Social and Structural Determinants ofPrevalence and Treatment of Sexually Transmitted Infections in
Southwestern Uganda
by
Benjamin West Bellows
B.A. (University of Michigan) 1996M.P.H. (University of California, Berkeley) 2004
A dissertation submitted in partial satisfaction of the requirements for the degree of
Doctor of Philosophy
in
Epidemiology
in the
GRADUATE DIVISION
of the
UNIVERSITY OF CALIFORNIA, BERKELEY
Committee in charge:
Professor Arthur Reingold, ChairProfessor Malcolm PottsProfessor Alan Hubbard
Spring 2009
Social and Structural Determinants of Prevalence and Treatment of Sexually Transmitted Infections in Southwestern Uganda
Copyright 2009
by
Benjamin West Bellows
Abstract
Evaluating Output-Based Aid for the Treatment of Sexually Transmitted Infections in Southwestern Uganda
by
Benjamin West Bellows
Doctor of Philosophy in EpidemiologyUniversity of California, BerkeleyProfessor Arthur Reingold, Chair
One approach to delivering health assistance in developing countries is output-
based aid (OBA), which reimburse health care providers for treating voucher-bearing
patients. In 2006, an OBA program was established in southwestern Uganda to increase
treatment for sexually transmitted infections (STIs), particularly among the poor. The
objectives of this research were to assess the appropriateness of the OBA strategy, to
evaluate whether the intervention was successful, and to measure social capital within
this population and examine its role in STI treatment.
The data for this research were generated in two cross-sectional surveys
conducted in southwestern Uganda in 2006 and 2007. The findings are described in three
manuscripts. The first manuscript measured the prevalence of STI symptoms and the
utilization of private and public healthcare in the region. Poor respondents were more
likely to have STI symptoms and were less likely to seek treatment for symptoms.
Respondents with symptoms expressed a preference for seeking treatment at private
health providers. Overall, the findings indicated that an OBA program for treatment in
private facilities was an appropriate strategy in the region.
The second manuscript measured changes in knowledge of STI symptoms,
1
utilization of STI treatment services, and the prevalence of syphilis. Between 2006 and
2007, knowledge of STI symptoms increased and the prevalence of syphilis decreased.
Among respondents with any STI symptoms, those close to OBA clinics had a larger
increase in the proportion using STI services. The prevalence of syphilis decreased more
for respondents living closer to OBA clinics. The findings indicated that the OBA
program was successful in achieving several of its goals.
In the third manuscript, two measures of social capital (cognitive and structural)
were developed and validated. There were significant associations between cognitive
social capital and health behaviors, including increased disclosure of STI test results
among respondents with high cognitive social capital. Disclosure is an important factor in
STI treatment. Social capital can also be used to draw economic resources to pay for
transport and medical services.
These findings contribute to understanding of economic and social barriers to
healthcare in southwestern Uganda and have implications for similar low-income regions.
________________________________________
Arthur Reingold, M.D., Chair
2
Dedication
For my wife, Nicole
i
Table of Contents
Chapter I: Introduction to dissertation 1 – 17
A. Background on OBA voucher programs 1
B. Uganda vouchers for STI treatment 6
C. Survey design 7
Chapter II: Factors predicting utilization of treatment services for sexually transmitted infections in southwestern Uganda
18-49
A. Abstract 18
B. Background 20
C. Research objectives 23
D. Methods 24
E. Results 31
F. Discussion 33
Chapter III: Impact of an output-based aid voucher program on knowledge of STI symptoms, utilization of STI treatment services and prevalence of syphilis in southwestern Uganda
50-83
A. Abstract 50
B. Background 53
C. Research objectives 60
D. Methods 61
E. Results 68
F. Discussion 72
Chapter IV: Social capital and health - testing the reliability and validity of a social capital instrument in southwestern Uganda using item response theory
84-133
A. Abstract 84
B. Background 86
C. Methods 94
ii
Table of Contents
D. Results 108
E. Discussion 114
Chapter V: Summary of findings and conclusions 134-140
A. Main findings 134
B. Policy Implications 135
C. Future directions 138
References 141
iii
List of Tables and Figures
Tables
Table I-1 Range of indicators for evaluating an OBA program 11
Table I-2 Selected Parishes for Survey (30 Mbarara, 11 Bushenyi) 12
Table I-3 Selection of 82 Villages 14
Table I-4 Summary of sampling frame 17
Table II-1Comparison of sex and age in 2002 Uganda Census population and the survey population in 2002, 2006 and 2007
44
Table II-2 Description of respondents in 2006 and 2007 by sex 45
Table II-3Relationship between four dimensions of poverty and three evaluation outcomes among respondents on the 2006 and 2007 surveys in the Mbarara region.
47
Table II-4
Utilization of STI treatment in previous six months by four different measures of poverty at public and private facilities, including drug shops and traditional healers, among respondents in the 2006 and 2007 surveys in the Mbarara region
49
Table III-1Distance between village of residence and contracted clinics for patients using vouchers in Mbarara region 2006-2008
78
Table III-2GenMatch balance on the means of matched variable after matching
79
Table III-3Relationship between four dimensions of poverty and three evaluation outcomes among respondents on the 2006 and 2007 surveys in the Mbarara region
80
Table III-4
Among poor respondents in four alternate definitions of poverty, knowledge of STI symptoms, use of STI treatment and the prevalence of syphilis in the Mbarara study population 2006 and 2007
81
Table III-5 Prevalence of syphilis by distance to a contracted clinic 82
Table III-6Distributions of matching variables in the unmatched and matched datasets
83
iv
List of Tables and Figures
Table IV-1 Social capital items 120
Table IV-2Description of respondents’ demographic factors, economic status, and household characteristics by survey year
124
Table IV-3 Percent missing values for each of the social capital items 125
Table IV-4Average logits for respondent ability at each response category
126
Table IV-5 Correlation matrix between explanatory variables 127
Table IV-6 Cognitive social capital and health-related behaviors 128
Table IV-7 Structural social capital and health-related behaviors 129
Figures
Figure II-1Directed acyclic graph of the proposed causal factors in utilization of STI treatment
38
Figure II-2Directed acyclic graph of the proposed causal factors in STI symptoms
39
Figure II-3Distribution of education levels among 5,198 respondents, Mbarara region surveys in 2006 and 2007
40
Figure II-4Distribution of the number of common household assets (0-7 assets) among 5,088 respondents in Mbarara surveys in 2006 and 2007
41
Figure II-5Distribution of household food insecurity scale (0-27) among 5,098 respondents in Mbarara region surveys in 2006 and 2007. Higher values indicate greater food insecurity
42
Figure II-6Distribution of monthly household expenditure among 5,137 respondents in Mbarara region surveys in 2006 and 2007 ($1 = 2000 UgSh)
43
Figure III-1Direction of financial flows under supply-side and demand-side strategies
76
Figure III-2A directed acyclic graph of the effect of distance to contracted STI clinics on 1) knowledge of STI symptoms, 2) any STI treatment seeking, and 3) the prevalence of syphilis
77
v
List of Tables and Figures
Figure IV-1 Item response curve for “trust of pharmacies” in 2006 survey 130
Figure IV-2Information curve for the item ‘trust of pharmacies’ from the 2006 survey
131
Figure IV-3 Item response curve for “trust of pharmacies” in 2007 132
Figure IV-4Wright Map of respondents on cognitive social capital items from 2006 survey
133
Figure V-1Percent of households below poverty line by subcounty (National Census 2002)
140
vi
Acknowledgements
I want to recognize the many efforts of many folks who made it possible for me to
submit this dissertation. The German Development Bank (KFW) funded the surveys and
evaluation of the output-based aid program in southwestern Uganda. In particular, I
would like to thank Claus Janisch and Martin Schmid for entrusting a graduate student
with the evaluation of the program.
Several individuals were instrumental in the completion of the surveys in Uganda.
My colleagues at Mbarara University of Science and Technology in Uganda, Drs Fred
Bagenda and Edgar Mulogo, were exceedingly patient as we worked out the sampling
frame, the logistics, and the myriad details needed to launch two large surveys. The data
collection teams were sensitive and knowledgeable during each of the surveys. Christine
Namayanja and the staff at Marie Stopes International Uganda were very accommodating
during my time in Kampala and inspiring in the good work in reproductive health
services they provide in the country.
In the United States, Martha Campbell, Melodie Holden and everyone at Venture
Strategies were essential in coordinating the research and budgets. Additionally,
individuals at the Bixby Program were supportive in this endeavor, particularly by
funding graduate student researchers Richard Lowe and Matt Hamilton to help conduct
aspects of the evaluation.
