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Mortality and Cause of Death in the Pacific Karen Lesley Carter
BAppSci (Env. Health), MPHTM
41545625
A thesis submitted for the degree of Doctor of Philosophy at
The University of Queensland in 2013
School of Population Health
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Abstract
Accurate data on mortality levels and causes of death is critical to assist
governments to improve the health of their populations; by identifying priority areas
for intervention, leveraging resources for such interventions, and to monitor the
impact and effectiveness of health programs. To date, there has been little data
available to agencies on mortality and causes of death in the Pacific Islands, and
there is significant disparity in published estimates, with many considered
implausible. Further, there are indications that improvements in life expectancy (LE)
across the region have stagnated in contrast to previously published estimates, most
likely as a consequence of premature adult mortality from non-communicable
diseases (NCDs).
In this research, routine vital registration collections from Pacific Island countries are
examined to determine whether these data can improve our understanding of
mortality and causes of death in the region. The work presents a review of available
data, examines the potential flaws and biases in the data collections, and examines
the methods best suited to working with the identified collections to generate valid,
reliable estimates of mortality level and cause of death, disaggregated by age group
and sex. Due to the scale and diversity of the Pacific Islands, six countries
representing a broad range of population size, level of development, and all three
sub-regions were selected for review. These were: Nauru, Fiji, Kiribati, Palau, Tonga
and Vanuatu.
Reporting systems were evaluated to identify potential impacts on the quality and
completeness of available data using an assessment framework that considers the
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societal, administrative and system (administrative, technical and ownership)
influences on the reporting system. Substantially more data was found to be
available than has been previously analysed or published. Medical certificates of
death are completed for some deaths in all of the study countries, and are required
for all deaths in Nauru, Fiji, Tonga and Palau.
Analytical strategies for deriving estimates of mortality level by age and sex were
selected from available approaches based on the system assessments, data
available and level of migration. Methods employed included direct calculation of
mortality level and evaluation of trends over time (Nauru and Palau), evaluation of
published data to establish plausible trends (Fiji and Tonga), direct demographic
methods to assess completeness - the Brass Growth Balance analysis (Fiji), and
capture-recapture analyses (Tonga and Kiribati). Only Vanuatu had insufficient data
from routine collection systems to reliably generate mortality estimates. Findings
from the use of model life tables in comparison to mortality estimates from empirical
data in the other study countries were therefore reviewed to evaluate the plausibility
of published estimates of mortality level from Vanuatu.
Estimates of cause–specific mortality were calculated directly from the medical
certificate data for Palau where the system review indicated a high level of content
validity. A medical record review was conducted in both Tonga and Nauru, and a
death certificate review conducted in Fiji where access to the medical records was
not possible. Other routine collections for cause of death were explored in Vanuatu
and Kiribati where medical certificates were not available for all deaths.
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Findings from this study demonstrate that LE across the selected countries is lower
than previously reported, but that this indicates a stagnation in LE rather than a
reversal in LE improvements as earlier estimates did not adequately account for
premature adult mortality. Estimates of LE for males ranged from 49 and 58 years in
Kiribati (2005-2009) for males and females respectively, to 65 and 70 years for
males and females respectively in Tonga (2005-2008) and Vanuatu (2005). Adult
mortality was high across all countries, with NCDs the leading cause of death in
adults aged 15-59 years in all countries. In Tonga, it has been possible to
demonstrate significant increases in age-standardised mortality, from the early
2000’s to the present, in ischaemic heart disease, diabetes and lung cancer.
Estimates of IMR and U5 mortality remain fairly high in Kiribati at 52 and 72 deaths
per 1,000 live births respectively, and in Nauru at 28 and 46 deaths per 1,000 live
births respectively. Causes of death in children 0-4 years in both countries reflect a
high proportion of deaths from preventable causes: including perinatal conditions,
malnutrition and infectious diseases. For all other study countries, IMR was
estimated to be below 20 deaths per 1,000 live births. These figures indicate that
while there are still gains to be made in improving child health in the region, infant
and child mortality are not the major influence contributing to the low life
expectancies seen across countries.
Despite a need for improvements in routine reporting systems to address poor
coverage, completeness and reporting practices, locally generated empirical data
from routine reporting systems was found to be able to generate plausible estimates
of mortality and add substantially to our understanding of cause of death in the
region.
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Declaration by author
This thesis is composed of my original work, and contains no material previously
published or written by another person except where due reference has been made
in the text. I have clearly stated the contribution by others to jointly-authored works
that I have included in my thesis.
I have clearly stated the contribution of others to my thesis as a whole, including
statistical assistance, survey design, data analysis, significant technical procedures,
professional editorial advice, and any other original research work used or reported
in my thesis. The content of my thesis is the result of work I have carried out since
the commencement of my research higher degree candidature and does not include
a substantial part of work that has been submitted to qualify for the award of any
other degree or diploma in any university or other tertiary institution. I have clearly
stated which parts of my thesis, if any, have been submitted to qualify for another
award.
I acknowledge that an electronic copy of my thesis must be lodged with the
University Library and, subject to the General Award Rules of The University of
Queensland, immediately made available for research and study in accordance with
the Copyright Act 1968.
I acknowledge that copyright of all material contained in my thesis resides with the
copyright holder(s) of that material. Where appropriate I have obtained copyright
permission from the copyright holder to reproduce material in this thesis.
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Publications during candidature
Published
Karen L Carter, Chalapati Rao, Alan D Lopez and Richard Taylor. Mortality and cause-of-death reporting and analysis systems in seven pacific island countries. BMC Public Health 2012, 12:436 doi:10.1186/1471-2458-12-436
Carter K, Cornelius M, Taylor R, Ali SS, Rao C, Lopez AD, Lewai V, Goundar R,
Mowry C. Mortality trends in Fiji. Aust N Z J Public Health. 2011 Oct;35(5):412-20.
doi: 10.1111/j.1753-6405.2011.00740.x.
Carter K, Soakai TS, Taylor R, Gadabu I, Rao C, Thoma K, Lopez AD. Mortality trends and the epidemiological transition in Nauru. Asia Pac J Public Health.
2011 Jan;23(1):10-23.
Karen L Carter, Gail Williams, Veronica Tallo, Diozele Sanvictores, Hazel Madera,
Ian Riley Population Capture-recapture analysis of all-cause mortality data in Bohol, Philippines. Population Health Metrics 2011, 9:9 (17 April 2011).
doi:10.1186/1478-7954-9-9
Hufanga S, Carter KL, Rao C, Lopez AD, Taylor R. Mortality trends in Tonga: an assessment based on a synthesis of local data. Population Health Metrics, 2012
Aug 14;10(1):14.
Carter K, Hufanga S, Rao C, Akauola S, Lopez AD, Rampatige R, Taylor R. Causesof death in Tonga: quality of certification and implications for statistics. Population Health Metrics, 2012 Mar 5;10(1):4.
Kuehn R, Fong J, Taylor R, Gyaneshwar R, Carter K. Cervical cancer incidence and mortality in Fiji 2003-2009. Aust N Z J Obstet Gynaecol. 2012 Aug;52(4):380-
6. doi: 10.1111/j.1479-828X.2012.01461.x.
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Carter, K, Rao, C, Lopez, A and Taylor, R (2011). Routine Mortality and Cause of Death Reporting and Analysis Systems in Seven Pacific Island Countries. In:
IEA World Congress of Epidemiology, Edinburgh, United Kingdom, (A421-A421). 7-
11 August 2011. Journal of Epidemiology and Community Health.
doi:10.1136/jech.2011.142976o.48
Carter K, Cornelius M, Taylor R, Ali SS, Rao C, Lopez AD, Lewai V, Goundar R,
Mowry C. An assessment of mortality estimates for Fiji, 1949-2008: findings and life tables. Health Information Systems Knowledge Hub 2010; 12
Submitted papers
Taylor R, Carter KL, Linhart C, Azim S, Rao C, Lopez AD, Wainiqolo I, Jatra N,
Mowry C. Mortality trends by ethnicity in Fiji. ANZ Journal of Public Health. In
review 2012
Conference presentations and posters
Scientific Poster- “Capture-recapture analysis of all-cause mortality data in Bohol,
Philippines” – Global Epidemiological Congress (Edinburgh, 2011)
“Mortality Trends and the Epidemiological Transition in Fiji and Nauru” – Australian
Association for the Advancement of Pacific Studies. (Melbourne 2010)
“Mortality Surveillance Systems in the Pacific Islands” – Western Pacific Regional
Epidemiological Association Meeting (Tokyo 2010)
“Mortality Surveillance Systems in the Pacific Islands” UQ SPH RHD Conference
(2009)
“The Epidemiological Transition in Fiji” – Western Pacific Regional Epidemiological
Association Meeting (Tokyo 2010)
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“The Epidemiological Transition in Fiji” –Queensland Branch Australian Public
Health Association Meeting (Brisbane, 2009),
“Mortality and Cause of Death in the Pacific Islands” – UQ SPH School Presentation
(2009)
Scientific Poster- “Capture-Recapture Analysis of Deaths in Bohol, Philippines” –
Presented at the Queensland Branch Australian Public Health Association Meeting
(Brisbane, 2009),
“Mortality and Cause of Death Surveillance in the Pacific Islands” – Pasifika Medical
Association Conference, Cook Islands, July 2009
Mortality data system reviews- debrief presentations to Government participants
and key in-country stakeholders (i.e. WHO, UNICEF, Save the Children etc) – Fiji,
Tonga, Solomon Islands, Vanuatu, Kiribati, Nauru, Palau. 2007-2009
“The Stagnation of Mortality Decline in Fiji” – Research Higher Degree Conference
2008 (Highly Commended)
(also accepted at the 2008 Australian Public Health Association Conference – but
presented by Prof Richard Taylor due to prior travel commitments)
“Capture-recapture methods and mortality estimation in Bohol, Philippines” –
presentation to Gates GC13 project group in Bohol September 2008.
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Publications included in this thesis
Karen L Carter, Chalapati Rao, Alan D Lopez and Richard Taylor. Mortality and cause-of-death reporting and analysis systems in seven pacific island countries. BMC Public Health 2012, 12:436 doi:10.1186/1471-2458-12-436 -
incorporated into Chapter 4
Contributor Statement of contribution
Karen Carter
(Candidate)
Developed and trialled the system assessment framework and data
collection tools, conducted the field visits and interviews, collated
the findings and extracted key themes, and drafted the analysis and
paper. (65%)
Other Authors CR: assisted with the identification and clarification of key themes,
reviewed the data analysis and interpretation of results; revised the
manuscript critically for structure, clarity and important intellectual
content (25%). AL: revised the manuscript critically for important
intellectual content (5%). RT: reviewed the data analysis and
interpretation of results; and revised the manuscript critically for
important intellectual content (5%). RT and AL both conceived and
coordinated the broader study under which this project was
undertaken. All authors read and approved the final manuscript.
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Carter K, Cornelius M, Taylor R, Ali SS, Rao C, Lopez AD, Lewai V, Goundar R,
Mowry C. Mortality trends in Fiji. Aust N Z J Public Health. 2011 Oct;35(5):412-20.
doi: 10.1111/j.1753-6405.2011.00740.x. – incorporated into Chapter 5
Contributor Statement of contribution
Karen Carter
(Candidate)
KC: Tabulation of MoH data, analysis of MoH data (including
assessment of completeness, Brass Growth Balance assessment,
interpolation of population figures, construction of life tables and
confidence intervals for all periods), Primary collation and analysis
of published cause of death data including conversion to standard
format, assessment of plausibility, tabulation and graphing,
development of criteria for plausibility of mortality level (with RT)
and application, critical interpretation of results and substantial
contribution to writing and shaping of discussion, formatting. (60%)
(60%)
Other Authors MC reviewed the draft paper and results critically for local context
and interpretation (5%): SA, VL and RG: reviewed the draft paper
and results critically for local context and interpretation, and
facilitated the collation and access to local data (2% each); CR
reviewed the draft paper and results critically for methodology (4%):
CM identified and tabulated published data and drafted the
preliminary analysis of trends before censoring for plausibility. RT
(20%) was responsible for the paper concept and original design
(with KC), ADL (5%) and RT conceived and coordinated the
broader study under which this project was undertaken, reviewed
and contributed to the interpretation of results, and revised the
manuscript critically for important intellectual content. RT also
generated the regression models to estimate IMR from childhood
mortality.
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Carter K, Soakai TS, Taylor R, Gadabu I, Rao C, Thoma K, Lopez AD. Mortality trends and the epidemiological transition in Nauru. Asia Pac J Public Health.
2011 Jan;23(1):10-23. – incorporated into Chapter 5
Contributor Statement of contribution
Karen Carter
(Candidate)
KLC: Primary collection and collation of 2002-2007 data, analysis
of data (including interpolation of population figures, construction of
life tables and confidence intervals for all periods, child mortality
measures, age standardised rates and confidence intervals, and
confidence intervals for external causes, construction of tables and
graphs,) interpretation of results, contributed to writing and shaping
of discussion, formatting. (55%)
Other Authors RT: Paper concept and original design, collection and collation of
data from 1986-1992, input on analysis methods, interpretation of
results, shaping and writing of all sections. (30%) KT: Collection
and collation of data from 1986-1992, interpretation of results from
early periods (5%). CR: Review of results interpretation, discussion
and epidemiological transition context (2%). SS: Review of results
interpretation, discussion and country context (2%). AL: Review of
results interpretation, discussion and epidemiological transition
context (4%). IG: Collection of population data, review of results
interpretation, discussion and country context (2%).
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Karen L Carter, Gail Williams, Veronica Tallo, Diozele Sanvictores, Hazel Madera,
Ian Riley Population Capture-recapture analysis of all-cause mortality data in Bohol, Philippines. Population Health Metrics 2011, 9:9 (17 April 2011).
doi:10.1186/1478-7954-9-9 – incorporated into Chapter 5
Contributor Statement of contribution
Karen Carter
(Candidate)
KC designed the study; conceived and conducted the system
review; designed and tested matching criteria; prepared the data
for the three-source analysis; performed the statistical analysis for
the two-source analysis and final model selection including
interpretation of final results; and drafted the manuscript.
(65%)
Other Authors GW made substantial contributions to analysis and interpretation of
data, performed the statistical analysis for the three-source analysis
and revised the manuscript critically (20%). VT coordinated data
collection, collation and matching, and made substantial
contributions to the interpretation of data (4%). DS coordinated
initial data cleaning, management, and tabulation of records, and
made substantial contributions to the interpretation of data (3%).
HM made substantial contributions to the acquisition and
interpretation of data (3%). IR conceived and coordinated the study
and revised the manuscript critically for important intellectual
content (4%).
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Hufanga S, Carter KL, Rao C, Lopez AD, Taylor R. Mortality trends in Tonga: an assessment based on a synthesis of local data. Population Health Metrics, 2012
Aug 14;10(1):14. – incorporated into Chapter 5
Contributor Statement of contribution
Karen Carter
(Candidate)
KC oversaw the project, conducted the system review, oversaw the
design and testing of matching criteria, checked the matching
process, assisted with data matching for the civil registration data
against other sources, performed the statistical analysis for both
the two-source analysis for 2001 to 2004 and the three-source
analysis, including the civil registry data and final model selection
including interpretation of final results and selection of final
estimates, supervised the other analysis, and drafted and led the
revision of the manuscript. (45%)
Other Authors SH conducted the system review, supervised the data collection
and collation processes, designed the matching criteria, conducted
the data matching, managed the ethics process and coordination
with data collection agencies, performed the statistical analysis for
the two source analysis of the HIS and PM data for 2005 to 2009,
completed the search for published estimates, assessed published
data sources for reliability, revised the manuscript critically, and
made substantial contributions to the interpretation of data (35%).
CR (5%) revised the manuscript critically for important intellectual
content. ADL (5%) and RT (10%) conceived and coordinated the
broader study under which this project was undertaken, reviewed
and contributed significantly to the interpretation of results, and
revised the manuscript critically for important intellectual content.
RT also generated the regression models to estimate IMR from
childhood mortality.
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Carter K, Hufanga S, Rao C, Akauola S, Lopez AD, Rampatige R, Taylor R. Causesof death in Tonga: quality of certification and implications for statistics. Population Health Metrics, 2012 Mar 5;10(1):4. – incorporated into Chapter 6
Contributor Statement of contribution
Karen Carter
(Candidate)
KC designed the study, developed the study forms and procedures,
conducted initial training of data extractors, reviewed trial extraction
forms, arranged coding and collation of data, analysed the
collected data, and drafted the manuscript (55%)
Other Authors SH coordinated data extraction in Tonga and provided significant
input to the manuscript (15%); CR assisted with reviewing trial
extraction forms and revised the manuscript critically for important
intellectual content (5%); SA reviewed the extracted data from both
countries to allocate cause of death from the medical record and
provided significant input into the discussion (5%); RR provided the
second review of deaths for which cause was originally assigned as
unknown and critically revised the manuscript (2.5%). ADL (2.5%)
and RT (15%) conceived and coordinated the broader study under
which this project was undertaken, reviewed and contributed
significantly to the interpretation of results, and revised the
manuscript critically for important intellectual content. RT also
made substantial contributions to the study design.
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Carter K, Cornelius M, Taylor R, Ali SS, Rao C, Lopez AD, Lewai V, Goundar R,
Mowry C. An assessment of mortality estimates for Fiji, 1949-2008: findings and life tables. Health Information Systems Knowledge Hub 2010; 12– incorporated
into the Appendices
Contributor Statement of contribution
Karen Carter
(Candidate)
KC: Tabulation of MoH data, analysis of MoH data (including
assessment of completeness, Brass Growth Balance assessment,
interpolation of population figures, construction of life tables and
confidence intervals for all periods), Primary collation and analysis
of published cause of death data including conversion to standard
format, assessment of plausibility, tabulation and graphing,
development of criteria for plausibility of mortality level (with RT)
and application, critical interpretation of results and substantial
contribution to writing and shaping of discussion, formatting. (70%)
Other Authors MC reviewed the draft paper and results critically for local context
and interpretation (2%): SA, VL and RG: reviewed the draft paper
and results critically for local context and interpretation, and
facilitated the collation and access to local data (2% each); CR
reviewed the draft paper and results critically for methodology (4%):
CM identified and tabulated published data and drafted the
preliminary analysis of trends before censoring for plausibility. RT
(15%) was responsible for the paper concept and original design
(with KC), ADL (3%) and RT conceived and coordinated the
broader study under which this project was undertaken, reviewed
and contributed to the interpretation of results, and revised the
manuscript critically for important intellectual content.
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Contributions by others to the thesis
Significant contributions made by others to the research presented in this thesis are
acknowledged above in the contribution to published papers. Prof Alan Lopez, Prof
Richard Taylor and Dr Chalapati Rao contributed significantly to this work as a whole
by reviewing and commenting on the study design, thesis structure and content,
including providing critical comments on the draft analysis and chapters. This
research was supported under a broader AusAID Australian Development Research
Award project to review vital registration systems in the Pacific Islands.
In addition, the following specific contributions by others were made to work
presented in this thesis other than the published case studies (which are note in the
previous section).
Chapter 5 Kiribati Capture-recapture study: Criteria for data matching between sources were
developed and tested with Tibwataake Baiteke from the Ministry of Health, and
Kabuaua Temaava and Tiensi Teea from the Civil Registration Offices by trialling
sub sets of data for review and discussion. These staff also reviewed the complete
matched data set to identify errors and potential missed errors. Both departments
provided input into the study design through extensive discussions with the
candidate on reporting processes.
Chapter 6 Nauru: Data extraction for the medical record review was completed by a team of
nurses and medical record officers following the protocol provided. Data extraction
was overseen by the director of nursing, periodically reviewed by myself. Location
and extraction of medical records for review was overseen by the manager of
medical records following the list provided. The medical records manager also
maintained the master list containing the identifiable data once study numbers were
assigned to each case in order to de-identify the data. Dr Sione Akuola reviewed the
extracted data from the medical record following the study protocol to complete a
causal sequence of death for the study. Cases for which an underlying cause of
death could not be identified were reviewed by Dr Rasika Rampatige.
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Fiji: Coding of each line of the certificate and selection of underlying cause of death
according to ICD rules undertaken by me as part of this thesis were checked by the
WHO collaborating centre on coding in Sri Lanka.
Kiribati: Moaiti Nubono from the Kiribati Ministry of Health worked with me to
translate causes of death from the civil registry data and review the collection
procedures to provide local cultural context.
Statement of parts of the thesis submitted to qualify for the award of another degree
None
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Acknowledgements
Firstly, I would like to thank my family for all their invaluable support during this
process; especially my sister Wendy who kept me supplied with chocolate, provided
distraction when needed, proof read pages and pages of early drafts and provided
unending support and encouragement; and my parents for their ongoing support and
patience.
Many thanks are due to my supervisors Prof Alan Lopez, Dr Chalapati Rao, and Prof
Richard Taylor for their guidance, support and for always challenging me to do
better. In particular Alan for setting me lofty standards to reach for, Chalapati for
keeping my feet on the ground, and Richard for having the patience and
perseverance to try to turn me into a researcher! I greatly appreciate your
persistence. Special mention is also due to Prof Gail Williams and Prof Ian Riley for
their encouragement and support. Many thanks are also due to Dr Deirdre
McLaughlin and Mary Roset the Postgraduate coordinator and administrator who
have shown endless patience and support, and whose dedication and endless good
humour when faced with students who are sleep deprived, stressed and dosed up on
coffee make the whole process work.
I would also like to thank the many colleagues and friends who helped me get here
in the first place especially Heather O’Donnell, Maireng Sengebau and Martyn Kirk;
Hazel Clothier for the nudge that first sent me out into the Pacific and introduced me
to this spectacular part of the world; the friends and colleagues who kept me sane
(or mostly sane) during the process – especially Misha, Heather, Linda, Kath, Jo,
Claire, Audrey, Sally, James, Damian and Danielle; my current boss Dr Haberkorn
for his ongoing support and encouragement; and of course the amazing health and
statistics professionals of the Pacific Islands without whom this research would not
have been possible and who have made the last few years much richer for this
experience.
I could not have done it without you all.
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Keywords
mortality, cause of death, epidemiology, public health, pacific islands, capture-
recapture, certification of death, civil registration, medical record review.
Australian and New Zealand Standard Research Classifications (ANZSRC)
ANZSRC code: 111706, Public Health: Epidemiology, 65%
ANZSRC code: 160304, Demography: Mortality, 30%
ANZSRC code: 111711, Public Health: Health Information Systems (including
Surveillance), 5%
Fields of Research (FoR) Classification
FoR code: 1117, Public Health, 70%
FoR code: 1603, Demography, 30%
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List of Key Acronyms
5q0 – Under 5 mortality, the probability of dying before age 5
45q15 – Adult mortality, the probability of dying between ages 15-59 inclusive
CEBCS – Children ever born/ Children surviving (an indirect method for obtaining
childhood survival)
CHIPs – Country Health Information Profiles (published annually by WHO)
CI- Confidence interval
CoD – Cause of death
COPD – Chronic obstructive pulmonary disease
CRVS – Civil Registration and Vital Statistics
DHS – Demographic and Health Survey
DSSs – Demographic surveillance sites
ICD – International Classification of Diseases
IMR – Infant Mortality Rate
ITC – Information technology and communication
GBD – Global Burden of Disease Study
HIS- Health Information Systems
HMN – Health Metrics Network
LE – Life expectancy (at birth)
MDG – Millennium Development Goals
MICS – Multiple Indicator Cluster Survey
MoH – Ministry of Health
MMR – Maternal mortality ratio (maternal deaths per 1,000 live births)
STEPS – WHO Stepwise NCD risk factor survey
PATIS – Patient Information System (Fiji’s HIS system)
PICTs – Pacific Island Countries and Territories
PNG – Papua New Guinea
NCDs – Non communicable diseases
WHO – World Health Organisation
UN – United Nations
UNICEF – United Nations Children’s Fund
U5M – Under 5 mortality (deaths in children aged 0-4 years divided by live births)
UQ HIS Hub – University of Queensland Health Information Systems Hub
VA – Verbal autopsy
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Contents
1� Introduction ........................................................................................................ 1�1.1� The importance of mortality and cause of death data ................................... 1�
1.2� The Pacific Islands ........................................................................................ 6�
1.3� Countries ....................................................................................................... 9�
1.3.1� Fiji ......................................................................................................... 10�
1.3.2� Kiribati ................................................................................................... 11�
1.3.3� Nauru .................................................................................................... 12�
1.3.4� Palau .................................................................................................... 13�
1.3.5� Tonga ................................................................................................... 14�
1.3.6� Vanuatu ................................................................................................ 15�
1.4� Measures of mortality and causes of death ................................................. 16�
1.5� An overview of mortality and cause of death data for the Pacific ................ 21�
1.5.1� Historical context – patterns of mortality and causes of death .............. 21�
1.5.2� Previous Data Quality ........................................................................... 24�
1.5.3� Recent data .......................................................................................... 26�
1.6� Research Objectives and Questions ........................................................... 34�
2� An overview of methods for collecting, analysing and assessing the quality of mortality level and cause of death data ........................................................... 38�
2.1� Sources of mortality and cause of death data ............................................. 39�
2.1.1� Census collections ................................................................................ 40�
2.1.2� Periodic Surveys ................................................................................... 45�
2.1.3� Routine vital registration collections ..................................................... 47�
2.1.4� Demographic surveillance sites ............................................................ 52�
2.1.5� Verbal Autopsy ..................................................................................... 52�
2.1.6� Summary of data collection approaches ............................................... 54�
2.2� Evaluating routine data collection and reporting systems ........................... 57�
2.3� Evaluating completeness for mortality level ................................................ 65�
2.3.1� Direct Demographic Methods ............................................................... 66�
2.3.2� Model Life Tables ................................................................................. 69�
2.3.3� Conclusions .......................................................................................... 73�
2.4� Evaluating biases in cause of death data .................................................... 74�
2.4.1� Review of Certification .......................................................................... 74�
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2.4.2� Assessment and correction of coding practices .................................... 78�
2.4.3� Multiple cause of death analysis ........................................................... 80�
2.4.4� Models for cause of death data ............................................................ 81�
2.5� Final Comments .......................................................................................... 83�
3� Routine reporting systems for deaths and cause of death in selected Pacific Islands ........................................................................................................ 87�
3.1� Assessment Framework for routine reporting systems ............................... 87�
3.2� Common elements of CRVS systems in the region and their effects on data
92�
3.2.1� Case Study 1: Reporting systems across seven PICTs (163) .............. 92�
3.2.2� Data availability and accessibility ....................................................... 103�
3.3� Summary of country specific CRVS systems ............................................ 104�
3.3.1� Fiji ....................................................................................................... 104�
3.3.2� Kiribati ................................................................................................. 105�
3.3.3� Nauru .................................................................................................. 106�
3.3.4� Palau .................................................................................................. 106�
3.3.5� Tonga ................................................................................................. 107�
3.3.6� Vanuatu .............................................................................................. 108�
3.4� Conclusion ................................................................................................ 108�
4� Further analytical considerations – small area estimates, off-island vital events and migration ........................................................................................... 110�
4.1� Introduction ............................................................................................... 110�
4.2� Population size and small-area estimates ................................................. 111�
4.3� Off-Island Events ....................................................................................... 114�
4.3.1� Off-island births .................................................................................. 122�
4.4� Migration ................................................................................................... 123�
4.5� Conclusions ............................................................................................... 128�
5� Assessing mortality level .............................................................................. 132�5.1� System evaluation findings and analytical strategies to assess mortality level
132�
5.2� Data aggregation and trend analysis ......................................................... 137�
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5.2.1� Case Study 2: Mortality Trends in Nauru. ........................................... 138�
5.2.2� Palau analysis and assessment ......................................................... 155�
5.3� Validity assessment of published data ...................................................... 156�
5.4� Brass growth balance analysis .................................................................. 157�
5.4.1� Case study 3: Mortality Trends in Fiji. ................................................. 161�
5.5� Capture-recapture analysis ....................................................................... 174�
5.5.1� Case study 4: Capture-recapture analysis in Bohol, Philippines – an
application in an “ideal” setting ........................................................................ 176�
5.5.2� Case study 5: Capture-recapture analysis in Tonga – application in a
Pacific Island with multiple data sources ......................................................... 187�
5.5.3� Using Capture-recapture analysis to set plausible mortality limits in
Kiribati 202�
5.6� Mortality Estimation in the absence of routine death reporting .................. 221�
5.7� Summary of method selection and application .......................................... 225�
6� Assessing cause of death distributions ...................................................... 229�6.1� Direct tabulation of cause of death from collected data ............................. 234�
6.1.1� Analysis of cause of death in Palau .................................................... 234�
6.2� Medical Record Review ............................................................................ 246�
6.2.1� Case study 6: Medical Record Review in Tonga ................................ 249�
6.2.2� Medical Record Review in Nauru ....................................................... 266�
6.3� Medical Certificate of Death Review ......................................................... 282�
6.3.1� Review of medical certification of death in Fiji .................................... 282�
6.4� Examining cause of death distributions from alternative data sources:
understanding causal patterns in the absence of nationally representative data
from medical certification .................................................................................... 293�
6.4.1� Comparison of cause of death distributions from hospital and community
data in Vanuatu ............................................................................................... 293�
6.4.2� Cause of death analysis from hospital, community nursing and civil
registry data in Kiribati ..................................................................................... 315�
6.5� Summary of method selection and application .......................................... 328�
7� Mortality patterns in the Pacific Islands ...................................................... 330�7.1� Summary measures of mortality ................................................................ 332�
� � �
xxiii
7.2� Mortality and cause of death distributions by age group ........................... 336�
7.2.1� Infant and child mortality ..................................................................... 336�
7.2.2� Adult mortality 15-59 years ................................................................. 339�
7.2.3� Mortality & Older adults ...................................................................... 352�
7.3� Country summaries ................................................................................... 354�
7.3.1� Fiji ....................................................................................................... 354�
7.3.2� Kiribati ................................................................................................. 356�
7.3.3� Nauru .................................................................................................. 359�
7.3.4� Palau .................................................................................................. 360�
7.3.5� Tonga ................................................................................................. 361�
7.3.6� Vanuatu .............................................................................................. 362�
7.4� Mortality in Transition ................................................................................ 364�
8� Conclusion and recommendations .............................................................. 367�8.1� Synopsis.................................................................................................... 367�
8.2� Recommendations .................................................................................... 373�
8.2.1� Recommendations for system improvement of CRVS systems .......... 374�
8.2.2� Recommendations for further research .............................................. 376�
9� REFERENCES ................................................................................................ 378�
Appendix 1: Brass Growth Balance Assessments for Kiribati ..................................... i�
Appendix 2: Additional country life tables ................................................................... iii�
Appendix 3: Additional analysis on mortality level in Fiji including life tables ........... xvi�
Appendix 4: Additional cause of death distributions by age and sex ........................ lvii�
Appendix 5: Medical record review handbook ...................................................... lxxxv�
� � �
xxiv
List of Tables
Table 1: Uses of cause of death data (11) ................................................................. 3�
Table 2: Land area, population and density by sub region: Pacific Islands (44) ........ 6�
Table 3: Pacific Island Countries and Territories, and their key characteristics .......... 8�
Table 4: Key measures of mortality and causes of death ......................................... 18�
Table 5: Published estimates at 2010 of Infant mortality rates (per 1,000 live births)
(Both sexes combined); Pacific Island Countries and Territories ............................. 27�
Table 6: Published estimates at 2010 of LE at birth (males and females); Pacific
Island Countries and Territories ............................................................................... 29�
Table 7: Extract from Fiji MDG Report: Statistics on Goal 4 - Fiji Millennium
Development Goals 2nd Report, 1990-2009 (113) ................................................... 31�
Table 8: Censuses in the Pacific Islands, 1950-2010 ............................................... 41�
Table 9: Type of data collection and methods of estimation used for calculation of life
tables in the most recent census round for selected Pacific Island Countries and
Territories ................................................................................................................. 44�
Table 10: Key surveys in the Pacific Islands; 1995-2012 ......................................... 46�
Table 11: Summary of Data Collection Approaches for Mortality Data .................... 55�
Table 12: Assumptions and data requirements for direct demographic analysis ...... 68�
Table 13: Routine death reporting system assessment framework outline: Key
aspects for review .................................................................................................... 90�
Table 14: Available routine mortality data collections by country and source ......... 103�
Table 15: Minimum number of years data would need to be aggregated to reach the
minimum person years of data required for acceptable mortality analysis as cited by
Begg et. al. by country (293) .................................................................................. 112�
Table 16: Registered deaths in Australia of new residents from the Pacific Islands by
age at death and duration of residence: 2001-2008 ............................................... 117�
� � �
xxv
Table 17: Deaths in Australia and New Zealand of persons usually resident overseas
by PICT of birth: 2001-2008 ................................................................................... 119�
Table 18: Migration rates by Country for 2010 (110) .............................................. 125�
Table 19: Reported deaths by source, Kiribati: 2000-2009 .................................... 207�
Table 20: Summary measures of mortality for Kiribati by data source and period,
2000-2009 .............................................................................................................. 210�
Table 21: Estimated average deaths per year by data source and method of
estimation: Kiribati, 2000-2009 by age group and sex ........................................... 216�
Table 22: Summary of data sources and methods used in assessment of mortality
level for the Pacific Islands ..................................................................................... 227�
Table 23: Deaths by cause, and proportional mortality for children 0-4 years, by
period for Palau; 1999-2009 (excluding 2007) ....................................................... 238�
Table 24: Cause-specific mortality by ICD chapter and period for adult males 15-64
years: Palau, 1999-2009 (excluding 2007) ............................................................. 244�
Table 25: Cause specific mortality by ICD chapter and period for adult females 15-64
years: Palau, 1999-2009 (excluding 2007) ............................................................. 245�
Table 26: Comparison of underlying cause of death from the medical certificate of
death (including the non-standard first line in the assessment of causal sequence)
against findings of the medical record review: Nauru, 2005-2009 .......................... 272�
Table 27: Comparison of age distribution for deaths with a medical certificate for
which no medical record was found; and deaths with a certificate which were
reviewed against the medical record. Nauru, 2005-2009 ....................................... 274�
Table 28: Deaths by cause (by ICD chapter), proportional mortality and cause-
specific mortality rates for children 0-4 years: Nauru, 2005-2009 .......................... 276�
Table 29: Deaths by cause (by ICD chapter) and proportional mortality for children 5-
14 years: Nauru, 2005-2009 ................................................................................... 277�
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xxvi
Table 30: Deaths by cause (by ICD chapter) and proportional mortality for adults 15-
54 years: Nauru 2005-2009 .................................................................................... 278�
Table 31: Number of selected medical certificates found by age group: Fiji, 2000-
2008 ....................................................................................................................... 285�
Table 32: Reviewed medical certificates by single versus multiple lines completed on
the certificate: Fiji, 2000-2008 ................................................................................ 285�
Table 33: Column of database where underlying cause of death from the original
certificate (specific disease and by chapter) were found, by age group: Fiji 2000-
2008 ....................................................................................................................... 287�
Table 34: Distribution of deaths by broad cause where underlying cause of death
was not recorded in the Ministry of Health database. Fiji 2000-2008 ..................... 288�
Table 35: Deaths, proportional mortality and death rate by selected cause (total
mentions on the certificate) for children 0-4 years: Fiji 2000-2008 ........................ 289�
Table 36: Deaths and proportional mortality by mentioned cause (total mentions on
the certificate) for selected causes for children 5-14 years: Fiji 2000-2008 ............ 290�
Table 37: Deaths and proportional mortality by underlying cause (by ICD chapter) for
adults 15-59 years: Fiji 2005-2008 ........................................................................ 291�
Table 38: Deaths and proportional mortality by mentioned cause (total mentions on
the certificate) for selected causes for adults 15-64 years: Fiji 2000-2008 ............. 292�
Table 39: Proportion of deaths (%) attributed to Ill-defined or unknown causes by
age group, gender and source: Vanuatu 2001-2007 .............................................. 299�
Table 40: Deaths, proportional mortality and cause-specific mortality rates (by
ICDv10 General Mortality List 3) for children 0-4 years: Vanuatu 2000-2007 ....... 301�
Table 41: Deaths, proportional mortality and cause-specific mortality rates (by
ICDv10 General Mortality List One) for children 5-14 years: Vanuatu 2000-2007 .. 304�
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xxvii
Table 42: Deaths and proportional mortality (excluding ill-defined causes) by cause
(ICDv10 General Mortality List 1) for adults 15-59 years: Vanuatu 2000-2007 ...... 307�
Table 43: Proportion of ill-defined or unknown causes and adjusted proportional
mortality (%) by age group and period: Kiribati males, 2000-2008 ........................ 321�
Table 44: Proportion of ill-defined or unknown causes and adjusted proportional
mortality (%) by age group and period: Kiribati females, 2000-2008 ...................... 323�
Table 45: Proportion (%) of hospital deaths of children aged 1-4 years where
malnutrition was reported as either the primary diagnosis or contributing factor,
Kiribati 2000-2008 .................................................................................................. 325�
Table 46: Minimum suicide rates (per 100,000 people) based on uncorrected data,
Kiribati 2000-2008 .................................................................................................. 326�
Table 47: Estimated Age-specific mortality rate from Diabetes (as reported through
civil registration and health system reporting) per 100,000 people ........................ 327�
Table 48: Summary of data sources and methods for used in assessment of cause
of death patterns for selected Pacific Islands ......................................................... 329�
Table 49: Summary of key measures of mortality for selected Pacific Islands ....... 333�
Table 50: Comparison of ‘Best estimates’ of life expectancy at birth (years) from this
research against Taylor et. al. 2005 review (2) ...................................................... 334�
Table 51: Key measures of childhood mortality (0-4 years), both sexes, for selected
Pacific Islands ........................................................................................................ 338�
Table 52: Adult mortality and proportional mortality (%) for key causes (by ICD
chapter) for selected Pacific Islands; Males ........................................................... 345�
Table 53: Cause-specific mortality rates for key causes (by ICD General Mortality
List 1) for selected Pacific Islands; Males 15-59 years ........................................... 346�
Table 54: Age-standardised incidence rates of specific cancer types 1980s-1990s
(cases per 100,000 population) by country extracted from Palafox et. al (368) ...... 347�
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xxviii
Table 55: Adult mortality and proportional mortality (%) for key causes (by ICD
chapter) for selected Pacific Islands; Females ....................................................... 350�
Table 56: Cause-specific mortality rates for key causes (by ICD General Mortality
List 1) for selected Pacific Islands; Females 15-59 years ....................................... 351�
Table 57: Life expectancy at 65 years for selected countries ................................. 353�
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xxix
List of Figures
Figure 1: Map of selected Pacific Island Countries included in this research ............. 9�
Figure 2: International standard medical certificate of death format (68).................. 19�
Figure 3: Recent published estimates of male LE at birth, Pacific Island Countries
and Territories .......................................................................................................... 30�
Figure 4: Recent published estimates of female LE at birth, Pacific Island Countries
and Territories .......................................................................................................... 30�
Figure 5: Thesis structure and content ..................................................................... 37�
Figure 6: Level of certainty of underlying cause of death by data source ................. 56�
Figure 7: The two source capture-recapture model .................................................. 69�
Figure 8: Sources of errors in medical certification of death from Maudsley and
Williams, 1994 (249) ................................................................................................. 75�
Figure 9: Routine death reporting system assessment framework ........................... 89�
Figure 10: Major medical referral pathways in the Pacific Islands .......................... 116�
Figure 11: Recommended analysis approach for assessment of mortality level in the
Pacific Islands ........................................................................................................ 133�
Figure 12: Age-specific mortality for Nauru 2002-2007, by sex .............................. 138�
Figure 13: Age-specific mortality for Palau 2004-2009 (excluding 2007), by sex ... 155�
Figure 14: Brass Growth Balance analysis of reconciled data from Tonga; Males
2005-2009 .............................................................................................................. 159�
Figure 15: Brass Growth Balance analysis of reconciled data from Tonga; Females
2005-2009 .............................................................................................................. 160�
Figure 16: Brass Growth Balance analysis for mortality data completeness: Fiji,
Males 2002-2004 ................................................................................................... 163�
Figure 17: Brass Growth Balance analysis for mortality data completeness: Fiji,
Females 2002-2004 ............................................................................................... 164�
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xxx
Figure 18: Reporting completeness of deaths in Tonga by age group, sex and data
source (2001-2009) ................................................................................................ 189�
Figure 19: Differences in total number of reported deaths by age group between Civil
registry data and MoH data for Kiribati, 2000-2009 ................................................ 211�
Figure 20: Estimated completeness of reported deaths from 2-source capture-
recapture analysis by source, sex and age group for Kiribati, 2000-2009 .............. 212�
Figure 21: Estimated average deaths per year (2000-2009) for Kiribati from census
data and routinely collected data ............................................................................ 214�
Figure 22: Empirical age-specific mortality compared with predictions from one and
two parameter models for Fiji, Males: 2002-2004 .................................................. 222�
Figure 23: Empirical age-specific mortality compared with predictions from one and
two parameter models for Fiji, Females: 2002-2004 .............................................. 223�
Figure 24: Collection and suggested review process for cause of death data ........ 233�
Figure 25: Proportional mortality by period for children aged 0-4 years both sexes:
Palau, 1999-2009 (excl 2007) ................................................................................ 237�
Figure 26: Proportional mortality by cause and period for Palauan males: Adults 15-
64 years for 1999-2009 (excluding 2007) ............................................................... 240�
Figure 27: Proportional mortality by cause and period for Palauan females: Adults
15-64 years for 1999-2009 (excluding 2007) .......................................................... 241�
Figure 28: Proportion of deaths due to external causes, by type of external cause for
males aged 15-64 years: Palau, 2004-2009 (excluding 2007) ............................... 243�
Figure 29: Age-distribution of deaths in Tonga by global burden of disease* category;
2005-2009 .............................................................................................................. 250�
Figure 30: Medical record review flowchart for Nauruan study ............................... 267�
Figure 31: Comparison of cause of death distributions by broad category for
reviewed* and non-reviewed^ deaths: Nauru, 2005-2009 ...................................... 273�
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xxxi
Figure 32: Age distribution of reported deaths by data source and gender; Vanuatu,
2001-2007 .............................................................................................................. 298�
Figure 33: Proportional mortality in Kiribati 2005-2008 for children 0-4 years,
excluding ill-defined causes, as reported through the Ministry of Health (hospital and
community health facilities) .................................................................................... 318�
Figure 34: Proportional mortality in Kiribati 2005-2008 for children 5-14 years,
excluding ill-defined causes, as reported through the Ministry of Health (hospital and
community health facilities) .................................................................................... 319�
Figure 35: Proportional mortality in Kiribati 2005-2008 for males 15-59 years,
excluding ill-defined causes, as reported through the Ministry of Health (hospital and
community health facilities) .................................................................................... 319�
Figure 36: Proportional mortality in Kiribati 2005-2008 for females 15-59 years,
excluding ill-defined causes, as reported through the Ministry of Health (hospital and
community health facilities) .................................................................................... 320�
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1
1 Introduction
The international community (1), researchers (2-4) and national governments
themselves (5, 6) have frequently lamented the lack of data, and the poor quality and
difficulties of accessing existing data on mortality and cause of death (CoD) data for
Pacific Island countries and territories (PICTs). This work will examine the empirical
data available on mortality and CoD from selected PICTs, the quality of these data,
and how they may be used to improve our knowledge of mortality and causes of
death in the region.
1.1 The importance of mortality and cause of death data
Mortality and CoD data are essential to quantify the health of a population and for
government planning. Statistics on mortality level have uses in generating population
estimates, assessing population dynamics and societal structure, workforce and
social security planning, economic development, and ensuring the effectiveness and
coverage of health systems (7). Registration of deaths through a civil registration
process also has benefits in terms of “legal status, and the protection of social, and
human rights” (8). For example, a legally recognised death certificate allows
remaining family members to access bank accounts, and transfer land or other
property ownership. Death registration flags some deaths as suspicious, requiring
investigation by police and civil authorities. A formal registration of death is also a
means of recognising the life which has passed as important, providing a measure of
societal accountability for how and when people die (7).
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2
Governments are tasked with supporting their populations to achieve an acceptable
health status both through broad environmental (including physical, social, and
economic) impacts, and the provision of quality preventative health services and
care. Complete and accurate statistics on mortality level and causes of death are
core elements of the information required to fulfil these functions (7, 9, 10). As noted
by the Health Metrics Network (HMN) “lost data, poor documentation, lack of access
to available knowledge, and reliance on memory all impede the delivery of high-
quality health care services” (8, 11) (12). Sufficient details to accurately disaggregate
data by key population characteristics (e.g. age group, ethnicity, sex, and location)
are critical to identify priority health gaps and target interventions effectively. While
predicted or modelled data have an important role in addressing data gaps where
empirical data are not available or reliable (13), they are not a substitute for empirical
data that has been assessed for validity and corrected for potential biases (14).
Predicted statistics are unsuitable for “monitoring progress towards agreed targets
and assessments of what works and does not” (15), (13, 16). By their nature,
predicted statistics cannot identify in smaller populations localised events or issues
affecting mortality patterns that are not represented in the assumptions or “usual”
patterns. It is these issues and events which are of greatest interest to local health
managers.
A useful summary of the applications of CoD data was developed by Byass in 2007
(11) and is reproduced below.
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3
Table 1: Uses of cause of death data (11)
Source(11): Byass P. Who needs cause of death data? PLoS medicine. 2007;4(11):1715
The routine collection and publication of CoD data began with the English Bills of
Mortality to monitor plague in the 1600’s and subsequent analysis by John Graunt
(17). Early efforts to standardise the causes of death (the precursors to the
International Classification of Diseases) are attributed to Sauvages, Linnaeus, and
Cullen in the 1700’s (17), with the quality of CoD statistics recognised as an issue of
scientific investigation dating from as early as 1940 (18). International interest in
mortality statistics and CoD certification procedures was renewed in the 1990’s with
the rise of the HIV epidemic in Africa and was solidified in 2000 with two events: a
legal case in the United Kingdom and the adoption of the Millennium Development
Goals (MDGs) by the United Nations (UN). The Shipman case involved a general
practitioner convicted of murdering 15 patients, but who was thought to have killed
up to 200 patients during his 24 year career (19, 20). That so many deaths could go
unnoticed sparked a major review of the medical certification and review process in
the UK and greater scrutiny of these processes more broadly (20-26). Among the
targets adopted under the MDGs were commitments to reduce childhood mortality
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4
by two thirds from 1990 levels, reduce maternal mortality, and reduce the incidence
(and impact) of malaria and HIV (27, 28). Billions of dollars of funding were
subsequently invested in addressing these challenges through structures such as
Global Fund (GFATM) established in 2002 (29) and The United States President’s
Emergency Plan for AIDS Relief (PEPFAR) established in 2004 (30).
The MDGs and subsequent investment in health infrastructure and programs has led
to significant pressure for governments to demonstrate progress towards mortality
related targets, and growing support to improve civil registration and vital statistics
(CRVS) systems (and more generally, national statistical systems and health
information systems (HIS)) to enable this. The Partnership in Statistics for
Development in the 21st Century (PARIS 21) established by the UN in 1999, adopted
the Marrakech Action Plan for Statistics in 2004 which, although focussed on
population and economic statistics, included the routine collection of vital events
(births and deaths) as a core element of a national statistical system (31). In 2005
the HMN was formed under the auspices of the World Health Organisation (WHO),
and in 2007 co-sponsored a series of articles in the Lancet on vital statistics and civil
registration. The “Who Counts” series (7, 32, 33), (34) highlighted the lack of reliable
and timely statistics on births and deaths (including causes of death) from low and
middle income countries (7) and emphasised the importance of these statistics in
ensuring government and international community accountability in supporting health
improvements where they are most needed (7, 35).
Mortality and CoD data is of particular importance in a setting such as the Pacific
Islands where most PICTs would easily be classified as low or middle income
settings (36). Only the Territories, Palau, and Tuvalu are classified as upper-middle
� � �
5
or high income settings by the World Bank (37). Governments must deal with
logistical issues of remote communities, high transport costs and limited resources
(38). Health budgets and priorities are heavily influenced by major funding agencies
and interest groups. Local empirical data is critical to ensure key issues (such as the
high premature mortality from NCDs (39)) are not overlooked if local health concerns
do not closely reflect those of the broader international community (40).
There are indications life expectancy (LE) has, in contrast to previously published
estimates, shown no improvement in several counties across the region including
Fiji, Tonga and Nauru (14), most likely as a consequence of premature adult
mortality from NCDs. Despite the region declaring an NCD crisis in 2011, the relative
contribution of specific NCDs to mortality levels, or how rapidly they are changing, is
not well understood. The Pacific Islands are also one of only two regions worldwide
(the other being Eastern Europe) where there appears to be excessively high levels
of adult compared to child mortality (41). These factors must be better understood for
policy action to improve health in the PICTs. To enable this, more reliable estimates
of underlying mortality patterns and trends are needed.
WHO reports “Most countries in the region are taking steps to align their information
systems to public health functions and needs. More active and rational use of
information is being observed in health programme management and health
planning. The majority of countries now also appreciate the need to monitor their
health information systems [including mortality and CoD data] continuously to
improve the quality and use of data” (42).
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6
1.2 The Pacific Islands
The PICTs consist of 21 countries and territories across three sub-regions:
Melanesia, Micronesia and Polynesia. Ranging from just over 1000 people (Tokelau)
(43) to 6.7 million (Papua New Guinea) (43), no two islands share a land border, and
most are separated by thousands of kilometres of ocean. While most have relatively
small land areas (as shown in Table 2), they all manage considerable marine areas
and many are spread over vast reaches of ocean (27, 36).
Table 2: Land area, population and density by sub region: Pacific Islands (44)
Region Land area (km²)
Estimated number of people Mid-year 2011
Population density (people/km²)
Melanesia 542,377 8,797,410 1,909,113 (excluding PNG)
16 (24 excluding PNG)
Micronesia 3,156 546,491 173
Polynesia 7,986 668,470 84
TOTAL 553,519 10,012,371 3,124,074 (excl. PNG)
18 (34 excl. PNG)
Over 800 languages are spoken across the region, with English and French the
primary official languages. There is significant cultural and political diversity (Table
3); from Tonga which has recently shifted power to a democratically elected
parliament, but remains the only Kingdom with an independent monarchy to have
avoided colonization; to Fiji which is currently under the rule of a military selected
government; to the territories of the United States (Guam and the Northern Mariana
Islands), France (New Caledonia and French Polynesia) and New Zealand
(Tokelau). There is also a vast Pacific diaspora in Australia, New Zealand and the
United States, with many communities maintaining close links with their home
� � �
7
islands and significant co-habitation across countries. Health services in the PICTs
are almost exclusively provided by the government sector.
PICTs are at different stages of the demographic transition, with populations such as
Kiribati and Vanuatu still experiencing relatively high mortality and fertility, while
others such as Palau manifest lower mortality and declining fertility (2). PICTs are
also passing through the epidemiological transition, with a progressive increase in
proportionate mortality from NCDs and consequent widening in the sex differential of
death rates (2).
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8
Table 3: Pacific Island Countries and Territories, and their key characteristics
Country or Territory
Status Sub-region Estimated population(2011) (44)
Land area (km2)(44)
No. of islands (45)
GDP(Constant)per capita USD - 2012 (46)
Fiji Islands Independent Melanesia 851,745 18,273 >320 5,233 New Caledonia
Territory (France)
Melanesia 252,331 18,576 12
Papua New Guinea
Independent Melanesia 688,297 462,840 Mainland + 600 islands
Solomon Islands
Independent Melanesia 553,254 30,407 992 (347 inhabited)
Vanuatu Independent Melanesia 251,784 12,281 >80 89,825 Fed States of Micronesia
Independent (Compact of association with USA)
Micronesia 102,360 701 607 2,115
Guam Territory (USA)
Micronesia 192,090 541 1
Kiribati Independent Micronesia 102,697 811 34 Marshall Islands
Independent (Compact of association with USA)
Micronesia 54,999 181 34
Nauru Independent Micronesia 10,185 21 1 N Mariana Islands
Semi-independent territory (Self-governing Commonwealth in union with the USA)
Micronesia 63,517 457 17 16,494
Palau Independent (Compact of association with USA)
Micronesia 20,643 444 >200 (8 inhabited)
American Samoa
Territory (USA)
Polynesia 66,692 199 7 7,874
Cook Islands
Independent (Compact of association with New Zealand)
Polynesia 15,576 237 15 11,109
French Polynesia
Territory (France)
Polynesia 271,831 3,521 118
Niue Independent (Compact of association with New Zealand)
Polynesia 1,446 259 1 16,494
Samoa Independent Polynesia 183,617 2,935 8 5,766 Tokelau Territory
(Administered by New Zealand)
Polynesia 1,162 12 3 atolls
Tonga Independent Polynesia 103,682 650 170 (36 inhabited)
3,677
Tuvalu Independent Polynesia 11,206 26 9 (8 inhabited)
1,770
Wallis and Futuna
Territory (France)
Polynesia 13,193 142 3
� � �
9
Note: GDP not available for other countries.
1.3 Countries
The scale and diversity of the region (Table 3) means it is not possible to focus on all
of the PICTs in this research. Consequently the thesis will focus on six countries: Fiji,
Kiribati, Nauru, Palau, Tonga and Vanuatu. These countries represent a range of:
population and land sizes, government structures, centralised and decentralised
health services, and economic situations. They also emanate from all three sub-
regions (Melanesia, Micronesia and Polynesia).
Figure 1: Map of selected Pacific Island Countries included in this research
Source: Pacific Islands Forum Secretariat (47)
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10
1.3.1 Fiji
Fiji is comprised of 322 islands, of which around two thirds are inhabited. The
population of Fiji is 837, 271 (2007) (48). It is the largest population in the PICTs
outside of PNG. Fiji is a predominantly mixed society of Indigenous Fijians
(Melanesians) or i-Tauki (475,739 people in 2007) and Indo-Fijians now called
Fijians of Indian descent (313,798 people in 2007) (48). Growth is slow due to a
falling birth rate and high levels of emigration. (48)
The population is concentrated on the two largest islands of Viti Levu and Vanua
Levu. Both Suva (the capital) and Nadi (the second largest city and site of the
international airport) are located on Vitu Levu. The main islands are mountainous.
Major industries include agriculture (particularly sugar cane), tourism, fishing and to
a lesser extent manufacturing. Traditional community structures (chiefs and
extended family relationships) remain very important in many areas.
Fiji has a centralized health system. There are hospitals in each of the four divisions
(central, east, west, and north). There are two government hospitals in Suva
including the Colonial War Memorial Hospital, the largest hospital in Fiji. Health
programs are managed predominantly from Suva and through Division offices.
Health Centres at the Provincial level (the next level down) provide basic inpatient
(and some basic surgical) care. Fiji also has a strong community health nurse
program, with community health nurses and midwives assigned to communities
throughout the country. Retention of staff (particularly skilled staff) is a major issue.
Pay-cuts introduced for all government employees have led to many nurses and
some doctors leaving Fiji for higher wages elsewhere.
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11
1.3.2 Kiribati
Kiribati is a group of 33 coral atolls spread across 4 million square kilometres of the
Pacific Ocean, spanning both the equator and the International Date Line (49). There
are three major island groups: the Gilbert Islands, the Phoenix Islands and the Line
Islands. While there are regular flights from the capital Tarawa to other islands of the
Gilbert Island group, there are no routine flights to the Phoenix or Line Islands.
International flights stop on Kirimati Island (in the Line Islands Group) between New
Zealand and Hawaii, however, all other access is by Sea. Kiribati gained
independence in 1971, having previously been a British Colony. The islands were
captured by the Japanese in World War II and were the site of several major battles
before being returned to British rule.
The population is approximately 100,000, with over one third of the population under
15 years of age (50). South Tarawa is home to 44% of the population and has a
population density of 2558 people per km square (50). This would be the 6th highest
population density in the world if South Tarawa Island was treated as a country (51).
Religion is important, with the population split approximately evenly between
Protestant and Catholic affiliations. Agriculture and industry are limited by the small
land area and topography. As atolls, soil quality is poor and predominantly sand,
limiting cropping opportunities. Average height above sea-level is less than 0.6m.
Today, Kiribati is heavily reliant on foreign assistance, and classified amongst the
least developed countries in the world (52, 53).
Health Services are provided through Tungaru Hospital, the national referral hospital
on South Tarawa Island, and two smaller hospitals at Betio (South Tarawa) and
Kirimati Island. Cuban doctors currently assist local doctors to staff the Tunguru
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Hospital and provide consultation services at a small number of health clinics in
South Tarawa. There are 96 health clinics throughout the islands staffed by a
medical assistant (equivalent to a nurse practitioner), registered nurses or nurse
aides. Traditional birth attendants operate throughout the islands but are not
regulated through the Ministry of Health (MoH). There are no private health facilities,
mortuary or morgue facility. There are 24 local doctors (in 2012), of whom
approximately 90% have trained through the Fiji School of Medicine. The remainder
were mostly trained in PNG.
1.3.3 Nauru
One of the smallest countries in the Pacific, Nauru has a land area of just 24 square
kilometres with a population of just 9,547 people, along with an expatriate workforce
of around 4002 (2006) (54).This is changing rapidly due to the re-opening of the
immigration detention facility in conjunction with Australia, and the associated
support personnel. The island is a raised coral atoll, with phosphate mining the only
major industry. Mining commenced early in the 1900s but ceased in 2003 with easy
to access deposits exhausted. Mining operations recommenced under a national
corporation in 2006 at a significantly reduced scale (55).
Originally annexed by Germany and subsequently becoming a protectorate under
the then League of Nations following World War One (WWI); the island was
occupied by Japan then Australia during World War Two (WWII), and subsequently
placed under the joint management of Australia, New Zealand and Britain. Nauru
gained independence in 1968. Although one of the most prosperous countries in the
region in the early 1990’s (based on GNP per capita), Nauru is now heavily reliant on
foreign aid (56) to support government functions and health care.
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There is one hospital on the island, following the 2005 merger of the public hospital
with the private hospital previously run by the mining corporation. The previous
public hospital site has been renovated and now functions as the public health and
dialysis clinic. All health services operated from these two facilities.
1.3.4 Palau
Palau is a chain of islands in the Marianas group in the northern Pacific. It stretches
from Helen Reef in the south near the equator, to Kyangel in the north. Of the
hundreds of islands and islets, only a few are permanently inhabited. The bulk of the
population of approximately 20,000 are based in Koror and Babeldaob (57). The
three main islands, Koror, Babeldaob and Pelelieu, lie within a single barrier reef
system, with smaller centres at Anguar and Kyangel (just south and north of this reef
respectively) and in the South-West Islands (some 600 kilometres from the main
group). Previously occupied by Spain then Germany, Palau was the Japanese
Pacific headquarters during WWII before it became a trust territory of the United
States. The country obtained independence in 1994 under a “Compact of Free
Association” with the USA. Funding under the original agreement expired in 2010
(58), with a revised agreement currently before the US House of Representatives for
approval following formal endorsement of the revised funding arrangements by the
Palau Parliament and US House of Representatives sub-committee in 2012 (58).
Palau has 16 states, each with its own government, along with a strong network of
traditional chiefs and women’s associations. The national government offices moved
to Melekeok state on the large island of Babeldaob in 2005, while the business
capital remains in Koror (state and island). The society is matrilineal with property
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passed from mother to daughter, and traditional (male) chiefs selected by the senior
women. The major industry is tourism, with the country being renowned for its diving
sites. Palau also licenses a number of commercial fishing operations within its
waters.
Health services are provided through the national hospital and dispensaries in each
state. Each dispensary is staffed by at least one registered nurse, and is visited
monthly by a team (including doctors and other health staff) on a monthly basis to
conduct clinics.
1.3.5 Tonga
Tonga has four main island groups spread over a large expanse of ocean. These are
the Tongatapu group (where the capital Nuku a’lofa is situated), the Ha’apai group,
the Vava’u group and the two islands of the Niuas. Of the 171 islands, fewer than 40
are inhabited. Tonga has a population of 103, 036 (59). Most of the population (73%)
lives on the island of Tongatapu, with the second largest centre Vava’u located
approximately 200 kilometres to the north (57). Roughly one third of the population
lives in urban areas, with tourism and farming the major industries. The population is
Polynesian, with a small Chinese minority (many of whom have Tongan citizenship).
Women traditionally have a great deal of influence within the family, however only
men can own or inherit land.
The population is very mobile, both within Tonga and to and from other countries in
the region. There is a high rate of out-migration to Australia and New Zealand.
Subsequently the overall population growth rate is extremely low given the high
fertility rate (57).
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Tonga is a Kingdom with a monarch as the head of the government. Government is
managed by the Prime Minister. It is a structured, hierarchical society with strong
traditional expectations regarding both communication and roles. The first
democratic elections were held in Tonga in 2010 resulting in a parliament comprised
of 26 elected representatives and nine nobles nominated by the monarch (60) .
The main hospital is a 200 bed facility in Nuku a’lofa, which underwent extensive
renovation in 2011-2012. Health centres provide basic medical facilities and limited
inpatient services at the next level. Of these there are seven on Tongatapu, two in
Hapai group, four in the Vavau group and two in the Nua’s. Below these there are
approximately 30 maternal and child health clinics. A strong community
midwife/nursing program is accessible through most of the country.
1.3.6 Vanuatu
Vanuatu is a volcanic chain of approximately 82 islands, making up a total land area
of 12190 square kilometres. Vanuatu has a population of 234,023 people (2009)
(61), with only 21% living in urban areas. Agriculture (including beef cattle) and
tourism are the major industries. Offshore financial services are also a substantial
source of income. Vanuatu remains amongst the least developed countries in the
world under the UN development index (62). Managed jointly by both France and
England for 76 years prior to independence in 1980 (62), both languages continue in
official use. Bislama, a form of pidgin English, is the common language spoken. Most
people have either English or French as a third language, along with one of the
many indigenous languages that remain in use.
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Government is largely decentralised to the six provinces. The capital Port Vila is on
the island of Efate, in Shefa province. The provinces are reflected in the health
administration districts. There is one referral hospital, with 4 provincial hospitals and
25 private health care facilities. Local health services are provided through Aid posts
staffed by nursing aides, and area health centres staffed by enrolled nurses. Health
services are party to a Sector Wide Activity Plan (SWAP) negotiated with partners
such as AusAID, Global Fund and the World Bank. Measures of mortality, including
childhood mortality rate (under 5 years) (U5M), infant mortality rate (IMR) and
maternal mortality ratio (MMR) are considered critical performance evaluation
measures under key funding agreements such as the SWAP plan.
1.4 Measures of mortality and causes of death
The simplest measure of mortality level is a direct count of the crude number of
deaths in a given population over a given time. While counts of reported deaths (by
age group and cause) may be useful in small stable populations where little change
is anticipated, mortality levels are usually measured as rates or proportions to allow
comparison over time and between different populations. Crude death rates similarly
do not readily allow comparison between populations due to the effect of age
structure. Important measures of mortality level used for health planning include sex
and age-specific rates, IMR and under 5 mortality rate (probability of dying between
0-4 years inclusive, 5q0). Summary measures of mortality such as LE, adult mortality
(the probability of dying between ages 15-59 inclusive, 45q15) and age-standardised
rates are also important for comparison between countries, regions and sub-
populations to identify inequalities, evaluate trends over time, and to present
mortality trends in a meaningful way for policy makers (Table 4). There are much
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greater social and financial incentives for reporting deaths in productive adult age
groups, and therefore under-estimation of the total number of deaths tends to have a
greater effect on early childhood deaths and older adults (63, 64). As LE is
calculated from mortality rates disaggregated by age group rather than as a total
population, unless IMR is very high LE estimates tend to be much less sensitive to
errors in estimation of completeness than crude death rates (65) and are therefore a
more reliable measure of mortality.
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Table 4: Key measures of mortality and causes of death
Measure Definition Infant mortality rate (IMR) Deaths in children aged less than 1 year divided by the
total number of live births. Usually reported per 1,000 live births.
Under 5 mortality rate (U5M)
Deaths in children aged less than 5 years divided by the total number of live births. Usually reported per 1,000 live births. This is a probability of dying rather than a population based rate.
Age-specific mortality rates
Number of deaths in a specific age group for a defined period divided by the total (mid-period) population in that age group. Usually reported per 1,000 population.
Age-standardised mortality rate
The number of deaths that would be expected to occur in a given population based on local empirical age-specific mortality rates if the age structure reflected a defined standard (usually the WHO world standard population), divided by the standard population. Usually reported per 1,000 population.
Adult mortality (45q15) The probability of dying between ages 15 and 59 years of age inclusive. Expressed as deaths per 1,000 population or as a percentage.
Life expectancy at birth (LE0)
The average number of years a person born at a defined point in time would be expected to live if exposed to the age specific mortality rates of that time throughout their lifetime.
Proportional mortality by cause
The proportion of deaths (as a percent) attributed to a specific underlying CoD (as defined by the International Classification of Diseases version 10, ICDv10)
Cause-specific mortality rate
Number of deaths in a specific age group for a defined period attributed to a specific underlying CoD (as defined by ICDv10) divided by the total (mid-period) population in that age group. Usually reported per 100,000 population.
(66, 67)
Cause of death may be expressed as age/sex/cause specific death rates and as
proportional mortality by cause if data are under-enumerated. CoD is conventionally
reported as a single underlying cause; derived from the medical certificate of death
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(hereafter called the medical certificate) based on a prescribed set of rules outlined
in the International Classification of Diseases (ICD) (68) The international standard
medical certificate has two parts. Part I is the causal sequence leading backwards
from the immediate CoD in line one. Part II is for conditions that were present at the
time of death, but did not directly lead to the death.
Figure 2: International standard medical certificate of death format (68)
Changes in the ICD rules over time have resulted in some shifts in patterns of
underlying cause, making comparison between versions problematic (69). The
current revision in use is Version 10 (ICDv10) (68). There is an Australian
modification (ICDv10AM) (70) used by several PICTs for morbidity coding which is
where major differences in these two versions predominantly occur. Mortality coding
is essentially comparable. Changes from ICD version 9 (ICDv9) to ICDv10 has
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resulted in a significant shift in the recording of diabetes, leading to a higher
attribution of deaths to diabetes as the underlying cause in some countries (69, 71).
In ICDv9, diabetes was considered the underlying CoD only if the preceding causes
were directly attributable to complications of diabetes or diabetic renal disease (71).
ICDv10 recognizes the causal effect of diabetes in certain types of cardiovascular
disease. It allows diabetes as the underlying cause for deaths attributed to
cardiovascular causes in the preceding lines on the medical certificate, provided
diabetes is listed in Part I. Diabetes as a risk factor for coronary atherosclerosis
producing ischaemic heart disease (listed in Part II) may not qualify. As such,
diabetes rates as CoD are expected to be significantly higher under ICDv10 than
previous versions (69, 71).
The ICD attributes each death to one underlying cause (68). This ensures the sum of
all cause-specific deaths equals but does not exceed the total number of deaths.
Cause-specific death rates may be calculated for multiple cause analysis. In this
case, the numerator is the total deaths with any mention of the disease of interest
(total mentions) anywhere on the medical certificate (Part I or Part II). This is useful
for measurement of the total burden of mortality attributable to each cause, but is
less readily understood by policy makers, and less comparable.
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1.5 An overview of mortality and cause of death data for the
Pacific
1.5.1 Historical context – patterns of mortality and causes of death
Mortality associated with devastating epidemics of infectious diseases such as
measles, whooping cough, and influenza (including the 1918-19 pandemic) was
recorded throughout the region as a result of European and Asian contact and
continued through to the 1940’s (72, 73). Infectious disease, nutrition and maternal
causes were the major causes of death noted at this time (72, 73). Infant mortality
was high, estimated at 150 deaths per 1,000 live births for Fijians and 176 deaths
per 1,000 live births in Papua New Guinea in the 1920’s (74),(72).
Major land and sea battles occurring in Guam, the Solomon Islands, the Marshall
Islands, Kiribati (then known as the Gilbert Islands), Nauru, and Palau during WWII
(72, 75-79). Mass depopulation occurred as islands were affected by the fighting,
and troop movements spread dengue mosquitoes around the region (72, 73). At the
end of the war, most of the PICTs were under colonial control by British, French,
Australian, New Zealand and United States governments. During this period, public
health measures focussed on malaria prevention and improved sanitation (72).
Immediately post WWII, the major causes of death in Vanuatu, the Solomon Islands
and PNG were reported as malaria, pneumonia, tuberculosis, dysentery and
malnutrition. In the non-malarious areas of Melanesia these were pneumonia,
dysentery, enteric fevers, filariasis, yaws, tuberculosis, leprosy, syphilis, and
childhood infections (72). Death rates from infectious diseases gradually declined
from WWII to the end of 1950’s (3),(80), although they remained the leading CoD
(72).
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Between WWII and the turn of the century, many of the islands regained their
independence (43). This was a period of rapid transition in many of the Pacific
Islands. Populations grew from continued high fertility and declining premature
mortality, and age structures shifted as people lived longer. Changes in sanitation,
health care and nutrition saw patterns of mortality shift. From the 1950’s, public
health programs continued to expand at both national and regional levels. The
Expanded Programme for Immunization, managed through WHO, began in the
Pacific in the 1970's and achieved 80% coverage of children receiving the basic set
of vaccines by 1988 (81). By the early 1960’s, infectious diseases no longer
predominated as the leading CoD in many PICTs (39, 80).
The first cases of HIV were reported in the mid 1980’s in all three sub-regions of the
Pacific (82). By 2000, HIV had become a generalised epidemic (self-sustaining
through heterosexual transmission (83) ) in PNG and moving towards a generalised
epidemic in Tuvalu and Kiribati. Other countries remain within the threshold for a
concentrated epidemic (with prevalence <1% in the general population but more than
5% in at least one high risk groups (83)).
It is evident that there was no single pathway through this demographic and
epidemiological transition. Early transition populations, characterised by continued
high infant and maternal mortality and infectious diseases remaining a leading CoD,
include PNG, Kiribati, Vanuatu, and the Solomon Islands (2, 3, 84-89), In countries
such as Fiji, Tonga and Samoa infant and childhood mortality rates have fallen and
chronic NCDs have become more important as a CoD (39). Increases in proportional
mortality from NCDs in these countries, when coupled with the lack of ongoing
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improvements in LE beginning to be reported (89), suggest real age-specific
increases in NCD related deaths rather simply reflecting a decreasing impact from
infectious diseases. Obesity rates in the Pacific islands are now amongst the highest
in the world, with 75% of adults in seven PICTs either overweight or obese (40).
Six countries were considered to have high (>15%) proportional mortality from
cancer by 1985 (Guam, French Polynesia, Palau, Tonga, American Samoa and
Cook Islands) (90). As early as the 1970’s, diseases of the circulatory system were a
leading CoD in many PICTs (91). Reports note that despite the obvious health risks
associated with atmospheric nuclear testing carried out in French Polynesia from
1966 to 1974, the biggest health impact was from increased affluence and the
associated lifestyle and environmental changes (39).
By the 1980’s, the first suggestions of a stagnation in health improvement were also
noted, reflected in very minimal or no improvement in LE from previous periods. This
stagnation noted at the 1986 Solomon Islands census with LE estimated at 59.9
years for males and 61.4 years for females (92). Stagnation in LE has also been
more recently demonstrated in Fiji (93). This demonstrates the ‘double burden of
disease’, where increases in the incidence, prevalence and mortality of NCDs occur
alongside existing health problems of infectious diseases such as malaria,
respiratory infections and gastroenteritis (40).
NCDs are the leading cause of hospital based deaths in many of the islands,
including FSM (1990, (94)), Fiji (1996 (91)), and Tonga (2005 and 2007 (95, 96)) .
However, information on CoD in the community remains limited for most PICTs. In
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2011 Health Ministers declared an NCD crisis in the region, further highlighting the
need for improved information for evaluation and monitoring (5).
‘Best Estimates’ of LE for 2000 published by Taylor et. al. for the PICTs were as low
as 51-58 years in PNG and as high as 73-77 for Guam and 70-76 for New Caledonia
(2). These estimates were strikingly lower than those reported by WHO for the same
period, and suggest mortality in the PICTs was in fact higher than had previously
been considered (2). A range of available data sources were evaluated for quality,
identifying wherever possible the original data and analytical method and considering
these in light of their known strengths and weaknesses. A key finding of this work
was that many of the available sources provided limited information on how data had
been obtained and estimates derived. As such, for many of the countries the authors
were able to provide only a broad range of values in which LE was likely to fall. This
indicates a critical need to re-examine sources of primary data in the PICTs to
establish more current estimates that will withstand similar scrutiny of their methods
and to provide greater certainty around the current mortality levels in the Pacific.
1.5.2 Previous Data Quality
An assessment of available data by Taylor et. al. in 1989 (3) highlighted significant
variation in published estimates of LE and a lack of COD data (3). This assessment
was repeated by Taylor et. al. in 2005 for estimates around the year 2000 (2) as
noted in the previous section, resulting in a similar conclusion. Wide disparities in
published estimates of mortality across the region were noted, suggesting many
published estimates over-estimated LE and indicating a continuing stagnation or
decline in health status in the PICTs (2). Plausible estimates of LE around 2000 were
derived predominantly from census analyses. Vital registration was considered
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reliable for the territories and small countries of Nauru, Palau and the Cook Islands.
The assessment of data sources is largely consistent with other reviews on reporting
completeness available for this period. The Solomon Islands MoH is reported to
have captured less than 10% of deaths through routine reporting in 1985-1986 (92).
The Republic of the Marshall Islands reported “significant under-registration” of
deaths in 1995 (97). In 1997, death registration data from the MoH in the Federated
States of Micronesia was estimated to be less than 65% complete (98).
The absence of reliable data was highlighted repeatedly throughout the 1990’s and
2000’s (4), (2), (99, 100). The first Global Burden of Disease (GBD) study, published
in 1996 (101-104), found there was inadequate mortality data available for most
PICTs, and therefore data from other countries in the broader WHO Western Pacific
region were used to develop mortality level and CoD estimates (104),(105). This
process was repeated in the 2000 update released in 2005 (105). Regional
aggregations, largely dominated by the health situation in PNG as used in the GBD
studies, are of little use in health planning at a country or local level in other PICTs
due to the relative population sizes.
Even in PICTs where mortality data was considered reliable, there was a notable
lack of data on causes of death. At the end of 2003, only Niue was reported to have
recent reliable CoD data (4). “Unclassified” deaths or “others” were reported as the
highest CoD (20.4%) in the Marshall Islands in 1999 (106), while “unknown causes,
signs and symptoms” were the second and third highest CoD in Tonga in 2005 (95)
and 2007 (107) respectively. Where CoD was recorded, tabulations of deaths were
often based on immediate rather than underlying CoD (106). Recent published CoD
data for the PICTs is fairly limited, consisting predominantly of leading CoD lists by
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all ages and both sexes combined, as published in the WHO Country Health
Information Profiles (89), and some national MoH annual reports (95),(107)
1.5.3 Recent data
Mortality estimates for the PICTs are currently published regularly by a range of
international agencies, with additional reporting through national statistics offices and
Ministries of Health. Tables 5 and 6 provide IMR and life expectancies at birth for the
PICTs as published in 2010 by WHO Country Health Information Profiles (CHIPs)
(108), the Secretariat of the Pacific Community’s (SPC) PRISM website (46), and the
UN statistical yearbook (109). Male and female LE at birth as published by these
sources is shown in Figures 3 and 4.
The CHIPs report is the primary health status publication in the Western Pacific
Region and has been published annually since the early 1980’s (108). Data are
derived from a combination of country MoH sources sent directly to WHO by
member states on request, and WHO program data. The PRISM website is
managed by SPC on behalf of the governments of the PICTs. The program
maintains close links with the statistical agencies for each country. Estimates are
derived from country reports and census analyses (mostly conducted by SPC in
partnership with national statistical agencies). The UN estimates shown here are
derived from the 2010 statistical yearbook and report the most recent empirical data
held by the UN statistical agency for each country at that time. IMR is not published
in the UN yearbook.
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Table 5: Published estimates at 2010 of Infant mortality rates (per 1,000 live births) (Both sexes combined); Pacific Island Countries and Territories
Country WHO (CHIPs) (108) SPC (PRISM) (43)�Reference Year
IMR Reference Year
IMR
AMERICAN SAMOA 2007 11.8 2006-08 11.3 COOK ISLANDS 2007 10.5 2005-09 11.6 FIJI 2008 16 2000 37.5 FRENCH POLYNESIA 2005 5.3 2006-08 17.0 GUAM 2004 12.3 2007-08 5.8 KIRIBATI 2005 52 2005-07 11.7 MARSHALL ISLANDS 2007 23 2003 52.0 MICRONESIA, FS of 2006 33 2003-07 21.0 NAURU 2002 12.7 2006-07 45.8 NEW CALEDONIA 2007 6.6 2007 6.1 NIUE 2003 29.4 2001-06 7.8 NORTHERN MARIANA IS
2005 7.11 2006-08 4.9
PALAU 2004 16.22 2004-06 20.1 PAPUA NEW GUINEA 2006 49 2001-06 56.7 SAMOA 2005 13.7 2006 20.4 SOLOMON ISLANDS 2008 31.4 2002-07 24.3 TOKELAU 2000 33 2000-03 31.3 TONGA 2005 11.8 2004-05 19.0 TUVALU 2003 21.6 2006-08 17.3 VANUATU 2006 30 2009 21.0 WALLIS AND FUTUNA 2003 5.9 2005-08 5.2
As can be seen in the above table, even allowing differences in reference years,
there are significant disparities between estimates from CHIPs and PRISM. Notable
examples include French Polynesia, Kiribati and Tonga. The data suggest IMR has
fallen substantially in Fiji, Niue, the Marshall Islands and Vanuatu and is increasing
in French Polynesia, Nauru, Palau, Samoa, the Solomon Islands and the Federated
States of Micronesia. These apparent changes are reasonably unlikely and the
differences suggested by these data require investigation.
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The disparity in published estimates of LE is even more apparent (Table 6, Figures 3
and 4). LE in Kiribati ranges by more than 13 years for males and 16 years for
females. In part, this disparity is generated by the extreme difference in reference
time periods; however, as all of these publications purport to be the most recent data
available, there is significant potential for these to be misleading. Much of the data is
also implausible, with estimates from Marshall Islands showing an 8 year
improvement in LE for males between 2003 and 2004 (108, 110), and Kiribati
showing a significant decline in LE over a five year period from 2000-2005 (108,
110).
Notably there are missing and dated estimates from the UN. This suggests the most
recent mortality data available for Kiribati at 2010 was for the 1950’s and 1960’s.
None of the data post dates 1995. SPC included data as recent as 2009 while WHO
had data as recent as 2008 in their 2011 CHIPS report, although most estimates
were for more than five years prior to the publication. Despite being significantly
more recent than the UN reports, this highlights a delay of at least two to three years
in the most recent data available to international agencies.
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Table 6: Published estimates at 2010 of LE at birth (males and females); Pacific Island Countries and Territories
Country
LE at birth (years, Males) LE at birth (years, Females) Reference Years
WHO (CHIPS)
SPC (PRISM)
UN stats
WHO (CHIPS)
SPC (PRISM)
UN stats
WHO (CHIPS)
SPC (PRISM)
UN stats
AMERICAN SAMOA 72.27 69.3 65 79.62 75.9 69.1 2005 2000 1969-
1971 COOK ISLANDS 65 69.5 63.17 73 76.2 67.09 2005 2001-6 1974-
1978
FIJI 68 67.4 69.5 72 68.0 73.7 2008 2000 1990-1995
FRENCH POLYNESIA 73 63.8 68.3 76.9 67.7 73.8 2006 2001 1990-
1995
GUAM 75.34 72.0 72.2 81.64 76.6 76 2005 2005-07
1990-1995
KIRIBATI 58.9 71.1 56.9 63.1 76.1 59 2005 2000 1958-1962
MARSHALL ISLANDS 67 58.9 59.06 70.6 63.1 62.96 2004 2003 1989
MICRONESIA, FS of 67 63.7 64.4 70 67.4 66.8 2006 1999 1991-
1992 NAURU 58 55.2 61 57.1 2004 2006 NEW CALEDONIA 71.9 71.8 67.7 78.6 80.3 73.9 2006 2007 1994
NIUE 67.0 76 76.0 2004 2001-6 NORTHERN MARIANA IS 73.3 73.5 78.61 77.1 2005 1999-
01 PALAU 67.8 66.3 75.68 72.1 2004 2005 PAPUA NEW GUINEA 52.5 53.7 55.2 53.6 54.8 56.7 2000 2000 1990-
1995
SAMOA 71.8 71.5 65.9 73.8 74.2 69.2 2001 2006 1990-1995
SOLOMON ISLANDS 62.2 60.6 68.4 64.3 61.6 72.7 2005 1999 1990-
1995 TOKELAU 68.4 67.8 71.3 70.4 2000 1990
TONGA 70 67.3 72 73.0 2005 2004-05
TUVALU 61.7 61.7 65.1 65.1 2002 1997-02
VANUATU 67 69.6 63.5 70 72.7 67.3 2006 2009 1990-1995
WALLIS AND FUTUNA IS 73.1 72.7 75.5 75.9 2003 2005-
08
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Figure 3: Recent published estimates of male LE at birth, Pacific Island Countries and Territories
Figure 4: Recent published estimates of female LE at birth, Pacific Island Countries and Territories
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In addition to the WHO CHIPs and UN statistical reports discussed previously, both
agencies, along with the United National Children’s Fund (UNICEF), and the United
Nations Development Programme (UNDP) maintain databases of annual estimates
of mortality for all countries including LE (all agencies), U5M and IMR. In most of
these databases, the original data source or analytical method are not explicitly
stated. The UNDP database uses predicted estimates based on an assumed
improvement each year from earlier data (108).
Despite studies that suggest estimates derived from local census analyses are the
most reliable data on mortality for much of the region (excluding the small islands
and territories) (2, 93, 111, 112), country generated reports frequently rely on
estimates from international agencies. Table 7 is derived from the most recent MDG
progress report published by the Fijian government. The IMR estimates shown were
obtained from the World Bank, despite local data being available for the period
shown. There was no information available regarding the original data or methods
used to generate these estimates.
Table 7: Extract from Fiji MDG Report: Statistics on Goal 4 - Fiji Millennium Development Goals 2nd Report, 1990-2009 (113)
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Additionally, Table 7 demonstrates the use of annual estimates of mortality
measures for small populations of the Pacific. This potentially leads to significant
over-interpretation of data, resulting in annual estimates that are highly susceptible
to stochastic variation. Annual estimates are used in a range of annual reports from
Ministries of Health, the CHIPs reports (as shown in Tables 5 and 6) and the
statistical databases maintained by the UN and UNICEF (109). Only the estimates
published by SPC (Table 5 and 6) show any consistent use of aggregation over time.
Similarly, measures of maternal mortality in the PICTs are highly unstable due to
stochastic variation, particularly when generated for single years. The regional MDG
progress report published by SPC in 2004 stated the MMR was ‘not a suitable
measure’ for maternal mortality in the Pacific Islands due to the small population
sizes (114). Despite this, MMR continues to be used almost exclusively by both
country health departments and international agencies.
Information on the top ten leading causes of death is produced by several PICTs and
collated in the WHO CHIPs (108). As noted previously, estimates are submitted
directly by countries or WHO programmes. Data from the CHIPs publications show a
number of issues. Firstly, cardiovascular diseases, cancer and even diabetes feature
prominently in recent lists, demonstrating the importance of these chronic and
lifestyle disease. Secondly, as noticed with PICTs such as Kiribati, Palau and
Vanuatu, unknown and ill-defined causes appear in the list of top ten causes of
death (108). Finally, the lack of age-specific data on CoD makes it difficult to assess
the causes that contribute to the early adult mortality that must be present given the
stagnation in LE reported by Taylor et.al (2) and confirmed in studies presented in
this thesis (93).
� � �
33
Comparison of the CoD distribution between PICTs has the potential to provide
valuable information on major regional problems, and to highlight emerging issues in
the region or where individual countries and territories are being left behind in terms
of health status. It is not possible to make comparisons in CoD distributions between
PICTs from the CHIPS data as there is no standard format, resulting in various
combinations of specific diseases and categories of diseases to be used. That ill-
defined and unknown deaths feature in these lists of leading causes of death also
indicates a high proportion of deaths in the region do not have a specific CoD
assigned, and the proportional mortality reported may not reflect the true distribution
of causes.
While the PICTs have been undergoing an epidemiological transition from a high
proportional mortality from infectious disease to increasing proportional mortality
from NCDs, this has not been even across the region, and uncertainty remains over
the effect of this transition on mortality levels. Previous studies have demonstrated
stagnation in LE across the 1980’s and 1990’s (2, 3, 115, 116). Other than the work
presented here, there has been no comprehensive assessment of mortality level or
CoD since these studies. It is apparent that both the lack of data and wide disparities
noted twenty years ago remain. The disparity in current mortality estimates is
potentially highly misleading for decision makers, and the estimates themselves are
of little use in evaluating health status without further assessment of their validity,
and the ability to disaggregate CoD data by age and sex.
The growing importance of NCDs increases the need for reliable ongoing mortality
and CoD data, to guide health planning, prioritise funding and evaluate interventions.
This data can be produced through well functioning routine CRVS systems. At
� � �
34
present, little is known regarding the operation and potential biases of many of the
CRVS systems in the PICTs. Taylor et. al.’s assessment of data from the 1980’s and
1990’s (3, 115) suggest only the small islands (such as Nauru and the Cook Islands)
and territories were able to generate valid mortality estimates from routine data.
More recent analysis by Mathers et al. in 2003 (4) suggests not even these smaller
countries are producing reliable data.
Mortality data (both level and CoD) are fundamental information required to assess
the health of a population, plan appropriate services, evaluate programs and make
decisions around how limited resources are best directed. Additionally, an
understanding of how mortality has changed over time allows an assessment of
where each country or territory is in terms of epidemiological transition. If sufficient
information can be obtained to determine if there is a “typical” pattern of
epidemiological transition within the PICTs, countries still in the midst of this
transition will be better equipped to predict the speed and distribution of these
changes for health planning purposes. As these “early transition” Islands are also
predominantly those most reliant of international assistance to meet health funding
needs, the ability to plan for the future is vital to ensure required services can be
met.
1.6 Research Objectives and Questions
Investigating mortality and CoD draws on research methods from both epidemiology
and demography. This thesis aims to define and investigate the current gaps and
uncertainties in mortality and CoD profiles for the Pacific Islands by addressing the
following primary research questions for selected islands:
� � �
35
o Based on empirical data (from routinely collected data where possible), what are
the current mortality levels in the Pacific Islands by age and gender?
o What are the major causes of death contributing to these mortality levels?
o How can existing data collections and CRVS systems be better used to generate
valid, reliable data on mortality and CoD, significantly disaggregated by age group
and cause to inform health planning and evaluate health programs?
In order to answer these primary research objectives, this thesis also addresses
three secondary research questions.
1) what are the current empirical mortality data collections available for the Pacific
Islands and
2) what biases are inherent in these collections?
3) what methods are suitable to assess the quality and completeness of the data
available from the Pacific Islands?
As noted earlier, due to the scale and diversity of the region, it is not possible to
review all the PICTs in this research, and these questions are investigated in relation
to six selected countries that reflect this diversity.
Chapter two provides a literature review that is essentially the “toolbox” of methods
available for this work. The review covers the primary methods of data collection for
mortality and CoD data, methods for evaluating the design and performance of
routine CRVS systems and their impact on data quality, and the methods for
assessing the quality of data and for generating estimates of mortality and CoD
distribution from flawed data. Strengths and weaknesses of these methods are
discussed, and their potential applicability to the PICTs reviewed.
� � �
36
Chapter three examines the routine CRVS systems for deaths in the selected
countries to address the first two secondary questions above, 1) what are the current
empirical mortality data collections available for the PICTs and 2) what biases are
inherent in these collections? The chapter also provides recommendations for how
routine data collections could be improved to provide more reliable estimates in the
future. Potential biases are further discussed in Chapter four which outlines the
potential impacts of small population sizes and migration on mortality and CoD
estimates for the PICTs, in order to identify where additional care or corrections may
be required.
Chapter five provides an overview of methods used to analyse mortality levels in
selected countries, based on the tools and data collections available and discusses
their strengths and weaknesses in this setting. Best-estimates for age-specific
mortality are subsequently generated. Causes of death are similarly dealt with in
Chapter six through examination of validity and the need for data correction.
The mortality and CoD profiles for selected countries are summarised in Chapter
seven, with a discussion of the data in a regional context examining how patterns of
mortality have changed and implications of this for public health priorities.
This thesis concludes in Chapter eight, returning to the original research questions,
and providing recommendations for additional research.
� � �
37
An overview of how these chapters fit together in this structure is given in the
following diagram (Figure 5).
Figure 5: Thesis structure and content
�
� � �
38
2 An overview of methods for collecting, analysing and assessing the quality of mortality level and cause of death data
Literature relevant to this thesis falls into two broad categories:
1/ data collection approaches for mortality and CoD data and the types of data
collected through these systems, including an overview of their strengths and
weaknesses; and
2/ the methods available for assessing the quality of the data from CRVS systems
and deriving estimates of mortality level and CoD distributions.
� � �
39
Methods can be further categorized by:
a/ methods for evaluating and assessing routine CRVS systems for mortality and
CoD data, and the impact of system design and performance on data availability and
quality;
b/ methods for evaluating (and correcting as necessary) mortality level data for
quality and validity; and
c/ methods for evaluating (and correcting as necessary) CoD data for quality and
validity.
A literature review was conducted in Pubmed and Medline, and using the reference
lists of key articles. Data were also sought directly from country holdings, and key
historical collections held by the SPC library and Prof. Taylor. Documents were
critiqued for content and evaluated by theme, with key findings presented below.
2.1 Sources of mortality and cause of death data
Vital statistics may be collected through censuses, surveys, or routine vital
registration collections such as civil registration or registries operated through the
MoH (34). The UN identified the importance of taking into account the distribution of
mortality when designing a system to measure it, and that the three collection
methods are complementary, rather than alternative options (117). Additional
administrative data collections including records of deaths may also exist, and be
used for analysis of mortality level and CoD distribution. These include Health
Information Systems (HIS) and hospital discharge data, police records for attended
deaths, and social security records.
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2.1.1 Census collections
The primary source of mortality data in the PICTs has historically been through
periodic censuses (Table 8). The first census in Fiji was conducted in 1879 (118).
Regular censuses were held across the region as early as the 1920’s (119).
Undercount was a noted concern with these early censuses, with mortality measures
derived from direct retrospective questions on deaths in the household over a
nominated reference period (75, 119-123).
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�
41
Tabl
e 8:
Cen
suse
s in
the
Paci
fic Is
land
s, 1
950-
2010
C
EB
CS
+ R
egis
tratio
n da
ta -
mod
elle
d
No
mor
talit
y da
ta re
leas
ed
� � �
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In more recent census analyses, a more common approach is to derive measures of
childhood mortality, or both childhood and adult mortality, from indirect demographic
methods that distribute deaths over time based on the age of the respondent (124-
126) (Table 9). These measures have then been used as inputs for model life tables
that generate estimates of age-specific measures of mortality and LE. Models that
use a single input parameter based on childhood mortality have been shown to have
a tendency to under-estimate mortality in the PICTs where low infant mortality does
not necessarily imply low adult mortality, as demonstrated for Fiji in case study
three (included in Chapter five) (93). Models that use both childhood and adult
mortality generally perform better (1, 93). Childhood mortality can be derived using
the children ever born/children surviving technique (CEBCS), where women of child
bearing age are asked how many children they have ever had and how many are still
alive. Deaths are then distributed in time according to national fertility patterns and
the age of the respondent (127). Adult mortality can be estimated using sibling
survival (how many brothers and sisters the respondent has had, how many are still
alive, and age of respondent), widowhood (has the respondent ever been married, is
that spouse alive, and age of respondent), and/or orphanhood techniques (is the
respondent’s mother or father alive, and age of respondent) with deaths distributed
according to the age distribution of the respondents (127). The UN manual X (127)
provides a good overview of calculations associated with these methods.