I am very grateful for the assistance of my dissertation committee. This research
would not have happened without Professor Malcolm Potts who graciously introduced
me to Claus Janisch and the opportunity to work on this project. Alan Hubbard and Art
Reingold provided valuable feedback on my manuscripts and offered many helpful ideas
vii
for improvements. In addition to my dissertation committee, other faculty at Berkeley
have been essential in inspiring me and mentoring me, especially Ray Catalano, Ann
Swidler, and Bill Satariano.
I would like to thank my family and friends for their support during my time in
graduate school. I have dedicated my dissertation to my wife Nicole, who has listened to
countless hours of discussion on output-based aid and accompanied me to Uganda for
four months with our darling daughter Ani. As our family expands, I look forward to
facing our future adventures together.
viii
Chapter I: Introduction to dissertation
This dissertation is an examination of two surveys in southwestern Uganda that
were conducted to evaluate an output-based aid (OBA) voucher program for treatment of
sexually transmitted infections (STIs). From these surveys, three manuscripts were
developed to document several salient economic and social factors in sexual healthcare
utilization in the region.
Background on OBA voucher programs
The combined use of vouchers and output-based contracting is generally known
as output-based aid (OBA) (Gorter et al. 2003; Janisch and Potts 2005; Sandiford, Gorter
and Salvetto 2002). In traditional salaried positions in the health sector, staff may have
little incentive to raise their productivity or to be concerned with patient perceptions of
health care quality (Robinson 2001). OBA contracts, however, create incentives to
improve the quality of healthcare and increase the utilization of important health services.
OBA vouchers stimulate patient demand for healthcare and give the patient the
purchasing power to seek care from the full range of available providers.
OBA programs have the potential to improve healthcare and health outcomes at
facility-level and in the general population (see Table I-1). Improvements are grouped
into four broad categories of measures: knowledge, behavior (including utilization),
costs, and disease status (prevalence, incidence, patient disease stage).
1
Knowledge is measured among patients, providers, and general population.
Common metrics include knowledge of disease signs, program characteristics (i.e. where
to find a voucher or clinic location), and provider recall of treatment guidelines.
Improvements in behaviors in the context of a health systems intervention largely
encompass health-seeking behaviors. There may be other barriers to care, such as
distance, that would keep patients from seeking care; however, if cost is the principal
reason for poor service uptake among the ill, we expect to see an increase in utilization at
contracted facilities. If the burden of untreated disease is high in the general population, it
may be possible to detect a change in the utilization patterns of the general population as
well.
Cost metrics are another important area to monitor in OBA programs. To evaluate
OBA programs, monitoring patient out-of-pocket spending, facility revenue and costs,
and related expenses give insight into whether the facility contracts and patient subsidies
are making improvements in healthcare delivery and health outcomes.
The final area to monitor is population disease burdens. Populations can include
patients and general populations. Monitoring disease burden can be as complex or simple
as dictated by need to determine the impact, how ever that may be defined. Risk of new
disease in a population served by clinics newly contracted may be one measure. Odds of
exposure in clinic-based cases and controls may be another approach. Change in
prevalence in a before-after design may be yet another metric that indcates to the
administrators, funders, and other interested parties whether the program was a success.
Several countries have employed OBA strategies to deliver health services to low-
income populations. In addition to the Uganda program, four specific programs are
2
discussed here to detail the history of OBA program development and the various
locations where OBA programs have been implemented.
Taiwan voucher program for contraception
The first use of output-based vouchers for healthcare in low-income countries was
done on a large scale in Taiwan in the 1960s and 1970s. The Taiwan Ministry of Health
offered male and female sterilization services at a range of government and private
facilities (Cernada and Chow 1969). The system was set up to subsidize the cost for low
income couples, targeting the service to couples with two or more children. The program
also wanted to be efficient so as to save funds and increase the number of qualified
couples who could use the program. The voucher subsidized a routine health service in
which all couples seeking sterilization participated, regardless of their income level.
Couples that did not qualify for the subsidy purchased their voucher and selected their
provider. Low-income couples did not pay the fee but received the same voucher, chose
from the same network of providers and received the same level of care (Lin and Huang
1981).
Nicaragua voucher program treatment of STIs in the 1990s
After the Taiwan program, there is no record of other OBA programs until 20
years later when Nicaragua implemented two voucher programs to treat STIs in 1995.
One program targeted vouchers to commercial sex workers for treatment of sexually
transmitted infections (STIs) as an HIV intervention. One important rationale for STI
treatment as it relates to HIV prevention. Although there is debate on the magnitude, it is
3
generally recognized that some STIs can increase the probability of viral shedding from
HIV positive persons as well as increase the susceptibility of HIV-negative persons to
infection (Buchacz et al. 2004; Grosskurth et al. 2000; Holmes et al. 1999; O'Farrell
2002)1. Additionally, there is evidence to suggest that it is easier to persuade individuals
to make use of improved STI treatment services which are accessible, effective and free
of charge than to achieve substantial and lasting changes in promoting sexual behavior
change (Hayes et al. 1995) as cited in (Borghi et al. 2005).
The sex worker vouchers were distributed around Managua and used in 22,082
visits between 1995 and mid 2008. The adolescent vouchers were used 15,134 times in
the same period. Sandiford and colleagues noted that the introduction of the sex worker
voucher was accompanied by annual declines in the prevalence of syphilis (8.6%) and
gonorrhea (9.4%) among the poorest sex workers (2002). Utilization of adolescent
reproductive health services and use of contraceptives were higher in the voucher group
compared to a control group (OR 3.1, 95% CI 2.5-3.8) (Meuwissen, Gorter and
Knottnerus 2006b).
Gujarat safe delivery vouchers in 2005
In recent years, there has been increasing interest in OBA programs for
reproductive health services. There have been many voucher pilots in south Asia recently,
although often launched with a limited scope of work and poorly documented. One
1 Using data from two studies in East Africa, Grosskurth and colleagues estimated the attributable fraction
of HIV infection due to STIs at 10-43% depending on the magnitude of HIV epidemic and risk behaviors
of the population. Grosskurth, H., R. Gray, R. Hayes, D. Mabey, and M. Wawer. 2000. “Control of
sexually transmitted diseases for HIV-1 prevention: Understanding the implications of the Mwanza and
Rakai trials.” Lancet 355:1981-7.
4
exception is the program in Gujarat state in India with its drive to use vouchers for
maternal delivery, “Chiranjeevi Yojana” (‘eternal life scheme’), launched in December
2005. The objective of the program was to improve the institutional delivery rate by
subsidizing access to private medical providers for pregnant women living below the
official poverty line (BPL) in remote areas with the highest infant and maternal mortality.
The scheme was launched as a single year pilot project in five districts: Banaskantha,
Dahod, Kutch, Panchmahal, and Sabarkantha (Bhat et al. 2009).
Voucher holders were provided a transport stipend and private contracted
providers were reimbursed on a capitation payment basis. The payments were made for a
batch of 100 deliveries to take care of case-mix differences (i.e., normal or complicated
deliveries). The costs for normal and complicated deliveries were based on market
prevailing rates and using locally relevant probabilities of complicated and normal cases,
an average cost per delivery was worked out. The scheme used a voucher system to target
the people living below poverty line (Bhat et al. 2006).
An evaluation survey was conducted among 262 voucher-using mothers and 394
similar non-voucher-using mothers. A vast majority (97%) of the voucher beneficiaries
delivered in private facilities, 2.7% deliveries were conducted in government facilities,
and one voucher-purchaser had a home delivery. In the non-voucher group, 21% of
women delivered at home, 1% in government facilities and 77% in private institutions
(Bhat et al. 2009).
5
Bangladesh Safe Delivery Vouchers
Bangladesh vouchers are currently being distributed to poor pregnant women for
antenatal, delivery, and post-natal care. With funding from World Bank donors, poor
women are offered free transport and clinic service fees. The poor are identified in
several ways. In 11 of the targeted districts (upazila) household asset scoring was used to
select poor from non-poor. In the remaining nine districts, all women qualified. Although
the targeted number of vouchers was not stated in the report, there were an estimated
174,000 deliveries annually in the 21 districts. There is a lengthy reimbursement schedule
for specific services - from transport to food supplements following delivery. However,
on average reimbursements are 1648 taka (USD 24) (Koehlmoos et al. 2008; Ministry of
Health and Family Welfare 2007).
Uganda vouchers for STI treatment
The focus of this dissertation is the OBA voucher program for STI treatment.