Indirect methods, based on the age distribution of respondents, are generally
preferred by statistical agencies over direct questions related to the number of
deaths in the previous twelve months which have “not proved to be very satisfactory”
(117) due to serious concerns around recall bias and subsequent under-enumeration
of deaths (11). In particular, the UN notes experience with the widowhood technique
� � �
43
suggests it should not be relied upon for accurate estimation (117). Although the
other indirect methods are considered more reliable (128-131), they are also affected
by issues of recall, can be affected by bias due to cultural issues around discussing
deaths, and rely on assumptions around the relationship between the distribution of
deaths over time and respondents age (66). Estimates of adult mortality may also be
derived from changes in population (by age and sex) based on survival and
migration recorded between multiple census rounds. The technique of inter-censal
survival is particularly reliant on accurate migration data, which is frequently
unavailable at the level of disaggregation required in the PICTs (132).
Enumeration of deaths through a census is noted to be more difficult in developing
countries as there tends to be a wider family structure (less nuclear families) (117),
increasing the level of uncertainty in data captured through questions about family
(such as how many siblings do you have) or households (such as have there been
any deaths in the household in the previous 12 months). Some Pacific cultures also
have social conventions that mean death cannot be freely spoken about, which may
introduce response bias. Despite these concerns, censuses have remained the main
source of mortality data for the region over the past 60 years due to the absence of
reliable CRVS systems and concerns about data quality (38, 50, 133). Censuses
have been held in most PICTs every five or ten years since 1946 (Table 8) (48, 50,
57, 59, 61, 123, 134-147), facilitated by assistance from SPC in Melanesia and
Polynesia, and the US Census Bureau in Micronesia. Limitations of demographic
analyses of census data have been highlighted on many occasions. For example,
IMR estimates for the 1967 and 1979 Vanuatu censuses were reported as
implausible (122, 148, 149) while the 1989 Vanuatu census reported difficulty in
establishing an appropriate measure of adult mortality (144). Censuses in the
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44
Solomon Islands in 1986 and 1999 also reported difficulties establishing adult
mortality, with models used to derive these measures from the data providing
implausible results (92, 142). Until 2000, most of the censuses conducted in the
PICTs continued to use life table models with a single input parameter of IMR or
child mortality to estimate adult mortality and LE (141-144, 149-152). Although more
recent censuses have demonstrated a shift to including two input parameter life
tables where possible, mortality estimates for several countries including Tonga (59,
136) and Vanuatu (135) continue to be generated from single input parameter
models. Methods used in the most recent census round are outlined in Table 9.
Censuses do not collect data on causes of death and therefore cannot be used to
derive population based measures of cause-specific mortality.
Table 9: Type of data collection and methods of estimation used for calculation of life tables in the most recent census round for selected Pacific
Island Countries and Territories
Country Census Year
Method of estimation
Fiji 2007 No mortality data released (48) Kiribati 2005 Model life tables using single input parameter
(CEBCS) (50) Nauru 2011 Vital registration data smoothed using model life
tables (134) Palau 2005 Model life tables (input unclear – smoothed vital
registration data or CEBCS input parameter – both collected) (57)
Tonga 2006 Model life tables using single input parameter (CEBCS) (136)
Vanuatu 2009 Model life tables using single input parameter (CEBCS)
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45
2.1.2 Periodic Surveys
There are several standard survey formats commonly used to collect data as inputs
for mortality estimates. Unlike the census that collects information on the whole
population, surveys collect data for a proportion of the population considered to be
representative of the broader population of interest. As with censuses, surveys may
collect data to undertake direct estimation of mortality from reported deaths, or
indirect estimation based on the age distribution methods described previously.
Repeated household surveys may also be used to estimate mortality based on
survival and migration.
Surveys fill an important role in the provision of mortality estimates in the absence of
routine data collection. The reliability of estimates from survey data is driven by how
well the sample selection reflects the broader population of interest (66). Surveys are
also subject to recall bias as they collect information on events that may have
happened several years prior (153, 154), and response bias (where people that
complete the survey are different in some way to those who opt out or cannot be
contacted). As with censuses, further response bias may be introduced by cultural
issues around discussing deaths. The use of surveys to collect data for monitoring
health status and evaluating health programs can also prove costly (38).
Historically, surveys in the PICTs have generally been more concerned with infant
and child mortality, or deaths from specific causes such as maternal deaths than all-
age all-cause mortality (155, 156). Two important survey types for mortality
estimation in the PICTs are Demographic and Health Surveys (DHS) (38, 43, 133)
and the UNICEF Multi-Indicator Cluster surveys (MICS) (157).
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Table 10: Key surveys in the Pacific Islands; 1995-2012
DHS surveys have been implemented across the region since the early 2000’s
(Table 10). These are retrospective cohort studies that use a birth history (all births
with dates and outcomes) from respondents to collect inputs for estimation of infant
and child mortality. Estimates often become less plausible for earlier periods (5-10
and 10-15 years prior to the survey) (158, 159) due to recall bias (153, 155). To date,
the only MICS in the PICTs was conducted in Vanuatu in 2007 (157), although more
are planned for the region over the next several years. These collect data for
estimation of infant mortality using the CEBCS techniques as employed in the
censuses. Although the Vanuatu MICS achieved a response rate of 89%, the
response rate from 15-19 year old women was particularly poor, and one of the most
noted reasons for non-participation of households as a whole was the death of a
family member (157). These issues raise concerns that childhood mortality figures,
potentially more so than other indicators derived from this survey, may be biased
and therefore not nationally representative. The smaller sampling frame employed in
� � �
47
the MICS (157) compared with the universal data collection employed in the census
obviously also generates a greater range of uncertainty around the subsequent
estimates of infant and child mortality generated from the CEBCS method.
Both surveys can be used to derive life tables from the estimates of infant and child
mortality using model life tables as for censuses; however this is routinely done only
for DHS’s.
2.1.3 Routine vital registration collections
An efficient routine CRVS system, with medical certification of CoD, provides
ongoing and relatively low cost data collection and therefore timely mortality data for
decision making (34, 35, 117, 160). The medical certificate is considered the
reference standard for CoD information, in the absence of an autopsy, as a qualified
practitioner is required to assess the case and make an informed decision
concerning the sequence of events that led to the death (68, 117). This is particularly
attractive in the PICTs where small population sizes make complete routine
registration feasible, despite the challenges of dispersed populations and limited
infrastructure.
Routine data collections for deaths are usually managed through civil registration or
health system reporting systems, but may also rely on lay reporting by a local chief
or leader (161). Civil registration is managed by the Ministry of Justice (or local
equivalent) with local functions often delegated to local government (such as Town
or Island Councils). The process comprises of two major components, recording the
death in the official records and therefore establishing the civil status of the
� � �
48
deceased person, and the issuance of a legal document, the death certificate,
certifying the death for legal purposes such as insurance and land inheritance. A
death certificate may also be required prior to burial (117).
Official registration of death requires the family to provide proof of the death (often in
the form of a medical certificate completed by a qualified doctor) (161), attend the
local civil registration office to complete the required paperwork, and pay a
nominated fee. Although the civil registration process may record CoD, this is not the
primary purpose of the system (other than to rule out unnatural causes that may
require criminal investigation (7)), and may vary in usefulness according to the
source of the information. Systems that require a medical certificate are likely to be
more reliable than systems that record CoD as reported by family members. As the
legal record, civil registration of vital events is frequently legislated as the official
source for mortality data (34) despite not always being the most reliable data source
in country.
While civil registration is frequently upheld as the “gold standard” (7, 33-35, 127,
160, 162, 163) of mortality data due to the relatively low cost of data collection, the
ongoing nature of collection (and therefore timeliness) and the ability to combine this
function with medical certification to obtain CoD (117); only seven of the 23 countries
of the WHO Western Pacific Region were assessed by Mathers et. al. to have
complete data collection systems in 2003 (4). The 2004 assessment of mortality data
in the region by Taylor et. al. (2) also found mortality from civil registration was
implausible and demonstrated significant under-recording. As Byass succinctly
states “the chances of a death being registered and documented as to cause
depends strongly on the socioeconomic status of the community and the nation in
� � �
49
which it occurs” (11). In many developing countries, a substantial proportion of
deaths do not appear in the final tabulations, either because they are simply not
registered (163), or because of losses in the administrative chain from a local
registrar to the central processing unit (164). Countries with low civil registration are
also subject to substantial sudden change in completeness, often due to subtle
process changes (117).
Vital events such as births and deaths are also often recorded through routine data
collections within the health system. “Complete and reliable mortality systems are
the cornerstone of national health information systems” (34). In comparison to the
legal focus of the civil registration system, health data collections are primarily to
inform operational decisions, and CoD is central to this purpose. Health systems for
reporting deaths may include a vital registration system (a record of all deaths in
both the health facilities and the community) based on medical certificates, hospital
separation data (hospital records that indicate whether the patient was discharged
home from hospital, transferred to another facility or died), and/ or community
nursing data.
The medical certificate requires doctors to record “to the best of their ability” the
causal sequence that lead to the death (starting from the immediate cause and
working back through the chain of events) (165-167). There is also space to record
any other diseases that contributed to the death but were not strictly causal. In
practice, certification has been found to be affected by many factors including the
specialty of the physician, their experience and beliefs, as well as their education
and training on the subject (168-172).
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50
The medical certificate as recommended by WHO is divided into two parts (as shown
in Figure 2). Part I has three lines and requires the physician to list the CoD starting
with the immediate cause followed by the preceding cause and finally the underlying
CoD. Part II of the certificate provides space for other conditions that did not directly
cause the death to be recorded. Coding practice varies internationally as to whether
the immediate cause, underlying cause or all causes are coded for analysis (161).
Many have argued that even with doctors’ certification, accurate CoD reporting
requires routine autopsy to verify CoD patterns. Various studies show the rate of
error when medical certificates are compared to autopsy data (173, 174). These
include hospital based studies in the US that found 48% of cases reviewed missed
“myocardial infarction” as a CoD, while 25% of the deaths reviewed were falsely
attributed to this cause (175). Very few PICTs have routine autopsy procedures, with
most either performing no autopsies at all, or flying a pathologist in only for high
profile medico-legal cases. There have been suggestions that while these errors are
significant at the individual cause level, they are less significant when grouped to
broader categories. Studies of autopsied cases in the 1930’s in the US showed that
correlation between the medical certificate and the autopsy finding rose from 79% to
90% when assigned to broad category of disease (ICD chapter rather than specific
causes) and that careful recording of deaths shows mistakes go a long way to
cancelling each other out at the population level (10). This is supported by a more
recent study of asthma in the UK (176).
Numerous studies, including Iran (177), India (178) and China (63, 179, 180), found
major discrepancies may occur between the medical certificate and information from
the medical record, highlighting the importance of training and periodic verification of
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51
certification practices. Despite these concerns, in the absence of an autopsy,
certification by a medical practitioner is recognised as the most reliable source of
CoD data.
In the hospital setting, unit record data on deaths and CoD may be collected through
hospital separation data. In these collections CoD is based on the principal diagnosis
at the time of death (and hence discharge from hospital) as recorded by the
physician on the front sheet of the medical record (117). While these data are based
on cause as nominated by a trained physician, the reason for treatment is not always
the underlying cause that led to death (117) and therefore there will be some small
degree of error in using these data in place of a medical certificate.
Countries may also collect data on deaths through primary or community health care
nursing programs, with deaths recorded on a separate form to be sent to the local
area nursing manager and/or recorded on monthly reports (161, 164). Details
collected about each death vary between country and system, but usually include
name, place of death (or report), age at death, and may include CoD as reported by
the local health worker nurse or family members (181). These systems may focus on
specific categories of deaths such as maternal or child deaths. There has been
limited analysis (at least in the published literature) of CoD based on this type of data
collection (182), and the usefulness of these data may be limited, or restricted to
very broad categories of cause. Similarly lay reporting by community leaders, such
as utilised in Tonga (164), can provide accurate collection of mortality level but is
limited in scope for collecting anything more than broad CoD. CoD is collected
through these systems essentially to rule out whether a death is suspicious and
requires investigation (14).
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2.1.4 Demographic surveillance sites
In the absence of universal civil registration, countries such as India and China have
demonstrated success with sample demographic surveillance sites (DSSs) (63, 117,
180, 183-186). These use smaller populations to provide a representative overview
of both mortality level and CoD distribution from medical certification. Jha notes
“sample registration systems are probably the most useful method to obtain CoD
information” in the absence of medical attention at death (187), and the Chinese
system is an excellent randomised cluster selection of urban and rural areas
accounting for around 1% of the total population and providing robust information for
decision makers (179, 180). This technique has been used in PNG (188) but it is
unlikely to be a suitable solution in other PICTs where the small population numbers
mean rates are often quite unstable and already need to be averaged over several
years to be interpretable (189)). A further disadvantage of DSSs is that focussing
enumeration in these areas over a number of years could result in a shift in
community knowledge and attitude to health interventions and reporting that may not
be reflected in the broader population. These sites may therefore become less
representative of the broader population over time (190).
2.1.5 Verbal Autopsy
The shortcoming of using CoD information from medical certificates is that in many
of the PICTs, a large proportion of deaths occur outside the hospital system without
a medical attendant (72). This means either CoD is not provided, or is based on the
determination of someone without a health background (such as the police or local
registrar). The verbal autopsy (VA) arose out of work in “population laboratories” in
India and Bangladesh in the 50’s to 70’s to address this issue. It is now recognised
internationally as a viable alternative for collection of accurate CoD statistics, at a
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53
population level, where universal certification is not practiced (181, 191-193). They
are usually associated with either demographic surveillance sites or surveys as they
are both labour and resource intensive (14).
The term “verbal autopsy” was first proposed by Keilman and associates working in
the population laboratory of Narangwal India as a “procedure to exploit the
information provided by the relatives of a deceased person” (194) in order to
establish a medically acceptable CoD, and was further developed by the MATLAB
site in Bangladesh (195). The technique has since been substantially refined to
reconcile differing approaches (190). A standardised questionnaire on symptoms
displayed by the deceased prior to death is completed by a trained interviewer who
visits family members, with CoD established by analysis of the questionnaire (either
by a physician, or more recently by automated analysis) (194).
Studies such as carried out in Tanzania by Setel et. al. in 2006 (196) found VA
effective for determining CoD from a list of locally important diseases (such as HIV,
malaria, TB and cardiovascular diseases among others) for both adults and children,
as verified against medical records. Limitations of VA are the focus on specific
common causes of death by broad age group (rather than all causes), and reliance
on well trained interviewers (often not medically trained) (195). VA has been utilised
in PNG using a questionnaire that pre-dates the current international standard (197).
There is no literature suggesting the technique has been applied elsewhere in the
region, however it may have application for the larger populations in the PICTs such
as the Solomon Islands and Vanuatu, where universal certification is currently not
feasible.
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In 2012, WHO also released a short version of the standard VA tool designed for use
in conjunction with routine reporting. This however is yet to be tested in the field
(198) . Standardised approaches with trained interviewers are a significant
improvement on earlier systems of lay reporting of CoD through community leaders
which had limited quality control around recording or standardisation of collection.
2.1.6 Summary of data collection approaches
As outlined in the preceding sections, and summarized in Table 11, while all of the
data collection approaches have strengths and weaknesses, routine vital
registration, done well, should be the preferred approach for mortality data in the
PICTs due to the continuous nature of collection (7, 117), ability to generate data on
both mortality level and CoD distributions (7, 117), and the small populations sizes
which result in a great deal of uncertainty when dealing with rare events such as
deaths, especially when disaggregated by age and sex as required to generate
meaning data for policy. Although censuses remain the primary data collection in the
region, they are not able to capture CoD, and in most instances rely on extrapolation
of adult mortality and LE from childhood mortality based on model life tables, rather
than measuring actual events. DHS and censuses will continue to be an important
source of data in the absence of reliable routine CRVS systems, and for generating
data to assess their performance.
Direct methods of estimating mortality from routine reporting have the advantage of
timeliness, can better recognise differentials, and are better suited for providing
estimates for infant and child mortality (127); although indirect methods are less
expensive (199).
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55
Tabl
e 11
: Sum
mar
y of
Dat
a C
olle
ctio
n A
ppro
ache
s fo
r Mor
talit
y D
ata
Dat
a So
urce
Pe
riodi
city
Sam
ple
fram
e Pe
riod
of
inte
rest
Dat
a co
llect
ion
(mor
talit
y le
vel
data
)C
oD D
ata
Col
lect
ed?
Cen
sus
Per
iodi
c –
5-10
yea
rs
Who
le P
opul
atio
n R
etro
spec
tive
Dire
ct -
(dea
ths
in th
e ho
useh
old)
In
dire
ct –
par
tial b
irth
hist
ory
(CE
B/C
S) &
orp
hanh
ood
data
No
Sur
vey
DH
S
Per
iodi
c –
~ 5
year
s S
elec
ted
sam
ple
– re
pres
enta
tive
of w
hole
po
pula
tion
Ret
rosp
ectiv
eD
irect
– c
ompl
ete
birth
his
tory
N
o
MIC
S
Per
iodi
c –
~ 5-
10
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(CE
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S)
No
Oth
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surv
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Usu
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opul
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n (d
epen
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on
cove
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) C
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Yes
Hea
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(dep
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rout
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– us
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ites
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es
� � �
56
As noted in this chapter, CoD can be obtained through medical certification, hospital
discharge records, verbal autopsy, nursing reports or lay reports from family and
other community representatives. As doctors are trained in diagnosis of disease,
sources that collect data based on direct observation by the doctor, provided the
doctor is aware of the patients history, are obviously likely to be more accurate than
reporting by nurses or family members who do not have the same level of training.
While both medical certificates and diagnosis for hospital discharge records are
completed by doctors, the medical certificate is specifically structured to capture
underlying CoD and there is a clear set of coding rules in order to identify this (ICD
10). In comparison the hospital discharge record captures the “primary diagnosis”
which may not necessarily reflect the cause that initiated the sequence of conditions
that ultimately resulted in the death occurring.
Figure 6: Level of certainty of underlying cause of death by data source
Highest certainty
Although medical certification is therefore accepted as the preferred source of CoD
data, the quality of this information varies significantly, with numerous studies
� � �
57
identifying discrepancies between the medical certificate and information from
medical records. This is discussed further in section 2.4.
2.2 Evaluating routine data collection and reporting systems
Although routine vital registration through either health or civil registry offices has
been identified as the preferred data source for vital statistics and CoD data in the
Pacific, both the design of the data collection processes and system performance
can have a profound effect on the quality of the data collected, and subsequently on
the validity of the estimates generated. It is therefore necessary to assess the
strengths and weaknesses of routine data collection and reporting systems to
understand how these may bias the data available.
The most comprehensive guide to the elements and processes necessary for
reliable and valid mortality level and CoD statistics was published by the UN in 1987
(and updated in 2001) (117). Simultaneously, the UN completed a survey of the
status of vital registration in member states. Although this forms the foundation for all
future work in system assessment, little action was taken at the time, and there was
little connection between this work and the subsequent information technology and
communication (IT) approaches.
Early approaches to assess HIS and CRVS systems developed from the burgeoning
field of IT applications for managing clinical health information. It was recognised that
an initial assessment of processes was needed before existing systems could be
replaced or adapted to computer-based applications. Craig et. al. (200) describe
assessment as a process that “looks at information in terms of needs and availability
� � �
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and its gathering, recording, transmission, processing, reception and use. These
questions are primarily examined with respect to the questions who, what, where,
when, why, how and how much” (200). He also describes the need to obtain all
documentation relating to the system. This approach recognised interviews as critical
to understanding the “informal processes” at work within the system, the way things
actually work as opposed to the way they are designed to work.
Interviews and document review remain at the heart of all future approaches to
record how data moves through the system (179, 186, 201). While further tools were
developed to “quantify” some of the aspects of the system operation, the qualitative
information obtained through this approach is critical to developing a rich
understanding of how and why the processes work the way they do. However, there
remained a need to organise key findings of the system assessment into a set of
coherent themes and ideas that could be used to focus further work. As later stated
by HMN “What has been missing in these efforts has been an overall vision of a
comprehensive health information system and the interlocking of the various parts”
(7).
A number of different frameworks were developed and trialled during the 1990’s and
2000’s to address this gap, most focussing on HISs more broadly rather than vital
registration or CoD specifically. The HMN was established in 2005 under WHO to
assist countries to improve the use of data in health planning by supporting
improvements in HISs (202). They describe the HIS in terms of demand (who needs
the data and what for), supply (the tools and methods available to obtain this data)
and level (the level of the system at which data are generated and used) (8). This
categorisation would later be developed into a comprehensive assessment tool (the
� � �
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HMN assessment) designed to assist countries to conduct their own HIS review. The
HMN assessment (203) covers key areas of:
• Content (Cost efficiency and effectiveness);
• Capacity to collect data, manage and analyse the results;
• Application of agreed standards;
• Dissemination and analysis; and
• Linkages to other data sources.
Whilst mortality data are collected though multiple different systems and
organisations, the HMN assessment is primarily focussed on health system
reporting. It does however make a specific point of emphasising the linkage to other
sources. Macfarlane (204) argues a key measure of success of a HIS is the
integration of information systems from other sectors. While fairly non specific about
what this should look like, she suggests possible means to achieve integration
including conformity of sources, definitions and reporting periods; rationalisations
and sharing of software, boundaries and maps; agreement on minimum health
related requirements for household surveys; and coordination with non-governmental
information systems.
Applied specifically to mortality data in Thailand, a workshop of key participants
reported the HMN tool provided a useful assessment framework (203). Interestingly,
this assessment found mortality data in Thailand to be of moderate or good quality,
whereas an assessment of the same data conducted shortly before found it to be of
poor quality (9). The HMN assessment relied on measures of completeness from a
national household survey (conducted 10 yearly) which asks for the registration
document for all deaths noted from the household. This is likely to be highly biased
� � �
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due to recall and the fact unregistered deaths may be less likely to be reported in the
survey, especially if participants know registration documents will be requested. The
95% completeness noted in the national survey seems unlikely, especially as the
demographic surveillance site noted in the same article suggests 12.5% under-
reporting in a site that has been closely monitored for over 10 years (203).
The key strength of the HMN assessment is that it generates consensus for areas to
focus on improvements, however the questions include predominantly subjective
criteria that would be difficult to apply uniformly given it is a self assessment. In order
to minimise possible bias the approach relies heavily on the formation of a
committee to conduct the assessment. The assessment tool has been applied in
both Fiji and Tonga (205, 206). Although both countries identified a need to improve
CoD data collection and reporting, given their broad focus of the assessment, there
is little discussion regarding the specific barriers or review of options for
improvement.
Concurrently with an increased focus on HIS, the 1990’s and 2000’s saw an increase
focus on assessing the IT used for health information. In particular, why so many HIS
had failed or were performing poorly. Most evaluations focussed on why a particular
software was chosen, or presented only a very general picture of costs and benefits,
driven primarily on the economic aspects of the system (207). These were
conducted by panel discussions, structured questionnaires or “expert” evaluation. A
literature review in 2007 found none addressed broader implications of IT on the
CRVS system as a whole (207). One assessment however advocated the use of a
framework to assess the “design-reality” gap (208). This framework considered
elements of information, technology, processes, objectives and values, staffing and
� � �
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skills, and other resources (i.e. time and cost). Although designed as a risk-
assessment tool to consider whether systems were likely to fail, rather than
specifically looking at the impact of the system on the data collected, this approach
highlights the effect of differences between system design and the “informal
processes" that develop within the system.
Following the first questionnaire from the UN in 1987 (161), it was not until the mid
2000’s that researchers and institutions began to develop more targeted approaches
to assessing routine reporting systems for deaths and CoD in their own right as
rather than simply as part of the broader HIS. Early approaches by Ruzicka and
Lopez (209), guided in large by the original UN civil registration guidelines (117),
were developed by Rao et. al. into a comprehensive framework to organise and
present key system aspects for collecting and reporting mortality and CoD data.
Applied to Africa (64) this framework divides the system into the following
components: administrative (legal framework, organisational issues, system design);
technical issues (data standards, capacity building, quality control aspects); and
societal issues (political will, public awareness).
Rao et. al.’s framework (64) assumes the person conducting the assessment has an
in-depth knowledge of mortality data collection and analysis questions to tease these
broad guidelines into a comprehensive assessment. The major advantage of this
approach is the flexibility of the framework to accommodate the different reporting
structures that may be encountered. The themes are also substantially consistent
with the major components considered essential in an assessment of public health
surveillance systems as outlined by the US Centres for Disease Control (210):
system considerations (purpose, stakeholders, and operation), outbreak detection
� � �
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(timeliness, validity) and system experience (usefulness, flexibility, acceptability,
portability, stability – resilience to changes, costs). While there are obvious
differences in data collections for mortality data and outbreak investigation (primarily
in terms of timeliness and strength of evidence required), both data sets are primarily
to inform decision making in health. This common purpose is reflected in the
similarity of the key issues considered important in system assessment.
Over the same period, the earlier work of Ruzicka and Lopez (209) was developed
further in parallel by Prof. Lopez and others at the University of Queensland; initially
to develop a set of questions that could be used by researchers to guide in-country
assessments (as carried out in Oman (211)), and subsequently into a series of tools
published in conjunction with WHO to guide countries through a self-assessment
process. These include a rapid assessment of 25 questions (212), and a broader
comprehensive assessment (213). The WHO assessment framework is based on
five key areas: legal basis and resources for civil registration; registration practices,
coverage and completeness; certification and CoD; ICD mortality coding practices;
data access, use and quality checks (212, 214). The prescriptive questions make the
assessment more appropriate for self-assessment, but lose a degree of flexibility that
Rao et. al.’s more thematic framework is able to accommodate when assessing
approaches and systems that do not fit the expected norms upon which the
questions in the WHO tool were based. Both have an important place in assessing
routine CRVS systems, and despite their differences share many similarities given
their common origins. While both approaches are focussed on identifying key areas
for improvement from a system perspective in order to improve future data
collections, only Rao et. al.’s framework attempts to relate these system
� � �
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characteristics to the effect on the data collected and subsequently how data can be
used.
One of the differences in approach is who should undertake the assessment. While
self-assessment may result in stronger ownership of the results, and therefore
possibly a greater likelihood to invest in recommended improvements, it requires a
high level of understanding of both the current CRVS system operations and best
practice CRVS system theory to move beyond very broad findings useful at an
advocacy level to specific actionable steps. Given the small specialist workforce
available in many PICTs, and the long and ongoing calls for improvements, it is felt
an external assessment process would be more appropriate in this setting, although
this could subsequent feed into a country led process.
Although implied under the various approaches, the need to document the flow of
information through a mapping exercise or flow chart describing what information is
collected and how it moves through the system, is not explicitly described. This is a
critical step in obtaining this rich qualitative data, as it allows the assessor to identify
breaks in process where data may be lost, delayed or changed.
Another concept gaining importance over recent years is the link between HIS and
health governance. This is broadly referenced to some degree under “administrative”
aspects in Rao et. al.’s framework, but is not covered beyond legislation in the WHO
tools. None of the approaches outlined previously focus specifically on the questions
of Who owns the data, Who is responsible for the data, For what purposes will the
data be used, If the data will (or may) be used, and what controls will be put in place
to govern that use (215). Siddiqi (216) applied the following questions in an
� � �
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assessment of health governance in the Eastern Mediterranean under the heading
“Intelligence and Information”:
• How efficient and up to date are communication processes (forms etc)?
• What information is available about the health system and health in the
country and how accessible is it?
• What is the reliability of information available for development of policies?
• What evidence is there for the use of information in the decision making
process?
• How is the relevant information about health generated?
• How is implementation of health policies monitored?
As noted by McGrail (215), the experience of developed countries demonstrates
failure to address these questions early in CRVS system development can result in
situations where data are not available or can only be accessed by a few specific
people, even when the system as a whole is working well, thus negating much of the
value of the collected data. The integration of some components of the frameworks
that have been developed for assessing health governance, such as the WHO
“Domains for Stewardship” (217) , into the assessment of CRVS systems may begin
to address this gap. Existing approaches also did not adequately highlight the
important relationship-driven personal elements of the systems which are critical to
the success or failure of CRVS systems in this region.
An amalgam of existing approaches, rather than one specific tool would therefore be
considered the best approach to adequately assess system issues that affect data
collection, capture, analysis and reporting in the PICTs in order to make plausible
� � �
65
judgements about the validity and reliability of published data, and to understand the
bias inherent in the raw data.
The approaches presented here all address system issues rather than providing a
structure for assessing data independently to the CRVS system through which it was
gathered. This has been covered thoroughly by a further data quality assessment
framework developed by Rao et. al. (218) which considers nine criteria under four
key data attributes. These are: generalizability (coverage and completeness); validity
(use of non-specific codes, content validity, incorrect or improbable age and sex
patterns); reliability (general mortality level and consistency of cause-specific
proportions over time); and policy relevance (timeliness and geographical
disaggregation) (179, 218). This has been applied successfully in both China (179)
and Brazil (218). Alone, this approach may have limited application in the PICTs, as
most of the data are expected to be of “poor” quality and may be considered of
limited use without a further understanding of how the data were obtained. However,
it has enormous potential to add to the value of a full system assessment by adding
a quantitative understanding of exactly how system issues are affecting estimates to
add to the qualitative understanding of the CRVS system review. The components of
this data assessment also provide the link from the system understanding to making
appropriate “corrections” to the data as demonstrated by Rao et. al. in Brazil (218).
2.3 Evaluating completeness for mortality level
Means of measuring mortality level fall into two broad categories; direct and indirect
methods. Direct methods record every death, either as it occurs or retrospectively,
such as through routine CRVS systems, demographic surveillance sites or surveys
� � �
66
and census that record each death in detail. These can then be assessed for
completeness and adjusted accordingly. Indirect methods collect information from
relatives regarding which family members have died and use age distributions from
the remainder of the population to distribute these deaths over time and age group,
subsequently generating age-specific mortality rates as described in Chapter one. As
noted in an earlier section of this chapter, direct reporting of deaths through vital or
civil registration processes is the preferred source of data for measuring mortality
and causes of death when data have been obtained from a well functioning CRVS
system (where biases in the data collection can be assessed) and the data have
been assessed and corrected if necessary for completeness. Preston notes the fact
empirical data are often “incomplete” tends to be used to justify ignoring findings
based on these data (219, 220). He goes on to note that for many data sets, if the
data can be assessed in terms of its completeness it is nearly as useful as a
complete data set. He sets the threshold for a data set being useful at around 60%
completeness (221), a limit also recognised by the Australian Bureau of Statistics.
2.3.1 Direct Demographic Methods
Methods to assess the completeness (or consistency between the death and
population data) of the reported deaths (through vital registration, census or survey)
include those developed by Brass, Preston, Bennet and Horiuchi (131, 199, 222-
225). The Brass “Growth Balance Method” assumes population stability, so that
growth rate is constant with age. It is based on the true growth of a population aged
x and over is equal to partial birth rate (people who reach age x in a given year
divided by the person years lived above age x in that year) minus the partial death
rate (deaths in a year in persons aged x at last birthday and above divided by person
years lived above age x in that year). Adapting this equation so the recorded number
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of deaths equals the completeness (C) of the data set multiplied by the true number
of deaths provides a linear equation where the Partial Birth rate = growth ( r) + 1/C *
(partial death rate). The slope of the line formed by this equation is 1/C and can be
used to both assess the data and correct for under-reporting. This method is very
sensitive to error in the data which changes the slope (and therefore the estimation
of completeness of the data source) and to changes in the assumption of stability
(225, 226). The selection of age groups to “Crop” in order to fit the line may also
have a significant impact on the estimate of completeness (124). Murray et.al.
examined optimal age ranges against developed country data in 2009 (124). While
they found the optimum cut as 40-70, the results from the age trims were found to be
highly susceptible to systematic age misreporting, and while not specifically noted in
the paper, this would also imply susceptibility to differentials in reporting
completeness by age, a scenario likely in the PICTs. As there were several age
trims found to perform well, the results indicate a need to consider optimal age trims
to fit the line in the context of local data. (227)
Modifications include the Preston-Coale method, which uses growth rates instead of
the age distribution of deaths used by Brass, and the Bennet and Horiuchi method
which discards the assumption of stability (225). These methods require inter-censal
data which is often not available (227).
An overview of the assumptions and data required for each method is shown in
Table 12 below.
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Table 12: Assumptions and data requirements for direct demographic analysis
Method Data Required Assumption Time period Brass Growth Balance
Age-distribution of population
Stable population
Can be matched to available data
Preston-Coale Age-distribution of population and population growth rate
Stable population
Can be matched to available data
Generalised Growth Balance
Age-distribution of population from two censuses
Closed population
Inter-censal
Bennett-Hounichi Age-distribution of population from two censuses
Closed population
Inter-censal
A second approach for assessing completeness is direct comparison with another
source or “capture-recapture” techniques (127). These methods, adapted originally
from population biology, compare the overlap between the different sources to
estimate the number of deaths missed by all sources (228-230) (see Figure 7). Two
source, or dual record methods such as described by Sekar and Deming (231), also
known as the “Peterson method” (232), assume independence between the two
sources (228). That is, reporting the death to one source should not affect the
probability that the death will be reported to the other source, a scenario unlikely to
hold true in most settings. Log-linear methods that use three or more sources
address this issue by producing multiple models with different combinations of
dependence between the data sources. The most appropriate model is selected
based on Bayesian selection criteria such as the Bayesian Information Criterion or
Aikaike Information Criterion, and on a qualitative understanding of the relationship
between the data sources (229, 230). Although the three source model is more
robust (233), it is uncommon (especially in the PICTs) to have access to multiple
data sets.
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Figure 7: The two source capture-recapture model
(229, 230).
Other assumptions of the capture-recapture method include a closed population and
equal “catchability” of deaths. That is, every death has an equal probability of being
reported. While this is unlikely across an entire population given the incentives for
reporting a death for an adult (who has property etc) are likely to be greater than for
a child, the advantage of the capture-recapture method is that analysis can be done
by sub-group (such as by age group) where these assumptions are more likely to
hold.
2.3.2 Model Life Tables
Model life tables may be used to impute age-specific mortality and LE where
empirical data is unavailable or incomplete using information from childhood
mortality (from CEBCS) alone, or with adult mortality (from methods such as sibling
survival). This involves fitting the known (or estimated) input parameter(s) to a model
life table such as the Coale and Deming life tables (234) derived primarily from
Europe and the Far East, or the UN life tables which use a slightly wider group of
countries (including some developing country life tables from Africa and eastern
Europe) (13, 66, 117). Although the UN life tables, and the newer tables designed by
� � �
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Murray et.al. (235) are an improvement on earlier tables as they include a wider
variety of sources including both developed and developing country data, in the main
these tables reflect country experiences where a low U5M equals a low adult
mortality rate and subsequent high LE. This association between U5M and adult
mortality levels was observed repeatedly across Europe, parts of Asia and Africa
(where data were available and subsequently used as inputs to the various model
life table systems (234) and reflects the earlier varieties of the epidemiological
transition where decreases in mortality from infectious diseases, primarily due to
improved nutrition and sanitation, resulted in marked improvements in longevity and
LE (234). Reductions in mortality from infectious diseases over the years (as
described in Chapter one) have largely (or in some cases completely (112)) been
offset by increasing mortality from NCDs, resulting in high adult mortality from NCDs
with lower IMR as infectious diseases have diminished in importance (2).
Subsequently, single input parameter models such as used by census estimates (61,
135) are likely to underestimate adult mortality and overestimate LE, although the
choice of model family (reflecting the broad geographical areas from which the
source data for the model was drawn) and subsequent ratio U5M to adult mortality
will affect to what extent. Modelled estimates based on single parameter input
models may be more suitable for the few PICTs which still report relatively high
IMRs, such as PNG, Kiribati, and the Solomon Islands, and have a high prevalence
of infectious diseases (108). However even Kiribati and the Solomon Islands are
beginning to note a dual burden of disease as NCDs such as diabetes and cancer
become more important (as noted through the reported leading causes of
hospitalisation (108) which would be expected to adversely affect adult mortality.
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Model systems such as the modified Brass logit system developed by Murray et al
(235) and developed into the WHO Modmatch software, may have greater
application for the region as they allow two input parameters (usually 5q0 and 45q15,
both of which can be derived from indirect demographic methods such as in a
census) forcing the selection of models that more accurately reflect the adult
mortality pattern.
Model life tables are generally calculated using input parameters from indirect
measures of mortality obtained through a census or survey as described in part one
of this chapter (127). In this case, final age-specific estimates of mortality are
affected by the uncertainty of both the model selection, and the distribution of events
over time based on age patterns inherent in the indirect estimates (127). Models may
also be used to “smooth out” age-specific mortality estimates that are unstable, or
show somewhat unexpected patterns due to stochastic variation from small
population sizes such as used in Niue and Nauru censuses (137). Given the small
population sizes in the PICTs, this application of model life tables may have
application in generating a valid, reliable age and sex specific mortality profile for
countries even where data collections are relatively complete.
Another method used to provide mortality estimates in the absence of recent data is
to use the last available empirical estimates and extrapolate to a current mortality
level. This is inherently flawed and as noted by Murray (13) when the change in LE is
examined there is “little left except the UN assumed rate of improvement”.
Unfortunately this approach is used to provide estimates many years distant to the
timeframe for which valid data are available, and does not account for the known
effects on LE if economic and social situations do not improve, if there is a HIV
� � �
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epidemic that takes hold, or if NCDs in adults cause premature deaths. Murray notes
in late 1980’s annual estimates by the WHO and UN for many PICTs were still based
on data from the early 60’s. (13)
A step further than the model life table systems are the more advanced models
developed by researchers such as Lopez, Rajatnaram and Murray to estimate
mortality trends over time for infant and child mortality(236, 237) , adult mortality
(41) and specific cause mortality ( such as maternal mortality) (238), in large
developed for the 2005 GBD study (239). These multivariate models use a wider
range of input data including economic measures such as GDP, patterns of mortality
in neighbouring countries with known data, as well as available country mortality
data fitted using Gaussian regression to estimate mortality trends over time. Such
models have an important role in filling information gaps and providing an indicative
assessment of the long term trends (upwards of 20 years) , however the wide
uncertainty shown in such estimates and the complexity of the approach limit the
usefulness of these methods at country level. They are therefore best suited to
academic assessments at a broad regional level, or to extrapolate trends in countries
where multiple reliable data sources are available at various points over a long
period of time. The validity of these models has been demonstrated successfully
multiple times at a broad regional level (240). However, as the models rely on
neighbouring country data to successfully provide context, their validity in the Pacific
may be limited given the diversity in the PICTs and documented lack of data upon
which to draw. Resultant estimates need to be further evaluated against empirical
estimates for the region where possible.
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2.3.3 Conclusions
In spite of the shortcomings outlined, model life tables based on single input
parameters generated though census collections (Table 9), remain the staple
method of generating age-specific mortality measures for many PICTs including
Tonga, Vanuatu, Kiribati and the Solomon Islands, highlighting a need to further
explore the potential for using direct demographic methods and capture-recapture
analysis to assess and correct mortality data from CRVS systems in the region
wherever there is sufficient data to do so. While the Brass-Growth Balance method
(241) has limitations in relation to its ability to account for changes in population
structure, the simplicity of approach and the subsequent greater availability of the
necessary input data mean it could prove a useful first “screening” tool in populations
for which migration is low (migration is discussed further in Chapter four) and the
system assessment supports an assumption of a reasonably high level of
completeness of reported deaths. Neither the Brass Growth Balance nor capture-
recapture methods are suitable for application in populations with high migration, and
in the absence of more suitable methods, modelled estimates will remain important
where CRVS systems generate incomplete data or the completeness of reporting
cannot be adequately anticipated through a review of the system design and
performance. Where model life tables do remain necessary, the use of two input
sources (both child and adult mortality) should be routine to avoid misleading
estimates.
The importance of assessing the plausibility of mortality estimates regardless of the
method of data collection and analysis, such as through consideration of the design
and performance of the CRVS system as noted above, has been highlighted by a
range of papers. These include the UN (117) and Sibbai (242) who noted a range of
� � �
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considerations such as access and timeliness which should be examined. In 2005,
Rao et. al. (179) developed this concept further generating a rating framework for
quality of CoD data for application in China. This was based on generalizability
(coverage and completeness), reliability (consistency of cause data with mortality
pattern by age and consistency of cause specific rate over time), validity (content
validity, use of ill-defined categories, incorrect/implausible age or sex dependency),
and policy relevance (timeliness and geographical disaggregation). This framework,
which extends to data on cause as well as mortality level, is useful as the measures
can be applied to the data themselves without a lot of additional information about
the system. The examination of consistency with long term trends will be particularly
important in the PICTs where small population sizes are likely to result in substantial
stochastic variation in mortality estimates.
2.4 Evaluating biases in cause of death data
The quality of CoD data as collated from medical certificates is dependent upon not
just the practice of certification, but how this information is then coded according to
the ICD rules and tabulated.
2.4.1 Review of Certification
As noted by Morton, Omar et al.(243) the medical certificate was not originally
intended for epidemiological research, however it remains the primary source of data
on CoD worldwide.
Determining the underlying CoD is an inherently difficult task, particularly if the
patient did not die while under medical care. A range of factors determine how
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closely the recorded sequences of deaths match the “truth” (Figure 8). These include
differences in practices by country (244) education and language (242). Local names
may exist for specific diseases or symptom complexes (242). While education can
improve accuracy of certification (245, 246), experience has been found not to be a
major factor (247). Studies based on case vignettes in Taiwan suggest individuals
produce very different results from the same information, especially when choosing
between an acute complication of a chronic disease and the chronic disease itself,
suggesting variations in certification were due to differences in interpretation of the
available information not knowledge of the process itself (248)
Figure 8: Sources of errors in medical certification of death from Maudsley and Williams, 1994 (249)
Some causes of death are more likely to be correctly identified than others. In
England and Wales, studies comparing medical certificates with autopsy results has
shown high levels of accuracy for ischemic heart disease and unnatural causes but
poor accuracy for a range of other diseases (250). In countries with good diagnostic
facilities, medical certificate studies have found high accuracy with recording
cancers. In the UK, studies have found breast cancer to be underreported by only
4% (251), and colorectal cancer by only 5% (252). In comparison, a study of brain
donors in the UK found less than half the patients who were clinically diagnosed with
� � �
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Parkinson’s disease prior to their death had this recorded on the certificate (either in
the causal sequence or in the contributory causes) (253).
Even common causes of death are often misidentified. A community hospital in the
US found 48 % of deaths due to myocardial infarction did not have this recorded in
Part I of the medical certificate and 25% of deaths that did have this recorded should
actually have been attributed to a different cause (173). Another study in the US
found medical certificates missed 35% of cancers known to the cancer registry in
four US states (254). In an interesting study that looked at how certain doctors felt
about their ability to identify the CoD, Karwinski found increased certainty of
diagnosis (due to better diagnostic technology) did not result in increased accuracy
on the medical certificate (255).
Age also affects the likelihood of an accurate CoD being recorded on the certificate.
As chronic disease advances there is also more likelihood of co-morbidity making
the underlying cause more difficult to determine (256). Acute conditions with high
case fatality are likely to be reported more accurately in death statistics than chronic
conditions which may progress slowly or produce non-specific symptoms. This has
been demonstrated in studies of asthma in the UK (257) and diabetes in France
(258).
Causes of death are even more difficult in neonatal deaths. Studies in an Australian
hospital neonatal intensive care unit show that even using just seven broad
categories, the main CoD was incorrectly recorded for 34% of deaths (259).
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Interestingly, causes of death may be deliberately incorrect on the medical
certificate. Legislation in the UK (no longer in force) requiring alcohol related deaths
be reported to the coroner appeared to result in under-reporting, with doctors likely to
amend the CoD to something less controversial (260, 261). Also in the UK, a study
of hospital doctors and GP’s found that of those who said they would always use the
best possible CoD statements more than half the doctors would modify the “true”
CoD to avoid distressing relatives (247). As noted by (262) while the information on
medical certificates remains non confidential, inaccuracy in certification will occur.
In busy hospitals in the developed world, certification often falls to the most junior
member of the team (263). Although this may not be the case in the PICTs, chronic
understaffing and extremely high workloads mean doctors are unlikely to complete
certificates with the care required for accurate statistics unless they understand the
importance of the data created. As Robertson said about the physician in his
address to the American Public Health Association in 1929, “beyond all he must be
admitted as a partner into this vast undertaking” (264). This remains true today.
There are two options for reviewing the quality of death certification, depending on
the data to which the researcher has access. The first is a review of the medical
certificates themselves, in terms of plausibility of sequences, the number of causes
listed, consistency with age and sex of the deceased etc. This is useful in reviewing
the quality of the processes employed in completing the certificate and the
plausibility of the results, but cannot give an indication of the consistency of the
findings with the medical history of the deceased and is therefore limited in terms of
being able to apply correction factors to the collected data. The second is a review of
the certificate against the medical record for the deceased (265-271), in which a
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medical practitioner or team of practitioners reviews the case history and completes
a “mock’ medical certificate to be compared against the original. This approach relies
on access to identifiable data, and assumes the reviewing physician or team is both
independent and is more accurate than the original physician. While this approach
has merit, in the PICTs the small medical community on island and often poor state
of the medical records mean findings from a review are not necessarily more
accurate than the original certificates. Implementation of such an investigation would
therefore require some modification to this approach.
2.4.2 Assessment and correction of coding practices
Coding is inherently affected by the quality of the medical certificates. Incomplete
data and inappropriate sequences are major problems (272). Standard practice is for
written causes of deaths (from certificates, verbal autopsy etc) to be coded using the
WHO ICD. The current version of this classification system is ICDv10. Deaths are
then tabulated by code and other disaggregations (such as age and sex). WHO
publishes guidelines on appropriate tabulations based on the level of coding
undertaken (273).
Although coding should be standardised, several studies have noted local practices
may develop over and affect coding and the international comparability of findings
(274-276). Even developed countries with well developed CRVS systems need
periodic review to correct systematic misclassification that can occur (187). Both
Kelson (275) and Mackenbach (276) found coding practices across Europe had
developed to “account for” common problems in the certification of cancer and went
some way to evening these discrepancies out.
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Changes in coding practice can also have a significant affect on cause specific
mortality. In the US, ischemic heart disease increased by a factor of 1.15 due to
changes in coding when ICDv7 was replaced with ICDv8 (171). These changes are
not consistent across countries due to the differences in certification and coding
practice. The change from ICDv9 to ICDv10 created a fall in COPD deaths in France
from 1998/99-2000/02 and in increase of around 10% in the US (69).
CoD has historically been measured and reported according to the underlying cause,
either by cause-specific mortality rates, or by proportional mortality. In order to
calculate these rates and/or proportions, the effect of errors in certification and the
often substantial number of deaths assigned to “ill-defined and unknown” causes
must be accounted for. In European data, inverse correlations have been found
between the proportion of “unknown” category deaths and suicides (277) There has
also been suggestion that cardiovascular disease in particular may be affected by
the proportion of deaths considered to be “unknown”. (276).
Some papers class errors in certification and coding into major and minor, although
this has been disputed as subjective (278). Given the state of data in PICTs much of
the CoD data cannot be coded to specific subsections and must therefore be
analysed at a broader category (i.e. diabetes rather than subtypes of diabetes).
Therefore there is value in differentiating between errors that result in classification
to a different category as opposed to those which affect coding only at sub-category
level.
While the mistakes are important, at a population level there is some evidence to
suggest inaccuracies balance out. A study of asthma mortality in Northern Ireland
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found false positives to be balanced by the number of false negatives when reviewed
against medical records and relative interviews about patient history (176).
2.4.3 Multiple cause of death analysis
While analysis of CoD has traditionally been carried out in relation to the underlying
cause, there has been a trend towards analysis of multiple causes of death in
recognition that several diseases may contribute to a patient outcome. This is
perhaps more relevant for public health practice, as it more accurately reflects the
actual burden of a disease on the population. Whilst there has been work put forward
to examine the use of multiple COD models to determine causality (including
frequency with which specific causes are recorded on the same certificate and odds
ratios) (279) this is fraught with danger, as the diagnosis is the physicians “best
guess” and substantial selection bias would be expected (i.e. causes already not
considered to be causal would not be reported as such). The most straight forward
analysis of multiple CoD data is the “total mentions”. That is, all deaths that have the
disease of interest on the certificate (in part I or part II) are counted (279). Of deaths
autopsied in Georgia (US), only 42 % of certificate had the correct underlying cause
– but when analysed using multiple causes of death, 72% had the correct underlying
cause somewhere on certificate (280).
Although multi cause data may improve the usefulness of the statistics, it does not
solve the issue of international comparison as the ratio between total mentions and
deaths with the cause of interest as the underlying cause may differ, reflecting the
different reporting practices of different areas (281). The frequency between which a
cause is reported (at all) and reported as an underlying CoD is affected by disease
type with acute conditions having a higher ratio (282). This suggests estimates of
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death due to acute conditions may provide similar estimates using either underlying
CoD data or multiple CoD data, but that multiple CoD analysis may be particularly
useful for chronic conditions.
2.4.4 Models for cause of death data
In many places medical certificates or other CoD data are often only available for the
proportion of the deaths that occur in or near a hospital or major health facility.
Murray and Lopez (283) have recently developed a method for using facility based
proportional mortality to estimate national proportional mortality by age and cause.
This still depends on the availability of the requisite data, and is highly context
dependant (that is a significant proportion of deaths need to occur in hospital) (11).
The method uses cause specific mortality fractions for the proportion of deaths that
occur in hospital, using a subset of the population for which there is good VR data, or
a similar population. The method was found to be fairly robust when tested in Mexico
and was not overly sensitive to the size of the population used to estimate
proportions. (15). The method could prove useful in PICTs where many areas have
poor data outside the hospital system, but may face difficulties due to different
access to health services by different populations and the lack of comparable
populations with good vital registration data.
Salomon and Murray (284) note current estimates indicate around 30% of all deaths
worldwide are covered by CRVS systems. In the complete absence of empirical
data, indirect methods have been developed for assigning CoD from the age
distribution of deaths. The best known of these methods were developed for the
GBD studies by Salomon and Murray (284). The “CODMOD” program is a
spreadsheet that uses probability distributions based on regression models to
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determine the proportion of deaths by cause from all-cause mortality and income per
capita.
Morris and colleagues (285) describe a similar regression method specifically for
estimating the distribution of deaths by cause in children under five years of age
when there is insufficient empirical data. This does not distinguish between neonatal
causes and essentially looks at diarrhoea, malaria, measles and pneumonia (as well
as undetermined deaths). More recently, Rao et al (286) have developed a model
which addresses these gaps but excludes malaria, an important CoD in children in
parts of Melanesia, from the generated CoD distributions.
The model by Morris was developed on data from SE Asia and Africa where studies
were available and child mortality was greater than 26 per 1,000. As data were not
available from other regions, they were not incorporated into the design, and the
model could not be validated against these other populations (285). Arguably the
same would be true of the Solomon and Lopez model, and when examined closely
there has been little empirical data for the PICTs used as input data, with included
data from the early 1990’s in one or two countries only; thus calling into question the
relevance of these models on the small scale applicable to the PICTs
Models may provide causal distributions by broad cause group (such as infectious
and maternal illnesses, NCDs and external causes) yet they cannot provide the
detail that health planners and governments need to address the very real health
issues in the PICTs. Taking just one group of NCDs as an example, the risk factors
and subsequently the health interventions required at a population level to address
rheumatic heart disease, ischaemic heart disease and cerebrovascular diseases are
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very different. Without the ability to disaggregate at this level, health planners are
mostly guessing as to where interventions should best be targeted.
2.5 Final Comments
As presented in this chapter, there are a range of methods available for collecting
and analysing mortality and CoD data. Censuses and surveys are well accepted in
the region as a means of collecting mortality data, and provide periodic data for
analysis that may simply be unavailable through routine CRVS systems. The use of
census data to generate mortality estimates across all age groups is however simply
no longer suitable (if it ever was) in the PICTs in the face of the changing
epidemiology of premature adult mortality and mortality from NCDs. Model life tables
based on single input parameters of child or infant mortality do not adequately
capture premature adult mortality, as this “uncoupling” of child and adult mortality
levels was not the experience of most countries from which the data used in the
models was drawn. While this could potentially be overcome using a second adult
mortality input in the model life tables, generated from indirect methods such as the
orphanhood or widowhood methods; these have had limited use in the region and
are known to be prone to both recall and selection bias as outlined earlier. Similarly,
methods reliant on inter-censal population changes to assess mortality by age group
are limited by the poor quality of migration data for much of the region, and may be
affected by poor recording of age of respondent. As such, there is a need to focus on
alternative means of generating reliable mortality estimates by age and sex through
the use of routinely collected data, wherever possible.
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As discussed in Chapter one, routine civil and vital registration collections in the
PICTs are known to be incomplete and may have issues around coverage,
completeness, timeliness and quality. In order to use the collected data to generate
reliable estimates of mortality, a qualitative and quantitative assessment of these
aspects is required. The system assessment approaches described together
address the range of system aspects that may have a bearing on data quality, while
both direct demographic and capture-recapture approaches methods have merit in
the PICTs. Other than the Brass Growth Balance method, the direct demographic
methods rely on data periods that align with an inter-censal period or require
additional data on migration, while the capture-recapture methods require multiple
data sets and assume all deaths have the same probability of being reported. The
choice of method to assess the data from each PICT will depend on the best fit of
available data with the data required and assumptions made for each approach.
What is apparent in the methods described in this chapter is the assumptions (such
as whether the population is stable or whether there is dependence between
sources), which are an inherent part of each of these approaches, have a significant
impact on the final estimates of mortality by age. Selections regarding the approach
used must therefore be made in the context of a qualitative system assessment to
ensure appropriate selections are made and final results are plausible.
Censuses and surveys are also clearly limited in their ability to collect CoD data.
While causal distributions by age and sex can be estimated from models based on
mortality level, this is not an adequate substitute for nuanced empirical data that is
able to monitor real rather than anticipated changes over time. Further, as described
in this section, the final estimates of CoD distributions generated in this manner may
be a long way from any original data and incorporate a substantial range of
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assumptions. They are used in scenarios where routine collection of CoD is simply
not available or complete and in this way, CoD estimates may be modelled from age
and sex specific mortality rates that were themselves derived from models using only
U5M as an input. CoD distributions derived from models therefore have a potential
role in evaluating priorities at a very broad scale where no empirical data is available,
but limited value in monitoring trends over time or for health planning at a detailed
level. As such, there is no real substitute for medical certification of death data
routinely collected at a nationally representative level.
The quality of CoD data relies on the diagnostic ability of the person reporting the
data in selecting the underlying CoD and recording the sequence of events leading
to the death. As noted in this chapter, this is an inherently difficult task even for
trained medical practitioners, and is cause is frequently misreported. Systematic
biases in certification, coding or tabulation may need to be assessed through a
medical record or certificate review according to the level of access to original source
data. The medical record review is more robust in its ability to assess and correct
reported CoD as it is possible, through this process, to evaluate each of the steps
from diagnosis and certification, to coding, tabulation and reporting. There is
however an assumption in this approach, as employed in Iran (185), that the medical
record itself is properly documented and complete, a scenario which is known to be
untrue in a number of PICTs including Tonga and Nauru (14). A death certificate
review does not allow the diagnosis to be reviewed beyond the plausibility of the
recorded sequence, and therefore allows only some of the revision possible through
the medical record review. While the use of a systematic VA may provide useful CoD
data, the size of Pacific Island populations means this would be unlikely to be a
viable option for data collection at a nationally representative level, although there
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may be scope for this approach to be used in the future to improve data collection
through community nursing reports from remote islands where medical certification is
not possible.
The limitations of models for CoD data as described above mean even for countries
where there is no representative national collection of CoD data from medical
certificates or verbal autopsy, empirical data collections on cause that do not
conform to these quality standards, such as hospital records, lay reporting through
civil registration or community nursing reports should be explored. While these
cannot be used to make national estimates, they may provide additional depth of
information for policy makers about key diseases and conditions of public health
interest, and what age groups these are impacting, that are not otherwise available.
This thesis will therefore focus on improving understanding and use of routinely
collected data to generate estimates of mortality and CoD profiles by age and sex.
Methods of evaluating data quality and correcting for systematic biases will be
employed from the methods described above as determined by both the data
available and access to the original source material. Available data for selected
countries will be examined more closely in the following chapter. As noted by the
UN, whichever method of data collection and analysis is selected, the final analysis
must be considered in the face of inherent plausibility (117).
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3 Routine reporting systems for deaths and cause of death in selected Pacific Islands
Examination of mortality level and CoD profiles should begin with a clear
understanding of the data sources available and how the data collection, collation,
analysis and reporting processes may affect these data. This is essential for
selecting appropriate methods to work with the data and critical assessment of the
plausibility of mortality level and CoD estimates derived from it. As discussed in the
previous chapters, this assessment will focus on routine government CRVS systems.
3.1 Assessment Framework for routine reporting systems
CRVS systems are frequently complex, spanning multiple departments or
organisations, and influenced by a diverse array of factors from community attitudes
through to available infrastructure. As discussed in the previous chapter, while there
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are a range of existing approaches to assessing CRVS systems, it was felt an
amalgam of these existing approaches was required to adequately capture all the
major features of the system that may affect data availability and quality.
The framework presented in this chapter was developed to provide a logical means
of identifying both the planned and actual operation of the systems and subsequent
effects on data quality and availability; and presenting these in a manner to allow key
strengths and weaknesses to be identified, and broad comparisons to be made
across systems and countries whilst maintaining flexibility in the approach. The
framework was developed specifically for application in the PICTs. It draws on
lessons from previous approaches, but is primarily based on previous work by Rao
et. al.(63, 64) presented in Chapter two; incorporating questions from tools such as
the HMN HIS assessment (202), the IT focused assessments of Heeks, Kaufman,
and Yusof (287-291) and previous work by Ruzicka and Lopez (209) under Rao’s
thematic headings (64). Initially developed as a data collection tool, the framework
was refined during and applied to country assessments for Fiji, Kiribati, Nauru,
Palau, Tonga, the Solomon Islands and Vanuatu between 2008 and 2010.
Amendments were also incorporated to reflect the layers of administration and
governance that dictate the way CRVS systems operate and interact, and the
importance of social structures and interpersonal relationships that are the
cornerstone of successful systems in this region. Key revisions, drawn primarily from
the body of work on health governance (216), included the separation of
administrative issues at the system or organisational and national levels, and the
addition of concepts of responsibility and authority (under administration aspects).
This is particularly important in the PICTs where seniority may be heavily influenced
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by community standing and age, rather than job title alone. A separate theme to
address staff ownership of the CRVS systems or the acceptability of the systems to
those directly involved in their operation was also added.
The framework presented here structures system findings into five key aspects: the
socio-political environment, national administrative framework, system
administration, system technical characteristics and system ownership (Figure 9).
The first two aspects refer to the overall context in which routine death reporting is
carried out within the country, while the final three are system specific. A summary of
key aspects for review is shown in Table 13. CRVS systems may be contained
entirely within a single organisation, or operated by a combination of government
and non-government agencies. Each distinct reporting system should be assessed
separately, and in the context of its relationship to other existing systems.
Figure 9: Routine death reporting system assessment framework
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90
Tabl
e 13
: Rou
tine
deat
h re
port
ing
syst
em a
sses
smen
t fra
mew
ork
outli
ne: K
ey a
spec
ts fo
r rev
iew
Cat
egor
y To
pic
Key
Des
ign
aspe
cts
for r
evie
w
Key
Per
form
ance
asp
ects
for r
evie
w
Soc
ieta
l iss
ues
Fune
ral
arra
ngem
ents
Ti
me-
fram
e to
bur
ial
Bur
ial l
ocat
ion
(priv
ate
vs. C
emet
ery)
Pub
lic s
uppo
rt R
equi
rem
ents
for s
ocia
l sec
urity
pay
men
t R
ole
of th
e ch
urch
lead
ers
Com
mun
ity a
war
e of
the
need
to re
port
deat
hs
Com
mun
ity a
ccep
tanc
e of
gov
ernm
ent r
ole
Pol
itica
l su
ppor
t P
rese
nce
of in
tere
st g
roup
s/ in
tern
atio
nal
stak
ehol
ders
M
orta
lity
data
use
d to
info
rm p
oliti
cal d
ecis
ions
Adm
inis
trativ
e en
viro
nmen
t G
eogr
aphy
G
eogr
aphi
cal d
istri
butio
n of
ser
vice
s re
ason
able
acc
ess
to c
ivil
regi
stra
tion
cont
act
Infra
stru
ctur
e av
aila
ble
Nat
iona
l int
erne
t net
wor
k R
elia
bilit
y of
pow
er s
uppl
y A
cces
s (tr
ansp
ort a
nd m
ail r
oute
s)
Com
mun
icat
ion
chan
nels
ava
ilabl
e
Rea
sona
ble
syst
ems
are
suita
ble
for t
he in
frast
ruct
ure
avai
labl
e
Lega
l fra
mew
ork
Per
sons
requ
ired
to re
port
Tim
efra
me
for r
epor
t E
vide
nce
requ
ired
for r
egis
tratio
n S
till-b
irths
incl
uded
in c
olle
ctio
n
Lega
l ins
trum
ent e
xist
s (y
ear)
R
epor
ting
of d
eath
s re
quire
d R
epor
ting
befo
re b
uria
l req
uire
d O
vers
eas
deat
hs a
nd s
tillb
irths
cle
arly
iden
tifie
d O
rgan
izat
iona
l ro
les
Gov
ernm
ent a
nd s
take
hold
er o
rgan
isat
ions
in
volv
ed in
rout
ine
repo
rting
or u
se o
f dat
a V
ital r
egis
tratio
n fu
nctio
ns c
lear
ly a
ssig
ned
Inte
rnat
iona
l con
tact
poi
nts
clea
rly d
efin
ed
Coh
esio
n /
inte
grat
ion
Coo
rdin
atio
n be
twee
n ag
enci
es
Nat
iona
l sta
tistic
s co
mm
ittee
mee
ts re
gula
rly
Qua
lity
cont
rol c
heck
s be
twee
n ag
enci
es
Form
al a
gree
men
ts fo
r dat
a sh
arin
g / a
cces
s S
taff
know
thei
r cou
nter
parts
in re
late
d or
gani
satio
ns
Sys
tem
issu
es
– adm
inis
tratio
n
Des
ign
and
inte
nt?
Pur
pose
of s
yste
m (i
.e. m
easu
ring
CoD
, civ
il st
atus
etc
) S
yste
m is
sui
tabl
e fo
r the
pur
pose
and
ava
ilabl
e in
frast
ruct
ure
Org
anis
atio
nal s
truct
ure
Pro
vinc
ial p
roce
sses
are
alig
ned
with
org
. stru
ctur
e M
anag
emen
t su
ppor
t
Dat
a us
ed to
info
rm m
anag
emen
t dec
isio
ns
Dat
a m
anag
emen
t sta
ff ha
ve a
cces
s to
man
ager
s R
espo
nsib
ility
Res
pons
ibili
ty fo
r all
proc
esse
s cl
early
des
igna
ted
Res
pons
ibili
ties
docu
men
ted
and
agre
ed w
ith s
taff
Aut
horit
y A
ppro
vals
requ
ired
to a
cces
s an
d re
leas
e da
ta
Dat
a m
anag
emen
t sta
ff ab
le to
che
ck a
nd c
orre
ct d
ata
Sta
ff ab
le to
rai
se p
robl
ems
outs
ide
imm
edia
te u
nit
Res
ourc
es
A
dequ
ate
staf
f and
fund
ing
reso
urce
s fo
r sys
tem
as
desi
gned
.
��
�
91
Sys
tem
issu
es
– te
chni
cal
Pro
cess
es
(incl
udin
g qu
ality
con
trol)
Det
erm
inat
ion
of c
ause
of d
eath
R
ole
of th
e po
lice
in c
ertif
ying
dea
ths
Fiel
d co
llect
ion
and
reco
rdin
g pr
oces
s C
olla
tion
and
entry
pro
cess
es
Exi
sten
ce o
f reg
iste
rs /o
ther
reco
rds
Dis
ease
spe
cific
regi
ster
s R
outin
e vs
. on-
dem
and
anal
ysis
Pro
cess
doc
umen
tatio
n ex
ists
and
mat
ches
pra
ctic
e P
roce
sses
avo
id d
uplic
atio
n be
twee
n pr
ogra
ms
Res
pons
ibili
ty fo
r dat
a ch
ecki
ng a
ssig
ned
and
cond
ucte
d Q
ualit
y co
ntro
l / s
yste
m re
view
car
ried
out r
egul
arly
R
epor
ting
timef
ram
es a
nd fo
rmat
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� � �
92
Key system aspects can be classified by performance and design elements.
Performance elements are those for which there are clearly recognised international
standards or best practices with which to compare, such as the use of the
internationally recognised format of the medical certificate (68) or coding for CoD
according to the ICDv10 (68). Design elements of the system encapsulate the
suitability of the system for the environment, infrastructure and administrative
framework under which it operates. The design elements are neither inherently
“good” or “bad” but have a significant influence on the ability of a CRVS system to
generate high quality accessible mortality level and CoD data.
3.2 Common elements of CRVS systems in the region and their
effects on data
An overview of the key data sources available and the major biases associated with
these data collections across the study countries is presented in case study one. A
brief summary of country specific findings follows the case study.
3.2.1 Case Study 1: Reporting systems across seven PICTs (164)
Karen L Carter, Chalapati Rao, Alan D Lopez, Richard Taylor. Routine death
reporting systems and implications for data in seven Pacific Island countries BMC
Public Health 2012, 12:436 doi:10.1186/1471-2458-12-436
This case study presents an assessment of the routine CRVS systems in Fiji,
Kiribati, Nauru, Palau, the Solomon Islands, Tonga and Vanuatu, based on the
� � �
93
assessment framework described earlier in this chapter. Assessments were
conducted to achieve three primary aims:
� To identify the key routine data collections available in the study countries
and where these data are held.
� To identify aspects in the design and performance of the CRVS systems
that could affect data accessibility and quality (including coverage and
completeness potential biases such as age or gender reporting
differentials); and
� To identify key priorities for system improvement based on the strengths
and weaknesses common across the countries to improve future data
collections.
Staff interviews, observation, document review and mapping of all processes (both
as they are meant to operate and how these actually functioned were key aspects o
the approach used to conduct the assessments.
While substantial differences in the CRVS systems (and subsequently the available
data) were evident across the study countries, common themes affecting how well
the systems functioned emerged. Significant biases that may affect mortality and
CoD data were present in all systems and included issues such as coverage of outer
islands, differentials in incentives for reporting deaths at different ages, and off-island
referrals for high risk cases.
Carter et al. BMC Public Health 2012, 12:436http://www.biomedcentral.com/1471-2458/12/436
CORRESPONDENCE Open Access
Mortality and cause-of-death reporting andanalysis systems in seven pacific island countriesKaren L Carter1,2*, Chalapati Rao1*, Alan D Lopez1 and Richard Taylor1,3
Abstract
Background: Mortality statistics are essential for population health assessment. Despite limitations in dataavailability, Pacific Island Countries are considered to be in epidemiological transition, with non-communicablediseases increasingly contributing to premature adult mortality. To address rapidly changing health profiles,countries would require mortality statistics from routine death registration given their relatively smallpopulation sizes.
Methods: This paper uses a standard analytical framework to examine death registration systems in Fiji, Kiribati,Nauru, Palau, Solomon Islands, Tonga and Vanuatu.
Results: In all countries, legislation on death registration exists but does not necessarily reflect current practices.Health departments carry the bulk of responsibility for civil registration functions. Medical cause-of-death certificatesare completed for at least hospital deaths in all countries. Overall, significantly more information is available thanperceived or used. Use is primarily limited by poor understanding, lack of coordination, limited analytical skills, andinsufficient technical resources.
Conclusion: Across the region, both registration and statistics systems need strengthening to improve theavailability, completeness, and quality of data. Close interaction between health staff and local communitiesprovides a good foundation for further improvements in death reporting. System strengthening activities mustinclude a focus on clear assignment of responsibility, provision of appropriate authority to perform assigned tasks,and fostering ownership of processes and data to ensure sustained improvements. These human elements need tobe embedded in a culture of data sharing and use. Lessons from this multi-country exercise would be applicable inother regions afflicted with similar issues of availability and quality of vital statistics.
Keywords: Mortality, Death registration, Vital statistics, Cause of death, Pacific islands.
BackgroundAccurate mortality statistics are essential for populationhealth assessment, and to design and monitor healthintervention programs. Despite extensive collection ofhealth data in Pacific island countries, these data arerarely analysed or utilised [1], as they are considered tobe incomplete or unreliable [2-4].Despite the data shortcomings, there is a general ap-
preciation that non-communicable diseases are increas-ingly contributing to premature adult mortality inPacific Island Countries [2,5]. Cancer and cardiovascular
* Correspondence: [email protected]; [email protected] of Population Health, University of Queensland, Herston, Australia2Statistics for Development Programme, Secretariat of the PacificCommunity, Noumea, New CaledoniaFull list of author information is available at the end of the article
© 2012 Carter et al.; licensee BioMed Central LCommons Attribution License (http://creativecreproduction in any medium, provided the or
disease have been recognised as important causes ofdeath in these countries since the 1980s [6-10], andobesity rates are amongst the highest in the world [10].Timely and reliable data on mortality and causes ofdeath is needed to effectively monitor and combat thisimpending epidemic. The ideal mortality data source isan efficient death registration system with medical certi-fication of cause-of-death. In Pacific Island Countries,small population sizes make complete routine registra-tion an attractive proposition, but there is a concomitantchallenge of servicing widely dispersed, low densitypopulations.Since the late 1990’s there has been an increasing
international emphasis on improving health informationsystems and mortality reporting [11-13]. More recently,
td. This is an Open Access article distributed under the terms of the Creativeommons.org/licenses/by/2.0), which permits unrestricted use, distribution, andiginal work is properly cited.
Carter et al. BMC Public Health 2012, 12:436 Page 2 of 9http://www.biomedcentral.com/1471-2458/12/436
the Health Metrics Network has highlighted the paucityof vital statistics in developing countries [14]. The Mil-lennium Development Goals (MDG’s) have also pres-sured governments to demonstrate progress towardsthese targets [15]. Despite this increased attention, littleinformation is available on the registration and statisticalsystems in Pacific Island Countries, and the influence ofthese processes on data quality.This paper examines the structure and operations of
routine death reporting systems in seven Pacific IslandCountries; Fiji, Kiribati, Nauru, Palau, Solomon Islands,Tonga and Vanuatu. Strengths and limitations commonacross national systems are identified, and system char-acteristics are related to data availability and quality.This review also makes specific recommendations tostrengthen these systems and improve data availabilityand quality at the national and regional level.
MethodsCountries were selected in order to represent Pacific Is-land States with a range of: population and land sizes,government structures, distribution of health services,and economic development [1] [see Additional file 1].These countries represent all three regions of the PacificIslands: Melanesia (Fiji, Solomon Islands, Vanuatu, andKiribati), Micronesia (Palau and Nauru) and Polynesia(Tonga). A standard analytical framework [16] was usedto assess practices for data collection, management, ana-lysis, reporting and utilisation in each country.Assessments were conducted through collaboration
between the authors and key national stakeholders, in-cluding representatives from the Ministry of Health andBureau of Statistics. Other stakeholders representedincluded: government departments such as the civilregistry office, finance, and central planning agencies;local non-government organisations; and country officesof WHO, UNICEF and UNFPA.The assessment comprised a field visit guided by a
detailed data collection tool [16], and involved compre-hensive document review, observation of procedures andprocesses, discussions with staff across organisations andlevels, and examination of available mortality data. Infor-mal interview respondents were asked to discuss theirrole in data collection, management or use, and percep-tions of the strengths and weaknesses of the system.Where practices differed from policy or managementperspectives, both were documented. For each country,a flow chart was assembled to depict the reportingprocess from the initial recording of each death to itsrepresentation in a national statistical dataset.All information was subsequently reviewed using a
standard assessment framework that classifies perform-ance and design aspects [16] of the system according tothe socio-political environment, national administrative
framework, system administration, technical characteris-tics and ownership [16]. The first two refer to the overallcontext in which routine death reporting is carried outwithin the country, while the final three are system spe-cific. This framework was adapted from earlierapproaches [17-19] with the additional assessment ofthree human elements that are central to system per-formance in the Pacific Islands; responsibility and au-thority under administrative aspects of the system, andorganisational ownership [16].Country and system specific findings were distilled
into key themes that reflect strengths and weaknessescommon to multiple systems across the countriesreviewed, that have the potential to significantly impactthe coverage, completeness and quality of data, or its ac-cess and utility. Finally, potential impacts of these sys-tem characteristics were reviewed in relation to availabledata.
Results and discussionCommon elements of routine reporting systemsRoutine reporting of deaths in the Pacific Island coun-tries is predominantly managed by civil registration sys-tems or Health departments (Figure 1). The importanceof health reporting systems in supporting civil registra-tion was evident across countries. In particular, despiteexisting legislation, civil registration of deaths was notactive in the Solomon Islands or Vanuatu.Within health departments, deaths are reported by med-
ical practitioners and community nursing staff (Figure 1).Medical certification of death is practiced in hospitals inall countries, however data from death certificates is rou-tinely collated only in Fiji, Nauru, Palau, Tonga, and inPort Vila hospital in Vanuatu. Collated data is codedaccording to the International Classification of Disease(ICD) version 10 in Fiji, Tonga and Port Vila hospital,while Nauru has subsequently started coding since thistime. Until recently only hospital discharge data wascoded in Palau (using ICD version 9), although recentchanges subsequent to this review have seen the introduc-tion of coding for medical certificates of death using ICD9. In Kiribati, the Solomon Islands, and other hospitals inVanuatu, data for hospital deaths is compiled from dis-charge data and coded according to ICD version 10.In Fiji, Palau, and Nauru, medical certification is also
conducted for deaths occurring in the community. In allcountries except Nauru, community deaths are reportedthrough monthly nursing reports. These are only rou-tinely collated in Fiji, Kiribati and Vanuatu. Reportingthrough the hospitals accounts for nearly all reporteddeaths in the small islands of Palau and Nauru, and ahigh proportion of reported deaths in Tonga; while amuch larger proportion are reported through monthlynursing reports in the other countries. Only in Kiribati
Figure 1 Routine Death Reporting Pathways in the Pacific Islands. Dotted lines indicate link does not apply to all study countries.
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is there a large proportion of deaths reported throughcivil registration that are not also reported via the healthsystem.Other routine death recording systems in these coun-
tries are operated by churches, cemeteries and the po-lice. However, these additional sources have limitedapplication in improving the completeness of deathrecords as they pertain only to specific sub-groupswithin the population, the records are of doubtful qual-ity, or the data cannot be easily extracted.
Key strengths and weaknessesStrengths and weakness of the routine reporting systemsare reviewed below, with key themes common acrosscountries reviewed outlined in Table 1.
Societal issuesInitial reporting and formal registration of deaths reliesprimarily on family members. Social factors such as bur-ial arrangements, public support, and political interest indata for planning purposes therefore have a significantbearing on the likelihood of the death being reported,and the quality of the data captured.Burial in cemeteries (parts of Vanuatu) require specific
approvals, which promotes registration. Such approvals
are not necessarily required for burial on private lands(as in Kiribati). Funeral customs that require extendedfamily to gather, as observed in Palau and Tonga alsoencourage reporting. In these cases, the death certificatefacilitates airline reservations, and the body is held forseveral days at a mortuary. Social incentives for report-ing are limited, although access to pension plans or landtransfers may encourage reporting, particularly for adultmale deaths. A notable exception is Nauru where a gov-ernment funeral assistance payment strongly encouragesuniversal reporting. Local health personnel are closelyintegrated into the community across these countries,and are an important foundation for developing publicsupport. Across the region, there is increasing evidenceof data use in deciding national health priorities [20-22],with the Pacific Health Ministers meeting in 2009recommending that countries “improve the quality andreliability of data” [23].
National administrative environmentThe collection and compilation of national mortality andcause-of-death statistics involves multiple governmentagencies (Figure 1). There is significant potential for du-plication of effort and inconsistencies in the data, in theabsence of appropriate coordination at the central level.
Table 1 Key Strengths and Weaknesses of Mortality Reporting Systems in the Pacific Islands
Category Strengths Weaknesses
Societal Issues Social incentives for registration Private land burials without officialapproval
AdministrativeEnvironment
Existing legal framework Inadequate/inconsistent implementation of laws
Health systems involvement inCivil registration and vitalstatistics operations
Passive registration i.e. onus to reporton citizen
Registration process for “off-island”deaths unclear
National statistics committees Complex statistical reportingrequirements
System Issues –Administration
Community nurses formally taskedto notify vital events
Improper emphasis on communitynurses to report cause of death
Routine compilation of mortalitydata by different healthdepartments
Need for better coordination togenerate one reconciled mortalitydataset from the health system
Private health intuitions rarelyintegrated adequately into reportingsystems
No clear delineation of responsibilityacross institutions, leading to taskduplication
Personnel lack authority toquery/clarify data
System Issues –Technical
Standard international medicaldeath certificate (except Nauru)
Lay reporting of cause for deathsoutside facilities in some countries
Medical certificates of death notroutinely tabulated in all countries
Trained ICD coders High turnover of trained staff
Key personnel adequately skilledfor data management at nationallevel
Statistical analysis limited to tenleading causes of death
Insufficient data quality assessmentand control
Initiatives to set data standards forhealth information
Dysfunctional/outdated softwareprograms not amenable to modificationor upgrade
System Issues –Ownership
Strong ownership ofsystems/interest at national levelsthat has contributed substantiallyto ongoing survival of the systems
Generally poor feedback to local levelstaff
Many systems are highly dependent onone or two key individuals with astrong interest in providing health data
Source data: [16].
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Equally, inadequate delineation of organisational respon-sibilities could result in significant data gaps.Legislation requiring registration of deaths exists for
all islands [24-31], although not fully implemented, suchas in rural areas of Vanuatu [24]). Current practices arealso not necessarily reflected in the legislation, as in Fijiwhere deaths cannot be registered prior to burial or cre-mation. While all countries have a process for holdingan inquest or inquiry for a death suspected to have
resulted from a criminal act, fire or other causes of spe-cific interest, only Fiji and Kiribati have legislation thatdefines responsibility for ensuring findings are subse-quently forwarding to the civil registry and used tocomplete the registration process [25,26]. In Vanuatuand the Solomon Islands, health systems have evolved toperform the role of civil registration. Compilation ofcause-of-death data was generally recognised as a role ofthe health department despite not being defined in
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legislation, except in Fiji where coding was duplicated atthe Bureau of Statistics. All countries currently havefunctional inter-agency committees to meet reportingrequirements for the Millennium Development goals[15]. These have had a clear impact in improving coord-ination and collaboration between departments.
System issues – administrationCompilation of mortality statistics also involves a widearray of organisational units within and across agencies(Figure 1). In the larger countries, provincial governmentstructures add to this complexity (Additional file 1). Theoperations and subsequent data quality are thereforeinfluenced by administrative aspects within the system.In most countries, the system design was appropriate
for available resources, with a clear mandate for datacompilation. However, in Vanuatu, an over-reliance ontechnology was noted, that did not have the requisite in-frastructure. In most departments, responsibilities forreporting and analysis of data were not clearly assigned;or, were assigned to staff without adequate training,resulting in poor use of existing data sets. A strong de-mand for health information in Palau, Tonga and Kiri-bati translates to better access for data managers toquery or clarify issues with doctors and senior staffregarding the data. In contrast, data managers in Vanu-atu, the Solomon Islands, and Fiji had limited authority,either real or perceived, to raise concerns. In decentra-lised civil registration systems, local registrars are notdirectly under the authority of the national Civil Regis-trar, leading to confusion in the processes for resolvingissues.
System issues – technicalDuplication of data compilation was observed in allcountries. This was particularly apparent between pro-vincial and national systems (for both health and civilregistration); and in the parallel reporting systems forspecific health programs (Figure 1). For example, com-munity nurses in the Solomon Islands use up to nineforms to report a death. Databases that cannot be modi-fied, or which use obsolete software for which technicalsupport is no longer available, as seen in Vanuatu andKiribati, contribute to the proliferation of multiple data-bases to meet changing needs. The international stand-ard medical certificate of death [32] is used in allcountries except Nauru. Much of the collected data wasnot available for analysis, such as in Kiribati where thehealth department does not retain a complete copy ofthe certificate. Quality control processes are limited, ex-cept in Palau where completed medical certificates areroutinely reviewed by senior doctors resulting in a lowproportion of deaths being assigned to ill-defined causes.Recording of infant deaths was inconsistent as result of
unclear procedures, a lack of data standards, and com-plicated forms; with early deaths potentially missed(Kiribati) or stillbirths included in the data (Nauru).In Fiji and Tonga, data entry and coding practices
leading to alterations in the causal sequence on the med-ical certificate result in difficulties in applying ICD rulesto select the underlying cause [32]. A high staff turnoverfor doctors (Nauru, Solomon Islands and Kiribati),nurses (Fiji) and key analytical roles (Tonga and Fiji) hasalso adversely affected knowledge of reporting processesand depleted the skill set available in some areas.
System issues – ownershipA sense of ownership in individuals as well as institu-tions is essential for smooth operation of vital registra-tion systems. In general, a broader sense of sharedownership provides the system with greater flexibility torespond to changed circumstances such as staff turnoveror natural disasters.Feedback to staff is an important mechanism to foster
ownership among personnel at different levels. This hasbeen provided through annual public health conferences,(e.g. Palau, Tonga and the Solomon Islands); and fieldvisits by data managers (e.g. Tonga and the SolomonIslands). In Tonga, such visits include local civil registrarstaff and community leaders. At local levels, there isconsiderable variation in the quality of registers and sub-mission of statistical returns by community nurses inKiribati, Solomon Islands and Vanuatu, which reflectsthe degree of ownership perceived by them. At the sametime, a strong sense of ownership at the national level ofthe health information system in Vanuatu helped sustainthe overall data compilation process. In health facilities,physicians tended to store medical records for deathsseparately, to preserve them from loss or destruction.This practice limits access to such data for reporting andanalysis.
Data availabilityAs shown in Table 2, significantly more data was identi-fied as available for reporting and analysis than previ-ously recorded by international sources. Additional datasources (Figure 1) that were not tabulated (and thereforenot accessible for analysis) were also found.System characteristics indicate that under-registration
of deaths is likely from civil registration data sets exclud-ing Nauru [42] and Palau. Health department deathreporting systems were estimated to be more than 90%complete in Fiji [41] and Palau (Table 2), and sufficientlycomplete in Tonga to produce reliable estimates of mor-tality when corrected for under-reporting [43]. ICDcoded [32] cause-of-death data based on medical deathcertificates were available for both hospital and commu-nity deaths in Fiji, and Tonga, with un-coded data based
Table 2 Tabulated mortality data available for selected Pacific Islands, 2000–2009: WHO databases, as compared withlocally available data
Mortality data by age and sex Cause of death data
Country EstimatedDeaths(2011)#
WHO*(Year/Completeness)
Locallyavailable data+
(estimated completeness$)
WHO}
(Year/Quality)Locallyavailabledata+
(frommedicalcertificates) +
Fiji 7185 No data(1999/90-100%)
MoH reports (>95%) 2000/Low MOH reports
Kiribati 827 2001/>75% MoH reports (40-60%)Civil registration (40-60%)
2002/Low Not tabulated
Nauru 88 No data Civil Registration (>95%) 1996/not rated MoH reports
Palau 158 No data MoH reports (>95%) No data MoH reports
Solomon Islands 4039 No data MoH reports (not estimated) No data Not tabulated
Tonga 683 No data MoH^ reports (>80%) 1998/Low MoH reports
Vanuatu 1311 No data MoH reports (not estimated) No data MoH reports(hospitaldeaths only)
# Estimated deaths extracted from Secretariat of the Pacific Islands Population data [33].* Data extracted from World Health Organisation Statistical Information System [34], [35-40]: data last updated March 2011.+ Most complete source listed only. MoH=Ministry of Health. Most complete available source or reconciled data: Fiji (MoH), Kiribati (reconciled MoH and civilregistry data), Solomon Islands (MoH), Vanuatu (MoH), Nauru (MoH/Civil registry data), Palau (MoH), Tonga (MoH).$ Estimates of completeness source: Fiji [41], Kiribati (capture-recapture analysis, unpublished), Solomon Islands (system assessment), Vanuatu (system assessment),Nauru [42], Palau (Brass analysis), Tonga [43].} WHO assessment, 2003 [3].^ MoH data from 2009 onward is routinely reconciled against civil registry data.
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on medical certificates available for Nauru and Palau.Very few deaths in Palau are assigned to ill-definedcauses, due to quality control measures described previ-ously. Data from both civil registration and healthreporting systems in Kiribati appear incomplete, al-though a reconciled data set derived from both systemswould be more plausible. Only Vanuatu and the Solo-mon Islands did not have routine reporting systems cap-able of generating estimates of the mortality level. Allthree had systems from which cause-of-death could beextracted for hospital deaths; with some cause-of-deathinformation available for deaths in the community, ei-ther based on family or community nurses reports.
ConclusionsDespite the data gaps identified in this analysis, signifi-cantly more information on mortality of populations inthe Pacific is available than currently used (Table 2).Findings suggest that while significant investment isrequired to improve capture of cause-of-death, it shouldbe possible for all the countries reviewed to be able togenerate reliable data on mortality level within 4–5 years.Causes of death reported by nurses are currently of lim-ited value due to use of broad categories and high pro-portions of "ill-defined" causes. The current system isappropriate to record only demographic aspects ofdeath, unless procedures such as verbal autopsy [44] areintroduced. This should be considered for systems such
as Kiribati, Solomon Islands and Vanuatu where univer-sal medical certification is not currently feasible. All ofthe health department reporting systems reviewed wereaffected by problems with data extraction, duplicate datasets, coding and data entry issues and difficulties acces-sing records in the database that adversely affect dataquality.Legislation requiring registration of deaths exists for
all islands, but does not necessarily reflect current prac-tices. Health departments are carrying the bulk of re-sponsibility for supporting routine data collection andcivil registration functions, and their importance cannotbe overemphasised. Efforts to improve data collection,use, and acceptability to decision makers will need to ei-ther focus on these systems, or ensure their integrationinto official reporting processes. A clear strength in allcountries reviewed was the close interaction betweenhealth staff and local communities, including the oppor-tunities this creates for building strong reporting rela-tionships at the local level.Significant duplication of data collection and entry
exists across all systems, reflecting issues with data own-ership, obsolete and unresponsive technology and frac-tured management of the reporting systems. Theassessment identified three critical human elements thatinfluence the effectiveness of civil registration and vitalstatistics operations. These are responsibility, authorityto effectively undertake assigned responsibilities, and
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ownership or the value that staff place on the reportingsystem and their role within it.The approach used in this study shares many similar-
ities with the comprehensive assessment tool for vitalstatistics systems developed by researchers at the Uni-versity of Queensland and WHO [19] which is primarilya policy tool that aims to lead countries through a crit-ical self-appraisal of their systems as a basis for develop-ing a planning document for system improvement. Theframework presented here was developed from a similarhistory, however is intended to provide a structured crit-ical evaluation of system characteristics to inform the in-terpretation of available and published data from theregion, yet also allows findings to be used in a policy set-ting to guide decisions around future system improve-ments required. Although conducted in partnership withthe Ministry of Health and Bureau of Statistics, andthrough close collaboration with other local partners;the assessment reported here was led by researchers ex-ternal to the systems being evaluated. This could poten-tially result in less ownership of the findings by those ina position to effect system improvements. However thisapproach allows an assessment by objective observers,with a broad range of experience across different report-ing systems, and using a standardised framework; thussubsequently allowing for a regional comparison and forcommon themes to be distilled from individual countrydata. Further, this approach allows staff at all levels to
Table 3 Key Regional Priorities for Action
Assessment Category Recommendations
Societal Issues Recognise the importance of communthe local community) and provide suphealth planning. [Long term]
Administrative Environment Conduct operational research to validainternal management support and con
Formalise MOU or similar between heaand cause of death information. [Short
System Issues – Administration Identify and minimise duplication betwthrough data sharing agreements. [Sho
Assign staff (and adequate resources) t
Investigate ways of better engaging prwith civil registration, and support this
System Issues – Technical Support countries to identify staff respothem to meet minimum skill and know
Encourage tabulation and use of medihospital discharge data. [Short Term]
Design databases to capture medical ccause to avoid changes to recorded se
Encourage use of standard statistical msystem limitations to aid in appropriate
Assess and build staff capacity, particul
System Issues –Ownership Build supportive relationships betweenmanagers to develop sufficient authori
Source data: [16].
discuss their views privately, and provides an opportun-ity for in-depth review where resources may not other-wise be available.While many of the systems reviewed were highly com-
plex, the framework used in this study provides a usefulmeans of identifying themes for further consideration.This paper is designed to provide an insight into themortality and cause-of-death reporting in the region.Several of the issues are identified here for the first time.Although previous studies have been limited, the issuesraised are consistent with those noted in this assessment.These include little incentive to register vital events [45]particularly in the case of children [46], a lack of any “at-tempt to estimate and correct for under-enumeration”[2], and referral biases in cause-of-death analysis due tothe reliance on hospital data in the absence of robustcommunity based reporting [47].Across the region, both registration and statistics sys-
tems need strengthening to improve the access, com-pleteness, and quality of data. Close interaction betweenhealth staff and local communities provides a good foun-dation for further improvements in death reporting. Sug-gested priorities to strengthen systems are noted inTable 3. Addressing issues of authority and support, andreducing the duplication of functions across systemswould assist in alleviating resource pressure. Longerterm priorities relate to broader shifts in practice andwould require significant investment.
ity nurses in the reporting process (including links and influence withport for increased training and feedback on how their data is used in
te reporting completeness and quality in functioning systems to buildfidence to use results. [Short Term]
lth departments and civil registration offices on data sharingTerm]
een parallel systems (particularly within health departments),rt Term]
o analysis role. [Short Term]
ovincial governments (island councils, municipal governments etc.)with feedback at a provincial level. [Long Term]
nsible for each process and provide in-country training to supportledge level. [Short Term]
cal certificates as a source of information rather than relying solely on
ertificates as written. Create an additional field for selection of underlyingquence. [Short Term]
easures (confidence intervals, and rolling averages) and reporting ofdata interpretation. [Short Term]
arly in data analysis and certification practices. [Short Term]
data managers, senior management and doctors that will allow dataty to question data as needed. [Long Term]
Carter et al. BMC Public Health 2012, 12:436 Page 8 of 9http://www.biomedcentral.com/1471-2458/12/436
System strengthening activities must focus on ele-ments such as clear assignment of responsibility,provision of appropriate authority to perform assignedtasks, and fostering ownership of processes and data toensure sustained improvements. These human elementsneed to be embedded in a culture of data sharing anduse. Although there are common priorities for systemimprovement across the region, the diversity of infra-structure, social context and existing system capacity willrequire action to address priorities to be locally appro-priate. Governments and international agencies shouldalso support the use of local data wherever possible. Les-sons from this multi-country exercise would be applic-able in other regions where vital statistics systems aresimilarly characterised by duplication, inadequatelytrained staff, and poor use of good quality data on birthsand deaths in informing policy decisions.
Additional file
Additional file 1: Appendix 1. Country Characteristics
AcknowledgementsThis work was conducted in conjunction with many government and non-government agencies, and was made possible by the efforts of manyindividuals within those departments. In particular the authors would like toacknowledge the participation and assistance of the following organisations(and specific individuals in particular): Fiji Ministry of Health (Laite Kavu,Shareen Ali); Fiji Islands Bureau of Statistics (Vasemaca Lewai); Fiji CivilRegistration Office, Ministry of Justice; Fiji School of Medicine; Kiribati Ministryof Health; Kiribati Bureau of Statistics; Kiribati Civil Registration Office; NauruMinistry of Health (Gano Mwareow, David Dowiyogo); Nauru Bureau ofStatistics (Ipia Gadabu); Palau Ministry of Health (Losii Samsel, MairengSengebau); Palau Bureau of Statistics (Rhinehart Silas); Solomon IslandsMinistry of Health (Baakai Iakoba and Christine Bishop); Tonga Ministry ofHealth (Sione Hufanga); Tonga Bureau of Statistics (Sione Lolohea); TongaCivil Registry Office; Vanuatu Ministry of Health (Joao Costa); World HealthOrganization (Country Liaison Officers and Western Pacific Regional Office);and the Secretariat of the Pacific Community. Funding for this research wasprovided under the AusAID Australian Research Development Awards(HS0024). The funding body had no role in study design; in the collection,analysis, and interpretation of data; in the writing of the manuscript; or inthe decision to submit the manuscript for publication.
Author details1School of Population Health, University of Queensland, Herston, Australia.2Statistics for Development Programme, Secretariat of the PacificCommunity, Noumea, New Caledonia. 3School of Public Health andCommunity Medicine, University of New South Wales, New South Wales,Australia.
Conflicts of interestThe authors declare that they have no conflicting interests.
Authors’ contributionsKLC: developed and trialed the system assessment framework and datacollection tools, conducted the field visits and interviews, collated thefindings and extracted key themes, and drafted the analysis. CR: assisted withthe identification and clarification of key themes, reviewed the data analysisand interpretation of results; revised the manuscript critically for structure,clarity and important intellectual content. AL: revised the manuscript criticallyfor important intellectual content. RT: reviewed the data analysis andinterpretation of results; and revised the manuscript critically for importantintellectual content. RT and AL both conceived and coordinated the broader
study under which this project was undertaken. All authors read andapproved the final manuscript.
Received: 6 September 2011 Accepted: 13 June 2012Published: 13 June 2012
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Health Surveillance and Response 2005, 12(2):155–158.2. Taylor R, Bampton D, Lopez AD: Contemporary patterns of Pacific Island
mortality. Int J Epidemiol 2005, 34(1):207–214.3. Mathers CD, Fat DM, Inoue M, Rao C, Lopez AD: Counting the dead and
what they died from: an assessment of the global status of cause ofdeath data. Bull World Health Organ 2005, 83(3):171–177.
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5. Khaleghian P: Non-communicable diseases in Pacific Island Countries: Diseaseburden, economic costs and policy options. Noumea: Secretariat of the PacificCommunity and World Bank; 2003.
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10. Coyne T: Lifestyle diseases in Pacific communities. Noumea: Secretariat of thePacific Community; 2000.
11. Hill K, Lopez AD, Shibuya K, Jha P: Interim measures for meeting needs forhealth sector data: births, deaths, and causes of death. Lancet 2007,370:1726–1735.
12. AbouZahr C, Cleland J, Coullare F, Macfarlane SB, Notzon FC, Setel P, SzreterS: and on behalf of the Monitoring of Vital Events (MoVE) writing group.The way forward. The Lancet 2007, 370:1791–1799.
13. Pacific Islands Health Officers Association: Declaring a Regional State ofHealth Emergency Due to the Epidemic of Non-Communicable Diseases.: BoardResolution #48-01; http://new.pihoa.org/files/resolutions/NCD_Emergency.pdf Accessed [2 April 2011].
14. Setel Philip W, Macfarlane Sarah B, Simon Szreter, Lene Mikkelsen, PrabhatJha, Susan Stout, Carla AbouZahr, on behalf of the Monitoring of VitalEvents (MoVE) writing group: Who Counts? 1: A scandal of invisibility:making everyone count by counting everyone. Lancet 2007, doi:doi:10.1016/S0140-6736(07)61307-5.
15. Secretariat of the Pacific Community: Pacific Islands Regional MillenniumDevelopment Goals Report 2004. Noumea: Secretariat of the PacificCommunity in cooperation with the United Nations; 2004. http://www.arab-hdr.org/publications/other/undp/mdgr/regional/asia-pacific/mdg-pacific-04e.pdf Accessed [2 April 2011].
16. Carter KL, Rao C, Taylor R, Lopez AD: Routine mortality and cause of deathreporting and analysis systems in seven Pacific Island countries. University ofQueensland: Health Information Hub; 2010:8. http://www.uq.edu.au/hishub/index.html?page=154695&pid=116774 Accessed [2 April 2011].
17. Rao C, Osterberger B, Anh TD, MacDonald M, Chuc NT, Hill PS: Compilingmortality statistics from civil registration systems in Viet Nam: the longroad ahead. Bull World Health Organ 2010, 88(1):58–65.
18. Rao C, Bradshaw D, Mathers CD: Improving death registration andstatistics in developing countries: Lessons from sub-Saharan Africa.SAJDem 2004, 9(2):79–97.
19. Mikkelsen L, Lopez AD: Improving the quality and use of birth, death andcause-of-death information: guidance for a standards-based review of countrypractices. Geneva: Health Information Hub, University of Queensland andWorld Health Organization; 2010.
20. Ministry of Health: Corporate Plan 2007–2009. Nuku'alofa: Government ofTonga; 2007.
21. Ministry of Health: Annual Corporate Plan for the Financial Year ending on31st December 2010. Suva: Government of Fiji; 2010.
22. Ministry of Health and Medical Services: Strategic Plan 2008–2011. Tarawa:Republic of Kiribati; 2009.
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23. Pacific Islands Ministers of Health: Eight Meeting of the Ministers of Health ofthe Pacific Island Countries.: World Health Organization and Secretariat of thePacific Community. PNG; 2008. DPM/03-E http://www.wpro.who.int/internet/resources.ashx/PIC/2009/8thPIC_meeting+report.pdf Accessed[2 April 2011].
24. Civil Status (Registration) Act (Chapter 61). Republic of Vanuatu. 1971.25. Births, Deaths and Marriages Registration Act. Government of Fiji. 1975.26. Kiribati Births, Deaths and Marriages (Amendment) Act. Republic
of Kiribati. 1997.27. Nauru Births, Deaths and Marriages Ordinance. Republic of Nauru. 1957.28. Palau National Code. Republic of Palau. 1985.29. Solomon Islands Births and Deaths (Registration) Act. Government of the
Solomon Islands; 1988.30. Births, Deaths and Marriages Registration (Amendment) Act. Kingdom of
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and Related Health Problems. Geneva: Tenth Revision; 2007. http://www.who.int/classifications/icd/en/Accessed [26/11/2010].
33. Secretariat of the Pacific Community: Pacific Island Populations - Estimatesand projections of demographic indicators for selected years. http://www.spc.int/sdp/Accessed [23 April 2012].
34. World Health Organization: World Health Statistics. 2010. http://www.who.int/whosis/whostat/2010/en/index.html Accessed [2 April 2011].
35. SPC: Pocket Statistical Summary. Noumea: Secretariat of the PacificCommunity; 2009. http://www.spc.int/sdp/index.php?option=com_docman&task=cat_view&gid=28&Itemid=42 Accessed [2 April2011].
36. ABC Island descriptions. http://www.abc.net.au/ra/pacific/places/country/palau.htm Accessed [2 April 2011].
37. Stanley D: South Pacific Organizer., http://www.southpacific.org/guide/tonga.html Accessed [2 April 2011].
38. Ramsar Sites Information Service. http://ramsar.wetlands.org/Portals/15/Republic_of_Kiribati.pdf Accessed [2 April 2011].
39. UNDP: Human Development Report. 2009. http://hdrstats.undp.org/en/indicators/87.html Accessed [2 April 2011].
40. World Health Organization, Western Pacific Regional Office: Western PacificCountry Health Profiles (CHIPS) 2010 Revision. Manila: World HealthOrganization; 2008.
41. Carter K, Cornelius M, Taylor R, Ali SS, Rao C, Lopez AD, Lewai V, Goundar R,Mowry C: Mortality Trends in Fiji. Aust N Z J Public Health 2010,35(5):412–20. In press.
42. Carter KL, Soakai TS, Taylor R, Gadabu I, Rao C, Thoma K, Lopez AD:Mortality Trends and the Epidemiological Transition in Nauru. Asia Pac JPublic Health 2011, 23:10.
43. Hufanga S: Evaluation of published estimates of mortality measures forTonga, and Capture-recapture analysis of mortality data in Tonga.University of Queensland: School of Population Health; 2010. WorkplaceProject Portfolio [Masters dissertation].
44. Setel PW, Sankoh O, Rao C, Velkoff VA, Mathers C, Gonghuan Y, Hemed Y,Jha P, Lopez AD: Sample registration of vital events with verbal autopsy:a renewed commitment to measuring and monitoring vital statistics. BullWorld Health Organ 2005, 83(8):611–617.
45. Taylor R, Lewis ND, Levy S: Societies in transition: mortality patterns inPacific Island populations. Int J Epidemiol 1989, 18:634–646.
46. Finau SA: Pacific Health: An Analysis for Training New Leaders. Asia Pac JPublic Health 1992, 6(2):46–53.
47. Hufanga S, Bennett V: Mortality Data in the Kingdom of Tonga: A Reviewof Changing Trends over the Ten Years since 1996. Health InformationManagement Journal 2007, 36(2):43–48.
doi:10.1186/1471-2458-12-436Cite this article as: Carter et al.: Mortality and cause-of-death reportingand analysis systems in seven pacific island countries. BMC Public Health2012 12:436.
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3.2.2 Data availability and accessibility
Significantly more data on mortality level and CoD is available from routine CRVS
systems than has previously been used or documented, although accessing these
data remains problematic. An overview of the available data sources is given in the
Table 14.
Table 14: Available routine mortality data collections by country and source
Data Source Fiji Kiribati Nauru Palau Tonga SolomonIslands Vanuatu
Civil Registration Available Available Available Available Available Not available
Limited
Health Reporting
Medical Certificate Data Hospital Deaths Community Deaths
Available Available
Available No
Available Available
Available Available
Available Available
Available Not available
Available Not available
Hospital Separation Data
Available Available Available Available Available Available Available
Community Nursing Data
Available Available Not available
Available Available Available Available
Other reporting systems
Police (accidents)
NA Treasury Mortuary Prime Minister’s Office
NA NA
Access to data was hampered by complex and slow approval and ethics review
mechanisms, a lack of clarity around who could approve access to data (and hence
a propensity to refer all decisions to a ministerial level), and in some cases an
unwillingness of individual data managers to allow scrutiny or use of the data,
despite higher approvals. In part, this was explained by concerns around privacy of
the data, which were able to be addressed through study protocols and relationship
building with the officers involved. Other cultural and historical influences were more
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difficult to identify or address. This includes the concept that knowledge should only
be passed to worthy individuals as reward or currency for a political or social role,
rather than being generally available; and an exhaustion with a legacy (real or
perceived) of past research that has limited country involvement or ownership of
outcomes.
3.3 Summary of country specific CRVS systems
A brief overview of routine CRVS systems in each country is given below.
3.3.1 Fiji
All deaths in Fiji are required to have a medical certificate. If the death occurs
outside the hospital, the body is either brought in by the family or police, or a doctor
is required to attend.
The medical certificates of death are collated nationally and entered into the HIS,
which also houses the hospital separation data entered at the hospital. Community
deaths are also reported through monthly nursing reports which are checked against
the medical certificates received.
To register a death through the civil registry, the family is required to present a
medical certificate, coroner’s or police report; and provide details of the burial site to
the local government offices. Deaths are entered into the registry’s database which
is managed by the government IT organisation (ITC). Deaths are registered by
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completing a paper form which is sent as hard copy to the provincial office where the
death certificate is issued, and the death is either entered onto the national registry
database or details are forwarded to the national civil registry office. Periodically, the
Bureau of Statistics requests details of the registered deaths through ITC, which are
coded against the ICD and re-entered into a separate database.
3.3.2 Kiribati
Although incomplete, civil registration is operational in Kiribati. Families are required
to present evidence (either a medical certificate, radio or newspaper notice of the
death or statutory declaration from a church minister) to the registrar to register the
death. Provincial offices complete a hard copy monthly report for the national civil
registry office, where all deaths are entered into a database.
Within the health system, medical certificates of death are issued for deaths in the
national referral hospital and major provincial hospitals staffed by doctors. They are
not collated centrally, and much of the data collected under this process is lost.
Hospital separation data (and therefore deaths in hospital) are recorded in the
electronic HIS. Deaths in the community are recorded on monthly reports completed
by nurses or nurse aids at the community health centres and aid posts. Both the
hospital separation data and monthly reports are entered into a database at the
national health information section. The Bureau of Statistics currently relies on
census analyses and does not use any routinely collected data from either the MoH
or Civil Registrar for mortality estimation.
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3.3.3 Nauru
All deaths in Nauru are required to have a medical certificate and are recorded
through the MoH. If the death occurs outside the hospital, the body is either brought
in by the family or police, or a doctor is required to attend. The medical certificate is
entered by nursing staff into a spreadsheet, as well as into the civil registry
database. Both the spreadsheet and database were established recently, with data
prior to these maintained only in hard copy. A hard copy of the certificate is
forwarded to the Civil Registry Office, with the original kept at medical records. Data
prior to 2005, before the two hospitals merged, is not available at the MoH. A
notification of death is also completed by nursing staff and forwarded to the Minister
of Health.
The government of Nauru provides a payment to the family to assist with expenses
when a death occurs. As such, it is also possible to obtain an almost complete list of
deaths (although name details are often unclear and the records do not include age
or date) from the treasury records. There is no routine analysis or publication of
mortality data other than with the national census, managed by the Bureau of
Statistics, which utilises data derived from civil registration records.
3.3.4 Palau
Like Nauru, Palau has a small population, and all deaths are required to have a
medical certificate. If the death occurs outside the hospital, the body is either brought
in by the family or police, or a doctor is required to attend. The medical certificate is
entered by the Public Health unit into a spreadsheet and separate database. Hard
copies of the certificate remain with hospital medical records and a copy is sent to
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the Ministry of Justice for civil registration purposes (although the family is still
required to attend the office to complete the registration process). Hospital
separation data (and therefore deaths) are recorded in the electronic HIS.
Community deaths are also reported through monthly nursing reports, although
these are not further collated. The Bureau of Statistics obtains data for reports
directly from the MoH upon request. Data are routinely analysed by the public health
epidemiologist for health planning and reporting.
3.3.5 Tonga
Tonga has multiple systems for routine reporting of deaths. Civil registration may be
completed through the national civil registry office or in provincial centres. Deaths
are recorded manually in the registry book, with those registered in Nuku’alofa (the
capital) also entered into an electronic database. Registry books from provincial
centres are forwarded to the national office when full, where they are entered into the
database. Deaths are also reported through local unpaid leaders called “Town
officers”, who are required to record all deaths in their area on a monthly report form,
covering a range of local issues, that is forwarded to the Prime Minister’s office.
All deaths are required to have a medical certificate. These are forwarded and
entered nationally by the Health Information Section into a spreadsheet and separate
database. Deaths in the community are recorded on “Notice of death” forms and
monthly reports completed by nurses or nurse aides at the community health centres
and aid posts, which are forwarded to the closest hospital where a medical certificate
should be prepared; before being forwarded to the Public Health nursing coordinator
at the national hospital.
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3.3.6 Vanuatu
Deaths in Vanuatu are only required to be registered when they occur within the
urban areas of Port Vila and Luganville. In practice, the civil registration system is
only recording births, with fewer than 20 deaths recorded in 2008. Routine reporting
of deaths is solely through the MoH. Medical certificates of death are issued for
deaths in the national referral hospital and the provincial hospital in Luganville where
there are doctors (there is a single doctor permanently located at only one of the
remaining provincial hospitals). Only medical certificates for deaths occurring at the
Port Vila hospital are collated electronically (as an excel spreadsheet). Hospital
separation data (and therefore deaths) are also recorded in the electronic HIS at the
major provincial hospitals. This information is sent electronically (or collected on disc
periodically) to the national HIS. Deaths in the community are recorded on “Notice of
death” forms and monthly reports completed by nurses or nurse aides at the
community health centres and aid posts. CoD is given as free text on a single line.
Notice of deaths forms are sent to the provincial offices as hard copy for entry into
the database, with data forwarded to the national health information office either as
electronic files or hard copy. Most data are entered (or re-entered) manually at the
national level.
3.4 Conclusion
As described in this chapter, each of the study countries is currently collecting some
data on deaths and CoD through routine CRVS systems. The extent of these data
collections and the quality of the data collected do however vary significantly
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109
between countries due to the different systems in place and the environments in
which they operate. This necessitates the use of a range of evaluation and correction
measures as described in Chapter two, rather than using a single approach across
the region.
While each country has a unique set of challenges and potential biases that should
be considered when evaluating the plausibility of reported measures of mortality, or
when estimating mortality and CoD patterns from raw data; there were a number of
common issues identified. These include greater social incentives for registering
deaths in wage earners and property holders (usually adult males) that could lead to
under-representation of women and children in the reported data, coverage issues
away from the major centres of population, significant issues around late reporting
that may affect the timeliness of the data for analysis, and a propensity to use
hospital discharge records for CoD tabulations (based on primary cause or
immediate CoD) rather than underlying cause, which could skew CoD patterns
towards broad categories such as cardiovascular diseases (from deaths reported
with an immediate cause such as “heart failure” or “heart attack”). Other potential
biases on mortality estimates include the different procedures for dealing with vital
events that occur off-island and the impact this can have in small populations, as in
these study countries. The impact of off-island events, migration and small
populations are discussed further in the next chapter.
�
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110
4 Further analytical considerations – small area estimates, off-island vital events and migration
4.1 IntroductionIn addition to the system biases outlined in Chapter three, any discussion of mortality
patterns in the Pacific Islands must specifically consider the impact of small
population sizes, off-island events and migration. In the context of generating
measures of mortality, PICTs have inherently small populations. The number of
deaths occurring in these countries each year is very low, and subsequently
population measures of mortality may be significantly affected by both stochastic
variation and small changes both in the numerator (the number of deaths) and
denominator (population, or for some measures, the number of live births). Changes
to the numerator (through deaths happening off-shore for example) would have the
greatest effect given the comparative size of the small number of events and the
population.
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111
4.2 Population size and small-area estimatesPopulation size in PICTs ranges from just over 1000 people in Tuvalu and Niue to
more than 1.5 million in PNG. Excluding PNG, Fiji has the largest population in the
Pacific with just over 800,000 people (44). The small population sizes mean
stochastic variation can have a significant impact on mortality rates, with one or two
deaths substantially changing mortality rates. In Tonga for example, a difference of
2 maternal deaths more than doubles the MMR (292). It is therefore critical data are
aggregated over multiple years to provide greater stability, and estimates are
reported with appropriate confidence intervals. The small population sizes mean
even at a national level, mortality estimates for PICTs can be regarded as small area
estimates. The field of small area estimation has several lessons that may prove
useful for understanding mortality in the PICTs.
Examining minimum sample size within demographic surveillance sites Begg et. al.
(293) cite a minimum of 852,900 person years of observation in a high mortality
(adult and child) population as necessary to obtain acceptable cause-specific data
(data with sufficiently narrow confidence intervals for most age groups to enable use
to inform planning decisions). Outside PNG, only Fiji has a sufficiently large
population to generate sufficient person years of exposure from routine registration
of deaths to allow data to be examined on an annual basis; although much
uncertainty remains and age groups must be kept very wide. For all of the other
study countries, multiple years must be aggregated before analysis by age group is
possible, as shown in Table 15. The years shown assume universal coverage and
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112
completeness of the CRVS system, and are therefore the minimum aggregation of
years required.
Table 15: Minimum number of years data would need to be aggregated to reach the minimum person years of data required for acceptable mortality
analysis as cited by Begg et. al. by country (293)
Country Population (2011)(44) Minimum aggregation of years
Fiji 851,745 1 Kiribati 102, 697 9 Nauru 10, 185 84 Palau 20,643 42 Solomon Islands 553, 254 2 Tonga 103,682 9 Vanuatu 251,784 4
Table 15 shows few PICTs could consider annual estimates of mortality and CoD to
be stable, and highlights the need to aggregate across several years in order to
reach sufficient person years of data to generate reliable measures. There is
however a “trade-off” between generating reliable data, and producing timely data to
meet policy and planning needs. Aggregating over more than 80 years for example,
as would be required in Nauru (Table 15) is simply not useful or possible in practical
application. Additional approaches to interpreting data, such as trend tests across
multiple years will need to be considered.
Small area analysis has been used extensively in Australia for mortality and cancer
analysis (189). At a sub-national level, difficulties generating mortality estimates for
small populations are likely to be compounded by issues such as numerator and
denominator sources which have incongruent geo-coding where data comes from
different sources (i.e. civil registration and census areas). Taylor (189) further notes
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113
that aggregation between adjoining areas often combine districts with very different
characteristics. This is particularly true in the PICTs, where customs, transport,
access to health services and lifestyle may vary significantly between adjoining
islands and regions. Aggregation over several years to provide sufficient data for
analysis may therefore be preferable. Despite these problems, Taylor argues small
area estimates are sufficiently useful that synthetic estimates are justifiable to
produce them (189).
More recent work in the UK on small area analysis has demonstrated that summary
measures of mortality, such as LE and adult mortality, can be generated reliably from
smaller population sizes using standard life table methods (without smoothing by
model life tables). Testing small random subsets of larger populations demonstrated
life tables can be generated with reasonable validity (results close to those
generated by the larger data set) and reliability (sufficiently narrow confidence
intervals to be useful) for populations of at least 5,000 people (over a single year),
even when there were zero deaths recorded in some age groups. These results
were found to be conditional on some deaths being recorded in the open ended
(oldest) age group (294, 295). This approach could be replicated in the PICTs by
aggregating over time to ensure 5,000 person years of data. The UK team also
evaluated several approaches to calculating confidence intervals for small area
estimates (294), and found the Chiang method (296) most appropriate, as it coped
best with missing values that may occur with small population sizes (294). This
approach has been adopted in Australia (297) and New Zealand (298, 299) for small
area analysis, and will subsequently be adopted in this work for the PICTs.
Stochastic variation will be minimised by aggregation over multiple years, with long
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114
term trend analysis applied where possible. Age groups of 5 or 10 years will be used
to ensure sufficient data in each in age group, with the open end age group set
according to expected mortality levels (for most countries at 65 years of age) to
ensure sufficient deaths recorded in this age group for reliable analysis.
4.3 Off-Island Events
As noted previously, while changes to both the numerator and denominator may
affect mortality rates, changes in the numerator produce the greatest effect due to
the relative sizes of the number of events and the population. Of the issues
presented in this chapter, deaths that occur off-island and missed from numerator of
mortality measure calculations would have the greatest impact on final estimates of
mortality.
There are three categories of travellers of interest. In decreasing order of risk of
dying, these are:
� People referred off-island for medical treatment through official
channels;
� People who “self-refer” and travel at their own expense (and without
any official record of the reason for travel) for off-island medical
treatment; and
� People who travel for non-medical reasons (holiday, employment,
visiting relatives etc).
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115
The last two categories are extremely difficult to measure as there is often no official
record of reason for travel. Anecdotal evidence from contacts at health departments
across the region suggest most long term residents who die overseas are returned
home for burial due to cultural affiliations and land issues; however there is often no
accessible record of these deaths (described in Chapter three). Where recorded,
these deaths could be included in the calculations, as with any other event in the
resident population. Where data are not available, deaths in the first category could
be used as “proxy events” to signal the level of impact off-island deaths may likely
have on total mortality and causes of death.
Deaths are most likely to occur amongst travellers referred overseas through official
channels for medical treatment due to the severity of their illness or condition. In
theory, data on these events should be captured through government reporting
systems (since the government has funded the travel and in some cases, the
treatment). In practice, many countries do not capture information on these deaths,
or are unable to collate these data with their primary death reporting processes (see
Chapter three).
All PICTs have a medical referral program, although the extent and destination
country vary. Primary destination countries include Australia, New Zealand, the USA
(primarily the Pacific Islander Health Care program at Tripler Army Hospital in
Hawaii) and the Philippines. Fiji is also an important referral destination for
surrounding PICTs. More recently, rising costs in traditional destination countries and
the burgeoning medical tourism market have seen India and Thailand added to the
list of potential destinations (used by Tokelau and Tuvalu respectively). Although
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116
exact numbers of referrals are difficult to obtain, these account for up to 20% of the
health budget in some PICTs (36). Major patient referral pathways, updated from
earlier work by Pryor et. al. (300) are shown in Figure 10.
Figure 10: Major medical referral pathways in the Pacific Islands
(adapted from Pryor, 2000 (300))
Registered deaths of Pacific Islanders in Australia for less than two years are shown
in Table 16.
�
�
�
�
Samoa
Wallis�&�Futuna
American�Samoa
French�Polynesia
Cook�Islands
Niue
Tonga
Fiji Tuvalu
Kiribati
Nauru PNG
Solomon�Islands
Vanuatu
New�Caledonia
Australia
New�Zealand
Guam
F.S.M
Palau
Tokelau
Hawaii
C.N.M.I.
RMI
Philippines
France
Polynesia
Micronesia
Melanesia
� � �
117
Table 16: Registered deaths in Australia of new residents from the Pacific Islands by age at death and duration of residence: 2001-2008
Duration of residence <1 year
Duration of residence 1 to <2 years
Country of birth 0-14 years
15-54 years
55+ years Total
0-14 years
15-54 years
55+ years Total
New Caledonia 6 24 24 54 4 11 6 21 Fiji np np 35 54 0 7 21 28
Total Melanesia, Micronesia and Polynesia 11 64 83 158 5 32 49 86 np not available for publication but included in totals where applicable, unless otherwise indicated
Data provided by ABS (unpublished)
The bulk of referred patients to Australia from the PICTs, and subsequently deaths
as shown in the table, come from New Caledonia due to agreements between the
Australian and New Caledonian governments regarding treatment costs. Fiji is the
only other sending country for which there were more than 5 cases in any age group
(the ABS criteria for disaggregation). The table shows most deaths were of people
aged greater than 55 years, where there would be minimal impact on life tables and
LE. A substantial proportion (39%) also occurred in younger adults, which would
have some impact on estimated deaths rates, with (7%) of these deaths in children.
These data are noted to be affected by issues of under-reporting (due to poor
capture of duration of residence) and reflect ethnicity rather than country of previous
residence. Although the total number of deaths of interest is likely to be higher, these
data provide an indication of the age groups that may be most affected. Similar data
was not available by age group for New Zealand. Deaths in Australia and New
Zealand for 2001-2008 of visitors (non-residents) of Pacific Islander background are
shown in Table 17. As with Table 16, these figures are likely to be affected by
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difficulties capturing usual place of residence and therefore may be somewhat
under-representative. They do however provide an indication of the scale of the
issue. The average number of deaths in Australia and New Zealand reported for
non-residents (visitors) from the PICTs was 42 in Australia and 38 in New Zealand.
On average, a further 30 deaths could be expected per year in Australia amongst
those who were recorded as having resided in Australia for less than two years
(short term residents) (Table 16). Excluding New Caledonia, this equates to an
average of 46 deaths reported deaths amongst visitors and new residents in
Australia who were born in the PICTs (across all PICTs).
While there are problems with recording residence that mean this data could be
under-reported, as country of origin is only available as country of birth, this data is
likely to include a substantial number of visitors and immigrants from New Zealand
who were born in the PICTs. The average number of deaths per year of those who
have arrived directly from the PICTs would be smaller again and the number of
deaths this equates to for each PICT would be negligible.
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Table 17: Deaths in Australia and New Zealand of persons usually resident overseas by PICT of birth: 2001-2008
Country or Territory of Birth
Australia New Zealand
New Caledonia 137 1 Papua New Guinea 35 Fiji 84 56 Solomon Island 4 Vanuatu 7 Kiribati 6 5 Nauru 13 Cook Islands 5 28 French Polynesia 9 39 Samoa 15 68 Tonga 12 36 Wallis and Futuna 8 1 Niue 7 Tokelau 2 Polynesia 339 243
Data supplied by ABS (301), and New Zealand Statistics (302)
Most deaths of non residents in New Caledonia between 2000 and 2007 were from
the French territories (55%) and mainland France (21%). All other nationalities (not
just PICTs) contributed to only 24%, or between 2-5 deaths per year (303). The
overall impact of these deaths on national reporting for PICTs would therefore be
negligible when spread across the sending countries.
While off-island deaths are likely to affect all mortality estimates to some extent, the
greatest impact will be to skew age-specific measures such as IMR and causal
patterns of death. High risk pregnancies are more likely to be referred off-shore, and
these births represent the greatest risk of early neonatal and maternal deaths.
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Although probably representing an extremely small number of total events, if the
outcomes of overseas referrals are not captured, the rarity of these events could
have a significant impact on underestimation of key measures. Similarly, referral
scheme criteria would result in a greater likelihood of specific causes of death in
overseas referrals.
For example, of the American associated territories, only Guam and to a limited
extent CMNI, have chemotherapy treatment available on-island. None have
radiological treatment available (304). Therefore, cancer patients are more likely to
be referred overseas for treatment than a patient with diabetes for example; which
may result in specific causes being under-represented in the national statistics. A
2000 review of the Guam cancer registry demonstrates the biases that may arise
from overseas referrals, both in terms of total number of cases (and subsequent
deaths) and the types of cancer recorded. As a receiving destination for cancer
treatment, the Guam cancer registry records cases of cancer diagnosed on Guam
for both residents and non-residents. For 1995-99, 10% of cases were non-
residents, primarily from other Micronesian PICTs. There were 66 cases of cancer
recorded in Micronesians who were non-residents in Guam between 1970-2000, an
average of 2.2 cases per year. Of these non-resident cases, 55% were Chamorro
(primarily resident in CNMI), 30% were from FSM, 14% from Palau and 1% from the
Marshall Islands. Thirty three percent of cases were for cancers of the digestive
tract, with a further 6% recorded as cancers of the mouth and pharynx. Respiratory
cancers accounted for 27%, cancer of the female genital organs was 8% and breast
cancer was 6% of cases diagnosed (305, 306). These figures demonstrate the type
of cases that may not be adequately captured in the source country data. Although
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overall they represent a very small number of events, they represent both common
and rarer causes of death, such as mouth cancer.
While it is difficult to obtain reliable figures by type of illness or treatment required,
the two major receiving centres in Hawaii (Tripler and Shiners) report the majority of
patients seen under the Pacific Referral Program are treated by the surgical
departments; primarily for cardiac care, orthopaedic procedures, cancer treatments,
emergency care and paediatrics (305-308).
While an important issue, the proportion of cases referred overseas has decreased
over recent years for many countries due to the costs involved, financial pressure on
governments (309-311). The rise of telemedicine, including rapid advances in
imaging (312, 313), has also allowed a greater number of conditions to be remotely
treated with expert advice.
The findings presented here indicate off-island events should have minimal impact
on estimates of mortality level, and errors introduced from these occurrences are
likely to be of smaller magnitude than the uncertainty in these figures due to the
small population sizes. Estimates of mortality level will therefore not be adjusted, but
will be reported with uncertainty analysis wherever possible. In comparison, while
off-island events may have little impact on cause-specific rates for some broad
categories, the patterns of referral may have significant impact for specific causes.
As there is no reliable way to correct for these biases without adequate data on
medical referrals and outcomes, estimates will be made primarily for broad cause
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categories. Reporting for specific causes should be considered for plausibility in light
of a system review identifying whether systematic biases in the findings are likely.
4.3.1 Off-island births
Off-island births are important, as they may be excluded from denominators for
mortality measures if the birth is not reported in the family’s country of residence. If
these births are not recorded (or reported too late for inclusion in current data sets)
both infant and childhood mortality may be inflated from true levels if births are used
as a denominator. Travelling overseas to give birth is common in several PICTs,
including Palau (with many deliveries occurring in birthing centres in Guam or the
United States) and the Cook Islands (with medical services sought in New Zealand
(314)). The majority of off-island births are arranged privately rather than through
official medical referral programs and as such, there is no simple way of tracking the
number that may be included in this category.
Overseas births affect not only the denominators of IMR and U5M, but also the
numerators, as they tend to include a higher proportion of high-risk births (hence the
referrals) and may therefore be more likely to be associated with infant deaths. As
discussed for overseas deaths generally, changes to numerators could potentially
have a significant impact on mortality estimates. It is therefore important to capture
these events wherever possible. Changes to denominators would have less impact,
and would only be likely to have a substantial effect where populations are extremely
small and overseas referrals common (such as in Niue or Tokelau), where primary
data capture of these events is critical to generating reliable mortality estimates. In
most of the PICTs, the impact is likely to be much less substantial. Although the
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Cook Islands, for example, is considered to have a high proportion of overseas births
due to both the government referral program and self referral (as Cook Island
residents qualify for government health care in New Zealand), overseas births are
estimated to be less than 5% of total births to resident women (314). The Cook
Islands reported an IMR of 7.8 deaths per 1,000 live births in 2009 (315), based on
255 reported births. If 5% of all births were off-island and not reported, the total
estimated births would be 268.42 and IMR would be 7.4 deaths per 1,000 live births.
Given IMR ranged from 3.8 to 21.4 over the period 2005 to 2009, the error
introduced by off-island births is negligible when compared to the stochastic error
due to the small population size and is therefore not a significant problem. Deaths
associated with overseas births would remain a greater issue.
4.4 MigrationAs noted at the introduction to this section, changes to the numerator (through
deaths happening off-shore for example) would have the greatest effect on mortality
rates given the comparative size of the small number of events and the population.
Nevertheless, changes to the denominator (population) through migration would also
potentially have an impact on mortality rates if:
a) the proportion of the population that is either removed (through out-
migration) or added (through in-migration ) to the denominator is different to the
proportional change in the number of deaths. That is, if the migrant population was
significantly more or less likely to die than the background resident population; or
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b) population estimates used in the denominator did not adequately
account for the extent of migration. This may occur if migration patterns have
changed substantially since the previous inter-censal period.
Migration figures show three distinct country groupings. New Caledonia and Guam
both have net in-migration (Table 18). The high rate of migration into Guam in 2010
(13 per 1,000 population) is a substantial increase from previous years and reflects
the significant up-scaling of US defence force personnel in the territory, while in
migration to New Caledonia is largely driven by the mining boom. In both cases,
migrants entering the country are more likely to be healthy individuals moving for
work, and could therefore be expected to have a lower mortality rate (at entry) than
the broader resident population. In-migration would therefore be expected to
increase the population denominator to a greater extent than it would contribute
additional deaths to the numerator. Thus decreasing calculated mortality rates and
potentially masking health concerns particularly within sub-groups of the population
who may have different health and social characteristics than represented in the
influx of migrants. Correlating trends in summary measures of mortality against
changes in migration would be important in interpreting mortality estimates for these
countries.
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Table 18: Migration rates by Country for 2010 (110)
Migration Category and Country
Crude net migration rate (‰) per 1000 pop
Number of net migrants
Annual population growth (%)
Net In-migration New Caledonia 4.65 1173.33 1.55 Guam 13.00 2400.00 2.67 Net Out-migration Niue -28.14 -42.11 -2.31 Marshall Islands -18.43 -1000.00 0.69 Samoa -16.68 -3050.00 0.30 Tonga -16.58 -1711.36 0.33 Tokelau -16.22 -18.91 -0.18 Northern Mariana Islands -15.85 -1000.00 -0.06 Federated States of Micronesia
-14.69 -1632.65 0.42
Wallis and Futuna -13.16 -175.00 -0.64 Tuvalu -8.78 -97.67 0.51 Fiji Islands -7.67 -6488.89 0.46 American Samoa -7.10 -465.12 1.20 Cook Islands -6.30 -97.61 0.32 Negligible migration – Kiribati -1.00 -100.00 1.85 Papua New Guinea 0.00 0.00 2.13 Solomon Islands 0.00 0.00 2.69 French Polynesia 0.00 0.00 1.16 Vanuatu 0.00 0.00 2.54 Palau 0.00 0.00 0.59 Nauru 0.00 0.00 2.08
The largest group of PICTs have net out-migration. This includes Niue, Marshall
Islands, Samoa, Tonga, Tokelau, Northern Mariana Islands, Federated States of
Micronesia, Wallis and Futuna, Tuvalu, Fiji Islands, American Samoa and the Cook
Islands (Table 18). All have out-migration rates higher than six per 1,000 with eight
PICTs having rates of more than 13 per 1,000 population.
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The effect of out-migration is to decrease population denominators, the result of
which is greater uncertainty around estimated rates. If the migrating population is
predominantly healthier than average (i.e. those with a high level of education and
income and most likely to quality for visas or employment etc), the number of deaths
is less likely to fall as people move away, thus artificially increasing calculated
mortality rates and potentially over-stating health concerns. Of course, if people
leaving the country through out-migration have a similar or higher risk of death than
the population average (people with illness going to receive treatment elsewhere for
example as discussed in the previous section), then the numerator (number of
deaths) would be reduced in proportion to the smaller denominator and an inflation
of mortality rates would not be expected. In countries where there are no reciprocal
residency arrangements in place such as in Tonga, Samoa, and Fiji etc the former
scenario is far more likely as people need to meet visa and employment conditions in
their new countries, whereas outmigration from countries such as the Cook Islands
and Niue which have automatic residency rights in New Zealand, or American
Samoa with residency rights in the US are more likely to see a broader spectrum of
migrants and therefore a smaller “inflationary” effect on mortality rates.
As under-reporting of deaths is likely in these countries (Chapter three), the impact
of migration on mortality estimates is likely to be far less significant than the under-
reporting of deaths. One is likely to completely cancel the other, resulting in empirical
estimates without correction that form the lower boundary of a “plausible envelope of
mortality”. For example, approximately 4,000 to 6,000 people have migrated from Fiji
each year between 2001-2009 (110). This equates to approximately 2.5 to 7.5% of
the population. Ideally, mortality estimates could be adjusted for the impact of
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migration, however, this would require much more detailed data on migration
patterns by age group than is available for the study countries (given migration is not
consistent by age and tends to be highest in young adults and their children) (66). In
the absence of more detailed migration data, it is not possible to exactly calculate the
potential impact on mortality rates. An indication could be made by adjusting
denominators of calculated mortality rates to increase the population based on
migration rates and making the (very poor) assumption that age differences are
minimal. In the case of Fiji, with a moderate level of migration (as above), increasing
the denominator by 7% across all groups would give a LE estimate of 65.8 for males
and 70.0 for females for 2002-2004, compared with the estimated LE of 64.3 (64.0-
64.6) and 69.1 (68.8-69.4) calculated from empirical data and presented in case
study three (Chapter five). As seen in this example, the difference in estimated
population resulted in estimates nearly two years higher for males and one year
higher for females. This is slightly outside the calculated confidence intervals,
although is without any adjustment for undercount in the Fijian data; as would be
expected in collections for most other countries. For most of the other study
countries, greater adjustments were made for undercount in the CRVS system as
presented in Chapter five (including Tonga which has the highest out-migration of
the selected study countries). As such, the unadjusted figure would provide a
plausible lower limit to mortality levels, even allowing for the impacts of migration.
The results show while migration should be considered in the interpretation of results
for countries where migration is moderate or high, there is insufficient data to
accurately correct estimates for the effect of migration and insufficient impact to
warrant doing so in the absence of detailed data. The greater effect of migration is
therefore in limiting the analytical options available for assessing and correcting
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mortality data, as methods may assume closed or stable populations such as the
Brass Growth Balance method and capture-recapture method.
Net migration was negligible in seven countries: Kiribati, Papua New Guinea,
Solomon Islands, French Polynesia, Vanuatu, Palau, and Nauru. Migration was -1
per 1,000 population for Kiribati, one of the larger populations, while remaining
countries were reported as zero. At a national level, overall migration should
therefore have minimal impact on mortality estimates and allows many of the
methods to correct under-reporting to be applied. Caution must still be applied at the
sub-national level of estimation for the larger countries of this group, including
Kiribati, PNG, Solomon Islands and Vanuatu, where there is substantial internal
movement from rural areas and outer islands to the urban centres (50, 61, 133, 135).
4.5 Conclusions Direct calculations of mortality (from empirical data) can be affected through impacts
on both the numerator and denominators of mortality measures, both in terms of
overall numbers (summary measures) and through the introduction of biases in the
data, particularly in relation to age group and causes of death. Deaths that occur off-
island and subsequently not counted in the national statistics may reduce mortality
rates. In the few PICTs that receive people travelling for medical treatment such as
Fiji, New Caledonia and Guam, deaths from this non-resident population may
artificially increase rates, biasing the results, particularly for specific causes of death
which reflect the treatments available. Movement of large numbers of people through
migration changes the denominators for mortality rates and if large enough, may
impact summary measures of mortality. Net migration away from the country may
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reduce denominators and increase rates. Migration may also have more subtle
effects on mortality estimates by introducing biases due to the selection criteria
generally applied to immigration applications.
In addition to the impacts on direct calculation of measures of mortality, population
movement defines the scope of methods of estimation available to derive mortality
estimates where empirical data is either not available or not complete.
The work presented in this chapter demonstrates the importance of data aggregation
over time, although this alone is not sufficient to generate reliable estimates for
disaggregated data in many of the smaller countries. Summary measures of
mortality, such as LE are therefore critical in understanding mortality profiles in the
smaller countries, and data for all countries should be aggregated over several years
to improve reliability. Even in the larger populations of Fiji and PNG, where
population sizes are sufficiently large to generate annual estimates of mortality
based on Begg’s calculations (293) this would assume complete reporting which is
unlikely, and a minimum aggregation of 3-5 years is recommended. For smaller
countries, collapsing age groups from 5 years and older to ten year groupings (0-4,
5-14, 15-24...etc) or even to broad groups of 0-4, 5-14, 15-59 and �60 may be
required to generate more robust estimates.
While it is not practical to capture all off-island events, it would be possible in most of
the study countries to count vital events (births and deaths) in the high risk medically
referred population in national statistics. The significant impact of these events on
mortality indicators, particularly for cause of death, would highlight a need to do so.
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In countries that already count these events to some extent (Nauru and Palau),
these will be included (where possible) in the analysis in the following chapters.
Nauru and Palau are in fact the smallest of the study countries, where the off-island
events are likely to have the greatest impact on estimates. In the remaining study
countries where these events are not currently captured, their impact on crude and
most age-specific mortality level estimates is likely to be declining due to the
increase of telemedicine and cost of patient referrals leading to reductions in these
programs, and small number of events. While capture of these events should be
encouraged for future data collections, in overall numbers, they are likely to have
less impact than stochastic variation and under-reporting (as outlined in Chapter
three). Where these events are more likely to have a significant impact is in the
estimation of infant and child mortality, and cause-specific mortality measures
(including MMR) which are affected by differential referral patterns. Findings should
be considered in light of the referral program, and interpreted with care when
discussing implications and trends.
Although potentially of less impact than changes to the numerators of mortality
estimates from off-island deaths, migration remains a potentially important influence
on mortality measures in several countries. With limited data on migration, both from
the outbound and destination countries, there is limited adjustment that can be made
to direct calculations of mortality to account for this, and improvements in data
capture are required. Methods to evaluate and correct mortality level and CoD data
can be carefully selected to ensure the most suitable method for the context is used.
Selection of the population of interest may also be possible to limit the effect of
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migration. For example, deaths in Nauru have long been separated by Nauruan vs.
non-Nauruan ethnicity, due to the much greater mobility in the expatriate community.
Assessment of plausibility is critical in working with the data limitations identified.
One potential source of information to help make this assessment is mortality
estimates by ethnicity within receiving countries such as for New Zealand (316-318),
and these should be reviewed for comparison wherever possible.
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5 Assessing mortality level
5.1 System evaluation findings and analytical strategies to assess
mortality level
Study countries can be grouped into three broad categories based on the system
assessments in Chapter three: those expected to have complete or near complete
reporting of deaths; those with partial reporting that may be able to be corrected
sufficiently to generate estimates from the empirical data; and those where data
collection is mostly absent and insufficient for use in establishing measures of
mortality. The analytical strategies selected for each country are dependent upon
which category the country falls into, whether more than one data source is
available, and whether the population is affected by high levels of migration or off-
island events. Approaches for deriving mortality measures for each of these
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categories have been tested in this chapter and developed into the following flow
diagram (Figure 11).
Figure 11: Recommended analysis approach for assessment of mortality level in the Pacific Islands
The system assessments described in Chapter three identified the small countries,
Nauru and Palau, were likely to have complete or almost complete recording of
deaths due to their small population size, relatively limited geographical distribution,
social incentives for reporting and reporting processes. Estimates of mortality can
thus be derived directly from the routinely collected data; provided care is taken
where more than one collection exists to select the most complete (as in Palau) and
Single data source only
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to avoid over interpretation of data generated over short periods of time due to
stochastic variation. Mortality estimates presented in this chapter for Palau were
derived from the medical certificate data collated by the MoH Epidemiologist, as this
is the most timely, complete data source. Analysis of data from Nauru is presented in
case study two and demonstrates the critical importance of evaluating mortality
data in small populations in the context of long term trends to avoid over
interpretation. While no data “correction” is required before analysis for PICTs in this
category, resultant estimates should be critically considered for consistency with
expected values based on long term trends, comparable overseas values and
internal consistency between results. Frameworks for assessing data quality, such
as outlined by Rao et. al. for use in China (179) or the more recent work by UQ HIS
Hub, may be useful for this purpose. Other PICTs reasonably expected to be
included in this first category, with reasonable mortality level data sources, are the
Cook Islands, Niue, CNMI and Guam (319).
Although routinely collected data for Fiji, Tonga and Kiribati, are considered less
likely to be fully enumerated than for the small islands, there are sufficient data
available to warrant further examination of completeness to determine if corrected
estimates of mortality could be derived. A range of methods for assessing and
correcting incomplete data are outlined in Chapter two. While there are strengths
and weaknesses to each of these, two are considered particularly useful in the
PICTs, as demonstrated in this chapter.
The analysis for Fiji in case study three presents the results of the Brass Growth
Balance analysis of completeness. As discussed in Chapter two, the Brass Growth
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Balance method requires less data than other direct demographic methods that
require analysis to match an inter-censal period (131), and provides a useful
screening tool for level of completeness. As with the small island countries with
good data, derived mortality estimates should be considered in light of longer term
trends derived from plausible estimates. The Brass Growth Balance method was
also applied to assess data completeness from unit record sources in Kiribati
(Appendix one). Results from this assessment showed the data sources were too
incomplete to be reliably adjusted in this manner.
The second method of assessing the completeness of reported deaths presented in
this chapter is capture-recapture analysis. Strengths and weaknesses of this are
examined through two case studies: firstly application of the method in an ideal
island setting in Bohol in the Philippines (case study four), where there are three
good quality data sources; then in Tonga, which shares many of the strengths of the
Bohol study (case study five). Although two source capture-recapture analysis
should not be used to generate definite estimates of all-cause mortality, it may be an
effective means of setting boundaries around the plausibility of estimates produced
by indirect estimation where there are insufficient data to generate estimates directly
from empirical sources. This approach is explored further for Kiribati, where
complete three source analysis has not been possible. Instead a two source capture-
recapture analysis is used to examine existing mortality estimates.
A system assessment would be required to determine other PICTs could be included
in this category of having some data that could be used to improve current estimates
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of mortality level. This could reasonably be expected to include Samoa, which
operates both health reporting and civil registration systems (319).
Finally, the system analysis presented in Chapter three identified that reporting in the
remaining study country, Vanuatu, was far less likely to be complete, and deaths
would subsequently be significantly under-enumerated. The civil registration of
deaths is virtually non-functional in Vanuatu and the health system captures only
around 60% of the deaths expected according to census estimates. Routinely
collected data are therefore considered too incomplete to be used as the basis for
estimates of mortality level. Coverage was also considered insufficient to be
representative at a national level. In larger countries, and where sufficient data exists
to do so, it may be possible to focus on smaller geographical areas to develop
estimates at a sub-national level using the techniques described for the previous
category. Otherwise, other sources of mortality data, such as census data and
survey data, must be used. Estimates from indirect methods and recall methods
employed in census and survey analyses must be assessed for validity in light of
what is known for other PICTs to determine the plausible range of estimates. For
example, it is unlikely Vanuatu, with more limited health facilities, would in 2010 have
had lower infant or child mortality than a non-malarious country such as Fiji with
more universal access to health care (108), while it is entirely plausible there would
be lower cause-specific mortality from NCDs, consistent with Vanuatu being at an
earlier stage of the epidemiological transition (2). Both PNG and the Solomon
Islands would also be expected to also fall within this category with incomplete
routine reporting of deaths (319).
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5.2 Data aggregation and trend analysis
As discussed in Chapter four, population sizes in the PICTs are small and thus
stochastic variation can have a significant impact on mortality rates. It is therefore
critical that data are aggregated over multiple years to provide greater stability and
reported with appropriate statistical confidence intervals.
For many countries, aggregation alone is insufficient to reach the population of
852,900 person years required to generate reliable estimates by age, sex and cause
as cited by Begg et. al. (293). In Nauru for example, this level of certainty would
require aggregation of 88 years of data (Table 15), a situation that is neither
plausible in regard to data availability nor useful for planning and policy. Therefore,
while aggregation remains important, another approach is needed to assess the
plausibility of measures of mortality level from the smaller PICTs, even when
complete data is available. A review of long term trends and how these relate to the
most recent estimates is one way of providing this context, as demonstrated in case
study two for Nauru.
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5.2.1 Case Study 2: Mortality Trends in Nauru.
Carter KL, Soakai TS, Taylor R, Gadabu I, Rao C, Thoma K, Lopez AD. Mortality
trends and the epidemiological transition in Nauru. Asia Pac J Public Health. 2011
Jan;23(1):10-23
This study provides an example of the use of data aggregation and assessment of
long term trends in understanding mortality levels in a small population.
Age-specific mortality rates, as shown for 2002-2007 in Figure 12 demonstrate a
plausible pattern of mortality level (320) albeit with significantly higher mortality in
males across all adult groups up to 65 years of age.
Figure 12: Age-specific mortality for Nauru 2002-2007, by sex
As demonstrated in this case study, neither childhood mortality nor adult mortality
(for males and females) has improved over a period of approximately 50 years.
0.1
1
10
100
1000
0�4 5�14 15�24 25�34 35�44 45�54 55�64 65�74
Mortality�rate(deaths�per�1,000�pop)�
Age�group
Males
Females
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Despite the lack of improvement in child mortality, U5M is sufficiently low (46.0
deaths per 1,000 live births) that this is not the primary factor influencing the low LE
estimated in this study. LE at birth for 2002-2007 is estimated as 52.8 years for
males, and 57.5 years for females.
LE for males remains one of the lowest in the region, with decreases in mortality
from external causes offset by increases in NCDs (112). Adult mortality (15-59
years) (at 510 deaths per 1,000 population for males and 401 deaths per 1,000
population for females) is three to four times higher than in Australia or New
Zealand. These levels of LE at birth are comparable with South Africa, which has a
high mortality from HIV/AIDs (321). Life tables from Nauru which are not included in
the paper are presented in Appendix two. Although not specifically reported in the
paper, the plausibility of the data across all broad age groups suggest reported
deaths could also be used to measure IMR with a reasonable level of reliability
across this period. IMR was subsequently estimated from the U5M using a
regression analysis based on international data as 28 deaths per 1,000 live births
(95% CI: 12 – 55 deaths per 1,000 live births).
http://aph.sagepub.com/Asia-Pacific Journal of Public Health
http://aph.sagepub.com/content/23/1/10The online version of this article can be found at:
DOI: 10.1177/1010539510390673
2011 23: 10Asia Pac J Public HealthAlan D. Lopez
Karen Carter, Taniela Sunia Soakai, Richard Taylor, Ipia Gadabu, Chalapati Rao, Kiki Thoma andMortality Trends and the Epidemiological Transition in Nauru
Published by:
http://www.sagepublications.com
On behalf of:
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Asia-Pacific Journal of Public Health23(1) 10 –23
© 2011 APJPHReprints and permission: http://www. sagepub.com/journalsPermissions.nav
DOI: 10.1177/1010539510390673http://aph.sagepub.com
Mortality Trends and the Epidemiological Transition in Nauru
Karen Carter, BAppSci (Env Health), MPHTM1, Taniela Sunia Soakai, Dip Health Admin, MBA2, Richard Taylor, PhD, FAFPHM1,3, Ipia Gadabu4, Chalapati Rao, MBBS, PhD1, Kiki Thoma, DSM2, and Alan D. Lopez, PhD, HonFAFPHM1
Abstract
This article aims to examine the epidemiological transition in Nauru through analysis of available mortality data. Mortality data from death certificates and published material were used to construct life tables and calculate age-standardized mortality rates (from 1960) with 95% confidence intervals. Proportional mortality was calculated from 1947. Female life expectancy (LE) varied from 57 to 61 years with no significant trend. Age-standardized mortality for males (15-64 years) doubled from 1960-1970 to 1976-1981 and then decreased to 1986-1992, with LE fluctuating since then from 49 to 54 years. Proportional mortality from cardiovascular disease and diabetes increased substantially, reaching more than 30%. Nauru demonstrates a very long period of stagnation in life expectancy in both males and females as a consequence of the epidemiological transition, with major chronic disease mortality in adults showing no sustained downward trends over 40 years. Potential overinterpretation of trends from previous data due to lack of confidence intervals was highlighted.
Keywords
mortality, life expectancy, Nauru, cause of death, Pacific Islands
Introduction
Nauru is a small (24 km2) raised atoll in the mid-Pacific. Originally colonized by Germany in 1888, it was administered by Australia, Britain, and New Zealand from 1914 until independence in 1968.1 Phosphate mining began in the early 1900s, but by the early 1990s most deposits had been extracted. Mining ceased entirely between 2003 and 2006,1 before recommencing at a
1University of Queensland, Brisbane, Queensland, Australia2Nauru Ministry of Health, Yaren, Nauru3University of New South Wales, Sydney, New South Wales, Australia4Nauru Bureau of Statistics, Yaren, Nauru
Corresponding Author:Richard Taylor, PhD, University of New South Wales Main Campus, Samuels Building, Level 2, Room 223, Botany St, Gate 11, Randwick, Sydney, New South Wales 2052, AustraliaEmail: [email protected]
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Carter et al. 11
significantly reduced scale. The Nauruan (Micronesian) population in 2006 was approximately 9547, along with an expatriate workforce of around 400.2 This expatriate workforce is signifi-cantly reduced from the 1000 or so phosphate workers (primarily from other Pacific Islands) present in 1992.3 Although entirely economically self-sufficient in the early 1990s, Nauru is now almost entirely dependent on foreign aid.1
Nauru gained notoriety following independence due to the considerable revenues from phos-phate mining. Profits were used for running government and providing services, invested in vari-ous enterprises, and paid to land owners.4 Annual per capita income implied from the value of phosphate exports during the 1970s and 1980s was relatively high at approximately US$10 000 to US$20 000.4,5 The ready availability of cash enabled considerable consumption expenditure on imported items such as food, alcohol and sweetened beverages, cigarettes, vehicles, and ste-reo and video equipment. References to adverse effects on health and high accident rates as a result of this affluence were noted beginning in the mid-1970s.3-5 Community surveys revealed an extraordinary high prevalence of diabetes in adults—30% in a 1976 survey6,7 and 24% in 19828—as well as considerable hypertension,9 hyperuricaemia,10 coronary heart disease,11 obe-sity,12 and microalbuminuria.13 Extensive morbidity and mortality14-16 associated with these con-ditions was evident, including diabetic eye disease,17 lower limb amputations,18 and higher mortality in diabetics.19 Obesity was common but not seemingly independently related to mortality.12 Genetic and environment factors are thought responsible for the prevalence of diabetes.17 A decline in the incidence of diabetes during the 1980s may indicate a partial exhaustion of the genetically susceptible.20 Tobacco smoking was frequent21 and related to higher mortality.12 Continuing high levels of diabetes, hypertension, serum cholesterol, obesity, and tobacco and alcohol consumption were demonstrated in a 2004 noncommunicable disease risk factor survey.22
Nauru provides an example of a deviation from the classical epidemiological transition described by Omran,23 in that mortality increased with changes of causes of death to noncom-municable disease and injuries.14-16 This study examines the unique epidemiological transition in Nauru by a secular analysis of mortality data to 2007—from 1947 for cause of death and from 1960 for level of mortality.
MethodsMortality data have only been sporadically available from Nauru, and the present compilation contains gaps where studies or assessments were not available. Data from 1986-1992 and 2002-2007 are reported here for the first time. Analysis is limited to Nauruan nationals because of significant changes in expatriate populations over time. Given the extensive timeframe covered, it is inevitable that there is variation in data sources and collection methods, as outlined below.
Population data for 2002-2007 were obtained by linear interpolation from the Nauruan popu-lations from the 2002 census3 and the 2006 Household Income and Expenditure Survey (HIES).2 The HIES data included people of unknown ages, and populations were redistributed to the age categories according to proportions reported. Population data for 1992-2002 were obtained from data published with the 2002 census.3 Population data for 1986-1992 were obtained by linear interpolation from the 1983 census24 and the 1992 census,25 whereas population data for previous periods were obtained from published sources.14-16 Population data for 1959 and 1972-1974 used in the examination of external causes of death (injuries, poisonings, suicide, homicide, etc) were extrapolated from population data for 1960-197026 based on the average growth rate of the adja-cent 5 years (1960-1964 and 1968-1971, respectively).
Details of the number of deaths for 2002-2007 were extracted (by KC) from death certifi-cates held at the national hospital and the National Registration Office, which receives the certificates from the hospital. Nauruan deaths were identified by recorded ethnicity on the death
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12 Asia-Pacific Journal of Public Health 23(1)
certificates. Details of sex, age at death, year of death, and causes of death were collected. Details of Nauruan deaths and mortality rates by age group for the periods 1997-2002 and 1992-1997 were obtained from data extracted from death certificates and registers of the Secretariat of the Pacific Community (SPC).3,27,28 Prior to 2000 there were 2 hospitals in Nauru, and data of the deaths for the years 1986-1992 were extracted from registers at both hospitals in 1993 by 2 of the authors (RT and KT); Details of Nauruan deaths were identified by name since all Nauruan families were known to one of the authors (KT). Variables were abstracted as for later data. Mortality data for periods before 1986 were obtained from published material,14-16,26 with 1976-1981 deaths extracted by RT and KT. For the period 1982-1985, mortality rates for ages less than 15 years were not available,16 and these were interpolated using 1976-198114-26 and 1986-1992 data. For all periods prior to 1986, the oldest open age group was more than 65 years.
Age-specific mortality rates, for age groups of 0-4 years then decades (because of small num-bers) to 74 years for each sex, were obtained by dividing deaths by population for each of the peri-ods analyzed. The mortality rate for age group 65 to 74 years before 1986 was estimated by linear extrapolation of the logarithm of the age-specific death rate from the age range of 35 to 64 years.
Data from 1992-1997 and 1997-2002 were published as 5-year age groups.3,27,28 These were converted to decade age groups for consistency with the other periods. Childhood mortality (0-4 years) was calculated for both sexes combined to reduce stochastic variation due to the small population size using age-specific mortality rates.
Life tables were constructed according to standard methods29 from age-specific mortality rates by sex for each period and life expectancy (LE) at birth reported. Confidence intervals (CIs) for both LE and childhood mortality (probability of dying aged 0-4 years) were calculated according to the Chiang method.30 Age-standardized mortality rates for adults (15-64 years) were calculated using the direct method based on the 2000-2007 population (both sexes). CIs were calculated using the Poisson distribution for all rates due to the small number of deaths, except for age-standardized mortality rates where there were sufficient deaths to use the normal approx-imation of the binomial.31 LE and age-standardized mortality rates were graphed to illustrate trends (Figures 1 and 2). The linear trend in death rates for males for the 3 time periods from 1976 to 1992, adjusted for age, was assessed using Poisson regression31: loge(deaths/population) was modeled on age group and period.
Causes of death for 2002-2007 and 1986-1992 were obtained from death certificates and/or death registers, with data for 1986-1992 supplemented by information from the patients’ files. A single underlying cause of death was determined according to the International Classification of Diseases (ICD), 9th revision,32 to maintain consistency between time periods and reported according to ICD9 chapter headings.
Cause of death data for other periods were obtained from published material,14-16,33 with 1976-1981 data extracted by RT and KT. Data from early periods are given by major cause ICD chapter. Deaths from external causes for the period 1982-1985 were obtained by inflating the deaths ≥15 years using the ratio of all external deaths to external deaths <15 years deaths for both the previous and succeeding periods (inflation factor 1.19).
Proportional mortality by ICD chapter was graphed over time to identify trends. Disease chapters were subsequently grouped further to demonstrate major trends from those that are predominately infectious in nature (infectious diseases, respiratory diseases, and diseases of the digestive tract), major chronic diseases (diseases of the circulatory system, neoplasms, and endo-crine, metabolic, and nutritional diseases), external causes, and all others (Figure 3).
Mortality rates due to external causes (all available years) were calculated for the total popu-lation and for males only where data were available, with Poisson CIs.31 Data for external causes is more specific by nature allowing this analysis, whereas disaggregation of other causes is made difficult by the nonstandard format of the medical certificate of death used in Nauru.
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Carter et al. 13
0
2
4
6
8
10
12
14
16
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
Time Period+
Dea
ths
per
1,0
00 p
op
ula
tio
n
Figure 1. Child mortality (deaths per 1000 population), 1960-2007, 0-4 years, both sexesNote: See Table 1 for age-specific mortality and probability of dying by period.+Results were plotted at the midpoint of each time period as follows: 1960-1970 (1965), 1976-1981 (1978.5), 1982-1985 (1983.5), 1986-1992 (1989), 1992-1997 (1994.5), 1997-2002 (1999.5), and 2002-2007 (2004.5).
0.0
5.0
10.0
15.0
20.0
25.0
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
Females
Males
Time Period+
Mo
rtal
ity
rate
per
100
,000
Figure 2. Nauruan age-standardized mortality rate (per 100 000), 1960-2007, 15-64 years, by sex+Results were plotted at the midpoint of each time period as follows: 1960-1970 (1965), 1976-1981 (1978.5), 1982-1985 (1983.5), 1986-1992 (1989), 1992-1997 (1994.5), 1997-2002 (1999.5), and 2002-2007 (2004.5).
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14 Asia-Pacific Journal of Public Health 23(1)
Results
Child mortality (deaths <5/1000 population) in Nauru fluctuated over the time periods exam-ined from 6.0 to 10.8; all 95% CIs overlap. In 2002-2007, the child mortality rate was 9.5 deaths per 1000 population, equivalent to a probability of dying (0-4 years) of 46 per 1000 live births (Table 1, Figure 1).
40
45
50
55
60
65
70
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
Males
Females
Lif
e ex
pec
tan
cy (
year
s)
Time Period+
Figure 3. Nauruan life expectancy at birth, 1960-2007, by sex+Results were plotted at the midpoint of each time period as follows: 1960-1970 (1965), 1976-1981 (1978.5), 1982-1985 (1983.5), 1986-1992 (1989), 1992-1997 (1994.5), 1997-2002 (1999.5), and 2002-2007 (2004.5)
Table 1. Child Mortality in Nauru, 1960-2007, 0-4 Years, Both Sexesa
Period Deaths for PeriodDeaths per 1000
PopulationProbability of Dying 0-4
Years per 1000 Live Births (5q0b)
1960-1970 46 7.5 (5.5-10.0) 36.7 (26.3-47.1)1976-1981 45 10.2 (7.5-13.7) 49.4 (35.4-63.5)1982-1985 35 8.6 (6.0-12.0) 41.9 (28.3-55.5)1986-1992c 49 6.0 (4.5-8.0) 29.6 (21.4-37.8)1992-1997 47 6.6 (4.9-8.8) 32.4 (23.3-41.6)1997-2002 70 10.8 (8.4-13.6) 52.1 (40.3-64.0)2002-2007d 62 9.5 (7.2-12.0) 46.0 (34.8-57.1)
aProbability of dying calculated from age-specific mortality rates. Source: 1960-197041; 1976-198123,24; 1982-198525; 1992-19973,42,43; 1997-2002.42,43
b5q0 is the demographic notation for the probability of dying before the age of 5 years.cData extracted by RT and KT in 1993 (previously unpublished).dData extracted and analyzed by authors.
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Carter et al. 15
For Nauruan males, LE at birth between 1960-1970 and 1976-1981 declined from 58 to 47 years, and the age-standardized mortality rate (15-64 years) more than doubled, with 95% CIs indicating significant differences (Table 2, Figures 2 and 3). From 1976-1981 to 1986-1992, mortality progressively decreased (Table 2), as evidenced by increasing LE to 50 years in 1982-1985 and 54 years in 1986-1992 and a decline in age-standardized mortality rate (15-64 years; c2
(1) = 15.4, P < .001; Figure 3). Comparison of 1976-1981 to 1986-1992 indicated that the larg-est decline in mortality occurred in the 25- to 34-year age group, with the mortality rate reduc-ing by two thirds; this is statistically significant according to 95% CIs (Table 2). Mortality again increased from 1986-1992 to 1997-2002, with male LE at birth in 1997-2002 falling signifi-cantly (according to 95% CIs) to 49 years,3 before improving again between 1997-2002 and 2002-07 to reach 53 years, a level similar to 1986-1992 but still less than the LE recorded in 1960-1970 (Table 2). The increase in LE between 1997-2002 and 2002-2007 was not statisti-cally significant. Female LE in Nauru has been higher than for males since 1976-1981, but no improvement in female LE occurred between 1960-1970 (57 years) and 2002-2007 (57 years; Table 3, Figures 2 and 3).
Major trends are discernable in causes of death over the past 60 years (1947-2007; Table 4, Figure 4). Infection was a major cause of death in the 1940s and 1950s but decreased signifi-cantly thereafter. Proportional mortality from respiratory diseases and diseases of the digestive tract, often substantially associated with infectious causes, show a similar pattern, although the decline is less dramatic.
Cardiovascular disease and diabetes increased from a small proportion of deaths to comprise 20% to 30% of deaths from the 1970s. Although deaths from endocrine and metabolic diseases (including diabetes) rose from 5% in 1976-1981 to almost 10% in 2002-2007, the proportion of deaths with diabetes recorded anywhere on the death certificate rose from 26% to 36% over the same period. Cancer was not reported in the early periods, but constituted around 10% to 12% of deaths by the 1980s.
External causes increased from less than 10% prior to 1970 to peak at 24% in the mid-1980s, then declined to the current level of 10%. The mortality rate from external causes (mostly in males) was significantly lower in 1986-1992, compared with previous periods (Table 5).
DiscussionAlthough Nauruans are a small population, mortality analysis can be undertaken by aggregation of several years’ data, and 95% CIs assist with interpretation of trends. The mortality data reported in this and previous studies over 4 decades3,14-21,24-28,33 are likely to be complete because of the small number of deaths, all well known to medical staff, and centralized hospital-based death certification. A review of the death reporting systems confirmed very little potential for underreporting of deaths to have occurred. Furthermore, cause of death data should be reason-ably accurate since they emanate from death certificates, supplemented by other relevant infor-mation. Systematic biases in either death counts or population counts are likely only among the highly unstable expatriate community in Nauru, and this was dealt with by restricting the analy-sis to Nauruans only. Records were sufficiently detailed to confidently exclude non-Nauruan deaths. There is reasonable consistency in the estimates over time when individual years were examined within the aggregated periods analyzed. Estimates of mortality from registration data 1992-2002 are congruent with indirect demographic methods of estimation from the 2002 Census.3 The orphanhood method revealed a higher survival of mothers (78%) than fathers (64%), and the widowhood method showed only 19% of men ≥60 years were widowed, whereas 61% of women in the same age group were widowed.3 The recently published Demographic and Health Survey for Nauru gives a 0 to 4 years mortality of 38 (95% CI = 18-57) per 1000 live births for
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Tab
le 2
. Mal
e N
auru
an M
orta
lity
(Age
s 0-
74)
and
Life
Exp
ecta
ncy
at B
irth
, 196
0-20
07a
Age
G
roup
(Y
ears
)
1960
-197
019
76-1
981
1982
-198
519
86-1
992b
1992
-199
719
97-2
002
2002
-200
7c
Dea
ths
for
Peri
odR
ate
per
1000
(9
5% C
I)d
Dea
ths
for
Peri
odR
ate
per
1000
(9
5% C
I)d
Dea
ths
for
Peri
odR
ate
per
1000
(9
5% C
I)d
Dea
ths
for
Peri
odR
ate
per
1000
(9
5% C
I)d
Dea
ths
for
Peri
odR
ate
per
1000
(9
5% C
I)d
Dea
ths
for
Peri
odR
ate
per
1000
(9
5% C
I)d
Dea
ths
for
Peri
odR
ate
per
1000
(9
5% C
I)d
0-4
[26]
e8.
0 (5
.3-1
1.8)
2310
.1 (
6.4-
15.1
)[1
8]8.
7 (5
.1-1
3.6)
22
5.2
(3.3
-7.9
) 2
46.
5 (4
.2-9
.6)
45
13.4
(9.
7-17
.9)
39
12.1
(8.
5-16
.3)
5-14
[3]
0.5
(0.1
-1.6
)10
2.8
(1.3
-5.1
)[2
]1.
3 (0
.2-4
.7)
9
1.4
(0.6
-2.6
) 1
21.
9 (1
.0-3
.3)
5
0.8
(0.2
-1.8
)
60.
9 (0
.3-1
.9)
15-2
4[9
]3.
5 (1
.6-6
.8)
226.
9 (4
.3-1
0.4)
2310
.5 (
6.7-
15.8
) 2
24.
9 (3
.1-7
.5)
14
3.2
(1.7
-5.4
) 3
06.
1 (4
.1-8
.7)
13
2.3
(1.2
-3.8
)25
-34
[9]
4.5
(2.1
-8.6
)27
16.7
(11
.0-2
4.4)
1610
.1 (
5.8-
16.4
) 1
85.
5 (3
.2-8
.6)
20
6.4
(3.9
-9.8
) 2
67.
9 (5
.2-1
1.6)
24
6.3
(4.0
-9.3
)35
-44
[15]
9.0
(5.0
-14.
8)23
19.6
(12
.5-2
9.5)
711
.6 (
4.7-
23.9
) 2
512
.9 (
8.3-
19.0
) 4
018
.8 (
13.4
-25.
5) 4
620
.0 (
14.6
-26.
6) 4
216
.1 (
11.4
-21.
5)45
-54
[9]
14.5
(6.
4-26
.7)
2931
.5 (
21.1
-45.
2)16
28.4
(16
.2-4
6.1)
24
29.3
(18
.8-4
3.7)
28
36.3
(24
.1-5
2.5)
57
52.5
(39
.8-6
8.0)
55
33.1
(24
.6-4
2.7)
55-6
4[2
2]49
.0 (
31.3
-75.
5)25
73.3
(47
.4-1
08.2
)20
66.4
(40
.6-1
02.5
) 4
274
.5 (
53.7
-100
.7)
27
56.6
(37
.3-8
2.3)
29
66.7
(44
.3-9
5.1)
36
68.8
(47
.7-9
4.2)
65-7
499
.9{2
6}13
3.1
(86.
9-19
5.0)
{17}
106.
1 (6
1.8-
169.
9) 1
797
.1 (
56.6
-155
.5)
29
154.
7 (1
03.6
-222
.1)
15
72.5
(41
.2-1
21.3
) 1
779
.6 (
45.8
-126
.0)
Age
-sta
ndar
dize
d ra
tef
15-6
464
7.7
(4.2
-11.
2)12
617
.1 (
11.9
-22.
2)82
16.2
(11
.1-2
1.2)
131
11.9
(7.
5-16
.2)
129
12.6
(8.
1-17
.1)
188
16.6
(11
.5-2
1.8)
170
11.8
(7.
5-16
.1)
Life
exp
ecta
ncy
(yea
rs)
At
birt
h58
.1 (
55.7
-60.
5)47
.4 (
45.1
-49.
8)50
.3 (
47.4
-53.
2)54
.0 (
52.0
-56.
1)52
.3 (
50.3
-54.
3)49
.0 (
47.4
-51.
6)52
.8 (
51.1
-54.
5)
Abb
revi
atio
n: C
I, co
nfid
ence
inte
rval
.a S
ourc
e: 1
960-
1970
41; 1
976-
1981
23,2
4 ; 19
82-1
98525
; 199
2-19
973,
42,4
3 ; 19
97-2
002.
42,4
3
b Dat
a ex
trac
ted
by R
T a
nd K
T in
199
3 (p
revi
ousl
y un
publ
ishe
d).
c Dat
a ex
trac
ted
and
anal
yzed
by
auth
ors
2008
.d A
vera
ge d
eath
rat
e pe
r 10
00 p
opul
atio
n pe
r an
num
.e [
] D
eath
s im
plie
d by
pop
ulat
ion
and
deat
h ra
te. {
} D
eath
s ag
ed ≥
65 y
ears
. 200
2-20
07 d
eath
s in
clud
e de
aths
of u
nkno
wn
age
redi
stri
bute
d by
age
gro
up.
f Age
-sta
ndar
dize
d ra
te b
ased
on
2002
-200
7 po
pula
tion
and
by d
irec
t m
etho
d.
16
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Tab
le 3
. Fem
ale
Nau
ruan
Mor
talit
y (A
ges
0-74
) an
d Li
fe E
xpec
tanc
y at
Bir
th, 1
960-
2007
a
Age
G
roup
(Y
ears
)
1960
-197
019
76-1
981
1982
-198
519
86-1
992b
1992
-199
719
97-2
002
2002
-200
7c
Dea
ths
for
Peri
odR
ate
per
1000
(9
5% C
I)d
Dea
ths
for
Peri
odR
ate
per
1000
(9
5% C
I)
Dea
ths
for
Peri
odR
ate
per
1000
(9
5% C
I)
Dea
ths
for
Peri
odR
ate
per
1000
(9
5% C
I)
Dea
ths
for
Peri
odR
ate
per
1000
(9
5% C
I)
Dea
ths
for
Peri
odR
ate
per
1000
(9
5% C
I)
Dea
ths
for
Peri
odR
ate
per
1000
(9
5% C
I)
0-4
[20]
e7.
0 (4
.2-1
0.7)
2210
.4 (
6.5-
15.8
)[1
7]8.
7 (5
.1-1
3.9)
276.
9 (4
.6-1
0.1)
236.
8 (4
.3-1
0.2)
258.
0 (5
.2-1
1.8)
237.
0 (4
.4-1
0.4)
5-14
[5]
1.0
(0.3
-2.3
)7
2.1
(0.8
-4.2
)[2
]1.
3 (0
.2-4
.7)
30.
5 (0
.1-1
.4)
40.
7 (0
.2-1
.7)
61.
0 (0
.4-2
.1)
20.
3 (0
.0-1
.2)
15-2
4[1
2]5.
5 (2
.7-9
.2)
82.
6 (1
.1-5
.0)
62.
7 (1
.0-5
.9)
61.
4 (0
.5-3
.1)
61.
5 (0
.5-3
.2)
81.
7 (0
.7-3
.3)
81.
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17
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18 Asia-Pacific Journal of Public Health 23(1)
Table 4. Proportional Mortality by Major Cause Group in Nauruans, 1947-2007, Both Sexes Combineda
Period 1947-1958 1959-1968 1972-1974 1976-1981 1982-1985 1986-1992b 2002-2007c
Deathsd 207 144 114 273 157 317 459Proportional mortality (%) Infection 41 4 15 9 13 7 Cancer 2 8 12 10 13 Endocrine\
metabolic 4 5 14 10 9
Nervous system diseasee
7 13 3 2 2
Cardiovascular disease
11 15 28 21 29 26 22
Respiratory disease 7 10 11 6 5 7 8 Digestive diseasef 7 15 5 6 5 5 Genitourinary
disease 5 7 3 1 3 8
Pregnancy and child birthg
5 3 1
Perinatal conditions 2 5 7 h 6 6 External causesi 4 8 15 20 24 10 10 Other causesj 11 16 44 7 8 7
aMajor cause according to ICD9 chapters. Source: 1947-195823; 1959-196823; 1972-197448; 1976-198123,24; 1982-198525; 1992-19973,42,43; 1997-2002.42,43
bData extracted by RT and KT in 1993 (previously unpublished).cData extracted and analyzed by authors 2008.dIncludes diabetes; allocation of deaths to diabetes is sensitive to the coding criteria employed.eIncludes ear and eye disease.fIncludes liver disease.gThese percentages should be doubled to obtain the approximate proportional mortality for females.h1982-1985 data are for age ≥15 years only.iMost deaths from external causes are in males.jIncludes unknown and ill-defined.
2003-2007 and 41 (95% CI = 22-60) for 1998-2007, based on retrospective maternal history and child survival from a sample of women aged 15 to 49 years in 2007.34 These figures are some-what less than recorded mortality for similar periods for the entire population of 52 (1997-2002) and 46 (2002-2007), but 95% CIs overlap.
There is substantial stochastic variation apparent in all the mortality measures reported here. This demonstrates the importance of avoiding overinterpretation of data from small populations, such as Nauru, by considering mortality measures in light of secular trends and their statistical CIs. Despite aggregation of data over several years, the stochastic variation in these measures has previously led to suggestions of significant recent increases in childhood and infant mortality,3 which are not supported by the analysis presented here.
Changes in proportional mortality for diseases of the circulatory system and endocrine/metabolic diseases (including diabetes) are likely to be affected by certification practices. Although this may have resulted in fewer deaths coded to diabetes, the proportion of deaths with diabetes noted anywhere on the certificate has increased dramatically since 1976-1981, confirming the continu-ing importance of this condition in Nauru. Recent declines in proportional mortality from cardio-vascular disease may largely be attributed to improvements in certification resulting in more specific diagnoses in other chapters. Cause of death was reported by ICD chapter to provide
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Carter et al. 19
Time Period+
0
10
20
30
40
50
60
Pro
po
rtio
nal
Mo
rtal
ity
Infectious, Respiratory andDigestive Tract Diseases
Endocrine\metabolic, Cardiovascular diseases and Cancer*
External causes~
All other causes
Figure 4. Trends in major causes of death in Nauru, 1947-2007, all ages, both sexes combinedNote: See Table 4 for numbers of deaths by period. Deaths from infection interpolated for 1972-1974. Unknown and ill-defined included in “All other causes.”*Endocrine deaths not included 1947-1958, and 1959-1968 cancers not listed separately (included in “Other”).~External causes include injury and accidents (including road accidents, falls, burns, etc), homicide, and suicide.+Results were plotted at the midpoint of each time period as follows: 1947-1958 (1952.5), 1959-1968 (1963.5), 1972-1974 (1973), 1976-1981 (1978.5), 1982-1985 (1983.5), 1986-1992 (1989), and 2002-2007 (2004.5).
Table 5. Mortality in Nauruans From External Causes, 1947-2007, All Agesa
Period
Both Sexes Males
Deaths for Period
Rate per 100 000b (95% CIs)
Deaths for Period
Rate per 100 000b (95% CIs)
1947-1958 8 43 (19-85)1959-1968 12 45 (23-79)1972-1974 17 138 (80-221)1976-1981 54 208 (156-272) 42 315 (227-425)1982-1985 [45]c 249 (181-333) [34]c 376 (261-526)1986-1992d 31 71 (49-101) 25 114 (74-168)2002-2007e 45 92 (66-122) 33 134 (92-189)
Abbreviation: CI, confidence interval.aAge adjusted using the indirect method to the 2002-2007 rates as a standard. Confidence intervals calculated according to the Poisson distribution. Source: 1947-195823; 1959-196823; 1972-197448; 1976-198123,24; 1982-198525; 1992-19973,42,43; 1997-2002.42,43
bAverage death rate per 1000 population per annum.c[ ] Deaths less than age 15 years estimated (see Methods).dData extracted by RT and KT in 1993 (previously unpublished).eData extracted and analyzed by authors 2008.
consistency with previously published data14-16 and allow the assessment of trends over time. As Nauru uses a death certificate that is not consistent with international guidelines, aggregating the data by ICD chapter also avoids comparability difficulties for specific causes created by the certificate format.
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20 Asia-Pacific Journal of Public Health 23(1)
The major finding of this study is that there has been no sustained reduction in mortality in Nauru over the past half century (1960-2007) in children or adults. For child mortality, neither the decrease from 10.2 deaths per 1000 population in 1976-1981 to 6.0 in 1986-1992 nor the subsequent increase to 9.5 in 2002-2007 was statistically significant. These variations are likely to represent stochastic fluctuations due to the small population.
After an increase from 1960-1970, Nauruan male mortality declined significantly between 1976-1981 and 1986-1992, partly due to a decrease in deaths from external causes, but gains in LE were not sustained. Improvement in the latest male LE (2002-2007) is within expected sto-chastic variation given the population size and is not statistically significant using 95% CIs compared with the previous period. Female Nauruan LE has shown no improvement over the study period with LE in both 1960-1970 and 2002-2007 at 57 years. Fluctuations in LE over this period (between 57 and 61 years) were not statistically significant.
Prior to the 1950s, infectious diseases (particularly tuberculosis) were the major cause of death, and injury accounted for only a small proportion of reported deaths. Hepatitis B, including infection with delta agent,35-37 was prevalent during the 1980s; however, universal immunization has since been implemented.37 As infectious diseases fell, chronic diseases began to increase in importance, and by 1976-1981, diseases of the circulatory system was the most significant cause of all deaths, followed by external causes, although the latter were the major cause of years of life lost.14-16 Diabetes makes a considerable contribution to mortality in Nauru, both directly and as a major risk factor for cardiovascular disease.12,14-16
Despite declines in proportionate infectious disease mortality, male LE (at birth) declined by 10 years from 57.6 years (1960-1970) to 47.2 years (1976-1981). This was the lowest national LE for males in the Pacific Island region during this period.38,39 The decline in LE in Nauru in concert with the epidemiological transition was reported prior to declines in LE in the former Soviet Union and Eastern European countries, also mainly due to noncommunicable disease and injuries in adults.40-43
The decline in male mortality between 1976-1981 and 1986-1992 was led by the reduction in deaths due to external causes in adults in the period 1986 to 1992. Previous analyses indicated that most external cause deaths at that time were motor vehicle accidents (MVA), 5 times that of Australian males for the same period,14,15 and frequently associated with alcohol intoxication, although other injuries, also often alcohol related, make up this category.14,15 Although it was not possible in this analysis to reliably separate MVAs from other external causes, the decline in external cause mortality by more than 50% in more recent periods implies a substantial reduction in MVA mortality. Evidence from key informants (including one of the authors, KT) suggests that driving motor vehicles or riding motor cycles while intoxicated with alcohol has declined substantially from the mid-1980s. This important change in behavior is attributed, in part, to the effect of the clergy, both Catholic and Protestant, who used the funerals of those who died from MVAs to deliver dire warnings from the pulpit on the dangers of drink driving. The awareness of the clergy was no doubt derived from their own observations but probably reinforced by analyses documenting the massive mortality from this cause.14-16 The message to avoid drunk driving would have fallen on fertile ground, particularly among women, who recognized that Nauru had become a “land of widows.”44 There is no evidence that the fall in external cause mortality was due to any legal or policing actions, restrictions on alcohol supply or sale, or organized health promotion interventions—since there were none at that time. Anecdotal evidence is that alcohol consumption did not substantially change during the period of rapid decline in external cause mortality and the loss in economic prosperity and hence purchasing power for both vehicles and alcohol did not occur until later.
Nauru demonstrates unusual aspects of the health transition. First, the emergence of non-communicable disease and injuries vastly exceeded the decline in infectious disease mortality in males, initially leading to a reduction in LE; this phenomenon has now been documented in
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Carter et al. 21
other populations.41-43 In females, the decline in infections and maternal mortality was approximately balanced by the rise in noncommunicable diseases and accidents. While stagnation in growth of LE as a consequence of the substitution of infections by noncommunicable conditions occurred in Australia and similar countries around 1950-1970, especially in males,45,46 the severity of the effect in Nauru was much greater and occurred at a lower LE. Declines in LE have been noted in populations of Eastern Europe and the former USSR since the 1960s and continuing through the 1990s, especially in males, as a consequence of cardiovascular disease, cancer, and other tobacco- and alcohol-related conditions and injuries—but at a higher LE than in Nauru.41-43 During the 1990s, Indigenous Australians (particularly males) demonstrated low life expectan-cies compared with the general population, predominately as a consequence of high mortality from noncommunicable disease and injury in adulthood, at comparable life expectancies to Nauru.47-50 Nevertheless, the predominant pattern in the Pacific Island region, as in many other developing countries such as the newly industrializing countries of Asia51 and historically in Western Europe, North America, and Australasia, is for continual improvements in LE despite the emergence of noncommunicable diseases as the major cause of morbidity and mortality, but with initial periods of stagnation in growth of LE.27,28 Additional work is currently underway to examine trends in the epidemiological transition in other Pacific Islands as recent empirical data on mortality levels and cause of death by age group is largely unpublished or unavailable. Second, the Nauru experience demonstrates that external cause mortality in males (the major category being MVAs while intoxicated) can be reduced through actions by civil society in the absence of specific interventions. Third, Nauru demonstrates a very long period of stagnation in LE in both males and females as a consequence of the epidemiological transition, with major chronic disease mortality in adults showing no sustained downward trends over the past 40 to 50 years. In Australia, the period of stagnation in LE for males was 25 years (1945-1970) and for females 10 years (1960-1970)45 as a consequence of increased risks of death from noncom-municable disease and injuries, before progressing to the fourth stage of the epidemiological transition,52 characterized by delayed mortality from noncommunicable diseases to older ages and increases in LE. Evidence from populations such as Nauru, Indigenous Australians, and former socialist economies in Europe suggest that these populations may endure longer peri-ods of stagnation of life expectancy unless effective public health measures are urgently implemented.
Acknowledgments
The authors would like to thank Andreas Demmke from the Secretariat of the Pacific Community (SPC) Statistics and Demography Programme for comments on the article and the staff of the Ministry of Health, the Registrar’s office, and the Bureau of Statistics in Nauru for their assistance.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interests with respect to the authorship and/or publication of this article.
Funding
The author(s) disclosed receipt of the following financial support for the research and/or authorship of this article:
Funding for this work was provided under an AusAID Australian Development Research Award (Grant no: 44738)
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5.2.2 Palau analysis and assessment
As for Nauru, the system assessment in Chapter three demonstrated reporting in
Palau is likely to be mostly complete for the data held by the MoH Epidemiologist.
Data included in this assessment were aggregated over the period from 2004-2009,
excluding 2007 (when there was no epidemiologist on island) which was found to
contain a number of irreconcilable errors. Deaths were also aggregated using 10
year age groups (for deaths at age 5 and over) to calculate age-specific mortality
and life tables directly. Life tables for Palau are attached in Appendix two with age-
specific mortality shown in Figure 13.
Figure 13: Age-specific mortality for Palau 2004-2009 (excluding 2007), by sex
Age-specific mortality shows a plausible pattern of deaths with relatively low infant
and child mortality, and high adult mortality, with age-specific mortality rates rising
substantially in age groups from 25 years of age and older. As the number of births
0.1
1
10
100
1000
0-4 5-9 15-24 25-34 35-44 45-54 55-64 65-74 75+
Deaths per 1,000
population
Age group
MalesFemales
� � �
156
was not available, IMR for 2004-2009 was estimated from the population mortality
rate (population <1year) and converted to a probability using life table methods.
Confidence intervals were derived using the Poisson distribution applied to infant
deaths. IMR is therefore estimated at 16.7 deaths per 1,000 live births (95% CI:
10.2-25.3). Twenty five deaths were recorded in children less than 5 years of age for
2004-2009 (excluding 2007), with U5M estimated from the life table (using Chiang
calculation of confidence intervals) as 17.9 deaths per 1,000 live births (95% CI:
10.9-24.8). The excessive premature mortality seen in the age-specific mortality
rates is reflected in a very high adult mortality (15-59 years), estimated at 596 deaths
per 1,000 population for males and 431 deaths per 1,000 population for females.
This is consistent with the high prevalence of traffic related deaths and diabetes
prevalence in Palau. The high adult mortality, particularly for males, is reflected in LE
which is estimated at 65.8 years (95% CI: 64.4-67.2 years) for males and 71.2 years
(95% CI: 69.9-72.5 years) for females. These results are similar to the 2005 census
which estimated LE at 66.3 years for males and 72.1 years for females (no
confidence intervals given), calculated by smoothing empirical data with a Coale-
Demeny West model life table.
5.3 Validity assessment of published data
Case study two demonstrates the importance of evaluating data in the context of
long term trends in Nauru. Records from vital registration with the health department
and civil registry were available for much of the previous 50 years, allowing this
assessment to be conducted using primary data and previously published estimates
generated using a very similar methodology to the current analysis. For many other
� � �
157
PICTs, it is unlikely that primary data, especially primary data of reliable quality,
would be available for such lengths of time; thus requiring researchers to turn to
secondary data sources such as censuses and surveys to establish these longer
term trends.
As noted in Chapter one, many of the published estimates of mortality level for the
PICTs are either implausible and/or show substantially different results for similar
periods. These estimates should not be used to establish trends in mortality levels
without first establishing whether individual estimates are likely to be plausible.
Reliable published sources can then be used to establish mortality trends for
comparison to current calculated measures. Case study three, demonstrates such
an assessment for published data in Fiji. A similar assessment was performed for
Tonga (case study four), however there were so few data points considered reliable
after the assessment that it was only possible to draw very general conclusions
regarding trends in respect to infant mortality.
5.4 Brass growth balance analysis
The system assessment in Chapter three indicated that although most deaths were
likely to be reported in Fiji, an assessment of completeness would be required to
evaluate and correct the available data as necessary. In accordance with Figure 11,
direct demographic methods (as outlined in Chapter two) were considered to
complete this assessment. As only one primary source of data, the Fijian MoH
records, was identified as being likely to be substantively complete, case study
three includes a Brass Growth Balance analysis of routinely collected mortality data.
� � �
158
As described in Chapter two, the Brass Growth Balance method uses the internal
consistency of the data to assess whether the age distribution of deaths is plausible.
To conduct this analysis, the partial (by age) death rate is compared at each age
with the partial birth rate, as shown in Figures 12 and 13. Completeness of the
reported deaths is then given by the inverse of the slope of the line fitted to these
points.
The Brass Growth Balance Method does not rely on having data for a full inter-
censal period and is therefore the most accessible method of assessing
completeness where migration is low to moderate. Other direct demographic
methods account better for population movement (14) if it is significant. The Brass
Growth Balance method was used as the additional information (such as migration
data) required for the other direct demographic methods was not readily available,
and the time period of the data collection available did not fit an inter-censal period.
Fiji has moderate out-migration of approximately 7.6% in 2010 (110). This appears to
have a minimal effect on the analysis for the total population. Despite this, the Brass
Growth Balance method has proven unsuitable for calculation of mortality rates by
ethnicity in Fiji due to the significant out-migration in the Indian population (322).
Implausible results were also obtained from a Brass Growth Balance analysis of the
Tongan data (where migration is -16.9% 2010 (110) (Appendix one), and population
has not increased significantly for around 30 years despite high fertility) and were
subsequently disregarded.
� � �
159
Both data from the MoH and reconciled data (Figures 14 and 15) from the MoH and
civil registry data (2005-2009) (case study five) in Tonga were assessed using a
Brass Growth Balance analysis. Analysis of the MoH data alone indicated a reporting
completeness as >99% for males and females, and the assessment of the reconciled
data indicated a reporting completeness as >99% for males but only 63% for
females. These results are clearly incompatible, both in regard to the single data
source, compared with reconciled data, and in relation to the sex differential in
reporting in Tonga. While some difference in reporting completeness by sex is
expected based on the system assessment (in terms of social incentives such as
land inheritance), there is no indication that suggests it should be this large.
Figure 14: Brass Growth Balance analysis of reconciled data from Tonga; Males 2005-2009
� � �
160
Figure 15: Brass Growth Balance analysis of reconciled data from Tonga; Females 2005-2009
As seen in Figure 14, for males, data did not fit comfortably on the straight line of
partial death rate graphed against the partial birth rate, indicating problems with the
age distribution of the source data. The Brass Growth Balance analysis performed
better (in terms of fit), and gave results similar to the capture-recapture analysis
(described later in this chapter) when limited to deaths on Tongatapu (the main
island) only, where there is perhaps less impact (on a per capita basis) from
migration (59, 136).
These examples demonstrate the problem of using a method of assessing reporting
completeness which is based on an internal consistency and age distribution only.