With funding from the German Development Bank (KFW), an OBA pilot in Uganda was
launched in 2006 to provide an improved standard of care for laboratory-based STI
diagnosis and treatment. The non-profit Marie Stopes International-Uganda (MSIU)
managed the pilot program’s operations, service provider contracts, claims and payment,
and fraud control. Sixteen clinics were contracted by the program launch with the goal to
sell more than 10,000 vouchers a year. At the request of the donor German Development
Bank (KFW) and the Uganda Ministry of Health, a population evaluation of the pilot
program was conducted by the OBA technical adviser at the Berkeley-based NGO,
6
Venture Strategies for Health and Development, researchers from the University of
California at Berkeley, and faculty at the Mbarara University of Science and Technology.
This dissertation uses data from two population-based surveys in which
respondents were interviewed about selected STI outcomes and health-seeking behaviors.
Study participants were sampled from three groups: communities near contracted private
clinics in Mbarara, Kiruhura, Ibanda and Isingiro districts; communities near comparable
non-contracted private clinics in a comparison district, Bushenyi; and communities in
Mbarara without nearby private facilities.
The first manuscript in this dissertation explores the patterns of STI symptoms
and STI treatment utilization at the appropriateness of an OBA voucher strategy; the
second manuscript evaluates the impact of the program between year 1 and year 2; and
the third manuscript develops two measures of social capital and explores the
relationships between social capital and health-related behaviors and outcomes.
Survey design
All three manuscripts are based on two population-based surveys collected in
southwestern Uganda. The paired population surveys shared a common design. Both the
baseline and follow-up survey were intended to select a representative sample of men and
women between 15 and 49 years of age from 82 villages in Mbarara, Kiruhura, Ibanda,
Isingiro and Bushenyi districts. In both surveys, a sample of respondents was selected
from a four-stage design using population weights from the 2002 Uganda census.
7
Sample size
Each survey drew a stratified sample selected using a multi-stage cluster design.
In the four stage design, the first stage sample of parishes was selected by probability
proportional to population size, followed by a second stage sample of the villages from
selected parishes, again by probability proportional to population size. The third stage
sample consisted of households enumerated from selected villages. Economic
information at the household level was measured in the survey. The final stage sample
was comprised of one household resident interviewed at each selected household.
First stage selection
The Ugandan Bureau of Statistics (UBOS) has census data freely available down
to parish level. There are 240 parishes in the old Mbarara district, ranging in size from
438 to 22,032 inhabitants (old Mbarara district had 1,088,356 persons in the 2002
census). There are 170 parishes in Bushenyi district, ranging in size from 622 to 8,608
inhabitants (Bushenyi district had 731,392 persons in the 2002 census). In 2006, the
Mbarara district was split into four new administrative districts. However, for the
purposes of the sampling frame, the previous administrative boundaries were used.
Parishes (and the analogous “municipal ward”) constituted the first-stage sampling units
for sample selection. For the first selection stratum, parishes were stratified by whether
they had either no private clinics or one or more private clinics. By including the 15
Mbarara parishes with OBA clinics in the first stage, respondents were oversampled from
parishes with an OBA clinic (see Tables I-2 and I-3). Eleven parishes from Bushenyi
containing one or more private clinics were also included (Table I-2). Fifteen Mbarara
8
parishes without an OBA clinic were sampled from the remainder of Mbarara parishes
(225) using probability proportional to size (PPS) systematic sampling without
replacement, where size was defined by the total parish population (Tables I-2 and I-4).
If is the population in parish , then the probability of including the parish in
the sample is given by:
where is the number of parishes selected in the sample in that district and is the
total number of persons in the 225 non-OBA parishes of Mbarara district.
Second stage selection
At the second stage, two enumeration areas (EAs) or “villages” from each parish
were selected with probability proportional to parish size without replacement (Table I-
3). If is the population in enumeration area (EA) , then the probability of including
the EA in the sample is given by:
where is the number of EAs selected in the sample in that parish and is the total
number of persons in the parish (all potential EAs).
Third stage selection
At the third stage, survey teams took a sample of households from each selected
village and municipal cell. Teams were to select a sample of 36 individuals from each of
82 villages and cells (total=2,952) at random households in the village. This was done by
9
first randomly selecting a household from an enumerated list of all households in the
village or cell. At each selected household, all potential respondents were enumerated.
Then, one individual between 15-49 years of age was selected at random to complete the
survey.
The original selection of controls was made so as to measure health status in
parishes without contracted OBA clinics (selected from Mbarara, Ibanda, Isingiro, and
Kiruhura districts) and areas that had non-contracted private clinics (selected parishes in
neighboring Bushenyi district). No other factors were taken into consideration in parish
selection.
All survey data were double entered into an EpiData (version 3.1) database
containing a range of logic checks. Item response models were fit using ConstructMap
(version 4.2 University of California at Berkeley). All statistical analyses for the
multivariable modeling were done in STATA (version 10.1).
Human subjects approval was granted for both surveys by the institutional review
boards at the University of California, Berkeley (#2005-8-24) and the Mbarara University
of Science and Technology.
10
Table I-1: Range of indicators for evaluating an OBA program that treats STIs
LEVEL Knowledge Behavior Costs Disease burden
Healthcare Facility
Provider knowledge of treatment or reporting protocols
Clinic utilization before and after program launch
Fraction of patients at clinic who used voucher
Claims-based reimbursements
Claims-based diagnosis & treatment
General population
Voucher recognition
Heard marketing messages
Recognize need for service
Self-reported healthcare use
Percent of voucher used by targeted population
Self-reported patient out-of-pocket
Lab confirmed syphilis
Self-reported STI symptoms
11
Table I-2: Selected Parishes for Survey (30 Mbarara, 11 Bushenyi)
District Sub-county Parish Parish Population
1 MBARARA IBANDA BUFUNDA 13,937
2 MBARARA KASHARI KABARE 4,713
3 MBARARA IBANDA KAGONGO 8,791
4 MBARARA MBARARA MUN. KAKOBA 22,032
5 MBARARA KASHARI KAKYERERE 5,186
6 MBARARA MBARARA MUN. KAMUKUZI 15,676
7 MBARARA MBARARA MUN. KAMUKUZI 15,676
8 MBARARA MBARARA MUN. KAMUKUZI 15,676
9 MBARARA KAZO KAZO 7,195
10 MBARARA RWAMPARA NYEIHANGA 3,030
11 MBARARA MBARARA MUN. RUHARO 7,794
12 MBARARA NYABUSHOZI RUSHERE 4,988
13 MBARARA NYABUSHOZI RUSHERE 4,988
14 MBARARA MBARARA MUN. RUTI 4,824
15 MBARARA ISINGIRO MABONA 4,619
16 MBARARA KASHUMBA KASHUMBA 8,338
17 MBARARA KASHUMBA KIGARAGARA 5,927
18 MBARARA BISHESHE NYAKATOKYE 5,805
19 MBARARA KICUZI KANYWAMBOGO 2,532
20 MBARARA KIKYENKYE KEIHANGARA 7,896
21 MBARARA NYAMAREBE KYENGANDO 6,976
22 MBARARA NYAKITUNDA NYAKARAMBI 4,358
23 MBARARA BUBAARE RUGARAMA 3,390
24 MBARARA BUREMBA KIJOOHA 4,838