There is no clear “point” at which migration becomes too high for this method to be
used, rather it is a gradient where the results may have less and less reliability with
increasing migration. The Brass Growth Balance method provides the only option for
direct assessment of completeness in countries with a single accessible data source
� � �
161
and limited access to age-specific population and migration estimates, as required
by the other direct demographic methods. This approach provides a useful overview
of whether the data are likely to be complete, but should be used cautiously when
correcting data.
5.4.1 Case study 3: Mortality Trends in Fiji.
Carter KL, Cornelius M, Taylor R, Ali SS, Rao C, Lopez AD, Lewai V, Goundar R,
Mowry C. Mortality trends in Fiji. Aust N Z J Public Health. 2011 Oct;35(5):412-20.
doi: 10.1111/j.1753-6405.2011.00740.x
Case study three presents a similar assessment of trends over time as for Nauru in
case study two, but adds existing published estimates and demonstrates the
importance of critical appraisal of published data. A set of core criteria assessing the
validity of published data were developed and applied to the estimates of IMR and
LE in Fiji. These criteria are as follows:
o the source publication noted the data were derived from estimation models
that assume a given improvement in LE or IMR by year, or the data source
showed a perfectly linear improvement in LE or IMR by year when graphed
indicating use of models that incorporate the above assumption;
o multiple incompatible estimates were given by the same source for a single
year or incompatible estimates were given by the same source for adjacent
years;
o the source data included estimates that were implausible given accepted
levels of LE and IMR in other countries (for example – sources that listed IMR
� � �
162
in Fiji well below the level in Australia or New Zealand at an equivalent point in
time were excluded); and/or
o calculations were documented or strongly suspected to be based on
uncorrected vital registration data where no assessment of completeness of
registration was provided.
As demonstrated in this case study, many of the published estimates are either
unclear regarding their primary data source and method, employ a series of
assumptions to extrapolated data from previous years, or estimate mortality levels
based on patterns of mortality from accepted model life tables. The empirical data
illustrates a rather different picture of the health status in Fiji than that presented by
many of the published estimates; demonstrating the risk of overlooking serious
health concerns when assumptions of improvement are made. The full list of sources
presented in the paper and reasons for exclusion are provided in Appendix three.
As discussed in the paper, many published sources excluded from the analysis were
from international agencies. This highlights one of the key problems facing decision
makers in small countries, where senior managers who are removed from the actual
collection and analysis of data in their own country may be influenced to accept
these estimates due to the authority with which they are presented; rather than local
data that may in fact be more reliable.
Case study three also presents a Brass Growth Balance analysis of the MoH data,
as the system assessment (Chapter three) demonstrated although CRVS systems
were relatively robust, there was some potential for under-reporting. Alternative
� � �
163
analyses such as the Bennet-Hounichi (199, 224) and capture-recapture methods
were not possible, as death data could not be accessed for the full inter-censal
period and only one data source was available (reporting through the civil registry
was found to be extremely incomplete).
The Brass Growth Balance analysis for Fiji (Figures 16 and 17, plus Appendix one)
shows the data from the MoH were more than 90% complete for most years, and
more than 80% complete for females for 2002 to 2004. With such high levels of
completeness, mortality estimates can be derived directly from the reported deaths
for comparison to long term trends.
Figure 16: Brass Growth Balance analysis for mortality data completeness: Fiji, Males 2002-2004
� � �
164
Figure 17: Brass Growth Balance analysis for mortality data completeness: Fiji, Females 2002-2004
Completeness is estimated by 1/K where K=the slope of the line
As shown in the figures, the slope indicates reporting for males is more than 99%
complete (1/K). As over-reporting is unlikely given the CRVS system (as described in
Chapter three), this can be interpreted as essentially complete reporting, indicating
the data does not need to be adjusted for undercount.
Key findings from the analysis in Fiji as outlined in the case study are that LE for
both males and females has shown no improvement over the last 20 years,
remaining stable at around 64 years for males and 69 years for females. Infant
mortality is stable below 20 deaths per 1,000 live births and therefore cannot account
for this low LE, which is being driven primarily by premature adult mortality.
412 AUSTRALIAN AND NEW ZEALAND JOURNAL OF PUBLIC HEALTH 2011 vol. 35 no. 5© 2011 The Authors. ANZJPH © 2011 Public Health Association of Australia
As c o u n t r i e s m ove t h r o u g h
demographic and epidemiological
transitions, declines in under-
nutrition and infectious diseases are reflected
in improvements in survival which result in
improvements in life expectancy (LE) at birth.
However, real increases in adult mortality
from non-communicable diseases (NCDs)
and injury can slow or even reverse mortality
decline, if of sufficient magnitude.1-3 This
requires urgent action to reduce premature
adult mortality, such as occurred in Australia
and New Zealand.4,5
Fiji is a multi-ethnic society (Melanesian
57%, Indian 38%) of 827,900 people (2007).6
NCDs such as cardiovascular disease and
diabetes have been documented as public
health concerns since the 1970s.7,8 Mortality
Mortality trends in Fiji
Karen CarterSchool of Population Health, University of Queensland
Margaret CorneliusFiji Ministry of Health
Richard TaylorSchool of Population Health, University of Queensland and School of Public Health and Community Medicine, University of New South Wales
Shareen S. AliFiji Ministry of Health
Chalapati Rao, Alan D. LopezSchool of Population Health, University of Queensland
Vasemaca LewaiFiji Islands Bureau of Statistics
Ramneek GoundarFiji School of Medicine
Claire MowrySchool of Population Health, University of Queensland
Submitted: September 2010 Revision requested: January 2011 Accepted: February 2011Correspondence to: Richard Taylor, University of New South Wales Main Campus, Samuels Building, Level 2, Room 223, Botany St, Gate 11, Randwick, NSW 2052; e-mail: [email protected]
Abstract
Objectives: Mortality level and cause
of death trends are evaluated to chart
the epidemiological transition in Fiji.
Implications for current health policy are
discussed.
Methods: Published data for infant
mortality rate (IMR), life expectancy
(LE) and causes of death for 1940-2008
were assessed for quality, and compared
with mortality indices generated from
recent Ministry of Health death recording.
Trends in credible mortality estimates
are compared with trends in proportional
mortality for cause of death.
Results: IMR declined from 60 deaths
(per 1,000) in 1945 to below 20 by 2000.
IMR for 2006-08 is estimated at 18-20
deaths per 1,000 live births. Excessive LE
estimates arise by imputing from the IMR
using inappropriate models. LE increased,
but has been stable at 64 years for males
and 69 years for females since the late
1980s and early 1990s respectively.
Proportional mortality from diseases of
the circulatory system has increased from
around 20% in the 1960s to more than
45%. Extensive variation in published
mortality estimates was indentified,
including clearly incompatible ranges of
IMR and LE.
Conclusions: Mortality decline has
stagnated. Relatively low IMR and
proportional mortality trends suggest this is
largely due to chronic diseases (especially
cardiovascular) in adults.
Implications: Reconciliation of mortality
data in Fiji to reduce uncertainty is urgently
needed. Fiji’s health services and donor
partners should place continued and
increased emphasis on effective control
strategies for cardiovascular disease.
Key words: Cardiovascular disease,
epidemiological transition, Fiji, mortality
Aust NZ J Public Health. 2011; 35:412-20
doi: 10.1111/j.1753-6405.2011.00740.x
measures for Fiji are published by government
sources and a range of international agencies.
This paper evaluates mortality levels to chart
the progress of the epidemiological transition
in Fiji by assessing available information for
1940-2008. Levels of adult mortality are
compared with similar measures for Australia
and New Zealand. Published data on causes of
death are used to explain trends in all-cause
mortality, and implications of these findings
for current health policy are discussed.
MethodsData Collection
Measures of level of mortality were obtained
from two sources: (1) previously unpublished
data on reported deaths aggregated by age
group and sex for 1996-2004 from the Fiji
Trends Article
2011 vol. 35 no. 5 AUSTRALIAN AND NEW ZEALAND JOURNAL OF PUBLIC HEALTH 413© 2011 The Authors. ANZJPH © 2011 Public Health Association of Australia
Ministry of Health (MoH); and (2) published reports on all-
cause and cause-specific mortality from 1940-2008 by local and
international agencies. Data from published reports7,9-69 were sought
through direct contact and website searches from Fiji government
departments, United Nations agencies, the Secretariat of the Pacific
Community (SPC), the World Bank (WB), Asian Development Bank
(ADB) and non-government organisations. A literature search was
undertaken in PubMED and Medline.
For published data on mortality levels, the method of data
collection and analysis was rarely available. Limited data on causes
of death have been published for Fiji, many of which included only
leading causes of death, usually not tabulated by age group or sex,
or ill-defined and unknown causes could not be excluded from
proportional mortality.
Data Assessment All-cause mortality
The infant mortality rate (IMR) and life expectancy (LE) were
chosen as measures of all-cause mortality due to availability. IMRs
from Fiji MoH death registration and published reports were graphed
over time by major source of data (Figure 1). Data sources were
assessed for reliability and plausibility of estimates on the basis of
method of estimation, original source of data, and data consistency.
Unreliable sources were censored from further analysis if they
met any of the following criteria: (a) data were derived, or were
considered likely to have been derived, from models assuming
a given improvement by year, as evidenced by a perfectly linear
improvement in LE or IMR by year; (b) multiple incompatible
estimates were given by the source for a single year or adjacent
years; (c) source data included implausible estimates (based on
equivalent measures for developed countries); (d) calculations were
based on uncorrected vital registration data known to be significantly
under-reported or with no assessment of reporting completeness.
An exponential trend was fitted to the average IMRs of each year
from data remaining after censoring (Microsoft Excel). Childhood
mortality (probability of dying before five years of age) was largely
unavailable from published reports. Recent measures were estimated
by applying the proportion of childhood mortality accounted for
by infant mortality (78%) from credible data sources for which
both measures were available (analyses from the 198637 and 199638
censuses, and MOH data 1996-2004), to credible estimates of IMR
for 2006-08.54
Age-specific mortality rates and life tables70 were calculated
from the MoH death data and populations from SPC.71 Data
were aggregated by triennia to reduce stochastic variation (1996-
98, 1999-2001 and 2002-04). Mortality rates were analysed for
completeness using the Brass growth-balance method70 for age
groups 35-74 years, a method that has been recently validated as
a way of measuring reporting completeness.72 No adjustment was
made to reported deaths as registration was assessed to be over
85% complete (more than 95% for all except females 2002-04).
LE and adult mortality (probability of dying between 15 and 59
years of age inclusive) were calculated (Table 1). Adult mortality
for 2002-04 was compared to measures derived from published
life tables for Australia and New Zealand for the same period73-76
(Table 1). LE from all sources were graphed over time by major
source of data (Figures 2-3), with unreliable estimates censored from
further analysis. A quadratic function was fitted to the remaining
life expectancy estimates (Microsoft Excel) (Figures 2-3) to obtain
final estimates and overall trend.
Empirical measures of LE derived from MoH data for 1996-
2008 were compared with LE predicted by the WHO Model Life
Table system77. First, LE was predicted using empirical childhood
mortality as a single input; and, second, using probabilities of
childhood mortality and adult mortality (two parameters).
Causes of death Available data on cause-specific mortality were examined using
a range of measures and cause aggregations. Data are presented
as proportional mortality (PM) by International Classification of
Disease (ICD) chapter78 as several sources did not provide total
deaths and data are frequently under-enumerated. Cause of death
was calculated for the total population as there was insufficient
information available to examine by age group, sex, or ethnicity. PM
excluding unknown and ill-defined cases was calculated for sources
where this was possible. PM, and corrected PM, were graphed over
time for each ICD chapter.
ResultsAll-cause mortality
Extensive variation was found in published estimates of IMR
(Figure 1), with several reporting levels below those of developed
countries in the region.74,76 Of 16 IMR sources, 12 were censored
from further analysis with a further two partially censored. The trend
Table 1: Probability of dying between ages 15 and 59 years (%) by age group in Fiji, New Zealand and Australia, males and females, 1996-2004.Age group (years) 15-34 35-59 15-59Sex Males Females Males Females Males Females
Fiji
1996-1998 3.6 2.5 26.8 18.5 29.5 20.5
1999-2001 4.1 2.9 27.5 18.4 30.5 20.8
2002-2004 3.7 2.9 24.9 17.9 27.6 20.3
New Zealand 2002-2004 2.0 0.9 7.9 5.5 9.7 6.4
Australia 2002-2004 1.7 0.7 7.2 4.4 8.9 5.1Source data: Fiji – calculated from deaths reported to MoH (previously unpublished); New Zealand 75,76; Australia 73,74
Trends Mortality trends in Fiji
414 AUSTRALIAN AND NEW ZEALAND JOURNAL OF PUBLIC HEALTH 2011 vol. 35 no. 5© 2011 The Authors. ANZJPH © 2011 Public Health Association of Australia
is consistent with IMR falling significantly over the last several
decades (1940–2006) to a level below 20 deaths per 1000 live births.
Credible sources indicate IMR for 2006-08 was 18 -20 deaths per
1000 live births, suggesting s a childhood mortality rate (<5 years)
of 23-26 deaths per 1000 live births (see methods).
Analysis of the MoH reported deaths shows LE was stable
between 1996 and 2004, as was adult mortality which was 2 to
4 times higher in Fiji than in Australia or New Zealand (Table
1). LE estimates from MoH data were notably lower than most
published estimates for the same period, but similar to estimates
from demographic analysis of censuses.38 Published estimates of LE
at birth for 1995-2000 vary by 11 years in males (61 to 72 years),
and by eight years in females (68 to 76 years) (Figures 2 and 3).
Of the 16 sources identified for LE data, 13 were censored from
the final analysis. Remaining estimates suggest improvements in
LE began to stall in the 1980s for both sexes, and that it has not
Carter et al. Article
Figure 1: Estimated infant mortality rate in Fiji, by data source, 1940-2004.
1a) All estimates
0
10
20
30
40
50
60
70
80
90
1940 1950 1960 1970 1980 1990 2000Year
Infa
nt M
orta
lity
Rate
(per
100
0 bi
rths
)
Fiji Fertility Survey (Total) (33)
Vital Registration (Fijian Male) (12,19,40)
Vital Registration (Indo-Fijian Male) (12,19,40)
Vital Registration (Fijian Female) (12,19,40)
Vital Registration (Indo-Fijian Female) (12,19,40)
Census (Total) (13,37,38,40,47)
Census (Male) (13,37)
Census (Female) (13,37)
Census (Fijian) (13,37)
Census (Indo-Fijian) (13,37)
MOH Reported Deaths (Total) (10,14-16,20,21,25,27,29,34,35,43,44,54)MOH Reported Deaths (Fijian) (14-16)
MOH Reported Deaths (Indo-Fijian) (14-16)
Adjusted MOH Reported Deaths (total) (21,26)
Adjusted MOH Reported Deaths (Fijian) (1,26)
Adjusted MOH Reported Deaths (Indo-Fijian) (21,26)
UNDP (total) (36,53,55)
UNICEF (total) (41,43,52,64,65,67)
Fiji Ministry of Information (total) (42)
SPC (total) (39,62,63)
Fiji Co-ordinating Committee on Children (Total) (31)
Fiji Government (Total) (28)
World Bank (Total) (32,63)
Asian Development Bank (59,60)
WHO (49,51,56)
WHO - WPRO (46,50,57)
UN ESCAP (69)
1b) Estimates censored by method
0
10
20
30
40
50
60
70
80
90
1940 1950 1960 1970 1980 1990 2000
Year
Infa
nt M
orta
lity
Rate
(per
100
0 bi
rths)
Average of censored sources by year
1a) All estimates
0
10
20
30
40
50
60
70
80
90
1940 1950 1960 1970 1980 1990 2000Year
Infa
nt M
orta
lity
Rate
(per
100
0 bi
rths)
Fiji Fertility Survey (Total) (33)
Vital Registration (Fijian Male) (12,19,40)
Vital Registration (Indo-Fijian Male) (12,19,40)
Vital Registration (Fijian Female) (12,19,40)
Vital Registration (Indo-Fijian Female) (12,19,40)
Census (Total) (13,37,38,40,47)
Census (Male) (13,37)
Census (Female) (13,37)
Census (Fijian) (13,37)
Census (Indo-Fijian) (13,37)
MOH Reported Deaths (Total) (10,14-16,20,21,25,27,29,34,35,43,44,54)MOH Reported Deaths (Fijian) (14-16)
MOH Reported Deaths (Indo-Fijian) (14-16)
Adjusted MOH Reported Deaths (total) (21,26)
Adjusted MOH Reported Deaths (Fijian) (1,26)
Adjusted MOH Reported Deaths (Indo-Fijian) (21,26)
UNDP (total) (36,53,55)
UNICEF (total) (41,43,52,64,65,67)
Fiji Ministry of Information (total) (42)
SPC (total) (39,62,63)
Fiji Co-ordinating Committee on Children (Total) (31)
Fiji Government (Total) (28)
World Bank (Total) (32,63)
Asian Development Bank (59,60)
WHO (49,51,56)
WHO - WPRO (46,50,57)
UN ESCAP (69)
1b) Estimates censored by method
0
10
20
30
40
50
60
70
80
90
1940 1950 1960 1970 1980 1990 2000
Year
Infa
nt M
orta
lity
Rate
(per
100
0 bi
rths)
Average of censored sources by yearAverage of censored sources by year
Year
Year
2011 vol. 35 no. 5 AUSTRALIAN AND NEW ZEALAND JOURNAL OF PUBLIC HEALTH 415© 2011 The Authors. ANZJPH © 2011 Public Health Association of Australia
improved (Figures 2 and 3). LE for the period 2006-08 is estimated
at 64 years for males and 69 years for females.
LE and adult mortality predictions from model life tables using the
childhood mortality and adult mortality from MoH death recording
varied less than one year from results calculated using age-specific
mortality rates (not shown). However, LE predicted from childhood
mortality alone over-estimated LE by 4-5 years (similar to censored
estimates) and were excluded from further analysis.
Cause specific mortality The proportion of deaths coded to ill-defined and unknown causes
since 1960 generally fluctuated below 8% of deaths, with higher
peaks (up to 18%) in the early 1980s and early 2000s (Figure 4). PM
attributable to ‘Diseases of the Circulatory System’ (ICD Chapter
IX) shows a significant and ongoing linear increase over 1960-2000,
from around 20% of deaths to more than 45% of all deaths (Figure
4), apparent before and after ill-defined and unknown causes are
excluded. Early data relating to 1960 were only available as PM
Trends Mortality trends in Fiji
Figure 2: Male life expectancy at birth, in Fiji, by data source, 1940-2006.
ADB – Asian Development Bank; BoS – Fiji Bureau of Statistics; MoH – Fiji Ministry of Health; SPC – Secretariat of the Pacific Community; UNDP – United Nations Development Program; WHO – World Health Organization; WPRO – WHO Western Pacific Regional Office; VR – Vital registration. NB: Census data co-published by SPC with Fiji Bureau of Statistics listed under BoS only. References shown in brackets after each source within figure.
2a) All estimates
40
45
50
55
60
65
70
75
80
1940 1950 1960 1970 1980 1990 2000 2010
Period (year)
Life
Exp
ecta
ncy
at B
irth
(yea
rs)
Registrar General - VR (12,19,40)
MOH Reported Deaths (Brass adjustment) - Fijian (21,26)
MOH Reported Deaths (Brass adjustment) - Indo-Fijian (21,26)BoS Census (13,37,38,40,47)
BoS Census - Fijian (13)
BoS Census - Indo-Fijian (13)
United Nations Population Division (36,53,55)
Fiji Coordinating Committee on Children - Male / Femalecombined total (31)World Bank - Male/ Female combined total (32,63)
Fiji Department for Women and Culture (30)
SPC (39,62,63)
Fiji Ministry of Information (42)
WHO - World Health Statistics (49,51,56)
MOH reported deaths (unpublished data)
WHO - WPRO (46, 50, 57)
ADB (59,60)
UNFPA (69)
UN ESCAP (58, 68)
2b) Estimates remaining after censoring by method
40
45
50
55
60
65
70
75
80
1940 1950 1960 1970 1980 1990 2000 2010
Year
Life
Exp
ecta
ncy
at B
irth
(yea
rs)
MOH Reported Deaths (Brass adjustment) (21,26)
BoS Census (13,37,38,40,47)
MOH reported deaths (unpublished data)
Year
Year
416 AUSTRALIAN AND NEW ZEALAND JOURNAL OF PUBLIC HEALTH 2011 vol. 35 no. 5© 2011 The Authors. ANZJPH © 2011 Public Health Association of Australia
>5 years7; even though this would over-estimate all-ages PM from
cardiovascular disease, the observed proportion (15-18%) was the
lowest ever observed.
PM due to infectious diseases showed no significant trend,
fluctuating between 5% and 12% of all deaths. Respiratory diseases
fell linearly from 14% to 7%, and injuries varied between 4% and
10%, with a gradual decline in evidence since the mid 1980s. PM
from conditions arising in the perinatal period fell from 8% in the
1970s to 2 %. There were no clear trends for any other ICD disease
categories; including neoplasms, endocrine and metabolic diseases
or diseases of the digestive tract and genitourinary tract.
DiscussionCredible data sources demonstrate that IMR has declined
steadily to below 20 deaths per 1000 live births. While IMR has
not fallen significantly since the late 1980s, at these levels it is a
minor influence on the overall LE70. The decline in infant and child
mortality since 1990 is insufficient for Fiji to meet the Millennium
ADB – Asian Development Bank; BoS – Fiji Bureau of Statistics; MoH – Fiji Ministry of Health; SPC – Secretariat of the Pacific Community; UNDP – United Nations Development Program; WHO – World Health Organization; WPRO – WHO Western Pacific Regional Office; VR – Vital registration.
Figure 3: Female life expectancy at birth, in Fiji, by data source, 1940-2006.
3a) All estimates
40
45
50
55
60
65
70
75
80
1940 1950 1960 1970 1980 1990 2000 2010
Year
Life
Exp
ecta
ncy
at B
irth
(yea
rs)
Registrar General - VR (12,19,40)
MOH Reported Deaths (Brass adjustment) - Fijian (21,26)
MOH Reported Deaths (Brass adjustment) - Indo-Fijian (21,26)BoS Census (13,37,38,40,47)
BoS Census - Fijian (13)
BoS Census - Indo-Fijian (13)
United Nations Population Division (36,53,55)
Fiji Coordinating Committee on Children - Male / Femalecombined total (31)World Bank - Male/ Female combined total (32,63)
Fiji Department for Women and Culture (30)
SPC (39,62,63)
Fiji Ministry of Information (42)
WHO - World Health Statistics (49,51,56)
MOH reported deaths (unpublished data)
WHO - WPRO (46, 50, 57)
ADB (59,60)
UNFPA (69)
UN ESCAP (58, 68)
3a) Estimates remaining after censoring by method
40
45
50
55
60
65
70
75
80
1940 1950 1960 1970 1980 1990 2000 2010
Year
Life
Exp
ecta
ncy
at B
irth
(yea
rs)
MOH Reported Deaths (Brass adjustment)(21,26)
BoS Census (13,37,38,40,47)
MOH reported deaths (unpublished data)
Carter et al. Article
Year
Year
2011 vol. 35 no. 5 AUSTRALIAN AND NEW ZEALAND JOURNAL OF PUBLIC HEALTH 417© 2011 The Authors. ANZJPH © 2011 Public Health Association of Australia
Development Goals by 2015;79 meeting these targets would require
an IMR comparable to that of Australia and New Zealand in 199074,75
where specialist medical services and technology were already
available – and nutritional and environmental conditions were better.
LE rose through the 1970s and 1980s, then stabilised around 64
years for males and 69 years for females.
The most reliable all-cause mortality estimates are those
from demographic analyses of censuses and recent MoH death
registration. LE produced from MoH data were amongst the lower
estimates found in available data. No adjustment for under-reporting
was made to the MOH data as they were found to be substantially
complete, however it this had been performed it would have resulted
in an even lower measure of LE. LE calculated from MoH death
registration (direct methods) correlates closely with published
Census mortality estimates (indirect methods), thus providing
independent verification of these figures, and by implication,
completeness of death reporting by Fiji MoH.
Bias was minimised by selecting the most commonly reported
mortality measures (i.e. IMR rather than childhood mortality) and by
converting all-cause of death information to proportional mortality
where possible; although this provides only an approximate picture
of cause of death patterns. As changes in the proportion of deaths
due to ‘Ill-defined’ and ‘Unknown’ causes may significantly affect
PM trends, PM excluding these categories was calculated where
possible. The increasing trend in circulatory diseases was evident
both before and after this refinement. As PM was tabulated by ICD
chapter, changes in coding over time should have minimal impact
as these were predominantly within chapters.80
In Australia, LE for males did not improve from 1945 to 1970
as increased mortality, mainly from cardiovascular diseases, offset
declines in death from infectious and related diseases.1,2,81 Similar
stagnation in LE has been reported in the 20th Century in Western
Europe, North America and New Zealand in association with the
epidemiological transition.3 Subsequent declines in cardiovascular
mortality and improvements in LE in Australia and New Zealand
from the 1970s have been driven by decreases in serum cholesterol,
blood pressure and cigarette smoking,4,5 as well as improved health
care, even though body weight has increased.82
Adult mortality was lower than recently published predictions for
201083 based on adjusted civil registration data from 1970-1988.
The adult mortality levels in Fiji for 2002-04 were 2-4 times higher
than in Australia or New Zealand. The probabilities of dying (for
2002-04) between ages 35 and 59 years in Fiji were 25% for males
and 18% for females, compared to 7–8% for males and 4–6% for
females in Australia and New Zealand.73-76 This segment of the adult
age group is where cardiovascular disease is a significant cause of
death. PM in Fiji from cardiovascular disease increased from just
below 20% of deaths around 1960 to more than 45% of deaths in
2001. While this shift in PM may also be influenced by aging of the
population, the age structure and LE in Fiji suggest that population
aging alone does not explain this trend. The PM from cardiovascular
diseases is substantially higher than that for infectious diseases
and respiratory illnesses. While a similar stagnation in LE may be
driven by increasing mortality from HIV/AIDS, Fiji is estimated
to have only 0.1% HIV prevalence in the adult population, and a
low mortality from AIDS.50
The increase in PM from cardiovascular diseases and levels of
adult mortality in Fiji are consistent with documented trends since
the 1970s, in both hospital admissions for cardiovascular disease
and risk factor exposure for these conditions7,82. A national survey
Figure 4: Proportional mortality from diseases of the circulatory system in Fiji, 1950-2010.
Trends Mortality trends in Fiji
Source data: 7-9, 11, 14-17, 20, 25,
26, 29, 35, 44, 51.
418 AUSTRALIAN AND NEW ZEALAND JOURNAL OF PUBLIC HEALTH 2011 vol. 35 no. 5© 2011 The Authors. ANZJPH © 2011 Public Health Association of Australia
of risk factor prevalence in 19807 showed adult prevalences of
hypertension (5.1-9.9%) (using ≥160 mm hg systolic and/or ≥95
mm hg diastolic and/or on treatment for hypertension) and diabetes
prevalence of 1.1-11.8% (using two hour post 75 g glucose load
blood glucose of ≥11.1 mm/L and/or on treatment for diabetes),
with the lowest prevalences in rural melanesians. The Fiji STEPS
survey in 200284 indicated adult prevalence of hypertension of 19%
(using ≥140 mmhg systolic and /or ≥90 mmhg diastolic and/or on
treatment for hypertension) and diabetes prevalence of 12% (using
fasting blood glucose of ≥7.0 mm/l and/or on treatment for diabetes)
Cigarette sales in Fiji rose 273% from 1956 to 1984, three times
the population increase (88%)7.
Dietary intake surveys in Fiji and elsewhere in the 1980’s
demonstrated that urban populations were more obese, had
higher prevalence of diabetes and hypertension, higher salt intake
and generally higher serum cholesterol levels, than their rural
counterparts, despite a lower overall calorie intake; indicating
the impact of physical exercise and dietary differences on
cardiovascular risk factors.85 The relationship of Fiji adult mortality
to cardiovascular risk factors was demonstrated in an 11-year follow-
up81 of the 1980 national risk factor survey,7 which found systolic
blood pressure, total serum cholesterol and two hour (post load)
plasma glucose associated with cardiovascular disease mortality,
with differences by sex and ethnicity. These studies suggest that the
prevalence of circulatory disease risk factors in Fiji is consistent
with the high adult mortality and increasing PM from cardiovascular
diseases. It is unclear when decline in cardiovascular disease and
subsequent increase in LE could be expected in Fiji as previously
experienced in Australia and New Zealand, however, on-going
monitoring of risk factors in the population by the Fiji Ministry of
Health84 will provide indications.
This study shows a range of eight years between various published
estimates of LE for Fiji in the early 2000s. Spurious increases in
LE imply that the health situation is improving when this is clearly
unsupported by more reliable data. Extensive variation in published
estimates of IMR by source (Figure 1) was also noted, with several
reporting improbable levels below those of developed countries in
the region such as Australia (IMR of 5.0 in 2007)67 and New Zealand
(IMR of 6.0 in 2007)67 (Figure 1). A previous study found substantial
uncertainty about mortality conditions in Pacific Island populations
with variations of 10 years or more in LE in the 1990s, depending
on the source.86 Principal issues include: under-enumerated vital
registration data; annual stochastic fluctuations in mortality in small
populations; errors in the imputation of adult mortality from infant
and childhood rates; implausible results from indirect demographic
methods; use of inappropriate model life tables; and inadequately
described and implausible projections.86
Difficulties in assessment of published data were the lack
of information concerning primary data sources, methods of
calculation, and assumptions. In order to deal with this, strict
exclusion criteria were specified and unreliable estimates censored
from further analyses. Many of the previously published estimates of
LE are implausible, and it is likely that many were produced through
the use of single parameter models (using infant or childhood
mortality) which under-estimate adult mortality in Pacific Island
populations, as shown in these results. These subsequently over-
estimate LE, thus “masking” the importance of cardiovascular
disease as a public health issue.
The considerable variation in mortality estimates for Fiji is a
significant concern for public policy. This also occurs in other
Pacific Island states.86 Less than seven of the 27 countries of the
WHO Western Pacific Region, including Fiji, have reliable mortality
data.87 These findings suggest that research is urgently required
to determine whether this pattern of premature adult mortality
has become the ‘typical’ picture of epidemiological transition
throughout the Pacific Islands. Reconciliation of mortality data to
reduce uncertainty is urgently needed. The quality assessment in
this study indicates that measures provided by Fijian government
sources based on indirect demographic methods from censuses and
direct MoH death recording provide the most reliable estimates of
mortality and LE at birth in Fiji. As such, these directly observed
data should be used by decision makers as key tools in health
planning rather than estimates from other sources.
The stagnation in life expectancy, at a relatively low IMR,
combined with trends in cardiovascular disease mortality, morbidity
and risk factors, suggests that NCDs are having a profound limiting
effect on further mortality decline in Fiji. Other contributing factors
need to be considered and require more investigation, including
out-migration of skilled workers (who may be healthier than non-
migrants), social dislocation, declines in health expenditure, and
effects of unemployment. While further research is required, there
is an urgent need for health services and donor partners to place
increased emphasis on effective prevention and treatment strategies
for cardiovascular disease.
AcknowledgementsThe authors would like to acknowledge the assistance of the Fiji
Ministry of Health and Bureau of Statistics.
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174
5.5 Capture-recapture analysis
An alternative method of assessing reporting completeness is a capture-recapture
analysis, considered a direct method as completeness is estimated based on
reported deaths from two or more sources, rather than referring to the age
distribution of the deaths. The advantages of this method is that under-reporting is
not assumed to be consistent across all age groups, and estimates of completeness
can be made directly by 5 and 10 year age groups, including for children (228, 323).
As described in Chapter two, the capture-recapture method reconciles different
sources of data to determine the proportion of these records which overlap. The
extent of the overlap in comparison to the overall number of records is used to
estimate the number of deaths not reported by either source (or “missing” records)
and therefore the total number of deaths (233).
Used extensively in the past to assess disease specific prevalence and mortality
(324, 325), for all-cause mortality this method has generally been used to compare
deaths recorded through routine CRVS systems to an independent survey. Use of
capture-recapture methods to assess all-cause mortality from two or more routine
reporting systems without an independent survey is relatively rare, with only one
recent example found in literature from Malawi (although no details of the model
selection procedures were documented) (326).
There are several considerations that must be taken into account when applying this
method to all-cause mortality that are perhaps less of an issue with cause or age-
specific assessments. These are outlined below:
� � �
175
• Independence: due to the structure of CRVS systems, policy and legislation
requiring evidence to be presented to officially register a death or bury/cremate, the
body, routinely collected data sources may not be independent of each other. Two
source analysis relies on the assumption the sources are independent (228), that
reporting the event to one source has no impact on the probability of the death being
reported to the other (233). The choice of model selected in three source
assessments is based on whether or not the sources are independent (and the
combination of these dependencies) (327, 328).
• Catchability: when studying all-cause mortality, the population from which
deaths are captured is much more heterogeneous than would be the case in many
disease specific studies, thus not all deaths in the community may have the same
probability of being captured (228).
• Population: the basic premise is events are drawn from the same population
(228), so only systems with the same coverage can be used as sources of data.
Alternatively, data may need to be separated by geographical area to meet this
criterion.
The following two case studies, for Bohol in the Philippines and Tonga, present
examples of the use of capture-recapture methods for all-cause mortality, and
lessons for how these may be applied in the PICTs. A two source capture-recapture
analysis is also presented for Kiribati, a population that does not have three distinct
data sources available, to set boundaries for plausibility around mortality estimates.
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176
5.5.1 Case study 4: Capture-recapture analysis in Bohol, Philippines – an
application in an “ideal” setting
Karen L Carter, Gail Williams, Veronica Tallo, Diozele Sanvictores, Hazel Madera,
Ian Riley Population Capture-recapture analysis of all-cause mortality data in Bohol,
Philippines. Population Health Metrics 2011, 9:9 (17 April 2011). doi:10.1186/1478-
7954-9-9
Case study four presents a two and three source analysis for the Bohol province in
the Central Visayas region of the Philippines. Although not a PICT, Bohol shares
many of the physical and social characteristics of the PICTs in regard to small widely
distributed populations. The region is essentially a “best-case” scenario for the
application of capture-recapture methods for assessing all-cause mortality data, as
there are three reporting systems all of reasonable quality, and therefore sufficient
information on each death to allow individuals in the different data collections to be
reconciled. These sources include two government sources, civil registry and MoH
data, and records from the Catholic church, which maintains records of births and
deaths following a standard format across all their parishes. The region is 99%
Catholic (329).
Key findings, as outlined in the paper, show capture-recapture methods as useful for
correcting incomplete data and generating estimates of all-cause mortality. They
may therefore have application in the PICTs, where many countries are likely to have
some, but not complete data. The method is particularly useful as it allows
examination of the data by age group, which is not possible with direct demographic
� � �
177
methods. This is particularly important in the PICTs, as the system assessments in
Chapter three demonstrated a high likelihood of differential reporting completeness
by age across most of the study countries in the absence of direct financial
incentives for reporting (such as in Nauru).
The study also highlights that even in this ideal setting, capture-recapture analysis
must be applied with a thorough understanding of the reporting system structure and
operation from which the data has been generated. This is particularly important in
the three-source analysis where there would otherwise be a significant potential for
inappropriate model selection (327, 330). While there are mathematical approaches
(such as the Aikake Information Criterion (328) and the Bayesian Information
Criterion (328) as described in the paper) to selecting the correct model, the results
show there may be several models that could be described as a “close-fit” based on
these criteria, but which give vastly different estimates of the scale of unreported
deaths. An understanding of the reporting system, such as through a thorough
system assessment, is therefore critical to appropriate model selection and
subsequently generating reliable estimates of mortality level. Three source analyses
are more robust than a two source analysis, as death reporting systems are rarely
completely independent in the real world.
Finally, the study demonstrated that even CRVS systems considered to generate
“good” quality data, as in the Philippines (4) should be assessed for completeness
and measures of mortality corrected accordingly. Findings from the study found
similar results to previous assessments in the Philippines (331, 332), supporting the
use of this method for correcting mortality data.
RESEARCH Open Access
Capture-recapture analysis of all-cause mortalitydata in Bohol, PhilippinesKaren L Carter1*, Gail Williams1, Veronica Tallo2, Diozele Sanvictores2, Hazel Madera2 and Ian Riley1*
Abstract
Background: Despite the importance of mortality data for effective planning and monitoring of health services,official reporting systems rarely capture every death. The completeness of death reporting and the subsequenteffect on mortality estimates were examined in six municipalities of Bohol province in the Philippines using asystem review and capture-recapture analysis.
Methods: Reports of deaths were collected from records at local civil registration offices, health centers andhospitals, and parish churches. Records were reconciled using a specific set of matching criteria, and both a two-source and a three-source capture-recapture analysis was conducted. For the two-source analysis, civil registry andhealth data were combined due to dependence between these sources and analyzed against the church data.
Results: Significant dependence between civil registration and health reporting systems was identified. There were8,075 unique deaths recorded in the study area between 2002 and 2007. We found 5% to 10% of all deaths werenot reported to any source, while government records captured only 77% of all deaths. Life expectancy at birth(averaged for 2002-2007) was estimated at 65.7 years and 73.0 years for males and females, respectively. This wasone to two years lower than life expectancy estimated from reconciled reported deaths from all sources, and fourto five years lower than life expectancy estimated from civil registration data alone. Reporting patterns varied byage and municipality, with childhood deaths more underreported than adult deaths. Infant mortality wasunderreported in civil registration data by 62%.
Conclusions: Deaths are underreported in Bohol, with inconsistent reporting procedures contributing to thissituation. Uncorrected mortality measures would subsequently be misleading if used for health planning andevaluation purposes. These findings highlight the importance of ensuring that official mortality estimates from thePhilippines are derived from data that have been assessed for underreporting and corrected as necessary.
BackgroundEffective planning and monitoring of health servicesrequires accurate measures of mortality by age and sex.Additionally, population health targets such as the Mil-lennium Development Goals [1] require reliable data tomeasure progress. Mortality data in the Philippines arecollected through the civil registration system, the healthsystem, and the parish records of local churches.Despite the importance of mortality data, official
reporting systems rarely capture every death [2]. Thechallenge for public health planners in many countriesis to meaningfully interpret the available data. A global
assessment of mortality data in 2003 listed the Philip-pines as having only “medium” quality death records,with reporting completeness estimated at 77% based ondata supplied to the World Health Organization [2].The completeness of death reporting and its impact onmortality estimates was examined in six municipalitiesof Bohol province in the Philippines using a systemreview and capture-recapture analysis. Variations inreporting patterns by age group, sex, and municipalitywere also examined.Bohol is a province of the Central Visaya region of the
Philippines. More than 95% of the provincial populationand study area is Catholic [3], although there is a smallbut localized Muslim population near the provincialcapital, Tagbilaran City. Major industries are agricultureand fishing [4]. The province has a population of
* Correspondence: [email protected]; [email protected] of Population Health, University of Queensland, Herston (Brisbane),Queensland, AustraliaFull list of author information is available at the end of the article
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© 2011 Carter et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative CommonsAttribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction inany medium, provided the original work is properly cited.
approximately 1,227,809 (2007) [3] living in 47 munici-palities and one city.Capture-recapture analysis uses the number of deaths
recorded by each combination of sources to estimatethe number of unreported deaths, and therefore the esti-mated total number of deaths [5]. Capture-recapturemethods have been used extensively to obtain estimatesof disease incidence and to assess coverage of disease-specific surveillance systems and registers [6-10]. Forall-cause mortality, this method provides an additionaltool to quantify and correct underregistration of deaths.Other methods compare reported deaths to expectedpatterns based on age distribution or require repeatedcensuses to evaluate survival [11,12].
MethodsThe study included Baclayon, Balilihan, Cortes, Dauis,and Panglao municipalities, and Tagbilaran city. Thesewere selected as part of a broader study to improveexisting methods for estimating mortality levels fromincomplete data and verbal autopsy methods for assign-ing cause of death under the Gates Grand Challenges inGlobal Health program [13]. The municipalities selectedwere included in this larger study due to their proximityand subsequent access to hospitals in Tagbilaran city.
System reviewA system review was conducted to assess data collectionand management within municipal mortality reportingsystems. The review comprised observation andmapping of processes; examination of forms, policydocuments, and electronic systems; and key informantinterviews held with local civil registrars, health officersand health center staff, hospital staff, church officials,cemetery managers, and staff of the provincial branch ofthe National Statistics Office (NSO).
Data CollectionProject staff visited local civil registration offices, healthcenters and hospitals, and parish churches regularlyduring 2006-2007 and extracted death report data fromlog books and registers using a standard form. Key vari-ables collected were: municipality of report, municipalityof residence, name of agency, nature of agency (localcivil registry [LCR], health center [HC], or church), fullname, sex, date of birth, date and place of death, andage at death. Population by age group, sex, and munici-pality was derived from the 2000 [14] and 2007 [15]censuses using exponential interpolation to provideestimates for intermediate years.
Data reconciliationEach death was assigned a unique number. Recordswere reconciled to create a single list of unique deaths
indicating source(s) in which each was recorded. Projectstaff manually matched deaths by comparing dataextraction forms from all sources. At least three of fivecriteria had to be met for records to be considered amatch: same full name (minor variations in spelling, var-iations in name order or a phonetic match wereallowed), age at death within one year, date of deathwithin five days, same municipality of residence, orsame sex. If more than one record was found as amatch, each record was required to share three criteriawith another record for the individual. A large numberof church records could not be matched because ofincomplete entries, and it was consequently decided thatoutstanding records matching on full name and date ofdeath would also be included. Records were thenentered into an Access database [16] that was analyzedto confirm all matches met stated criteria and nomatches had been missed, to remove duplicate records,and to correct minor data entry errors. Deaths withmissing age for a particular source were redistributedaccording to two imputed age distributions: (a) for thatsource, and (b) for all sources combined. In cases wherematched records did not have identical municipality orage at death, these variables were recorded separately,and sensitivity analyses were used to examine the influ-ence of such mismatches.The following measures of mortality were calculated
using standard methods [12]: age-specific mortality rate;infant mortality rate (IMR) (probability of dying before1 year of age); childhood mortality (probability of dyingbefore 5 years of age); adult mortality (probability ofdying between 15 and 59 years of age inclusive); and lifeexpectancy at birth (LE).
Two-source capture-recapture analysisA key assumption in a two-source analysis is thatsources are independent, i.e., reporting to one sourcedoes not influence the chance of the event beingreported to the other source [17-19]. The system reviewrevealed that LCR and HC were highly interdependentand so they were combined; this combination is hence-forth referred to as the “official” source. Unreporteddeaths were estimated using a two-source capture-recapture analysis of official and church sources. Datawere analyzed using Excel and SAS. Under the assump-tion that official and church sources are independent,the number of missing deaths (x) may be estimated asbc/a, where a, b, c are the numbers of deaths found byboth sources (a), only in the official records (b), andonly in the church records (c) [17]. Confidence intervalswere calculated using the maximum likelihood estima-tor. Unreported deaths were estimated for total deaths,with subgroup analyses for sex, age, and municipality.The estimated percentage of underreporting was
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examined by subgroup to identify variation in reportingpatterns. Dependence between official data and churchdata was assessed using the method of Hook and Regal[20], with a value of 1 signifying sources are indepen-dent, and a positive and negative dependency betweensources indicated by values less than and greater than 1,respectively.
Three-source capture-recapture analysisThree-source capture-recapture analysis allows for pair-wise dependence between sources [6-8,17], and usedLCR, HC, and church data separately. Data wereanalyzed using the SAS CATMOD procedure [21] fortotal deaths as well as by municipality and age group.Age was grouped as 0-4 years, 5-14 years, and 15+years. Each three-source analysis resulted in eight possi-ble models (Table 1). The minimal three-source modeluses three parameters, one for each source, and assumesindependence of all sources [7,17]. An extended modelincorporates three additional parameters, which allowsthe estimation of the three possible pair-wise dependen-cies (LCR and HC, LCR and church, and HC andchurch), generating three models with a single pair-wisedependency combination, and three models incorporat-ing two pair-wise dependencies. The “saturated model”has an additional parameter for modification of eachpair-wise dependence by the third source [7,17]. Modelswere selected on the basis of the system evaluation(qualitative assessment) and statistical criteria such asthe significance of associations between sources (usinglikelihood ratio tests) and goodness-of-fit criteria(Bayesian Information Criterion (BIC) and the AkaikeInformation Criterion (AIC)) [17]. Excel and STATAwere used for analysis [22].
Final mortality estimatesFinal estimates of the total number of deaths werederived from both the two- and three-source analyses.Models derived from each of these were assessed for
plausibility and fit based on the statistical techniquesdescribed above and the system assessment. Measures ofmortality, including IMR, childhood mortality, LE, andadult mortality, were then recalculated using the cor-rected estimates.
ResultsSystem AnalysisFilipino legislation [23,24] requires families of thedeceased person, regardless of the cause of death, toobtain a medical certificate of death and to present thisto the Local Civil Registrar to obtain an official deathcertificate prior to burial. Nearly all funerals are over-seen by the local Catholic Church, which also keeps arecord of deaths. Stillbirths are recorded using separateforms and do not appear on the death registers.At the local registry office, the death is recorded in the
register book and a death certificate issued to the family.A copy of the death certificate is sent to the NationalStatistics Office along with an electronic file if the muni-cipality also maintains an electronic database. Healthrecords may be derived from clinic log books, hospitalseparation data, or the medical certificate of death.While any medical practitioner can issue a certificate ofdeath, Filipino law requires that each medical certificateof death is submitted to the local municipal health offi-cer for review and signature [24]. A record of the deathshould be available from the health center even if themedical certificate was not originally issued there.Church records include the name of the deceased, dateof death, age at death, usual place of residence, andplace of death. These records are maintained in a stan-dardized format as set out in log books sold to theparishes through the church hierarchy.The system review demonstrated significant depen-
dence in the reporting of deaths between the LCR andthe HC. Families often approached both health and civilregistry offices independently, but medical certificates ofdeath were also forwarded directly by several municipal
Table 1 Models from three-source capture-recapture analysis (all ages) for Bohol study area, 2002-2007
Model* d.f G2 p-value AIC BIC Estimator (x) Total (N) Lower 95% CI Upper 95% CI
Independent 3 1900.00 <0.0001 1894 1879 147 8222 8208 8236
1-2 2 77.43 <0.0001 73 63 382 8457 8422 8493
1-3 2 1800.00 <0.0001 1796 1786 69 8144 8130 8157
2-3 2 1900.00 <0.0001 1896 1886 148 8223 8206 8240
1-2,1-3 1 69.75 <0.0001 68 63 520 8595 8476 8715
1-2, 2-3 1 16.50 <0.0001 15 9 552 8627 8560 8695
1-3, 2-3 1 1800.00 <0.0001 1798 1793 62 8137 8123 8150
1-2, 1-3, 2-3 0 -0.0002 1.000 0 0 908 8983 8748 9218
Source 1 - LCR data; Source 2 - health data; Source 3 - church data.
* Notation indicates associations between sources taken into account by the model, i.e., the 1-2 model accounts only for an association between source 1 (LCR)and source 2 (health center).
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health offices to the LCR. HCs either recorded deathsdirectly from the medical certificate of death at thetime it was issued or reviewed, or else kept no recordof the death at that time and later relied on either amonthly report of deaths from the LCR or on thefamily returning a copy of the official death certificateto the health office after registration. There was noofficial contact between the churches and governmentoffices and no apparent dependence in reportingpractices.In practice, there was considerable variation in report-
ing processes across the six municipalities. Noted varia-tions included whether families were required to firstreport the information to the local registrar or healthcenter, whether there was someone present at the healthcenter who could certify cause of death, and the chargesassociated with reporting. Practices for dealing with thenonavailability of a medical officer to sign the medicalcertificate also varied. Panglao and Dauis had a recipro-cal arrangement between their health centers. In Cortes,the mayor was the alternative authorized person, whilein the other municipalities, the reporting waited untilthe doctor returned.
Observed DeathsCombining all sources, 8,075 unique deaths wererecorded in the study area between 2002 and 2007, giv-ing an observed crude mortality rate of 7.06 deaths per1,000 population. Official sources (LCR and HC)recorded 6,696 (83%) of these deaths for a crude mortal-ity rate of 5.85 deaths per 1,000 population. Of the offi-cially recorded deaths (6,696), 6,297 (94%) wererecorded by the LCR, and 4,999 (75%) by the HC. Thechurch recorded 6,621 deaths.Observed LE at birth for the study area (2000-2007)
was 67.3 years for males and 74.2 years for females.IMR was 19.2 deaths per 1,000 live births for bothmales and females, with childhood mortality 23.2 and23.5 deaths per 1,000 live births for males and females,respectively.
Two-source capture-recapture analysisThe overall two-source analysis of aggregated officialrecords and church records estimated a total of 8,151deaths in the study area for 2002-2007. Aggregation ofsubgroup analyses within strata of age and municipalityprovided estimates that differed little from the overallestimate. Sensitivity analyses revealed that the source ofinformation on age and municipality (in cases of non-match on these) did not affect results, and the LCRrecord was used wherever possible to assign values. Offi-cial data were found to be virtually independent fromchurch data, with dependence coefficient values greaterthan 0.99.
Recording completeness varied by age (Figure 1), withchild deaths less likely to be recorded than adult deaths.Age had the greatest effect on health center records.Both official sources of data showed increasing comple-teness with increasing age at death. Variations in report-ing by age were not consistent across municipalities.Childhood deaths were less likely to be recorded inBaclayon, Dauis, and Panglao than in the othermunicipalities.
Three-source capture-recapture analysisEstimates of total deaths ranged from 8,137 to 8,983based on aggregate data, depending on the modelselected (Table 1). This equates to deaths from allsources being underregistered by 1% to 10%. Estimatesof total deaths varied further when disaggregated by agegroup and totaled to produce an overall figure.The saturated model was selected as the best fit (with
the lowest significant value for the AIC and BIC) fromthe analysis of the aggregate data. This also generatedthe highest estimate of 8,983 total deaths (Table 1). Thenext best fit falls to any of the models incorporating theassociation between the LCR and health data.Pair-wise dependence between sources was, however,
not consistent for all age groups. Table 2 shows mea-sures of association between the three sources and con-firms the dependence identified in the system reviewbetween the LCR and health reporting systems. Theodds ratio is high in the dataset as a whole (14.7) and inall three age groups. It is particularly high in persons 5-14 years (12.39) and in persons 15 years and older(15.86). There is additional evidence, however, of anassociation between the other combinations of sources,although the magnitude of this association as measuredby the odds ratio was much smaller (Table 2). Thisassociation reaches statistical significance in the datasetas a whole and in persons 15 years and older.Taking both the system review and these statistical
tests into account, these results did not clearly identifyany one model as the only reliable solution, but ratherindicated several plausible options depending on thelevel of age disaggregation used.
Final mortality estimatesSix models were identified as plausible and a good fit forthe data from the two- and three-source analyses. Threeyielded low values, and three yielded high values for thetotal number of deaths (Table 3). These models allaccounted for the principal association demonstratedbetween the LCR and health data, and the differences inreporting by age.The three low estimates were derived from those
models that used: a) a two-source analysis (with LCRand health center data combined as a single source
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against church data) including unknown age deaths(with age redistributed according to source category) byage; b) a three-source analysis with the model account-ing for association between the LCR and health datausing ages redistributed according to total age distribu-tion; and c) the three-source analysis as defined in (b)with estimates calculated separately for broad agegroups (<5 years, 5-14 years, and 15+ years) andsummed, rather than as a single application for all agescombined.The three high estimates were derived from models
that used: a) a three-source analysis (including unknownage deaths without age distribution) using the saturatedmodel (model accounting for an association betweeneach possible pair of sources); b) a three-source analysiswith the saturated model using ages redistributedaccording to total age distribution (then calculated sepa-rately for <5 years, 5-14 years, and 15+ years); and c) athree-source analysis using ages redistributed according
Table 2 Association between reporting sources byage-group - three-source analysis, Bohol studyarea, 2002-2007
Age group Association OR Phi p
All ages S1 * S2 14.74 (12.79,17.00) 0.52 (0.01) < 0.0001
S1 * S3 1.64 (1.30, 2.08) 0.06 (0.02) < 0.0001
S2 * S3 1.75 (1.53, 1.99) 0.11 (0.01) < 0.0001
0-4 years S1 * S2 4.81 (3.26, 7.10) 0.36 (0.04) < 0.0001
S1 * S3 0.58 (0.31, 1.09) -0.11 (0.06) 0.0726
S2 * S3 0.85 (0.51, 1.42) -0.04 (0.06) 0.5373
5-14 years S1 * S2 12.39 (4.57,33.58) 0.52 (0.08) < 0.0001
S1 * S3 0.94 (0.17, 5.13) -0.01 (0.12) 0.9399
S2 * S3 0.98 (0.36, 2.62) -0.00 (0.10) 0.965
15 + years S1 * S2 15.86 (13.42, 18.74) 0.50 (0.01) < 0.0001
S1 * S3 2.01 (1.54, 2.61) 0.08 (0.02) < 0.0001
S2 * S3 1.78 (1.55, 2.04) 0.11 (0.01) < 0.0001
Source 1 - LCR data, Source 2 - health data, Source 3 - church data.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Reporting completeness
Age Group
Reconciled data
LCR
Health
Church
Figure 1 Proportion of estimated deaths reported, Bohol study area, 2002-2007: two-source analysis.
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to total age distribution, calculated separately for <5years (1-2 model), 5-14 years (1-2 model), and 15+ years(saturated model) (Table 3). Low estimates equate to 5%of all deaths in the study area going unreported by anyof the sources, while the high estimates set unreporteddeaths at 10%.Final estimates were derived by selecting the average
of both high and low models and the extreme high andlow values of the 95% confidence intervals for any ofthese models selected. As such, total deaths in the studyarea for 2002-2007 are estimated to be 8,729, with arange of 8,079 to 9,445 deaths.The final estimates for total deaths imply that for the
period 2002-2007, only 77% (95% CI: 71%-83%) ofdeaths in the study were reported to official sources,with 93% (95% CI: 85%-100%) being reported to anysource, including the church. Civil registration was only72% complete (95% CI: 67%-78%). The crude mortalityrate for the study area is therefore estimated at 7.62deaths per 1,000 population (95% CI: 7.19-8.13). Lifeexpectancy at birth is estimated at 65.7 years for malesand 73.0 years for females, one to two years lower thanlife expectancy estimated from reconciled reporteddeaths from all sources, and four to five years lowerthan life expectancy estimated from civil registrationdata alone. The corrected IMR was 22.7 and 22.2 deathsper 1,000 live births for males and females, respectively,indicating that infant deaths were underreported fromall sources by 15% and by 62% from the LCR data(Figure 2). Childhood mortality was 27.3 and 27.1 deathsper 1,000 live births for males and females, respectively,again indicating underreporting by all sources of 13% to
15%. Adult mortality was estimated at 267.2 deaths per1,000 population for males and 137.3 deaths per 1,000population for females, indicating underreporting by theLCR of 24% to 29%.
DiscussionThese findings demonstrate that reporting of deaths inBohol to both official and church sources is incomplete.Estimates suggest that only 77% of deaths are recordedin official LCR or health records, while 5% to 10% of alldeaths are not reported to any source. Civil registrationwas estimated to be only 72% complete for all ages.Although the impact of underreporting of deaths onsummary mortality measures such as LE at birth was inthe order of four to five years, there was a much greaterimpact on age-specific measures of IMR and childhoodmortality, which were 15% to 18% higher than those cal-culated from the unadjusted reconciled data. Infantdeaths were underreported by 62% in the civil registra-tion data. Adult mortality was also 11% higher than theoriginal estimate from the reconciled data.The three-source analysis allows for departure from
the assumption of independence between sources, essen-tial for the two-source analysis. However, the addition ofany further source to the capture-recapture analysisincreases the uncertainty in the estimate and the widthof confidence intervals [17,19]. The additional modelsgenerated for each possible set of associations betweensources as additional sources are added [17] results in agreater onus on researchers to select an appropriatemodel based on their knowledge of the reporting sys-tems. Conducting the three-source analysis is therefore
Table 3 Final estimates of total deaths by method, Bohol study area, 2002-2007
Method/Model Total estimated deaths Crude mortality rate (deathsper 1,000 population)
Low estimates
2-source analysis (combined LCR and health center) including unknown age deaths(with age redistributed by source category) by age
8459 (8226-8692) 7.39 (7.19 - 7.60)
3-source analysis with the 1-2 model using ages redistributed by total age distribution 8457 (8422-8493) 7.39 (7.36 - 7.42)
3-source analysis with the 1-2 model using ages redistributed by total age distributioncalculated separately for <5 yrs, 5-14 yrs, and 15+ years)
8481 (8413 - 8546) 7.41 (7.35 - 7.47)
High estimates
3-source analysis (including unknown age deaths without age distribution) - saturatedmodel
8983 (8748-9218) 7.85 (7.64 - 8.06)
3-source analysis with the saturated model using ages redistributed by total agedistribution (calculated separately for <5 yrs, 5-14 yrs, and 15+ years)
8964 (8676 - 9305) 7.83 (7.58 - 8.13)
3-source analysis calculated separately for <5 yrs (1-2), 5-14 yrs (1-2), and 15+ years(saturated)
9006 (8734 - 9275) 7.87 (7.63 - 8.11)
Averages
Average of all accepted estimates with maximum 95% confidence intervals (ascalculated by any method)
8725 (8354 - 9266) 7.62 (7.19 - 8.13)
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only justified if the third source contributes deaths notrecorded by the other two sources to the analysis. Thetwo-source analysis found very weak associationbetween official and church records. Although thethree-source analysis confirmed this, the association wasstatistically significant and thus should not be ignored(Table 2). The two-source analysis is viewed as a lowerlimit for the estimate of total deaths, as the associationindicates that deaths reported to official sources aremore likely to also be reported to the church, resultingin an underestimation of unreported deaths. The three-source results accounting for this dependence thereforerange upward from the two-source estimate. Since onemodel cannot be identified as the only model to fit thedata, the various models are used to set upper andlower limits of the estimate of unreported deaths, andtherefore total deaths, for the study area.Population estimates were derived from the 2000 [14]
and 2007 [15] censuses, using linear interpolation toprovide estimates for intermediate years, and are thusmore likely to have overestimated the population forintermediate years, biasing mortality rates downward.The broad matching criteria used to reconcile the datasources and multiple checks of the matching processminimize the risk of significant errors in the reconcilia-tion of data sources. Further, in this setting, under-matching due to incomplete data is more likely thanovermatching. This would lead to overestimation of the
number of deaths not reported [17], and therefore leadto an inflated estimate of mortality, thus supporting theuse of the higher estimates of total deaths as an upperlimit.The small number of deaths in the study area meant
that while disaggregation by 5-year age group or munici-pality was possible with the two-source analysis, manyof the results became unstable when the data wereeither disaggregated further by year, or when the officialsources were separated for the three-source analysis.The two-source analysis was therefore used to assessvariations in reporting patterns. The important differ-ence in reporting completeness between childhooddeaths and adult deaths was accounted for in the three-source analysis by disaggregating data by broader agegroups (0-4, 5-14, and 15+ years).Capture-recapture analysis requires that: the record
sources capture deaths from the same population, thepopulation is closed, and that every death has the sameprobability of being reported [17,18]. The analysis is alsoaffected by the dependence between the data sources,with independence required for an accurate result froma two-source analysis. These conditions, however, arerarely fully met with human populations, and such ana-lysis therefore requires a solid understanding of report-ing systems in order to assess the magnitude of anydependence and population movements in or out of thearea. In the Bohol study area, while the population is
0 5 10 15 20 25
Civil Registration
All observed deaths
C-RC estimates
Deaths per 1000 live births
Data source
Figure 2 Estimated infant mortality rate by source, Bohol study area, 2002-2007.
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not strictly closed, migration both in and out is low,with the latest census reporting an annual growth rateof 1.06 percent for the period 2000 to 2007, althoughmost of this change is accounted for through births anddeaths [3]. Dependence between the LCR and healthreporting systems was accounted for in both the two-and three-source analyses, and the high proportion ofCatholics in the study population meant that for nearlyall deaths, there was a reasonable potential for thatdeath to be reported to the local parish and thus beincluded in this study. As such, while there were minordeviations from the assumed conditions for a capture-recapture analysis, Bohol is an ideal situation in whichto conduct this analysis, and results are highly unlikelyto have been affected by assumptions of the methodbeing violated. The selection of municipalities based onproximity to the urban center of Tagbilaran would likelyresult in the estimates of completeness presented herebeing considered a best-case scenario, with reportingfrom outlying areas likely to be even less complete.Additionally, the gender and age distribution in theseareas is almost identical to that for the broader province[14,15], suggesting that there is some generalizability tothe local area. However, the variation in reporting pat-terns and procedures by municipality identified by thisstudy, despite national legislation and procedural policy,indicates that it is not appropriate to apply these find-ings and assessment to other areas of the Philippineswithout an understanding of reporting processes at alocal level.While there have been very few studies on reporting
completeness in the Philippines, our findings of reportingcompleteness to official sources are consistent with a2003 evaluation of mortality data in the Philippines basedon data obtained by the World Health Organization [2].Completeness of death registration is often assessed aspart of the analyses of census data; however, this has notbeen reported in either of the two most recent censuses,2000 and 2007, in the Philippines [14,15]. Although esti-mates for this study were calculated from only six of the48 municipalities in Bohol, they are significantly higherthan official published estimates that put IMR for Boholprovince at 14.6 in 2000 and 8.53 in 2004 [4]. Publishedestimates of LE at birth for Bohol vary, with estimates for2000-2005 from official sources in the Philippines ran-ging from 65.3 to 68.19 for males and 71.0 to 72.9 forfemales (2000-2005) [3,4]. LE estimates from this studyare consistent with the lower published estimates fromthe National Statistics Office (NSO) for males, but arehigher than the published estimates for females [3]. Asboth sets of published data are derived from civil registra-tion within the province, it is probable that estimatesreleased by the NSO have been corrected for someundercounting in the data.
ConclusionsThis study demonstrates that even though the Philip-pines has national legislation and policies on deathregistration, there is significant underreporting of deathsto official sources in Bohol. While this had only a smallimpact on summary measures of mortality, such as thecrude death rate and LE, the impact of underreportingon age-specific measures such as IMR and childhoodmortality was much greater. Uncorrected mortality mea-sures would subsequently be misleading if used forhealth planning and evaluation purposes. These findingshighlight the importance of ensuring that official mortal-ity estimates from the Philippines are derived from datathat have been assessed for underreporting and cor-rected if necessary.Official LCR and health sources were more incomplete
that church records across nearly all age groups (Figure1), highlighting the potential to improve reporting toofficial sources by developing stronger links betweenlocal civil registrars and parish churches in this setting.Completeness of death reporting within the health sys-tem could be substantially improved by reducing lossesfrom the system through ensuring that a copy of eachmedical certificate of death is either kept or entered intoa logbook rather than relying on the data being returnedfrom the LCR. Variations in local reporting practicesfurther indicate that additional improvements in report-ing completeness may be possible through adaptinglocal practices to align with a standard nationalapproach.Finally, capture-recapture methods are an important
tool for assessing and correcting mortality data forreporting completeness, and have been demonstratedhere to provide improved understanding of mortalityestimates derived from a system generally recognized tobe of reasonable quality. While not an entirely newapplication of capture-recapture analysis, the methodhas potential to significantly improve our understandingof mortality in settings where multiple incomplete data-sets are available, particularly in small areas (such as thesmall countries of the Pacific Islands or provinces of thelarger countries of Asia) where it is possible forresearchers to develop a strong understanding of systemprocesses in order to make sensible decisions aboutboth model selection and matching criteria.
AcknowledgementsThe authors would also like to thank Dr Marilla Lucero, Dr Alvin Tan, and theother staff from the Research Institute of Tropical Medicine, Manila, workingon the Bohol field site for their data collection and guidance during thiswork. Funding for this research was provided under the Population HealthMetrics Research Consortium project of the Gates Grand Challenges inGlobal Health program. The funding body had no role in study design; inthe collection, analysis, and interpretation of data; in the writing of themanuscript; or in the decision to submit the manuscript for publication.
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Page 8 of 9
Author details1School of Population Health, University of Queensland, Herston (Brisbane),Queensland, Australia. 2Research Institute of Tropical Medicine, Manila,Philippines.
Authors’ contributionsKC designed the study; conceived and conducted the system review;designed and tested matching criteria; prepared the data for the three-source analysis; performed the statistical analysis for the two-source analysisand final model selection including interpretation of final results; and draftedthe manuscript. GW made substantial contributions to analysis andinterpretation of data, performed the statistical analysis for the three-sourceanalysis and revised the manuscript critically. VT coordinated data collection,collation and matching, and made substantial contributions to theinterpretation of data. DS coordinated initial data cleaning, management,and tabulation of records, and made substantial contributions to theinterpretation of data.HM made substantial contributions to the acquisition and interpretation ofdata. IR conceived and coordinated the study and revised the manuscriptcritically for important intellectual content. All authors read and approvedthe final manuscript.
Competing interestsThe authors declare that they have no competing interests.
Received: 23 December 2010 Accepted: 17 April 2011Published: 17 April 2011
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Global Uncertainty: Asia-Pacific Regional Report 2009/10. United NationsEconomic and Social Commission for Asia and the Pacific Statistics, AsianDevelopment Bank, United Nation Development Funds. Bangkok; 2010[http://www.mdgasiapacific.org/regional-report-2009-10].
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8. Lewden C, Jougla E, Alioum A, Pavillon G, Lièvre L, Morlat P, Salmon D,May T, Chêne G, Costagliola D, the Mortalité 2000 Study Group: Number ofdeaths among HIV-infected adults in France in 2000, three-sourcecapture-recapture estimation. Epidemiology and Infection 2006,134(6):1345-1352.
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16. Microsoft: Access: Computer program 2007.17. Hook E, Regal R: Capture-Recapture Methods in Epidemiology: Methods
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20. Hook E, Regal R: Validity of Methods for Model Selection, Weighting forUncertainty and Small Sample Adjustment in Capture-RecaptureEstimation. American Journal of Epidemiology 1997, 145(12):1138-1144.
21. SAS: The CATMOD Procedure , Second[http://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#catmod_toc.htm], SAS/STAT(R) 9.2 User’s Guide..
22. Heiden M: RECAP: Stata module to perform capture-recapture analysisfor three sources with Goodness-of-Fit based confidence intervals.Statistical Software Components S456859, Boston College Department ofEconomics 2007.
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doi:10.1186/1478-7954-9-9Cite this article as: Carter et al.: Capture-recapture analysis of all-causemortality data in Bohol, Philippines. Population Health Metrics 2011 9:9.
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5.5.2 Case study 5: Capture-recapture analysis in Tonga – application in a
Pacific Island with multiple data sources
Hufanga S, Carter KL, Rao C, Lopez AD, Taylor R. Mortality trends in Tonga: an
assessment based on a synthesis of local data. Population Health Metrics, 2012
Aug 14;10(1):14.
Case study five examines the application of capture-recapture analysis techniques
in Tonga. There are three primary data sources in Tonga: two separate collections
from the MoH (monthly nursing reports and vital registration through the HIS), and
civil registration through the Ministry of Justice. The Prime Minister’s office also
records deaths reported through local community leaders (164) for electoral
purposes, although this collection is considered to be very incomplete, and is
affected by data quality issues that affect the accuracy of reconciliation with other
sources. Out-migration in Tonga is relatively high (-16.6% in 2010 (110)) making
methods such as the Brass Growth Balance method unsuitable, as noted previously.
The analysis was therefore restricted to the main island of Tongatapu (80% of the
population, 2006 Census) where the larger population centre (and possibly the
greater employment and social opportunities) minimise the effect of migration on a
per capita basis.
Differences in collected variables, the quality of data collection and societal
influences, such as the use of different names and titles (including customary titles,
maiden and married names, baptismal names and nicknames) in different settings,
make reconciling the data sources one of the key issues to overcome in a capture-
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188
recapture analysis. In this study, criteria for matching individuals were adapted from
the previous work in the Philippines (333).
Key findings from the paper show the process of reconciling data from multiple
sources is of significant benefit in improving the completeness of death reporting and
subsequently improving the validity of data generated, even if further analyses are
not undertaken. However, data sets such as that held by the Prime Minister’s office,
which have low coverage of the population, should not be used for capture-recapture
analysis, as they break the assumption of equal catchability for each death, and may
introduce significant biases into the results.
As discussed in the paper and seen in Figure 18, findings demonstrate patterns of
reporting completeness vary substantially by age and source, as expected from the
system assessment in Chapter three, and highlight the advantage of capture-
recapture methods over direct demographic methods in the ability to examine age
groups separately.
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189
Figure 18: Reporting completeness of deaths in Tonga by age group, sex and data source (2001-2009)
The reconciled data set provides a minimum value for the mortality estimates derived
through capture-recapture analysis. Published IMR estimates varied significantly
through to the late 1990’s where most estimates converge to a narrower range
between 10 and 20 deaths per 1000 live births. Findings from the empirical data
were consistent with this range, and did not demonstrate any significant trend.
Published estimates of LE from 2000 onwards varied from 65-75 years for males,
and 68-74 years for females, with most clustered around 70-71 for males and 72-73
for females. The reconciled empirical data for 2005-2009 produces an estimate of LE
of 65.2 years (95% CI: 64.6-65.8) for males, and 69.6 years (95% CI: 69.0–70.2) for
females; several years lower than both the MoH and Census estimates. However,
based on the CRVS systems and results of the data analysis presented here, it is
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190
highly likely even reconciled data are under-enumerated and mortality may be even
higher than these reconciled estimates indicate. Final plausible ranges of LE for
2005-2008 from the study are therefore 60.4 - 64.2 years for males and 65.4 - 69
years for females.
Hufanga et al. Population Health Metrics 2012, 10:14http://www.pophealthmetrics.com/content/10/1/14
RESEARCH Open Access
Mortality trends in Tonga: an assessment basedon a synthesis of local dataSione Hufanga1,2, Karen L Carter2,3*, Chalapati Rao2, Alan D Lopez2 and Richard Taylor2,4
Abstract
Background: Accurate measures of mortality level by age group, gender, and region are critical for health planningand evaluation. These are especially required for a country like Tonga, which has limited resources and worksextensively with international donors. Mortality levels in Tonga were examined through an assessment of availablepublished information and data available from the four routine death reporting systems currently in operation.
Methods: Available published data on infant mortality rate (IMR) and life expectancy (LE) in Tonga were soughtthrough direct contact with the Government of Tonga and relevant international and regional organizations. Datasources were assessed for reliability and plausibility of estimates on the basis of method of estimation, originalsource of data, and data consistency. Unreliable sources were censored from further analysis and remaining dataanalysed for trends.Mortality data for 2001 to 2009 were obtained from both the Health Information System (based on medicalcertificates of death) and the Civil Registry. Data from 2005 to 2009 were also obtained from the ReproductiveHealth System of the Ministry of Health (MoH) (based on community nursing reports), and for 2005–2008, datawere also obtained from the Prime Minister’s office. Records were reconciled to create a single list of unique deathsand IMR and life tables calculated. Completeness of the reconciled data was examined using the Brassgrowth-balance method and capture-recapture analysis using two and three sources.
Results: Published IMR estimates varied significantly through to the late 1990s when most estimates converge to anarrower range between 10 and 20 deaths per 1,000 live births. Findings from reconciled data were consistent withthis range, and did not demonstrate any significant trend over 2001 to 2009.Published estimates of LE from 2000 onwards varied from 65 to 75 years for males and 68 to 74 years for females,with most clustered around 70 to 71 for males and 72 to 73 for females. Reconciled empirical data for 2005 to 2009produce an estimate of LE of 65.2 years (95% confidence interval [CI]: 64.6 - 65.8) for males and 69.6 years (95% CI:69.0 – 70.2) for females, which are several years lower than published MoH and census estimates. Adult mortality(15 to 59 years) is estimated at 26.7% for males and 19.8% for females. Analysis of reporting completeness suggeststhat even reconciled data are under enumerated, and these estimates place the plausible range of LE between 60.4to 64.2 years for males and 65.4 to 69.0 years for females, with adult mortality at 28.6% to 36.3% and 20.9% to27.7%, respectively.
Conclusions: The level of LE at a relatively low IMR and high adult mortality suggests that non-communicablediseases are having a profound limiting effect on health status in Tonga. There has been a sustained history ofincomplete and erroneous mortality estimates for Tonga. The findings highlight the critical need to reconcileexisting data sources and integrate reporting systems more fully to ensure all deaths in Tonga are captured and theimportance of local empirical data in monitoring trends in mortality.
Keywords: Mortality, Cause of death, Noncommunicable diseases, Medical record review, Death certification, Tonga,Pacific Islands
* Correspondence: [email protected] of Population Health, University of Queensland, Brisbane, Australia3Secretariat of the Pacific Community, Noumea, New CaledoniaFull list of author information is available at the end of the article
© 2012 Hufanga et al.; licensee BioMed CentraCommons Attribution License (http://creativecreproduction in any medium, provided the or
l Ltd. This is an Open Access article distributed under the terms of the Creativeommons.org/licenses/by/2.0), which permits unrestricted use, distribution, andiginal work is properly cited.
Hufanga et al. Population Health Metrics 2012, 10:14 Page 2 of 11http://www.pophealthmetrics.com/content/10/1/14
BackgroundMortality level is a key measure of population health,and accurate measures of mortality level by age group,gender, and region are critical for health planning andevaluation. Additionally, population health targets suchas the Millennium Development Goals [1] require reli-able data to measure progress. Reliable health statisticsare crucial for countries like Tonga, which have limitedresources and the international community of partnersand donors that support health programs.Tonga is a small Pacific Island country with 36 inhab-
ited islands. The main island groupings are Tongatapu(including the capital Nuku’alofa) which houses 70% ofthe population, and Ha’apai, Vava’u, Eua, and Niua’s.Tonga has an estimated population of 103,000 (2009)[2], of which 38% are under 15 years of age [2]. Healthservices are provided through a national hospital, threeouter island hospitals, 11 reproductive health centers,and 15 community health centers [3].Despite the importance of mortality data, official
reporting systems rarely capture every death [4]. A glo-bal assessment of mortality data in 2003 listed Tonga ashaving “low” quality death records, with reporting com-pleteness estimated at 86% [4]. Substantial variation inpublished estimates of mortality, particularly for infantand childhood mortality, have been noted [5] and makesassessment of health trends over time difficult.Deaths in Tonga are recorded through three organiza-
tions and four systems. These are: the Health Informa-tion System (HIS) and Reproductive Health SurveillanceSystem (RHS) operated by the Ministry of Health(MoH), the Civil Registry (CR) system managed by theMinistry of Justice, and reporting through the PrimeMinister’s office (Figure 1). Coverage of these systems, inprinciple, is nationwide. Burials for deceased persons in
Town Officer
Civil Registrar’s O(Ministry of Justi
Death Certificate
N
Family
District Officer
Prime Minister’sOffice
Monthly report
Quarterly report
Figure 1 Routine mortality data reporting systems in Tonga.
Tonga occur primarily on community land that is mana-ged by local leaders and do not generally require a for-mal burial permit.Tongan legislation [6] requires families to report
deaths to the Registrar or the nominated Magistrate whoperforms this function for outer islands. The CR officeoften requests that the family produce a medical certifi-cate of death in order to complete the registration; how-ever, this is not required by legislation. Deaths ofTongan citizens that occur overseas may be registered inthe same manner.For deaths in a hospital or health center, the attending
doctor will issue a medical certificate of death. For homeand community deaths, local nurses complete a “noticeof cause of death,” which is used by the certifying doctorto complete a medical certificate of death [7]. A copy ofthis certificate is sent by the hospital to the Health Infor-mation Section, where the data are coded and enteredonto the HIS database. District nurses are also requiredto submit monthly reports, including details of knowndeaths. These forms are forwarded to the reproductivehealth office. Data are aggregated by hand for inclusionin the annual report.Reporting to the Prime Minister’s office is primarily
for the purposes of maintaining the electoral roll, al-though deaths at all ages are collected. Reports are sub-mitted monthly by locally elected community leaders[8].In this study, mortality levels in Tonga were examined
through an assessment of available published informa-tion and data available from the four routine deathreporting systems in operation. Empirical data fromthese systems were reconciled to derive summary mea-sures of mortality, including infant mortality rate (IMR),childhood mortality (probability of dying before the age
fficece)
ational Statistics Office
National Planning Office / Process
HealthSystem
Doctor* Nurse #
Health PublicInformation HealthSystem Nursing
MedicalCertificateof Death
Monthlyreport
NoticeofDeath
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of 5, 5q0), average life expectancy at birth (LE), and adultmortality (probability of dying between ages 15 and 59,
45q15). The reconciled dataset was further examined forpotential under-enumeration of deaths using both aBrass analysis [9] and capture-recapture techniques [10],and the plausibility of the derived mortality estimates isdiscussed.
MethodsMeasures included in the analysis and reported in thispaper are from two sources: (1) previously publishedreports [11-60] and (2) empirical data on deaths by agegroup, sex, and period for 2001 to 2009 obtained fromlocal sources. As key informant interviews with theregistrars’ office indicated, most overseas deaths regis-tered are for Tongan residents. All deaths registered inTonga were included.
Published data collection and analysisData collectionAvailable published secondary data [11-60] on mortalityin Tonga were sought through direct contact with thegovernment of Tonga and international and regionalorganizations with an interest in health or mortality inTonga. A literature search was conducted, including aninternet search of websites for United Nations agencies(UNDP, UNICEF, UNFPA, and WHO); regional institu-tions such as the Secretariat of the Pacific Community
Figure 2 Published estimates of Infant Mortality Rate (IMR) by data soMoH = Ministry of Health, SPC = Secretariat of the Pacific Community, WHOrange of best estimates from this study (Table 2) for 2001–2004 and 2005–
(SPC), the World Bank (WB), and the Asian Develop-ment Bank (ADB); and non-governmental organizations.
Data assessmentIMR and LE were chosen as measures of all-cause mor-tality due to availability. IMRs from published reportswere graphed over time by major source of data(Figures 2 and 3). Data sources were assessed for reli-ability and plausibility of estimates on the basis ofmethod of estimation, original source of data, and dataconsistency. Unreliable sources were censored from fur-ther analysis if they met any of the following criteria: (a)data were derived, or were considered likely to havebeen derived, from models assuming a given improve-ment by year, as evidenced by a perfectly linear improve-ment in LE or IMR by year; (b) multiple incompatibleestimates were given by the source for a single year oradjacent years; (c) source data included implausible esti-mates (based on equivalent measures for developedcountries); (d) calculations were based on uncorrectedvital registration data known to be significantly underre-ported or with no assessment of reporting completeness.An exponential trend was fitted to the average IMRs
of each year from data remaining after censoring(Microsoft Excel). A linear trend was fitted to IMRs post1996, excluding the MoH estimates, which were retainedafter censoring but later shown to be incomplete basedon the empirical data analysis. LE from all sources were
urce, 1939–2009. SDT = Statistics Department of Tonga,= World Health Organization, ADB = Asian Development Bank. The
2009 is shown at 2002 and 2007, respectively.
Figure 3 Credible published and empirical estimates of IMR by source and method of adjustment, 1939–2009. SDT = StatisticsDepartment of Tonga, MoH = Ministry of Health, CRC = Capture-recapture analysis y = 200 + 21e-0.023x / y = 0.0025x + 12.16 (fitted to credibleestimates excluding MoH estimates as described in the methods).
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graphed over time by major source of data (Figures 4and 5), with unreliable estimates (based on the same cri-teria as described for IMR) censored from further ana-lysis. Insufficient data remained after censoring to fit atrend curve.
Empirical data collection and analysisData collectionData for 2001 to 2009 were obtained from both the HIS(derived from medical certificates of death) and CR.Data from 2005 to 2009 were obtained from the RHSrecords (community nursing reports), and data for 2005to 2008 were obtained from the Prime Minister’s office(Table 1). Additional years from the RHS and Prime
Figure 4 Published estimates of life expectancy at birth by source, m(Table 2) for 2001–2004 and 2005–2009 is shown at 2002 and 2007, respec
Minister’s office were not available at the time of datacollection. Key variables collected were: island of resi-dence, data source, full name, sex, date of birth, dateand place of death, and age at death. Population by agegroup, sex, and island was derived from the 1996[14,15] and 2006 [16,17] censuses using exponentialinterpolation to provide estimates for intermediate andadditional years. Total births for 2005 to 2009 wereobtained from the MoH annual reports for calculationof IMR [21].
Reconciliation of reported deathsEach death was assigned a unique number. Records werereconciled to create a single list of unique deaths
ales, 1930–2009. The range of best estimates from this studytively.
Figure 5 Published estimates of life expectancy at birth by source, females, 1930–2009. The range of best estimates from this study(Table 2) for 2001–2004 and 2005–2009 is shown at 2002 and 2007, respectively.
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indicating source(s) in which each was recorded. Investi-gators manually matched deaths through a repeatedsorting of lists by key criteria, and the final list of uniquedeaths was reviewed by two investigators (SH, KC). Cri-teria were established for records to be considered amatch: (1) same surname (minor variations in spelling,variations in name order, or a phonetic match wereallowed), (2) same first name (again, minor variationsallowed), (3) age at death within one year, (4) date ofdeath within the same month, and (5) either same placeof residence or same place of death. Records were con-sidered to match if they met three criteria including sur-name or four criteria excluding surname.Deaths with missing ages were then redistributed
according to imputed age distributions for that source.Deaths only reported through the Prime Minister’sOffice (Table 1) were excluded from the reconciled data,
Table 1 Reported deaths by source, all Tonga: 2001-2009
Year CivilRegistration
HIS (deathcertificates)
RHS (comnursing r
2001 473 567
2002 446 588
2003 434 598
2004 418 543
2005 406 534 133
2006 470 493 146
2007 400 526 144
2008 398 484 543
2009 468 567 393
HIS = Health Information System, RHS = Reproductive Health System.
as the limited information provided and poor data qual-ity meant that it was not possible to determine whetherthese were additional deaths, or whether there was sim-ply insufficient information to identify an existingmatch.The following measures of mortality were calculated
using standard methods [61]: age-specific mortality rate,IMR (probability of dying before 1 year of age), child-hood mortality (probability of dying before 5 years ofage), adult mortality (probability of dying between 15and 59 years of age inclusive), and LE. Childhood(<5 years) mortality was also calculated based on a re-gression of the IMR as a proportion of childhood mor-tality against childhood mortality from internationalestimates for 2006 [62]. Estimated reporting complete-ness by data source was calculated using the total recon-ciled deaths.
munityeports)
Prime Minister’s office(town and districtofficer reports)
Reconciled data(all sources)
605
627
629
574
36 657
44 713
74 735
60 957
836
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Assessment of completeness of reconciled dataReconciled data were assessed for under enumeration ofdeaths by (1) the Brass growth-balance method, whichcompares the age distribution of reported deaths withthe expected age distribution of deaths based on theobserved population age-sex structure (assuming aclosed population), applied to ages 25 to 64 years [61,63]and (2) capture-recapture techniques, which use theproportion of reported deaths recorded by each combin-ation of sources to estimate the number of unreporteddeaths [10,63]. Data from the Prime Minister’s officewere excluded from the capture-recapture analysis asvery few deaths were reported (Table 1), and these werefrom a very small area of Tonga, thus violating the as-sumption of equal catch ability for each death [10].A two-source capture-recapture analysis was con-
ducted for HIS data against CR data for 2001 to 2004. Akey assumption in a two-source analysis is that sourcesare independent. Data from the HIS and RHS for 2005to 2009 were therefore reconciled as a single source(henceforth referred to as MoH data) as both systemsare operated by the MoH and therefore demonstratestrong interdependency. MoH data was then assessedagainst CR data for 2005 to 2009. Confidence intervals(CIs) were calculated using the maximum likelihood es-timator [10].Three-source capture-recapture analysis allows for
pair-wise dependence between sources [10,64] and usedHIS, RHS, and CR data separately for 2005 to 2009.Each three-source analysis results in eight possible mod-els [10,64,65]. Models that did not account for depend-ence between the two MoH sources were excludedbased on the evaluation of the death recording systemswith final model selection based on statistical criteriasuch as the significance of associations between sources(p-value) and goodness-of-fit criteria (Bayesian Informa-tion Criterion and the Akaike Information Criterion)[10,65]. HIS data for 2001 to 2004 were adjusted byreporting completeness for the HIS data for 2005 to2009 to generate an adjusted estimate for this periodbased on the three-source analysis. Unreported deathswere estimated for total deaths, with subgroup analysesfor sex, age, and island group for both the two- andthree-source capture-recapture analyses to identify vari-ation in reporting patterns.Reporting completeness for Tongatapu was applied to
all islands to derive adjusted estimates of total deaths, asmost deaths are from Tongatapu and it was anticipatedthat data from the main island group (where data arechecked more regularly) should be of the highest qualityand would therefore be less likely to be under matcheddue to data recording issues. Adjusted measures of LEand IMR were then compared with reconciled data andcredible estimates from the published data.
ResultsAnalysis of published dataThirteen sources of published mortality estimates werefound for Tonga, with 10 of these sources reporting LE.There was very little information on methods of data col-lection and analysis available in the published sources.
Infant mortality ratePublished IMR estimates varied significantly through thelate 1990’s when most estimates converge to between 10and 20 deaths per 1,000 live births. Despite the variation,it is apparent IMR has fallen substantially over theperiod investigated and is now relatively low (Figures 2and 3). Four sources remained after applying the censor-ing criteria, including MoH estimates from reporteddeaths. Significant variation remains in recent estimatesdespite censoring. Based on credible published estimates,IMR for Tonga appears relatively stable for recent years:between 10 and 19 deaths per 1,000 live births.
Life expectancyTen sources of published estimates of LE were found forTonga since 1939. There was a wide variation found inpublished estimates of LE (around 20 years’ differencefor a single year for each sex), as shown in Figures 4 and5. Eight sources were censored from further analysis,leaving only uncorrected MoH data and an earlier studythat used CR data for 1982 to 1986 and 1987 to 1992adjusted for completeness [19]. After censoring, pub-lished estimates of LE were 69.6 years for males and72.9 years for females for 2005 to 2008 (Table 2).
Analyses of reported deathsReconciled dataThere were 3,874 unique deaths recorded for Tonga be-tween 2005 and 2009 (Table 1) and 2,435 deathsrecorded between 2001 and 2004. The MoH recorded3,361 (87%) of total reconciled deaths for 2005 to 2009,while the CR recorded 2,128 deaths (55%). The PrimeMinister’s office recorded 214 deaths for 2005 to 2008,of which only 14 were not able to be matched with an-other source.From the reconciled deaths, IMR was directly calcu-
lated for 2005 to 2009 as 14.7 deaths per 1,000 livebirths, with childhood mortality as 24.5 deaths per 1,000live births for males and 19.5 for females. Imputation ofIMR from these levels of childhood mortality yielded anestimate of 18.2 deaths per 1,000 live births. Adult mor-tality is estimated at 26.7% for males and 19.8% forfemales (Table 2). LE at birth (2005 to 2009) was 65.2(95% CI: 64.6 – 65.8) years for males and 69.6 (95% CI:69.0 - 70.2) years for females. LE is lower than publishedestimates presented above and substantially lower thanpublished estimates that were censored. If overseas
Table 2 Summary estimates of mortality (all Tonga) by method and period
Mortality dataset Period IMR /1,000 IMR /1,000imputed fromchild mortality
Child mortality<5 years /1,000
Adultmortality (%)
Life expectancyat birth (years)
M F M F M F
Crediblepublishedsources
Civil Registry data(adjusted by Brassanalysis) [29]
1982-1986 25.9 - - - - - 66.3 69.5
1987-1992 22.9 - - - - - 68.1 72
Ministry of Health dataonly (unadjusted) [5]
2005-2008 16.4 - - - 18.4 19.1 69.6 72.9
Reconcileddata
2001-2004 8.9 12.6 16.7 11.5 21.8 15.5 67.7 71.7
2005-2009 13.7 18.2 24.5 19.5 26.7 19.8 65.2 69.6
Adjustedreconcileddata
Adjusted by Brassanalysis (>5 years)
2001-2004 NA NA NA NA 21.6 22.1 67.9 68.3
2005-2009 NA NA NA NA 26.7 23.6 65.2 67.8
Adjusted by two-sourceCRC analysis (all ages)
2001-2004 9.1 12.8 17.0 11.8 22.2 15.8 67.5 71.6
2005-2009 14.7 19.4 26.6 20.7 28.6 20.9 64.2 69.0
Adjusted separatelyfor three-source CRCanalysis for 0–4 yearsand remaining deathsby gender
2001-2004† 10.4 21.7 32.1 21.8 39.4 29.8 58.8 64.2
2005-2009 19.1 24.7 34.8 27.6 36.3 27.7 60.4 65.4
Final mortality estimates 2001-2004 9.1 – 21.7 17.0 – 32.1 11.8 – 21.8 22.2 -39.4 15.8 – 29.8 58.8 - 67.5 64.2 - 71.6
2005-2009 14.7 – 24.7 26.6 – 34.8 20.7 – 27.6 28.6 – 36.3 20.9 – 27.7 60.4 - 64.2 65.4 – 69.0
CRC = Capture-recapture analysis.Credible estimates of IMR are shown in Figure 3.†Estimates generated by applying reporting completeness for HIS data from 2005 to 2009 three-source analysis (final disaggregation by 0 to 4 years, andremaining deaths by gender) to HIS data for 2001 to 2004.
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deaths are excluded to minimize the possibility of nonre-sidents being included in the analysis, there is less than0.5 years increase in LE for both males and females.There is no change to IMR.Reporting completeness for individual data sources
(2005 to 2008), as determined from a comparison withtotal reconciled deaths, is estimated at 55% for CR, 67%for HIS, 35% for RHS, and 87% for the combined MoHdata. For Tongatapu, reporting completeness was esti-mated at 56% for CR, 70% for HIS, 42% for RHS, and91% for the combined MoH data (Table 3).
Brass analysisBrass analysis of the reconciled data estimates thatreporting completeness for 2005 to 2009 (all sources) is>99% for males and 63% for females; however, the dis-parity between these estimates is not plausible. For Ton-gatapu only, reporting completeness was estimated as>99% for males and 82% for females. The Brass analysisplot of partial births against partial deaths for males (forall deaths and Tongatapu only) did not readily allow astraight line to be fitted regardless of age groupsselected, indicating this estimate of completeness isunreliable.
Capture-recapture analysisThe overall two-source analysis of aggregated healthrecords and civil registration records estimated a total of
2,491 (95% CI: 2465–2517) deaths in Tonga for 2001 to2004 and 4,400 (95% CI: 4295–4506) deaths for 2005 to2009. Compared to these estimates, reconciled data fromboth the CR and health reporting systems were found tobe 98% (95% CI: 97–99) complete for 2001 to 2004 and88% (95% CI: 85–90) for 2005 to 2008 for both sexes.For Tongatapu only, the reconciled data were estimatedas 98% (95%: CI 97–99) complete for both sexes for2001 to 2004 and 93% (95%: CI 89–95) complete for2005 to 2009 (Table 3). Recording completeness variedby age, with child deaths less likely to be recorded thanadult deaths through CR. However, aggregation of sub-group analyses within strata of age and island group pro-vided summary measures of mortality, which differedlittle from the overall aggregated estimates. Reportingcompleteness for individual data sources (2005 to 2008)in Tongatapu, as determined by the two-source analysis,is estimated at 53% for CR data, 66% for HIS data, 40%for RHS data, and 84% for the combined health data(HIS plus RHS).A three-source analysis was performed for HIS, RHS,
and CR data for 2005 to 2009. Estimates of total deathsranged from 4,404 to 6,695 based on aggregate (total)data, depending on the model selected. The modelselected as the best fit was that which included depend-ence between the HIS and RHS and between the HISand CR. This is consistent with the assessment of thedeath reporting system. Based on this model, the
Table 3 Estimated reporting completeness (%) of deaths (all ages, both sexes) by source and method of assessmentfor Tongatapu
Method of assessment Years Completeness (%) and 95% confidence intervals
Reconcileddeath data
Civil registrationof deaths
HIS (deathcertificates)
RHS (communitynursing reports)
MoH(combined)
Comparison against reconciled death data 2001-2004 - 70 96 NA NA
2005-2009 - 56 70 42 91
Brass growth-balance assessment 2001-2004 96 (M) / 63 (F) - - - -
2005-2009 >99 (M) / 82 (F) - - - -
Two-source capture-recapture analysis 2001-2004 98 (97 – 99) 69 (68–70) 94 (93 – 95) NA NA
2005-2009 93 (91–95) 53 (52–54) 66 (65–67) 40 (39–40) 84 (83–86)
Three-source capture-recapture analysis(aggregated ages)
2005-2009 68 (63–73) 37 (34 – 40) 46 (43 – 50) 28 (26 – 30) 63 (58 – 67)
Three-source capture-recapture analysis(final disaggregation*)
2005-2009 69 (57 – 77) 38 (31 – 42) 47 (39 – 52) 28 (23 – 31) 63 (52–70)
95% confidence intervals shown in parentheses where applicable.HIS = Health Information System, RHS = Reproductive Health System, MoH = Ministry of Health (HIS and RHS data combined).* Capture-recapture analysis disaggregated by the following subgroups: 0 to 4 years (both sexes), males 5 years or older, and females 5 years or older.
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reconciled data are estimated to be 58% (95% CI: 52 –63) complete. If only Tongatapu is included, the recon-ciled data are estimated to be 70% (95% CI: 63–76)complete for males and 67% (95% CI: 58–74) completefor females. As reporting patterns varied by age (particu-larly for very young deaths) and sex, final estimates ofcompleteness from the three-source analysis werederived from models of best fit for disaggregated datafor children aged 0 to 4 years (both sexes) and theremaining ages by sex. Based on these models, thereconciled data for Tongatapu were found to be 70%(95% CI: 32–91) complete for children aged 0 to 4 years,69% (95% CI: 61–76) complete for males 5 years andover, and 68% (95% CI: 59–75) complete for females5 years and over. Adjusted estimates are shown inTable 2.
Final estimates of mortalityBoth the Brass method analysis and capture-recaptureanalyses indicate that deaths remain under enumeratedfollowing data reconciliation. The Brass method, how-ever, provided implausible results, due most likely to thesensitivity of this method to high levels of migration andsmall numbers [66]. The two-source capture-recaptureanalysis is less sensitive but is known to be affected byprobable positive dependence between the civil registryand health data due to registration practices; therefore, itis expected to overestimate reporting completeness. Assuch, the two-source analysis provides a plausible lowerlimit to mortality levels and subsequent upper limit forLE. Equally, the three-source analysis allows for this de-pendence and can therefore be used to set a plausibleupper limit for mortality levels and hence a lower limitfor LE. Final estimates for 2005 to 2009 place IMR be-tween 14.7 to 24.7 deaths per 1,000 births, with the best
estimate for this period at 19 deaths per 1,000 births, asestimated from childhood mortality from the two-sourcecapture-recapture analysis, direct calculation from thethree-source analysis, and the 2006 census. Final esti-mates place the plausible range of LE at 60.4 to64.2 years for males and 65.4 to 69.0 years for females.Adult mortality is estimated to be between 28.6% to36.3% for males and 20.9% to 27.7% for females.
DiscussionThese findings demonstrate that there has been a sus-tained history of erroneous mortality estimates forTonga, with many published estimates of LE and IMRimplausible and presented without an indication of themethod used. Many of the IMR estimates presentedprior to 2000 (Figure 3) fall below current levels of IMRin Australia (IMR of 5.0 in 2007 [58]) and New Zealand(IMR of 6.0 in 2007 [58]). Credible data sources demon-strate that IMR has declined steadily to below 20 deathsper 1,000 live births, fluctuating in recent years between10 and 19 deaths per 1,000 live births. Although higherthan previous MoH estimates of IMR, reconciled esti-mates of IMR are consistent with this range, while sev-eral adjusted estimates are slightly higher. The range ofplausible estimates suggests that progress toward theMillennium Development Goal targets has been min-imal, with best estimates from the empirical data from2005 to 2009 at very similar levels to census estimatesfor 2006. Although IMR demonstrates no significanttrend since the late 1990s, at these levels it is a minor in-fluence on the overall LE [61].Published estimates of LE from 2000 onwards varied
from 65 to 75 years for males and 68 to 74 years forfemales, with most clustered around 70 to 71 for malesand 72 to 73 for females. Very few of the published
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estimates were assessed as reliable, with only MoH dataremaining for recent years. These MoH data estimatedLE for 2005 to 2008 as 69.6 years for males and72.9 years for females. The 2006 census estimates of67.3 years for males and 73.0 years for females were cen-sored from the final results as LE was calculated fromregistration data adjusted using a model with a single in-put parameter (childhood mortality) [16,17].The reconciled empirical data for 2005 to 2009 pro-
duces an estimate of LE of 65.2 years (95% CI: 64.6 -65.8) for males and 69.6 years (95% CI: 69.0 – 70.2) forfemales, which is several years lower than both the MoHand census estimates. These findings demonstrate thatLE is substantially lower than previously reported forTonga. Further, the reconciled data indicate LE mayhave fallen from 2001 to 2004, although this is not evi-dent in the range of plausible published estimates foreach period.Although there remains some uncertainty regarding
the extent of underreporting to all data sources, the datacollection systems and results of the assessments pre-sented here indicate that even reconciled data underenumerated. The Brass analysis, particularly for males,appears significantly affected by migration, which is quitehigh in Tonga (−16.58 per 1,000 population in 2010[67]). However, the capture-recapture assessments pre-sented here allow limits of plausibility to be established.The two-source analysis for 2005 to 2009 accounts forthe major dependency between the HIS and RHS bycombining these data; however, there is also probablepositive dependency between the HIS and CR due to theCivil Registry requesting medical certificates of death. Assuch, this analysis would overestimate reporting com-pleteness and underestimate the total deaths, thus pro-viding a plausible upper limit for LE. The three-sourcecapture-recapture analysis allows these dependencies tobe taken into account, and can therefore be used as alower limit for LE, producing similar results to the Brassanalysis for females. As migration data for Tonga are notcomplete, it is not possible to use changes in populationrecorded through the census as an alternative methodfor assessing reporting completeness for deaths.The low LE is clearly being predominantly driven by
adult mortality. Even prior to adjustment for under-counting, adult mortality based on reconciled data inTonga is roughly three times that seen in Australia,where there is a probability of dying of 8.9% for malesand 5.1% for females in 2002 to 2004 [68], or NewZealand, where there is a probability of dying of 9.7% formales and 6.4% for females in 2002 to 2004 [69]. Thehigh adult mortality is consistent with the limited infor-mation available on noncommunicable disease risk factorprevalence available for Tonga. Provisional results from a2004 survey found that 69% of adults were obese (body
mass index ≥ 30 kg/m2), 23% of adults were affected byhypertension (systolic blood pressure ≥ 140 mmHg and/or diastolic blood pressure ≥ 90 mmHg or currently onmedication), and 18% reported to be diabetic (self-report)[70]. In 2004, Tonga acted to recognize the extensivecommunity burden on noncommunicable diseases bydeveloping the first non-communicable disease strategyin the Pacific region [71]. This pattern of high adult mor-tality limiting LE is also consistent with recent findingselsewhere in the Pacific, such as in Fiji and Nauru[72,73].Difficulties encountered in assessment of published
data were the lack of information concerning primarydata sources, methods of calculation, and assumptions.In order to deal with this, exclusion criteria were speci-fied and unreliable estimates censored from further ana-lyses. The matching criteria used to reconcile the sourcesof death reporting and multiple checks of the matchingprocess minimize the risk of significant errors in the rec-onciliation of the data sources. These criteria were devel-oped and tested to reduce the risk of matching recordsbeing missed, with the matching process checked by twoinvestigators (SH, KC). Removing the Prime Minister’soffice data from the analysis further reduced the potentialfor under matching due to incomplete data, which wouldhave lead to overestimation the number of deaths notreported [10,63]. Completeness estimates for the main is-land group of Tongatapu, as the best quality subset ofdata, were also applied to all data to minimize the poten-tial for overestimating unreported deaths due to mis-alignment in the populations covered by each source orpoorer data quality from the outer islands.The wide range of plausible estimates for the period
2001 to 2004 limit any assessment of trends between thetwo time periods investigated. This uncertainty is largelya result of less data being available for these years. How-ever, as MoH efforts to improve reporting began afterthis period, HIS data for 2001 to 2004 could reasonablybe expected to be less complete than in 2005 to 2009.This is reflected in the increase in the total reporteddeaths per year in the reconciled data between the twoperiods, as shown in the results. The true level of mor-tality is therefore likely to fall closer to the high end ofthe range of plausible mortality estimates for 2001 to2004 (and therefore the lower estimates of LE) asderived by applying reporting completeness for the HISfrom the three-source assessment for 2005 to 2009.While there have been very few studies on reportingcompleteness in Tonga, our findings of reporting com-pleteness for the reconciled data are consistent with a2003 evaluation of mortality data in Tonga based ondata obtained by the World Health Organization. Thisreport found Tonga to have “low” quality mortality andcause of death data, with completeness estimated at 86%
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and coverage estimated at 70% [4], compared with anestimated completeness for the reconciled data between68% and 93% for 2005 to 2008, as found in this study.
ConclusionsLE for Tonga for 2005 to 2008 is estimated to be be-tween 60.4 to 64.2 years for males and 65.4 to 69.0 forfemales, well below previously published estimates. Thelow LE, at a relatively low IMR and high prematureadult mortality, suggests that non-communicable dis-eases are having a profound limiting effect on health sta-tus in Tonga.These findings demonstrate that much of the mortality
data that have been previously available to health policymakers in Tonga as well as to international donors andagencies has been misleading, potentially masking theurgency of the public health action need to address adultpremature mortality in Tonga. Additionally, the findingshighlight the critical need both to reconcile existing datasources and integrate reporting systems more fully toensure all deaths in Tonga are captured, as well as theimportance of analysis of local empirical data in moni-toring trends in health status. While census estimatesfor IMR were found to be consistent with best estimatesfrom the routinely reported data, the census was foundto have underestimated adult mortality and thereforeoverestimated LE.
Competing interestsAL is one of the Editors-in-Chief of Population Health Metrics.
Authors’ contributionsSH conducted the system review, supervised the data collection andcollation processes, designed the matching criteria, conducted the datamatching, managed the ethics process and coordination with data collectionagencies, performed the statistical analysis for the two-source analysis of theHIS and PM data for 2005 to 2009, completed the search for publishedestimates, assessed published data sources for reliability, revised themanuscript critically, and made substantial contributions to the interpretationof data. KC oversaw the project, conducted the system review, oversaw thedesign and testing of matching criteria, checked the matching process,assisted with data matching for the civil registration data against othersources, performed the statistical analysis for both the two-source analysisfor 2001 to 2004 and the three-source analysis, including the civil registrydata and final model selection including interpretation of final results andselection of final estimates, supervised the other analysis, and drafted andled the revision of the manuscript. CR revised the manuscript critically forimportant intellectual content. ADL and RT conceived and coordinated thebroader study under which this project was undertaken, reviewed andcontributed significantly to the interpretation of results, and revised themanuscript critically for important intellectual content. RT also generated theregression models to estimate IMR from childhood mortality. All authors readand approved the final manuscript.
AcknowledgmentsThe authors would also like to thank the staff and management of theTongan Ministry of Health, Civil Registry office, and Prime Minister’s office fortheir assistance and cooperation with this research. Particular thanks are dueto Mrs. Sela Paasi, Dr. Toa, Dr. Malakai ‘Ake, Mr. Pita Vuki, and Mrs. ManakoviPahulu. We would also like to thank Claire Mowry for performing the originalpublication search. Funding for this research was provided under the AusAIDAustralian Research Development Awards (HS0024). The funding body hadno role in study design; in the collection, analysis, and interpretation of data;
in the writing of the manuscript; or in the decision to submit the manuscriptfor publication.
Author details1Ministry of Health, Kingdom of Tonga, Tonga. 2School of Population Health,University of Queensland, Brisbane, Australia. 3Secretariat of the PacificCommunity, Noumea, New Caledonia. 4School of Public Health andCommunity Medicine, University of New South Wales, Paddington, Australia.
Received: 7 September 2011 Accepted: 2 August 2012Published: 14 August 2012
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doi:10.1186/1478-7954-10-14Cite this article as: Hufanga et al.: Mortality trends in Tonga: anassessment based on a synthesis of local data. Population Health Metrics2012 10:14.
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5.5.3 Using Capture-recapture analysis to set plausible mortality limits in
Kiribati
As demonstrated in case study five, even when data sources are largely
incomplete, it is possible to use capture-recapture analysis to quantify upper and
lower boundaries to define the range of plausible estimates for mortality. In Kiribati,
previous estimates of mortality level have all been obtained through surveys and
census analyses based mainly on indirect methods; with age-specific mortality
patterns, LE and adult mortality derived through single input model life tables (50,
152). There has been no assessment of mortality patterns based on empirical data
as available sources were considered too incomplete (50, 152). As discussed in
Chapter two, single input parameter model life tables may underestimate adult
mortality across all age groups (but especially adult ages when U5M is low) and
subsequently overestimate LE. Although Kiribati is still relatively early in the
epidemiological transition (334) and therefore may more closely reflect standard
model life tables, the existence of a double burden of disease, as indicated by the
high prevalence of diabetes and other NCDs (335) demonstrates a need to evaluate
the plausibility of these findings.
Only two sources of mortality data are available in Kiribati, civil registration and the
HIS, however the system assessment in Chapter three indicates these operate
largely independently, with no direct data sharing and limited provisions to require a
medical certificate prior to registration of a death. While three source capture-
recapture methods are more robust, the limited interaction between these data
sources indicates a two source analysis could be used. Capture-recapture analysis
� � �
203
therefore offered an opportunity to use the empirical data from these sources for the
first time to provide mortality estimates.
Methods
Methods used in the study closely reflected those employed in Bohol and Tonga.
Recorded deaths were sought from the MoH and Civil Registration Office for 2000-
2009. MoH data were first reconciled between the HIS death registry, hospital
records and nurses reports. Duplicate records were identified based on the same
criteria as used for matching the records between sources (outlined below). Where
duplicates were found, the data from each record was combined, and any
differences in key variables noted in the combined record.
Criteria for matching records within the two sources were based on the previous
work in Tonga and Bohol, and the variables available. These were developed in
conjunction with the Civil Registration Office and MoH staff. Data trials were
conducted to identify a set of criteria that would minimise the potential for over
matching (falsely matching records together that do not refer to the same individual),
and therefore underestimating the deaths not recorded in either source; and equally
minimise potential for under-matching, which would overestimate the missing
number of deaths.
The final criteria for records to be considered to be related to the same individual
were:
� � �
204
• Same surname (minor spelling variations allowed) plus at least two of: first
name (minor spelling variations allowed, or English versions of the same name),
and/or island (place of death), and/or date of death (within 5 days); or
• Same first name, and island, and date of death, and age and gender; or
• Either the same surname or first name, within same month of death, and all of
the following: island, age, gender, and cause (by broad category).
For cause to be considered a match, only specific causes were included (such as
stroke, diabetes, suicide etc) with ill-defined causes such as “old” or “sick” not
included. Cause was also considered a match if the records contained the same
cause in English and I-Kiribati.
These criteria were designed to account for the following issues:
• People may have different first names due to various usage of given versus
baptismal names, major variations in spelling, and interchangeable I-Kiribati and
English spelling (for example “Tiaon” and “John”).
• Adoption (including informal adoption) is common practice in Kiribati. People
may use either (or both) the name of their birth father, or the name of their adoptive
father.
• Surnames in Kiribati are usually the fathers’ first name. People may informally
use different surnames depending upon the head of the household where they
currently reside, marital status, and “nicknames” or name variations commonly used
� � �
205
by their father. In a significant proportion of records, this means no surname match is
possible.
• Minor spelling variations are common both with surnames and first names.
• Date of death was not routinely used for the MoH data until 2003. Prior to this,
the date was recorded only as month of report from the health centre. This was not a
reliable means of verifying potential matches.
The flexible matching criteria required all matching to be carried out manually, by
sorting the data sets by each of the matching criteria successively to identify
potential matches for review. Matches were identified, moved to the final data set
and re-sorted by place of death (by island), then by first name, then by surname.
This process was repeated for each matching criteria (i.e. sorting remaining records
first by place, date of death, name etc. in varying combinations). Each potential
match was then reviewed to ensure the matching criteria were met, and only those
that met these criteria were counted as the same deceased individual. Where
variables about an individual differed, the variable value as recorded in the civil
registration data set was considered to be the true value.
The analysis of data followed Hook and Regal’s (230, 336) method for two sources
as outlined in the two previous case studies. As the two systems operate quite
separately to each other and do not require documentation from the other to register
a death, it can be reasonably assumed the systems are largely independent. This
assumption was tested using the “r” and “B(r)” measures of dependence as outlined
by Hook and Regal (230, 336). Confidence intervals (95%) were also calculated
based on variance using the formula outlined by Sekar Deming (231).
� � �
206
It was noted in discussion with both MoH and civil registration staff that neonatal
deaths are frequently recorded as stillbirths in both reporting systems. Therefore, two
separate tabulations were generated and analysed, one including stillbirths and one
excluding them.
Population by age group, and sex was derived from the 2005 census (as the mid-
point of the study period). Population data by time period for 2000-2004 and 2005-
2009 were derived from the 2001(50) and 2005 censuses (50) using exponential
interpolation/projection to provide estimates for other years.
As the capture-recapture results for deaths in children (0-4 years) appear implausibly
high, final estimates of total deaths by age group and gender were derived using
reconciled data (including stillbirths) for children (0-4 years), and adjusted figures
based on the capture-recapture analysis by age group for all remaining ages.
Summary measures of mortality were then re-calculated based on this adjusted data
for 2000-2009 combined and by 5 year period. As the number of live births was not
known (and could not be verified), IMR was estimated from U5M calculated from the
life tables in each method, using a regression equation based on data from 1990 and
2006 for all countries that equates IMR (x) with U5M (y) mortality derived by Prof
Taylor as follows:
� � �
207
Results
There were 8,728 unique deaths reported (through all sources) in 2000-2009 in
Kiribati, an average of 873 deaths per year. If stillbirths are included in the data set,
this figure was 8,840 deaths in total or 894 deaths per year (Table 19). Including
stillbirths, 59% of these deaths were recorded through civil registration and 63%
through MoH. There were no significant changes in the number of reported deaths
by year.
Table 19: Reported deaths by source, Kiribati: 2000-2009
Age group
Reported deaths by source Civil Registration
Ministry of Health
Reconciled TOTAL
0-4 437 1254 1530 5-14 166 198 294 15-24 327 282 509 25-34 419 355 633 35-44 704 524 953 45-54 851 681 1183 55-64 685 618 1029 65-74 687 650 1073 75-84 473 359 687 85+ 130 81 183 Unknown 313 606 766 Total 5192 5608 8840
NB/ Table includes deaths reported as stillbirths, as noted previously.
There were 22 deaths in overseas residents recorded in the civil registration data
which were excluded from the study, as they were not from the resident population of
Kiribati. The age range of these cases was 22-74 years, with cases residents of Fiji
(8), Nauru (4), USA (2), Germany (2), or others (6). More than 80% of these deaths
� � �
208
were due to drowning, boating or diving accidents. A further 24 deaths (20 from civil
registration and four from MoH data) were excluded as there were no names
recorded.
A large number of duplicate records were removed from the final data set. While
some of these resulted from the multiple systems in use at MoH, many were the
result of duplicate reporting through the local island registrars. Up to 14 entries were
found per death for deaths occurring in Nanouti, with up to 19 entries for a single
death that occurred in Tarawa.
As there was little evidence of secular trends over the study period (Table 20),
summary measures are presented for 2001-2009 inclusive. U5M (both sexes
combined) was estimated at 67.3 deaths per 1,000 live births, excluding stillbirths;
and 73.6 deaths per 1,000 live births, if stillbirths are included. This equates to an
IMR of 48.9 and 52.9 deaths per 1,000 live births respectively using a regression
analysis (developed by Prof R Taylor) of U5M against IMR based on data from the
WHO databank (presented in case study three).
Adult mortality (45q15) was estimated at 46.6-47.1% for males and 27.8-27.9% for
females, depending on whether stillbirths were included in the data set (thereby
adjusting the distribution of deaths of unknown ages). LE for 2001-2009 (total) is
estimated at 54.6-55.3 years for males and 63.4-64 years for females.
Summary measures of mortality from the analyses for 2001-2009 are presented in
the following table by period. Confidence intervals for estimates from the reconciled
� � �
209
data were derived from the life tables (using the Chiang method) with confidence
intervals for the capture-recapture analysis based on estimated reporting
completeness of the source data. Uncertainty for best estimates was derived by
applying the greater of these two values.
��
�
210
Tabl
e 20
: Sum
mar
y m
easu
res
of m
orta
lity
for K
iriba
ti by
dat
a so
urce
and
per
iod,
200
0-20
09
Dat
a so
urce
IM
R
(dea
ths
per
1000
live
bi
rths)
U5M
(pro
babi
lity
of d
ying
(p
er 1
000
pop)
from
age
s 0-
4 in
clus
ive)
(9
5% C
I)
Adu
lt m
orta
lity
(pro
babi
lity
(%) o
f dy
ing
from
age
s 15
-59
incl
usiv
e)
Life
Exp
ecta
ncy
at b
irth
(yea
rs)
(9
5% C
I)
(Bot
h se
xes)
(B
oth
sexe
s)
Mal
es
Fem
ales
M
ales
Fe
mal
es
2001
-20
04
2005
-20
09
2001
-200
4 20
05-2
009
2001
-20
04
2005
-20
09
2001
-20
04
2005
-20
09
2001
-20
04
2005
-20
09
2001
-20
04
2005
-20
09
Cen
sus
(200
5)^
52
69
58.9
63.1
Rec
onci
led
data
In
cl.
Stil
lbirt
hs
52.0
51
.6
72.1
(6
7.5-
76.
7)
71.5
(6
6.8-
76.4
)
48.1
46
.8
29.1
27
.1
54.2
(5
3.8-
54
.6)
55.1
(5
4.6-
55.4
)
63.1
(6
2.6-
63.6
)
64.2
(6
3.7-
64
.6)
Exc
l. st
illbi
rths
49.0
41
.8
67.5
(6
3.0-
71.
9)
56.4
(5
2.3-
60.9
)
48.2
40
.5
29.1
22
.6
54.4
(5
4.0-
54.8
)
58.9
(5
8.4-
59
.3)
63.4
(6
3.0-
63.9
)
68.1
(6
7.7-
68
.6)
Cap
ture
-re
capt
ure
anal
ysis
Incl
. S
tillb
irths
10
6.8
92.7
16
4.8
(1
58.3
- 17
1.0)
139.
6
(133
.6-
146.
2)
61.2
60
.0
44.3
37
.4
43.7
(4
3.2-
44.1
)
45.0
(4
4.5-
45
.4)
49.8
(4
9.2-
53.0
)
54.0
(5
4.4
– 55
.5)
Exc
l. S
tillb
irths
95
.7
71.3
14
4.9
(1
38.7
-15
0.9)
103.
2
(97.
8-
109.
0)
61.3
52
.9
44.3
31
.8
44.4
(4
3.9-
44
.8)
49.7
(4
9.2-
52
.0)
51.2
(5
0.7-
51
.8)
59.2
(5
8.7-
59
.7)
Bes
t Est
imat
e*
52.0
51
.6
72.1
(6
7.5-
76.
7)
71.5
(6
6.8-
76.4
)
61.2
60
.0
44.3
37
.4
47.9
(4
7.5-
48
.2)
48.6
(4
7.2-
48
.3)
55.7
(5
5.3-
56
.1)
57.9
(5
5.0-
56
.3)
^ C
ensu
s an
alys
is b
ased
on
mod
el li
fe ta
ble
from
sin
ge in
put p
aram
eter
– c
hild
hood
mor
talit
y de
rived
from
CE
BC
S m
etho
d. N
o co
nfid
ence
inte
rval
s av
aila
ble
* B
ased
on
capt
ure-
reca
ptur
e an
alys
is fo
r old
er a
ge g
roup
s an
d re
conc
iled
data
for c
hild
ren
(incl
udin
g st
illbirt
hs)
+ C
onfid
ence
inte
rval
s ba
sed
on re
porti
ng c
ompl
eten
ess
for c
aptu
re-re
capt
ure
anal
ysis
and
Chi
ang
life
tabl
e m
etho
ds fo
r re
conc
iled
data
N
ote:
Adu
lt m
orta
lity
varie
s as
add
ing
stillb
irths
affe
cts
all a
ges
thro
ugh
age
redi
strib
utio
n.
� � �
211
Although there were substantial differences in assigned age between the civil
registration and MoH data, the largest differences were in adjacent age groups and
balanced each other out, therefore having negligible effect on summary measures of
mortality (Figure 19).
Figure 19: Differences in total number of reported deaths by age group between Civil registry data and MoH data for Kiribati, 2000-2009
Capture-recapture analysis
The capture-recapture analysis suggests overall the total reported deaths (reconciled
data) is 59% (95% CI: 58-60%) complete. This is plausible in light of what is known
regarding the CRVS systems in Kiribati, particularly as there is little social incentive
for registration of deaths and burial occurs at home. Assessed as a combined total
(not disaggregated by age group), civil registration data were estimated to be 35%
(95% CI: 34-36%) complete and MoH data 38% (95% CI: 37-39%) complete. There
has been no sustained improvement in completeness over the study period for either
�30
�20
�10
0
10
20
30
F
M
� � �
212
reporting system. Estimates from the capture-recapture analysis place completeness
for the civil registration data between 32 and 38%, and the MoH data between 30
and 45%.
Based on the capture-recapture analysis, reporting patterns varied significantly by
age; with significant under-reporting in younger ages, particularly in the CRVS
system, and lower reporting rates for females than males in the older age groups
(Figure 20).
Figure 20: Estimated completeness of reported deaths from 2-source capture-recapture analysis by source, sex and age group for Kiribati, 2000-2009
Sources were found to be largely independent (with “B(r)”, a measure of dependence
where 1 = independent (230)) estimated to equal 0.99). Based on the system design,
particularly the Civil Registrar’s requirement that evidence such as a medical
certificate be presented to complete the registration, any dependence that did affect
the calculations would be anticipated to be positive, that is deaths reported to one
0.00
0.10
0.20
0.30
0.40
0.50
0.60
<�5 5�14 15�24 25�34 35�44 45�54 55�64 65�74 75�84 85+
Reporting�Completeness
Age�Group
Civil�Registry�� MalesCivil�Registry�� FemalesHealth�� MalesHealth�� Females
� � �
213
source would also be more likely to occur in the other. This is supported by the “r”
value as used by Hook and Regal (336), which is estimated at 0.21 in this analysis
(as r > 0 indicates positive dependence), and would result in an overestimation of
reporting completeness. On the other hand, poor data quality may have resulted in
some cases which should have been matched between both sources being missed,
which would lead to an underestimation of reporting completeness.
Based on the capture-recapture analysis, U5M (both sexes combined) for 2001-2009
was estimated at 135.6 deaths per 1,000 live births, excluding stillbirths, and 156.2
deaths per 1,000 live births, if stillbirths are included in the data set. Using regression
methods, this equates to an IMR of 90.4 and 102.1 deaths per 1,000 live births
respectively. Adult mortality was estimated at 60.0% for males and 40.3% for
females depending on whether stillbirths were included in the data set (thereby
adjusting the distribution of deaths of unknown ages). LE is estimated at 44.1- 45.2
years for males and 51.5 - 53.2 years for females.
Best estimates of summary measures of mortality
As noted in the methods, the capture-recapture analysis produced improbably high
estimates for total deaths in the 0-4 year age group, but appeared plausible for other
age groups (Figure 21). This is plausible as there is likely to be greater dependency
between the MoH and Civil Registration Offices given local emphasis on child health
programs, which would encourage reporting of known childhood deaths. Names and
other identifiable data are also poorly recorded in this age group (with names
changed as parents settle on a name for their child), making it more likely that under-
matching would occur. This is apparent from the number of estimated deaths by age
� � �
214
group as shown in Figure 21. This issue with data matching and dependence is
largely confined to the under 5 age group, and as such, the capture-recapture
estimates derived by age and sex were used to adjust all other age groups. Thus
final estimates of mortality were derived from reconciled data (including stillbirths) for
children (0-4 years) and adjusted figures based on the capture-recapture analysis by
sex and age group for all remaining ages. This results in an estimate of 1,307 deaths
per year, or a crude death rate of 14 deaths per 1,000 population.
Figure 21: Estimated average deaths per year (2000-2009) for Kiribati from census data and routinely collected data
The best available estimate of IMR (2001-2009) is from the reconciled data as
described earlier, and is 52.9 deaths per 1,000 live births, with a plausible range
from 48.9 - 113.7 deaths per 1,000 live births based on the analysis of
� � �
215
completeness. U5M is estimated at 73.6 deaths per 1,000 live births, with a plausible
range from 67.3 - 117.3 deaths per 1,000 live births.
Adult mortality for 2001-2009 is significantly higher in males than females, and LE is
significantly lower. Adult mortality is estimated at 60.0% for males (plausible range
46.6 = 63.3%) and 40.3 for females (plausible range 27.8 - 44.0%). LE is estimated
as 48.5 years for males (plausible range 44.1 - 55.3 years), and 56.9 years for
females (plausible range 48.6 - 64.0 years).
There was no significant change in any of the summary measures of mortality
between 2000-2004 and 2005-2009. The only indication of possible improvement
over this period was a slight reduction in estimated adult mortality for females and a
subsequent improvement in best estimates of LE from 55.7 years to 57.9 years
(Table 20).
��
�
216
Tabl
e 21
: Est
imat
ed a
vera
ge d
eath
s pe
r yea
r by
data
sou
rce
and
met
hod
of e
stim
atio
n: K
iriba
ti, 2
000-
2009
by
age
grou
p an
d se
x
M
ale
Fem
ale
Tota
l
Age
gr
oup
Cen
sus
Rec
onci
led
data
(no
adju
stm
ent)
Cap
ture
re
capt
ure
estim
ate
Cen
sus
Rec
onci
led
data
(no
adju
stm
ent)
Cap
ture
re
capt
ure
estim
ate
Cen
sus
Rec
onci
led
data
(no
adju
stm
ent)
Cap
ture
re
capt
ure
estim
ate
< 5
8888
19
580
8118
816
816
938
25-
14
1619
31
1114
2126
3352
15-2
4 23
41
8119
1624
4256
103
25-3
4 22
46
7722
2440
4270
118
35-4
4 38
69
9432
3756
7010
615
045
-54
5284
12
040
4765
9313
118
555
-64
6569
93
4945
8811
411
417
365
-74
7264
96
7055
9514
211
919
075
-84
3834
57
5142
7488
7613
185
+ 6
7 11
1413
3620
2042
Incl
udes
stil
l birt
h an
d re
-dis
tribu
ted
for u
nkno
wn
� � �
217
Discussion
The reconciled data demonstrate LE for males over 2001-2009 was at most 54
years, several years lower than the 2005 census estimate of 59 years. For females,
LE from the reconciled data was estimated to be 63 years, matching findings from
the 2005 census. However, the findings presented here, along with knowledge of the
CRVS systems in Kiribati, suggest it is unlikely that even the reconciled data are fully
enumerated. As described in the results, best estimates place LE for 2001-2009 at
48.5 years for males and 56.9 years for females, however this could be as low as 44
and 49 years respectively. IMR from the reconciled data was estimated at 52.9
deaths per 1,000 live births, almost identical to the 2005 census findings, although
capture-recapture analysis suggests this figure could be higher.
There were no apparent secular trends in any of the summary measures from 2000-
2004 to 2005-2009, in either reconciled data or adjusted data based on the capture-
recapture analysis, with the possible exception of a slight improvement in female
adult mortality. This indicates that while Kiribati is not undergoing any significant
decline in health status, there were also no discernible improvements for most
sectors of the population, despite significant attention from international donors to
improving public health programs over the last several years. There has been little
change in IMR or U5M and Kiribati is highly unlikely to meet its MDG targets (49) in
this area.
The total estimated deaths by age group and sex per year from the census analysis
(using model life tables derived from U5M from CEBCS methods) (50) was very
� � �
218
similar to the total deaths reported (reconciled data). This would indicate either
nearly all deaths in Kiribati are reported to at least one of the routine reporting
systems, or the census has substantially underestimated mortality, as would be
expected from the use of a single input parameter model. The capture-recapture
analysis indicates that overall, the total reported deaths (reconciled data) is 59%
(95% CI: 58-60%) complete. Reporting completeness varied much more significantly
by age group, with the Civil Registration Office recording a much lower proportion of
childhood deaths than MoH. In older age groups, reporting completeness was lower
for females than for males, particularly in the MoH data.
It is possible that unreported deaths may have been overestimated through the
capture-recapture analysis due to poor data quality, resulting in deaths reported in
both the Civil Registration and MoH data not being recognised as a match. The
variation in names used to identify an individual in Kiribati increased the potential for
deaths remaining unmatched if other variables were recorded poorly or absent.
Despite these issues, it is unlikely that missed matches account for the bulk of the
difference between the census estimates and reconciled data, and the estimates
from the capture-recapture analysis due to the matching criteria employed. Matching
criteria were developed and trialled on a sample data set taken from the records,
with multiple variations reviewed by staff from both Civil Registration and MoH to
ensure the least number of missed matches. The final criteria selected are very
broad and allow names to be excluded from the requirements for a match to account
for the problems outlined earlier. The matched data set (including unmatched
deaths) was reviewed independently by both MoH and Civil Registration staff to
minimise possible errors. Missed matches are more likely to have occurred amongst
� � �
219
young children, as this is the age group in which baptism and adoptions are usually
carried out, and individuals are less likely to have settled on their own name. While
the inclusion of stillbirths in the data set may also have led to some overestimation,
the decision to include these in the data set was based upon discussion with a range
of Civil Registration and MoH staff. These discussions produced a general
consensus that early neonatal deaths (up to about 3 days old) are routinely recorded
as stillbirths in both the Civil Registration and MoH (community nursing) data, as this
is more socially acceptable for the family.
Capture-recapture analysis requires: the record sources capture deaths from the
same population, the population is closed, and every death has the same probability
of being reported (229, 230). There is very little external migration to or from Kiribati,
indicating that while individual islands should be treated as open populations, the
population is largely closed at a national level. While capture-recapture analysis is
able to account for dependence between the data sources if three or more data
sources are available, a two source analysis assumes complete independence. The
system assessment in Kiribati identified positive dependence between the Civil
Registration and MoH data, which could result in overestimation of reporting
completeness (and subsequently underestimation of mortality), although the
magnitude of this effect was very small with sources found to be close to
independent. Further, the findings of the capture-recapture analysis are consistent
with both an understanding of how the system operates (164) as well as previous
reports (including the 2005 census (4) which have suggested routinely collected data
were too incomplete for use). Differences to the census estimates may have resulted
from the use of models with a single input parameter (from U5M) to derive them (50).
� � �
220
Assessments from other PICTs, including Fiji (93) and Tonga (111), have
demonstrated these models overestimate LE where adult mortality is high, as
reported here in both the reconciled and adjusted data. For females, where
estimates of adult mortality were lower, the single parameter model may be more
suitable and account for the similarity of findings.
The findings here show LE in Kiribati is lower than previously reported (108), and
may be significantly lower than estimates from the reconciled data. Estimates of LE
in Kiribati from the reconciled data are higher than recent estimates from
neighbouring Nauru (LE 2002-2007 estimated at 52.8 years for males and 57.5
years for females). Nauru also has high adult mortality, but does not have the
overcrowding, malnutrition and sanitation problems noted in Kiribati and would
reasonably be expected to have a higher LE than Kiribati, despite a long history of
NCDs.
The findings presented here demonstrate two critical issues. Firstly, this analysis
demonstrates the importance of using empirical data, despite its flaws, to examine
mortality and health issues at a country level. These findings show the health
situation in Kiribati is worse than recognised through census analysis, highlighting
the problems associated with relying on census estimates that impute adult mortality
from childhood mortality. There is clearly a need to improve routine data collections
through standardising the data collected in key fields, reconciling data between
sources (particularly within MoH), managing duplicates in reported data and
educating data collectors in standard definitions (such as for stillbirths). However, the
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need to improve data collection and reporting should not be seen as a reason not to
examine and report on trends using available collected data.
Secondly, regardless of the reconciled data, adjusted data from the capture-
recapture analysis or best estimates from a combination of both, show the health
situation in Kiribati needs urgent attention. IMR and U5M are higher than most other
PICTs (108), (Table 5) with the exception of PNG, with little progress made towards
improving these figures under the MDGs. There is also significant premature male
mortality, which has led to some of the lowest estimates of LE for the region (Table
6). A preliminary review of data from the Civil Registration Office suggests this is at
least in part driven by a very high incidence of suicide, and further investigation into
the causes of death contributing to these low LE’s is urgently needed.
5.6 Mortality Estimation in the absence of routine death
reporting
The system assessment in Chapter three demonstrated that although routine
reporting systems for deaths exist in Vanuatu, these had limited coverage (therefore
may not be representative at a national level) and were largely incomplete, rendering
the data gathered through these systems insufficient to generate empirical estimates
of mortality. National estimates are reliant on estimates from census and survey
data. If all criteria used earlier in this chapter to assess published estimates were
applied, only U5M and IMR from the census and a few surveys remain. Given the
absence of other reliable data, it is necessary to rely on census estimates for LE,
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adult mortality and age-specific mortality rates, which were calculated based on
single input parameter (5q0) model life tables.
Published estimates from census and survey sources should still be considered in
light of their plausibility. As demonstrated in the material included in this chapter for
both Fiji and Kiribati, single input parameter model life tables based on U5M alone
are likely to underestimate adult mortality, and therefore overestimate LE. To
demonstrate this more clearly, empirical estimates of mortality for Fiji were input into
both one and two parameter models in Modmatch (235), the results of this are
shown in the figures below.
Figure 22: Empirical age-specific mortality compared with predictions from one and two parameter models for Fiji, Males: 2002-2004
Model: WHO Modmatch (235)
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Figure 23: Empirical age-specific mortality compared with predictions from one and two parameter models for Fiji, Females: 2002-2004
Model: WHO Modmatch (235)
Both Figures 22 and 23 show substantially lower death rates across all age groups
from models calculated using a single input parameter of 5q0 when compared with
the empirical rates or estimates derived with two input parameters models (5q0 and
45q15). Similar results occur for Tonga if U5M (24.5 deaths per 1,000 live births for
2005-2008) is inputted into a single parameter model life table system. Modmatch
estimates LE at 69 and 73.4 years for males and females respectively, several years
higher than what has been demonstrated to be plausible in this research (case
study five).
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While it could be argued that this effect may be more pronounced in Fiji, given Fiji
has moved through the epidemiological transition with NCDs now the leading causes
of death , results in Kiribati, which is earlier in the transition (and therefore not
dissimilar to Vanuatu), also demonstrated that census LE estimates based on single
parameter inputs were potentially significantly overestimated. Census estimates LE
and adult mortality for Vanuatu, should be considered more as a lower limit of
mortality across age groups 5 years and above.
U5M from the Vanuatu census (2009) was 24 per 1,000 live births. Adult mortality
(45q15) was estimated based on model life tables as 172 deaths per 1,000
population for males and 121 deaths per 1,000 population for females, with LE for
males and females estimated as 69.9 and 73.5 years respectively. These would
represent substantially lower mortality than all other study countries in this work,
which is unlikely given the level of infrastructure in Vanuatu; and as such should be
treated as a lower limit rather than a true reflection of mortality levels.
Adult mortality estimates for Vanuatu derived by Rajaratnam et. al. (41) using
multivariate models including economic and a vast range of international mortality
data, are likely to be more representative, with estimates of adult mortality in 2010 of
around 275 deaths per 1,000 population for males and 170 deaths per 1,000
population for females. These models however, show a continued improvement in
mortality rates over time, which is inconsistent with the patterns seen in neighbouring
countries (as presented in this research) which show a clear stagnation or worsening
in adult mortality, and should be further investigated in Kiribati. Computing life tables
from two parameter inputs including this adult mortality (using Modmatch (235))
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gives a LE of approximately 65 years for males, and 70 for females; figures which
are more plausible given findings presented through the rest of the chapter, but
which would still put Vanuatu amongst the highest life expectancies amongst the
study countries. While Vanuatu is yet to move far into the epidemiological transition,
as some other PICTs, such as Nauru and Tonga have; the most recent Vanuatu
STEPS survey (337) certainly indicates NCDs are likely to be an issue due to the
high prevalence of diabetes and risk factors such as hypertension. Further data
collection on adult mortality in Vanuatu is therefore critical to determine whether
Vanuatu has in fact managed to so far avoid the high premature adult mortality from
NCDs seen in the rest of the PICTs, and if so, to examine how best to learn from the
experiences of the rest of the PICTs with NCDs to avoid following the same path.
5.7 Summary of method selection and application
Although not all PICTs have complete reporting of deaths, it is possible to generate
reliable measures of mortality level from routinely reported data in many of them by
evaluating the data and correcting for undercount using the methods described in
this chapter. Even where routinely collected data is insufficient to generate measures
directly, they may be useful in establishing plausible ranges of mortality in order to
help interpret data from other sources, such as censuses and surveys. In spite of
these findings, countries such as Vanuatu, with very under-developed reporting
systems, do not collect sufficient data to accurately estimate mortality levels either
directly or with correction. In this setting, improving CRVS systems must be a key
focus for improving the availability and reliability of mortality data for planning and
evaluation.
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As noted at the beginning of this chapter, PICTs fall into three broad categories,
those which have complete or nearly complete reporting, those where CRVS
systems are mostly functional but the level of reporting (or patterns of under-
reporting) is largely unknown, and those which do not collect sufficient information
for analysis. Methods for deriving mortality measures for each of these categories,
as documented in Figure 11, were applied to each of the study countries to
demonstrate the potential for deriving a greater understanding of current mortality
levels and trends from routinely collected data than has previously been available.
In countries where collected data require correction, initial corrections could be made
by reconciling data from different sources of death recording. Where this was
insufficient, either capture-recapture methods, or evaluation and correction using the
Brass Growth Balance Method could be used to correct the data or provide plausible
ranges. Both methods must be applied in the presence of strong system
understanding in order to accurately interpret results, fit the age groups in the Brass
Growth Balance Method, or to select an appropriate model using capture-recapture
methods.
A summary of the data sources and methods used to generate measures of mortality
for each of the study countries is shown in the table below.
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Table 22: Summary of data sources and methods used in assessment of mortality level for the Pacific Islands
Country Data Source(s) Used in Final Calculations
AssessmentsApplied*
Adjustmentfor under-enumerationmade to reporteddeaths
Years of dataavailable
Fiji MoH Assessment of Published Data Brass Analysis
No 2002-2004
Kiribati MoH and Civil registry
Reconciliation of sources 2 source capture-recapture analysis
Yes 2000-2009
Nauru MoH/Civil registry (shared data)
Mortality level calculated directly from empirical data
No 2002-2007
Palau MoH (Epidemiology spreadsheet)
Mortality level calculated directly from empirical data
No 2004-2009 (excl 2007)
Tonga MoH (HIS and Community nursing data) and Civil registry
Reconciliation of sources 3 source capture-recapture analysis
Yes 2001-2009
Vanuatu Census and survey estimates
Plausibility Assessment of Published Data
Secondary data only available
2007-2011
* A system assessment and trend analysis was conducted for all countries in
addition to the assessments shown in the table.
Age groups for each country were selected on the basis of population size and
disaggregated population data available. Where there were sufficient deaths to do
so, infant mortality was examined separately. Ten year age groupings were used in
Nauru and Palau, where total population size is very small. Statistical confidence
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intervals for life tables for all countries were obtained using the Chiang method (296).
A summary of findings across all countries is given in Chapter seven.
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6 Assessing cause of death distributions
While mortality level at various ages is important for measuring the health of a
population and identifying key disparities between populations, it is robust data on
CoD distributions that allows targeted interventions to be shaped to affect these
mortality outcomes. In this chapter, the data available from each of the selected
study countries is explored in more detail in relation to the availability and quality of
the CoD data, in order to generate CoD distributions by age where possible.
The system assessment in Chapter three identified that there is significantly more
data on CoD available from the study countries than is routinely collated or reported,
although the quality of these data are likely to vary significantly (Table 14). Medical
certificates, considered the best source of CoD data when completed correctly, are
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required for at least some deaths in all of the study countries. In Nauru, Palau,
Tonga and Fiji, medical certificates are required either by policy or law for all deaths,
although not all of these data are routinely coded or collated; while in Vanuatu and
Kiribati, certificates are completed only for those deaths that occur in hospital.
Additional reporting systems, such as the community health reporting in Vanuatu,
Tonga and Palau, were also identified.
The extent and quality of CoD information available for each country is a function of
which deaths are medically certified, and the procedures and practices at three key
stages of the collection and collation process: (1) designation of a diagnosis for
CoD, and completion of the medical certificate by the medical practitioner: (2) coding
of these data and selection of underlying CoD according to the ICD rules (68); and
(3) tabulation and presentation of the data on deaths by sex, age and cause group.
As with data on mortality level, all data on causes of death should be reviewed for
plausibility. Both the framework for evaluating CoD statistics developed by Rao et. al.
(63, 64) and the more recent framework published by the UQ HIS Hub -
(subsequently developed into the ANACOD tool published by WHO) (320), both
provide useful guidance on determining whether national CoD data are plausible.
Both include aspects such as coverage, completeness and timeliness, which are
equally applicable for all-cause and cause-specific mortality data. They also include
aspects such as the use of ill-defined causes, age and sex dependency, consistency
over time and consistency with general mortality levels.
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Ill-defined categories includes those cases which have no known cause, those
attributed to signs and symptoms (such as fever or cough) rather than specific
conditions or diseases, conditions that do not explain the death (such as old age and
senility) and those for which only a “mode” of death (such as stopped breathing) is
recorded. The GBD (103), and the UQ HIS Hub framework advocate redistributing
into specific conditions the so-called “garbage” codes, that is, deaths attributed to
causes which tend to be used as “catch all” categories when more specific
information is not available - such as “cardiac failure” or “heart attack”. This
redistribution requires the data set to be adjusted has been coded accurately, and
assumes a redistribution pattern based on broad global experience (103). What is
considered to be ill-defined may vary based on the detail one is trying to obtain from
the data. As Naghavi et. al. point out, many ICD chapters contain a ‘generic’
category, such as neoplasms of unspecified site (265). While correction for these
causes is not required to look at trends across broad causes, these categories do
not allow detailed analysis of specific diseases within the chapter without further
redistribution.
Cause dependency by sex refers to ensuring that causes of death which are sex-
specific have not been wrongly attributed (such as prostate cancer recorded for a
female, or maternal causes recorded for a male). Age dependency refers to
plausibility of the age distributions by cause. For example, as Rao et. al.
demonstrates for China (63), deaths from maternal haemorrhage occur between the
ages of 15 and 40years, peaking between 20-25 years, with no deaths recorded
outside these age groups. The data are therefore consistent with the ages at which
women are in their childbearing years and potentially at risk of maternal
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haemorrhage. Similarly, deaths from lower respiratory illnesses are highest in infants
and children, with very few recorded deaths from this cause in the following age
groups until late adulthood, when numbers again begin to rise. This is consistent with
known distributions of infectious disease mortality and demonstrates internal
consistency within the data. The UQ HIS Hub framework (213) demonstrates this
using the GBD categories (rather than specific disease categories), with Group 1
diseases (primarily infectious and respiratory diseases) following a similar pattern in
Venezuela to the age distribution of respiratory deaths demonstrated by Rao et.al for
China (63).
Both the Rao et. al. (63) and UQ HIS Hub (320) frameworks provide a useful
approach to assessment of the plausibility of CoD data collections, once analysed
and reported. In addition to the above aspects, Rao et.al.’s framework (63) also
describes the concept of content validity, which is a subjective assessment of the
process used to obtain and collate the data, and the impact this may have on data
quality. For example, as the China Disease Surveillance Point System examined by
Rao (63) uses interviews with family members as the primary source of data on CoD,
Rao et. al. (63) noted the collection may have limited content validity. For collections
where the system assessment (as described in Chapter three) has identified
potential content validity concerns due to procedures around the collection and
tabulation of data, further investigation of the data quality through a medical record
or certificate review as described in Chapter two is warranted.
Figure 24 shows the processes involved in collecting CoD data for health monitoring
and evaluation; and the assessments which can be applied to determine the validity
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of the data generated through these processes. The ability to conduct these
assessments is largely governed by access to the original data.
Figure 24: Collection and suggested review process for cause of death data
As CoD data from all study countries was obtained as either unit record uncoded raw
text (which was subsequently coded according to ICD rules), or as ICD coded data
without the original text, an audit of coding practices (as described in Chapter two)
for the study countries could not be undertaken. There are several good examples of
coding audits, such as undertaken in Australia and England, recorded in the
literature which demonstrate how this could be undertaken (338-340).
Of the four countries where medical certificates of death are required for all deaths,
the system assessment identified CoD data from Palau as likely to have a high
content validity, indicating estimates could be generated directly from the collected
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data and assessed for plausibility. For Tonga, Fiji and Nauru, the system
assessments raised queries regarding the content validity that warrant further review
and potential correction of the data. A medical record review was conducted in
Tonga (case study six) and Nauru. A medical certificate review and multiple CoD
analysis was conducted in Fiji where the same access to medical records was not
available.
For Vanuatu and Kiribati, where medical certificates are only completed for deaths in
hospital, alternative data sources are explored. Community nursing reports are
contrasted against hospital and medical certificate data from Vanuatu. In Kiribati,
selected causes of death of particular public health importance are reviewed through
community reporting to the civil registry office. While these alternative data sources
are not an adequate replacement for reliable CoD data on community deaths
through medical certification or verbal autopsy, these reviews do provide an
indication of the impact of key causes of death, such as external causes (injuries and
suicide), malnutrition and maternal causes, which has not been adequately captured
in country reporting to date.
6.1 Direct tabulation of cause of death from collected data
6.1.1 Analysis of cause of death in Palau
Aim and Objectives
The system assessment in Chapter three identified a robust quality control process
to ensure medical certificates in Palau are completed according to the best available
information, and in accordance with the ICD standards. Each certificate is formally
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reviewed and countersigned by the medical director, who checks both the plausibility
of the assigned cause and the sequence on the certificate. Certificates which do not
meet these standards are referred back to the treating doctor to review. All deaths
are also reviewed periodically through a mortality review meeting of hospital doctors.
As a result, Palau has a very low percentage of deaths reported as “Ill-defined” or
“garbage” codes and is expected to have a high content validity, allowing direct
analysis of the data without correction through a medical record or medical certificate
review process.
Palau does not routinely code or report their CoD data, other than as leading causes
of death for all ages (in the regional CHIPs reports). Causal distributions of deaths by
age and sex for 1999-2003 and 2004-2009 were therefore generated by direct
analysis of the tabulated data from the medical certificates.
Methods
Data from medical certificates was obtained from the MoH for 2004-2009 as an
Excel file, capturing each field on the medical certificate as uncoded text exactly as
written on the original certificate. Underlying CoD was then identified [by KC]
according to ICDv10 rules. Due to the small number of deaths, underlying cause was
tabulated directly to the ICDv10 General Mortality List One (103 causes) (68) for
analysis by broad age group and sex. Deaths were initially tabulated in ten year age
groups so the broad groupings used were 0-4 years, 5-14 years, 15-64 years and
�65 years. Confidence intervals for proportional mortality were calculated using the
Poisson distribution due to the small number of events. Confidence intervals for
cause-specific rates were subsequently derived from the uncertainty of the
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proportional mortality, without adjustment for uncertainty in the overall mortality
envelope.
As noted in the previous chapter, an unusually low number of deaths were reported
in 2007, so this year was excluded from the analysis as being unrepresentative.
Results
For children aged 0-4 years, congenital issues (1-093) and perinatal conditions (1-
092) were approximately 70% of all deaths, although there was some shift between
these categories across the two periods (Figure 25). The cause-specific death rate
for perinatal causes was 133 per 100,000 population (95% CI: 0-376.6 deaths per
100,000 population) for 2004-2009. Only one death was attributed to unknown
causes. The remainder were spread over a number of cause categories. The overall
number of deaths, even when aggregated over five years of data is very low, and as
such significant uncertainty remains in the cause-specific calculations as shown in
Table 23.
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Figure 25: Proportional mortality by period for children aged 0-4 years both sexes: Palau, 1999-2009 (excl 2007)
n=20 n=25
Key 1-001 Infectious diseases 1-026 Neoplasms 1-048 Diseases of the blood 1-051 Endocrine, nutritional and other metabolic disorders 1-058 Diseases of the nervous system 1-064 Diseases of the circulatory system 1-072 Diseases of the respiratory system 1-078 Diseases of the digestive tract
1-082 Diseases of the skin 1-084 Diseases of the genito-urinary system 1-092 Perinatal conditions 1-093 Congenital conditions 1-094 Signs, symptoms and other ill-defined causes 1-095 External causes (injuries)
1�0585% 1�064
5%
1�07810%
1�09251%
1�09319%
1�09510%
1999�2003
1�0484%
1�0584% 1�064
4%
1�0728%
1�0844%
1�09264%
1�0934%
1�0944%
1�0954%
2004�2009�(excl.�2007)�
��
�
238
Tabl
e 23
: Dea
ths
by c
ause
, and
pro
port
iona
l mor
talit
y fo
r chi
ldre
n 0-
4 ye
ars,
by
perio
d fo
r Pal
au; 1
999-
2009
(exc
ludi
ng
2007
)
� � �
239
There were nine deaths in 5-14 year olds certified from 1999-2003 and six from
2004-2009 (excluding 2007). The proportion of deaths attributed to external causes
fell from 44% in the earlier period to 16% (one death) in the latter. Although this trend
was not significant when confidence intervals were calculated, this is largely a
function of the small number of events, and the decrease is certainly relevant at a
policy level. Remaining deaths were spread across other cause categories with only
single deaths appearing in each, making further interpretation of cause patterns in
this age group difficult.
Examining proportional mortality for certified deaths shows very different patterns of
mortality between males and females. As shown in Figure 26, while cancers (1-026)
account for 20-30% of deaths in both men and women, this is substantially higher for
women than men, despite the decline between the two periods (Table 24 and 25).
Breast cancer has risen from 3% of all deaths in women in this age group, with a rate
of 6.8 deaths per 100,000 population (95% CI: 0.8–24.5) in 1999-2003 to 4% of
deaths in 2004-2009, with a cause-specific rate of 15.7 deaths per 100,000
population (95% CI: 5.1–36.7). The proportion of deaths due to cervical cancer has
fallen from 7% in 1999-2003 to 3% in 2004-2009 with a slight (but non-significant) fall
in the cause-specific rate from 13.6 deaths per 100,000 population (95% CI: 3.7 -
34.8) to 12.6 deaths per 100,000 population (95% CI: 3.4-32.3). The leading cancer
deaths in males were from liver and lung cancer, both of which rose slightly over the
two periods reviewed, from 5% and 4% respectively in 1999-2003, to 7% and 5%
respectively in 2004-2009, although these changes were not significant and may
reflect stochastic variation. Lung cancer rates are very low, consistent with the
historically low smoking rates in Palau. In 1998, Palau was the only PICT to report
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smoking prevalence lower than Australia, with 22% of men and 8% of women over
20 years of age self classifying as smokers.
Figure 26: Proportional mortality by cause and period for Palauan males: Adults 15-64 years for 1999-2009 (excluding 2007)
Key 1-001 Infectious diseases 1-026 Neoplasms 1-048 Diseases of the blood 1-051 Endocrine, nutritional and other metabolic disorders 1-058 Diseases of the nervous system 1-064 Diseases of the circulatory system 1-072 Diseases of the respiratory system 1-078 Diseases of the digestive tract
1-082 Diseases of the skin 1-084 Diseases of the genito-urinary system 1-092 Perinatal conditions 1-093 Congenital conditions 1-094 Signs, symptoms and other ill-defined causes 1-095 External causes (injuries)
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
1�00
11�
026
1�04
81�
051
1�05
51�
058
1�06
41�
072
1�07
81�
082
1�08
31�
084
1�09
21�
093
1�09
41�
095
M�1999�2003
M�2004�2009
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241
Figure 27: Proportional mortality by cause and period for Palauan females: Adults 15-64 years for 1999-2009 (excluding 2007)
Key 1-001 Infectious diseases 1-026 Neoplasms 1-048 Diseases of the blood 1-051 Endocrine, nutritional and other metabolic disorders 1-058 Diseases of the nervous system 1-064 Diseases of the circulatory system 1-072 Diseases of the respiratory system 1-078 Diseases of the digestive tract
1-082 Diseases of the skin 1-084 Diseases of the genito-urinary system 1-092 Perinatal conditions 1-093 Congenital conditions 1-094 Signs, symptoms and other ill-defined causes 1-095 External causes (injuries)
The cause-specific mortality rate from external causes in 2004-2009 was 37.8
deaths per 100,000 population in females (95% CI: 19.5-66.0) and 170.8 deaths per
100,000 population in males (95%CI: 132.4-217.0), indicating a statistically
significant difference between sexes, although there has been a notable decline
across both sexes between the two periods. In 2004-2009, deaths from external
causes accounted for 10% of deaths in females aged 15-64 and 27% of deaths in
males in this age group.
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
1�00
11�
026
1�04
81�
051
1�05
51�
058
1�06
41�
072
1�07
81�
082
1�08
31�
084
1�09
21�
093
1�09
41�
095
F�1999�2003
F�2004�2009
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242
This high mortality from external causes in males is clearly of public health interest
and there is a need to understand the specific causes contributing to this category of
deaths. As shown in Figure 28, 58% of deaths from external causes in males aged
15-64 from 2004-2009 are attributed to “other external causes” when classified
strictly according to the ICD rules. This reflects a tendency of certifying doctors to
record the injuries sustained rather than how the injury occurred and indicates a
need for further training, despite the extensive review processes in place. Data on
external causes is therefore currently of limited use for health planning, although a
medical record or medical certificate review may allow greater disaggregation of
deaths within this causal group. Of the deaths for which a specific cause could be
identified, there were 39 deaths in 2004-2009 in this age group attributed to suicide,
accounting for a rate of at least 22.9 deaths per 100,000 population (95% CI: 10.5 -
43.6 unadjusted for ill-defined external causes).
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Figure 28: Proportion of deaths due to external causes, by type of external cause for males aged 15-64 years: Palau, 2004-2009 (excluding 2007)
Figures 26 and 27 also show the high proportion of deaths attributed to heart
diseases (1-064) in both males and females; which has increased for both sexes
over the two periods. While the proportion of deaths attributed to diabetes (1-051)
has fallen for females and risen only slightly in males, the proportion of deaths
attributed to genitourinary diseases (1-084) has almost doubled for both sexes and
may indicate diabetes is not being recorded appropriately on the medical certificates.
Rates for causes of death by ICD chapter are shown in Tables 24 and 25, with rates
for specific causes (according to ICD General Mortality List One) attached in
Appendix four.
1�096�Transport�accidents
13% 1�098�Falls5%1�099�Drowning�
2%
1�100�Accidental�poisoning
2%1�101�
Intentional�Self�Harm13%
1�102�Assault7%
1�103�Other�external�causes
58%
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244
Table 24: Cause-specific mortality by ICD chapter and period for adult males 15-64 years: Palau, 1999-2009 (excluding 2007)
1999-2003 2004-2009 Cause of death Deaths Rate (per
100,000) Lower 95% CI
Upper 95% CI
Deaths Rate (per 100,000)
Lower 95% CI
Upper 95% CI
1-001 Infectious diseases
6 18.9 6.9 41.1 20 51.0 31.2 78.8
1-026 Neoplasms
35 110.2 76.8 153.3 49 125.4 92.4 165.2
1-048 Diseases of the blood
3 9.4 1.9 27.6 1 2.5 0.1 14.2
1-051 Endocrine, nutritional and other metabolic disorders
14 44.1 24.1 74.0 19 48.4 29.2 75.7
1-055 Mental and behavioural disorders
0 0.0 0.0 11.6 4 10.2 2.8 26.1
1-058 Diseases of the nervous system
1 3.1 0.1 17.5 3 7.6 1.6 22.4
1-064 Diseases of the circulatory system
30 94.5 63.7 134.9 55 140.2 105.7 182.5
1-072 Diseases of the respiratory system
4 12.6 3.4 32.3 9 22.9 10.5 43.6
1-078 Diseases of the digestive tract
4 12.6 3.4 32.3 9 22.9 10.5 43.6
1-082 Diseases of the skin
0 0.0 0.0 11.6 0 0.0 0.0 9.4
1-083 Diseases of the musculoskeletal system and connective tissue
4 12.6 3.4 32.3 1 2.5 0.1 14.2
1-084 Diseases of the genito-urinary system
10 31.5 15.1 57.9 12 30.6 15.8 53.5
1-092 Perinatal conditions
0 0.0 0.0 11.6 0 0.0 0.0 9.4
1-093 Congenital conditions
0 0.0 0.0 11.6 0 0.0 0.0 9.4
1-094 Signs, symptoms and other ill-defined causes
1 3.1 0.1 17.5 2 5.1 0.6 18.4
1-095 External causes (injuries)
12 37.8 19.5 66.0 67 170.8 132.4 217.0
�
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245
Table 25: Cause specific mortality by ICD chapter and period for adult females 15-64 years: Palau, 1999-2009 (excluding 2007)
Cause of death Deaths Rate (per 100,000)
Lower 95% CI
Upper 95% CI
Deaths Rate (per 100,000)
Lower 95% CI
Upper 95% CI
1-001 Infectious diseases
6 20.4 7.5 44.4 6 18.9 6.9 41.1
1-026 Neoplasms
22 74.7 46.8 113.1 35 110.2 76.8 153.3
1-048 Diseases of the blood
0 0.0 0.0 12.5 3 9.4 1.9 27.6
1-051 Endocrine, nutritional and other metabolic disorders
9 30.6 14.0 58.0 14 44.1 24.1 74.0
1-055 Mental and behavioural disorders
0 0.0 0.0 12.5 0 0.0 0.0 11.6
1-058 Diseases of the nervous system
0 0.0 0.0 12.5 1 3.1 0.1 17.5
1-064 Diseases of the circulatory system
12 41.2 21.1 71.2 30 94.5 63.7 134.9
1-072 Diseases of the respiratory system
2 6.8 0.8 24.5 4 12.6 3.4 32.3
1-078 Diseases of the digestive tract
1 3.4 0.1 18.9 4 12.6 3.4 32.3
1-082 Diseases of the skin
0 0.0 0.0 12.5 0 0.0 0.0 11.6
1-083 Diseases of the musculoskeletal system and connective tissue
2 6.8 0.8 24.5 4 12.6 3.4 32.3
1-084 Diseases of the genito-urinary system
3 10.2 2.1 29.8 10 31.5 15.1 57.9
1-092 Perinatal conditions
0 0.0 0.0 12.5 0 0.0 0.0 11.6
1-093 Congenital conditions
0 0.0 0.0 12.5 0 0.0 0.0 11.6
1-094 Signs, symptoms and other ill-defined causes
0 0.0 0.0 12.5 1 3.1 0.1 17.5
1-095 External causes (injuries)
11 37.4 18.6 66.8 12 37.8 19.5 66.0
Discussion
Despite the limited change in deaths attributed to diabetes, both proportional
mortality and age-specific mortality (adults aged 15-59 years) from NCDs including
heart disease, cancer and genitourinary diseases have increased across the two
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246
periods examined. Although these changes were not statistically significant, they
provide strong supportive evidence for calls for urgent public health action to address
premature mortality from NCDs.
6.2 Medical Record Review
While medical certification practices in Palau are monitored and have quality review
mechanisms in place, this level of scrutiny is not present (nor necessarily
practicable) in most PICTs. Indeed, as shown in the example from Palau, where
uncertainty was identified in both capture of external causes and diabetes-related
deaths, even a system with known quality control procedures may show indications
that further review of the data quality (and indeed training to improve collection in the
first place) may be warranted.
Where there is sufficient access to the original data, a medical record review allows
comparison of the underlying CoD as derived from the medical certificate, with the
available information from cases’ symptoms and history preceding death, to
determine whether the recorded sequence is plausible, and whether it is the most
reasonable diagnosis that could have been made. Medical record reviews were
conducted in both Tonga (case study six) and Nauru to assess the quality of CoD
data derived from certification, with data adjusted for systematic reporting biases
based on study findings and applied to the overall mortality envelope (all-cause
mortality rates) calculated in Chapter five to derive cause-specific mortality rates.
� � �
247
The methodology for medical record reviews has been developed over a number of
investigations and case studies in the Philippines, Thailand and Australia (269, 341-
344). Reported methods had to be substantially modified for application in the Pacific
Islands (for both the Tonga and Nauru case studies) for several reasons.
Previous medical record reviews have primarily relied on a doctor (or panel of
doctors) independent from the system being reviewed; in that they have not worked
within the system under review, treated patients or family members of patients who
may appear in the cases to review or otherwise have an interest in running the
health services that may have serviced these patients. It is however helpful if
reviewing doctors are familiar with local terms and practices that may appear in the
notes. In a larger country, it is possible to identify independent doctors who are
familiar with local practices. In the PICTs, the small population and close medical
community mean a medical practitioner available on island could not be reasonably
expected to be independent from the system being reviewed.
In order to limit biases in the study, rather than reviewing the original medical chart
directly, key details from each case were extracted by a trained nurse or medical
records officer using a standardised form and de-identified. The form was then
reviewed by a Pacific doctor in Australia who was familiar with the context in which
the cases occurred, but who had been absent from the local islands health setting for
a number of years, and was therefore unlikely to have had contact with any of the
cases under review. Although significantly adapted, the extraction form was
modelled after a similar concept employed for the verbal autopsy study in the
Philippines which used a standard form to extract information from the medical
� � �
248
record to compare with the verbal autopsy information (341). Data extractors
received training in completing the form based on information provided in the
medical record, without adding their own interpretations to these data, with regular
reviews of the completed data extraction forms conducted throughout the studies. An
example of the data form and instructions for interviewers developed for the Tonga
review is provided in Appendix five.
Previous studies have also started from the assumption that the medical records are
substantially correct and well kept; they are in fact the “standard” against which the
diagnosis on the medical certificate should be judged (269, 341-344). However, it is
widely known that many of the health systems in the PICTs have poorly kept medical
records, with notes and results frequently missing or poorly recorded in the patients
chart, or charts missing altogether. The medical records in this study were seen as a
separate source of CoD data which could be compared to the death certification
data, but they could not automatically be assumed to be more accurate than the
certificate Results were therefore seen as a measure more of the uncertainty and
possible variation in the proportion of deaths attributed to each major cause, rather
than a straightforward measurement of error.
� � �
249
6.2.1 Case study 6: Medical Record Review in Tonga
Carter K, Hufanga S, Rao C, Akauola S, Lopez AD, Rampatige R, Taylor R. Causes
of death in Tonga: quality of certification and implications for statistics. Population
Health Metrics, 2012 Mar 5;10(1):4.
The system assessment in Tonga identified contributory causes of death may be
moved into the causal sequence of Part I of the medical certificate during data entry
into the HIS; and CoD tabulations, when performed, are generated from the
immediate CoD (Line one, Part I). There is also a large proportion of deaths coded
as ‘unknown’ or ‘ill-defined’ causes. This paper demonstrates the impact of poor data
management procedures on the final CoD tabulations, leading to potentially
misleading information on CoD distributions being provided to health planners.
Unit record data from the medical certificate was obtained as coded data only,
therefore neither a certificate review or coding audit could be completed. A review of
the plausibility of the causal sequence (as entered in the database) was undertaken,
and a medical record review conducted to examine the accuracy of the diagnosis
and certification.
As contributory causes of death could not be adequately separated from the CoD
sequence, cause-specific rates were calculated to provide a range of estimates
based on both the inclusion and exclusion of these causes from Part I of the medical
certificate. This sensitivity analysis primarily affected the distribution between
diabetes and cardiovascular related deaths, however both showed a significant
� � �
250
increase over 2001-2004 to 2005-2008, regardless of any effect from these
tabulation practices.
As noted in the paper, and in Figure 29, NCDs (group II diseases based on GBD
categories) are the leading CoD in adults over 35 years of age. Table 4 in the paper
shows age-standardised mortality by cause, clearly demonstrating a significant
increase in a range of NCDs between 2001-2004 and 2005-2009, independent of
any changes in age structure, therefore reflecting a genuine increase in premature
mortality from these causes.
Figure 29: Age-distribution of deaths in Tonga by global burden of disease* category; 2005-2009
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0�4 5�14 15�2425�3435�4445�5455�6465�7475�84 85+
Proportion�of�deaths�(%)
Age�Group
I
II
III
RESEARCH Open Access
Causes of death in Tonga: quality of certificationand implications for statisticsKaren Carter1*, Sione Hufanga2, Chalapati Rao1, Sione Akauola3, Alan D Lopez1, Rasika Rampatige1 andRichard Taylor1,4
Abstract
Background: Detailed cause of death data by age group and sex are critical to identify key public health issuesand target interventions appropriately. In this study the quality of local routinely collected cause of death datafrom medical certification is reviewed, and a cause of death profile for Tonga based on amended data ispresented.
Methods: Medical certificates of death for all deaths in Tonga for 2001 to 2008 and medical records for all deathsin the main island Tongatapu for 2008 were sought from the national hospital. Cause of death data for 2008 werereviewed for quality through (a) a review of current tabulation procedures and (b) a medical record review. Datafrom each medical record were extracted and provided to an independent medical doctor to assign cause ofdeath, with underlying cause from the medical record tabulated against underlying cause from the medicalcertificate. Significant associations in reporting patterns were evaluated and final cause of death for each case in2008 was assigned based on the best quality information from the medical certificate or medical record. Cause ofdeath data from 2001 to 2007 were revised based on findings from the evaluation of certification of the 2008 dataand added to the dataset. Proportional mortality was calculated and applied to age- and sex-specific mortality forall causes from 2001 to 2008. Cause of death was tabulated by age group and sex, and age-standardized (all ages)mortality rates for each sex by cause were calculated.
Results: Reported tabulations of cause of death in Tonga are of immediate cause, with ischemic heart disease anddiabetes underrepresented. In the majority of cases the reported (immediate) cause fell within the same broadcategory as the underlying cause of death from the medical certificate. Underlying cause of death from themedical certificate, attributed to neoplasms, diabetes, and cardiovascular disease were assigned to other underlyingcauses by the medical record review in 70% to 77% of deaths. Of the 28 (6.5%) deaths attributed to nonspecific orunknown causes on the medical certificate, 17 were able to be attributed elsewhere following review of themedical record. Final cause of death tabulations for 2001 to 2008 demonstrate that noncommunicable diseases areleading adult mortality, and age-standardized rates for cardiovascular diseases, neoplasms, and diabetes increasedsignificantly between 2001 to 2004 and 2005 to 2008. Cause of death data for 2001 to 2008 show increasingcause-specific mortality (deaths per 100,000) from 2001-2004 to 2005-2008 from cardiovascular (194-382 to 423-644in 2005-2008 for males and 108-227 to 194-321 for females) and other noncommunicable diseases that cannot beaccounted for by changes in the age structure of the population. Mortality from diabetes for 2005 to 2008 isestimated at 94 to 222 deaths per 100,000 population for males and 98 to 190 for females (based on the range ofplausible all-cause mortality estimates) compared with 2008 estimates from the global burden of disease study of40 (males) and 53 (females) deaths per 100,000 population.
Discussion: Certification of death was generally found to be the most reliable data on cause of death in Tongaavailable for Tonga, with 93% of the final assigned causes following review of the 2008 data matching those listed
* Correspondence: [email protected] of Population Health, University of Queensland, Herston (Brisbane),Queensland, AustraliaFull list of author information is available at the end of the article
Carter et al. Population Health Metrics 2012, 10:4http://www.pophealthmetrics.com/content/10/1/4
© 2012 Carter et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative CommonsAttribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction inany medium, provided the original work is properly cited.
on the medical certificate of death. Cause of death data available in Tonga can be improved by routinelytabulating data by underlying cause and ensuring contributory causes are not recorded in Part I of the certificateduring data entry to the database. There is significantly more data on cause of death available in Tonga than areroutinely reported or known to international agencies.
Keywords: Mortality, Cause of death, Noncommunicable Diseases, Medical record review, Death Certification,Tonga, Pacific Islands
IntroductionCause of death data by age group and sex is critical infor-mation both to identify key public health issues contribut-ing to premature mortality and to design interventionsthat are appropriately targeted. Several studies have pre-viously noted that there is an important gap in availabilityof data on mortality and cause of death patterns from thePacific Islands [1,2], and the World Health Organization(WHO) mortality database currently lists Tonga as havingno cause of death data available [3].Tonga is a Pacific Island country with an estimated
population of 103,000 (2009) [4], of which 38% are under15 years of age [4]. Seventy percent of the populationreside on the main island of Tongatapu [4]. Recent (2005to 2009) estimates of adult mortality (probability of dyingbetween ages 15 and 59 years inclusive) based on recon-ciled empirical data from the Ministry of Health (MoH)and Civil Registration office, are nearly three times thatfor Australia and New Zealand [5]. Estimated life expec-tancy (LE) from these data is between 60.4 to 64.2 yearsfor males and 65.4 to 69.0 years for females [6] whenassessed for underenumeration, several years lower thanpreviously reported [5-7]. Given the high level of prema-ture adult mortality and the impact on LE, it is criticalthat health planners have access to accurate informationon causes of death.Health services in Tonga are provided through a
national hospital, three outerisland hospitals, 11 repro-ductive health centers, and 15 community health centers[8]. Prior to 2007, medical certification of death was con-ducted by the attending doctor for deaths occurring athealth facilities or by the treating doctor for deaths incommunity if requested by the family. In January 2007,the Tongan MoH introduced a new policy on reportingdeaths as part of a comprehensive effort to improve vitalregistration [9]. This policy requires all deaths to be certi-fied by a medical practitioner. Tonga uses a medical cer-tificate of death (hereafter referred to as the medicalcertificate) that is consistent with the international stan-dard medical certificate of death from the 10th version ofthe International Classification of Diseases (ICD-10) [10].This lists the cause of death sequence starting with theimmediate cause of death (the condition that directlyresulted in the death) and working backwards in Part I ofthe certificate and contributory causes (conditions
present at the time of death, but which did not directlycause the death) in Part II (Additional file 1).In accordance with ICD-10, underlying cause of death
(the condition that started the chain of events thatresulted in the death) is generally selected by workingbackwards from the immediate cause to the cause on thelowest line in Part I, which is able to directly cause thecondition above it. If the cause listed on the lower line isnot a direct cause of the condition listed above (forexample if cancer was listed below ischemic heart dis-ease), the lower line is not included in the causalsequence and the line above is selected as the underlyingcause (ischemic heart disease in the previous example).Modification and selection rules also apply for certainconditions [10]. In Tonga, data from the medical certifi-cates are currently coded line by line (without selectingunderlying cause) by medical records staff who havecompleted a recognized training course in ICD codingand collated in an electronic database. Tabulations aregenerated from this database as required, and until thisstudy, were based on the immediate cause of death (line1 of Part I of the certificate). The Country Health Infor-mation Profile, developed from local data and publishedby the Western Pacific Regional Office of WHO, listsonly five leading causes of death for Tonga (based on2007 data) reported for all ages and both sexes combined.These are: 1) Diseases of the circulatory system; 2) Neo-plasms; 3) Symptoms, signs, and ill-defined conditions;4) Diseases of the respiratory system; and 5) Certaininfectious and parasitic diseases [8].This paper examines the accuracy of the locally gener-
ated cause of death data for Tonga for 2008 as collectedthrough the medical certificates. Cause of death asrecorded on the certificate is compared to informationcontained in the patients’ medical record to assessreported patterns of cause of death and to identify (andcorrect) any systematic misclassifications that may affectselection of underlying cause of death or analysis oftrends. Findings from this assessment were applied tocause of death data from the medical certificates of deathfor 2001 to 2007, which were then added to the dataset.Proportional mortality by age and sex was calculated andapplied to age-specific all-cause mortality for 2001 to2008 [5] to produce age-, sex- and cause-specific mortal-ity for Tonga and assess trends over time.
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MethodsAn overview of the study approach is given in Figure 1.
Quality review of cause of death data for 2008Data from the medical certificates were sought from theMoH for all reported deaths in Tonga for 2008 (n = 945).Cause of death data from the certificate (both Part I andPart II) were entered into the database by MoH medicalrecord staff as the ICD-10 code only [10]. During dataentry for some deaths, contributory causes listed in Part IIof the original medical certificate were entered onto thefirst available line in the database sequence in Part I. Thiscould erroneously lead to their consideration within thecausal sequence recorded in the database, resulting in thepossibility that such a contributory cause may be selectedas the underlying cause (according to the General Princi-ple for mortality coding in ICD-10), if it could plausiblylead to the condition listed above it.The quality of population cause of death data obtained
through medical certification is directly affected by threestages in the data collection process: original diagnosis andcertification of the death, coding of the data from the cer-tificate, and the data entry and tabulation practices used to
produce aggregate data. As data from the certificates ofdeath were obtained as an extraction from the MoH data-base (with cause of death available only as ICD codes), cer-tification and coding could not be examined separately.Deaths for 2008 were therefore examined using: (a) areview of the data entry and tabulation practices; (b) amedical record review; and (c) assignment of a final under-lying cause from either the medical certificate of death ormedical record based on the best available information.Review of tabulation practicesAn underlying cause of death was assigned for each deathbased on ICD-10 [10]. When the unit record listed morethan one cause, the underlying cause of death wasassigned assuming no movement of contributory causesinto Part I of the certificate. Assigned underlying causeswere then reviewed to assess whether the selection ofunderlying cause would differ if it was assumed that con-tributory causes had been recorded in empty lines forPart I of the certificate when entered in the database.Coded data for both the immediate and underlying causeof death from the medical certificate of death were aggre-gated to one of the 103 causes listed in the GeneralMortality Tabulation List 1 of ICD-10 (hereafter referred
Figure 1 Overview of study design.
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ICD list 1) [10]. These were crosstabulated to comparethe effect of current MoH tabulation practices (based onimmediate cause) on the cause of death profile (Table 1).Medical record reviewMedical records were sought from the national hospitalfor all deaths that occurred in 2008 in Tongatapu whereproximity to the major hospital suggests that deceasedindividuals were more likely to have received care at thehospital and therefore have a medical record. Data fromthe medical record for each death were extracted using astandardized form adapted from similar work in the Phi-lippines [11,12], de-identified, and assigned a study num-ber. Data extraction was completed by qualified nurseswho underwent initial training in data extraction proce-dures based on the written study protocol. Extractionforms were regularly reviewed throughout the project [byKC].An independent medical doctor was provided with
medical record extracts and requested to complete amedical record review form for each death based on theinternational standard medical certificate of death [13].Doctors were provided with both written and verbalinstruction on completing the medical record reviewform based on ICD-10 guidelines [10]. Where age atdeath was less than 60 years and cause of death could notbe determined, the record extracts were reviewed by asecond independent medical doctor. For each cause ofdeath assigned, the doctor was requested to indicate thelevel of certainty of the diagnosis (definite, strong cer-tainty, some certainty, and limited certainty) based onthe available evidence [12]. Assigned causes were subse-quently grouped into two diagnostic evidence categories:high (definite and strong) and low (some and limited)[12].Underlying cause of death from the medical record
review form was then coded according to ICD-10 [10] andmatched to the medical certificate of death by study num-ber. Underlying cause of death as noted on both the certi-ficate of death and the medical record review form wascrosstabulated using ICD list 1 [10]. Differences betweenthe two underlying causes were classified as either minor(different cause as tabulated using ICD list 1, but sameICD chapter) or major (different ICD chapter), and signifi-cant associations in reporting patterns were examined.Assignment of final underlying cause of deathPrevious medical record review studies [14-16] havestarted from the presumption of a reference standard,where one source (the medical record) is assumed to bemore accurate than the other (the medical certificate).However, such an assumption was not valid in this study.A final underlying cause of death was assigned for eachdeath based on the best available data, whether from themedical certificate or the medical record. In general, the
medical certificate-based underlying cause of death wasselected, except in the following instances:a) where cause on the medical certificate was
unknown or coded as nonspecificb) an injury leading to the death was noted in the
medical record, but not mentioned on the deathcertificatec) the underlying cause identified from the medical
record could have led to the underlying cause on themedical certificate of deathd) for all remaining deaths not due to external causes,
the underlying cause from the medical record was con-sidered to be of high certaintyProportional mortality by cause by age and sex was
then tabulated and 95% confidence intervals were calcu-lated using the Poisson distribution.
Cause of death tabulations, 2001 to 2008Based on the assessment of deaths in 2008, it was deter-mined that medical records added little to our understand-ing of underlying cause of death at a population level,particularly for adult deaths. For 2008, 93% of the finalassigned causes of death were consistent with the medicalcertificate of death (see results) following the medicalrecord review. Based on this finding, de-identified datafrom the medical certificates for all deaths for 2001 to2007 were added to the 2008 data set. As immediate causediffered from the underlying cause (ICD list 1) in 37% ofcases for 2008 (see results), each death (2001 to 2007) wasreviewed and assigned an underlying cause of death basedon ICD-10 [10], by either assuming no movement of con-tributory causes into Part I of the certificate or assumingthat contributory causes had been recorded in empty linesfor Part I, as completed for the 2008 data. This resulted intwo separate underlying causes, and tabulations by agegroup and sex were generated for both variables. Propor-tional mortality for both tabulations was calculated (withexclusion of ill-defined and unknown deaths) for 2001-2004 and 2005-2008 and applied to estimates of totaldeaths for Tonga to calculate mortality rates by age, sex,and selected causes (ICD list 1). Total deaths for 2001-2004 and 2005-2009 (the closest available period to 2001-2008) were extracted directly from life tables generated bya recent study that established upper and lower mortalityscenarios based on adjusted empirical data reconciledfrom both MoH and Civil Registry sources [5]. Bothunderlying cause of death tabulations described abovewere applied to the upper and lower mortality scenariosfrom this recent study [5], generating four estimates fortotal deaths by cause by age group and sex for each period.The upper and lower values from these four estimateswere used to generate a plausible range of deaths andmortality rates by cause.
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Table 1 Comparison of original cause of death tabulation based on immediate cause from the medical certificate against underlying cause of death from themedical certificate†: ICD 103 cause list (selected causes), Tonga, 2008Original tabulation Underlying cause of death from medical certificate
1
1-001-1-025*
1-012 1-019 1-026 1-052 1-059 1-064-1-071+
1-065 1-067 1-069 1-072 1-080 1-082 1-084 1-087 1-092 1-093 1-094 1-095 Other Unknown Total
1-001 - 1-025* 3 4
1-012 1 2 2 10 3 2 4 2 1 3 30
1-019 1 1
1-026 1 55 56
1-052 12 12
1-064 -1-071+
2 9 37 2 5 6 1 1 63
1-067 6 28 1 35
1-069 2 1 15 2 20
1-072 3 4 4 5 1 34 51
1-080 5 2 6 13
1-083 1 1 2
1-084 1 2 7 10
1-092 13 13
1-093 3 3
1-094 2 5 1 1 1 1 10 1 3 25
1-095 0
Other 1 2 15 1 2 10 31
Injury~ 24 24
Unknown 1 1 36 38
Total 10 4 9 67 59 4 47 2 35 17 42 6 6 12 1 15 3 10 29 17 36 431
†Determined according to ICD principles [10], excluding potential impact of contributory causes (Part II) in the causal sequence (Part I)
Single causes from ICD list 1 are recorded in italics, disease groups are shown in plain text.
1-001-1025 Infectious and parasitic diseases (other than septicemia and viral hepatitis)
1-012 Septicemia
1-019 Viral hepatitis
1-026 Neoplasms
1-052 Diabetes
1-059 Meningitis
1-064 - 1071 Diseases of the circulatory system (excluding rheumatic, ischemic, and cerebrovascular heart disease)
1-065 Rheumatic heart diseases
1-067 Ischemic heart disease
1-069 Cerebrovascular heart disease
1-072 Diseases of the respiratory system
1-080 Diseases of the liver
1-084 Diseases of the genitourinary system
1-087 Pregnancy, childbirth, and the puerperium
1-092 Certain conditions originating in the perinatal period
1-093 Congenital malformations, deformations, and chromosomal abnormalities
1-094 Symptoms, signs, and abnormal clinical and laboratory findings not elsewhere classified
1-095 Injury, poisoning, and certain other consequences of external causes
Other All other causes
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Population by age group and sex was derived from the1996 [17] and 2006 [7] censuses using exponential inter-polation to provide estimates for intermediate and addi-tional years. Age-standardized mortality rates for allages were then calculated using the world-standardpopulation [18] to examine trends over time (2001-2004and 2005-2008).Final cause of death was also classified to the WHO
Global Burden of Disease (GBD) Study categories: I(infectious, perinatal, and maternal conditions), II (non-communicable diseases), and III (external causes) [19].Proportional mortality by GBD category for males wascompared with that derived from Codmod [20] to assessbroad plausibility of the results. Codmod is a modelingprogram that generates expected cause of death distri-butions [20] from inputs of level of childhood and adultmortality and is based on associations between theseparameters as recorded in over 100 years of data from awide range of countries. This was not performed forfemale deaths as existing Codmod models [20] do notallow input of the high level of adult mortality estimatedin females in Tonga concurrently with the low estimatesof childhood mortality [5].
ResultsReported deathsOf 945 deaths recorded in Tonga for 2008 based on recon-ciled MoH and civil registry data [5], 431 deaths had a cer-tificate recorded in the MoH database; 390 of these werefrom Tongatapu and therefore had a medical recordsought for review. As such, certificates were not availablefor 54% of the reported deaths (reconciled from the CivilRegistry, nursing records, and health information system).There were 2,310 deaths extracted from the MoH data-
base (based on medical certification) for 2001 to 2004, ofwhich 1,902 cases included cause of death data. For 2005to 2008, 2,025 cases were extracted from the database, ofwhich 1,885 included cause of death data.
Quality review of deaths in 2008Review of tabulation practicesExamination of case data for 2008 from the certificatesrevealed that in 63% of deaths the cause of death originallytabulated on immediate cause was the same as the finalunderlying cause of death when aggregated to the ICD list1 [10] (Table 1). However, a substantial proportion ofdeaths originally tabulated as septicemia (1-012) (93%),cardiovascular diseases (1-064 to 1-071) (25%), respiratoryillnesses (1-072) (33%), and liver disease (1-078) (54%)should have been attributed to other causes (Table 1)based on ICD rules [10], reflecting various nonspecificcauses, such as heart failure and respiratory arrest, whichoften appear on the first line of the medical certificate ofdeath. Twelve of the 17 deaths (71%) recorded as “signs,
symptoms or ill-defined conditions” should have beenattributed to more specific causes (Table 1), even with nofurther information available from the medical record.Diabetes (1-052) was notably underrepresented in the
original tabulation, with 47 of 59 (80%) deaths for whichdiabetes was selected as the underlying cause originallyassigned to other causes, notably septicemia and cardio-vascular disease. Neoplasms (1-026) were also underrepre-sented in the original tabulations with 12 (18%) cancerdeaths assigned to other causes. Although they accountedfor only a small number of deaths, some deaths attributedon the medical certificate of death to underlying causes ofparticular public health importance, such as rheumaticheart diseases and maternal causes, were not apparent inthe original tabulation of immediate cause.Upon review, 36 (8%) deaths for 2008 were found to be
potentially affected by the entry of contributory causesfrom Part II of the certificate into the Part I sequence. Inall other deaths application of the ICD rules would resultin the same underlying cause (when collated to ICD list 1)[10]. Only one of these deaths involved a death assigned toInfectious Diseases, which would have otherwise beencoded to pneumonia (Respiratory Disease); all other deathsoccurred amongst those assigned to noncommunicablediseases - both in the original tabulation of immediatecause and selected underlying cause of death. Four ofthese deaths were minor variations within categories ofcardiovascular disease, while 23 cases were diabetes-related deaths that would be assigned to cardiovascular orrespiratory diseases if the diabetes code had been recordedin Part II of the certificate (as a contributory cause).
Medical record reviewMedical records were located for 294 (75%) certifieddeaths that occurred in Tongatapu (Figure 2) followingexclusion of duplicate records and overseas deaths. Causeof death was identified from the medical record for 190cases, 65% of those with a matching medical record. Diag-nosis of underlying cause of death was considered to be ofhigh certainty by the medical reviewer in only 73 (25%) ofthese records.The sex and age distribution of deaths for which records
could not be located was similar to that for the deathswhere a medical record was found, except for older ages(> 64 years), which had a higher proportion of missingrecords. Twenty-eight percent of deaths missing a medicalrecord occurred at the national hospital. Total reporteddeaths followed a similar age and sex distribution as thedeaths for which a record was located.There were 93 major discrepancies (32% of deaths)
and 19 minor discrepancies (6% of deaths) identifiedbetween the medical death certificate and medicalrecord review, following collation to ICD list 1 andtabulation according to underlying cause of death. If the
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dataset was limited to deaths with a diagnosis of highcertainty, there were 18 major discrepancies (24% ofdeaths) and four minor discrepancies (5% of deaths). Asdetermined from the medical certificate, 70% of deathsattributed to neoplasms, 77% of deaths attributed to dia-betes, and 70% of deaths attributed to cardiovasculardisease were assigned to other underlying causes by themedical record review (Table 2). The most commonunderlying causes of death found by the medical recordreview to have been originally certified as due to othercauses (Table 2) were infectious diseases, in particularsepticemia (100% of deaths), cerebrovascular diseases(65% of deaths), and respiratory diseases (71% ofdeaths). If redistribution of deaths is limited to deathswith a diagnosis of high certainty, nearly all the deathswould be assigned to the same broad ICD category byboth the certificate and medical record review.Of the 28 deaths attributed to nonspecific or unknown
causes on the medical certificate, 17 (61%) were able tobe attributed elsewhere following review of the medical
record. Eight (29%) of these unknown cases occurred inchildren less than 5 years of age and were assigned toperinatal or congenital causes following the medicalrecord review. Evidence for this attribution was definiteor strong in each of these cases. Even following medicalrecord review, 11 (6% of all deaths) remained assignedto unknown causes.Assignment of final underlying cause of deathFor 208 deaths (71%) for which a medical record wasfound, the underlying cause of death as determinedfrom the medical record review did not match underly-ing cause as certified. The medical certificate of deathwas used in 179 (86%) and the medical record in 29(14%) of these cases to determine final cause. As notedin the methods, the medical record was used ininstances where it added additional information to thecausal sequence indicated on the certificate or where anexternal cause of death was indicated that was notrecorded on the medical certificate. The medical recordswere not considered to be inherently more reliable than
Figure 2 Flowchart of process for medical record review in Tongatapu.
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Table 2 Comparison of underlying cause of death from the medical certificate of death† and from the medical record review: ICD 103 cause list (selected causes),Tongatapu, 2008
Underlying cause of death from medical record review
Underlying cause of death from medical certificate 1-001- 1-025* 1-012 1-019 1-026 1-052 1-064- 1-071 1-067 1-069 1-072 1-080 1-082 1-083 1-084 1-092 1-093 1-095 Other Unknown Total
1-001 - 1-025* 5 1 1 7
1-012 1 1 1 3
1-019 2 3 2 1 8
1-026 1 15 1 2 1 1 1 2 4 23 50
1-052 1 1 10 1 1 8 1 1 3 1 1 14 43
1-059 1 1 1 1 4
1-064 - 1-071+ 2 4 4 1 1 2 18 32
1-065 1 1 2
1-067 1 1 6 1 1 10 20
1-069 1 1 6 1 3 12
1-072 2 1 1 5 10 19
1-080 1 1 2
1-082 4 1 5
1-084 1 1 4 6
1-087 1 1
1-092 1 11 1 1 14
1-093 3 3
1-094 1 1 3 5
1-095 1 1 11 6 19
Other 2 1 1 9 2 16
Unknown 1 3 1 7 3 8 23
Total 12 10 4 20 11 7 11 17 17 4 9 2 4 19 8 17 18 104 294
†Determined according to ICD principles [10], excluding potential impact of contributory causes in the causal sequence
Single causes from the ICD list 1 are recorded in italics, disease groups are shown in plain text.