25 MBARARA KANONI ENGARI 5,427
26 MBARARA KAZO RWAMURANGA 2,533
27 MBARARA SANGA RWABARATA 2,673
12
District Sub-county Parish Parish Population
28 MBARARA BUGAMBA KIBINGO 3,787
29 MBARARA RUGANDO MIRAMA 3,638
30 MBARARA RUGANDO NYABIKUNGU 4,988
31BUSHENYI KABWOHE-
ITENDERO T.CKABWOHE 4,628
32 BUSHENYI SHUUKU KISHABYA 5,901
33 BUSHENYI KYEIZOBA KITWE 4,506
34 BUSHENYI KIGARAMA MABARE 6,166
35 BUSHENYI KYAMUHUNGA MASHONGA 8,170
36 BUSHENYI RYERU NDEKYE 4,619
37BUSHENYI KABWOHE-
ITENDERO T.CNYANGA 4,332
38 BUSHENYI MITOOMA RUSHOROZA 3,684
39 BUSHENYI BUSHENYI TC WARD I 6,028
40 BUSHENYI BUSHENYI TC WARD III 7,592
41 BUSHENYI BUSHENYI TC WARD IV 3,899
13
Table I-3: Selection of 82 Villages
District Parish Name Village NameVillage Households
Village Population
1 MBARARA KASHUMBA BURAMA 115 474
2 MBARARA KASHUMBA KASHUMBA 184 759
3 MBARARA KIGARAGARA KAMISHWA 157 799
4 MBARARA KIGARAGARA RWAMACUMU 66 336
5 MBARARA NYAKATOKYE RWEBIYENJE I 44 218
6 MBARARA NYAKATOKYE BIGYERA 47 233
7 MBARARA KANYWAMBOGO KABUHWEJU 67 308
8 MBARARA KANYWAMBOGO KISABO I 126 579
9 MBARARA KEIHANGARA NGANGO I 108 521
10 MBARARA KEIHANGARA KANYEGANYEGYE 72 347
11 MBARARA KYENGANDO RWENKUREJU I 89 388
12 MBARARA KYENGANDO KOBUHURA A. 76 332
13 MBARARA NYAKARAMBI OMUBUSHAMI 122 548
14 MBARARA NYAKARAMBI OMUKINIKA 140 629
15 MBARARA RUGARAMA NKAAKA 153 792
16 MBARARA RUGARAMA RUGARAMA I 187 968
17 MBARARA KIJOOHA MUSHAMBYA 136 690
18 MBARARA KIJOOHA BUREMBA 185 938
19 MBARARA ENGARI RUSHANGO 113 603
20 MBARARA ENGARI NYABUBARE II 75 400
21 MBARARA RWAMURANGA MIRAMA 152 852
22 MBARARA RWAMURANGA RWAMURANGA 126 706
23 MBARARA RWABARATA RWAMUHUKU 192 774
24 MBARARA RWABARATA RWONYO 139 560
25 MBARARA NGUGO/KIBINGO NTSINGWA I 65 327
26 MBARARA NGUGO/KIBINGO RUSHANJE 101 509
27 MBARARA MIRAMA RWEMIYENJE 72 355
28 MBARARA MIRAMA MIRAMA II 49 242
29 MBARARA NYABIKUNGU MIKAMBA 69 367
14
District Parish Name Village NameVillage Households
Village Population
30 MBARARA NYABIKUNGU KABOBO 68 361
31 MBARARA BUFUNDA MPIIRA STREET 194 853
32 MBARARA BUFUNDA NYAKATEETE II 92 405
33 MBARARA KAGONGO KAFUNDA 73 357
34 MBARARA KAGONGO KASHAKA II 133 650
35 MBARARA MABONA MABONA 71 334
36 MBARARA MABONA KYAMUDIMA 84 390
37 MBARARA KAMUSHOKO RWEMPOGO 146 794
38 MBARARA KAMUSHOKO RWAMBABANA 98 533
39 MBARARA RWENSHANKU RWENTURAGARA 169 841
40 MBARARA RWENSHANKU RWENSHANKU 106 528
41 MBARARA KABARE NSHOZI 52 198
42 MBARARA KABARE KARUHAMA 99 376
43 MBARARA KAKYERERE BWIZIBWERA TR. A 124 575
44 MBARARA KAKYERERE RWANYAMAHEMBE 131 607
45 MBARARA KAZO KAZO II 228 1161
46 MBARARA KAZO KAZO I 195 993
47 MBARARA KAKOBA KISENYI 'B' 353 1405
48 MBARARA KAKOBA LUGAZI ‘A’ 549 2186
49 MBARARA KAMUKUZI KAKIIKA 'B' 603 2315
50 MBARARA KAMUKUZI KASHANYALAZI 286 1098
51 MBARARA RUHARO NKOKONJERU 'A' 309 1453
52 MBARARA RUHARO KIYANJA 396 1862
53 MBARARA KATETE KATETE CENTRAL 'A' 251 1106
54 MBARARA KATETE NYAMITANGA 'A' 165 727
55 MBARARA RUTI KAFUNDA 99 402
56 MBARARA RUTI KATEERA 'A' 147 596
57 MBARARA RUSHERE RUSHERE T/C 'A' 252 1254
58 MBARARA RUSHERE RUSHERE T/C 'B' 172 856
59 MBARARA NYEIHANGA NYEIHANGA 40 183
15
District Parish Name Village NameVillage Households
Village Population
60 MBARARA NYEIHANGA RWABAJOJO 59 270
61 BUSHENYI NDEKYE RYERU I 142 638
62 BUSHENYI NDEKYE RYERU II 117 561
63 BUSHENYI WARD I CENTRAL CELL 'A' 281 1348
64 BUSHENYI WARD IV CELL C 372 1756
65 BUSHENYI WARD III CELL B 'A' 217 982
66 BUSHENYI KITWE KITWE 99 488
67 BUSHENYI MASHONGA NYAKATEMBE 123 569
68 BUSHENYI WARD I CENTRAL CELL 'B' 253 1213
69 BUSHENYI KITWE RWENTUHA TC 215 1060
70 BUSHENYI WARD III CELL B 'B' 484 2190
71 BUSHENYI MASHONGA KAYANGA 118 546
72 BUSHENYI WARD IV CELL D 338 1595
73 BUSHENYI RUSHOROZA NYAKASHOJWA 67 359
74 BUSHENYI RUSHOROZA MITOOMA TOWN 205 1099
75 BUSHENYI MABARE NYAKAMBU 143 696
76 BUSHENYI KISHABYA KISHABYA 90 465
77 BUSHENYI KISHABYA KYENJOJO 75 388
78 BUSHENYI NYANGA KIGIMBI 146 612
79 BUSHENYI NYANGA KABWOHE TOWN B 451 1889
80 BUSHENYI MABARE KATWE 59 287
81 BUSHENYI KABWOHE KABWOHE TOWN A 406 1729
82 BUSHENYI KABWOHE KAMWEZI 44 187
16
Table I-4: Summary of sampling frame
Mbarara OBA parishes
Mbarara Non-OBA parishes
Bushenyi parishes
Total
Population 119,824 968,532 731,392 1,819,748
Number of parishes 15 225 170 410
Number of parishes in sample 15* 15 11* 41
Number of selected villages in parishes
30 30 22 82
Number of households 5,107 3,473 4,341 12,921
Number of selected households^
1,080 1,080 792 2,952
Total village population 22,922 16,184 20,181 59,287
* purposively sampled (probability of selection =1) ^ 36 households per EA village were planned in the survey
17
Chapter II: Factors predicting utilization of treatment services for
sexually transmitted infections in southwestern Uganda
Abstract
Rationale: There is growing interest in the potential for private sector healthcare
to meet public health needs in low-income countries. Policymakers have a number of
options to choose when considering how to utilize private sector healthcare for public
health goals.
One strategy for extending public health planning into private facilities is through
the use of output-based contracts for sexual and reproductive health services. The Uganda
Ministry of Health and the German Development Bank (KfW) launched a project in July
2006 using output-based aid (OBA) contracts to subsidize treatment of sexually
transmitted infections (STIs) at eighteen private clinics in four districts of southwestern
Uganda.
Objectives: Using population survey data from 2006 and 2007, this study aimed
to examine four independent measures of poverty and determine the association of
poverty measures with STI outcomes and risk behaviors, in order to better understand the
population most in need of STI services. An additional objective was to determine
whether individuals prefer private or public providers for STI treatment.
Methods: Data from two cross sectional surveys of approximately 2,600
respondents in 82 villages were used to fit logistic models of general healthcare
utilization and STI treatment services at public and private healthcare providers.
Explanatory variables were household asset score, food insecurity score, education level,
respondent age, respondent sex, and respondent’s partnership status. Respondents were
18
also asked whether certain structural factors (i.e. ability to pay, transport, and provider
availability) were significant problems when accessing medical advice. The three
outcomes of interest were whether the respondent reported STI symptoms in the previous
six months, whether the respondent sought any care for STI symptoms, and respondent’s
choice of public or private health facility when service was sought.
Results: The poverty scores were not highly correlated with each other (highest
pairwise Pearson coefficient r= 0.28), suggesting the scores measured different
dimensions of poverty. Overall, those with higher poverty scores were more likely than
those with lower poverty scores to report having had one or more STI symptoms in the
past six months. Among respondents who reported having any STI symptom in the past
six months, those with higher poverty scores were less likely to have used STI treatment
than those with lower poverty scores. There was a clear preference among all respondents
for using private facilities for STI treatment. Private clinics account for a large proportion
of STI treatment visits. Depending on the poverty measure used, 48-54% of poor
respondents went to private facilities for STI treatment.
Conclusions: There is evidence that the poor have a high STI burden and that the
private sector is a significant source of STI treatment. There is also evidence that the poor
use private facilities as much, if not more, than they use public facilities. Interventions to
improve STI treatment services in the private sector could reach a large proportion of the
population given the current utilization pattern.
19
Background
Sexually transmitted infections (STIs) constitute a large health and economic
burden. Seventy-five to 85 percent of the estimated 340 million annual new cases of the
four most common curable STIs (gonorrhea, syphilis, trichomoniasis, and chlamydia)
occur in low-income countries, and STIs, excluding HIV, account for 17 percent of
economic losses due to illness in 15-44 year old women (Mayaud and Mabey 2004). STIs
also facilitate the sexual transmission of HIV, thereby indirectly imposing additional
morbidity and mortality burdens on developing countries (Grosskurth et al. 2000;
Grosskurth et al. 1995).
Private sector plays a large role in healthcare delivery
In a study of Demographic and Health Survey (DHS) data from 22 African
countries, Prata and colleagues (2005) found that the poorest quintile of children had the
highest burden of diarrhea and acute respiratory infection and the lowest use of treatment
services for those conditions. However, among children who were seen by a medical
provider for diarrhea and ARI, most of those (77% for diarrhea and 74% for ARI) from
the poorest quintile who received care went to private providers (Prata et al. 2005). A
study from 2008 found that in Nigeria and Uganda, people in the lowest economic
quintile received more than 60 percent of their healthcare in the private for-profit sector
(Ghatak, Hazlewood and Lee 2008).
The proportion of a national population treated by private providers differs across
sub-Saharan Africa (Ghatak et al. 2008), but in most countries a large proportion of STI
treatment occurs in private sector healthcare facilities (Adu-Sarkodie 1997; Brugha and
20
Zwi 1998; Jacobs et al. 2004; Voeten et al. 2001; Wawer et al. 1999; WHO 2001).
Although there is vigorous debate about the role of the public and private sectors in
meeting population healthcare needs (Mayor 2009; Over 2009), policymakers recognize
that private providers have a significant role in healthcare delivery and can serve as a
complement to public sector healthcare in sub-Saharan Africa (Bennett et al. 2005;
Ghatak et al. 2008; Hanson et al. 2008; Mills et al. 2002).
What is the “private sector”?
Definitions of “private health sector” vary; however, there is agreement that it
represents non-state healthcare providers. In the widest definition, private sector
providers include traditional healers; unregulated drug shops; registered for-profit,
independent clinics run by senior nurses or medical doctors; secular non-profit clinics
(NGOs); mission or faith-based facilities; and large networks of for-profit clinics and
hospitals (Mills et al. 2002). A 2009 report by Oxfam International underscored the
political challenges when discussing private sector healthcare (Mayor 2009). The report
expressed a legitimate concern with including informal providers, like drug shops,
chemists, traditional healers, and small clinics in a broad “private sector” category. The
report stressed that informal providers generally lack capacity to treat many complicated
conditions, unlike large, better equipped private and public facilities. However, informal
providers are similar to larger private facilities in that they are responsive to adjustments
in incentives and can be considered potential participants in policy interventions to
improve population health (Mills et al. 2002; Patouillard et al. 2007; Peters, Mirchandani
and Hansen 2004).
21
Various types of private providers make up significant segments of the market for
sexual and reproductive health (SRH) services in different African countries. For
instance, among 291 respondents in Nairobi, Kenya who reported STI symptoms, most
men and women had sought care in private independently owned clinics (72 percent and
57 percent, respectively) (Voeten et al. 2001). Of men who sought treatment for STIs in
Ghana and Cameroon, 75 percent in Ghana and 50 percent in Cameroon sought treatment
through the informal private sector, generally using traditional healers and drug shops,
prior to visiting a government health center (Adu-Sarkodie 1997; WHO 2001). Self-
medication and purchase of over the counter (OTC) treatment from pharmacies and other
private sector sources accounted for an estimated 90 percent of antimicrobial STI
treatment in Ghana (Brugha and Zwi 1999). Dartnell and colleagues reported that in
South Africa, traditional healers were the first source of STI care for 80 percent of
patients who attended a formal health sector provider (1997). In Mwanza, Tanzania, 30
percent of men seeking STI treatment sought treatment in the informal private sector or
traditional treatment (Jacobs et al. 2004). In the Rakai district in Uganda, Wawer and
colleagues reported that fewer than 20 percent of adults with symptomatic STIs attended
government clinics (1999).
Is the private sector a significant source of STI treatment in western Uganda?
To increase the utilization of STI treatment services and improve the quality of
care, the Uganda Ministry of Health began in July 2006 to subsidize STI treatment at
accredited private clinics in four southwestern districts. At the program’s launch, it was
not well known whether the demand-side program, with its economic subsidy, would
reach a large portion of the population with STI symptoms in need of treatment services.
22
To measure the program’s impact, an evaluation was designed based on surveys
conducted before and 16 months after program launch, among communities identified as
control and intervention areas.
Before measuring the program’s impact, it is important to know what share of the
population seeks care at private facilities. This study set out to explore poverty-related
differences in the prevalence of reported STI symptoms, proportion of those with STI
symptoms who sought STI treatment, and the proportion of those who sought STI
treatment who did so at private and public facilities.
Research Objectives
Using data from a combined dataset of two cross sectional surveys, the following
three questions and hypotheses were explored:
1. Are the poor more likely to report STI symptoms compared to the non-poor?
Hypothesis: Individuals with higher poverty scores are more likely to report
having one or more STI symptoms in the six months prior to a survey
compared to individuals with lower poverty scores.
2. Among those with one or more STI symptoms, are the poor less likely to seek
treatment for STI symptoms?
Hypothesis: Individuals with STI symptoms who have higher poverty scores
are less likely to seek treatment for STI symptoms compared to individuals
with lower poverty scores.
23
3. Among those seeking treatment for STI symptoms, are the poor less likely to seek
treatment in the private sector?
Hypothesis: Individuals with higher poverty scores who have sought treatment
for STI symptoms are less likely to report having sought treatment in the
private sector compared to individuals with lower poverty scores.
The proposed mechanism for utilization of STI treatment is presented as a
directed acyclic graph (DAG) in Figure II-1. Three general types of STI risk factors are
present: economic, demographic and sexual behavior and knowledge. Figure II-2 presents
a similar DAG for self-reported STI symptoms as the outcome.
Methods
Sampling frame
The sampling frame was designed to select a representative sample of 2952 men
and women between 15 to 49 years of age from five districts: Mbarara, Kiruhura, Ibanda,
Isingiro and Bushenyi. The sample was selected in a four-stage design using the 2002
Uganda census. The first stage consisted of a sample of parishes (local administrative unit
of 5,000-20,000 population). 15 parishes were selected by probability proportional to
population size (PPS) and 26 parishes were purposively selected because of the presence
of a clinic. In the second stage, villages were sampled from parishes by probability
proportional to village population size. The third stage sample randomly drew households
enumerated within selected villages. The final stage of the survey selected one household
resident for interview at each selected household.
24
Primary explanatory variables
The primary explanatory variables of interest were four measures of poverty. In
order to assess which individuals had high levels of poverty, we used four common
measures of socioeconomic status: education, household assets index, household monthly
expenditure, and household food insecurity access scale.
Poverty measure 1: Education
The amount of education an individual attained can be used to estimate the level
of economic deprivation during the respondent’s youth. Although schooling has often
been completed years before the interview, there are long term social, economic and
health sequelae associated with the amount of completed education. In this study,
education levels were grouped initially into five categories: no primary school, some
primary school, completed primary school, some secondary school, and completed
secondary school. The five category ordinal education variable had a bimodal
distribution, with peaks for completing primary and completing secondary (see Figure II-
3). For this study, a dichotomous variable (completed primary versus some secondary and
above) was created from the five category ordinal variable.
Poverty measure 2: Household assets index
A household assets index was based on seven questions about respondents’
household assets and living conditions: having electricity, a radio, a TV, a telephone, a
refrigerator, a lantern, and a cupboard. Using household assets indices to measure relative
poverty is common in the absence of income data (Filmer and Pritchett 1998). In Filmer
25
and Pritchett’s method, which has been used extensively by the World Bank, an asset
index is weighted by a scoring factor that is assigned to each variable in the linear
combination of the variables that constitute the first principal component. This principal
component, developed from classical test theory, is conceptualized as the unobservable
latent poverty variable. Each household asset variable is normalized by its mean and
standard deviation, and the weights are the standardized first principal component of the
observed household assets (Expert Group on Poverty Statistics 2006; Falkingham and
Namazie 2002). The Filmer and Pritchett index includes additional questions about
housing materials, water access, and household disease control measures, such as use of
bed nets. For this study, the household asset score (see Figure II-4) was restricted to
seven binary questions on durable household objects and the presence of electricity, all of
which are indicative of longterm economic status.