1-001-1025 Infectious and parasitic diseases (other than septicemia and viral hepatitis)
1-012 Septicemia
1-019 Viral hepatitis
1-026 Neoplasms
1-052 Diabetes
1-059 Meningitis
1-064 - 1071 Diseases of the circulatory system (excluding rheumatic, ischemic, and cerebrovascular heart disease)
1-065 Rheumatic heart diseases
1-067 Ischemic heart disease
1-069 Cerebrovascular heart disease
1-072 Diseases of the respiratory system
1-080 Diseases of the liver
1-084 Diseases of the genitourinary system
1-087 Pregnancy, childbirth, and the puerperium
1-092 Certain conditions originating in the perinatal period
1-093 Congenital malformations, deformations, and chromosomal abnormalities
1-094 Symptoms, signs, and abnormal clinical and laboratory findings not elsewhere classified
1-095 Injury, poisoning, and certain other consequences of external causes
Other All other causes
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the death certificate, as has been assumed in other stu-dies of this type, because of the large proportion ofincomplete or missing records, the most recent entry inthe record for many cases preceding the death by manyyears or months, and the poor legibility and completionof records. An overview of the sources of final cause ofdeath data and reasons for selection is shown in Addi-tional file 2; 93% of the final causes assigned matchedthe underlying cause given by the medical certificate.Perinatal conditions were the leading cause of death in
children (Table 3). Neoplasms were the leading cause ofdeath in adult females, with cardiovascular diseases theleading cause in adult males (Table 4). Diabetes was thesecond leading cause of death in adult males (19%) andthe third leading cause of death in adult females (18%)(Table 4) based on ICD-10, where diabetes is taken as anunderlying cause if stated in Part I of the certificate. Half(50%) of all cardiovascular disease was found to be due toischemic heart disease. Viral hepatitis (4.5% of total adultdeaths) was the most frequently reported infectious dis-ease causing mortality in adults.
Causes of death, 2001 to 2008Cause of death data for 2001 to 2008 were compiled fromthe best available data on underlying cause from either themedical certificate of death or medical record for deaths in2008, and de-identified medical certificate data for 2001 to2007. Infectious diseases and neonatal causes were theleading causes of death in 0 to 4 year olds, with injury andexternal causes the leading cause of death for 15 to 24year olds. From 25 years of age onwards, proportionalmortality from injury and external causes decline withchronic diseases leading proportional mortality for all agegroups above 25 years. Age-standardized rates (all ages)for selected diseases are shown in Figures 3 and 4 (Addi-tional file 2), and indicate a significant increase in mortal-ity rates (for males and females) between 2001-2004 and2005-2008 for a range of noncommunicable diseases,
despite the uncertainty in the estimates described in themethods. For 2005 to 2008, the plausible ranges for non-communicable disease death rates (per 100,000) were car-diovascular diseases (males: 423 - 644 and females: 194 -321), diabetes (males: 94 - 222 and females: 98 - 190), andlung cancer (males: 34 - 49 and females: 17 - 23). Therewas a notable increase in age-standardized mortality dueto intentional self harm in males, with the age-standar-dized rate estimated as 9 to 12 deaths per 100,000 in 2005to 2008.The overall cause distribution by GBD category from
the empirical data for Tongan males for both 2001-2004and 2005-2008 was very similar to the modeled distribu-tion produced by Codmod [20], which reflects the antici-pated cause distribution based on mortality levels asderived from a wide range of previous mortality data sets.However, the Tongan empirical data shows a higher pro-portion of deaths from noncommunicable diseases inearly adult ages than the modeled data, which is consis-tent with the low life expectancy for males in Tonga overthis period. As noted in the methods, Codmod distribu-tions could not be derived for female deaths due to thehigh adult mortality.
DiscussionCertificates were not available for 54% of the reporteddeaths in 2008 despite the current MoH policy [9] requir-ing certification of all deaths. Current tabulation prac-tices based on the immediate cause of death havecontributed to a lack of reliable evidence for decision-making in Tonga. Causes of death such as ischemic heartdisease and diabetes were underrepresented in these ori-ginal tabulations. The tabulated cause was found to fallwithin the same cause aggregated to ICD list 1 as theunderlying cause of death in 63% of instancesDespite data entry procedures potentially moving con-
tributory causes into the causal sequence, the overalleffect of this on selection of underlying cause is likely to
Table 3 Underlying causes of death in Children 0-4 years, Tonga 2008
Cause of death* Reported deaths Proportional mortality (%)
1-092 Perinatal conditions 21 36
1-001 Infectious diseases 8 14
1-093 Congenital abnormalities 6 10
1-072 Respiratory diseases 6 10
1-026 Neoplasms 5 9
1-095 External Causes 4 7
1-059 Meningitis 3 5
1-078 Disease of digestive system 2 3
Other 3 5
* Tabulated according to ICD list 1 [10]. “Infectious diseases” in the table refers to those conditions classified according to chapter 1 of the ICD. Meningitis iscoded as a disease of the nervous system under the ICD [10] and is therefore listed separately, while pneumonia and other infectious respiratory illnesses areincluded in the respiratory diseases chapter
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be minor, other than for diabetes, if ICD selection rulesare rigorously applied. The general principle of ICDrules require selection of the lowest completed line onPart I of the certificate as the underlying cause of deathonly if the condition “could have given rise to all theconditions entered above it” [10], thereby excluding thecontributory cause in most cases even if recorded in thecausal sequence of Part I of the certificate. However,while the ICD rules allow cardiovascular conditions tobe considered as a consequence of diabetes, the actualrecording of diabetes in Part 1 or Part 2 in such cases isdriven by individual certifier preferences rather than aninternationally recognized causal relationship betweendiabetes and cardiovascular disease. This affects compar-ability of underlying cause statistics between populations
and across time. In this study, the process of generatingtwo tabulations based on different assumptions of howcontributory causes were treated (as described in themethods) in effect also reflects this potential ambiguityin the results [21]. The increases in cause-specific mor-tality from 2001-2004 to 2005-2008 from noncommu-nicable diseases, including cardiovascular diseases anddiabetes, are significant, with the range of estimatesfrom each period not overlapping, indicating thatincreases in cause-specific mortality from diabetes arenot an artifact of changes in coding conventions. Non-communicable diseases are clearly leading the high ratesof adult mortality, although injury and external causesremain the leading cause of death in 15- to 24-year-olds. Current tabulations of data in Tonga code external
Table 4 Underlying causes of death in adults aged 15-64 years, Tonga, 2008
Cause of death* Males Females
Reporteddeaths
Proportionalmortality (%)
Reporteddeaths
Proportionalmortality (%)
1-064 Diseases of the circulatory system 28 28 16 23
1-067 Ischemic heart diseases 18 18 4 6
1-069 Cerebrovascular diseases 1 1 4 6
1-065 Acute rheumatic fever and chronic rheumatic heart diseases 1 1 1 1
1-051 Endocrine, nutritional, and metabolic diseases (all diabetes(1-052))
19 19 13 18
1-095 External causes of morbidity and mortality 18 18 3 4
1-096 Transport accidents 6 6 0 0
1-101 Intentional self-harm 4 4 0 0
1-026 Neoplasms 15 15 20 28
1-036 Malignant neoplasm of breast 0 3 4
1-037 Malignant neoplasm of cervix uteri 0 2 3
1-039 Malignant neoplasm of ovary 0 2 3
1-031 Malignant neoplasm of liver and intrahepatic bile ducts 3 3 1 1
1-034 Malignant neoplasm of trachea, bronchus, and lung 3 3 0
1-029 Malignant neoplasm of stomach 2 2 2 3
1-030 Malignant neoplasm of colon, rectum, and anus 2 2 2 3
1-001 Certain infectious and parasitic diseases 10 10 3 4
1-019 Viral hepatitis 6 6 2 3
1-072 Diseases of the respiratory system 4 4 3 4
1-078 Diseases of the digestive system 3 3 3 4
1-084 Diseases of the genitourinary system 2 2 3 4
1-094 Symptoms, signs, and abnormal clinical and laboratoryfindings, not elsewhere classified
1 1 1 1
1-058 Diseases of the nervous system 0 0 3 4
1-082 Diseases of the skin and subcutaneous tissue 0 0 2 3
1-087 Pregnancy, childbirth, and the puerperium 0 1 1
Other categories 1 1 0 0
TOTAL 101 100 71 100
* Tabulated according to ICD list 1 [10]. The table shows cause categories by ICD chapter in bold, with important individual cause categories from ICD list 1shown in small print under their assigned ICD chapter
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cause by injury type rather than cause (such as fall,motor vehicle accident, etc.), and there was insufficientdetail in many cases (both on the certificate or medicalrecord) to assign a more specific cause category. Injurymortality data are thus currently of limited use to targetpublic health interventions, and doctors should beencouraged to record more detail for these events onthe medical certificate of death.The data for 2008 indicate 93% of the final assigned
causes matched those listed on the medical certificate ofdeath (ICD list 1). There were, however, some clear pat-terns of misreporting on the certificate, primarily for chil-dren, where additional information from the record couldhave led to a more specific diagnosis. Inconsistencieswere also noted in the reporting of diabetes and cardio-vascular disease, with these conditions being the mostlikely underlying causes from the medical certificate tobe assigned to other causes by the medical record review.This demonstrates the importance of certifying doctorsreferring to the medical record, particularly in cases dueto chronic disease, or where death is attributed to an ill-defined or unknown cause. There is also a critical needto improve the quality of medical records and availability
of this information to the certifying doctor. Even follow-ing review, 6% of deaths remained assigned to unknowncauses. In part, this may reflect the community atten-dance (or lack thereof) at the hospital and subsequentlylimited opportunity for data collection. However, 28% ofthe deaths for which no medical record could be locateddied at the hospital and therefore should have had a med-ical record available. In part these missing records maybe due to cases that were “dead on arrival” at the hospitaland therefore certified without a record being generated.For the remainder, as the study was conducted somemonths after the conclusion of 2008, these missingrecords cannot be accounted for by delays in transfer ofthe record from the ward to the medical records office.This highlights a critical need to improve data manage-ment procedures to ensure that data are not lost oncecollected and that all records can be accessed centrally.The medical record review of deaths that occurred in
2008 was conducted in accordance with the principles setout by Johanssen et al. [14] to ensure the transparencyand reproducibility of medical record review studies.However, this work varied from previous applications ofthis technique as the small population and health service
Figure 3 Age-standardized mortality for males (selected causes) for Tonga, 2001-2009 (plausible range based on upper and lowermortality scenarios). Age-standardized mortality rates for all ages were calculated using the world-standard population [18].
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arrangements meant that an independent review in-country was not possible, thus introducing an additionalstep of extracting data from the medical record ratherthan having a physician review the record directly. Pre-vious studies have also started from the presumption of areference standard, where one source is assumed to bemore accurate than the other (i.e., the certificate whenreviewing verbal autopsy findings or the medical recordwhen reviewing the medical certificate), which was notappropriate in this setting. In order to ensure the integ-rity of the study given these design differences, dataextractions from the medical record were guided by astandardized form and instruction manual [12], extrac-tions were completed by nurses with a good understand-ing of medical terminology, and forms were routinelyreviewed by the project team for quality. Causes of deathwere coded according to ICD rules [10] and collated tothe recommended tabulations in the ICD [10]. Theapproach used in this study allowed the flexibility toensure the most reliable data from either the medicalrecord or medical certificate of death were used to assignunderlying cause. This was an important considerationgiven the unknown quality of cause of death data in boththe medical certificates of death and medical records at
the outset of the study. A complete review of informationin the medical record, rather than the principal diagnosisas recorded on the discharge summary was used for com-parison to the medical certificate, as the principal diagno-sis is the reason for the admission and is frequently amanifestation of an underlying cause that may not berecorded. For example, a patient admitted for congestiveheart failure and who subsequently dies would have thisrecorded as the principal diagnosis rather than theunderlying cardiopathy that led to this condition. Ascause of death data were obtained as ICD codes it wasnot possible to differentiate whether errors were intro-duced during original certification or coding, and afurther coding audit may also prove useful.There is very little information currently published on
causes of death in Tonga. Of the published data available,the WHO Child Health Epidemiology Reference Groupindicates similar estimates of proportional mortality for2008 in children under 5 years of age from external causes(7%) but higher mortality from respiratory diseases (pneu-monia) (17%) and congenital abnormalities (17%) com-pared to this study [22]. In adults, while significantuncertainty remains due to both previous data entry prac-tices and the small number of deaths in the study, the
Figure 4 Age-standardized mortality for females (selected causes) for Tonga, 2001-2009 (plausible range based on upper and lowermortality scenarios). Age-standardized mortality rates for all ages were then calculated using the world-standard population [18].
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findings demonstrate that diabetes is a much greater pub-lic health issue in Tonga than previously reported. Age-standardized mortality in Tonga from diabetes for 2005 to2008 is estimated at 94 to 222 deaths per 100,000 formales and 98 to 190 for females. This is significantlyhigher than the estimated age-standardized rates of 16(males) and 11 (females) deaths per 100,000 populationrespectively for New Zealand (2008) [23] and previousGBD estimates for Tonga (2008) of 40 (males) and 53(females) deaths per 100,000 [19], although the latter mayhave been based on tabulations of immediate cause. Thehigh estimates of cause-specific mortality from diabetes inTonga are, however, broadly consistent with previous stu-dies for New Zealand’s Maori, a group that also demon-strates high mortality from diabetes at a lower prevalenceof disease. A 2004 survey estimated prevalence of diabetesin the Tongan population at 18% (based on self-report)[24], while prevalence (by self-report) in the Maori popula-tion was estimated at 8% for males and 12% for females in2002 [25]. Age-standardized mortality from diabetes forMaori males was estimated at 61 deaths per 100,000 and39 deaths per 100,000 for females in 2004 [25]. Whilelower than the estimates of age-standardized mortalityfrom diabetes in Tonga, the Maori figures are based onthe older WHO standard population (the Segi age distri-bution) [18] and would be substantially higher if thenewer WHO world standard population (which has ahigher proportion of the population in the adult agegroups) had been used, as applied in this study.A study of diabetic patients in New Zealand also
found that Tongan patients tended to have poorer com-pliance with treatment and a more fatalistic attitude totheir illness [26]. This may also be a factor in the highmortality rates seen in Tonga and is consistent with thehigh proportion of medical record extracts collected inthis study in which the case’s diabetes was referred to as“uncontrolled” and “unmanaged.”Age-standardized death rates for ischemic heart disease
and neoplasms were substantially higher in males inTonga compared to Australia and New Zealand. For Ton-gan males, age-standardized mortality from ischemic heartdisease is estimated at 139 to 210 deaths per 100,000(2005-2008), compared with 97 in New Zealand [23] and78 in Australia [19] in 2008. Age-standardized mortality inmales from neoplasms is estimated at 189 to 255 deathsper 100,000 (2005-2008) in Tonga compared with 155deaths per 100,000 in New Zealand and 146 in Australiain 2008 [19]. Age-standardized mortality from ischemicheart disease and neoplasms for females (2005-2008) wasvery similar to estimates reported for New Zealand [23]and slightly higher than those for Australia in 2008 [19].The higher age-standardized rates for cardiovascular dis-ease and neoplasms would contribute to the relatively lowlife expectancy for males in Tonga (60.4 to 64.2 years,
2005-2009) [5], highlighting the critical importance ofaddressing chronic diseases in Tonga to improve lifeexpectancy. Age-standardized rates for ischemic heart dis-ease remain below those seen in Australia (472 deaths per100,000 for males and 246 deaths per 100,000 for femalesat the height of the cardiovascular disease epidemic(1968)) [27].The high age-standardized mortality rates from cardio-
vascular diseases are consistent with the limited informa-tion on noncommunicable disease risk factors available forTonga. A 2004 survey found that 67% of adults wereobese (body mass index ≥ 30 kg/m2), with 23% of adultsaffected by hypertension (systolic blood pressure ≥ 140mmHg and/or diastolic blood pressure ≥ 90 mmHg orcurrently on medication) [24]. The higher age-standar-dized rate of lung cancer in males of 34.4 to 49.0 deathsper 100,000 compared with 16.5 to 23.0 for females (2005-2009) reflects significantly higher smoking prevalenceamongst males (64%) than females (14%), as recorded in apopulation-based survey undertaken in 1992 [28]. Theserates of lung cancer are substantially higher than previousestimates from the GBD study (2008) of cause-specificmortality of 15 deaths per 100,000 for males and eightdeaths per 100,000 for females [19].Increases in age-standardized mortality from infectious
diseases were largely accounted for by cause-specific mor-tality from hepatitis B and septicemia and indicate a needfor further consideration for early universal vaccination forhepatitis B and improved reporting of underlying condi-tions that may result in septicemia (such as diabetes) onthe medical certificate.
ConclusionThis paper presents the first published comparative vali-dation study of cause of death data in a Pacific Islandcountry and demonstrates clearly the impact of non-communicable diseases on life expectancy in Tonga.This is supported by the significant increase in age-specific mortality from a range of noncommunicablediseases noted between the two periods, including dia-betes, lung cancer, and cardiovascular disease, althoughspecific subcategories of disease such as ischemic heartdisease are more likely to be affected by quality issues inthe earlier period (prior to the new MoH policy on cer-tification) and therefore will be less reliable than thebroader categories. Although Tonga was the first coun-try in the region to develop a strategy to combat non-communicable diseases [29], more clearly needs to bedone to address this growing health issue.This research also outlines the potential for improving
cause of death data available in Tonga. Changes havealready been made to data entry and tabulation practicesin order to separate contributory causes in Part II of thecertificate from the causal sequence in Part I and to
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ensure tabulations from the MoH database are generatedfrom the underlying cause of death. However, despite thepotential for further improvements in cause of deathdata, there are significantly more data on cause of deathavailable from Tonga than are routinely reported orknown to international agencies, and it is imperative thathealth planners are able to access this information.
Additional material
Additional file 1: World Health Organization. International StatisticalClassification of Diseases and Related Health Problems. Tenth Revision.Geneva: 2007 [cited 26/11/2010] http://www.who.int/classifications/icd/en/.
Additional file 2: Source data for selection of final cause-of-death inmatched records, and reason for selection; Tongatapu (2008).
AcknowledgementsThe authors would like to acknowledge the management and staff of theTonga Ministry of Health for their support and involvement in this project.Particular recognition is due to Dr. Toa, Dr. Ake, Trish Ryan and the medicalrecords unit in Tonga, and, of course, our data extractors Hulita Fihaki and‘Elaine Faletau. We would also like to acknowledge Dr. SamanthilakaGamage for coding the forms.
Author details1School of Population Health, University of Queensland, Herston (Brisbane),Queensland, Australia. 2Ministry of Health, Tonga, Australia. 3Western Health,Melbourne, Australia. 4School of Public Health and Community Medicine,University of New South Wales, Randwick (Sydney), NSW, Australia.
Authors’ contributionsKC designed the study, developed the study forms and procedures,conducted initial training of data extractors, reviewed trial extraction forms,arranged coding and collation of data, analyzed the collected data, anddrafted the manuscript; SH coordinated data extraction in Tonga andprovided significant input to the manuscript; CR assisted with reviewing trialextraction forms and revised the manuscript critically for importantintellectual content; SA reviewed the extracted data from both countries toallocate cause of death from the medical record and provided significantinput into the discussion; RR provided the second review of deaths forwhich cause was originally assigned as unknown and critically revised themanuscript. ADL and RT conceived and coordinated the broader studyunder which this project was undertaken, reviewed and contributedsignificantly to the interpretation of results, and revised the manuscriptcritically for important intellectual content. RT also made substantialcontributions to the study design. All authors read and approved the finalmanuscript.
Competing interestsADL is an Editor-in-Chief of Population Health Metrics.
Received: 7 September 2011 Accepted: 5 March 2012Published: 5 March 2012
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what they died from: an assessment of the status of global cause ofdeath data. Bull World Health Organ 2005, 83(3):171-177.
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doi:10.1186/1478-7954-10-4Cite this article as: Carter et al.: Causes of death in Tonga: quality ofcertification and implications for statistics. Population Health Metrics 201210:4.
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6.2.2 Medical Record Review in Nauru
Aim and Objectives
Certification practices are of particular interest in Nauru for two main reasons. Firstly,
Nauru is the only study country to use a medical certificate that is not compliant with
the international standard. In the Nauru certificate there is an additional line labelled
cause of death” above the first line of Part I (the causal sequence). Secondly, there
is a very high occurrence of co-morbidities for chronic NCDs and it is not uncommon
to see medical certificates (or medical records) with three or more of the following
diseases: gout, hepatitis, liver failure, ischemic heart disease, kidney failure,
diabetes, neoplasms, and rheumatic heart disease. This high level of co-morbidity
across all adult age groups may lead to difficulties in selecting a clear causal
sequence leading to death and subsequent completion of the medical certificate.
Methods
The medical record review in Nauru followed similar methods as the study in Tonga.
Medical records were sought for 2005-2009. Prior to 2005, many records were
discarded when the public hospital moved to its present site.
Variations from the methods used in Tonga were that medical certificates had to be
manually coded and entered and the search for medical records included many
found amongst so called “active” patient files. Where a medical record was not
available for an infant who had died in the first few days after birth, the mother’s
record was reviewed for information on the pregnancy and child’s symptoms.
Although a medical record is to be created for all children born, in practice, early
notes are frequently recorded on the mother’s chart, with a separate chart for the
� � �
267
child not established for several days. Similarly, if a file consisted of several parts,
but these could not all be located, the partial file was used. In both cases, the extract
was marked to indicate the variation. Reviewed records are shown in the following
flow diagram.
Figure 30: Medical record review flowchart for Nauruan study
Where two CoD sequences were possible from the medical record review, if one was
consistent with the medical certificate, this was taken as the correct sequence. As
with the study in Tonga, the reviewing doctor was familiar with the country and
medical facilities, but had not actively worked in this setting for a number of years.
Training was conducted with the doctor by reviewing the study protocol (Appendix
five) and completing a series of example cases that were discussed with the
N:�43
� � �
268
researcher. Any cases where CoD could not be determined were reviewed by a
second doctor with experience in medical record review studies. Reviewing doctors
indicated their level of certainty when identifying the causal sequence resulting in
death (outlined in Appendix five).
Selection of underlying CoD from the original medical certificate, for comparison to
the findings from the medical record review, was done twice; both applying ICD rules
to the causal sequence including the non-standard additional line on the Nauruan
certificate, and again, excluding the additional line and using only the causes listed in
Part I and II of the certificate as it complies with the ICD standard.
The age, sex, and causal distributions (by proportion) of deaths for which a medical
record was found, and those for which no record was found, were compared. As
deaths for which no medical record was found were predominated by older age
categories and ‘ill-defined’ causes, only deaths for which a medical record was found
and could be reviewed were used to determine final causal distributions.
The underlying CoD for each reviewed case could then be selected. In each case
the underlying CoD from the certificate was selected except in the following
scenarios, where data was corrected.
a. Where the underlying cause identified by the two sources did not match but
was potentially related, the more specific cause was selected. If the more
specific cause was from the medical record, this was only selected when the
certainty of diagnosis was considered reasonable (categorised as “definite” or
“some certainty” by the reviewing doctor).
� � �
269
b. If the two causes were not related, and the medical certificate indicated an
“external cause”, this was selected.
c. If the underlying cause from the medical certificate was attributed as “signs,
symptoms or ill-defined causes” the underlying cause was taken from the
medical record review.
d. An unrelated underlying cause was identified from the medical record review
and the diagnosis was classified as “certain” by the reviewing doctor.
CoD distributions were than calculated by age and sex for 2005-2009. Confidence
intervals were based on Poisson distribution of uncertainty from the proportional
mortality, not accounting further for uncertainty in the mortality envelope.
Results
Of the 382 deaths identified in Nauru, a medical record was found and reviewed for
288 (75%) cases.
When medical records were reviewed and compared with the medical certificates,
specific underlying CoD defined according to the ICDv10 General Mortality List One
categories (66), was found to match in 48% of cases if the first (non-standard) line of
the certificate was included. Agreement increases to 58% when using broad
categories (by ICD chapter). Table 26 shows the consistency between the medical
record and medical certificate when including all lines on the certificate. Excluding
the non-standard line, using only the section compliant with the ICD standard, there
was 43% agreement between the medical records and medical certificates at by
specific causes and 54% by ICD chapter.
� � �
270
Using only the additional line before Part I of the Nauruan certificate, compared to
the medical records, there is an 18% match with specific causes and 25% by ICD
chapter.
Many cases with discrepancies in the underlying cause between the medical record
and death certificate had potentially related conditions identified. For example, two
cases certified as septicaemia on the medical certificate were identified from the
medical record as due to diabetes and meningitis, respectively. Both plausibly
related diagnoses. Similarly, cases certified as due to tuberculosis were identified
from the medical record as due to lung disease, although there was insufficient detail
for a specific diagnosis; again, plausibly the same condition. This was similar for a
case of hepatic cancer, certified as due to infectious hepatitis, although this
diagnosis was not identified on the medical record.
The greatest discrepancies (Table 26) were found in cases certified as due to
diseases of the genitourinary tract, with only 10% agreement with the medical
records. A further 30% were found to have an underlying CoD as diabetes in the
medical records. Only 50% of cases certified as due to diabetes on had this
identified as the underlying CoD in the medical record, although a further 24% (22
cases) were attributed to potential complications of diabetes without a diagnosis of
diabetes in the medical record. In contrast, known cases of diabetes, where diabetes
was selected as the underlying CoD from the medical record review, were also
poorly certified. Only 65% of these cases had diabetes as an underlying CoD on the
� � �
271
medical certificate. A further 19% (13 cases) were certified as being due to causes
that are potential complications of diabetes.
Six of 21 deaths (29%) certified as due to perinatal conditions on the medical
certificate were found to be stillbirths upon medical record review. As discussed in
Chapter three, government funeral payments to assist families following a death,
while creating a strong incentive for complete reporting, have also led to pressure
(real or perceived) on doctors to certify late term stillbirths as infant deaths to ensure
families are eligible for this assistance.
��
�
272
Tabl
e 26
: Com
paris
on o
f und
erly
ing
caus
e of
dea
th fr
om th
e m
edic
al c
ertif
icat
e of
dea
th (i
nclu
ding
the
non-
stan
dard
firs
t lin
e in
the
asse
ssm
ent o
f cau
sal s
eque
nce)
aga
inst
find
ings
of t
he m
edic
al re
cord
revi
ew: N
auru
, 200
5-20
09
� � �
273
As noted in the methods, the age, sex and causal distributions of cases for which a
medical record was found were compared to those for which no record was found to
identify whether any significant systematic bias would be introduced by restricting the
analysis to those cases for which a medical record could be reviewed.
Figure 31 shows the underlying cause distribution (by proportion) of deaths for which
a medical record could and could not be found. Causal distribution of deaths for
which a medical record was identified was determined based on underlying cause on
the original certificate (assuming inclusion of the first line), and the corrected
underlying CoD from the medical record review (as outlined in the methods).
Figure 31: Comparison of cause of death distributions by broad category for reviewed* and non-reviewed^ deaths: Nauru, 2005-2009
* deaths reviewed against the medical record; and ^non-reviewed deaths for which no record was found
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35 Missing�RecordsCause�from�DC�for�found�recordsAmmended�cause�based�on�review
� � �
274
As seen in Figure 31, corrections had very little impact on the causal distribution of
deaths by ICD chapter for the reviewed cases. There was however a significant
difference between the causal distributions of deaths for which a medical record
could and could not be found. Deaths with missing records were more much more
likely to be reported as due to ‘ill-defined conditions’ and non-specific circulatory
causes (such as ‘heart stopped’ or ‘heart attack’).
Cases for which a medical record could not be found also had a much higher
proportion of older adults 65 years and above (30% compared to 14% of those for
which a record was located) (Table 27).
Table 27: Comparison of age distribution for deaths with a medical certificate for which no medical record was found; and deaths with a certificate which
were reviewed against the medical record. Nauru, 2005-2009
Age Group
Cases Proportion (%) Not found Found Not found Found
<5 years 11 27 0.12 0.10 5-14 years 5 0.00 0.02 15-24 years 3 11 0.03 0.04 25-34 years 5 22 0.05 0.08 35-44 years 10 34 0.11 0.12 45-54 years 14 98 0.15 0.35 55-64 years 23 46 0.24 0.16 65+ years 28 39 0.30 0.14 Total 94 282 100 100
Based on these findings, the overall cause distributions by age and sex were
generated only from the deaths for which a medical record was found and could be
reviewed, rather than simply using the medical record review to correct individual
records as was done in Tonga (where a much smaller proportion of deaths could not
� � �
275
be located in the medical records). This decision may result in some selection bias,
particularly in the oldest age group, although this should be minimal as there is no
reason to expect any systematic “loss” of medical records based on the certified CoD
(such as a doctor keeping the files of patients with a specific illness). Restricting
cases to those for which cause could be validated should minimise reporting bias
that would potentially result in artificial overestimation of cause-specific mortality
from disease categories such as cardiovascular diseases and diabetes, which may
be used when the causal sequence is unknown.
As seen in Table 28, the majority (44%) of deaths in children aged 0-4 years in
Nauru are due to perinatal conditions, although external causes and respiratory
conditions are also important. Both external causes and respiratory causes featured
as important causes of death for 5-14 year olds (Table 29), although the total
number of deaths is very small and rates are therefore unstable, even when
aggregated over multiple years as shown here.
��
�
276
Tabl
e 28
: Dea
ths
by c
ause
(by
ICD
cha
pter
), pr
opor
tiona
l mor
talit
y an
d ca
use-
spec
ific
mor
talit
y ra
tes
for c
hild
ren
0-4
year
s:
Nau
ru, 2
005-
2009
Row
Lab
els
Dea
ths
Prop
ortio
nal
mor
talit
y (%
)
Low
er
95%
CI
(%)
Upp
er95
% C
I (%
)
Tota
l rat
e (d
eath
s pe
r 1,
000)
Low
er 9
5%
CI (
deat
hs
per 1
,000
)
Upp
er 9
5%
CI (
deat
hs
per 1
,000
) 1-
001
Infe
ctio
us d
isea
ses
0 0
0 9
0.0
0.0
1.1
1-02
6 N
eopl
asm
s 1
4 0
14
0.4
0.0
1.7
1-04
8 D
isea
ses
of th
e bl
ood
0 0
0 9
0.0
0.0
1.1
1-05
1 E
ndoc
rine,
nut
ritio
nal a
nd o
ther
m
etab
olic
dis
orde
rs
0 0
0 9
0.0
0.0
1.1
1-05
8 D
isea
ses
of th
e ne
rvou
s sy
stem
1
4 0
14
0.4
0.0
1.7
1-06
4 D
isea
ses
of th
e ci
rcul
ator
y sy
stem
0
0 0
9 0.
0 0.
0 1.
1
1-07
2 D
isea
ses
of th
e re
spira
tory
sy
stem
4
15
6 26
1.
4 0.
4 3.
1
1-07
8 D
isea
ses
of th
e di
gest
ive
tract
0
0 0
9 0.
0 0.
0 1.
1 1-
082
Dis
ease
s of
the
skin
1
4 0
14
0.4
0.0
1.7
1-08
4 D
isea
ses
of th
e ge
nito
-urin
ary
syst
em
2 7
1 18
0.
7 0.
1 2.
2
1-09
2 P
erin
atal
con
ditio
ns
12
44
35
53
4.2
2.5
6.4
1-09
3 C
onge
nita
l con
ditio
ns
2 7
1 18
0.
7 0.
1 2.
2 1-
094
Sig
ns, s
ympt
oms
and
othe
r ill-
defin
ed c
ause
s 1
4 0
14
0.4
0.0
1.7
1-09
5 E
xter
nal c
ause
s (in
jurie
s)
3 11
3
22
1.1
0.3
2.7
Tota
l 27
100
100
100
9.5
7.2
12.0
� � �
277
Table 29: Deaths by cause (by ICD chapter) and proportional mortality for children 5-14 years: Nauru, 2005-2009
Row Labels Deaths lower upperProportional mortality lower upper
1-048 Diseases of the blood 1 0.0 5.6 20% 2% 48%1-072 Diseases of the respiratory system 1 0.0 5.6 20% 2% 48%1-095 External causes (injuries) 3 0.6 8.8 60% 38% 75%Total 5 2 12 100% 100% 100%
For adults aged 15-59 years, endocrine, metabolic and nutritional diseases were
responsible for the highest proportion of deaths for both males (42%) and females
(32 %), consistent with the findings for all-age proportional mortality from the case
study presented in Chapter five. Cancers were responsible for 22% of deaths in adult
females, with leading cancers including breast cancer (4% of total deaths), lung
cancer (4% of total deaths) and cervical cancer (6% of total deaths). Lung cancer
was the most common cancer identified as the underlying CoD for males in this age
group, but was responsible for a significantly lower proportion of deaths (2%).
Proportional mortality and cause-specific rates for specific diseases are shown in
Table 30.
��
�
278
Tabl
e 30
: Dea
ths
by c
ause
(by
ICD
cha
pter
) and
pro
port
iona
l mor
talit
y fo
r adu
lts 1
5-54
yea
rs: N
auru
200
5-20
09
Cause�of�death�
Female�
Male�
Deaths�
Prop
or�
tiona
l�Mortality�
(%)�
Upp
er�
95%�
CI�
Lower�
95%�
CI�
Rate�
(deaths�
per�
100,000�
peop
le)�
Lower�
95%�
CI�
Upp
er�
95%�
CI�
Deaths
Prop
or�
tiona
l�Mortality�
(%)��
Upp
er�
95%�
CI�
Lower�
95%�
CI�
Rate�
(deaths�
per�
100,000�
peop
le)�
Lower�
95%�
CI�
Upp
er�
95%�
CI�
1�00
1�In
fect
ious
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seas
es�
6�6.
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911
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.221
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4.5
1.8
8.8
43.8
17.5
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eopl
asm
s�21
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27.9
168.
712
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210.
711
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crin
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ition
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nd�o
ther
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olic
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rder
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279
Cause�of�death�
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95%�CI
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� � �
281
Discussion
The results show high cause-specific mortality rates from NCDs in adults aged 15-59
years. Cause-specific mortality from endocrine and metabolic diseases (nearly all of
which is due to diabetes) is estimated at 241 and 412 deaths per 100,000 population
for females and males respectively. In comparison, age-standardised mortality
(based on Segi’s world population) from endocrine diseases in New Zealand in 1996
was 210 per 100,000 population for males and 190 per 100,000 population for
females (345). The high cause-specific mortality from NCDs, including diabetes,
demonstrates these diseases are having a direct impact on premature mortality in
Nauru, resulting in the stagnation in LE reported in Chapter five (case study two).
The study also demonstrated quality issues in both the medical certificates and the
medical records. Numerous cases, as discussed in the results, were found to be
missing a record of specific tests or diagnoses (such as hepatitis infection, or
diabetes), even when these were later implied in the medical certificate. Equally,
many certificates contained no mention of conditions clearly recorded in the medical
record. As such, there is a need to improve documentation and recording across
both sources. There was also limited agreement between the underlying CoD
identified from the medical record review with that identified on the additional line on
the Nauruan certificate. Agreement between sources was much greater when this
additional line was treated as part of the causal sequence when applying the ICD
rules to selecting CoD. In this way, the Nauruan certificate is treated as though there
are four lines in Part I. The extra line, which currently sits outside the causal
sequence section of the certificate should therefore be removed (and a 4th line added
to Part I if required) to ensure the medical certificate in Nauru conforms to the
international standard.
� � �
282
6.3 Medical Certificate of Death Review A medical record review relies on the ability to access and match data from the
medical certificate and the medical record. Where access to the medical records is
not available, it is still possible to make a determination about the quality of the
certification process based on the plausibility of the sequence, the specificity of the
information provided, and consistency with the cases’ age and sex, by reviewing the
original medical certificate.
6.3.1 Review of medical certification of death in Fiji
Aim and Objectives
In Fiji, MoH policy requires a medical certificate for all deaths (both hospital and
community deaths). These are collated in their HIS (PATIS) and routinely reconciled
with monthly nurses’ reports and hospital discharge data. The system assessment
presented in Chapter three identified previous tabulations of these data were
primarily based on the CoD reported on the certificate that coding staff considered to
be of most importance to public health. It was further noted the sequence of causes
was often altered upon data entry to ensure this “most important” cause was entered
first (and would therefore be included in the tabulations); while contributory causes
were entered onto the first available line in the sequence rather than in the labelled
field, and as such may have been moved into the causal sequence recorded. As
access to the medical records was not possible, a medical certificate review was
conducted to evaluate the impact of these practices on the CoD tabulations and
where possible, correct any systematic biases to generate causal distributions of
death by age and sex.
� � �
283
Methods
CoD data were obtained for all deaths reported to the MoH for 2000-2008. Data were
extracted from the MoH databases (PATIS for 2007 and 2008, and separate Access
databases for each earlier year). Medical certificates are coded and entered into
these databases by MoH staff at the national office. Data is checked against monthly
community nursing reports and hospital separation data to ensure all deaths are
captured. Variables extracted for each case included certificate number, sex,
ethnicity, date of birth or age, date of death, place of death (by region), and causes
of death (recorded as ICD codes). Names were not extracted to ensure data were
not identifiable.
Accuracy of the CoD data as collated in the MoH databases, was evaluated by
comparing a random sample of entered cases against the original medical certificate.
A random number generator was used to assign a number to each case extracted
from the database. These were sorted by assigned number and the first 48 records
for each year were selected (a total of 350 records) to provide a random sample.
Original medical certificates were sought for these cases from paper records kept by
the MoH. Age, sex, and causes of death (by line number) were then entered into an
excel spreadsheet from the certificate. Each line of the certificate and underlying
CoD was coded according to ICD rules by the researcher and checked by a WHO
collaborating centre for coding (in Sri Lanka). These fields were then compared to
data extracted from the MoH databases by certificate number.
The original medical certificate for each case was initially assessed to determine: a)
the causal sequence and contributory causes for each death; b) whether the causal
� � �
284
sequence as recorded on the certificate is plausible; and c) the underlying CoD
according to ICD rules. The medical certificate was then compared to the data
extracted from the MoH databases to identify: d) whether causes entered on the
database match those recorded on the certificate; e) which data fields each cause
line on the certificate has been entered into in the database; f) whether the code
assigned to each CoD in the database is the closest possible match for the data
provided; and g) which field (if any) contains the underlying CoD as determined by
applying the ICD rules to the original certificate.
Patterns of misreporting are then used to correct tabulated data from 2000-2008 to
generate a CoD profile for Fiji by age group and gender. Proportional mortality was
applied to the total mortality envelope by age group and sex previously estimated
and reported in Chapter five, to generate age-specific mortality rates. Confidence
intervals (95% CI’s) were generated based on the uncertainty of the cause
distribution (excluding uncertainty in the mortality envelope itself) using Poisson
distribution due to the small number of deaths by individual cause.
As some uncertainty may remain regarding the final allocation of CoD due to
previous data entry and tabulation procedures, a multiple CoD analysis was also
conducted for specific diseases of public health interest (based on ICDv10 General
Mortality List One (103 cause list) (68)) with age specific and age-standardised
mortality rates calculated according to total mentions on the certificate.
Results
Of the 384 records randomly selected for review, the original certificate was located
for 329 (86%) cases as shown in Table 31.
� � �
285
Table 31: Number of selected medical certificates found by age group: Fiji, 2000-2008
Age group (years) <1 1 to 4 5 to 14 15-59 60+ Total
No certificate found 10 5 10 18 12 55 Certificate found 42 7 54 132 94 329 Total records sought
52 12 64 150 106 384
% found 81 58 84 88 89 86
Of the original medical certificates located, only 51 were found to be of the current
certificate form (introduced in late 2007), with the remainder consisting of two
previous versions (both of which used three lines to report the causal sequence of
death, rather than four lines as per the newer certificate) and three deaths with the
causal sequence reported directly to PATIS.
More than half (59%) of all original certificates were recorded with a single CoD (with
only one line completed), consistent across all age groups (Table 32). Of those with
more than one line completed, 52% were found to have a clearly implausible causal
sequence, however in nearly all cases, the ICD rules would still have identified a
plausible underlying cause.
Table 32: Reviewed medical certificates by single versus multiple lines completed on the certificate: Fiji, 2000-2008
Recording on certificate
Age group (years)<1 1 to 4 5 to 14 15-59 60+ Total
Multiple lines completed on certificate
11 3 24 58 39 135
Single cause of death listed only
31 4 30 74 55 194
Total 42 7 54 132 94 329
� � �
286
Data entry practices were found to be less consistent, with the order of some, but not
all certificates, modified upon entry into the database.
Only 44% of original certificates were found to have the code associated with the text
on the first line also in the first line of the database. In order to allow for coding
errors, this was also reviewed by ICD chapter, where 69% of certificates were found
to have the same ICD chapter associated with the text on the first line also in the first
line of the database. This was limited to 60% if only certificates with more than one
line completed were selected. These findings indicate at least 30% (and up to 66%)
of deaths are either coded incorrectly or have causes entered into the database in a
different order to that appearing on the certificate (as anticipated from the system
assessment). As this selective reordering is not consistent however, it is not possible
to simply tabulate deaths from the first line of the database, as has been done for
previously reported data from Fiji. The proportion of deaths for which the underlying
cause was found in line one of the database is shown by age in the Table 33. As
seen in this table, 36% of the deaths which could be reviewed had the underlying
CoD recorded on the first line of the certificate. This increased to 61% when
evaluated by ICD chapter.
� � �
287
Table 33: Column of database where underlying cause of death from the original certificate (specific disease and by chapter) were found, by age group:
Fiji 2000-2008
Deaths Proportion (%)
Age group (years) <1 1 to
45 to 14
15-59 60+ Total <1 1 to
45 to 14
15-59
60+ Total
FIRST LINE (EXACT CAUSE)
11 1 15 45 47 119 27 14 28 34 50 36
FIRST LINE (ICD CHAPTER)
19 4 31 80 66 200 46 57 57 61 70 61
LAST LINE (EXACT CAUSE)
7 1 4 22 10 44 17 14 7 17 11 13
LAST LINE (ICD CHAPTER)
9 1 4 23 13 50 22 14 7 18 14 15
None 12 2 13 20 9 56 29 29 24 15 10 17
The similarity of these figures with the proportion of deaths for which the cause on
the certificate matched the database by ICD chapter, suggests this may be
accounted for, at least in part, by poor coding rather than a deliberate reordering of
the sequence. Only 13% of cases were found to have the underlying cause selected
according to ICD rules on the last line of the database, as would be expected if
certificates were completed correctly and entered as written.
Most concerning is that 17% of cases were found not to have the underlying cause
from the certificate recorded anywhere in the database sequence, resulting in all of
these cases being attributed to an incorrect category. This cannot be adequately
corrected from a medical certificate review and would require the original hard copy
of each certificate to be re-coded. These findings were relatively consistent across
gender, year, age group and ethnicity.
� � �
288
The proportion of deaths by cause for cases where the underlying cause was not
identified in the database is shown in Table 34.
Table 34: Distribution of deaths by broad cause where underlying cause of death was not recorded in the Ministry of Health database. Fiji 2000-2008
Cause Number Proportion (%) 1-001 Infectious Diseases 7 14 1-026 Neoplasms 5 10 1-048 Diseases of the blood and blood forming organs
1 2
1-051 Endocrine, nutritional and metabolic diseases
6 12
1-058 Diseases of the nervous system 4 8 1-064 Diseases of the circulatory system 6 12 1-072 Respiratory Illnesses 1 2 1-087 Maternal causes 1 2 1-092 Perinatal conditions 4 8 1-093 Congenital conditions 4 8 1-094 Signs, symptoms and ill-defined 2 4 1-095 External causes 10 20 Total 51 100
As seen in the above table, causes were distributed across ICD chapters, although
deaths due to external causes were particularly poorly captured.
Given uncertainty in both the original coding and reordering of the causal sequence
during data entry, it was determined not possible to select one line of the database
as the underlying cause by specific disease, nor would it be possible to re-select
underlying cause from the database for 2000-2008 with any accuracy without
reviewing each original certificate. As the original certificates were not available, a
multiple CoD analysis for selected causes of specific public health importance was
� � �
289
conducted. These included maternal deaths, external causes, diabetes and
genitourinary diseases, malnutrition, cancers, hepatitis, TB and septicaemia.
Underlying cause by ICD chapter was also re-selected from the tabulated data for
2000-2008 (excluding 2007 for which not all data was available), to provide an
indicative picture of CoD across age groups, although some uncertainty will remain
in these estimates as the medical certificate review indicated that some underlying
causes were not represented in the tabulated data.
Table 35: Deaths, proportional mortality and death rate by selected cause (total mentions on the certificate) for children 0-4 years: Fiji 2000-2008
�� Females� Males�Total�deaths�
Proportion�of�deaths�with�cause�mentioned
Rate�(deaths�per�100,000�population)
Lower�95%�CI�
Upper�95%�CI�
Meningitis� 23� 28� 51 2.2 7.9 0� 28.9Pneumonia� 49� 49� 98 4.2 15.2 0� 44.2Malnutrition� 20� 23� 43 1.8 6.7 0� 25.9Septicaemia� 97� 97� 194 8.3 30.0 0� 70.9Rheumatic�fever/�RHD� 3� 5� 8 0.3 1.2 0� 9.5Total� 1196� 1146� 2342 100.0 �� �� ��
Excluding deaths from 2007 as not all required variables in the data set were available from the extracted data
� � �
290
Table 36: Deaths and proportional mortality by mentioned cause (total mentions on the certificate) for selected causes for children 5-14 years: Fiji
2000-2008
Cause�of�death��Deaths
Proportion�(excluding�ill�
defined)�
Lower�95%�CI�
Upper�95%�CI�
1�001�Infectious�Diseases� 224� 7.7� 2.8� 14.4�1�026�Neoplasms� 91� 3.1� 0.6� 8.8�1�048�Diseases�of�the�blood�and�blood�forming�organs� 14� 0.5� 0.0� 3.7�
1�051�Endocrine,�nutritional�and�metabolic�diseases� 124� 4.2� 1.1� 10.2�
1�055�Mental�Disorders� 1� 0.0� 0.0� 3.7�1�058�Diseases�of�the�Nervous�System� 103� 3.5� 0.6� 8.8�
1�064�Diseases�of�the�circulatory�system� 236� 8.1� 3.5� 15.8�
1�072�Respiratory�Diseases� 288� 9.8� 4.1� 17.1�1�078�Diseases�of�the�digestive�system� 44� 1.5� 0.0� 5.6�
1�082�Diseases�of�the�skin� 13� 0.4� 0.0� 3.7�1�083�Musculoskeletal�Diseases� 1� 0.0� 0.0� 3.7�1�084�Diseases�of�the�genitourinary�tract� 29� 1.0� 0.0� 3.7�
1�087�Maternal�deaths� 7� 0.2� 0.0� 3.7�1�092�Perinatal�Conditions� 1285� 43.9� 31.1� 57.9�1�093�Congenital�Conditions� 259� 8.8� 3.5� 15.8�1�094�Ill�defined� 96� 3.3� 0.6� 8.8�1�095�External�causes� 208� 7.1� 2.8� 14.4�Total�� 3023�Total�excluding�ill�defined� 2927� 100.0�
��
�
291
Tabl
e 37
: Dea
ths
and
prop
ortio
nal m
orta
lity
by u
nder
lyin
g ca
use
(by
ICD
cha
pter
) for
adu
lts 1
5-59
yea
rs:
Fiji
2005
-200
8
Cause�of�death��
Females�
Males�
Deaths�
Prop
ortio
n�(excluding�
ill�defined
)�Lower�
95%�CI�
Upp
er�
95%�CI�
Deaths�
Prop
ortio
n�(excluding�ill�
defin
ed)��
Lower�
95%�CI�
Upp
er�
95%�CI�
1�00
1�In
fect
ious
�Dise
ases
�18
16.
22.
213
.123
05.
31.
65.
6�1�
026�
Neo
plas
ms�
610
20.9
12.2
30.9
328
7.6
2.8
7.2�
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8�Di
seas
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f�the
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nd�b
lood
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rmin
g�or
gans
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1.0
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3.7
200.
50.
03.
7�1�
051�
Endo
crin
e,�n
utrit
iona
l�and
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etab
olic
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ases
�68
623
.514
.634
.575
717
.49.
917
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ases
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23.2
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34.5
1679
38.7
26.9
38.1
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Resp
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stem
�82
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7.2
145
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skel
etal
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ases
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ases
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tern
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292
Tabl
e 38
: Dea
ths
and
prop
ortio
nal m
orta
lity
by m
entio
ned
caus
e (to
tal m
entio
ns o
n th
e ce
rtifi
cate
) for
sel
ecte
d ca
uses
for
adul
ts 1
5-64
yea
rs: F
iji 2
000-
2008
Caus
e�of
�dea
th�
Fem
ale�
Mal
e�
Deat
hs�
Prop
ortio
n�of
�dea
ths�
with
�cau
se�
men
tione
d
Rate
�(d
eath
s�per
�10
0,00
0�po
pula
tion)
Low
er�
95%
�CI�
Upp
er�
95%
�CI�
Deat
hs�
Prop
ortio
n�of
�dea
ths�
with
�cau
se�
men
tione
d
Rate
�(d
eath
s�per
�10
0,00
0�po
pula
tion)
Low
er�
95%
�CI�
Upp
er�
95%
�CI�
Tube
rcul
osis�
47�
0.7
2.4
0.0
7.9�
850.
84.
60.
011
.5�
Hepa
titis�
15�
0.2
0.8
0.0
3.9�
410.
42.
20.
07.
6�Di
abet
es�
1391
�21
.370
.540
.510
0.4�
1534
15.3
83.5
50.9
116.
1�Rh
eum
atic
�Hea
rt�
Dise
ase�
157�
2.4
8.0
0.0
18.0
�13
51.
37.
30.
017
.0�
Isch
aem
ic�H
eart
�Di
seas
e�63
6�9.
732
.212
.052
.5�
3083
30.8
167.
912
1.7
214.
0�
Sept
icae
mia
�85
7�13
.143
.419
.966
.9�
868
8.7
47.3
22.7
71.8
�Ca
ncer
�12
49�
19.1
63.3
34.9
91.6
�67
86.
836
.915
.258
.6�
Tota
l�65
37�
����
����
1001
4�
��
��
� � �
293
6.4 Examining cause of death distributions from alternative data
sources: understanding causal patterns in the absence of
nationally representative data from medical certification
Ideally, health planners require medically certified CoD for the whole population, or a
representative sample. Yet many countries have to rely on hospital data for
reporting and monitoring, since that is the only data available. While this is a useful
proxy to monitor changes in specific CoD, it is important to remember CoD
distributions for hospital data may not be representative of the total population (283).
Deaths which occur suddenly from external causes, stroke and myocardial infarction,
where not all cases make it to hospital for treatment, may be under-represented
(283), while treatment practices and the level of services available may affect the
likelihood of death from NCDs (such as specific types of cancer and liver disease)
occurring in hospital.
Differences in reporting patterns from hospital and community deaths are explored
below in relation to Vanuatu, which has three CoD data collections: hospital
separation data (based on primary diagnosis) medical certificates for hospital deaths,
and community nursing reports.
6.4.1 Comparison of cause of death distributions from hospital and
community data in Vanuatu
Aim and Objectives
As noted in Chapter three, reporting completeness from routine CRVS systems in
Vanuatu is currently too low to enable data to be corrected and used directly to
derive estimates of mortality level (61). The most reliable data on mortality level
� � �
294
comes from survey (such as the MICs described in chapter 2 which examined U5M
and IMR)(157), and census analyses (61, 138, 144). However, neither MICS nor
census analyses collect information on CoD. CoD data, when reported, are usually
derived from hospital separation data based on the primary diagnosis and reported
as leading causes of death for all ages.
As national representative CoD data are not available, hospital data (medical
certification and hospital separation data) and community health reporting data are
reviewed to generate proportional mortality distributions by age and sex. Similarities
and differences in the resultant causal distributions are reviewed to: a) identify key
public health issues contributing to premature mortality by cause; b) quantify at a
broad level the contribution of these disease categories to premature mortality where
possible; c) identify public health issues from the community data that may have
been previously unrecognised in published data from Vanuatu based on hospital
reporting; and d) highlight systematic biases that may occur when CoD profiles are
obtained from hospital data alone.
Methods
De-identified unit record data on deaths for 2001-2007 were obtained during in-
country visits from three sources: hospital separation data, medical certificates for
deaths in hospital and notices of death submitted with monthly nursing reports from
smaller health facilities (area health centres, dispensaries and aid posts).
CoD data are collected for deaths in the two main hospitals: Vila the national referral
hospital at Port Vila on Efate(the main Island), and the secondary district hospital at
Luganville, in Santo province. These are two of only three hospitals in Vanuatu
� � �
295
routinely staffed by doctors (Tanna hospital is staffed by a single doctor and the
remaining two district hospitals are staffed by nurse practitioners).
Hospital separation data were extracted from the HIS. This is an Access database
maintained at the national level, with data entry for hospital deaths completed locally
at Vila Central and Luganville hospitals, and copied to the national office periodically
when staff travel to or from the national office. Local copies of the database were
used as they are considered to be more complete than the copy held at the national
office. CoD is recorded according to principal diagnosis, with up to two additional
fields for other causes, and fields for accidents and external causes, and medical
procedures. These fields appear to have been completed as a causal sequence, as
found on a medical certificate (with immediate cause recorded first, working
backwards to the underlying cause), so underlying cause was selected and coded
according to ICDv10 rules for medical certificates (68). Causes are coded according
by medical record staff at the hospital, with both text and code entered in the
database.
Medical certificates are completed semi-routinely (and particularly upon request by
family members) for hospital deaths. They are not routinely coded or collated for
Luganville, and although collated for Port Vila hospital are not routinely coded,
analysed or reported. Data were extracted from the original medical certificates at
Luganville hospital and from an Excel spreadsheet maintained by medical records
staff at Vila Central. The Excel spreadsheet included all causes listed in Part I of the
certificate as text, identified by line. Contributory causes were not recorded. Medical
certificates are predominantly completed by the attending doctor, although ni-
Vanuatu doctors in Luganville noted they completed most certificates as
� � �
296
internationally trained doctors based at the hospital were not always comfortable
undertaking this task due to language difficulties. Most certified deaths occur in
hospital, however doctors occasionally certify deaths that occur in the community.
Place of death was not well recorded on either the original certificate or the
spreadsheet; and it is therefore not possible to separate certified deaths according to
location. Underlying cause was selected and coded [by KC] according to ICDv10
(68).
Deaths in the community are captured through health system reporting by health
centres, dispensaries, and aid posts. Nurses or nurse aides at each facility are
required to submit a monthly report of activities including any deaths in their area. A
notice of death form is also collected for deaths in the community. This includes
name, age, date of death, place of death and CoD as a single free-text field. The
system is designed so deaths are collated and coded according to ICDv10 before
being entered into the national HIS database at the provincial level, although in
practice this is mostly performed at the national HIS office.
Data were sorted according to health facility code and deaths that occurred in Vila or
Luganville hospitals were removed. For deaths where specific age was not recorded,
deaths recorded as “adults” were attributed to age group 15-59 years and “old” or
“elderly” to age group 60+ years.
The approximate proportion of deaths accounted for by each data set was calculated
based on an average of 1,394 deaths per year (as estimated by the Vanuatu
National Statistics Office) (346), assuming little variation over the period under
investigation. Deaths were tabulated by broad age group (0-4 years, 5-14 years, 15-
� � �
297
59 years, 60+ years) to address the differences in CoD distribution by age, while
allowing for a lack of age detail in some of the data. Foetal deaths were identified
and removed from all data sets. Cases where there was uncertainty whether the
death was a stillbirth or early neonatal were counted as neonatal, as separate
reporting mechanisms exist for stillbirths which mean in practice, they should not be
recorded in these data.
All data sets were aggregated to ICDv10 General Mortality List One (103 causes),
with childhood deaths (0-4 years) aggregated to ICDv10 Condensed List three for
U5M. Tabulations of reported deaths by cause, proportional mortality and
proportional mortality adjusted to exclude unknown and ill-defined deaths were then
generated for each data set. Comparisons by broad category of cause, as derived
from the GBD, are also presented.
Results
a) Hospital Separation Data:
There were 954 deaths reported in the hospital separation data. This is an average
of 136 deaths per year, or nearly 10% of the expected deaths in Vanuatu. Of these,
800 deaths were recorded at Vila Hospital and 154 at Luganville. There were 13
foetal deaths removed from the data (eight from Vila Central and five from
Luganville). A further 14 deaths had no age attributed and were excluded from
further analysis.
b) Medical Certificates of Death:
There were 1790 deaths medically certified between 2001 and 2007. This is an
average of 256 deaths per year, or approximately 18% of the expected deaths in
� � �
298
Vanuatu. Of these, 1466 were recorded at Vila Central Hospital and 324 at
Luganville Hospital. One hundred foetal deaths were also certified at Vila Central
Hospital, and a further 33 at Luganville hospital. These were subsequently removed
from the data set. There were an additional 78 deaths excluded from further analysis
as they had no age recorded.
c) Health Facility records
There were 2,563 deaths recorded by health facilities between 2001 and 2007, an
average of 366 deaths per year, or approximately 26% of the expected deaths in
Vanuatu. Twenty one deaths reported from Vila Hospital were removed from the
data set. All were adult deaths (13 aged 15-59 years and eight aged 60 or older).
There were also 102 foetal deaths removed from the data. The age and gender
distribution of deaths reported through the health facilities is shown in Figure 32.
Forty six deaths had no age details and were excluded from further analysis.
Figure 32: Age distribution of reported deaths by data source and gender; Vanuatu, 2001-2007
MC = Medical certificate data, Hosp = Hospital discharge data, and HF = Health
Facility data. M= Males and F= Females.
� � �
299
The age distribution of deaths reported in the hospital separation data and medical
certificates were quite similar, with children aged 0-4 years accounting for 15% and
22% of reported deaths respectively. There were few deaths reported in the 5-14
year age group (4 % and 2% respectively), with the majority of deaths in the adult
age group of 15-59 years (48% and 41% respectively). Approximately 30% were
people aged 60 years or older at time of death (32% and 31% respectively for
hospital separation data and medical certificates) . The community health facility
data showed similar proportions of childhood deaths (16% for ages 0-4 years and
3% for 5-14 year olds) as other sources, but a lower proportion of adult deaths for
ages 15-59 years (29%) and a significantly higher proportion of deaths in people
aged 60 years or older (51%). There was a low proportion of deaths of unknown
ages (1-4%) in each of the data sources. As these accounted for only a small
number of deaths they were excluded from further analysis.
Deaths attributed to unknown or ill-defined causes (Code 1-064 ‘signs, symptoms or
ill-defined causes) are shown in Table 39.
Table 39: Proportion of deaths (%) attributed to Ill-defined or unknown causes by age group, gender and source: Vanuatu 2001-2007
Age group Gender Hospital Separation
Data
MedicalCertificates
Data
HealthFacility
Data0-4 years Both 1.4 3.9 22.6 5-14 years Both 8.6 2.7 24.7 15-59 years Males 1.8 5.9 18.1 15-59 years Females 1.8 6.3 22.1
� � �
300
For deaths in ages 0-4 years, both neonatal causes amenable to intervention
through maternal health status, antenatal care, and birthing practices, and infectious
diseases, were listed in the leading causes of death once unknown and ill-defined
causes were removed (Table 40).
Malaria was the leading CoD reported in both the hospital separation data and health
facility reporting for 5-14 year olds. It ranked second in the medical certificate data,
with meningitis ranked first. External causes of death, drowning and burns (exposure
to smoke fire or flames), appeared in the leading five causes of death for this age
group reported through the health facilities, but did not rank highly in the hospital
data (Table 41).
��
�
301
Tabl
e 40
: Dea
ths,
pro
port
iona
l mor
talit
y an
d ca
use-
spec
ific
mor
talit
y ra
tes
(by
ICD
v10
Gen
eral
Mor
talit
y Li
st 3
) for
chi
ldre
n 0-
4 ye
ars:
Van
uatu
200
0-20
07
Cau
se o
f Dea
th
Rep
orte
d D
eath
s P
ropo
rtion
al M
orta
lity
(%) e
xclu
ding
un
know
n an
d ill
-def
ined
cas
es
Hos
pita
lSe
para
tion
Dat
a
Med
ical
Cer
tific
ates
Hea
lthFa
cilit
y R
epor
ting
Hos
pita
lSe
para
tion
Dat
a
Med
ical
Cer
tific
ates
H
ealth
Faci
lity
Rep
ortin
g 3-
002
Dia
rrho
ea a
nd g
astro
ente
ritis
of
pres
umed
infe
ctio
us o
rigin
4
823
2.8
2.2
7.4
3-00
4 Tu
berc
ulos
is
1
10.
00.
30.
33-
005
Teta
nus
2
21.
40.
00.
63-
008
Men
ingo
cocc
al in
fect
ion
11
0.
70.
30.
03-
009
Sep
ticae
mia
3
3
2.1
0.8
0.0
3-01
3 O
ther
vira
l dis
ease
s 1
2
0.7
0.0
0.6
3-01
4 M
alar
ia
75
294.
91.
39.
43-
015
Rem
aind
er o
f cer
tain
infe
ctio
us a
nd
para
sitic
dis
ease
s
31
0.0
0.8
0.3
3-01
8 R
emai
nder
of m
alig
nant
neo
plas
ms
12
20.
70.
50.
63-
019
Rem
aind
er o
f neo
plas
ms
10.
00.
00.
33-
021
Ana
emia
s
12
0.0
0.3
0.6
3-02
2 R
emai
nder
of d
isea
ses
of th
e bl
ood
and
bloo
d-fo
rmin
g or
gans
and
cer
tain
di
sord
ers
invo
lvin
g th
e im
mun
e m
echa
nism
11
0.
70.
30.
0
3-02
4 M
alnu
tritio
n an
d ot
her n
utrit
iona
l de
ficie
ncie
s 4
128
2.8
3.2
2.6
3-02
5 R
emai
nder
of e
ndoc
rine,
nut
ritio
nal
and
met
abol
ic d
isea
ses
60.
00.
01.
9
3-02
7 M
enin
gitis
2
26
1.4
0.5
1.9
3-02
8 R
emai
nder
of d
isea
ses
of th
e ne
rvou
s sy
stem
5
31
3.5
0.8
0.3
��
�
302
3-02
9 D
isea
ses
of th
e ea
r and
mas
toid
pr
oces
s1
0.7
0.0
0.0
3-03
0 D
isea
ses
of th
e ci
rcul
ator
y sy
stem
3
86
2.1
2.2
1.9
3-03
2 P
neum
onia
10
1751
6.9
4.6
16.5
3-03
3 O
ther
acu
te re
spira
tory
infe
ctio
ns
12
80.
70.
52.
63-
034
Rem
aind
er o
f dis
ease
s of
the
resp
irato
ry s
yste
m
24
41.
41.
11.
3
3-03
5 D
isea
ses
of th
e di
gest
ive
syst
em
24
41.
41.
11.
33-
036
Dis
ease
s of
the
geni
tour
inar
y sy
stem
1
1
0.7
0.0
0.3
3-03
8 Fe
tus
and
new
born
affe
cted
by
mat
erna
l fac
tors
and
by
com
plic
atio
ns
of p
regn
ancy
, lab
our a
nd d
eliv
ery
341
402.
111
.012
.9
3-03
9 D
isor
ders
rela
ting
to le
ngth
of
gest
atio
n an
d fe
tal g
row
th
3484
2423
.622
.67.
8
3-04
0 B
irth
traum
a
1
0.0
0.0
0.3
3-04
1 In
traut
erin
e hy
poxi
a an
d bi
rth a
sphy
xia
1655
511
.114
.81.
63-
042
Res
pira
tory
dis
tress
of n
ewbo
rn
15
10.
04.
00.
33-
043
Con
geni
tal p
neum
onia
1
0.0
0.3
0.0
3-04
4 O
ther
resp
irato
ry c
ondi
tions
of
new
born
10
78
6.9
1.9
2.6
3-04
5 B
acte
rial s
epsi
s of
new
born
2
194
1.4
5.1
1.3
3-04
7 H
aem
orrh
agic
and
hae
mat
olog
ical
di
sord
ers
of fe
tus
and
new
born
1
44
0.7
1.1
1.3
3-04
8 R
emai
nder
of p
erin
atal
con
ditio
ns
56
283.
51.
69.
13-
050
Con
geni
tal h
ydro
ceph
alus
and
spi
nal
bifid
a 1
72
0.7
1.9
0.6
3-05
1 O
ther
con
geni
tal m
alfo
rmat
ions
of t
he
nerv
ous
syst
em
1
0.
70.
00.
0
3-05
2 C
onge
nita
l mal
form
atio
ns o
f the
hea
rt 3
91
2.1
2.4
0.3
3-05
4 D
own'
s sy
ndro
me
and
othe
r ch
rom
osom
al a
bnor
mal
ities
3
81
2.1
2.2
0.3
��
�
303
3-05
5 O
ther
con
geni
tal m
alfo
rmat
ions
5
1715
3.5
4.6
4.9
3-05
8 O
ther
sym
ptom
s, s
igns
and
abn
orm
al
clin
ical
and
labo
rato
ry fi
ndin
gs, n
ot
else
whe
re c
lass
ified
28
90
3-05
9 A
ll ot
her d
isea
ses
23
21.
40.
80.
63-
061
Tran
spor
t acc
iden
ts
13
10.
70.
80.
33-
062
Acc
iden
tal d
row
ning
and
sub
mer
sion
4
0.0
1.1
0.0
3-06
4 E
xpos
ure
to s
mok
e, fi
re a
nd fl
ames
2
61
1.4
1.6
0.3
3-06
5 A
ccid
enta
l poi
soni
ng b
y an
d ex
posu
re
to n
oxio
us s
ubst
ance
s 1
15
0.7
0.3
1.6
3-06
6 A
ssau
lt
5
0.0
0.0
1.6
3-06
7 A
ll ot
her e
xter
nal c
ause
s 3
53
2.1
1.3
1.0
��
�
304
Tabl
e 41
: Dea
ths,
pro
port
iona
l mor
talit
y an
d ca
use-
spec
ific
mor
talit
y ra
tes
(by
ICD
v10
Gen
eral
Mor
talit
y Li
st O
ne) f
or
child
ren
5-14
yea
rs: V
anua
tu 2
000-
2007
R
epor
ted
Dea
ths
Pro
porti
onal
Mor
talit
y (%
) exc
ludi
ng
unkn
own
and
ill-d
efin
ed c
ases
H
ospi
tal
Sepa
ratio
nD
ata
Med
ical
Cer
tific
ates
H
ealth
Faci
lity
Rep
ortin
g
Hos
pita
lSe
para
tion
Dat
a
Med
ical
Cer
tific
ates
H
ealth
Faci
lity
Rep
ortin
g 1-
003
Dia
rrho
ea a
nd g
astro
ente
ritis
of
pres
umed
infe
ctio
us o
rigin
1
0.0
0.0
1.8
1-00
5 R
espi
rato
ry tu
berc
ulos
is
11
13.
12.
81.
8 1-
006
Oth
er tu
berc
ulos
is
1
3.
10.
00.
0 1-
008
Teta
nus
10.
00.
01.
8 1-
012
Sep
ticae
mia
2
2
6.3
5.6
0.0
1-02
1 M
alar
ia
45
1012
.513
.918
.2
1-04
2 M
alig
nant
neo
plas
m o
f men
inge
s, b
rain
an
d ot
her p
arts
of c
entra
l ner
vous
sys
tem
2
1
6.3
0.0
1.8
1-04
3 N
on-H
odgk
in's
lym
phom
a 1
1
3.1
2.8
0.0
1-04
5 Le
ukae
mia
s
1 1
0.0
2.8
1.8
1-04
6 R
emai
nder
of m
alig
nant
neo
plas
ms
11
43.
12.
87.
3 1-
049
Ana
emia
s 1
3
3.1
0.0
5.5
1-05
0 R
emai
nder
of d
isea
ses
of th
e bl
ood
and
bloo
d-fo
rmin
g or
gans
and
cer
tain
di
sord
ers
invo
lvin
g th
e im
mun
e m
echa
nism
2
0.
05.
60.
0
1-05
3 M
alnu
tritio
n 3
1
9.4
0.0
1.8
1-05
4 R
emai
nder
of e
ndoc
rine,
nut
ritio
nal a
nd
met
abol
ic d
isea
ses
1
3.
10.
00.
0
1-05
9 M
enin
gitis
3
6 1
9.4
16.7
1.8
1-06
1 R
emai
nder
of d
isea
ses
of th
e ne
rvou
s sy
stem
1
1 4
3.1
2.8
7.3
��
�
305
1-06
3 D
isea
ses
of th
e ea
r and
mas
toid
pr
oces
s
1
0.0
2.8
0.0
1-06
7 Is
chae
mic
hea
rt di
seas
es
1
3.
10.
00.
0 1-
068
Oth
er h
eart
dise
ases
2
1 6
6.3
2.8
10.9
1-
069
Cer
ebro
vasc
ular
dis
ease
s 1
3.1
0.0
0.0
1-07
1 R
emai
nder
of d
isea
ses
of th
e ci
rcul
ator
y sy
stem
1
3.1
0.0
0.0
1-07
4 P
neum
onia
1
2
3.1
0.0
3.6
1-07
5 O
ther
acu
te lo
wer
resp
irato
ry in
fect
ions
1
0.0
2.8
0.0
1-07
6 C
hron
ic lo
wer
resp
irato
ry d
isea
ses
1
20.
02.
83.
6 1-
080
Dis
ease
s of
the
liver
1 3
0.0
2.8
5.5
1-08
1 R
emai
nder
of d
isea
ses
of th
e di
gest
ive
syst
em
10.
00.
01.
8
1-08
3 D
isea
ses
of th
e m
uscu
losk
elet
al
syst
em a
nd c
onne
ctiv
e tis
sue
2
0.
05.
60.
0
1-08
6 R
emai
nder
of d
isea
ses
of th
e ge
nito
urin
ary
syst
em
11
3.
12.
80.
0
1-09
3 C
onge
nita
l mal
form
atio
ns,
defo
rmat
ions
and
chr
omos
omal
ab
norm
aliti
es
31
9.
42.
80.
0
1-09
4 Sy
mpt
oms,
sig
ns a
nd a
bnor
mal
cl
inic
al a
nd la
bora
tory
find
ings
, not
el
sew
here
cla
ssifi
ed
31
18
1-09
6 Tr
ansp
ort a
ccid
ents
2
0.0
5.6
0.0
1-09
7 Fa
lls
11
13.
12.
81.
8 1-
098
Acc
iden
tal d
row
ning
and
sub
mer
sion
2 4
0.0
5.6
7.3
1-09
9 E
xpos
ure
to s
mok
e, fi
re a
nd fl
ames
1 4
0.0
2.8
7.3
1-10
2 A
ssau
lt
1
0.0
0.0
1.8
1-10
3 A
ll ot
her e
xter
nal c
ause
s
1 3
0.0
2.8
5.5
TO
TAL
3537
73
� � �
306
The leading causes of deaths for males aged 15-59 were heart diseases, liver
diseases and diabetes. Differences in specific cause distribution between other heart
diseases (ischaemic heart disease and cerebrovascular diseases) (Table 42)
between data sources in this age group possibly reflect both more accurate reporting
by doctors on the medical certificate, and greater likelihood of these deaths being
recorded as non-specific heart disease in health facility reports by a nursing aide
rather than reflecting a difference in underlying disease. For females in this age
group, leading causes of death also included heart diseases, liver disease and
diabetes; with cervical and breast cancer also ranking highly in both the hospital and
health facility data (Table 42). As with ages 15-59, chronic NCDs were the leading
causes of death in older adults (Appendix four).
��
�
307
Tabl
e 42
: Dea
ths
and
prop
ortio
nal m
orta
lity
(exc
ludi
ng il
l-def
ined
cau
ses)
by
caus
e (IC
Dv1
0 G
ener
al M
orta
lity
List
1) f
or
adul
ts 1
5-59
yea
rs: V
anua
tu 2
000-
2007
Caus
e�of
�dea
th�
MAL
ES�
FEM
ALES
�
Repo
rted
�Dea
ths
Prop
ortio
nal�M
orta
lity�
(%)*
��Re
port
ed�D
eath
sPr
opor
tiona
l�Mor
talit
y�(%
)*��
Hosp
MC�
HF�
Hosp
�M
C�HF
�Ho
spM
C�HF
�Ho
sp�
MC�
HF�
1�00
1�Infectious�Diseases�
2141
329.3
9.8�
9.6
2227
2310.1
9.1
8.4�
1�00
3�Di
arrh
oea�
and�
gast
roen
terit
is�of
�pr
esum
ed�in
fect
ious
�orig
in�
��2
30
0.5�
0.9
��1
10
0.3
0.4�
1�00
4�O
ther
�inte
stin
al�in
fect
ious
�dise
ases
1��
��0.
40�
01
11
0.5
0.3
0.4�
1�00
5�Re
spira
tory
�tube
rcul
osis�
34
91.
31�
2.7
15
80.
51.
72.
9�1�
006�
Oth
er�tu
berc
ulos
is�1
31
0.4
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Discussion
For deaths amongst 0-4 year olds, all sources of data reflect the importance of
neonatal causes of death, demonstrating the importance of antenatal care,
appropriate delivery services and improved maternal health in reducing mortality in
this age group. Infectious diseases (pneumonia, malaria and diarrhoea) represent a
significant proportion of deaths in this age group, as reported by all sources. Further
investment in Integrated Management of Childhood Illnesses (IMCI) programs, as
recommended by the World Health Organisation to target these areas at a
community level, will be necessary if Vanuatu is to show considerable progress
towards the MDGs to reduce infant and childhood mortality by 2015. For 5-14 year
olds, the differences in proportional mortality reflects the importance of external
causes (such as drowning and burns) and the high proportion of non-specific causes
recorded in health facility data
NCDs were the leading causes of death for adults in both the 15-59 year age group
and 60+ age group. For males, these were predominantly cardiovascular diseases,
liver diseases and diabetes; while for females, neoplasms were also important, with
both cervical and breast cancer appearing in the leading causes of death.
As data were collected in 2008, the estimates presented here are only for 2001-
2007. As Vanuatu continues to improve data collection and further data becomes
available, it will be important to repeat this analysis for later years. There has been
significant investment in both reproductive health programs and malaria control in
Vanuatu since these data were collected (62). As a consequence, proportional
mortality from neonatal causes and malaria could reasonably be expected to have
313
reduced, although it is anticipated that they will remain important causes of death for
the foreseeable future.
Hospital separation data would normally have the principal diagnosis recorded first,
but this field was recorded as the mode of death (cardio-respiratory arrest, heart
stopped, stopped breathing etc) in many of the cases reviewed, indicating that
tabulation based on the first field of the database would have been misleading.
Although it is not possible to provide national estimates of cause-specific mortality
rates due to the uncertainty in existing data collections, the data presented in this
paper clearly demonstrates Vanuatu is dealing with a double burden of disease, with
significant proportions of mortality attributed to both infectious diseases and NCDs.
This is further supported by the high proportion of deaths in females 15-59 years
attributed to maternal causes. While the high proportion of NCDs in adults from the
health facility data may in part be attributed to reporting of non-specific causes such
as “heart problem” and “heart-stopped”, that this pattern is reflected in the hospital
separation and medical certificate data adds to the evidence that mortality burden
from NCDs is significant. The high proportion of deaths attributed to NCDs such as
cardiovascular disease and diabetes in adults aged 15-59 is consistent with patterns
seen in other PICTs (such as Fiji, Nauru and Tonga (93, 112, 347)), where NCDs
have contributed to high premature adult mortality and subsequently limited
improvements in LE.
The high proportion of ‘other cancers’ reported in adults is (at least in part)
attributable to a high occurrence of thyroid cancer, consistent with published
incidence data for the 1980’s which suggested Vanuatu had one of the highest
314
occurrences of this type of cancer in the world (348). Previous studies also report a
higher incidence of cervical cancer and high-grade epithelial abnormalities (2.0%) on
screening than Australia (349), which is consistent with findings here that show
cervical cancer as an important CoD in adult females. Screening is however very
limited.
The high proportion of infant and childhood deaths attributed to premature birth or
low gestational weight for age found in this study is somewhat inconsistent with the
relatively low proportions of children born (regardless of outcome) in these
categories reported for 1982-2001 for Vila hospital. Thirty seven out of 1,000
deliveries were reported as premature during this time, with 45 per 1,000 deliveries
reported as small for age (350). This earlier study does report difficulties in
ascertaining gestational age and suggests a great deal of uncertainty in these
figures, with results suggesting either implausibly low capture of premature and low
weight births in hospital records, or high overall mortality rates within these high risk
infants (or both).
These findings highlight the need for more reliable data on CoD, particularly for
deaths that occur in the community, outside the hospitals. Despite a national
reporting system through community health facilities, these account for only 26% of
expected deaths nationally, far lower than the proportion of deaths that would occur
in the community. It is clear from the data presented here that deaths from external
causes are under-represented in the hospital data, and as such may not have been
adequately considered in public health priorities. Similarly, meningitis and malaria
appear more frequently in the community health facility reports than in the hospital
data. This may be due to a greater awareness of the importance of these diseases in
315
the community, resulting in better recognition (and subsequently reporting) than for
other causes of death. However, these data pre-date the large scale malaria
intervention in Vanuatu implemented over the last several years.
Despite data shortcomings, findings presented here provide national level CoD data
that will assist health planners and donor agencies ensure health programs can be
more readily targeted to the greatest areas of need.
6.4.2 Cause of death analysis from hospital, community nursing and civil
registry data in Kiribati
Aim and Objectives
Problems such as malnutrition, injuries and infectious diseases have been identified
as health priorities in Kiribati by the Ministry of Health and international organisations
such as the World Health Organisation (WHO) (108) and UNICEF (351), although
there has to date been limited information available to quantify the extent of these
health problems. As presented in Chapter 5, deaths are substantially under-reported
in Kiribati. Although medical certificates were sought from both medical records and
from each of the hospital ward, very few could be located with the only complete
data available coming from 2008 in Betio hospital (a minor health facility). Since
there were insufficient data for medical certificates of death to be used as a reliable
data source, and they were excluded from further analysis.
Methods
As CoD data could not be reliably extracted from medical certificates, both hospital
and civil registration data were examined for 2000 to 2008. Data were extracted
316
from: the database of hospital admissions administered by the medical records
section of the major hospital; the database of deaths from community health clinics;
and the database maintained by the Civil Registration Office. Stillbirths were
removed from the extracted records.
CoD as recorded in the civil registration data was translated from i-Kiribati to English
with the assistance of staff from the MoH, and coded to the underlying cause using
ICDv10 rules (68). CoD as reported through the Civil Registration Office is of limited
value, as it is derived from family reports and frequently consists of terms such as “e
boo” meaning “sudden death” or possibly “stroke” and “aoraki” meaning “sickness”,
along with English terms such as old age and senility; making attribution of cause
unreliable. The notable exception is for deaths due to external causes such as
suicide, vehicle accidents and drowning, where the cause is more immediate and
requires less interpretation. For injury related deaths, the data were also coded for
intent (intentional, unintentional or unknown) and whether the death was reported as
suicide.
Data were then tabulated by age group, gender and CoD. Similarly, hospital data
were tabulated by age group, ICD code of the principal diagnosis, and gender.
Cause, as proportional mortality by age group, is reported separately from the
hospital data and the civil registration data, as it was not possible to reconcile the
two sources from the de-identified data available. It is anticipated there would be
some overlap between these sources. Estimated age-specific mortality rates have
been calculated by applying proportional mortality from this study to the mortality
envelope (all-cause mortality) established in Chapter five for key causes of death of
public health importance. For deaths from external causes, age-specific mortality
317
rates were estimated as a ‘minimum rate’ and were not adjusted for either under-
reporting or “ill-defined” and “unknown” causes, as deaths from injury are often less
under-reported than those from other causes.
Cause is primarily reported by ICD chapter due to the lack of detail available and
small numbers of deaths given the population size. It is broken into more specific
categories (using ICDv10 General Mortality List One) when sufficient data were
available. Confidence intervals were calculated using Poisson distribution due to the
small number of events.
Results
Civil registration reported 2,639 deaths between 2000 and 2004 and 2,026 deaths
between 2005 and 2008. There were 263 deaths recorded in the hospital data from
2000-2004 and 652 deaths recorded from 2005-2008. A further 1,324 deaths were
recorded by the community health clinics in 2000-2004. For 2005 to 2008, there
were 65 males and 48 females for whom age group was not known, and 24 deaths
for which gender was not specified (one an adult 15-59 years, one older than 60
years and the remainder of unknown age). Although redistributed for analysis of
mortality level, these were excluded from further analysis for cause.
Proportional mortality from 2005 to 2008 by gender and broad age group is shown in
Figures 33-36 for deaths recorded through the MoH (including hospital and health
facility reporting), with both health data and civil registration data shown in Tables 43
and 44. A substantial proportion of reported deaths could not be attributed to a
cause, either due to no cause being reported, the death being coded to ‘signs and
symptoms’ or ‘ill-defined’, or in a small number of cases the condition or terminology
318
listed being indecipherable. The proportion of deaths reported through the civil
registration data not be attributed to a cause in 2005-2008 ranged from 25% for
males aged 15-59 years to 65% for males aged less than 1 year. Deaths reported
through the clinics had less than 20% of deaths fall into this category, with the
exception of females aged 15-59 where 23% could not be attributed to a cause.
Deaths occurring in hospital that could not be attributed to a cause ranged from 0%
up to 7.7% for males aged 5-14 years (Table 43).
Figure 33: Proportional mortality in Kiribati 2005-2008 for children 0-4 years, excluding ill-defined causes, as reported through the Ministry of Health
(hospital and community health facilities)
319
Figure 34: Proportional mortality in Kiribati 2005-2008 for children 5-14 years, excluding ill-defined causes, as reported through the Ministry of Health
(hospital and community health facilities)
Figure 35: Proportional mortality in Kiribati 2005-2008 for males 15-59 years, excluding ill-defined causes, as reported through the Ministry of Health
(hospital and community health facilities)
320
Figure 36: Proportional mortality in Kiribati 2005-2008 for females 15-59 years, excluding ill-defined causes, as reported through the Ministry of Health
(hospital and community health facilities)
321
Table 43: Proportion of ill-defined or unknown causes and adjusted proportional mortality (%) by age group and period: Kiribati males, 2000-2008
Age�Cat.�� Cause�of�Death�
MALES�
2000�2004� 2005�2008�
Civil�Reg.�Combined�
Health�Deaths�
Hospital�Deaths�
Clinic�Deaths� Civil�Reg.�
Combined�Health�Deaths�
Hospital�Deaths�
Clinic�Deaths�
<�1�Year�Ill�defined�/�Not�stated�or�Unclear� 40.7� 19.4 0.0 22.1 65.2� 13.3� 4.3 18.5
�� Infectious�diseases� 15.2� 10.1 0.0 11.9 0.0� 9.9� 4.4 13.6
��
Endocrine,�nutritional�and�metabolic�diseases� 0.0� 6.3 8.3 6.0 0.0� 11.7� 2.2 18.2
��Diseases�of�the�nervous�system� 12.1� 2.5 8.3 1.5 0.0� 2.7� 4.4 1.5
��Diseases�of�the�circulatory�system� 9.1� 0.0 0.0 0.0 12.5� 0.0� 0.0 0.0
��Diseases�of�the�respiratory�system� 30.3� 24.1 41.7 20.9 25.0� 18.9� 22.2 16.7
��Conditions�originating�in�the�perinatal�period� 21.2� 53.2 33.3 56.7 62.5� 52.3� 62.2 45.5
��Congenital�malformations� 0.0� 0.0 0.0 0.0 0.0� 0.9� 2.2 0.0
�� External�causes� 3.0� 2.5 8.3 1.5 0.0� 2.7� 0.0 4.5�� Other�causes� 6.1� 1.3 0.0 1.5 0.0� 0.9� 2.2 0.0
TOTAL� 100� 100 100 100 100� 100� 100 100
1�4�years�Ill�defined�/�Not�stated�or�Unclear� 40.7� 19.7 0.0 31.6 42.9� 8.7� 0.0 17.9
�� Infectious�diseases� 47.1� 44.9 43.5 46.2 18.8� 33.3� 33.9 32.6�� Neoplasms� 2.9� 0.0 0.0 0.0 6.3� 1.9� 1.7 2.2
��
Endocrine,�nutritional�and�metabolic�diseases� 17.6� 28.6 30.4 26.9 15.6� 36.2� 42.4 28.3
��Diseases�of�the�nervous�system� 2.9� 6.1 8.7 3.8 9.4� 6.7� 5.1 8.7
��Diseases�of�the�circulatory�system� 5.9� 2.0 0.0 3.8 9.4� 1.0� 1.7 0.0
��Diseases�of�the�respiratory�system� 5.9� 14.3 13.0 15.4 28.1� 15.2� 15.3 15.2
�� External�causes� 2.9� 4.1 4.3 3.8 9.4� 3.8� 0.0 8.7�� Other�causes� 8.8� 0.0 0.0 0.0 3.1� 1.9� 0.0 4.3
TOTAL� 100� 100 100 100 100� 100� 100 100
322
Age�Cat.�� Cause�of�Death�
MALES�
2000�2004� 2005�2008�
Civil�Reg.�Combined�
Health�Deaths�
Hospital�Deaths�
Clinic�Deaths� Civil�Reg.�
Combined�Health�Deaths�
Hospital�Deaths�
Clinic�Deaths�
5�14�years�
Ill�defined�/�Not�stated�or�Unclear� 20.6� 22.6 0.0 27.3 32.4� 6.9� 7.7 6.3
�� Infectious�diseases� 1.9� 26.8 11.1 31.3 0.0� 14.8� 16.7 13.3�� Neoplasms� 3.8� 0.0 0.0 0.0 8.3� 7.4� 8.3 6.7
��Diseases�of�blood�and�blood�forming�organs� 1.9� 7.3 22.2 3.1 8.3� 11.1� 25.0 0.0
��
Endocrine,�nutritional�and�metabolic�diseases� 1.9� 4.9 11.1 3.1 0.0� 0.0� 0.0 0.0
��Diseases�of�the�nervous�system� 3.8� 2.4 11.1 0.0 4.2� 14.8� 8.3 20.0
��Diseases�of�the�circulatory�system� 5.8� 2.4 0.0 3.1 8.3� 14.8� 25.0 6.7
��Diseases�of�the�respiratory�system� 0.0� 9.8 11.1 9.4 12.5� 0.0� 0.0 0.0
��Diseases�of�the�digestive�system� 30.8� 19.5 11.1 21.9 8.3� 7.4� 0.0 13.3
�� External�causes� 46.2� 26.8 22.2 28.1 45.8� 22.2� 8.3 33.3�� Other�causes� 0.0� 0.0 0.0 0.0 0.0� 7.4� 8.3 6.7
TOTAL� 100� 100 100 100 100� 100� 100 10015�59�years�
Ill�defined�/�Not�stated�or�Unclear� 23.4� 18.1 0.0 21.1 24.6� 5.0� 1.1 7.2
�� Infectious�diseases� 2.4� 6.0 5.7 5.1 3.9� 8.0� 11.4 6.0�� Neoplasms� 4.1� 2.4 2.7 8.5 6.3� 1.7� 2.8 1.1
��
Endocrine,�nutritional�and�metabolic�diseases� 6.4� 6.3 10.2 5.4 5.9� 9.3� 14.8 6.0
��Diseases�of�the�circulatory�system� 38.2� 33.7 20.3 36.6 43.4� 31.0� 17.0 39.6
��Diseases�of�the�digestive�system� 20.8� 23.0 27.1 22.1 21.9� 20.8� 25.6 17.9
��Diseases�of�the�genitourinary�system� 1.0� 0.9 1.7 0.7 0.7� 1.7� 2.8 1.1
��Pregnancy�and�childbirth� 0.0� 0.0 0.0 0.0 0.0� 0.0� 0.0 0.0
�� External�causes� 22.4� 21.8 10.2 24.3 14.5� 15.2� 5.7 21.1�� Other�causes� 3.7� 6.0 17.0 3.6 2.9� 11.8� 19.9 6.8
TOTAL� 100� 100 100 100 100� 100� 100 100
323
Table 44: Proportion of ill-defined or unknown causes and adjusted proportional mortality (%) by age group and period: Kiribati females, 2000-2008
Age�Cat.�� Cause�of�Death�
FEMALES�
2000�2004� 2005�2008�
Civil�Reg.�Combined�
Health�Deaths�
Hospital�Deaths�
Clinic�Deaths� Civil�Reg.�
Combined�Health�Deaths�
Hospital�Deaths�
Clinic�Deaths�
<�1�Year�Ill�defined�/�Not�stated�or�Unclear� 51.2� 11.4 5.3 13.3 38.9� 13.0� 5.4 16.9
�� Infectious�diseases� 13.6� 12.9 5.6 15.4 0.0� 7.4� 0.0 11.9
��
Endocrine,�nutritional�and�metabolic�diseases� 0.0� 10.0 11.1 9.6 0.0� 7.4� 2.9 10.2
��Diseases�of�the�nervous�system� 4.5� 5.7 0.0 7.7 0.0� 6.4� 2.9 8.5
��Diseases�of�the�circulatory�system� 4.5� 0.0 0.0 0.0 9.1� 0.0� 0.0 0.0
��Diseases�of�the�respiratory�system� 22.7� 14.3 16.7 13.5 27.3� 23.4� 20.0 25.4
��Conditions�originating�in�the�perinatal�period� 31.8� 50.0 66.7 44.2 54.5� 47.9� 68.6 35.6
��Congenital�malformations� 0.0� 1.4 0.0 1.9 0.0� 2.1� 0.0 3.4
�� External�causes� 0.0� 2.9 0.0 3.8 9.1� 1.1� 0.0 1.7�� Other�causes� 13.6� 2.8 0.0 3.8 0.0� 4.4� 5.8 3.4
TOTAL� 100� 100 100 100 100� 100� 100 100
1�4�years�Ill�defined�/�Not�stated�or�Unclear� 51.6� 11.0 0.0 17.0 44.2� 3.8� 2.3 5.7
�� Infectious�diseases� 59.4� 37.0 27.6 43.2 37.9� 26.7� 23.8 30.3�� Neoplasms� 0.0� 0.0 0.0 0.0 6.9� 0.0� 0.0 0.0
��
Endocrine,�nutritional�and�metabolic�diseases� 9.4� 31.5 37.9 27.3 17.2� 41.3� 40.5 42.4
��Diseases�of�the�nervous�system� 6.3� 12.3 17.2 9.1 3.4� 8.0� 9.5 6.1
��Diseases�of�the�circulatory�system� 0.0� 0.0 0.0 0.0 0.0� 1.3� 0.0 3.0
��Diseases�of�the�respiratory�system� 6.3� 9.6 13.8 6.8 10.3� 14.7� 21.4 6.1
�� External�causes� 9.4� 5.5 0.0 9.1 13.8� 8.0� 4.8 12.1�� Other�causes� 3.1� 4.2 3.4 4.6 10.3� 0.0� 0.0 0.0
TOTAL� 100� 100 100 100 100� 100� 100 100
324
Age�Cat.�� Cause�of�Death�
FEMALES�
2000�2004� 2005�2008�
Civil�Reg.�Combined�
Health�Deaths�
Hospital�Deaths�
Clinic�Deaths� Civil�Reg.�
Combined�Health�Deaths�
Hospital�Deaths�
Clinic�Deaths�
5�14�years�
Ill�defined�/�Not�stated�or�Unclear� 40.0� 21.7 0.0 26.3 35.3� 6.5� 0.0 12.5
�� Infectious�diseases� 8.3� 11.1 0.0 14.3 9.1� 20.7� 26.7 14.3�� Neoplasms� 0.0� 5.6 25.0 0.0 0.0� 3.4� 6.7 0.0
��Diseases�of�blood�and�blood�forming�organs� 8.3� 0.0 0.0 0.0 0.0� 3.4� 6.7 0.0
��
Endocrine,�nutritional�and�metabolic�diseases� 4.2� 11.1 25.0 7.1 4.5� 0.0� 0.0 0.0
��Diseases�of�the�nervous�system� 8.3� 5.6 25.0 0.0 4.5� 10.3� 6.7 14.3
��Diseases�of�the�circulatory�system� 4.2� 5.6 0.0 7.1 9.1� 6.9� 6.7 7.1
��Diseases�of�the�respiratory�system� 0.0� 0.0 0.0 0.0 13.6� 20.7� 20.0 21.4
��Diseases�of�the�digestive�system� 33.3� 38.9 0.0 50.0 31.8� 17.2� 13.3 21.4
�� External�causes� 33.3� 16.7 25.0 14.3 22.7� 13.8� 6.7 21.4�� Other�causes� 0.0� 5.6 0.0 7.1 4.5� 3.4� 6.7 0.0
TOTAL� 100� 100 100 100 100� 100� 100 10015�59�years�
Ill�defined�/�Not�stated�or�Unclear� 24.5� 23.2 1.7 30.9 30.6� 15.3� 1.9 22.8
�� Infectious�diseases� 4.5� 7.5 8.5 7.0 5.4� 12.6� 11.4 13.4�� Neoplasms� 14.7� 9.8 11.9 8.8 14.4� 13.8� 19.0 10.1
��
Endocrine,�nutritional�and�metabolic�diseases� 6.9� 12.7 13.6 12.3 11.3� 11.8� 18.1 7.4
��Diseases�of�the�circulatory�system� 20.7� 22.0 15.3 25.4 29.3� 23.6� 16.2 28.9
��Diseases�of�the�digestive�system� 29.9� 22.5 15.3 26.3 23.4� 15.0� 3.8 22.8
��Diseases�of�the�genitourinary�system� 4.5� 6.4 13.6 2.6 3.2� 2.4� 3.8 1.3
��Pregnancy�and�childbirth� 3.6� 5.8 6.8 5.3 0.5� 1.2� 1.9 0.7
�� External�causes� 6.9� 4.6 1.7 6.1 5.9� 4.3� 1.9 6.0�� Other�causes� 7.8� 8.2 11.9 6.2 6.8� 15.4� 23.9 9.3
TOTAL� 100� 100 100 100 100� 100� 100 100
325
The two major causes of death in children aged less than one from all sources were
conditions arising in the perinatal period and respiratory diseases. In children aged
1-4 years, 34% of the deaths occurring in hospital were attributable to malnutrition as
either the primary diagnosis or a contributing cause, equating to an age-specific
mortality rate of 2.5 per 1,000 in 2000-2004, to 4.5 per 1,000 in 2005-2008. In
children aged 5-14 years of age, external causes account for the greatest proportion
of deaths in both periods, with drowning and vehicle accidents featuring prominently.