The distribution was left-skewed, as seen in Figure II-4. For this study, the
variable was made dichotomous, with all values above the median grouped against all
values at and below the median.
Poverty Measure 3: Household food insecurity access scale
The Household Food Insecurity Access Scale (HFIAS) is calculated from nine
items measuring food availability, food access and food utilization in the 30 days prior to
the interview (Coates, Swindale and Bilinsky 2006). The HFIAS questions relate to three
different domains of food insecurity found to be common to cultures examined in a cross-
country literature review: anxiety and uncertainty about the household food supply,
insufficient food intake and its physical consequences, and insufficient quality of food
26
(Coates 2004; Coates et al. 2006; Food and Nutrition Technical Assistance (FANTA)
Project 2004). The HFIAS is a continuous variable bound by 0 and 27 and with a left-
skewed distribution, as seen in Figure II-5. For this study, the food insecurity variable
was made into a dichotomous variable with all values above the median grouped against
all values at and below the median.
Poverty Measure 4: Household monthly expenditures
Respondents were asked for the gross monthly expenditures in their household.
Amounts are reported in Uganda shillings. The distribution was highly left-skewed, as
demonstrated in Figure II-6. For this study, a dichotomous variable was created dividing
values above the median from those at and below the median.
Control variables
Data were also collected on respondents’ demographic characteristics (age, sex,
and marital status), whether an individual lived in an urban or rural village, and
respondents’ STI behavioral and knowledge risk factors (number of sex partners in the
previous six months, receiving money for sex in the previous six months, and knowledge
of STI symptoms). Respondent-level characteristics, behaviors, and knowledge were
determined by self-report. In assessing respondents’ knowledge of STI symptoms,
respondents were asked to name symptoms that could be the result of a sexually
transmitted infection. No prompts were given; interviewers checked responses against a
list of nine common possible symptoms.
27
Outcomes variables
Reporting of one or more STI symptoms
Respondents were asked whether in the previous six months they had experienced
foul smelling penile or vaginal discharge, burning irritation during urination, or non-
traumatic sores on the genitalia. A single additive index was created from the three
responses for male respondents. The same index was modified for female respondents to
include only burning irritation during urination and non-traumatic sores on the genitalia.
Foul smelling discharge was excluded as it was considered not specific. A new binary
variable was created to contrast respondents having one or more symptoms from those
without any symptoms.
STI treatment utilization
Among those who reported having one or more STI symptoms, respondents were
asked the number of times they sought treatment for STI symptoms in the past six
months.
Type of facility visited for STI treatment
Respondents who sought treatment were asked about the type of facility they
visited for their most recent STI symptom(s). Responses were limited to self-treatment,
traditional healer, drug shop, private clinic, private-not-for-profit (NGO) clinic, private
hospital, government clinic, government hospital, or mission hospital. These were then
grouped into modern private providers (drug shop, NGO clinic, private clinic, and private
hospital), and modern public providers (government clinic, government hospital and
28
mission hospital). Mission facilities coordinate with government and often have staff on
government salary and so are included in the public sector category. Excluded were
traditional healers, who, by definition, do not practice modern medicine, and self-
medication responses.
Analysis/Model building methods
In this study, poverty was alternatively defined by four constructs: household
assets, household monthly expenditures, household food insecurity, and respondent
education level. Each was made into a dichotomous exposure variable as explained
above. For the first three research questions, a logistic model was fit for each
combination of outcome and poverty measure, resulting in four models for each of the
first three questions.
The first question was whether respondent poverty was associated with self-
reporting of one or more STI symptoms in the previous six months. Separate logistic
models were fit for each poverty construct: household assets, household monthly
expenditures, household food insecurity, and respondent education level.
The second question asked whether a higher poverty score predicted greater odds
of STI treatment utilization, among respondents reporting any STI symptoms in the
previous six months. Separate logistic models were fit for each poverty construct:
household assets, household monthly expenditures, household food insecurity, and
respondent education level.
The third question was whether individuals with higher poverty scores (as
determined by the four alternative constructs) used STI care at private or public
29
providers. Private providers were defined as any drug shop, private-for-profit clinic,
private-for-profit hospital and NGOs. Public providers included government clinics,
government hospitals and mission hospitals. Traditional healers and self-medication were
excluded from this question. Separate logistic models were fit for each poverty construct:
household assets, household monthly expenditures, household food insecurity, and
respondent education level.
Categorical variables were reported as proportions and continuous variables were
reported as means with standard deviations (SD) or medians with interquartile range
(IQR). Bivariate and multivariate associations were reported using odds ratios and 95
percent confidence intervals. All statistical tests were two-sided and considered
significant at = 0.05. Statistical analyses were done using STATA version 10.1
(College Station, TX). Bivariate odds ratios were estimated to test for associations
between the independent variables and the primary outcomes. Multivariate (adjusted)
odds ratios are reported as tests for association between each poverty score and outcome
controlling for potential confounders.
Human subjects approval was granted by the institutional review boards at the
University of California, Berkeley (#2005-08-24) and the Mbarara University of Science
and Technology.
30
Results
Recruitment and description of the sample
Table II-1 describes the sample population. The sampling frame was designed to
reach 2,952 15-49 year old respondents in 82 villages. The survey response rate was 88
percent (2639 / 2952) in 2006 and 93 percent (2757 / 2952) in 2007. Compared to the
general population, there was a disporportionately high number of older respondents and
of female respondents.
Characteristics of surveyed population
Table II-2 describes the demographic, economic, and behavioral characteristics of
the men and women in the study. Men were more likely to be older and single than
women, although the difference was not statistically significant. Men also reported higher
monthly household expenditures. It is not clear if the women were coming from
households with lower monthly expenditures or if, perhaps, there were gender differences
in recall, knowledge, or reporting of household expenditures. It is also not known if
female respondents were in female headed households.
Based on reported behaviors, men had higher STI risk. Men reported a higher
frequency of unprotected sex (X2= 10.8, df=1, p=0.001) and were more likely to have
given money for sex (X2= 243.0, df=1, p<0.001) during the prior six months. However,
more men knew two or more STI symptoms compared to women (53 percent versus 50
percent, Χ2=5.8, df=1, p=0.016). Pairwise Pearson’s correlation tests between
independent variables demonstrated no collinearity.
31
In a pairwise Pearson’s test, the four measures of poverty were all significantly
associated (p<0.001) but not highly correlated. The largest correlation coefficient
(r=0.28) was between median household expenditures and median number of household
assets.
Finding 1: The poor have greater STI burden
Poverty was defined independently by four different dichotomous measures:
respondent education completed primary or not; household monthly expenditure above or
below the median; household asset score above or below the median; and household food
insecurity above or below the median. As shown in Table II-3, in two of the four
measures of poverty the “not poor” groups were less likely to have one or more STI
symptoms in the previous six months compared to the “poorer” groups, even after
controlling for demographic and behavioral factors (household median assets adjusted
OR=0.76 [95% CI=0.66-0.87], respondent education adjusted OR=0.68 [95% CI=0.59-
0.77]). Household median monthly expenditures and median food insecurity index did
not have a significant association with having one or more STI symptoms.
Finding 2: The poor use fewer STI treatment services
In this study, among respondents who reported having any STI symptoms in the
previous six months, the “not poor” were significantly more likely to use any type of STI
treatment service or product2 compared to the “poor” (household median monthly
expenditures adjusted OR=1.31 [95% CI=1.05-1.63], respondent education adjusted
OR=1.26 [95% CI=1.01-1.57]). Two of the four poverty measures were not significantly
associated with use of STI treatment (household median asset score adjusted OR=1.25
2 including self-medication, traditional healers, drug shops and full range of private and public facilities
32
[95% CI=0.99-1.57] and household median food insecurity index adjusted OR=0.88
[95% CI=0.71-1.10]).
Sixty-four percent of respondents with one or more STI symptoms failed to seek
any treatment. When asked why they did not seek any treatment for their STI complaint,
55% of respondents cited “lack of money”. “Not a serious condition” was the second
most common reason.
Finding 3: Private clinics account for a large proportion of STI treatment visits
Among the respondents who sought STI treatment in the previous six months, the
“not poor” were significantly more likely than “the poor” to use private providers,
including drug shops. Three of the four poverty measures had significant bivariate
associations with use of private providers (education crude OR=1.39 [95% CI=1.02-
1.89], median household assets crude OR=1.65 [95% CI=1.20-2.28], and food insecurity
index crude OR=1.43 [95% CI=1.04-1.96]).
Depending on the poverty measure used, 48-54% of poor respondents went to
private facilities, including drug shops, while 42-44% went to government facilities and
the remainder (7-9%) either self-medicated or saw a traditional healer (see Table II-4).
Discussion
Main findings
This study sheds light on the burden of STI symptoms in southwestern Uganda
and the need for appropriate health care interventions. There are several important
findings from this study. First, the data indicate that STI symptoms are common among
the population in southwestern Uganda, with 40% of individuals reporting at least one
33
STI symptom in the past six months. Moreover, the majority (63%) of these individuals
did not seek any form of treatment for their STI symptoms. The poor were more likely to
report having at least one STI symptom in the previous six months and were more likely
than the “not poor” to not use any treatment for their STI symptoms.
These findings reveal a need for health care interventions that enable individuals
to seek treatment for STI symptoms, as an increase in prompt treatment would not only
lessen the pain and suffering and possible sequelae among those who are experiencing
STIs, but could also decrease the transmission of STIs to partners and reduce the
incidence of STIs in a particular region. Among the reasons respondents gave for not
seeking treatment for STI symptoms, “lack of money” was the most frequently stated
reason (data not shown). This finding further underscores the importance of aiming
health policy interventions towards those with fewer economic resources.
Measuring poverty in the developing world is more challenging than the in
developed world, where poverty thresholds have been established through commonly
available income data and then tested and used for public policy purposes. The four
poverty measures used here represent significantly different concepts of poverty, as
confirmed by the relatively low correlation coefficients between the measures (r=0.28). A
below median number of household assets indicates long term deprivation. Having a low
education level represents deprivation experienced during childhood, although the effects
of low educational achievement typically carry forward to adulthood. The food insecurity
index measures consumption within the previous 30 days, as does the 30 day household
expenditures measure. Each of these poverty measures is an imperfect measure of overall
34
level of poverty; however, by examining all four, it is hoped that the latent concept of
poverty was more fully captured than by any poverty measure alone.
While this study found that those with higher poverty scores had a greater need
for STI services, the third research question explores the odds of using private versus
public facilities for STI treatment. Some might argue that health care interventions
directed at the poor should focus on bolstering government facilities in Uganda, where
services are nominally free, although informal charges are common. However, among
those who did visit a provider for STI treatment, respondents with high and low poverty
scores showed a preference for private facilities, including drug shops. The potential
reasons for this preference include perceived better quality of care at private facilities;
greater access to private facilities in terms of provider’s hours and proximity; and broader
level of treatment options available at private facilities.
While both the poor and “not poor” used private facilities more than
governmental facilities, the poor used private providers in lower proportions compared to
the “not poor”. These findings indicate that another viable option for improving access to
STI treatment for the poor is to enable them to obtain treatment in the private sector
through an output-based aid subsidy.
Limitations
It is important to acknowledge this study’s limitations. The first limitation is that
it is unknown whether any respondents from the first survey were interviewed in the
second survey. Non-independence of clustered observations within each survey year was
accounted for in the multivariable models; however, it is not known, and it is not possible
to control for, interviewing the same person in both surveys.
35
Additionally, although the survey had high response rates (88% in 2006 and 93%
in 2007), the survey population over-sampled older and female respondents and the study
did not reweight for the regional population structure. Sampling weights were not applied
not to the study data and quantified results are not generalized to the regional population.
Within the sample, the odds of having one or more STI symptoms did not differ by age or
gender; however, older individuals with STI symptoms were more likely to seek
treatment for their symptoms compared to younger individuals.
Another possible limitation might result from the stigma of reporting use of
traditional healers. Anecdotally, there is shame in reporting use of traditional healer
services to a modern medical professional. Our survey was carried out by medical
teaching faculty at Mbarara University and it is possible that respondents were reluctant
to mention their use of traditional medicine, leading us to underestimate the proportion of
the survey population using traditional healers. A greater use of traditional medicine than
indicated in this study would indicate, given the poor quality of the care provided by
traditional healers, a continued need to educate the population about higher quality
modern healthcare. If further investigations determine that use of traditional medicine is
more common than our study found, health policy interventions to increase modern STI
treatment will need to include elements of social marketing to convince a subset of the
population to shift away from traditional medicine towards modern treatment of STI
symptoms or traditional healers could be brought into the voucher program to refer
patients with STI symptoms to accredited clinics.
36
Conclusions
In spite of these limitations, this study has demonstrated that there is a substantial
need for STI treatment in southwestern Uganda, particularly among the poor. One
proposed strategy for providing STI services to the poor is through an output-based aid
intervention that subsidizes STI care in the private health facilities. Because those with
higher poverty scores indicated a preference for using private facilities and a pattern of
using private facilities for the treatment of STI symptoms, an output-based aid
intervention has the potential to be successful in reaching large numbers of poor patients
with STI symptoms. Future work should focus on evaluating output-based aid voucher
programs in this context.
37
Figures and Tables
Figure II-1: Directed acyclic graph of the proposed causal factors in utilization of STI treatment
*Facilities are defined as public hospitals, private hospitals, public clinics, private clinics,
W2: SPATIAL RISKRural versus urban location
Health outcome
1. Utilization of private and govt clinics in previous 6 months
2. Use of any facilities* for STI treatment in previous 6 months
W3. KNOWLEDGE & BEHAVORIAL RISKS: Unprotected sex previous 6
months Knowledge of STI symptoms Having more than 1 partner in
previous 6 months
A. ECONOMIC RISK: Food insecurity Household assets Education Household expenditure
W1. DEMOGRAPHIC FACTORS: Age Sex Marital status
38
Figure II-2: Directed acyclic graph of the proposed causal factors in STI symptoms
W2: SPATIAL RISKRural versus urban location
Health outcome
Having at least 1 STI symptom in past 6 months
W3. KNOWLEDGE & BEHAVORIAL RISKS: Unprotected sex in previous 6
months Knowledge of STI symptoms Having two or more partners in
previous 6 months
A. ECONOMIC RISK: Food insecurity Household assets Education Household Expenditure
W1. DEMOGRAPHIC FACTORS: Age Sex Marital status
39
Figure II-3: Distribution of education levels among 5,198 respondents, Mbarara region surveys in 2006 and 2007
40
Figure II-4: Distribution of the number of common household assets (0-7 assets) among 5,088 respondents in Mbarara surveys in 2006 and 2007.
41
Figure II-5: Distribution of household food insecurity scale (0-27) among 5,098 respondents in Mbarara region surveys in 2006 and 2007. Higher values indicate greater food insecurity.