A significant proportion of deaths in this age group is also attributed to disease of the
digestive tract
Table 45: Proportion (%) of hospital deaths of children aged 1-4 years where malnutrition was reported as either the primary diagnosis or contributing
factor, Kiribati 2000-2008
Hospital
Gender 2000-2004 2005-2008
Males 47.8 35.6
Females 48.3 32.6
Combined 48.1 34.3
The greatest proportion of deaths in both males and females 15-59 years for 2005 to
2008 was from diseases of the circulatory system (16-29% for females and 17-43%
for males). For males, 21-23% of deaths in both periods (based on civil registration
data and health system data) were attributable to diseases of the digestive system.
Causes in this category include hepatitis, liver disease and bleeding ulcers. For
males, deaths due to external causes were approximately 15% of those reported in
2005-2008, down from 22% in 2000-2004. Suicide was common, with the minimum
age-specific mortality rate for males (without adjusting for completeness or
326
percentage of deaths with cause unknown) calculated as 38.9 deaths per 100,000
population. Endocrine, nutritional and metabolic diseases, including diabetes, made
up only 6-9% of adult male deaths in 2005-2008. These were more important for
females, accounting for 7-13% of deaths in 2000-2004 and 12% of deaths in 2005-
2008. Neoplasms were also higher in females than for males, accounting for 14% of
deaths in 2005-2008. Maternal deaths accounted for 3.6-5.8% of adult female deaths
in 2000-2004, although this had reduced to 0.5-1.2% of deaths in 2005-2008.
Table 46: Minimum suicide rates (per 100,000 people) based on uncorrected data, Kiribati 2000-2008
Gender Intent
Age Group / Years 10-14 years 15-59 years
2000-2004
2005-2008
2000-2004
2005-2008
MaleDefinite suicide
11.4 8.7 51.3 38.9
All possible+ 19.0 21.8 80.3 55.6
FemaleDefinite suicide
4.2 0.0 5.6 1.4
All possible+ 12.5 4.6 6.0 3.2
Combined Definite suicide
7.9 4.5 30.5 20.4
All possible+ 15.8 13.5 44.9 30.4 *Civil registration data - not adjusted for “ill-defined” or “unknown” deaths or completeness of reporting. +Possible suicides are those for which intent was not recorded, including shootings, strangulation which may have been either self inflicted or as the result of assault.
327
Table 47: Estimated Age-specific mortality rate from Diabetes (as reported through civil registration and health system reporting) per 100,000 people
Gender Source Year
2000-2004 2005-2008
MaleCivil
Registration 72.0 81.1
Health 65.5 80.8
FemaleCivil
Registration 39.9 71.3
Health 73.3 59.4
Discussion
Despite the shortcomings of the data available, this study highlights that analysis of
even incomplete data can identify key public issues and provide better evidence for
decision making in health policy development and resource allocation in a setting
such as Kiribati. Although there has been a general acknowledgement amongst the
donor community that issues such as infectious diseases and malnutrition amongst
children are important in Kiribati, the scale of the contribution of malnutrition to
childhood mortality has perhaps been under-recognised, particularly given the
preventable nature of this problem. Additionally, the cause data has highlighted other
key population health issues that have perhaps been overlooked and need greater
attention in order to reduce the excess mortality in Kiribati. These issues include
injury related deaths (and suicide in particular), and digestive tract diseases (possibly
related to Hepatitis B prevalence) in adults.
328
6.5 Summary of method selection and application
As demonstrated in this chapter, both medical record and medical certificate review
provide a useful means of examining content validity of CoD data and correcting for
system errors in certificate completion and tabulation. The medical certificate review
is by necessity more limited, as the researcher has access to less of the original
information captured. As shown for Fiji, while this is useful to understanding data
limitations and interpreting estimates of CoD distributions, the medical certificate
review alone is unable to adequately correct for these concerns where significant
data quality errors are identified that are not accounted for by systemic practices.
Where medical certificates are not available for a representative population, the
studies from Vanuatu and Kiribati have demonstrated that while care must be taken
in generalising findings on CoD, it is possible to obtain a greater understanding of
the broad causes contributing to premature mortality in PICTs through the available
data from hospital, community health, and lay reporting sources. In particular, these
have proven useful in understanding previously under-recognised contribution of
external causes (particularly suicide and traffic accidents) in these communities.
These alternative approaches are however, not a long term substitute for quality
CoD data at a representative population level and cannot provide the detail to guide
planning and evaluation that is possible in Palau, Nauru and Tonga, where nationally
representative data is available. �
329
A summary of the data sources and methods used to assess CoD data and generate
CoD distributions is shown in the table below.
Table 48: Summary of data sources and methods for used in assessment of cause of death patterns for selected Pacific Islands
Country Data Source(s) Used in Final Calculations
Assessments and corrections applied*
Years of dataavailable
Fiji MoH – medical certificates Assessment of trends by broad category / Medical certificate review
2000-2008 (excluding 2004 /2007)
Kiribati MoH and Civil registry – hospital discharge data and community reported CoD
Analysis of broad categories only
2000-2009
Nauru MoH/Civil registry (shared data) derived from medical certificates & hospital records
Assessment of trends by broad category / Medical Record review
2005-2009
Palau MoH (Epidemiology spreadsheet) – tabulated data derived from medical certificates
Direct analysis from medical certificate (including coding and tabulation)
2004-2009 (excl 2007)
Tonga MoH HIS data from medical certificates
Medical record review 2001-2009
Vanuatu Hospital data (medical certificates and separation data) and community health centre reporting
Comparison of hospital and community data for broad categories
2007-2011
A summary of mortality and CoD patterns by age will be presented in the following
chapter for each of the selected countries.
330
7 Mortality patterns in the Pacific Islands
As outlined in the introduction to this thesis, there is a critical need for a greater
understanding of the mortality levels and causes of death contributing to these in the
PICTs. Given the available data, it has been possible to generate estimates of
mortality level for Fiji, Nauru and Palau, and to generate a range of plausible
estimates for Tonga and Kiribati. The plausible ranges presented provide greater
certainty around mortality level for health planning and evaluation. These mortality
estimates are presented in Table 49.
For Vanuatu, although there were insufficient data to use the vital registration
systems from either the MoH or Civil Registry office to obtain plausible estimates, it
has been possible to review existing sources of data such as the census analysis
and multi-variate modelling by Rajaratnam (41) for plausibility based on how these
methods performed in the other study countries. An “upper-limit” for LE (and hence a
331
lower limit for mortality) were generated based on these findings, that provides
greater context for planning than previously available (Chapter 5).
CoD distributions by age and sex were able to be generated for Palau, Nauru and
Tonga based on medically certified deaths. A plausible range for cause-specific
mortality rates was also able to be derived for Vanuatu from MoH data (from hospital
and community reporting sources), although this is based on primary diagnosis
rather than underlying CoD. For these countries, proportional mortality by age and
sex for selected causes from the ICD General Mortality List 1 (103 cause list) (68)
was applied to the mortality level derived in Chapter 5.
For Nauru, Palau and Tonga, confidence intervals for proportional mortality were
derived using a Poisson distribution and applied to the range of mortality estimates
(plausible limits for Tonga as described in Chapter 5, and 95% confidence intervals
based on normal approximation of the binomial for Palau and Nauru). A similar
approach was used in Vanuatu with the range of plausible estimates for proportional
mortality applied to the 95% confidence interval (based on a normal approximation of
the binomial) of the mortality rate.
Data from Kiribati and Fiji were both of insufficient quality to be reliable in deriving
cause-specific mortality rates for most diseases. In Fiji although there is near
universal certification of CoD, coding and tabulation errors are substantial but not
systematic (Chapter 6) and therefore cannot be readily corrected without re-coding
the original medical certificates. For Kiribati, deaths are reported through both the
health and civil registry system; with most causes of death captured through lay
reporting by the family or through nursing reports. For both countries however, it has
332
been possible to provide indicative CoD distributions (proportional mortality) by age
and sex by ICD chapter; with cause-specific rates for specific causes either where
reporting is less subject to diagnostic uncertainty (external causes in Kiribati) or
through multiple-CoD analysis (in Fiji) (Chapter 6).
Estimates of mortality and CoD from the studies in Chapter 5 and 6 are collated in
the remainder of this chapter to provide a picture of mortality and CoD by age group
and by country, with findings compared to previous estimates and assessed for
plausibility.
7.1 Summary measures of mortality Table 49 demonstrates a common pattern of low infant and child mortality and high
adult mortality, resulting in relatively low life expectancies across nearly all of the
study countries. Estimates of LE range from 48.6 years for males in Kiribati to 71.2
years for females in Palau. Males fare substantially worse than females across all
countries with an average LE 4-5 years lower than females, except in Kiribati where
the difference is even greater (even allowing for the range of plausible estimates).
IMR and U5 mortality remain fairly high in Kiribati at 48.9 to 52.9 and 67.3 to 73.6 per
1,000 live births respectively, and in Nauru at 28 and 46 deaths per 1,000 live births
respectively. For all other study countries, IMR was estimated to be below 20 deaths
per 1,000 live births. These figures indicate while there are still gains to be made in
improving child health in the region, infant and child mortality are not the major
influence contributing to the low life expectancies seen across countries.
333
Tabl
e 49
: Sum
mar
y of
key
mea
sure
s of
mor
talit
y fo
r sel
ecte
d Pa
cific
Isla
nds
Cou
ntry
R
efer
ence
Y
ear
IMR
(per
1,
000
live
birth
s)
U5M
(per
1,
000
live
birth
s)
Mal
e ad
ult m
orta
lity
(pro
babi
lity
of d
ying
be
twee
n ag
es 1
5-59
, %)
Fem
ale
adul
t m
orta
lity
(pro
babi
lity
of d
ying
bet
wee
n ag
es 1
5-59
, %)
Mal
e LE
at
birth
(y
ears
)
Fem
ale
LE
at b
irth
(yea
rs)
Fiji
2002
-200
4 19
(CI:
18-
20)
21.3
(CI:
20.1
- 22
.5)
27.6
20
.3
64.3
(CI:
64.0
–
64.6
)
69 (C
I: 68
.8
– 69
.4)
Kiri
bati
2000
-200
9 51
(p
laus
ible
ra
nge
41.8
-71.
3)
71.5
(6
6.8-
76.4
) 60
.0 (p
laus
ible
ra
nge
40.5
-60.
0)
37.4
(pla
usib
le
rang
e 22
.6- 3
7.4)
48
.6
(CI:
47.2
- 48
.3)
57.9
(C
I: 55
.0-
56.3
)
Nau
ru
2002
-200
7 28
(CI:
12-
55)
46.0
51
.0
40.1
52
.8
57.5
Pal
au
2004
-200
9 (e
xcl.
2007
) 16
.7
25
59.6
43
.1
65.8
(CI:
64.4
- 67
.2)
71.2
(CI:
69.9
- 72
.5)
To
nga
2005
-200
8 14
.7 –
24
.7
20.7
– 3
4.8
28.6
– 3
6.3
20.9
- 27.
7 65
.2 (C
I: 64
.6 -
65.8
69
.6 (C
I: 69
.0 –
70.
2)
Van
uatu
(low
er
plau
sibl
e lim
it fo
r mor
talit
y)*
2009
21
24
27
.5 (e
stim
ate)
17
.0 (e
stim
ate)
65
70
95%
Con
fiden
ce in
terv
als
(CI)
give
n in
bra
cket
s w
here
ava
ilabl
e.
* IM
R a
nd U
5M d
eriv
ed fr
om c
ensu
s da
ta (6
1), w
ith a
dult
mor
talit
y fro
m c
o-va
riate
mod
ellin
g by
Raj
arat
nam
et.
al. (
237)
334
The estimates of LE at birth as derived from the work presented in this thesis are
consistently lower than those published circa 2010 by WHO and presented in
Chapter 1 (Table 6). Rather than suggesting a decline in LE over the last few years,
the work presented through the previous chapters provides supporting evidence that
in many cases, previously published estimates, from WHO and other sources (2, 14,
115, 352), have under-estimated adult mortality in the Pacific Islands due to the use
of methods that did not adequately account for observed disparity between child and
adult mortality levels. In comparison, there was much greater similarity with the “best
estimates” identified by Taylor et. al. in 2005 (2) which were vetted for data source
and quality where possible (Table 50).
Table 50: Comparison of ‘Best estimates’ of life expectancy at birth (years) from this research against Taylor et. al. 2005 review (2)
Country Best estimate from 2005 review (2)
Best estimate from work presented in this thesis
Ref Year Male Female Ref Year Male Female Fiji 1996 65 69 2002-2004 64.3 69
Kiribati 1995 59 65 2000-2009 48.6
57.9
Nauru 1991-1993 54 61 2002-2007 52.8 57.5
Palau 1995 64 70 2004-2009 (excl. 2007)
65.8 71.2
Tonga 1997-1999 70 72 2005-2008 65.2 69.6 Vanuatu 1999 66 69 2009-2010 65 70
Despite the difference in time periods, estimates were similar for Fiji, Vanuatu, and
Palau. Estimates of LE for Tonga, Kiribati and Nauru were slightly higher than those
derived from the work in this thesis. In Tonga and Kiribati, the “best estimates”
selected by Taylor et. al. (2) were derived from demographic analysis of the most
recent census; both of which were based on model life tables derived from a single
335
parameter input (child survival) in the absence of any better data sources; and as
such are most likely to be based on an under-estimation of adult mortality (93, 112,
347).
For Nauru, the “best estimates” selected by Taylor et. al (2) were based on a direct
analysis of reported civil registration data that is estimated to be essentially complete
from an earlier time period. As such the difference in estimated LE may represent a
true decline over the last decade, although this is not significant and case study 2
demonstrated there has been no substantial change in LE for males or females in
Nauru over the last 50 years (112).
336
7.2 Mortality and cause of death distributions by age group
7.2.1 Infant and child mortality
As shown in Table 49, infant and childhood mortality varies substantially across the
selected study countries. So too does the CoD patterns associated with the mortality
levels. An overview of causes in this age group is given in the Table 51.
Countries such as Fiji, Palau and Tonga, with low U5M are dominated by perinatal
and congenital causes, indicating mortality is clustered in the first days and weeks of
life, and while some further gains may be possible in association with improvements
in ante and post-natal care; significant resource and technology investment may be
required to realise significant gains at these low levels. Kiribati had a much smaller
proportion of deaths attributed to congenital causes (1%) and perinatal causes
(27%), and more associated with causes such as respiratory illness (19%) and
infection (19%), reflecting continued mortality throughout this age group rather than
being concentrated only in the perinatal period.
Kiribati also had a high proportion (18%) of deaths in children (0-4 years) attributable
to Endocrine, nutritional and metabolic disorders, which in this age groups is
primarily accounted for by malnutrition and ‘electrolyte imbalance’. In comparison, in
Fiji where malnutrition (as part of the ICD chapter ‘Endocrine, nutritional and
metabolic diseases) did not feature as an underlying CoD, the multiple CoD analysis
identified malnutrition was a factor in a small proportion of deaths (<2%), with a
related mortality rate of 6.7 deaths per 100,000 population (95% CI: 0-25.9).
337
Measles, an important CoD in children globally (353), was not found to be a
significant CoD in any of the study countries, reflecting the high immunisation rates
in the region (354). Further investigation is warranted in Kiribati where there was a
high proportion of deaths in children from infectious diseases, but insufficient data
quality to allow disaggregation by cause to this level.
Malaria, which is responsible for 20.8% of deaths in children 1-4 years of age
globally (353) only occurs in Vanuatu of the study countries. Modelled estimates by
Murray and Rosenfeld et. al. in 2010 (355) estimated 1 death (95% CI: 0-3) per year
in children 0-4 years in Vanuatu due to most cases in Vanuatu being due to P.vivax
rather than the more serious P. falciparum. While hospital and community reporting
in Vanuatu (Chapter 6) reported only between 5 and 29 deaths respectively over 8
years (2000-2007), an average of <1 to 4 deaths per year, proportional mortality
ranged from 1.3 to 9.4% of deaths in this age group (depending on source),
indicating Malaria is potentially a greater problem in Vanuatu than previously
identified.
338
Tabl
e 51
: Key
mea
sure
s of
chi
ldho
od m
orta
lity
(0-4
yea
rs),
both
sex
es, f
or s
elec
ted
Paci
fic Is
land
s
IM
R (d
eath
s pe
r 1,0
00
live
birth
s)
U5M
(dea
ths
per 1
,000
live
bi
rths)
Pro
porti
onal
mor
talit
y (0
-4 y
ears
) (%
) R
ef. Y
ear
Infe
ctio
us
Dis
ease
s N
eopl
asm
sD
isea
ses
of th
e ne
rvou
s sy
stem
(p
rimar
ily
Men
ingi
tis)
Per
inat
al
Cau
ses
Con
geni
tal
Cau
ses
Res
pira
tory
Ill
ness
es
Inju
ry a
nd
othe
r ex
tern
al
caus
es
Oth
er
caus
es
Fiji
19 (C
I: 18
-20
) 21
.3 (C
I: 20
.1
- 22.
5)
8 3
4 44
9
10
7 15
20
02-2
004
Nau
ru
51
(pla
usib
le
rang
e 41
.8-
71.3
)
71.5
(C
I: 66
.8-
76.4
)
0 4
4 44
7
15
11
15
2000
-200
9
Kiri
bati
(Hea
lth
data
)
28 (C
I: 12
-55
) 46
.0
19
1 6
27
1 18
4
24
2002
-200
7
Pal
au
16.7
25
0
0 4
64
4 8
4 16
20
04-2
009
(exc
l. 20
07)
Tong
a 14
.7 –
24.
7 20
.7 –
34.
8 14
9
5 36
10
10
7
9 Le
vel:
2005
-20
08.
CO
D:
2001
-200
8
Van
uatu
21
24
6-
18
0.5-
1 1-
5 13
-15
6-11
6-
20
5-6
24-6
3 20
05-2
009
* ad
just
ed to
exc
lude
ill-d
efin
ed a
nd u
nkno
wn
caus
es
95%
Con
fiden
ce in
terv
als
(CI)
show
n
339
Estimates for Oceania from the newly released 2010 Global burden of disease study
(240) show fairly similar point estimates at 2010 as found in this study. U5M was
estimated for Fiji at 30 deaths per 1,000 live births, for Kiribati around 40 deaths per
1,000 live births, and Tonga at 20 deaths per 1,000 live births. Estimates for
Vanuatu, the other study country to be included in the GBD, were substantially
higher than the most recent census at around 32 deaths per 1,000 live births (240),
following closely the findings from the MICs survey which were based on a partial
birth history, and noted the potential for significant responder bias in the sample, with
households where a death had occurred less likely to take part in the survey (157).
The findings from the research in this thesis show significant inequalities in child
survival across the PICTs and indeed across the broader region, with IMR and U5M
in Australia and New Zealand having been stable below 5 deaths per 1,000 live
births for many years. While child mortality is not the primary driving factor behind
the stagnation in LE in the region, there are still significant gains to be made in this
area, and greater attention should be given to addressing those causes of death
which are amenable to intervention.
7.2.2 Adult mortality 15-59 years
Adult mortality was high across all the study countries, as shown in the following
tables, with men having a much greater probability of dying before their 60th birthday
than women. Adult mortality in Fiji and Tonga is nearly three times that in Australia
and New Zealand, with estimates for males in Palau, Kiribati and Nauru nearly
double that again (297, 356), highlighting the profound effect of premature mortality
in the region. As for child mortality, findings from the GBD study (240) are
340
reasonably similar at 2010 to the findings from this thesis (excluding female adult
mortality in Tonga), but show an ongoing decline in adult mortality which is not
consistent with the findings from the work presented in this thesis. The sustained
decline in adult mortality for males shown in Fiji (240) is inconsistent with the
stagnation shown for LE over the last 20 years (93). GBD estimates for female adult
mortality in Fiji show a similar decline over the previous decade although this is now
steady. Again this decline in adult mortality is inconsistent with reliable historical
estimates evaluated in Chapter 5, which show the stagnation in female LE began in
the mid 1980s, with no significant improvement since (93). Trends presented in
Chapter 5 (case study 3) which demonstrate this stagnation in LE are based on
both unadjusted empirical data from the Ministry of Health for 1996-2004 and
previously published estimates censored for reliability to exclude estimates for which
methods could not be verified or which include an assumption of mortality decline in
their methodology (such as estimates from the UN). Estimates of adult mortality
derived from the Ministry of Health data showed very little difference over 3 time
periods, with adult mortality (probability of dying between ages 15-59 inclusive)
estimated at 29.5% and 20.5% for males and females in 1996-1998; 30.5% and
20.8% for males and females in 1991-2001 and 27.6% and 20.3% for males and
females in 2002-2004 (93). This stagnation in LE and high adult mortality is
consistent with risk factor data from Fiji. A national survey of risk factor prevalence in
1980 showed adult prevalence of hypertension as 5.1–9.9% (using �160 mm hg
systolic and/or �95 mm hg diastolic and/or on treatment for hypertension) (357, 358)
while the Fiji STEPS survey in 2002 indicated adult prevalence of hypertension of
19% (using �140 mmhg systolic and /or �90 mmhg diastolic and/or on treatment for
hypertension) (359). Diabetes prevalence has remained at similar levels over this
341
time period, with prevalence reported as 1.1–11.8% (using two hour post 75 g
glucose load blood glucose of �11.1 mm/L and/or on treatment for diabetes) in the
1980 survey, and 12% (using fasting blood glucose of �7.0 mm/l and/or on treatment
for diabetes) in the 2002 steps survey (359). Although smoking prevalence has
declined from 46% of the population smoking in 1992 (NCD book) to 37% in 2002,
as reported in case study 3, cigarette sales in Fiji rose 273% from 1956 to 1984,
three times the population increase (88%) (359).
The modelled decline for Fiji may be a key driver of the more moderate declines in
adult mortality the GBD study portrays in Kiribati, Tonga, and Vanuatu, due to the
model drawing on neighbouring populations for which data was available (41) and
the relative size of Fiji (and therefore the number of deaths) to the other PICTs. For
Tonga, female adult mortality from the GBD study shows adult mortality around
13.5% (range approx: 11.5-17.0) (240) substantially lower than the findings
presented in Table 49 (111). This may potentially be explained by the model
anticipating a greater sex-differential in mortality than is seen in Tonga where adult
mortality is high for both men and women (Table 49), and from errors in the source
data. The input data displayed in the GBD modelling indicated it was derived from
vital registration, without correction for undercount, and shows significant
inconsistencies in level. As discussed in Chapters 3 and 5, the CRVS system in
Tonga is known to have some undercount issues, and there are notable differences
in completeness in the various routine collections from civil registry and the Ministry
of Health (all of which may be termed “vital registration data”) (164).
342
Key causes of death for adult males and females 15-59 years are shown in the
following tables (Tables 52-56). Although countries may be at different stages of the
epidemiological transition, NCDs dominate the causes of death in this age group.
Diabetes rates ranged from 38.2 (Palau) to 489 (Nauru) deaths per 100,000
population for males and 34.6 (Palau) to 268 (Nauru) deaths per 100,000 population
in females. Deaths related to diabetes in Fiji (2005-2008) were estimated from the
multiple CoD analysis in Chapter 6 at 83.5 deaths per 100,000 population for males
and 70.5 deaths per 100,000 population for females. Although rates were not
calculated for Kiribati, proportional mortality for mortality appears to be in a similar
range as the other study countries (excluding Nauru). While the extremely high
diabetes rates in Nauru could in part be a result of local certification preferences
given the low proportion of cardiovascular diseases in comparison, the cause-
specific mortality rates were derived following a medical record review with CoD
assigned from the medical record by a doctor external to the system. The high cause
specific mortality is therefore more likely to reflect the high prevalence of diabetes in
Nauru. In 2010, Nauru was estimated to have the highest prevalence of diabetes in
the world based on WHO figures at 30.9% of the population from 20-79 years of age,
with the second highest prevalence occurring in the United Arab Emirates with
18.7% prevalence; substantially lower than in Nauru (360).
Throughout the records reviewed across the study countries, it was noted deaths
due to diabetes were frequently related to septicaemia following infection of skin
abscesses or amputations. Many of the deaths reviewed noted the diabetes was
“uncontrolled”, and anecdotal discussions with physicians in the region suggest
343
many patients only present for care once the infection has spread or traditional
remedies have failed. Similar issues have previously been documented in the Pacific
population in New Zealand (361). This suggests while prevention of new cases is
important, it may also be possible to reduce mortality related to diabetes through
improvements in management of the disease in existing cases. Further work is
clearly needed to improve mortality data to allow reliable analysis of deaths by
specific sub-category of disease, in this case the specific complications of diabetes
according to the third digit of the ICD code; but also to identify current barriers to and
patterns of access to health care and ways of improving the ongoing care of patients
with chronic diseases.
Diseases of the circulatory system were among the top causes of death in all
countries. Both ischaemic and cerebrovascular diseases were found to be important
causes of death across the study countries. Rates could not be adequately identified
for either in Palau where most deaths were recorded as hypertensive heart disease
or other heart conditions, indicating the need for further review of certification
practices. The overall cause-specific mortality rate of 140.2 and 94.5 deaths per
100,000 people from diseases of the circulatory system in Palau (2005-2009) shows
a similar level of cardiovascular disease overall to Nauru and Vanuatu. While Palau
and Nauru are both recognised as being well into the epidemiological transition, that
diseases of the circulatory system occur at similar rates in Vanuatu (Tables 52 and
55) is perhaps a surprise, but is consistent with the risk factor data available for the
country. Small sample risk factor surveys were conducted by the Vanuatu Ministry of
Health in 1998 (62), 2007 (62) and 2011 (337). These surveys reflect a rapidly
increasing proportion of the population affected by NCD risk factors. Only 3% of the
344
population was reported to have diabetes in 1998 (62), this had increased to 12% in
2007 (62), and by 2011 was 18% (defined as percentage with raised fasting blood
glucose capillary whole blood value � 6.1 mmol/L or currently on medication for
raised blood glucose). Similarly hypertension prevalence increased from 13 to 15%
between 1998 and 2007, while those considered overweight (BMI � 25kgm3)
increased from 33% of the population in 1998 to 38% in 2007 and 51% in 2011 (62).
Proportional mortality from Kiribati also indicates similar levels of cardiovascular
disease. Cause-specific mortality related to Ischaemic heart disease in Fiji was
estimated at 167.9 and 32.2 deaths per 100,000 population for males and females
respectively indicating higher rates of ischaemic heart disease, but similar levels of
all cardiovascular diseases.
Rheumatic heart disease, which has previously been identified as an issue in the
Pacific (362-367) varied markedly across the study countries, and clearly remains a
public health issue in Nauru, and to a lesser extent in Palau, Tonga and Fiji (where
cause specific mortality associate with rheumatic heart disease was 7.3 and 8.0
deaths per 100,000 for males and females respectively in 2005-2008).
345
Tabl
e 52
: Adu
lt m
orta
lity
and
prop
ortio
nal m
orta
lity
(%) f
or k
ey c
ause
s (b
y IC
D c
hapt
er) f
or s
elec
ted
Paci
fic Is
land
s; M
ales
Cou
ntry
Adu
lt m
orta
lity
(pro
babi
lity
of
dyin
g be
twee
n ag
es 1
5-59
, %)
Age
gr
oup
for
caus
e of
de
ath
Ref
. ye
ar
Pro
porti
onal
mor
talit
y, e
xclu
ding
ill-d
efin
ed c
ause
s (%
)
Infe
ctio
us
dise
ases
N
eopl
asm
s
End
ocrin
e,
nutri
tiona
l and
m
etab
olic
di
seas
es
Circ
ulat
ory
dise
ases
R
espi
rato
ry
Illne
sses
Dis
ease
s of
the
dige
stiv
e tra
ct
Gen
ito-
urin
ary
dise
ases
Inju
ry a
nd
othe
r ex
tern
al
caus
es
Fiji
27.6
15
-59
2000
-20
08
5.3
7.6
17.4
38
.7
7.3
3.3
4.5
13.1
Nau
ru
51.0
15
-59
2005
-20
09
4.5
9.9
42.3
15
.3
7.2
2.7
5.4
9.9
Kiri
bati
(hea
lth
repo
rting
)
60.0
(pla
usib
le
rang
e 40
.5-
60.0
) 15
-59
2000
-20
09
4 2
9 31
6
21
1 15
Pal
au
59.6
15
-64
2004
-20
09
8.0
19.6
7.
6 21
.9
3.6
3.6
4.8
26.7
Tong
a 28
.6 –
36.
3 15
-59
2001
-20
09
5 16
8-
14
36-4
1 14
-15
4 2
6
Van
uatu
27
.5
(est
imat
e)
15-5
9 20
07-
2011
9-
10
12-1
6 6-
10
27-2
9 7-
9 8-
14
3-6
9-13
346
Table 53: Cause-specific mortality rates for key causes (by ICD General Mortality List 1) for selected Pacific Islands; Males 15-59 years
Cause� Crude�age�specific�mortality�rate�(deaths�per�100,000�population)�
Listcode Disease Palau 2004-
2009Tonga 2005-
2008Nauru 2002-
2007Vanuatu 2000-
2009
1-001 Certain infectious and parasitic diseases
51.0�(31.2���78.8)� 61.1���84.9� 53.2�(13.5���
141.7)� 60.0���75.6�
1-005 Tuberculosis 5.1�(0.6��18.4)� �*� 10.6�(0.2���112.5)� 6.5���20.8�
1-019 Viral hepatitis 22.9�(10.5���
43.6)� 18.4���24.7� 21.3�(2.0���87.7)� 13.6���37.0�
1-021 Malaria �NA� NA�� NA�� 3.2���23.2�
1-026 Neoplasms �125.4�(92.4���165.2)� 189.4���254.7� �116.9�(45.6���
239.0)� 74.2���119.6�
1-031 Malignant neoplasm of liver and intrahepatic bile ducts
43.3�(25.3���69.4)� 17.9���25.5� *�� 27.1���37.0�
1-034 Malignant neoplasm of trachea, bronchus and lung
30.6�(15.8���53.5)� 34.4���49� 21.3�(2.0��
87.7)� 3.9���13.9�
1-051Endocrine, nutritional and metabolic diseases
48.4�(29.2���75.7)� *�� 499.6�(286.5���
759.0)� 36.8���76.4�
1-052 Diabetes mellitus 38.2�(21.4���
63.1)� 94.2���222� 489.0��(279.4���745.1)� 34.9���74.9�
1-058 Diseases of the nervous system �*� 13.5���17.9� 21.3�(2.0���
87.7)� 14.2��37.0�
1-064 Diseases of the circulatory system
140.2�(105.7���182.5)� 423���644.2� 180.7�(82.2���
330.5)� 171.7���222.2�
1-065 Acute rheumatic fever and chronic rheumatic heart diseases
2.5�(0.1���14.2)� 8.7���11.6� 21.3�(2.0���
87.7)� 0.0���13.1�
1-067 Ischaemic heart diseases *�� 139.4���210.2� 74.4�(23.3���
175.1)� 3.9���94.9�
1-069 Cerebrovascular diseases *�� 40.4���66.4� 53.2�(13.5���
141.7)� 27.1���44.8�
1-072 Diseases of the respiratory system
22.9�(10.5���43.6)� 168.4���230.9� 85.0�(28.7���
191.4)� 46.5���65.6�
1-074 Pneumonia 2.5�(0.1���
14.2)� 14.8���20.2� 63.8�(18.3���158.6)� 3.2���23.9�
1-076 Chronic lower respiratory diseases
10.2�(2.8���26.1)� 92.7���124.6� 0.0�(0.0���
44.8)� 29.0���41.7�
1-078 Diseases of the digestive system
22.9�(10.5���43.6)� 46.6���63� 31.9�(5.1���
106.5)� 51.0���105.7�
1-080 Diseases of the liver 15.3�(5.6���
33.3)� 17.3���23.1� 21.3�(2.0���87.7)� 45.2���91.8�
347
1-084 Diseases of the genitourinary system
30.6�(15.8���53.5)� 25.1���36.6� 63.8�(18.3���
158.6)� 19.4���48.6�
1-095External causes of morbidity and mortality
�170.8�(132.4���217.0)� 68.2���91.6� *�� 56.8���97.2�
1-096 Transport accidents 22.9�(10.5���
43.6)� 13.1���17.5� 116.9�(45.6���239.0)� 5.8���30.9�
1-098 Accidental drowning and submersion
2.5��(0.1���14.2)� �*� 21.3�(2.0���
87.7)� 8.4���20.8�
1-101 Intentional self-harm 22.9��(10.5���
43.6)� 8.6���11.6� 31.9�(5.1���106.5)� 0.0���9.3�
1-102 Assault 12.7��(4.1����
29.8)� �*� 21.3�(2.0���87.7)� 2.6���16.2�
*NB/ not all rates available for all countries due to different study designs
Proportional mortality from neoplasms from the Ministry of Health data in Kiribati was
very low in comparison both to the other study countries and global norms; at 2% of
adult deaths for males, 10% for adult females. These figures were slightly higher in
the civil registry data at 4% and 13% respectively. The low mortality rates were
consistent with previously published data from Palafox et.al.(368) who reviewed
reported cancers from Ministry of Health data across Micronesia in the late 1990s,
and found Kiribati to have substantially lower reported incidence of a range of
cancers of public health importance. An extract from this data is shown below.
Table 54: Age-standardised incidence rates of specific cancer types 1980s-1990s (cases per 100,000 population) by country extracted from Palafox et. al
(368)
Cancer site Kiribati Palau Marshall Islands
Nauru
Breast 8.0 17.1 36.0 15.4 Cervix 4.5 37.5 60.5 55.0 Liver 0.5 19.4 10.2 5.7 Lung 4.4 34.6 41.1 42.8 Years 1989-1998 1985-1998 1985-1998 1985-1998
Note: age-adjusted to WHO world standard population, annualized.
Source: Palafox, Cancer in Micronesia (368)
348
As shown in the shaded column, incidence rates for each of these four key types of
cancer were substantially lower than would be expected. This is particularly true for
lung cancer given the high prevalence of smoking in Kiribati (74% of males and 45%
of females reported smoking daily in the 2006 STEPS survey) (335). Palafox et.al
noted in their paper the poor state of the records associated with cancer reporting in
Kiribati (368) and the difficulties of diagnosis given the limited diagnostic facilities
and access to higher level health services. It is likely these factors, are responsible
for both the low reported incidence of cancer as well as the low proportional
mortality, rather than a true absence of the disease in the population. This is
supported by Ou et al. whose review of reported cases in Kiribati for the same period
indicated nearly 18% of those cases who were treated, were not formally diagnosed
or treated for more than a year following the onset of symptoms (369). This lack of
diagnostic capacity may also be a factor in the relatively low proportion of deaths
attributed to respiratory disease (including chronic lower respiratory illness) given the
high smoking prevalence.
A significant increase in age-standardised mortality from Hepatitis B was shown in
Tonga between 2001-2004 and 2005-2008. Hepatitis, which codes to liver disease
(under Diseases of the digestive tract) when a causative virus is not recorded on the
medical certificate (68), is also likely to account for the high proportional mortality
from Diseases of the digestive tract reported for males in Kiribati (21% of adult
deaths), and to a lesser extent the proportional mortality attributed to this category in
the other study countries. As such, Hepatitis B represents a significant cause of
mortality in the region that is amenable to intervention. Universal Hepatitis B
vaccination in Pacific Island countries will take some years to generate a reduction in
349
mortality due to the current prevalence of the disease. A reduction in prevalence
(due to early vaccination) would reasonably be expected to result in reduced
mortality in future generations. All Pacific Island Countries offered universal
vaccination by the late 1990s, however by 2005, coverage in Fiji, Samoa, the
Solomon Islands, Tuvalu and Vanuatu remained below 80% (370).
Injury and external causes are a significant CoD for males in this region and need to
be explored further. CoD for these deaths was, as a whole, poorly recorded across
the region, with most datasets consistently capturing the type of injury but not how it
occurred. The available data suggest suicide is a significant issue, with estimates for
Kiribati (2005-2008) as at least 20 deaths per 100,000 population for males 15-59
(from civil registration data) not accounting for under-count in registration or ill-
defined cases. Deaths attributed to suicide in were also very high for males in Palau
and Nauru with cause specific mortality estimated at 22.9 and 31.9 deaths per
100,000 population respectively (without re-distribution of deaths attributed to ‘other
external causes’), with further work required in Fiji to further disaggregate by cause
the cause-specific mortality from external causes (70.7 deaths per 100,000
population for males 15-59 years in 2005-2008). These findings suggest while
methods of suicide may have changed, there has been very little progress in dealing
with this issue since the mid 1990’s when suicide rates in the Pacific were identified
as being amongst the highest in the world (371).
350
Tabl
e 55
: Adu
lt m
orta
lity
and
prop
ortio
nal m
orta
lity
(%) f
or k
ey c
ause
s (b
y IC
D c
hapt
er) f
or s
elec
ted
Paci
fic Is
land
s;
Fem
ales
Cou
ntry
Adu
lt m
orta
lity
(pro
babi
lity
of
dyin
g be
twee
n ag
es 1
5-59
, %
)
Age
gr
oup
for
caus
e of
de
ath
Ref
. ye
ar
Pro
porti
onal
mor
talit
y, e
xclu
ding
ill-d
efin
ed c
ause
s (%
)
Infe
ctio
us
dise
ases
N
eopl
asm
s
End
ocrin
e,
nutri
tiona
l an
d m
etab
olic
di
seas
es
Circ
ulat
ory
dise
ases
R
espi
rato
ry
Illne
sses
Dis
ease
s of
the
dige
stiv
e tra
ct
Mat
erna
l C
ause
s
Gen
ito-
urin
ary
dise
ases
Inju
ry
and
othe
r ex
tern
al
caus
es
Fiji
20.3
15
-59
2000
-20
08
6.2
20.9
23
.5
23.2
7.
2 2.
8 0.
7 4.
4 7.
1
Nau
ru
37.4
(p
laus
ible
ra
nge
22.6
- 37
.4)
15-5
9
2005
-20
09
6.4
22.3
31
.9
16.0
10
.6
4.3
1.1
5.3
2.1
Kiri
bati
(hea
lth
repo
rting
)
40.1
15
-59
2000
-20
09
4 10
7
29
6 23
1
1 6
Pal
au
43.1
15
-64
2004
-20
09
4.8
28.2
11
.3
24.2
3.
2 3.
2 0
8.1
9.7
Tong
a 20
.9- 2
7.7
15-5
9 20
01-
2009
5
20
14-2
0 28
-34
12
4 0-
1 1
3
Van
uatu
17
.0(e
stim
ate)
15
-59
2007
-20
11
8-10
28
-32
10-1
3 14
-16
6-7
5-7
3-10
4-
11
2-6
351
Table 56: Cause-specific mortality rates for key causes (by ICD General Mortality List 1) for selected Pacific Islands; Females 15-59 years
Cause� Crude�age�specific�mortality�rate�(deaths�per�100,000�population)�
Listcode Disease Palau 2004-
2009Tonga 2005-
2008Nauru 2002-
2007Vanuatu 2000-
2009
1-001 Certain infectious and parasitic diseases
18.9�(6.9���41.1)��
33.8 - 48.4 57.4��(15.4���144.2)� 30.6�����46.6�
1-005 Tuberculosis 0.0�(0.0���
11.6)� �*� 28.7�(1.9���141.3)� 1.8���13.4�
1-019 Viral hepatitis 9.4�(1.9���
27.6)�7.3 - 10.1
9.6�(0.2���61.5)� 5.1���8.3�
1-021 Malaria �NA� NA�� NA�� 6.2���11.5�
1-026 Neoplasms 110.2�(76.8���153.3)�
136.4 - 195.5 201.1�(90.7���354.4)� 102.0���145.3�
1-031 Malignant neoplasm of liver and intrahepatic bile ducts
9.4�(1.9���27.6)�
5.9 - 8.1 �*� 3.6���13.4�
1-034 Malignant neoplasm of trachea, bronchus and lung
9.4�(1.9���27.6)� 16.5���23.0� 38.3�(7.6���
113.1)� 0.0���4.6�
1-036 Malignant neoplasm of breast
15.7�(5.1���36.7)� 20.7�30.4� 38.3�(7.6���
113.1)� 13.1��43.4�
1-037 Malignant neoplasm of cervix uteri
12.6�(3.4���32.3)�
8.6 - 12.0 57.4�(15.4���
144.2)� 9.1���32.3�
1-051Endocrine, nutritional and metabolic diseases
44.1�(24.1���74.0)� *�� 287.2�(141.2���
472.8)� 35.7���61.4�
1-052 Diabetes mellitus 34.6�(17.3���
62.0)�98.3 - 190.4
268.1�(129.8���446.8)� 35.7���59.0�
1-058 Diseases of the nervous system *�� 17.9 - 24.8 9.6�(0.2���
61.5)� 5.1���13.4�
1-064 Diseases of the circulatory system
94.5�(63.7���134.9)�
194.2 - 321.3 143.6�(58.6���273.1)� 51.7���73.8�
1-065 Acute rheumatic fever and chronic rheumatic heart diseases
15.7�(5.1���36.7)� 10.2���14.3� 47.9�(11.3���
128.8)� 0.0���20.8�
1-067 Ischaemic heart diseases �*� 47.3 - 88.1
28.7�(4.3���96.8)� 0.0���11.1�
1-069 Cerebrovascular diseases *�� 31.3 - 46.7
47.9�(11.3���128.8)� 14.6���25.4�
1-072 Diseases of the respiratory system
12.6�(3.4��32.3)�
81.7 - 114.2 95.7�(33.5���203.0)� 22.6���33.7�
1-074 Pneumonia 0.0�(0.0���
11.6)�11.1 - 16.0
47.9�(11.3���128.8)� 2.6���8.3�
1-076 Chronic lower respiratory diseases
9.4�(1.9���27.6)�
23.4 - 33.6 19.1�(1.7���
79.8)� 14.9���23.5�
1-078 Diseases of the digestive system
12.6�(3.4���32.3)�
27.6 - 38.6 38.3�(7.6���113.1)� 16.8���31.8�
352
1-080 Diseases of the liver 6.3�(0.8���
22.8)�9.2 - 12.8
9.6�(0.2���61.5)� 11.7���26.8�
1-084 Diseases of the genitourinary system
31.5�(15.1���57.9)�
20.9 - 32.1 47.9�(11.3���
128.8)� 14.6���48.4�
1-087 Pregnancy, childbirth and the puerperium �*� 4.8 - 6.7 �*� 11.7���47.1�
1-095External causes of morbidity and mortality
37.8�(19.5���66.0)�
21.4 - 29.5 19.1�(1.7��
79.8)� 5.1���26.8�
1-096 Transport accidents 6.3�(0.8���
22.8)� �*� 19.1�(1.7���79.8)� 0.0���1.4�
1-098 Accidental drowning and submersion
0.0�(0.0���11.6)�
1.7 - 2.3 0.0�(0.0���
40.7)� 0.0���5.1�
1-101 Intentional self-harm 12.6�(3.4���
32.3)� *�� 0.0�(0.0���40.7)� 0.0���6.9�
1-102 Assault 3.1�(0.1���
17.5)� *�� 0� 3.3���8.3�
*NB/ not all rates available for all countries due to different study designs
Maternal mortality was responsible for a substantial proportion of deaths in Vanuatu
(ranging from 3-10 % of all female deaths from 15-59 years of age). This accounts
for between 11.7 and 47.1 deaths per 100,000 women. These estimates were
derived from 7 years of data (2001-2007) to minimise the impact of stochastic
variation. In Fiji, cause-specific mortality was estimated at 2.2 deaths per 100,000
women (2005-2008).
7.2.3 Mortality & Older adults
Understanding premature mortality in adults is of primary importance in improving
the health of Pacific Islanders, however the health of older adults is also of interest to
governments, the health sector and social security and pension funds, and to guide
decisions about the social and physical infrastructure required to support this group.
As noted in the previous section, premature adult mortality across the selected study
countries was high. This trend is continued through into older adulthood, with men
353
and women who reach 65 years of age (above the estimated LE at birth for most of
the study countries) only anticipated to live another 12 years for males and 8-13
years on average for females (Table 57). In Australia and New Zealand, men and
women reaching this age would on average be anticipated to live a further 18-19
years for males and 20-22 years for females (Table 57).
.
Table 57: Life expectancy at 65 years for selected countries
Country Reference Year Males Females Fiji 2002-2004 11.4 13.9 Kiribati 2005-2009 8.2 10.5 Nauru 2002-2007 12.4 7.9 Palau 2004-2009 12.2 13.6
Tonga 2005-2009 (observed / unadjusted)
11.5 13.5
Vanuatu (estimated)
2009-2010 12.2 14.7
Australia (372) 2009-2011 19.1 22.0 New Zealand (356) 2005-2007 17.9 20.6
Causes of death in older adults are typically more poorly recorded than in other
groups due to the high proportion of deaths that occur at home (14) and multiple co-
morbidities, which may make determining a single underlying CoD more difficult
(247). This is reflected in the high proportion of ill-defined causes seen in older
adults across the study countries. Leading causes of deaths across all countries in
this age group were NCDs.
354
7.3 Country summaries An overview of the key findings on mortality level and cause for each of the selected
study countries is provided below and briefly discussed in relation to known risk
factors and previous studies.
7.3.1 Fiji
Case study 3 (Chapter 5) which examined both MoH data from Fiji as well as
previously published estimates clearly demonstrated many past estimates of
mortality for Fiji have significantly underestimated adult mortality. When screened for
plausibility, long term trends demonstrate a long term stagnation in LE since the
early 1980s for both males and females, with no sustained improvements over the
previous 20 to 30 years. Although infant and child mortality estimates were found to
be higher than several previous estimates by the Fiji MoH and other government
sources (14), IMR fell significantly over the 1980s to 1990s and is now stable below
20 deaths per 1,000 live births; a level at which IMR could not be considered to be a
primary influence on the stagnation in LE reported in this work. While migration of
healthy adults from Fiji could potentially contribute to some of the stagnation of LE,
further analysis by ethnicity yet to be published, suggests that adult mortality is
increasing most notably amongst Fijian i-Tauki, especially for women. This is
inconsistent with migration which is known to be higher in the Fijian Indian
population.
Despite almost universal medical certification, coding and tabulation practices
currently limit the value of this data for establishing CoD patterns for specific causes,
with 17% medical certificates reviewed found not to have the underlying CoD
355
represented in the data tabulations. Further work is recommended to re-examine
completed certificates in order to correct these issues.
Despite the data uncertainty (as described in the previous chapters), NCDs are
clearly having a critical impact on premature adult mortality. Plausible published
estimates (case study 3) demonstrate the proportion of deaths from NCDs for all
ages has increased substantially over the previous few decades (93) while deaths
due to infectious disease and external causes have both fallen. The medical
certificate review presented in Chapter 6 demonstrates that this is clearly not a
function of additional deaths occurring from NCDs at older ages as people live
longer, with NCDs the leading CoD in both males and females from ages 15-54
years of age.
Given the uncertainty that remains in the data, tabulations were derived for broad
CoD categories only in Fiji. For adults 15-59 years the tabulations for both men and
women demonstrate the vast majority of deaths are due to NCDs, including diseases
of the circulatory system, endocrine and metabolic disease and neoplasms. The
multiple CoD analysis found 21% of deaths in this age group for females and 15%
for males were associated with diabetes. This equates to a cause-specific mortality
rate of 70.5 (95%CI: 40.5-100.4) and 83.5 (95%CI: 50.1 – 116.1) deaths per 100,000
population respectively. As these rates include all deaths for which diabetes was
listed on the medical certificate they are not comparable with other populations, but
do provide health planners an understanding of the scale of the issue in Fiji.
Ischaemic heart disease was a CoD in 30.8% of males and but only 9.7% of
females, indicating cause specific mortality of 167.9 (95%CI 121.7 0 214.0) and 32.2
(95%CI: 12.0 – 52.5) deaths per 100,000 population respectively. Rheumatic heart
356
disease was not a major CoD for adults, and was attributed as a CoD in only 2.4% of
women and 1.3% of males.
Hepatitis was listed as a CoD in only 0.2% of deaths for adult women and 0.4% of
deaths for adult males indicating the hepatitis vaccination program which has been
running for several decades in Fiji has been effective.
The high cause-specific mortality rates from NCDs are consistent with previous
studies in Fiji which note steady increases in hospital admissions from
cardiovascular diseases and diabetes in the 1980s (357). Changes in risk factors
over the last 20-50 years have also been noted, with trends towards consumption of
imported high energy foods (373, 374), and an increasing proportion of the
population leading a sedentary lifestyle (375). Prevalence of cardiovascular
diseases and diabetes has been documented as higher in populations where these
changes were more ubiquitous than in more traditional rural areas.(74, 376)
7.3.2 Kiribati
Results from Kiribati depict a country still in the early stages of the epidemiological
transition with high rates of infant and child mortality, and a high prevalence of
infectious diseases. The data from this study suggests Kiribati has one of the lowest
life expectancies in the region, estimated to be 48.6 years for males and 57.9 years
for females. This is several years lower than published estimates for Nauru (Table
6), and lower for males than published estimates for PNG (Table 6); countries
previously recognised as having the lowest LE in the region. These low life
expectancies reflect high mortality rates across all age groups.
357
Infant mortality was estimated at 51.6 deaths per 1,000 live births and U5M at 71.5
deaths per 1,000 live births. Malnutrition remains a serious contributor to childhood
mortality in Kiribati, with 23% of deaths in children 0-4 years recorded by the hospital
or community health facilities attributed to ‘Endocrine, metabolic and nutritional
disorders’ as defined in the ICD. If these figures are representative at a broader
community level, this would equate to approximately 12 of every 1,000 children born
dying directly from malnutrition before their 5th birthday. A further 27% of deaths in
this age group were attributed to infectious diseases or respiratory conditions; thus
highlighting the large proportion of preventable deaths.
Adult mortality is relatively high, estimated at 60% for males and 34% for females,
and clearly shows the double burden of disease with both infectious diseases and
NCDs present. For both males and females, Infectious diseases and Diseases of the
digestive tract were among the leading causes of death. Deaths attributed to
diseases of the digestive tract included those recorded as peptic ulcers and liver
disease, while infectious disease deaths included both septicaemia and hepatitis
related deaths. Previous studies have shown high prevalence of Hepatitis B in
Kiribati which would contribute deaths in both of these categories (through those
coded as infectious hepatitis, as well as liver disease), with prevalence of the surface
antigen estimated at 31% in 1987(377). The Kiribati immunisation schedule has
included vaccination for Hepatitis B since 1985 when coverage was estimated as
73%. Estimates of immunisation coverage have fluctuated in the years since (as low
as 35% in 1995) with the most recent available estimate at 86% coverage in 2009.
Although immunisation will not impact mortality rates from this disease for many
years, the data highlights the importance of this program in addressing future
358
mortality rates in Kiribati, and the need to focus on ensuring universal vaccination
coverage.
Maternal deaths account for 3.6 to 5.8% of all female deaths between 15-59 years,
depending on the data source. Civil registry data for maternal deaths is likely to be
more reliable than for other causes as there is a clear event which is recorded
(requiring less interpretation by a lay person determining CoD), and as such
maternal mortality is likely to be toward the higher end of range. This would equate
to an average of 11 deaths per year (95%CI: 5-20 using Poisson distribution) over
2005-2008, or a maternal mortality ratio of 444 (95%CI: 202-808) based on the 2475
births per year estimated by the 2005 census. This is substantially higher than
previous estimates from national MDG reporting and should be further investigated.
Of the cancer deaths reported through the hospital, 55% of male deaths were due to
neoplasms of the lung or oesophagus. While a similar number of deaths were
reported for women, these accounted for only 7% of the cancers, with breast or
cervical cancer most reported (44%). The high proportion of cancer deaths
attributable to lung cancer based on hospital mortality, is consistent with the high
smoking prevalence reported in Kiribati. The 2006 STEPs survey indicated 74% of
males and 45% of females smoked daily (335).
Suicide was highlighted as a major CoD for males from civil registry data, but is
almost entirely absent from the hospital data; thus demonstrating the importance of
not relying on hospital data sources in isolation from other information. Even without
adjusting these rates for undercount, the minimum level of mortality attributable to
this cause is 55.6 deaths per 100,000 population for males, on par with countries
359
such as Russia. While the rates appear to have fallen, they are still extremely high
and warrant further investigation urgently.
7.3.3 Nauru
LE in Nauru is low at an estimated 52.8 and 57.5 years for males and females
respectively as shown in case study 2, and has changed little over the last 50 years.
This stagnation is largely due to the extremely high premature adult mortality,
present in both males and females. Endocrine, nutritional and metabolic diseases
(nearly all diabetes) is the leading CoD for both males and females. This group of
causes accounted for 42% of deaths in males and 32% of deaths in females 15-59
years; a mortality rate of 412 and 241.deaths per 100,000 population respectively.
Although extremely high, this is consistent with previous studies that found a high
prevalence of diabetes in Nauru (24 % in 1982) (Zimmet 1998 (378); and a higher
risk of diabetes in Nauruans amongst multi-ethnic studies, independent of other risk
factors including physically inactive lifestyle (379). A 2004 survey found 90% of
Nauruans have a poor diet (defined by low fruit and vegetable intake), and nearly
50% have very low rates of exercise (380). Cardiovascular diseases and cancers
were also a significant CoD in adults 15-59 years for both men and women.
Unusually, respiratory illnesses accounted for a substantial proportion of deaths in
adults 15-59 years, 7% of deaths in males and 11% of deaths in females. In 2004,
45% of males and 53% of females smoked daily (381), which may have contributed
to these levels.
IMR and U5M are still above 30 deaths per 1,000 live births, with U5M having
changed very little over the last 50 years. From the medical record review 44% of
360
deaths for children aged 0-4 years were found to be due to perinatal causes, with
only 7% due to congenital causes. As perinatal causes are more amenable to
improvement through intervention, through improved ante-natal and post-natal care,
it is feasible further gains could be made in this area. These findings are consistent
with the 2007 DHS which found significant problems with both ante-natal and post-
natal care provision in Nauru, despite nearly all births being attended by a medical
professional. Only 40% of women had the recommended number of ante-natal visits
with 75% of women attending their first visit in the second trimester of pregnancy.
One in six did not receive a postpartum check-up (156)
7.3.4 Palau
LE in Palau is estimated at 65.8 years (95%CI: 64.4 – 67.2) for males and 71.2 years
(95% CI: 69.9 – 72.5) for females in 2004-2008, consistent with the census
estimates from 2005. Improvements in LE for both males and females have
stagnated in Palau as in Fiji and Nauru. Although this stagnation happened at slightly
higher LE’s (for females at least), comparison with census data shows no
improvement at all over 20 years, and for females may actually indicate a slight
decline (as there are no confidence intervals for the census data this could not be
demonstrated to be significant). Adult mortality in Palau is extremely high at 59.6%
and 43.1% for males and females respectively (2005-2008), and dominated by
NCDs including diabetes and cancer.
Previously published estimates of IMR and U5M for Palau (14) vary significantly and
have limited value as they are based on single year estimates which are highly
unstable in a population of this small size. This study found IMR for 2004-2008 to be
16.7 deaths per 1,000 live births when aggregated over the 4 years of data available
361
(excluding 2007 due to a problem with the records for that year) and 25 deaths per
1,000 live births for children 0-4 years. At these relatively low levels, U5M is not a
major influence on the stagnation in LE described above.
7.3.5 Tonga
Mortality and CoD patterns in Tonga have been discussed extensively in case
studies 5 and 6. As noted in these studies, data from the MoH and Civil Registry
were reconciled and subjected to a capture-recapture analysis. Estimates of
mortality from this process were also compared to published data sources screened
for plausibility. A medical record review was then conducted to assess certification
and establish CoD patterns.
Published estimates of IMR converge to a range between 10 and 20 deaths per
1,000 live births from the late 1990s. Findings from reconciled data were consistent
with this range, and did not demonstrate any significant trend over 2001 to 2009.
LE in Tonga is substantially lower than has been previously reported, although this
indicates a stagnation in LE rather than a reversal as previous estimates were shown
to have under-estimated adult mortality. Estimates of LE from the reconciled
empirical data for 2005 to 2009 were 65.2�years (95% CI: 64.6 - 65.8) for males and
69.6�years (95% CI: 69.0 – 70.2) for females, several years lower than published
MoH and census estimates. However the capture-recapture analysis of reporting
completeness suggests even reconciled data are under enumerated, placing the
range of plausible estimates as low as 60.4 �years for males and 65.4 �years for
females,
362
Given the reasonably low IMR, these estimates of LE are driven primarily by the high
adult mortality. Adult mortality (15 to 59�years) from reconciled empirical data is
estimated at 26.7% for males and 19.8% for females, while the capture-recapture
analysis placed the range at 28.6% to 36.3% and 20.9% to 27.7% for males and
females respectively. The data shows real age-specific increases in NCDs including
diabetes, cardiovascular diseases and cancer over 2001-2004 to 2005 to 2008 for
both men and women, and demonstrate NCDs are having a critical impact on
premature mortality. Mortality from diabetes for 2005 to 2008 is estimated at 94 to
222 deaths per 100,000 population for males and 98 to 190 for females, and
cardiovascular disease at 423-644 deaths per 100,000 population for males and
108-227 deaths per 100,000 population for females. The high mortality rates from
NCDs are consistent with the limited information on risk factors available for Tonga.
A 2004 survey found 67% of adults were obese (body mass index � 30 kg/m2), with
23% of adults affected by hypertension (systolic blood pressure � 140 mmHg and/or
diastolic blood pressure � 90 mmHg or currently on medication) (380).
7.3.6 Vanuatu
The system assessment identified there was very limited data in Vanuatu on
mortality and CoD, and the collected data was unlikely to be representative at a
national level. The best estimates of mortality level must still therefore come from
other sources such as censuses and surveys. As demonstrated in the work in this
thesis however, estimates of adult mortality and LE based on single parameter
model life tables tend to under-estimate mortality levels and are therefore not
suitable for application in the region. Covariate modelling methods such as those
developed by Ratnarjaram et al which draw on all available estimates as well as
patterns from neighbouring countries where more empirical data is available are
363
therefore more suited for use, although their applicability in the region needs to be
explored further and may be limited by the lack of neighbouring countries (other than
Australia and New Zealand which are significantly different in terms of size,
development and society) with empirical data which can be inputted into the models.
Best estimates of mortality for infant and child mortality in Vanuatu were therefore
derived from the census data, with age specific mortality for other ages (and LE)
derived by inputting both child mortality from the census and adult mortality from
Rajaratnam ‘s work (41) into a two source life table (using modmatch) (235). These
provide a plausible lower limit for mortality in Vanuatu (and therefore an upper limit
for LE) given the recall issues associated with collecting data through surveys or
censuses and the reporting bias likely in Vanuatu as a culture in which discussing
people who have died is particularly difficult. While LE from this approach is at the
higher end of the range of estimates for the region at 65 years for males and 70
years for females, these are much more plausible than the census estimates at 69.9
and 73.5 years respectively given the level of development and infrastructure in
Vanuatu.
As discussed in Chapter 6, the leading causes of death for adults in both the 15-59
year age groups and older age groups were NCDs. For males these were
predominantly cardiovascular diseases, liver diseases and diabetes; while for
females neoplasms were also important, with both cervical and breast cancer
appearing in the leading causes of death. External causes were under-represented
in the hospital data and therefore would not appear as a public health concern on the
leading CoD list compiled from this source for the CHIPs report. Maternal deaths
were equally under-represented in the health data accounting for 2% of deaths in
364
females 15-59 years, but representing 10% of deaths females in this age group in
the community facility data.
7.4 Mortality in Transition
As demonstrated in Table 50, there has been no sustained improvement in LE
across the study countries since mid 1990s. Although Table 50 indicates LE has
actually fallen in Tonga, the case study presented in Chapter 5 demonstrated this is
not actually the case, and that earlier estimates of LE were likely to be an over-
estimation (111).
For countries where longer trend data was available, including Tonga, Fiji and
Nauru, LE has been stagnant for several decades; indicating that these countries
“missed out” on the anticipated increases in LE associated with the decline in
infectious disease mortality seen in the classical pattern of the epidemiological
transition as described by Omran (382) based primarily on experiences in Europe.
Instead, gains in mortality from declining infectious diseases have, at a population
level, been almost entirely offset by increases in premature adult mortality due to
NCDs. This variation in the standard pattern of epidemiological transition has been
described previously for Nauru (112), but could equally apply to Tonga and Fiji, and
although data is more scarce, is consistent with LE and summary measures reported
for Kiribati and Vanuatu. Only Palau appears to have seen a moderate improvement
in LE over the last decade, despite one of the highest adult mortality rates.
There is a potential argument that the stagnation in LE across countries may in part
be being driven by the “healthy migrant effect” where healthy adults are more likely
365
to migrate away from the country for work, thus artificially increasing adult mortality
in some countries such as Fiji and Tonga. Migration from Kiribati, Nauru and
Vanuatu is however essentially negligible, and cannot account for the high adult
mortality in these countries. Additionally, research on the Pacific population in New
Zealand indicates that the mortality of Pacific people on migration to New Zealand
does not change significantly with duration in New Zealand, suggesting no marked
healthy migrant effect (318, 383). As such, migration is unlikely to have a significant
impact on these patterns of mortality. Mortality rates for “Pacific” people in New
Zealand however do appear to be lower than the mortality rates reported in this
paper. This may be due to the high proportion of Pacific Islanders in New Zealand
coming from countries such as Niue and the Cook Islands where life expectancy
(based on complete or near complete civil registration data) is comparatively high
(43), although further investigation and comparison by specific country of origin
would be useful.
Of particular interest is the consistency of this variant pattern of the epidemiological
transition with high adult mortality across the study countries, despite extensive
differences in societal, environmental and economic conditions and countries being
at vastly different stages in the transition. Vanuatu and Kiribati are at an early stage
of the transition, with relatively high infant and child mortality and a high proportion of
deaths attributed to infectious diseases. These early transition countries could still
realise the LE gains anticipated under the classical pattern of transition as
improvements are made in the areas of child and maternal health, and infectious
diseases. However the double burden of disease faced by these countries as they
concurrently deal with increasing premature mortality from NCDs and external
causes, as described in Tables 52 to 56, indicate it is likely these countries are
366
following the variant pattern of epidemiological transition already seen in their
neighbours.
367
8 Conclusion and recommendations
8.1 SynopsisAccurate data on mortality levels and causes of death is critical to assist
governments to improve the health of their populations; by identifying priority areas
for intervention, leveraging resources for such interventions, and monitoring the
impact and effectiveness of health programs. However, there is substantial disparity
in previous estimates of mortality, leaving decision makers without clear information
from which to work. Methods such as the use of single parameter input model life
tables, commonly employed in the region to obtain estimates of LE and adult
mortality from childhood mortality estimates derived from census and survey data
are inappropriate, and have been shown to lead to a substantial under-estimation of
adult mortality (Chapters 2 and 5). CoD data currently available is frequently limited
to a list of leading causes for all age groups (108), and as demonstrated in Chapter
368
3, often tabulated by immediate rather than underlying cause. In this environment of
limited and potentially misleading data, PICTs are facing a significant public health
issue as NCDs impact on premature adult mortality and are facing increased
pressure to report against MDGs on maternal and childhood mortality. More current,
reliable estimates of mortality and CoD by age and sex, based on empirical data
from within the countries, are therefore critically needed in order to inform
governments and health agencies in responding to their population health needs.
The research presented in this thesis demonstrates it is possible to use existing
routine data collections to significantly improve our knowledge of mortality and CoD
patterns in the PICTs. Although gaps remain, the lack of complete data should not
justify failure to critically analyse empirical data on mortality level and CoD.
There is significantly more mortality data available in the countries reviewed than is
currently used for reporting or decision making purposes. While the data are flawed,
as long as these flaws are understood by the researcher or health planner
appropriate judgements can be made about which methods of analysis are suitable
and what biases may affect the final estimates. The framework presented in Chapter
three is a flexible approach for evaluating CRVS systems to understanding content
validity of data and the methods that may be suitable for interrogating the data
further. While the utility of some data sets is limited due to quality concerns, as
outlined in relation to mortality level analysis for Vanuatu and CoD data for Fiji, for all
countries the routinely collected data was able to add to the existing knowledge of
mortality and CoD patterns.
369
Use of local data allows measurement of what is really happening in each country,
rather than estimates that reflect the assumptions of the models they are based on.
Work in Kiribati, Tonga and the Solomon Islands highlights the importance of
reconciling data sources where multiple reporting systems exist, prior to estimation
of mortality level. Both indirect methods of assessing completeness such as the
Brass Growth Balance method, and direct methods such as capture-recapture
analysis, can provide useful tools to correct incomplete mortality data in the region,
provided they are applied with a thorough understanding of the CRVS systems as
described in Chapter three. The methods outlined in Chapters five and six provide a
useful set of tools able to make the best possible use of the data available. In
particular, the framework presented in Chapter five is useful in identifying an
appropriate analytical approach to the data.
The analyses of mortality level presented in Chapter five demonstrates LE across
the study countries has remained steady in the low 60’s or less (with male LE for
both Nauru and Kiribati estimated in the 50’s). These estimates are lower than many
of those previously reported (including those from WHO and UN agencies) (108). At
the same time, infant and child mortality rates were reasonably low for most
countries, and do not appear to be the principal driving factor behind the low
estimates of LE.
Despite the reasonably low IMR and U5M, there remains scope for improvement;
particularly in Kiribati, the Solomon Islands and Vanuatu. Although specific CoD data
could not be obtained for the Solomon Islands, malnutrition (specifically protein
malnutrition) and infectious diseases accounted for a significant proportion of deaths
in children less than five years in Kiribati and Vanuatu.
370
Estimates of IMR appear to have changed little over the previous ten years for Fiji,
Nauru, Palau, and Tonga, suggesting that these countries will have difficulties
reaching the MDG targets to reduce infant and child mortality by two thirds of 1990
levels by 2015. However, estimates for all four countries place IMR at less than 20
deaths per 1000 live births for the mid 2000’s. As child mortality in these countries is
already reasonably low, many of the “easier” gains possible through improved
maternal and child health services and improved hygiene and sanitation have
already been made. Mortality is now clustered in the neonatal age group, and as
such, further improvements in IMR will require more focused interventions and
investment in specialised medical technology and services. Designing these
interventions will subsequently require more detailed CoD data. It is also important
that baseline data used to set MDG targets is assessed for validity to ensure that
countries are working towards plausible goals.
Premature adult mortality (deaths in the 15-59 year age group) was found to be high
for all study countries. Adult mortality in Fiji (2005-2008), Tonga (2005-2008) and
Nauru (2002-2007) was at least three times that for Australia and New Zealand over
a similar period (2002-2004). Although an aging population would be expected to
lead to an increase in proportional mortality from NCDs, proportional mortality from
these causes is increasing across the countries reviewed without a concurrent
increase in LE. This trend suggests NCDs (such as ischaemic heart disease, cancer,
diabetes and liver disease) are responsible for the high premature adult mortality. In
Tonga, where medical certification was assessed as high quality and sufficient data
was available to assess trends in cause specific mortality, it has been possible to
demonstrate significant increases in age-standardised mortality from the early 2000’s
371
to present in ischaemic heart disease, diabetes and lung cancer. These findings
provide supporting evidence NCDs are having a significant impact on premature
adult mortality in other PICTs that demonstrate similar patterns of IMR, LE and
proportional mortality.
The increase in premature adult mortality from NCDs is consistent with the data on
NCD prevalence and risk factors in the region. Hypertension has increased from the
1980’s across several countries. These include Fiji where prevalence rose from 5.1–
9.9% in 1980 (defined as �160 mm hg systolic and/or �95 mm hg diastolic and/or on
treatment for hypertension) to 19% (using �140 mmhg systolic and /or �90 mmhg
diastolic and/or on treatment for hypertension) in 2002 (359). Vanuatu similarly
reported an increase from 13% to 28.6% over 1998 to 2011. In Tonga 23% of adults
were affected by hypertension (systolic blood pressure � 140 mmHg and/or diastolic
blood pressure � 90 mmHg or currently on medication) in 2004, while Kiribati
reported a prevalence of 17.3% in 2009 (335).
Obesity is also recognised as an issue across the region. By 2000, 75% of adults in
seven PICTs were either overweight or obese (384). Tonga reported 67% of adults
as obese (BMI � 30 kg/m2) in 2004. While increases in the prevalence of people
considered overweight (BMI � 25kgm3) in Vanuatu from 33% in 1998 to 51% in 2011
demonstrate the rapid changes underway in the early transition countries.
Other risk factors such as poor diet and a lack of physical activity are also notable
across the region. It is estimated 90% of Nauruans have a poor diet (defined by low
fruit and vegetable intake), and nearly 50% have very low rates of exercise (381).
372
Smoking too remains a significant risk factor in Kiribati, with 74% of males and 45%
of females smoking daily in 2006 (335).
The use of local data allows countries to manage health responses more confidently,
and to have more control in priority setting and resource decisions. Through analysis
of local data in Nauru, it was demonstrated U5M had not increased significantly over
the 1990’s and 2000’s; contrary to some of the modelled estimates. Health planners
in Nauru were subsequently able to use this information in negotiations around child
health interventions with donor agencies. In Tonga, local data analysis demonstrated
U5M is not getting worse (as reported through some modelled data), while adult
mortality from NCDs is significantly higher than previously thought. This information
is being used by the Tongan MoH in planning and budget decisions, and by regional
agencies and donor partners to improve the appropriateness of assistance offered to
PICTs.
Despite the similarities in broad patterns of mortality level and CoD demonstrated
between countries in this thesis, the work has also shown significant differences,
highlighting the diversity in this region and need to tailor responses at a national (or
even sub-national) level. Countries such as Palau and Tonga have clearly moved
through the epidemiological transition, with adult deaths predominately due to NCDs.
Yet amongst these the patterns are very different with cause-specific mortality rates
from diabetes being nearly three times higher in Tonga despite similar proportional
mortality, to cancers playing a more important role in Palau. Kiribati, at the other end
of the spectrum early in the epidemiological transition, still faces substantial public
health issues related to infant and child mortality and infectious diseases across all
ages; yet is also experiencing rapid increases in the prevalence of risk factors for
373
NCDs and the subsequent double burden of disease in adults. Nauru, in comparison,
illustrates an unusual pathway through the epidemiological transition; with high
premature mortality due to NCDs and very few deaths in adults attributed to
infectious diseases, yet almost no progress over 50 years in reducing infant and
child mortality (112).
As mortality from infectious diseases falls, the early transition countries of Vanuatu
and Kiribati (and most likely their fellow Melanesian countries of PNG and the
Solomon Islands) present a real opportunity to divert these populations from
experiencing the same stagnation in LE as seen in the late transition countries. This
would require an urgent and significant investment in NCD prevention programs.
Additionally, the similarities across the study countries suggest may be possible to
develop a regional set of life tables that better reflect the experiences of the PICTs,
which could then be used to help fill gaps for those countries that do not yet have a
reliable CRVS system.
Recommendations from the work presented in this thesis, both in relation to
assessment and improvement of routine data collection and reporting systems and in
terms of public health priorities, are outlined in the following section.
8.2 Recommendations
Most importantly this work highlights the importance of using the available empirical
data, despite its shortcomings, rather than waiting until systems are perfect and
making decisions solely on modelled data.
374
8.2.1 Recommendations for system improvement of CRVS systems
CRVS systems across the region require strengthening to improve the completeness
and quality of data available. As demonstrated in the work included in this thesis,
much can be made of incomplete data from current systems and an imperfect
system should not be ignored as a data source. The data correction and review
techniques which have been presented are however no substitute for functional high
quality data collection and reporting from vital registration covering a representative
population with medical certification, or verbal autopsy where certification is not
possible.
Key recommendations for system improvement based on the strengths and
weaknesses common across the study countries were outlined in case study one.
These included: improving data sharing between departments, minimising
duplication in data collection and entry, improving resourcing of CRVS systems, and
allocating clear roles and responsibilities for data collection, analysis and reporting.
The importance of human elements, including responsibility and authority to
undertake assigned responsibilities, and ownership to ensure improvements are
sustained (164), should not be under-valued. Close interaction between health staff
and local communities provides a solid foundation for improvement in death
reporting.
Although there are common priorities for system improvement across the region, it
must be noted there is a great deal of diversity across PICTs in terms of
infrastructure, social context and existing reporting system capacity. As such, there
375
is no generic solution for these issues and any action to address these priorities will
need to be locally appropriate. There is significant work underway in the region
under the Pacific Vital Statistics Action Plan 2011-2014, endorsed by Pacific
governments under the Ten Year Pacific Statistics Strategy and supported by SPC,
the UQ HIS Hub, WHO, UNICEF and UNFPA and among others. The plan
encourages and assists countries to undertake a national assessment of their CRVS
system and develop a coordinated national improvement plan that can then be
supported by donor partners. Each of the study countries have commenced work on
national improvement plans (319).
A key priority for governments in addressing the current system gaps should be a
commitment to count every death, and that CoD reporting will be implemented in line
with the recommendations of the ICD, including medical certification and coding
practices. This requires adequate government financial and political commitment to
begin to address recommendations in a way that builds sustainable systems.
Despite current CRVS system flaws, there is a significant amount of data currently
collected that cannot be adequately accessed, interrogated or used for health
planning. There is a critical need to improve this situation to ensure available data is
used at a national level, and it is made available regionally and internationally
through reporting mechanisms such as the WHO CHIPs. It is this data that will give
the Pacific Islands a strong voice in international forums such as discussions for the
post 2015 development agenda (385), and it is imperative to establish a user
demand that PICTs should not be represented by modelled estimates when
plausible empirical data exists.
376
International and regional agencies must act to support countries in making data
more visible and accessible. This will encourage ongoing use of the data in health
planning and decision making, regional and international comparisons, and
monitoring and accountability (of both countries and donor agencies) towards
development goals; and in doing so, facilitate the ongoing improvement of data.
8.2.2 Recommendations for further research
The research presented in this thesis highlights a critical need for additional data on
the impact of migration on mortality patterns in the PICTs. To date, this has been
limited by the poor quality of migration data (132), and the difficulty of obtaining this
data given population mobility and large proportion of people that maintain multiple
homes in different countries. Given the recent release of the 2010 GBD (353), there
is also scope to examine the modelling methods used in that research, and update
these with empirical findings presented in this thesis to continue to provide more
accurate options for modelling data where vital registration is not yet fully reliable.
It is further recommended a system assessment is undertaken for those countries
that have not already done so, both to inform system improvements and identify and
understand existing data sources. The Health Minsters’ declaration of NCDs as an
emergency in the region (5), along with moves to incorporate NCDs into the post
2015 MDG agenda (385), mean governments will increasingly need to accurately
monitor the impacts of premature adult mortality from specific causes of death; not
just as a one off exercise when external research support is available, but through
the routine collection, analysis and reporting of robust CoD data. If such monitoring
and target setting is to be meaningful, there is also a need for all PICTs to establish
377
current baselines of adult mortality by cause, as done for selected countries within
this research.
The need for specific action to obtain more detailed data on specific causes of death
has also been discussed in this thesis. Particular areas of need are further
disaggregated data on deaths from external causes in light of the poor data
recording noted in this area, and the high mortality from suicide which has been
noted in Kiribati. The extent of this problem has largely gone unrecognised as
suicide related deaths are unlikely occur in hospital and are therefore not reported in
the hospital data.
Mortality level and CoD findings from Nauru and Kiribati further identified much of the
continued infant and child mortality is from causes that should be amenable to
intervention (386), such as malnutrition. Further investigation to inform an
intervention strategy should be urgently undertaken.
Finally, although limited to selected study countries, the research presented in this
thesis demonstrates serious disparities in health across the region, with LE having
remained steady at very low levels. Further monitoring of these disparities, and
raising greater awareness of these concerns with governments, funding agencies,
and civil society, is critical if there is to be tangible improvements in health status in
the PICTs in the foreseeable future. Targeted, resourced, and sustained support to
health agencies and governments will be required to realise substantial health
improvements and ‘kick start’ LE improvements in the region.
378
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) 19
99-2
003
Mal
es
Fem
ales
vi
2004
-200
9 (e
xcl.
2007
)
Mal
es
Fem
ales
vii
Life
tabl
es fo
r T
onga
(200
5-20
09)
Obs
erve
d –
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onci
led
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es
Age
x
Year
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arity
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1000
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0.5
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547
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264
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368
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474
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940.
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94
384
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002
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0.02
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53
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2168
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6.5
viii
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x
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435
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6
xi
Life
tabl
es fo
r K
irib
ati (
2000
-200
9)
Fina
l est
imat
es in
clud
ing
reco
ncile
d da
ta fo
r chi
ldre
n 0-
4 ye
ars
and
capt
ure-
reca
ptur
e an
alys
is c
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ctio
n (d
isag
greg
ated
by
age
and
sex)
for r
emai
ning
age
gro
ups.
Incl
udes
dea
ths
reco
rded
as
stillb
irths
in in
fant
dea
ths
(see
Cha
pter
5 fo
r det
ails
)
2000
-200
4
Mal
es
xii
Fem
ales
2005
-200
9
Mal
es
xiii
Fem
ales
xiv
Life
tabl
es fo
r V
anua
tu 2
009-
2010
Li
fe ta
bles
der
ived
from
Mod
mat
ch (2
inpu
t par
amet
ers)
usi
ng U
5M fr
om th
e 20
09 c
ensu
s an
d 20
10 e
stim
ates
of a
dult
mor
talit
y fro
m
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al.
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es
se
x ag
e lx
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n M
x qx
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60
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xv
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296
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.895
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.774
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560.
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4498
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0.00
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1.81
2414
xvi
Appendix 3: Additional analysis on mortality level in Fiji including life tables
KNOWLEDGE HUBS FOR HEALTHStrengthening health systems through evidence in Asia and the Pacific
An assessment of mortality estimates for Fiji, 1949-2008: findings and life
tables
Karen CarterHealth Information Systems Knowledge Hub, School of Population Health, University of Queensland
Margaret CorneliusFiji Ministry of Health
Richard TaylorSchool of Population Health, University of Queensland and School of Public Health and Community Medicine,
University of New South Wales
Shareen S AliFiji Ministry of Health
Chalapati RaoHealth Information Systems Knowledge Hub, School of Population Health, University of Queensland
Alan LopezHealth Information Systems Knowledge Hub, School of Population Health, University of Queensland
Vasemaca LewaiFiji Islands Bureau of Statistics
Ramneek GoudarFiji School of Medicine
Claire MowrySchool of Population Health, University of Queensland
HEALTH INFORMATION SYSTEMS KNOWLEDGE HUB
DOCUMENTATION NOTE SERIES NUMBER 12| NOVEMBER 2010
School of Population HealthUniversity of Queensland
Health Information Systems
Knowledge Hub
HEALTH INFORMATION SYSTEMS KNOWLEDGE HUB
Health Information Systems Hub (HIS) Knowledge Hub
2 DOCUMENTATION NOTE SERIES NUMBER 12 | NOVEMBER 2010
ABOUT THIS SERIES
The objective of the HIS Hub Documentation Series is to document in detail the methods and findings of the HIS Knowledge Hub’s activities in partner countries.
The Documentation Series also serves to describe the state of the health information system in a number of Asia and Pacific Island countries. This series provides a baseline for comparison of HIS between countries. It also provides a preliminary diagnostic analysis for use by countries in determining areas requiring improvement. The target audiences for these mappings are all stakeholders with an interest in the functioning or development of HIS, specifically those working in the Pacific and Asia region. The Series also reports on work in progress, particularly for large, complex or multi-year HIS Hub initiatives, or on specific components or aspects of such projects that may be of more immediate relevance to stakeholders
The opinions or conclusions contained in the Documentation Series are those of the authors and do not necessarily reflect the views of institutions or governments.
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ACKNOWLEDGEMENTS
The authors would like to acknowledge the assistance of the Fiji Ministry of Health and Bureau of Statistics. Funding for this research was provided under the AusAID Australian Research Development Awards (HS0024).
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ABSTRACT
Published mortality indices for Fiji and causes of death for 1940-2008 were assessed for quality, and compared with those generated from recent Ministry of Health reporting1. Trends in credible estimates are compared with proportional mortality for cause of death, and life tables generated from recent Ministry of Health data (1996-2004). Infant mortality rate (IMR) declined to below 20 by 2000 (per 1000). Life expectancy (LE) increased, but has been stable at 64 and 69 years since the 1980’s for males and females respectively. Proportional mortality from cardiovascular disease has increased from around 20% in the 1960s to over 45%. Credible estimates indicate stagnation in life expectancy in the context of a declining and relatively low infant mortality rate. Proportional mortality trends suggest this is largely due to the emergence of chronic diseases (especially cardiovascular) in adults. Incompatible variations in published estimates of infant mortality rate and life expectancy were indentified. Difficulties encountered in assessment of published data were the lack of information in many publications concerning the primary data sources, methods used for calculation, and the assumptions made. In order to deal with this lack of information, exclusion criteria were specified and unreliable estimates censored from further analyses. The use of single input parameter models to estimate life expectancy from childhood mortality results in estimates that are similar to many of the published estimates that were censored from the final analysis, and overestimates life expectancy by 4-5 years. The most plausible estimates of mortality are derived from two local sources: census analyses from the Fiji Bureau of Statistics and routine death reporting from the Fiji Ministry of Health, with these data found to be mostly more than 95% complete by the Brass method.
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INTRODUCTION
Mortality is an important measure of the health of a population. As countries move through the demographic and epidemiological transitions, declines in under-nutrition and infectious diseases are reflected in improvements in survival (especially child survival) which result in improvements in life expectancy at birth. Improvements in the economic, social, and physical environment, and developments in public health and health services result in declining age-specific mortality rates. However, many societies undergoing modernization also experience real increases in adult mortality from non- communicable diseases (NCDs) and injury, which, if of sufficient magnitude, can lead to a slowing, plateau or reversal in overall mortality decline2-4. Such a situation requires urgent public health action to reduce premature adult mortality from non- communicable diseases, such as occurred in Australia and New Zealand5,6.
Fiji is a multi-ethnic society (Melanesian 57%, Indian 38%) of 827,900 people in 20077. Located approximately 1,770 kms north of New Zealand, Fiji has over 106 inhabited volcanic islands. The two largest Vitu Levu and Vanua Levu cover over 87% of the land area and contains 84% of the population6. Mortality measures for Fiji are routinely published by government sources as well as by a range of international agencies.
Available data on mortality level and causes of death, over the period 1940-2008, are examined for plausibility to establish an understanding of recent mortality levels. Death reporting from the Ministry of Health is also examined for completeness and life tables generated for 1996-20041.
KEY FINDINGS
The infant mortality rate has fallen significantly over the period investigated to below 20 deaths per 1000 live births. Empirical estimates for 2002-2004 place the infant mortality rate at 17.4 deaths per 1000 live births for males and 16.0 deaths per 1000 live births for females.
LE rose over the period of observation but has stabilized at around 64 years for males and 69 years for females since the 1980s and there have been no indications of any sustained improvement since this time.
Adult mortality is two to three times higher in Fiji than in Australia or New Zealand. The high levels of premature adult mortality in concert with the significant increase in proportional mortality from diseases of the circulatory system suggest that there is a severe epidemic of cardiovascular disease that is preventing improvement in life expectancy. Many of the previously published estimates of life expectancy for Fiji are implausible. This work demonstrates that the most plausible estimates of mortality are derived from two local sources: census analyses from the Fiji Bureau of Statistics and routine death reporting from the Fiji Ministry of Health.
STUDY METHODS
Data collection
Measures of level of mortality included in this work are from two sources:
1. Published reports by local and international agencies from 1940 (Appendix 1 and 2). 2. Data on reported deaths by age group, sex and period for 1996-2004 obtained from the Fiji Ministry of Health –
previously unpublished as measures of population mortality, except for the infant mortality rate.
Available data from published reports8-69 on all-cause and cause-specific mortality in Fiji from1940-2008 were sought through direct contact with Fiji government departments (including the Ministry of Health and Fiji Bureau of Statistics) and international and regional organizations known to have an interest in health or mortality in Fiji. Internet searches
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included websites for United Nations agencies (UNDP1, UNICEF2, UNFPA3, and WHO4), regional institutions such as the Secretariat of the Pacific Community (SPC), The World Bank (WB), and Asian Development Bank (ADB), and non-government organisations. A literature search was also undertaken in PubMED and Medline.
Information on method of data collection and analysis was rarely available, with the notable exceptions of demographic analyses of the Fiji censuses by the Fiji Bureau of Statistics37,38. These census estimates of life expectancy were produced from separate estimates for childhood and adult mortality, with adult mortality calculated using the orphanhood technique, and childhood mortality calculated by a combination of indirect estimation using the children ever-born, children surviving technique, and a direct estimate from questions about the date of birth and survival of the most recent child born (partial birth history)37,38. Data sources and reasons for their exclusion from the final analysis are shown in Appendix 1. Limited data on causes of death have been published for Fiji, many of which included only leading causes of death and could not be adjusted to exclude ill-defined and unknown causes from proportional mortality. Sources used in this analysis are shown in Appendix 2.
Data assessment
All cause mortality
The infant mortality rate and life expectancy were chosen as measures of all-cause mortality because of availability. Levels of the infant mortality rate from Fiji Ministry of Health death registration (1996-2004) and published reports were tabulated and graphed over time by major source of data. Data sources were assessed for reliability and plausibility on the basis of method of estimation, original source of data and data consistency, with implausible or unreliable estimates censored from further analysis (shown in Appendix 1).
Data sources were removed from the analysis if they met any of the following criteria:
a) the source publication noted that the data were derived from models that assume a given improvement by year or data from a source showed a linear or curvilinear improvement in life expectancy or infant mortality rate by year (indicating use of models that incorporate this assumption);
b) multiple incompatible estimates were given by the source for a single year or incompatible estimates were given by the source for adjacent years;
c) source data included estimates that were extremely implausible (based on equivalent measures for developed countries within the region);
d) calculations were documented to be based on uncorrected vital registration data where there was either no assessment of completeness or data was known to be significantly under-reported (Appendix 1) .
An exponential trend was fitted to the average infant mortality rates of each year from data remaining after censoring (Microsoft Excel). Childhood mortality (probability of dying before 5 years of age) was largely unavailable from published reports. Recent measures were estimated by applying the proportion of childhood mortality accounted for by infant mortality (78%) from credible data sources for which both measures were available (analyses from the 198637 and 199638 censuses, and Ministry of Health data 1996-2004), to credible estimates of infant mortality rate for 2006-200854 .
Age-specific mortality rates and life tables71 for 1996-98, 199-2001, and 2002-04 were calculated from the MoH death data using census populations provided by the Secretariat of the Pacific Community (SPC)72. Due to the small population size, data were aggregated by triennia to reduce stochastic variation. Death recording by the Ministry of Health was analysed for completeness using the Brass growth-balance method71 and since registration was assessed to be mostly over 95% complete, no adjustment was made to number of the recorded deaths >1 year. Details of the Brass analyses are in Appendix 3.
Life expectancy and adult mortality (probability dying between the ages of 15 and 59 years inclusive) were then derived
1 United Nations Development Programme2 United Nations Children’s Fund3 United Nations Population Fund4 World Health Organization
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from the life tables for each period (Appendix 4). Age-specific mortality for 2002-2004 was compared to measures derived from published life tables for Australia and New Zealand for the same period73-76.
Empirical measures of life expectancy derived from the Ministry of Health data for 1996-2008 were compared with similar measures for Fiji predicted by the WHO Model Life Table system77. The model was used to predict two estimates of life expectancy; the first using an empirical probability of dying before age five years as a single input; and the second using two input parameters; probabilities of childhood mortality and adult mortality (probability of dying between ages 15 and 59 years). The details of these analyses are in Appendix 5.
Calculated life expectancy for the Ministry of Health death recording, and reported life expectancy from previously published sources were tabulated and graphed over time by major source of data, and implausible or unreliable estimates were censored from further analysis. Difficulties encountered in assessment of published data were the lack of information in many publications concerning the primary data sources, methods used for calculation, and the assumptions made. In order to deal with this lack of information, strict exclusion criteria were specified and unreliable estimates censored from further analyses.
Causes of death
Available data on cause-specific mortality were examined using a range of measures (rates, number of deaths and proportions) and levels of disease or cause aggregation (shown in the Appendix 2). To assess trends over time, data are presented as proportional mortality by International Classification of Disease chapter78 since in several instances total deaths are not given and data are frequently under-enumerated. Cause of death was calculated for the total population as there was insufficient information in available tabulations of data to examine by age group, sex, or ethnicity. proportional mortality, excluding unknown and ill-defined cases, was calculated for sources where all deaths were included in the data source (allowing redistribution by proportion), or the information could be extracted directly from the reported data. Trends in proportional mortality, and proportional mortality excluding unknown and ill-defined cases, were graphed over time for each International Classification of Disease chapter. See Appendix 6.
FINDINGS
All cause mortality
Published estimates
There is extensive variation in the published estimates of infant mortality by source, with several sources reporting levels that are below those of developed countries in the region70 (Figure 1).
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Figure 1: Estimates of infant mortality rate in Fiji, by source: 1940-2004
0
10
20
30
40
50
60
70
80
90
1940 1950 1960 1970 1980 1990 2000 2010
Infa
nt M
orta
lity
Rate
(pe
r 10
00 b
irths
).
Year
Fiji Fertility Survey (Total) (32)
Vital Registration (Fijian Male) (11,18,39)
Vital Registration (Indo-Fijian Male) (11,18,39)
Vital Registration (Fijian Female) (11,18,39)
Vital Registration (Indo-Fijian Female) (11,18,39)
Census (Total) (12,36,37,39,46)
Census (Male) (12,36)
Census (Female) (12,36)
Census (Fijian) (12,36)
Census (Indo-Fijian) (12,36)
MOH Registration (Total) (9,13-15,19,24,26,28,33,34,42,43,53)
MOH Registration (Fijian) (13-15)
MOH Registration (Indo-Fijian) (13-15)
Brass adjustment of Vital registration (total) (25)
Brass adjustment of Vital registration (Fijian) (25)
Brass adjustment of Vital registration (Indo-Fijian) (25)
WHO - WPRO (45,49,56)
UNDP (total) (35,52,54)
UNICEF (total) (40,42,51,63,64,66)
UNESCAP (68)
Fiji Ministry of Information (total) (41)
SPC (total) (38,61,62)
Fiji Co-ordinating Committee on Children (Total) (30)
WHO (48,50,55)
Figure adapted from Carter et al1.
Of 16 sources for infant mortality rate, 12 were censored from further analysis with a further two partially censored (Appendix 1). The overall trend is consistent with the infant mortality rate falling significantly over the last several decades (1940–2006) to a current level below 20 deaths per 1000 live births (Figure 2). The infant mortality rate in 2008 is estimated by projection at 15 deaths per 1000 live births which suggests a childhood (<5 years) mortality of 17 deaths per 1000 live births.
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Figure 2: Estimated infant mortality rate in Fiji, sources remaining after censoring: 1940-2004
0
10
20
30
40
50
60
70
80
1940 1950 1960 1970 1980 1990 2000 2010
Infa
nt M
orta
lity
Rat
e (p
er 1
000
birt
hs).
Year
Average of remaining estimates
Figure adapted from Carter et al1.
Published estimates of life expectancy at birth between 1995 and 2000 vary by more than 10 years in males (from 61 to 72 years), and by 8 years in females (from 68 to 76 years) (Figures 3 and 4).
Figure 3: Estimates of male life expectancy at birth in Fiji, by source: 1940-2006
Figure adapted from Carter et al1.
ADB- Asian Development Bank; BoS - Fiji Bureau of Statistics; MoH - Fiji Minstry of Health; SPC - Secretariat of the Pacific Community; UNDP - United Nations Development Program; WHO - World Health Organization; WPRO – WHO Western Pacific Regional Office; VR – Vital registrationNB – Census data co-published by SPC with the Fiji Bureau of Statistics listed under BoS onlyReferences shown in brackets after each source within figure.
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Figure 4: Estimates of female life expectancy at birth in Fiji, by source: 1940-2006
Figure adapted from Carter et al1.
ADB- Asian Development Bank; BoS - Fiji Bureau of Statistics; MoH - Fiji Minstry of Health; SPC - Secretariat of the Pacific Community; UNDP - United Nations Development Program; WHO - World Health Organization; WPRO – WHO Western Pacific Regional Office; VR – Vital registrationNB – Census data co-published by SPC with the Fiji Bureau of Statistics listed under BoS onlyReferences shown in brackets after each source within figure.
Of the 16 data sources identified for life expectancy data, 13 were censored from the final analysis (Appendix 1). Once unreliable sources are removed, the estimates suggest that life expectancy began to level out in the 1980’s, and has not shown a significant improvement since (Figures 5 and 6). Life expectancy for 2008 is estimated at 64 years for males and 69 years for females.
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Figure 5: Reliable estimates of male life expectancy at birth in Fiji, by source: 1940-2006
Figure adapted from Carter et al1.
Figure 6: Reliable estimates of female life expectancy at birth in Fiji, by source: 1940-2006
Figure adapted from Carter et al1.
Ministry of health death recording
The Ministry of Health death registration data, aggregated by year, sex and age group were obtained for the years 1996 to 2004. Application of the Brass growth-balance method for age groups 35 to 74 years suggests the recorded deaths to be mostly more than 95% complete (except for female deaths for 2002-2004 which was 85% complete) as shown in Figure 7 below and Appendix 3. The Brass analysis of data for 1996-2001 is shown in Appendix 3.
40
45
50
55
60
65
70
75
80
1940 1950 1960 1970 1980 1990 2000 2010
Life
Exp
ecta
ncy
at B
irth
(yea
rs)
Year
MOH Reported Deaths (Brass adjustment) (21,26)BoS Census (13,37,38,40,47)MOH reported deaths (unpublished data)
40
45
50
55
60
65
70
75
80
1940 1950 1960 1970 1980 1990 2000 2010
Life
Exp
ecta
ncy
at B
irth
(yea
rs)
Year
MOH Reported Deaths (Brass adjustment) (21,26)BoS Census (13,37,38,40,47)MOH reported deaths (unpublished data)
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Figure 7: Analysis for mortality data completeness by gender: Fiji, 2002-2004
a) Males b) Females
Completeness (c) is estimated by 1/K where K= the slope of the line
Age-specific mortality
As shown in Figure 8, mortality across all age groups is significantly higher than in Australia and New Zealand.
Figure 8: Age specific mortality rates: Fiji, Australia and New Zealand, 2002-2004
Source data: Fiji - calculated from deaths reported to the Ministry of Health (previously unpublished); New Zealand75, 76; Australia73-74
K=0.89C=>100%
K=1.18C=85%
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Life expectancy
Analysis of the Ministry of Health reported deaths shows life expectancy between 1996 and 2004 was stable at 64-65 years for males and 69 for females (see life tables in Appendix 4).
Table 1: IMR and life expectancy at birth for Fiji, Australia and New Zealand, by period and gender: 1996-2004
Age group (years) IMR (deaths per 1,000 live births LE at birthSex Males Females Males Females
Fiji1996-1998 17.4 (16.6-18.2) 16.0 (15.2-16.8) 65.0 (64.9-65.2) 69.2 (68.9-69.4)1999-2001 15.7 (14.9-16.5) 13.3 (12.6-14.0) 63.7 (63.4-63.9) 68.7 (68.4-69.0)2002-2004 16.9 (16.1-17.7) 15.1 (14.3-15.9) 64.3 (64.0-64.6) 69.1 (68.8-69.4)
New Zealand 2002-2004 5.7 5.0 77.0 81.3Australia 2002-2004 5.3 4.5 78.1 83.0
Source data: Fiji - calculated deaths reported to the Ministry of Health; New Zealand75-76; Australia73-74
Adult mortality
Adult mortality (probability of dying between 15 and 59 years) also did not change significantly over the period 1996-2004. The adult mortality levels in Fiji for 2002-2004 were three times higher than in Australia or New Zealand. The absolute difference is far greater between ages 35-59 years, than other adult ages. The probabilities of dying (for 2002-2004) between ages 35-59 years in Fiji were 25% for males and 18% for females, compared to 7 - 8% and 4 - 6%, for males and females, respectively, in Australia and New Zealand73-76(Table 2).
Table 2: Probability of dying between ages 15-59 years (%) by age group in Fiji, Australia and New Zealand, males and females, 1996-2004
Age group (years) 15-34 35-59 15-59Sex Males Females Males Females Males Females
Fiji1996-1998 3.6 2.5 26.8 18.5 29.5 20.51999-2001 4.1 2.9 27.5 18.4 30.5 20.82002-2004 3.7 2.9 24.9 17.9 27.6 20.3
New Zealand 2002-2004 2.0 0.9 7.9 5.5 9.7 6.4Australia 2002-2004 1.7 0.7 7.2 4.4 8.9 5.1
Table adapted from Carter et al1. Source data: Fiji- calculated from deaths reported to the Ministry of Health (previously unpublished); New Zealand75-76; Australia73, 74.
Comparison of empircal estimates with modelled estimates of mortality
Life expectancy and adult mortality predictions from model life tables using the empirical childhood mortality (<5 years) and adult mortality (15-59 years) from the Ministry of Health death recording varied less than 1 year from results calculated using empirical age-specific mortality rates (Figure 9 and 10). Modmatch analysis of data from earlier years is shown in Appendix 5 and 6. However, life expectancy predicted from the Ministry of Health childhood mortality alone (Figure 9), produced estimates similar to those from sources considered in this study as erroneous (Appendix 1), and over-estimated life expectancy by 4-5 years.
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Figure 9: Empirical age-specific mortality compared with predictions from one and two parameter models for Fiji, males: 2002-2004
Model: WHO Modmatch77
Figure 10: Empirical age-specific mortality compared with predicitions from one and two parameter models for Fiji, females: 2002-2004
Model: WHO Modmatch77
Summary of mortality estimates
Sources remaining after censorship for quality and method demonstrate that infant mortality rate has fallen steadily to below 20 deaths per 1000 live births. Empirical estimates from the Ministry of Health data placed the infant mortality rate for 2002-2004 at 17.4 deaths per 1000 live births for males and 16.0 deaths per 1000 live births for females (Appendix 4). Credible sources indicate the infant mortality rate for 2006-08 was 18 -20 deaths per 1000 live births, suggesting s a childhood mortality rate (<5 years) of 23-26 deaths per 1000 live births (see methods).
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Life expectancy rose but has stabilized at around 64 years for males and 69 years for females from the 1980’s. While the infant mortality rate has not fallen during the period of stagnation in life expectancy, it had fallen previously (Figure 2), and at levels since the late 1980s, the infant mortality rate is a minor influence on the overall life expectancy71.
The most reliable all cause mortality estimates are those from local census data analysis and recent Ministry of Health death registration. The estimates of life expectancy produced from the Ministry of Health data were amongst the lower estimates found in the published data. The Ministry of Health data were not adjusted for under-reporting as the data were found to be substantially complete, however if an adjustment had been made this would have resulted in an even lower measure of life expectancy. Level of mortality (and life expectancy) calculated from the Ministry of Health death registration (direct methods)71 correlates closely with the published Census mortality estimates (indirect methods)37,38, thus providing independent verification of these figures, and by implication, the completeness of death reporting by the Ministry of Health in Fiji.
Cause specific mortality
The proportion of deaths coded to ill-defined and unknown causes over the period since 1960 generally fluctuated below 8% of deaths, with substantially higher peaks (up to 18%) in the early part of the 1980’s and again in the early 2000’s.
The proportion of deaths attributable to “Diseases of the Circulatory System” (International Classification of Disease) Chapter IX) shows a significant and ongoing linear increase over 1960-2000, from around 20% of deaths to over 45% of all deaths. This trend is clearly apparent both before (Figure 11) and after the data are refined to censor ill-defined and unknown causes of death (Figure 12), although it is important not to over-interpret reported proportional mortality in all ages and both sexes which provides only an approximate picture of cause of death patterns.
Proportional mortality due to International Classification of Disease Chapter IX is far greater than for any other cause. Early data relating to 1960 were only available as proportional mortality >5 years7; even though this would lead to an over-estimate of all-ages proportional mortality from cardiovascular disease, the observed proportion (15-18%) was the lowest ever observed in Fiji. There were insufficient data available to examine disease-specific contributions within diseases of the circulatory system.The increase in proportional mortality from cardiovascular diseases, and levels of adult mortality in Fiji, are consistent with documented trends since the 1970s in both hospital admissions for cardiovascular disease, and with risk factor exposure for these conditions8, 83-87.
Figure 11: Proportional mortality from diseases of the circulatory system in Fiji: all ages, 1950-20101
Source data: 7-9, 11, 14-17, 20, 25, 26, 29, 35, 44, 51
Figure adapted from Carter et al1
0
10
20
30
40
50
60
70
80
1950 1960 1970 1980 1990 2000 2010
Prop
rotio
n of
dea
ths
(%)
Year
Diseases of the Circulatory System (all ages)
Diseases of the Circulatory System (age >5 years)
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Figure 12: Proportional mortality from diseases of circulatory system (excluding ill-defined and unknown causes) in Fiji: all ages, 1950-2010
0
10
20
30
40
50
60
1950 1960 1970 1980 1990 2000
Pro
po
rtio
n o
f dea
ths
(%)
YearDiseases of the Circulatory System
Source data: 7-9, 11, 14-17, 20, 25, 26, 29, 35, 44, 51
Figure adapted from Carter et al1
Proportional mortality due to the infectious diseases showed no significant trend fluctuating between 5-12% of all deaths over the period investigated.
Figure 13: Proportional mortality from infectious and parasitic diseases (excluding ill-definded and unknown causes) in Fiji: all ages, 1950-2010
Source data: 7-9, 11, 14-17, 20, 25, 26, 29, 35, 44, 51
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Respiratory diseases fell linearly from 14% to 7%, and injuries varied between 4-10% over this period, with a gradual decline in evidence since the mid 1980’s
Figure 14: Proportional mortality from diseases of the respiratory system (excluding ill-defined and unknown causes) in Fiji: all ages, 1950-2010
Source data: 7-9, 11, 14-17, 25, 26, 29, 35, 44, 51
There were no clear trends for any other International Classification of Diseases disease categories (except perinatal conditions); including neoplasms, endocrine and metabolic diseases or diseases of the digestive tract and genitourinary tract, particularly once data was adjusted to account for ill-defined and unknown causes (Appendix 6). Proportional mortality from these chronic disease categories together (20-30%), excluding diseases of the circulatory system, showed no obvious trend. Although only around 8% at the beginning of the 1970’s, proportional mortality from conditions arising in the perinatal period fell to around 2 %.
DISCUSSION
A difference in life expectancy at birth in 2000 of approximately 8 years is evident between various published estimates for Fiji. Many of the previously published estimates of life expectancy are implausible. Spurious increases in life expectancy imply that the health situation is improving when this is clearly unsupported by more reliable data. It is likely that many published estimates were produced through the use of single parameter models used to predict life tables (using infant or childhood mortality) which underestimate adult mortality in Pacific Island populations, and subsequently over-estimate life expectancy.
Extensive variation in the published estimates of infant mortality by source (Figure 1) was also noted, with several reporting levels that are below those of developed countries in the region such as Australia (IMR of 5.0 in 2007)67 and New Zealand (IMR of 6.0 in 2007)67 and are therefore highly improbable (Figure 1). A previous study found substantial uncertainty about mortality conditions in Pacific Island populations with variations of 10 years or more in life expectancy in the 1990s, depending on the source90. Principal issues include: under-enumerated vital registration data; annual stochastic fluctuations in mortality in small populations; errors in the imputation of adult mortality from infant and childhood rates; implausible results from indirect demographic methods; use of possibly inappropriate model life tables to adjust death
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data, or for indirect methods; and inadequately described and implausible projections90.Reconciliation of mortality data in Fiji to reduce uncertainty is urgently needed to ensure that policy and program initiatives to address health issues can be appropriately evaluated.
The quality assessment in this study indicates that the measures provided by Fijian government sources based on indirect methods from censuses and the Ministry of Health death registration data provide the most reliable estimates of mortality and life expectancy at birth in Fiji. As such, these directly observed data should be used by decision makers as key tools in health planning rather than estimates based on theoretical models.
Sources remaining after censorship for quality and method demonstrate that the infant mortality rate has fallen steadily to below 20 deaths per 1000 live births. Life expectancy rose over the period of observation but has stabilized at around 64 years for males and 69 years for females since the 1980s. While the infant mortality rate has not fallen during the period of stagnation in life expectancy, it had fallen previously, and at the levels since the late 1980s, the infant mortality rate is a minor influence on the overall life expectancy71. Proportional mortality in Fiji from cardiovascular disease increased from just below 20% of deaths around 1960 to over 45% of deaths in 2001 (adjusted for unknown and ill defined causes).The proportional mortality from circulatory diseases is substantially higher than that for infectious diseases (as defined in International Classification of Diseases Chapter I) and respiratory illnesses (including infectious respiratory diseases, such as TB). While a similar stagnation in life expectancy may be driven by increasing mortality from HIV/AIDS, Fiji is estimated to have only 0.1% HIV prevalence in the adult population50, and a low mortality from AIDS82.
CONCLUSION
This assessment demonstrates the extensive variation in published estimates of mortality in Fiji; and the neet to evaluate published estimates cricically. Local data from the Ministry of Health routine reporting and analysis of census data were shown to be the most reliable sources, with many others proven implausible.
The stagnation in life expectancy, at relatively low levels of infant mortality, in concert with the epidemiological transition indicated by the levels and trends in cardiovascular disease mortality, morbidity and risk factors, suggests that an epi-demic of chronic non-communicable diseases is having a profound limiting effect on further mortality decline in Fiji. While further research is required, there is an urgent need for Fiji’s health services to place continued and increased emphasis on effective prevention and treatment strategies for cardiovascular disease.
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78. WHO. International Classification of Diseases. World Health Organization. Geneva; 200779. Janssen F, and Kunst A.E. ICD coding changes and discontinuities in trends in cause-specific mortality in six European
countries, 1950–99. Bulletin of the World Health Organization 2004; 82(12): 891-97080. Bennet S and Magnus P. Trends in cardiovascular risk factors in Australia. Results for the National Heart Foundation’s
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1999–2000 Australian Diabetes, Obesity and Lifestyle Study (AusDiab). Medical Journal of Australia 2003; 178:427–432.
82. WHO, UNAids, Unicef. Epidemiological Fact Sheet on HIV and AIDS - Fiji. 2008 [cited 15/5/2010]; Available from: http://apps.who.int/globalatlas/predefinedReports/EFS2008/full/EFS2008_FJ.pdf
83. Pathik B, Ram P. Acute myocardial infarction in Fiji: a review of 300 cases. Medical Journal of Australia 1974;2: 922-4.84. Reed D, Feinleib M. Changing Patterns of Cardiovascular Disease in the Pacific Basin: Report of an International
Workshop. Journal of Community Health, Spring 1983; 8 (3). 182-205. 85. Ram P, Collins V, Zimmet P, Taylor R, King H, Sloman G, Hunt D. Cardiovascular disease risk factors in Fiji: the results
of the 1980 survey. Fiji Medical Journal1983; 11(7/8): 88-94.86. Taylor R, Zimmet P, Levy S, Collins V. Group comparisons of blood pressure and indices of obesity and salt intake in
Pacific populations. Medical Journal of Australia 1985, 142: 499-501.87. Taylor R, Badcock J, King H, Pargeter K, Zimmet P, Fred T, Lund M, Ringrose H, Bach F, Wang RL. Dietary intake
exercise, obesity and noncommunicable disease in rural and urban populations of three Pacific Island countries. Journal of the American College of Nutrition 1992. 11(3): 283-93.
88. Collins V, Dowse G, Cabealawa S, Ram P, and Zimmet P. High Mortality from Cardiovascular Disease and Analysis of Risk Factors in Indian and Melanesian Fijians. International Journal of Epidemiology 1996; 25(1): 59 – 69.
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90. Taylor R, Brampton D, Lopez A. Contemporary patterns of Pacific Island mortality. International Journal of Epidemiology 2005 (34):207-214. www.unstats.un.org/unsd/demographic/products/vitstats/Sets/SeriesA_%20Jan2006_complete.pdf
91. Mathers C, Ma Fat D, Inouie M, Rao C, Lopez A. Counting the dead and what they died from: an assessment of the status of global cause of death data. Bulletin of the World Health Organization 2005;83(3): 171 - 7.
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APPENDIX 1: Data sources and reliability for IMR and LE in Fiji, 1940-2008
SourceYears available
Censored from final estimates Comments and reason for exclusion
IMR LE IMR LE
Govt. - Registrar General
Vital Registration Data 11, 18, 39 46+, 56+, 86+, 96+
46, 56 Yes Yes Calculations were based on uncorrected vital registration data where no assessment of completeness of registration was provided.
Govt. – BoS BoS Census data 12, 36, 37, 39,46 57, 76, 86, 96
57, 66*, 73, 76*, 83, 86, 96
No No Estimates by ethnicity excluded in years where total was available. Total estimate for 1996 and 1976 calculated based on population proportions at the closest census.
Govt. – MoH MoH registration data adjusted for under-enumeration 20, 25
82 82* Partially No Brass adjustment 25 for IMR excluded as this method is not suitable for younger age groups.
LE from unpublished MoH data
Nil 99, 01, 03
NA No Present study
MoH Registration (Total) (9, 13,
14, 15, 19, 24, 26, 28, 33, 34, 42, 43, 53)76-81, 82*, 84, 90, 95-97, 00, 02-04, 06-08
Nil Partially NA 2008 IMR excluded as inconsistent with previous years. Estimates by ethnicity excluded in years where total was available. Total LE estimate for 1982 calculated based on population proportions at 86 census.
Govt. - other Fiji Coordinating Committee on Children 30
90, 93 90‡, 93‡ Yes Yes Source data not identified
Fiji Department for Women and Culture 29
Nil 86 NA Yes Source data not identified - appears to be an MoH estimate from an earlier period.
Fiji Ministry of Information 41 00, 00, Yes Yes
Fiji Fertility Survey (Total) 32 72 Nil No NA
Fiji Government (Total) 27 92 Nil Yes NA Original source not specified
SPC 38, 61, 62 94, 96, 97, 00, 02, 07
95, 97, 01
Yes Yes Incompatible estimates given by the same source for adjacent years
UN UNDP 35, 52, 54 48-88,91, 93-96, 98, 99, 05
53, 58, 63, 68, 73, 76, 83, 88, 93, 05
Yes Yes Source publication noted that the data were derived from estimation models that assume a given improvement in LE or IMR by year,
WHO - World Health Statistics
48, 50, 55, 90, 00, 06, 07
90, 00, 04, 06, 07, 08
Yes Yes Methods vary but not individually identified. These include census data and models derived from the WHO life table system.
WHO - WPRO 45, 49, 56 98, 99, 02, 05, 07, 08
02, 04, 05, 07, 08
Yes Yes Source/method described as local WHO office
UNFPA 68 Nil 02,03 NA Yes Source data not identified
UNESCAP 57, 67 00, 05, 06 00-08 Yes Yes Data showed a perfectly linear improvement by year when graphed indicating use of models that assume a given improvement in LE or IMR; IMR shows incompatible estimates given by the same source for adjacent years
UNICEF (total) 40, 42, 51, 63, 64, 66 97, 00, 01, 05, 07
Nil Yes Yes Estimates derived from other sources by committee consensus
World Bank 31, 62 85, 90, 00, 08
85‡, 90‡ Yes Yes Estimates not available by gender, method not specified
Asian Development Bank (ADB) 58, 59 80, 90, 98, 00, 07
80, 90, 95, 98, 99, 07,
Yes Yes Multiple incompatible estimates given for a single year ; estimates implausible given accepted levels of LE and IMR in other countries
+By gender and thenicity only, *By ethnicity only, ‡Both genders combined. NA: Not available, NB- Census data co-published by SPC with the Fiji
Bureau of Statistics listed under BoS only
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APPENDIX 2: Published cause of death data available Fiji, 1960-2008
Data pro-vided
CategoriesAll deaths included
Years Method of collection / analysis References
Deaths ICDNo 77,78
From the register for Cancers (split by ethnicity of the two main groups)
8
Yes71, 75, 79-81, 85-88, 90, 97
Reported Deaths - MoH10, 13-16, 19, 24, 26, 28, 34
Proportion
Broad Disease Groups
No
60Cardiovascular disease only – method not stated
6
02 Burden of Disease estimates 47
80,88 Vital Statistics 21
ICDNo
74, 78-90, 94, 98-01
Deaths reported to the health statistics section
8, 10, 15, 20, 22, 28, 33 43
Yes 82, 94, 99, 02 Reported Deaths - MoH 8, 10, 45, 52
ICD (combined)
No 77, 85 Reported Deaths - MoH 15, 28
Rate ICD No 71-85Hospital discharge records coded to 17 categories based on ICD. Only categories accounting for >5% included.
9
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APPENDIX 3: Brass analysis for mortality data completeness by gender, Fiji
Completeness (c) is estimated by 1/K where K= the slope of the line
a) 1999-2001 - Males
b) 1999-2001 - Females
K=0.74C=>100%
K=10..C=100%
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c) 1996-1998 - Males
d) 1996-1998 - Females
K=0.77C=>100%
K=0.9156C=>100%
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APPENDIX 4: Life tables calculated from Ministry of Health death reporting
Life table for Fiji 2002 - 2004, Males
Age Deaths Reported Population
Mortality Rate
Linearity Adjustment
Years in Interval
Probability of Dying
Individuals Surviving
Deaths in Interval x
Years Lived in Interval x
Cumulative Years Lived
Expectancy of Life at
Age x
x 5Dx 5Nx nMx a n nqx lx ndx nLx Tx Tx
<11-45-9
10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-80
80+
518139
8997
156222202188283435655807928
1117955
1008694
1226
29309115190143632133136134768123364
94662793157938878910659895221140757295481971011784
60133742
0.01770.00120.00060.00070.00120.00180.00210.00240.00360.00550.00990.01550.02280.03780.04850.08550.11540.3276
0.10.30.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.5
14555555555555555
0.01740.00480.00310.00360.00580.00900.01060.01180.01770.02720.04840.07440.10770.17270.21610.35240.4479
1
1000009826097788974859713196570957059468993574919218942185091787607027658140455762951716298
1740473302354561865
1016111616532499433063318484
1213612564160591321916298
98434391718488182486539484252480688475986470658463736453355436281409626372589321040259291187734114537101244
6495892639745860057405517558503101945467674066079359009331194352655699220234417660631356436
983847662807403515215781101244
64.9665.1161.4256.6051.8047.0842.4937.9133.3428.8924.6320.7517.2214.0011.40
8.857.316.21
Life table for Fiji 2002-2004, Females
Age Deaths Reported Population
Mortality Rate
Linearity Adjustment
Years in Interval
Probability of Dying
Individuals Surviving
Deaths in Interval x
Years Lived in Interval x
Cumulative Years Lived
Expectancy of Life at
Age x
x 5Dx 5Nx nMx a n nqx lx ndx nLx Tx Tx
<11-45-9
10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-80
80+
447109
7681
117151146159207308385602662878741823595117
0
27617108255135047124737126544116172
88074752127807276747646635352542767324512254514483
8186
6574
0.01620.00100.00060.00060.00090.00130.00170.00210.00270.00400.00600.01120.01550.02710.03290.05680.0727
0.1780
0.10.30.50.50.50.50.50.50.50.50.50.50.50.50.50.50.5
0.5
14555555555555555
0.01600.00400.00280.00320.00460.00650.00830.01050.01320.01990.02930.05470.07450.12670.15190.24880.3075
1
10000098405980099773497417969689634095545945409329591441887598390477652678135751543207
29919
1595395275317449628795
1005124518532682485562529839
102981430913288
29919
98564392512489359487878485963483269479711475211469587461841450502431659403892363664313321251805182813
193751
69153036816738642422659348675446989496102644777573998046352283430532472591406214090417092451305353
941690628369376564
193751
69.1569.2765.5560.7255.9151.1646.4841.8437.2632.7328.3424.1220.3716.8113.8910.93
8.72
6.48
27 NUMBER 12 | NOVEMBER 2010 DOCUMENTATION NOTE SERIES
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Health Information Systems Hub (HIS) Knowledge Hub
Life table for Fiji 1999- 2001, Males
Age Deaths Reported Population
Mortality Rate
Linearity Adjustment
Years in Interval
Probability of Dying
Individuals Surviving
Deaths in Interval x
Years Lived in Interval x
Cumulative Years lived
Expectancy of Life at
Age x
x 5Dx 5Nx nMx a n nqx lx ndx nLx Tx Tx
<11-45-9
10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-80
80+
470196139100156213221239354482637898917997
1031948727
1269
29603117430138977134619137510114383
89752842368440176600597494888836811267321753410654
54683855
0.01590.00170.00100.00070.00110.00190.00250.00280.00420.00630.01070.01840.02490.03730.05880.08900.13290.3292
0.10.40.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.5
14555555555555555
0.01570.00670.00500.00370.00560.00930.01220.01410.02070.03100.05190.08780.11720.17060.25640.36410.4988
1
1000009843497779972929693396386954939432492994910668824383665763226737755880415512642413244
1566655486359547893
1169133019272823457873438945
1149714328151281318013244
98590392163487677485563483297479697474543468295460151448275429771399968359247308141243578169938
9916981078
6369140627055058783875390710490514744218493942152346761029993152539164209089016611181261151
901904593763350185180247
81078
63.6963.7060.1255.4150.6045.8841.2836.7632.2527.8823.6919.8516.5213.3910.63
8.436.826.12
Life table for Fiji 1999- 2001, Females
Age Deaths Reported Population
Mortality Rate
Linearity Adjustment
Years in Interval
Probability of Dying
Individuals Surviving
Deaths in Interval x
Years Lived in Interval x
Cumulative Years Lived
Expectancy of Life at
Age x
x 5dx 5Nx nMx a n nqx lx ndx nLx Tx Tx
<11-45-9
10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-80
80+
374163
8792
124156138158230276410544624758770726561
1170
27901110474130369126460129976108066
84175819728255773679595005015138338291941937012833
68866311
0.01340.00150.00070.00070.00100.00140.00160.00190.00280.00370.00690.01080.01630.02600.03980.05660.08150.1854
0.10.40.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.5
14555555555555555
0.01330.00590.00330.00360.00470.00720.00820.00960.01380.01850.03390.05280.07820.12200.18090.24790.3384
1
1000009867498094977699741296950962539546894552932439151588412837437719867784555254176027630
1326580325357462696786916
130917283103466965459415
12259137651413027630
98806393303489656487952485904483007479303475050469487461894449817430388402354362455308270243211173475177976
68723086773502638019958905425402590491668644336793954377347932730098402547946209812916677411265386
902932594662351451177976
68.7268.6565.0460.2555.4650.7146.0641.4236.8032.2827.8423.7319.9116.3913.3210.71
8.426.44
28 DOCUMENTATION NOTE SERIES NUMBER 12 | NOVEMBER 2010
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Health Information Systems Hub (HIS) Knowledge Hub
Life table for Fiji 1996-1998, Males
Age Deaths Reported Population
Mortality Rate
Linearity Adjustment
Years in Interval
Probability of Dying
Individuals Surviving
Deaths in Interval x
Years Lived in Interval x
Cumulative Years Lived
Expectancy of Life at
Age x
x 5Dx 5Nx nMx a n nqx lx ndx nLx Tx Tx
<11-45-9
10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-80
80+
518173117
92172187176218301409708717817852919738676986
30295116570134542141550131306105237
921269026985982693375646444314335972372715910
948754993872
0.01710.00150.00090.00060.00130.00180.00190.00240.00350.00590.01250.01620.02430.03590.05780.07780.12300.2546
0.10.40.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.5
14555555555555555
0.01690.00590.00430.00320.00650.00890.00950.01200.01730.02910.06080.07780.11470.16480.25240.32570.4704
1
1000009831597735973109699696364955109460093465918468917583755772406838357115427012879415251
1685580425315632854910
113516192671542165148858
1126714414139081354315251
98483391868487613485765483399479685475275470164463280452554432325402488364058313745249541178737110111
94298
6433389633490659430385455425496966044862604006575353130030611362597856214530217129771310489
946432632687383146204409
94298
64.3364.4360.8156.0651.2446.5641.9537.3332.7528.2824.0620.4516.9713.8411.08
8.977.106.18
Life table for Fiji 1996- 1998, Females
Age Deaths Reported Population
Mortality Rate
Linearity Adjustment
Years in Interval
Probability of Dying
Individuals Surviving
Deaths in Interval x
Years Lived in Interval x
Cumulative Years Lived
Expectancy of Life at
Age x
x 5Dx 5Nx nMx a n nqx lx ndx nLx Tx Tx
<11-45-9
10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-80
80+
438141
6672
104122128144178216493469576667644572532
1082
28584109571126125133682125031
9919288738885478254367417567704484035244248681748510576
67065722
0.01530.00130.00050.00050.00080.00120.00140.00160.00220.00320.00870.01050.01630.02680.03680.05410.07930.1892
0.10.40.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.5
14555555555555555
0.01510.00510.00260.00270.00420.00610.00720.00810.01070.01590.04250.05090.07840.12570.16870.23820.3308
1
1000009848797981977269746597060964659577094992939739248188547840367744367705562854287928694
1513506255261405596695778
101814923934451165929738
11420134061418528694
98639392735489269487979486314483812480585476903472412466135452568431456403698362871309976247910178932185298
69074926808853641611959268505438871495255744687443988159351125630388442572710212014116886851284988
922116612140364230185298
69.0769.1365.4860.6555.8051.0346.3341.6436.9632.3427.8223.9420.0916.5913.6210.88
8.496.46
29 NUMBER 12 | NOVEMBER 2010 DOCUMENTATION NOTE SERIES
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APPENDIX 5: Empirical age-specific mortality compared with predictions from one and two parameter models for Fiji
1. 1999-2001, Males
2. 1999-2001, Females
30 DOCUMENTATION NOTE SERIES NUMBER 12 | NOVEMBER 2010
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3. 1996-1998, Males
4. 1996-1998, Females
31 NUMBER 12 | NOVEMBER 2010 DOCUMENTATION NOTE SERIES
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APPENDIX 6:
Proportional mortality in Fiji 1970-2004: all ages (unadjusted for ill-defined or unknown causes)
Source data: 7-9, 11, 14-17, 20, 25, 26, 29, 35, 44, 51
1. Infectious and parasitic diseases - unadjusted for ill-defined and unknown cases
2. Diseases of the respiratory system - unadjusted for ill-defined and unknown causes
32 DOCUMENTATION NOTE SERIES NUMBER 12 | NOVEMBER 2010
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3. Neoplasms
a) Unadjusted for ill-defined and unknown causes
b) Adjusted for ill-defined and unknown causes
33 NUMBER 12 | NOVEMBER 2010 DOCUMENTATION NOTE SERIES
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4. Endorcrine, nutritional and metabolic disorders
a) Unadjusted for ill-defined and unknown causes
b) Adjusted for ill-defined and unknown causes
34 DOCUMENTATION NOTE SERIES NUMBER 12 | NOVEMBER 2010
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5. Diseases of the nervous and sensory systems
a) Unadjusted for ill-defined and unknown causes
b) Adjusted for ill-defined and unknown causes
35 NUMBER 12 | NOVEMBER 2010 DOCUMENTATION NOTE SERIES
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6. Diseases of the digestive system
a) unadjusted for ill-defined and unknown causes
b) Adjusted for ill-defined and unknown causes
36 DOCUMENTATION NOTE SERIES NUMBER 12 | NOVEMBER 2010
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7. Diseases of the genitourinary system
a) Unadjusted for ill-defined and unknown causes
b) Adjusted for ill-defined and unknown causes
37 NUMBER 12 | NOVEMBER 2010 DOCUMENTATION NOTE SERIES
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8. Conditions arising in the perinatal period
a) Unadjusted for ill-defined and unknown causes
b) Adjusted for ill-defined and unknown causes
38 DOCUMENTATION NOTE SERIES NUMBER 12 | NOVEMBER 2010
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9. Congenital abnormalities
a) Unadjusted for ill-defined and unknown causes
b) Adjusted for ill-defined and unknown causes
39 NUMBER 12 | NOVEMBER 2010 DOCUMENTATION NOTE SERIES
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10. Injury and poisoning
a) Unadjusted for ill-defined and unknown causes
b) Adjusted for ill-defined and unknown causes
KNOWLEDGE HUBS FOR HEALTHStrengthening health systems through evidence in Asia and the Pacific
A strategic partnerships initiative funded by the Australian Agency for International Development
The Nossal Institute for Global Health
lvii
Appendix 4: Additional cause of death distributions by age and sex
Palau Children 0-4 years
1999�2003
�103�ICD�code�
Female�deaths�
Male�deaths�
Total�deaths�
Rate�(deaths�per�100,000�pop)�
Upper�95%�CI�
Lower95%��CI�
1�048� 0 0� 0 0.0 0.0 55.2�1�058� 1 0� 1 0.4 0.0 83.4�1�064� 1 0� 1 0.4 0.0 83.4�1�072� 0 0� 0 0.0 0.0 55.2�1�078� 0 2� 2 3.6 0.0 108.1�1�084� 0 0� 0 0.0 0.0 55.2�1�092� 2 8� 10 71.7 0.0 275.1�1�093� 3 1� 4 9.3 0.0 131.2�1�094� 0 0� 0 0.0 0.0 55.2�1�095� 1 1� 2 3.6 0.0 108.1��
2004�2009�(excl.�2007)
�103�ICD�code�
Female�deaths�
Male�deaths�
Total�deaths�
Rate�(deaths�per�100,000�pop)�
Upper�95%�CI�
Lower95%��CI�
1�048� 0 1� 1 0.4 0.0� 80.8�1�058� 0 1� 1 0.4 0.0� 80.8�1�064� 1 0� 1 0.4 0.0� 80.8�1�072� 1 1� 2 3.5 0.0� 104.7�1�078� 0 0� 0 0.0 0.0� 53.5�1�084� 0 1� 1 0.4 0.0� 80.8�1�092� 5 11� 16 132.6 0.0� 376.6�1�093� 0 1� 1 0.4 0.0� 80.8�1�094� 0 1� 1 0.4 0.0� 80.8�1�095� 0 1� 1 0.4 0.0� 80.8�
lviii
5-14 years
1999�2003
ICD�103�list�code�
Female�deaths�
Male�deaths�
Total�deaths
Proportion�(%)�
Rate�(deaths�per�100,000�pop)�
Upper�95%�CI�
Lower95%��CI�
1�001� 1�005� 0� 0� 0 0 0 0� 22.462761�012� 0� 0� 0 0 0 0� 22.462761�013� 0� 0� 0 0 0 0� 22.462761�017� 0� 0� 0 0 0 0� 22.462761�019� 0� 0� 0 0 0 0� 22.462761�020� 0� 0� 0 0 0 0� 22.462761�025� 0� 1� 1 11.11111 6.09E�05 0.152228� 33.92857
1�001�Total� 0� 1� 1 11.11111 6.09E�05 0.152228� 33.928571�026� 1�027� 0� 0� 0 0 0 0� 22.46276
1�029� 0� 0� 0 0 0 0� 22.462761�030� 0� 0� 0 0 0 0� 22.462761�031� 0� 0� 0 0 0 0� 22.462761�032� 0� 0� 0 0 0 0� 22.462761�034� 0� 0� 0 0 0 0� 22.462761�036� 0� 0� 0 0 0 0� 22.462761�037� 0� 0� 0 0 0 0� 22.462761�038� 0� 0� 0 0 0 0� 22.462761�039� 0� 0� 0 0 0 0� 22.462761�040� 0� 0� 0 0 0 0� 22.462761�041� 0� 0� 0 0 0 0� 22.462761�042� 0� 0� 0 0 0 0� 22.462761�043� 0� 0� 0 0 0 0� 22.462761�044� 0� 0� 0 0 0 0� 22.462761�046� 0� 0� 0 0 0 0� 22.462761�047� 0� 0� 0 0 0 0� 22.46276
1�026�Total� 0� 0� 0 0 0 0� 22.462761�048� 1�049� 0� 0� 0 0 0 0� 22.462761�048�Total� 0� 0� 0 0 0 0� 22.462761�051� 1�052� 0� 0� 0 0 0 0� 22.46276
1�053� 0� 0� 0 0 0 0� 22.462761�054� 0� 0� 0 0 0 0� 22.46276
1�051�Total� 0� 0� 0 0 0 0� 22.462761�055� 1�056� 0� 0� 0 0 0 0� 22.46276
1�057� 0� 0� 0 0 0 0� 22.462761�055�Total� 0� 0� 0 0 0 0� 22.462761�058� 1�059� 0� 0� 0 0 0 0� 22.46276
1�061� 0� 0� 0 0 0 0� 22.462761�058�Total� 0� 0� 0 0 0 0� 22.46276
lix
1�064� 1�065� 0� 0� 0 0 0 0� 22.462761�066� 0� 0� 0 0 0 0� 22.462761�067� 0� 0� 0 0 0 0� 22.462761�068� 0� 0� 0 0 0 0� 22.462761�069� 0� 0� 0 0 0 0� 22.462761�071� 0� 0� 0 0 0 0� 22.46276
1�064�Total� 0� 0� 0 0 0 0� 22.462761�072� 1�074� 0� 0� 0 0 0 0� 22.46276
1�076� 0� 1� 1 11.11111 6.09E�05 0.152228� 33.928571�077� 2� 0� 2 22.22222 0.000122 1.473567� 43.99388
1�072�Total� 2� 1� 3 33.33333 0.000183 3.769165� 53.383311�078� 1�080� 0� 0� 0 0 0 0� 22.46276
1�081� 1� 0� 1 11.11111 6.09E�05 0.152228� 33.928571�078�Total� 1� 0� 1 11.11111 6.09E�05 0.152228� 33.928571�082� 1�082� 0� 0� 0 0 0 0� 22.462761�082�Total� 0� 0� 0 0 0 0� 22.462761�083� 1�083� 0� 0� 0 0 0 0� 22.462761�083�Total� 0� 0� 0 0 0 0� 22.462761�084� 1�086� 0� 0� 0 0 0 0� 22.462761�084�Total� 0� 0� 0 0 0 0� 22.462761�092� 1�092� 0� 0� 0 0 0 0� 22.462761�092�Total� 0� 0� 0 0 0 0� 22.462761�093� 1�093� 0� 0� 0 0 0 0� 22.462761�093�Total� 0� 0� 0 0 0 0� 22.462761�094� 1�094� 0� 0� 0 0 0 0� 22.462761�094�Total� 0� 0� 0 0 0 0� 22.462761�095� 1�096� 0� 1� 1 11.11111 6.09E�05 0.152228� 33.92857
1�097� 0� 0� 0 0 0 0� 22.462761�098� 0� 0� 0 0 0 0� 22.462761�099� 0� 1� 1 11.11111 6.09E�05 0.152228� 33.928571�101� 0� 0� 0 0 0 0� 22.462761�102� 0� 1� 1 11.11111 6.09E�05 0.152228� 33.928571�103� 0� 1� 1 11.11111 6.09E�05 0.152228� 33.92857
1�095�Total� 0� 4� 4 44.44444 0.000244 6.63714� 62.36476Grand�Total� 3� 6� 9 100 0.000548 25.05672� 104.0326
� �
lx
2004�2009
ICD 103 list code Female deaths
Male deaths
Total deaths
Proportion (%)
Rate (deaths per 100,000 pop)
Upper 95% CI
Lower95% CI
1-001 1-005 0 0 0 0 0 0 21.012151-012 0 0 0 0 0 0 21.012151-017 0 0 0 0 0 0 21.012151-019 0 0 0 0 0 0 21.012151-020 0 0 0 0 0 0 21.012151-025 0 0 0 0 0 0 21.01215
1-001 Total 0 0 0 0 0 0 21.012151-026 1-027 0 0 0 0 0 0 21.01215
1-028 0 0 0 0 0 0 21.012151-029 0 0 0 0 0 0 21.012151-030 0 0 0 0 0 0 21.012151-031 0 0 0 0 0 0 21.012151-032 0 0 0 0 0 0 21.012151-033 0 0 0 0 0 0 21.012151-034 0 0 0 0 0 0 21.012151-036 0 0 0 0 0 0 21.012151-037 0 0 0 0 0 0 21.012151-038 0 0 0 0 0 0 21.012151-039 0 0 0 0 0 0 21.012151-040 0 0 0 0 0 0 21.012151-042 1 0 1 16.66667 5.7E-05 0.142397 31.737521-043 0 0 0 0 0 0 21.012151-044 0 0 0 0 0 0 21.012151-045 0 0 0 0 0 0 21.012151-046 0 0 0 0 0 0 21.012151-047 0 0 0 0 0 0 21.01215
1-026 Total 1 0 1 16.66667 5.7E-05 0.142397 31.737521-048 1-049 0 0 0 0 0 0 21.01215
1-050 1 0 1 16.66667 5.7E-05 0.142397 31.737521-048 Total 1 0 1 16.66667 5.7E-05 0.142397 31.737521-051 1-052 0 0 0 0 0 0 21.01215
1-053 0 0 0 0 0 0 21.012151-054 0 0 0 0 0 0 21.01215
1-051 Total 0 0 0 0 0 0 21.012151-055 1-056 0 0 0 0 0 0 21.01215
1-057 0 0 0 0 0 0 21.012151-055 Total 0 0 0 0 0 0 21.012151-058 1-061 1 0 1 16.66667 5.7E-05 0.142397 31.737521-058 Total 1 0 1 16.66667 5.7E-05 0.142397 31.737521-064 1-065 1 0 1 16.66667 5.7E-05 0.142397 31.73752
1-066 0 0 0 0 0 0 21.01215
lxi
1-067 0 0 0 0 0 0 21.012151-068 0 0 0 0 0 0 21.012151-069 1 0 1 16.66667 5.7E-05 0.142397 31.737521-070 0 0 0 0 0 0 21.012151-071 0 0 0 0 0 0 21.01215
1-064 Total 2 0 2 33.33333 0.000114 1.378406 41.152841-072 1-074 0 0 0 0 0 0 21.01215
1-075 0 0 0 0 0 0 21.012151-076 0 0 0 0 0 0 21.012151-077 0 0 0 0 0 0 21.01215
1-072 Total 0 0 0 0 0 0 21.012151-078 1-080 0 0 0 0 0 0 21.01215
1-081 0 0 0 0 0 0 21.012151-078 Total 0 0 0 0 0 0 21.012151-082 1-082 0 0 0 0 0 0 21.012151-082 Total 0 0 0 0 0 0 21.012151-083 1-083 0 0 0 0 0 0 21.012151-083 Total 0 0 0 0 0 0 21.012151-084 1-085 0 0 0 0 0 0 21.01215
1-086 0 0 0 0 0 0 21.012151-084 Total 0 0 0 0 0 0 21.012151-092 1-092 0 0 0 0 0 0 21.012151-092 Total 0 0 0 0 0 0 21.012151-093 1-093 0 0 0 0 0 0 21.012151-093 Total 0 0 0 0 0 0 21.012151-094 1-094 0 0 0 0 0 0 21.012151-094 Total 0 0 0 0 0 0 21.012151-095 1-096 0 0 0 0 0 0 21.01215
1-098 0 0 0 0 0 0 21.012151-099 0 0 0 0 0 0 21.012151-100 0 0 0 0 0 0 21.012151-101 1 0 1 16.66667 5.7E-05 0.142397 31.737521-102 0 0 0 0 0 0 21.012151-103 0 0 0 0 0 0 21.01215
1-095 Total 1 0 1 16.66667 5.7E-05 0.142397 31.73752Grand Total 6 0 6 100 0.000342 12.54236 74.38268
lxii
15-6
4 ye
ars
1999�2003
Cause�(103�cod
e)�
Females�
Males�
Deaths�
Prop
ortio
nRa
te�(p
er�
100,000)�
Lower�
95%�CI�
Upp
er�
95%�CI�
Deaths�
Prop
ortio
n
Rate�
(per�
100,000)�
Lower�
95%�
CI�
Upp
er�
95%�CI�
1�001�
1�005�
1.0�
1.5
3.4
0.1
18.9�
0.0
0.0
0.0
0.0
9.7
1�012�
1.0�
1.5
3.4
0.1
18.9�
3.0
1.6
7.9
1.6
23.0
1�013�
0.0�
0.0
0.0
0.0
12.5�
0.0
0.0
0.0
0.0
9.7
1�017�
0.0�
0.0
0.0
0.0
12.5�
2.0
1.0
5.2
0.6
18.9
1�019�
3.0�
4.4
10.2
2.1
29.8�
4.0
2.1
10.5
2.9
26.9
1�020�
0.0�
0.0
0.0
0.0
12.5�
1.0
0.5
2.6
0.1
14.6
1�025�
1.0�
1.5
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lxviii
65+
year
s
1999�2003
Fem
ales
M
ales
Dea
ths
Pro
porti
on
Rat
e (p
er
100,
000)
Lo
wer
95
% C
I U
pper
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% C
I D
eath
s P
ropo
rtion
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ate
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10
0,00
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er
95%
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er
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00
120.
4028
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00
166.
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012
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8087
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281
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Gra
nd T
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��
lxxi
2004�2009
Cau
se (1
03 li
st)
Fem
ales
M
ales
Dea
ths
Pro
porti
on
Rat
e (p
er
100,
000)
Lo
wer
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% C
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pper
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% C
I D
eath
s P
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rtion
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ate
(per
10
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er
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Upp
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lxxii
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7560
9812
7.72
7826
.354
537
3.26
32
1-06
4 1-
065
1 0.
5464
4828
.551
150.
7137
7915
9.08
70
00
015
7.06
26
1-06
6 9
4.91
8033
256.
9603
117.
488
487.
7964
42.
3414
6317
0.30
3746
.407
7743
6.06
27
1-06
7 13
7.
1038
2537
1.16
4919
7.63
1163
4.69
2125
14.6
3415
1064
.398
688.
836
1571
.265
1-
068
6 3.
2786
8917
1.30
6962
.869
6337
2.84
957
4.09
7561
298.
0315
119.
8087
614.
0727
1-
069
18
9.83
6066
513.
9207
304.
5837
812.
2231
1911
.121
9580
8.94
2748
7.02
6112
63.2
71
1-07
0 0
00
010
5.32
521
0.58
5366
42.5
7593
1.06
4398
237.
2331
1-
071
3 1.
6393
4485
.653
4517
.673
1625
0.30
791
0.58
5366
42.5
7593
1.06
4398
237.
2331
1-
064
Tota
l 50
27
.322
414
27.5
5710
59.5
6218
82.0
6357
33.3
6585
2426
.828
1838
.046
3144
.233
1-
072
1-07
4 8
4.37
1585
228.
4092
98.6
1567
450.
0518
10.
5853
6642
.575
931.
0643
9823
7.23
31
1-07
5 0
00
010
5.32
520
00
015
7.06
26
1-07
6 6
3.27
8689
171.
3069
62.8
6963
372.
8495
127.
0243
951
0.91
1226
4.01
3489
2.47
67
1-07
7 4
2.18
5792
114.
2046
31.1
2075
292.
4209
31.
7560
9812
7.72
7826
.354
537
3.26
32
1-07
2 To
tal
18
9.83
6066
513.
9207
304.
5837
812.
2231
169.
3658
5468
1.21
4938
9.35
6911
06.2
5 1-
078
1-08
0 0.
5 0.
2732
2414
.275
570
105.
3252
1.5
0.87
8049
63.8
639
1.06
4398
237.
2331
lxxiii
1-08
1 2
1.09
2896
57.1
023
6.90
9378
206.
2821
21.
1707
3285
.151
8710
.303
3830
7.61
11
1-07
8 To
tal
2.5
1.36
612
71.3
7787
6.90
9378
206.
2821
3.5
2.04
878
149.
0158
26.3
545
373.
2632
1-
082
1-08
2 1
0.54
6448
28.5
5115
0.71
3779
159.
087
10.
5853
6642
.575
931.
0643
9823
7.23
31
1-08
2 To
tal
1 0.
5464
4828
.551
150.
7137
7915
9.08
71
0.58
5366
42.5
7593
1.06
4398
237.
2331
1-
083
1-08
3 0
00
010
5.32
520
00
015
7.06
26
1-08
3 To
tal
0 0
00
105.
3252
00
00
157.
0626
1-
084
1-08
5 0
00
010
5.32
520
00
015
7.06
26
1-08
6 9
4.91
8033
256.
9603
117.
488
487.
7964
31.
7560
9812
7.72
7826
.354
537
3.26
32
1-08
4 To
tal
9 4.
9180
3325
6.96
0311
7.48
848
7.79
643
1.75
6098
127.
7278
26.3
545
373.
2632
1-
092
1-09
2 0
00
010
5.32
520
00
015
7.06
26
1-09
2 To
tal
0 0
00
105.
3252
00
00
157.
0626
1-
093
1-09
3 0
00
010
5.32
520
00
015
7.06
26
1-09
3 To
tal
0 0
00
105.
3252
00
00
157.
0626
1-
094
1-09
4 8.
5 4.
6448
0924
2.68
4898
.615
6745
0.05
188.
54.
9756
136
1.89
5414
7.05
7367
1.12
44
1-09
4 To
tal
8.5
4.64
4809
242.
6848
98.6
1567
450.
0518
8.5
4.97
561
361.
8954
147.
0573
671.
1244
1-
095
1-09
6 0
00
010
5.32
521
0.58
5366
42.5
7593
1.06
4398
237.
2331
1-
098
0 0
00
105.
3252
00
00
157.
0626
1-
099
0 0
00
105.
3252
00
00
157.
0626
1-
100
0 0
00
105.
3252
00
00
157.
0626
1-
101
0 0
00
105.
3252
00
00
157.
0626
1-
102
1 0.
5464
4828
.551
150.
7137
7915
9.08
70
00
015
7.06
26
1-10
3 7
3.82
5137
199.
858
80.3
4294
411.
7932
63.
5121
9525
5.45
5693
.752
2155
5.99
91
1-09
5 To
tal
8 4.
3715
8522
8.40
9298
.615
6745
0.05
187
4.09
7561
298.
0315
119.
8087
614.
0727
G
rand
Tot
al
183
100
5224
.86
2323
.036
3472
.591
170.
8333
100
7273
.389
3464
.148
5178
.383
POP
3502
.486
23
48.7
45
lxxiv
Ton
gaA
ge s
tand
ardi
sed
rate
s (s
tand
ardi
sed
to T
onga
pop
ulat
ion
2005
-200
8)
MA
LES
20
01-2
004
2005
-200
8
N
o ad
just
men
t for
co
ntrib
utor
y ca
uses
A
djus
ted
for e
ffect
of
cont
ribut
ory
caus
es
No
adju
stm
ent f
or
cont
ribut
ory
caus
es
Adj
uste
d fo
r effe
ct o
f co
ntrib
utor
y ca
uses
Lo
w
Mor
talit
y E
stim
ates
Hig
h M
orta
lity
Est
imat
es
Low
M
orta
lity
Est
imat
es
Hig
h M
orta
lity
Est
imat
es
Low
M
orta
lity
Est
imat
es
Hig
h M
orta
lity
Est
imat
es
Low
M
orta
lity
Est
imat
es
Hig
h M
orta
lity
Est
imat
es
1-00
1 C
erta
in in
fect
ious
and
pa
rasi
tic d
isea
ses
20.3
40.0
20.3
40.0
61.1
81.4
63.7
84.9
1-01
9 V
iral h
epat
itis
4.0
8.0
4.0
8.0
18.4
24.5
18.5
24.7
1-
026
Neo
plas
ms
66.1
130.
166
.113
0.1
191.
125
4.7
189.
425
2.4
1-02
9 M
alig
nant
neo
plas
m o
f sto
mac
h 5.
210
.15.
210
.115
.420
.515
.620
.8
1-03
1 M
alig
nant
neo
plas
m o
f liv
er a
nd
intra
hepa
tic b
ile d
ucts
9.
618
.99.
618
.919
.225
.517
.923
.9
1-03
4 M
alig
nant
neo
plas
m o
f tra
chea
, br
onch
us a
nd lu
ng
12.5
24.6
12.5
24.6
36.7
49.0
34.4
45.8
1-04
0 M
alig
nant
neo
plas
m o
f pro
stat
e 10
.420
.310
.420
.326
.034
.626
.935
.9
1-05
2 D
iabe
tes
mel
litus
26
.051
.325
.550
.216
6.5
222.
094
.212
5.6
1-05
8 D
isea
ses
of th
e ne
rvou
s sy
stem
5.
210
.05.
210
.013
.517
.913
.517
.9
1-06
4 D
isea
ses
of th
e ci
rcul
ator
y sy
stem
19
3.9
380.
519
4.4
381.
542
3.0
563.
948
3.2
644.
2
1-06
5 A
cute
rheu
mat
ic fe
ver a
nd
chro
nic
rheu
mat
ic h
eart
dise
ases
0.
20.
50.
20.
58.
711
.68.
711
.6
1-06
6 H
yper
tens
ive
dise
ases
11
.522
.412
.023
.525
.033
.427
.136
.1
1-06
7 Is
chae
mic
hea
rt di
seas
es
44.8
88.4
44.8
88.4
139.
418
5.8
157.
721
0.2
1-06
8 O
ther
hea
rt di
seas
es
119.
923
4.9
119.
923
4.9
203.
427
1.1
232.
631
0.1
1-06
9 C
ereb
rova
scul
ar d
isea
ses
17.5
34.2
17.5
34.2
40.4
53.9
49.8
66.4
lxxv
1-
072
Dis
ease
s of
the
resp
irato
ry
syst
em
55.0
107.
655
.010
7.6
168.
422
4.5
173.
323
0.9
1-07
4 P
neum
onia
14
.327
.814
.327
.814
.819
.715
.120
.2
1-07
6 C
hron
ic lo
wer
resp
irato
ry
dise
ases
25
.649
.925
.649
.993
.512
4.6
92.7
123.
5
1-07
8 D
isea
ses
of th
e di
gest
ive
syst
em
22.8
45.2
22.8
45.2
46.6
62.1
47.2
63.0
1-08
0 D
isea
ses
of th
e liv
er
8.6
17.2
8.6
17.2
17.3
23.1
17.3
23.1
1-
084
Dis
ease
s of
the
geni
tour
inar
y sy
stem
14
.328
.414
.328
.425
.133
.527
.536
.6
1-09
2 C
erta
in c
ondi
tions
orig
inat
ing
in th
e pe
rinat
al p
erio
d 1.
83.
41.
83.
411
.314
.911
.314
.9
1-09
5 Ex
tern
al c
ause
s of
mor
bidi
ty
and
mor
talit
y 29
.558
.329
.558
.368
.290
.868
.891
.6
1-09
6 Tr
ansp
ort a
ccid
ents
6.
813
.66.
813
.613
.117
.513
.117
.5
1-10
1 In
tent
iona
l sel
f-har
m
1.6
3.1
1.6
3.1
8.6
11.5
8.7
11.6
lxxvi
FEM
ALE
S 20
01-2
004
2005
-200
8
N
o ad
just
men
t for
co
ntrib
utor
y ca
uses
A
djus
ted
for e
ffect
of
cont
ribut
ory
caus
es
No
adju
stm
ent f
or
cont
ribut
ory
caus
es
Adj
uste
d fo
r effe
ct o
f co
ntrib
utor
y ca
uses
Lo
w
Mor
talit
y E
stim
ates
Hig
h M
orta
lity
Est
imat
es
Low
M
orta
lity
Est
imat
es
Hig
h M
orta
lity
Est
imat
es
Low
M
orta
lity
Est
imat
es
Hig
h M
orta
lity
Est
imat
es
Low
M
orta
lity
Est
imat
es
Hig
h M
orta
lity
Est
imat
es
1-00
1 C
erta
in in
fect
ious
and
pa
rasi
tic d
isea
ses
12.3
25.2
12.3
25.2
33.8
46.6
35.4
48.8
1-01
9 V
iral h
epat
itis
1.3
2.6
1.3
2.6
7.3
10.1
7.4
10.2
1-
026
Neo
plas
ms
57.5
118.
457
.511
8.4
141.
419
5.5
136.
418
8.5
1-02
9 M
alig
nant
neo
plas
m o
f sto
mac
h 3.
87.
93.
87.
97.
310
.16.
38.
7 1-
031
Mal
igna
nt n
eopl
asm
of l
iver
and
in
trahe
patic
bile
duc
ts
3.2
6.6
3.2
6.6
5.9
8.1
5.9
8.1
1-03
4 M
alig
nant
neo
plas
m o
f tra
chea
, br
onch
us a
nd lu
ng
2.5
5.3
2.5
5.3
16.5
22.8
16.6
23.0
1-03
6 M
alig
nant
neo
plas
m o
f bre
ast
14.8
30.1
14.8
30.1
22.0
30.4
20.7
28.7
1-
037
Mal
igna
nt n
eopl
asm
of c
ervi
x ut
eri
5.6
11.6
5.6
11.6
8.6
12.0
8.7
12.0
1-03
8 M
alig
nant
neo
plas
m o
f oth
er a
nd
unsp
ecifi
ed p
arts
of u
teru
s 4.
79.
64.
79.
66.
89.
55.
88.
0
1-03
9 M
alig
nant
neo
plas
m o
f ova
ry
3.0
6.2
3.0
6.2
7.7
10.6
7.7
10.6
1-
052
Dia
bete
s m
ellit
us
35.0
72.4
34.1
70.7
137.
819
0.4
98.3
135.
9 1-
058
Dis
ease
s of
the
nerv
ous
syst
em
2.5
5.1
2.5
5.1
17.9
24.6
18.0
24.8
1-06
4 D
isea
ses
of th
e ci
rcul
ator
y sy
stem
10
8.2
225.
310
9.1
227.
019
4.2
268.
523
2.5
321.
3
lxxvii
1-06
5 A
cute
rheu
mat
ic fe
ver a
nd
chro
nic
rheu
mat
ic h
eart
dise
ases
1.
22.
51.
22.
510
.214
.210
.314
.3
1-06
6 H
yper
tens
ive
dise
ases
7.
215
.07.
215
.012
.317
.016
.122
.3
1-06
7 Is
chae
mic
hea
rt di
seas
es
14.1
29.6
14.5
30.4
47.3
65.3
63.7
88.1
1-
068
Oth
er h
eart
dise
ases
70
.614
6.6
70.6
146.
689
.412
3.6
104.
814
4.9
1-06
9 C
ereb
rova
scul
ar d
isea
ses
15.2
31.7
15.6
32.6
31.3
43.3
33.8
46.7
1-
072
Dis
ease
s of
the
resp
irato
ry
syst
em
26.6
55.2
26.6
55.2
81.7
112.
882
.711
4.2
1-07
4 P
neum
onia
7.
816
.27.
816
.211
.115
.411
.516
.0
1-07
6 C
hron
ic lo
wer
resp
irato
ry
dise
ases
8.
617
.78.
617
.724
.333
.623
.432
.4
1-07
8 D
isea
ses
of th
e di
gest
ive
syst
em
14.3
29.6
14.3
29.6
27.6
38.1
28.0
38.6
1-08
0 D
isea
ses
of th
e liv
er
6.5
13.5
6.5
13.5
9.2
12.7
9.3
12.8
1-
084
Dis
ease
s of
the
geni
tour
inar
y sy
stem
8.
718
.18.
718
.120
.928
.923
.332
.1
1-08
7 Pr
egna
ncy,
chi
ldbi
rth
and
the
puer
periu
m
1.1
2.2
1.1
2.2
4.8
6.7
4.8
6.7
1-09
2 C
erta
in c
ondi
tions
orig
inat
ing
in th
e pe
rinat
al p
erio
d 1.
32.
41.
32.
410
.714
.410
.714
.4
1-09
5 Ex
tern
al c
ause
s of
mor
bidi
ty
and
mor
talit
y 7.
214
.87.
214
.821
.429
.520
.428
.2
1-09
6 Tr
ansp
ort a
ccid
ents
2.
04.
12.
04.
11.
72.
31.
72.
3 1-
101
Inte
ntio
nal s
elf-h
arm
0.
00.
00.
00.
02.
12.
92.
12.
9
lxxviii
Cause-of-death distributions by global burden of disease category (cumulative
%) for Tonga: Empirical data and modelled distribution based on mortality
level
a) Males, 2001-2004
Modelled estimates by GBD category obtained using Codmod models using inputs of
5q0 = 25 deaths per 1000 live births, 45q15 = 30.6% probability of dying (closest
possible model fit to midpoint of plausible range of mortality estimates), and GDP =
$2105 US per capita
Group I
Group
II
Group
III
lxxix
b) Males, 2005-2008
WHO Global Burden of Disease (GBD) Study categories: I (Infectious, perinatal and
maternal conditions), II (NCDs) and III (external causes)
Modelled estimates by GBD category obtained using Codmod models using inputs of
5q0 = 30 deaths per 1000 live births, 45q15 = 31.9% probability of dying (closest
possible model fit to midpoint of plausible range of mortality estimates), and GDP =
$2961 US per capita
�
Group
III
Group
II
Group I
lxxx
Nauru Deaths by cause (by ICD chapter) and proportional mortality (for children 5-14
years: Nauru, 2005-2009
Row Labels Deaths Proportionalmortality
Lower 95% CI
Upper95% CI
1-001 Infectious diseases 0 0% 0% 32% 1-026 Neoplasms 0 0% 0% 32% 1-048 Diseases of the blood 1 20% 2% 48% 1-051 Endocrine, nutritional and other metabolic disorders
0 0% 0% 32%
1-058 Diseases of the nervous system 0 0% 0% 32% 1-064 Diseases of the circulatory system
0 0% 0% 32%
1-072 Diseases of the respiratory system
1 20% 2% 48%
1-078 Diseases of the digestive tract 0 0% 0% 32% 1-082 Diseases of the skin 0 0% 0% 32% 1-084 Diseases of the genito-urinary system
0 0% 0% 32%
1-092 Perinatal conditions 0 0% 0% 32% 1-093 Congenital conditions 0 0% 0% 32% 1-094 Signs, symptoms and other ill-defined causes
0 0% 0% 32%
1-095 External causes (injuries) 3 60% 38% 75% Total 5 100% 100% 100%
lxxxi
Deaths in adults aged 65 years and older
Cause Female Male
ICD CHAPTER Deaths Propor-tional
mortality
Lower 95% CI
Upper95% CI Deaths
Propor-tional
mortality Lower 95% CI
Upper95% CI
1-026 Neoplasms 2 10% 2% 23% 3 17% 6% 31% 1-051 Endocrine, nutritional and other metabolic disorders
9 45% 34% 55% 2 11% 2% 25%
1-064 Diseases of the circulatory system 5 25% 13% 38% 5 28% 15% 41%
1-072 Diseases of the circulatory system 1 5% 0% 18% 5 28% 15% 41%
1-078 Diseases of the digestive tract 1 5% 0% 18% 1 6% 0% 20%
1-082 Diseases of the skin 1 5% 0% 18% 0% 0% 13%
1-084 Diseases of the genito-urinary system 1 5% 0% 18% 1 6% 0% 20%
1-095 External causes (injuries) 0% 0% 12% 1 6% 0% 20% Total 20 100% 100% 100% 18 100% 100% 100% Specific Causes (103 list) 1-034 1 11% 1% 33% 1 50% 10% 77% 1-037 1 11% 1% 33% 0% 0% 51% 1-046 0% 0% 22% 2 100% 100% 100% 1-052 9 100% 100% 100% 2 100% 100% 100% 1-067 0% 0% 22% 2 100% 100% 100% 1-068 3 33% 15% 51% 1 50% 10% 77% 1-069 2 22% 6% 42% 1 50% 10% 77% 1-071 0% 0% 22% 1 50% 10% 77% 1-074 0% 0% 22% 1 50% 10% 77% 1-075 0% 0% 22% 1 50% 10% 77% 1-076 1 11% 1% 33% 1 50% 10% 77% 1-077 0% 0% 22% 2 100% 100% 100% 1-081 1 11% 1% 33% 1 50% 10% 77% 1-082 1 11% 1% 33% 0% 0% 51% 1-086 1 11% 1% 33% 1 50% 10% 77% 1-103 0% 0% 22% 1 50% 10% 77% Total 20 222% 297% 181% 18 900% 4404% 394%
lxxxii
FijiDeaths and proportional mortality by mentioned cause (total mentions on the
certificate) for children 5-14 years: Fiji 2000-2008
Cause of death Deaths
Proportion (excluding ill defined)
Lower 95% CI
Upper 95% CI
1-001 Infectious Diseases 76 8.5 8.0 3.454 15.7631-026 Neoplasms 97 10.8 10.0 4.795 18.391-048 Diseases of the blood and blood forming organs 21 2.3 2.0 0.242 7.2251-051 Endocrine, nutritional and metabolic diseases 30 3.4 3.0 0.619 8.7671-055 Mental Disorders 1 0.1 0.0 0 3.6891-058 Diseases of the Nervous System 83 9.3 9.0 4.115 17.0851-064 Diseases of the circulatory system 122 13.6 13.0 6.922 22.231-072 Respiratory Diseases 117 13.1 13.0 6.922 22.231-078 Diseases of the digestive system 30 3.4 3.0 0.619 8.7671-082 Diseases of the skin 7 0.8 0.0 0 3.6891-083 Musculoskeletal Diseases 13 1.5 1.0 0.025 5.5721-084 Diseases of the genitourinary tract 27 3.0 3.0 0.619 8.7671-087 Maternal deaths 1 0.1 0.0 0 3.6891-092 Perinatal Conditions 4 0.4 0.0 0 3.6891-093 Congenital Conditions 29 3.2 3.0 0.619 8.7671-094 Ill-defined 30 3.4 3.0 0.619 8.7671-095 External causes 237 26.5 26.0 16.984 38.096Total 925 0 3.689Total excluding ill-defined 895 100.0 100.0 81.364 121.627
lxxxiii
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lxxxv
Appendix 5: Medical record review handbook
PROCEDURES MANUAL
TONGA MEDICAL RECORD
REVIEW
UNIVERSITY OF QLD / TONGAN MINISTRY OF HEALTH
V 1: 15 SEPTEMBER 2009
1 | P a g e
INTRODUCTION
The purpose of this study is to review underlying causes of death to provide an overview of the
major health problems causing premature deaths in Tonga. This study will help both resource
allocation and improving data collections.
The data extracted from the medical records file will be reviewed by a doctor who will assign
underlying and contributory causes of death based on this information. Causes will then be
compared to the death certificate and the data used to complement this information to
develop a complete picture of causes of death in Tonga.
PRIVACY ISSUES
The confidential nature of the data must be respected at all times during the study, and all data
must be de-identified before leaving Tonga. De-identification will be done by matching a study
number to each case. The master list will remain with Sione and is to be password protected.
Hard copies of this list will be provided for data extraction in batches of 50-100 names, and
must be returned to Sione once complete. Sione will ensure details of the completed records
are marked against the master sheet and that the hard copy is destroyed.
2 | P a g e
STUDY PROCEDURES
CONSTRUCTION OF STUDY FILES
Creation of the master list will be done by one person in the HIS section of the Tongan Ministry
of Health to protect privacy.
Use the following instructions to create the master list and associated de-identified files:
Pull up copy of HIS records of all deaths for 2008 (Include all fields at this stage)
Keep
- All deaths in Tongatapu
- All deaths in ANY hospital
Remove – all deaths outside hospital on the outer islands
Randomly sort all remaining records and add a new column for STUDY_ID. Assign each death a
3-digit number (you can use the drag and fill function in excel – but not formulas)
Save 2 copies of this file
1. MASTER LIST
Keep only variables: STUDY_ID, PATIENT_NO (Hospital Number), FIRST NAME, SURNAME, AGE,
DATE OF BIRTH and DATE OF DEATH. Empty variables for Sent, Returned, and Complete should
also be added. This file is to be locked and kept only by Sione.
For data extraction – sort by hospital and print list for each hospital for data extraction (list per
week rather than all at once). This should be provided in hard copy only to the Data extractor
and returned once the data has been extracted. Sione is to check that lists have been returned
and to ensure hard copies are destroyed.
2. DEATH CERTIFICATE FILE
Keep only variables: STUDY_ID, Village, Island, Place of death (hospital vs community) and AGE
(using variable names already in the system)
File to be kept by Sione and copy to Karen (this is de-identified data only)
3. RESULTS FILE
Create another empty spreadsheet called MRResults with variables STUDY ID, Study Cause 1
(Text + ICD Code), Study Cause 2 (Text + ICD Code), Study Cause 3 (Text + ICD Code), Study
Contributing Cause 1 (Text + ICD Code), Study Contributing Cause 1 (Text + ICD Code)
3 | P a g e
INSTRUCTIONS FOR MEDICAL RECORD DATA EXTRACTION
Although the task requires data to be extracted as it appears in the record without
interpretation, finding the appropriate information in the record requires a good understanding
of disease processes, medical terminology, laboratory tests and other investigations. This is the
most important part of the project!!
The form has been designed in consultation with Co-investigators, doctors, and HIS staff from
Tonga. However, this is a new process and your feedback on how the form is working (including
whether it is easy to follow or whether there should be any changes) will help improve the
study.
TRIAL
When starting data extraction, each person will be asked to complete a trial of ½ to 1 day (7-10
forms) before continuing with any further data extraction. These forms are to be sent to Karen
for review and feedback prior to further work. The intention of this trial is both to make sure
the data extractor has understood the intent of the questions and that the appropriate level of
detail has been provided, as well as to provide an opportunity to review the form itself before
further work is done.
GENERAL INSTRUCTIONS
- You will be given a list of patients for whom we need to collect data. You must not copy or
lose this file, and it must be returned to Sione when completed.
- You will either need to find the file using name and patient number or ask medical records
to find the file for you. If a file cannot be found please mark this on the list and let Sione
know when the list is returned.
- For each patient you will need to complete a data extraction form. Make sure the study ID
number is recorded on each page. The patient name or patient number should NOT be
marked anywhere on the extraction form.
- When completing the extraction form, please be as accurate as possible. If the doctor has
included abbreviations or local words, please record these as is rather than translating. We
are hoping to have a Tongan doctor review the data.
4 | P a g e
- You will only have access to the medical record. Please do not chase up additional data
from the death certificate or other data. While this may be the correct things to do for
surveillance system, it is not part of this study protocol and could affect interpretation of
the results.
- While much of the information in the first 4 sections may be found in the coversheet/
admission sheet – if additional information is included in the medical records, please
include this as well.
- Once you have completed the extraction form, please make sure you have
o Completed all sections
o Entered your name and date at the end
o Recorded the study ID on each page
o Ticked the individual off your list
o Returned the file to medical records (or other process for returning files as set out by
Sione)
o Put your completed form into the marked box.
- We have estimated that each record extraction should take up to 30-40 mins on average
(once you have done the first few which will of course be slower!). The time for each form
will vary according to the cause of death and information available. We therefore expect
this to be around 12-14 records per day. If you are consistently completing less than this,
please talk to Sione so we can look at why and whether our estimates need to be revised.
SPECIFIC INSTRUCTIONS BY SECTION
Section 1
1.1 Record the study number from the master sheet. Tick the name off on the master sheet
printout and mark that this has been done on the form.
1.2 Record the gender of the case
1.3 Record the age at death/ discharge from hospital (i.e. at the time of the last record). If one
year or older only years needs to be completed. For cases less than one year old at time of
death, record the age as months or days if case was less than one month old at time of death.
Leave the other sections of this question blank.
5 | P a g e
1.4 If case is a stillbirth – please note on form. There is no need to complete any further
information.
1.5 Format for date of birth is DD/MM/YYYY. If the case died outside hospital and there is no
information on the date of birth in the medical record please tick the box to indicate this.
1.6 Format for date of death is DD/MM/YYYY. If the case died outside hospital and there is no
information on the date of death in the medical record please tick the box to indicate this.
1.7 Record the usual residence (prior to admission to hospital) by island and village
Section 2
2.1 Record the hospital where the records were found
2.2 Indicate whether the case was in hospital at the time of death
2.3 If the case died outside hospital, record the date of discharge. Use the format
DD/MM/YYYY. If the case died in hospital indicate NA
Sections 3-5
If a section is not applicable please tick the box marked NA and move to the next section. Please
ensure that all relevant information available in the medical record is included – not just that
marked on the hospital coversheet.
Section 3
For males or females under 10 years of age indicate that the section is not applicable and move
to section four. Complete this section for all females 10 years or older or for whom age is
unknown.
3.1 Indicate whether the case was pregnant at the time of death
3.2 Indicate whether the case had delivered a child or had an abortion (deliberate or non-
deliberate) within 6 weeks of the death.
3.3 Record the details of the pregnancy (including progress and investigations) if either Q3.1 or
Q3.2 apply.
6 | P a g e
Section 4
Complete this section for all deaths where injury was a factor. For deaths that occurred from
other causes indicate that the section is not applicable and move to section five.
4.1 Indicate whether the intention of the injury was known. For example, suicide, homicide,
murder, assault are all intentional. Motor vehicle accidents (MVA’s) would most likely be
unintentional. If intent is not known (for example in a poisoning where it is not known in the
death was accidental or if the person committed suicide) record the intent as “unknown”.
4.2 If the death was known to be intentional in Q4.1, indicate whether the death occurred due
to suicide (i.e. caused by the individual who died) or homicide (i.e. caused by someone other
than the person who died).
4.3 Indicate the mode of the death, that is how the injury occurred (ie car accident, fell of ledge
etc).
4.4 Describe each injury, including what the injury was (i.e. burn, fracture, laceration etc) and
which body part was affected (ie base of skull, right lower arm, etc).
4.5 If any details of why the injury occurred are known, include this information here (i.e.
known alcohol consumption, kava etc)
Section 5
Complete this section for all deaths excluding those that occurred from injuries without
underlying natural causes. For example for a death from an MVA, skip this section unless the
accident was related to an underlying health problem. For injury deaths where this section is
not relevant , indicate that the section is not applicable and move to section six.
5.1 Include all relevant details of the illness or condition preceding death. Timeframe for this
question is not fixed and will vary from case to case based on cause. Please include all of the
most recent admission with details of any relevant events that led up to that visit (such as
concerns / tests / GP visits). Include the relevant sections of the record as originally recorded.
5.2 Tick if any of the tests in the list were performed either during the most recent admission or
in the events leading to this admission.
5.3 Make sure any test mentioned in Q5.1 or Q5.2 where a positive result was recorded, please
record the date of the test and the result as shown in the medical record.
7 | P a g e
5.4 Indicate if any imaging studies were performed (either locally or overseas) either during the
most recent admission/ illness or in the events leading to this admission.
5.5 For any imaging studies indicated in Q 5.4 that had positive or abnormal findings, record the
date and the results as shown. If you are unsure whether the results indicate an abnormal
finding, include them here.
5.6 If any histopathological investigations were performed, record the date of the procedure
and the result as shown.
5.7 Indicate whether the case had a chronic condition or disease. If no chronic condition is
recorded tick ‘No” and skip to 5.12. Common chronic diseases/ conditions may include:
After effects of stroke, Alcoholism, Asthma, Cancer (active or in remission), Cardiovascular
disease, Cirrhosis, COPD, Drug Use (long term), Diabetes, Disability (paraplegia etc),
Emphysema, Hepatitis B, Hepatitis C, Hypertension, Mental Disorders, Rheumatic Fever, etc.
Please note that we ask you to include chronic infectious diseases such as Tuberculosis at this
question as well as non-communicable diseases.
5.8 For cases with a chronic condition noted in the record state which one(s)
5.9 Record the evidence in the file that indicates the case had this condition (these may be
doctors clinical diagnosis, diagnostic tests or investigations). Include when the diagnosis was
made or the tests were done.
5.10 Record the month and year when this condition was first noted. If the case had multiple
chronic conditions indicate this timeframe for each.
5.11 Record details of any complications listed on file (e.g. cellulitis of the left leg from diabetes
etc). Indicate when these complications occurred. If there are no complications noted in the file
please note ‘None’.
5.12 Record whether the case had any surgery or invasive procedures in the four months prior
to death. If no, tick the appropriate box and skip to Q5.14
5.13 Describe any medication / treatment the case was taking in order to treat this illness at the
time of death.
5.14 Record details of any surgery or procedure indicated in Q5.12. Include date, what the
procedure was, where the procedure was performed (ie hospital, clinic, overseas), outcome,
complications, and length of stay if known.
8 | P a g e
5.15 Indicate whether the person has a positive HIV result on file. If there is a positive result,
indicate the date of the test as DD/MM/YYYY.
5.16 Review the of potential AID’s defining conditions for developing countries by WHO and
indicate if any of these conditions are noted on file. For any that are listed in the medical
record, please record the details of the condition from the record in the space provided.
Remember to include the date of findings.
END
Please ensure your name and the date are marked on the first page for follow up if data on
form is not clear. Also check the questionnaire is securely fastened together and the patient
number is clear.
ISSUES / QUESTIONS
Issues should be raised with Sione in the first instance for anything concerning obtaining and
returning records, name lists, missing records or completed forms. Issues related to content
should be discussed with Karen ([email protected])
9 | P a g e
INSTRUCTIONS FOR CAUSE OF DEATH REVIEW
INTRODUCTION
The Data Collection Form contains an extract of the case details as shown on the medical
record (without reference to any other data sources). Data is contained in the following
sections
1/ General/ demographic details
2/ Hospital/ admission details
3/ Details of female deaths (aged > 10 years)
4/ Details of injury-related deaths
5/ Case history including:
o Most recent illness
o Chronic disease history
o Previous surgery
o AIDs defining conditions
Please use the information provided to make a clinical judgment about the sequence of causes
that resulted in the death, as well as whether there were any additional causes that did not
contribute directly to this sequence. You will be asked to note these causes, including your level
of certainty regarding the cause, on the Review and Coding Sheet. Please do not write in the
section of the sheet marked “Coding” and ignore the text in italics (these are variable names to
assist with data entry).
SPECIFIC INSTRUCTIONS
1.1 Record the study ID on the form
1.2 Indicate the IMMEDIATE cause of death. This is the cause that directly resulted in the
death (i.e. “stroke”). Please note that this field should not include text such as “cardio-
respiratory arrest” or “stopped breathing”. Record only one cause per line.
1.3 If there is one, record the cause of death that resulted in the cause listed in Q1.2. In
other words, the cause of death listed in Q1.2 occurred DUE TO the one listed here. This
is the PRECIPITATORY cause of death. Record only one cause per line.
10 | P a g e
1.4 Continuing this sequence, if there was a further underlying cause of death that led
directly to the cause listed in Q1.3, please record it here. This should be there overall
UNDERLYING cause of death. Record only one cause per line.
1.5 Record any conditions that may have contributed to the death but were did not directly
result in the sequence recorded above in Q1.2 – Q1.4. Record only one cause per line.
1.6 For each cause of death listed in Q1.2 to Q1.5, indicate in the space marked how certain
you are of each diagnosis based on the following criteria.
o Definite – Clinical diagnosis is supported by positive tests and investigations in a
combination which could not be indicative of any other disease or sequence of
events (i.e. in the case of injury or external causes, the injuries could not have
occurred in any other way), or autopsy supports findings.
o Strong certainty – Clinical diagnosis is supported by positive tests and
investigations which are diagnostic for this disease, or in the case of injury and
external causes, the case history is consistent with the cause noted and there is
other evidence (such as a police report or file note) to support the diagnosis.
o Some certainty – Clinical diagnosis is supported by positive tests and
investigations which are indicative for this disease, or case history is consistent
with the disease and does not suggest a differential diagnosis, or in the case of
injury and external causes, the case history is consistent with the described
sequence of events.
o Limited certainty – Cause is the most likely interpretation of the data available.
1.7 For any death where the cause cannot be identified, please indicate this at this question
and provide details in the space provided.
1.8 Note your initials and the date you completed the review.
11 | P a g e
INSTRUCTIONS FOR CODING
You will be given a copy of the Review and Coding Sheet for each case. Code each cause of
death using ICD 10-AM, following the sequence as you would for a medical certificate of death.
The code should be written onto the space provided in the form.
Data is to be entered onto the RESULTS file. For each case enter each cause of death and the
associated ICD code onto the spreadsheet against the Study_ID. Variable names are shown on
the form. Once data from the Review and Coding Sheet has been entered into the spreadsheet,
tick the box at the bottom right of the form to indicate this is complete.
ADMINISTRATION ISSUES
WEEKLY TASKS FOR MOH CO_INVESTIGATOR
• Provide a list to the data collectors at the beginning of each week of records to be
reviewed.
• At the end of the week collect the paper lists, check the completed records marked on
the sheet against the completed forms, mark the completed records on the Master
sheet, and destroy the paper copy.
• E-mail Karen to advise number completed.
• At the end of every week collect the completed forms and either send to Karen by
registered post OR photocopy the forms and mail the original by regular post (the copy
should be kept on-site in a dedicated folder)
OTHER TASKS
• For each batch of coversheets returned for coding, mark the sheets returned against
their study ID numbers on the Master Sheet.
• Provide the coversheets to Nauna for coding and entry into the Record Review Results
File.
• Once entered, mark the ID number as complete in the Master Sheet.
• E-mail a copy of the MRResults to Karen as progress is made
• Send signed invoices to UQ for payment of agreed funds as milestones are reached.
12 | P a g e
FORMS
DATA COLLECTION FORM
See attached file
13 | P a g e
REVIEW AND CODING SHEET
Next Page
REVIEW AND CODING SHEET
1.1 PATIENT ID � � �
From the information in the medical record, please
indicate:
1.2 Immediate cause of death (IC_T)
________________________________________
1.3 Precipitatory cause of death (PC_T)
________________________________________
1.4 Underlying cause of death (ie the cause that
started the sequence) (UC_T)
________________________________________
1.5 Contributory Causes (that contributed to death, but
did not directly lead to the death)
(CC1_T)____________________________________
(CC2_T)____________________________________
(CC3_T)____________________________________
1.6 � Cause cannot be determined (XC)
________________________________________________
________________________________________________
1.8 Initials _________________________
Date: ______ / ______ / ________
Definite Strong
certainty
Some
certainty
Limited
certainty
Definite Strong
certainty
Some
certainty
Limited
certainty
Definite Strong
certainty
Some
certainty
Limited
certainty
Definite Strong
certainty
Some
certainty
Limited
certainty
Definite Strong
certainty
Some
certainty
Limited
certainty
Definite Strong
certainty
Some
certainty
Limited
certainty
ICD CODE
Coder’s use only
1.6 Certainty of Diagnosis
Initials :
______________
Date:
____ /____ / ____
Study ID 22 Sep 2009 v2
Page 1 of 6
MEDICAL RECORD DATA ABSTRACTION FORM
PATIENT DEMOGRAPHICS
1.1 Study ID Number: Name checked against Study ID
Master Sheet?
1.2 Sex 1 Male 2 Female
1.3 Age at Death _____ years (if ≥ 1 yr) _____ months (if < 1 yr) _____ days (if < 1 month)
1.4 Is case a stillbirth? ���� if yes, stop here (If uncertain, please continue)
1.5 Date of Birth /____/____/______/ No Info
dd mm yyyy
1.6 Date of Death /____/____/______/
dd mm yyyy
1.7 Residence of Deceased (as detailed as possible)
_______________________________________________ Village |____|____|
_______________________________________________ Island |____|____|
HOSPITAL DETAILS
2.1 Hospital Name: ______________________________________
2.2 Case died in Hospital? 1 YES 2 NO
2.3 Date of Final/Most Recent Admission /____/____/______/ Not Applicable
dd mm yyyy
2.4 If case died outside hospital –
Date of Most Recent Discharge /____/____/______/ Not Applicable
dd mm yyyy
Initials:
______________
Date:
____ /____ / ____
Study ID 22 Sep 2009 v2
Page 2 of 6
FEMALE DEATHS Not Applicable
3.1 Was this person pregnant 1 YES 2 NO
3.2 Did the death occur within 6 weeks of either a delivery or abortion?
1 YES 2 NO
3.3 If yes to either question:
Provide details – (include Date of Delivery, complications, single or multiple birth, child born
alive etc)
INJURY DEATHS Not Applicable
4.1 Intent of the injury
1 Intentional 2 Unintentional 3 Not known / Not stated
For Intentional Injuries:
4.2 Was this death: 1 Suicide 2 Homicide
For All Injuries:
4.3 Mode of death (i.e. fall / poisoning / hanging / stabbing / burn / MVA etc):
4.4 Describe the injuries (include body part and effect)
4.5 Additional Info: (How did the injury occur?)
i) road traffic accident – whether
pedestrian or occupant of vehicle,
type of vehicles involved
ii) accidental poisoning – nature of
poison, circumstances of poisoning
iii) fall – from where, how
iv) drowning – well, lake, river, sea
v) burns – how (stove burst, gas
cylinder, house fire, chemical burns
etc)
vi) bite of venomous animal
vii) other unintentional injuries such as
occupational injuries – nature of
work, mechanism of injury etc.
Study ID 22 Sep 2009 v2
Page 3 of 6
NON-INJURY RELATED DEATHS / (And injury related deaths where other disease factors were involved)
Not Applicable
HISTORY, EXAMINATION, INVESTIGATION AND PROGRESS
Recent History
5.1 Clinical case summary: (please write a brief description of the presenting illness and
clinical events during hospitalization in chronological sequence, culminating in either death or
discharge of the patient. Include any relevant investigation results and diagnoses, as recorded
by treating physicians in the case record).
Study ID 22 Sep 2009 v2
Page 4 of 6
5.2 Were any of the following laboratory investigations performed? (locally or overseas)
Test Performed? (Tick if Yes)
Test Performed? (Tick if Yes)
Haematology Biochemistry
Prostate specific antigen
Haemoglobin Infectious disease serology
White cell count Hepatitis B
Differential leucocyte count Hepatitis C
ESR HIV
Urinalysis Cardiovascular diagnostics
Glucose ECG
Albumin Echocardiography
Microscopy Cardiac perfusion scan
Biochemistry Coronary angiography
Biochemistry Glucose metabolism Cardiac enzymes – Ck-MB)
Fasting plasma glucose Troponins
2 hour post prandial plasma
glucose
Pulmonary diagnostics
Ketones Chest x ray
glycosylated haemoglobin Sputum for AFB
Biochemistry Liver function tests AFB culture report
Serum bilirubin Spirometry
ALT FEV1 %
AST Peak expiratory flow rate
Prothrombin time Ventilation perfusion scan
Biochemistry Renal function tests
Blood urea
Serum creatinine
Electrolytes
5.3 Provide details of all positive test results – include dates / test type and results
5.4 Were any of the following imaging studies performed? (locally or overseas)
Study Performed? Test Performed?
Barium contrast radiology Laparoscopy
Ultrasonography Endoscopy
CT Scan Colonoscopy
MRI Colposcopy/hysteroscopy
Other x rays (including
mammography)
Study ID 22 Sep 2009 v2
Page 5 of 6
5.5 Provide details of all positive imaging results – include dates / study type and results
5.6 Describe any Histopathology results (cancers, liver and renal biopsies)
Not Applicable
Chronic Diseases / Conditions
5.7 Did this person have a chronic disease or condition 1 YES 2 NO
5.8 What disease(s) / condition(s):
5.9 Was there any evidence recorded when this diagnosis was ORIGINALLY made (include
results and dates) (ie. If the case has chronic Hep B is there an antigen result on file etc)
5.10 What is the earliest date in record where this disease/condition was noted?
Month/Year: __________________________________________________________________
5.11 Any noted complications from the chronic condition? (Describe including date and
outcome):
Study ID 22 Sep 2009 v2
Page 6 of 6
5.12 Was the person taking any medication / treatment to control this disease or condition?
1 YES 2 NO � If yes, please describe below (include name/ dose)
Surgery / Invasive Procedures
5.13 Was any surgery / invasive procedure performed on this person within the last 4 months?
1 YES 2 NO
5.14 If yes � Describe the procedure (include date, outcome and complications if noted)
AIDS Defining Conditions
5.15 Is there a positive HIV test result on file 1 YES � ____ / ____ / ____ 2 NO
5.16 Does the medical record show evidence of AIDS defining conditions?
Karposi sarcoma YES NO
Pulmonary of extrapulmonary tuberculosis YES NO
Meningitis YES NO
Weight lost > 10% of body weight YES NO
Chronic diarrhoea > 1 month YES NO
Prolonged fever > 1 month YES NO
Persistent cough > 1 month YES NO
History of Herpes Zoster YES NO
Oropharyngeal candidiasis YES NO
Chronic or disseminated herpes simplex YES NO
Generalised lymphadenopathy YES NO
Persistent dermatitis YES NO
Neurological impairment (not related to known accident)
YES NO
Recurrent pneumonia YES NO
Invasive cervical cancer YES NO
END OF QUESTIONNAIRE – Remember to sign and date the front