42
Figure II-6: Distribution of monthly household expenditure among 5,137 respondents in Mbarara region surveys in 2006 and 2007 ($1 = 2000 UgSh)
43
Table II-1: Comparison of sex and age in 2002 Uganda Census population and the survey population in 2006 and 2007
2002 Census 2006 Survey 2007 Survey
Age Malen=379,164
Femalen=414,099
Male n=1,044
Female n=1,546
Male n=1,355
Female n=1,279
15-24 years
47% 47% 21% (217) 28% (425) 29% (387) 33% (426)
25-34 years
30% 30% 40% (419) 40% (621) 40% (545) 39% (500)
35-49 years
23% 23% 40% (408) 32% (500) 31% (423) 28% (353)
44
Table II-2: Description of respondents in 2006 and 2007 by sex
Women (n=2639) Men (n=2757) Chi-Square
Socio-demographic factors
Respondent age (mean in years)
29.9 years (n=2808)
SD= 8.8 years
31.1 years (n=2420)
SD= 9.1 years
Marital status
Single/widowed/divorced
Married or cohabitating
28% (n=786)
72% (n=2001)
32% (n=763)
68% (n=1636)
X2=7.9, df=1, p=0.005
Poverty
Monthly household expenditure (Uganda shillings)
Mean: 84000
(SD: 201000)
Median: 50000
(IQR: 30000-100000)
n=2737
Mean: 101,800
(SD 201,600)
Median:60,000
(IQR: 30,000- 100,000)
n=2399
Above vs below median
X2=24.9, df=1, p<0.001
Household food insecurity score (0-27)
Mean: 8 (SD: 5.5)
Median: 7
(IQR: 4-11)
n= 2738
Mean: 7 (SD: 5.5)
Median: 7
(IQR: 4-11)
n= 2359
Above vs below median
X2=0.16, df=1, p=0.690
Household assets (7 common goods)
Mean: 2 (SD 2)
Median: 2
(IQR: 1-3)
n= 2730
Mean: 2 (SD: 2)
Median: 2
(IQR: 1-3)
n= 2357
Above vs below median
X2=4.4, df=1, p=0.035
Education
No formal (n=651)
Some primary (n=1789)
Completed primary (n=880)
Some secondary (n=1113)
Complete secondary (n=764)
440 (16%)
1016 (36%)
420 (15%)
565 (20%)
347 (13%)
n=2788
211 (9%)
773 (32%)
460 (19%)
548 (23%)
417 (17%)
n-2409
X2=94.9, df=1, p<0.001
Village characteristics
Commercial villages
Rural villages
1565 (55%)
1265 (45%)
1388 (57%)
1055 (43%)
Above vs below median
X2=1.22, df=1,
45
Women (n=2639) Men (n=2757) Chi-Square
p=0.269
STI behavioral risks & knowledge in previous six months
Unprotected sex
Consistent condom
1945 (86%)
331 (14%)
1804 (89%)
228 (11%)
Gave money for sex 61 / 2609 (2%) 333 / 2307 (14%)
Knows 2+ STI symptoms
Knows only 0-1 symptoms
1338 (50%)
1347 (50%)
1250 (53%)
1098 (47%)
46
Table II-3: Relationship between four dimensions of poverty and three evaluation outcomes among respondents on the 2006 and 2007 surveys in the Mbarara region
One or more STI symptoms in previous six months among sexually active
Obtained any STI treament+ in previous six months among those reporting 1 or more STI symptoms
Obtained private STI treatment among those who sought any STI treatment at public or private facilities (incl drug shops)
Education
Total N (“poor” and not poor”) 4201 2058 653
No complete primary (“poor”) 971/ 1956(50%)
283/ 1011(28%)
164/ 309(53%)
Completed primary (“not poor”) 951/ 2245(42%)
341/ 1027(33%)
210/ 344(61%)
Bivariate OR 0.75*** 1.28** 1.39*
Bivariate 95% CI 0.66-0.84 1.06-1.55 1.02-1.89
Multivariate aOR^ 0.68*** 1.28* 1.32
Multivariate 95% CI 0.59-0.77 1.06-1.55 0.80-2.18
Household monthly expenditures
Total N (“poor” and not poor”) 4171 2000 652
Median and below (“poor”) 1001/ 2139 (47%)
284/ 1011(28%)
178/ 328(54%)
Above median (“not poor”) 896/ 2032(44%)
333/ 989(34%)
194/ 324(60%)
Bivariate OR 0.90 1.30** 1.26
Bivariate 95% CI 0.79-1.01 1.08-1.56 0.92-1.71
Multivariate aOR^ 0.91 1.27* 1.05-1.54
Multivariate 95% CI 0.79-1.04 1.05-1.54 0.72-1.55
47
Table II-3: Continued
One or more STI symptoms in previous six months among sexually active
Obtained any STI treament+ in previous six months among those reporting 1 or more STI symptoms
Obtained private STI treatment among those who sought any STI treatment at public or private facilities (incl drug shops)
Household asset score
Total N (“poor” and not poor”) 4121 2015 641
Median and below (“poor”) 1150/ 2382(48%)
256/ 1227(29%)
191/ 371(52%)
Above median (“not poor”) 743/ 1739(43%)
267/ 788(34%)
172/ 270(64%)
Bivariate OR 0.79*** 1.25* 1.65**
Bivariate 95% CI 0.71-0.91 1.02-1.55 1.20-2.28
Multivariate aOR^ 0.76*** 1.24* 1.41
Multivariate 95% CI 0.66-0.87 1.02-1.51 0.95-2.11
Household food insecurity in past 30 days
Total N (“poor” and not poor”) 4124 2015 638
Median and below (“poor”) 899/ 1950(46%)
312/ 1096(29%)
167/ 317(53%)
Above median (“not poor”) 993/ 2174(46%)
301/ 984(34%)
197/ 321(61%)
Bivariate OR 0.98 1.27* 1.43*
Bivariate 95% CI 0.87-1.11 1.04-1.56 1.04-1.96
Multivariate aOR^ 0.96 1.26* 1.49*
Multivariate 95% CI 0.84-1.10 1.03-1.55 1.01-2.21
*p < 0.05, **p < 0.01, ***p < 0.001
^ multivariate models control for respondent demographic factors (age, sex and marital status), village level access to healthcare (as approximated by commercial status of respondent’s village), and risk behaviors (unprotected sex, having two or more partners, giving money for sex).
+ “any STI treatment” includes self-medication, traditional healers, and the full range of allopathic providers
48
Table II-4: Utilization of STI treatment in previous six months by four different measures of poverty at public and private facilities, including drug shops and traditional healers, among respondents in the 2006 and 2007 surveys in the Mbarara region
Utilization of STI treatment in previous 6 months by the poor
Low education (n=339)
Few household assets (n=405)
Low household expenditure (n=356)
Low household food security (n=342)
All respondents who sought tx(n=724)
Govt clinics and hospitals
145 (43%) 180 (44%) 150 (42%) 150 (44%) (284) 39%
Private, including drugs shops
164 (48%) 191 (49%) 178 (50%) 167 (54%) (377) 52%
Traditional healers & self-treated
30 (9%) 34 (7%) 28 (8%) 25 (7%) (63) 9%
49
Chapter III: Impact of an output-based aid voucher program on the prevalence of syphilis and utilization of treatment services for sexually transmitted infections in southwestern Uganda
Abstract
Rationale: A voucher program subsidizing access to healthcare services to treat
sexually transmitted infections (STIs) began after a baseline survey in 2006. Sixteen
clinics were contracted and thirteen clinics saw more than 10,000 patients (the other three
clinics combined saw fewer than 200 total patients) between the program launch in July
2006 and the follow-up survey in November 2007.
Objectives: The study sought to determine the impact of the voucher program in a
before-and-after design measuring three important outcomes in year one and year two of
the evaluation: the proportion of respondents who recognized two or more STI
symptoms; among respondents having one or more STI symptoms, the proportion who
sought STI treatment; and the prevalence of syphilis. The same analysis was conducted
on four subgroups of the poor, alternately defined by the following dimensions of
poverty: median household monthly expenditures, median household assets, completed
primary school, and median household food insecurity score. The study also sought to
determine whether distance was inversely correlated with STI treatment utilization.
Finally, the study aimed to measure in populations <11 kilometers and ≥11 kilometers
from contracted OBA clinics the change between 2006 and 2007 on the proportion who
sought STI treatment, among respondents having one or more STI symptoms, and the
prevalence of syphilis.
Methods: Three data sets were used: the claims management database, population
surveys conducted in 2006 and 2007, and a spatial dataset for the region indicating
50
administrative boundaries, clinic locations and roads. We compared distance between
each patient’s village to the nearest contracted clinic for the 14,961 patients who used
vouchers for STI treatment to determine whether use of STI voucher treatment services
decreased with greater distance. Observations from the first survey were matched to
observations from the second survey using a nonparametric matching package
(GenMatch) in R (version 2.8.1) to control for potential confounding by the matched
variables. Logistic models were fit using the matched dataset. The “distance to contracted
clinic” cutpoint was set at the median value of the continuous version of “distance to
contracted clinic” before fitting it in logistic models with STI treatment utilization and
the prevalence of syphilis as outcomes of interest.
Results: A majority of the patients using an STI voucher (54%) sought care ≤10
kilometers of their village of residence. Distance was inversely correlated with use of STI
treatment services at contracted clinics (r= -0.78). Knowledge of STI symptoms
increased 18% between the first and second years (aOR=1.43 95% CI=1.22-1.68). STI
treatment utilization among those reporting having had one or more STI symptoms in the
previous six months increased 15% between the first and the second year; however, the
increase was not statistically significant (aOR=1.14, 95% CI=0.89-1.47). The prevalence
of syphilis, as measured by the VDRL test, decreased 42% between the two surveys
(aOR=0.63, 95% CI=0.48-0.79). There was a greater reduction in the prevalence of
syphilis among respondents between 2006 and 2007 who lived <11 kilometers from a
contracted facility compared to respondents who lived ≥11 kilometers from a contracted
clinic (57% decrease versus 20% decrease).
51
Conclusions: The OBA voucher program appeared to improve knowledge of STI
symptoms and reduce the prevalence of syphilis. There was not a significant
improvement in the utilization of STI treatment in the full study population. However, the
distance from village of residence to contracted OBA clinic appeared to be a significant
barrier to utilization of STI treatment. Greater distances significantly attenuated a local
area effect (<11 kilometers) of the voucher program on the utilization of STI treatment
utilization and the prevalence of syphilis.
52
Background
There is abundant evidence from many low-income countries that the poor benefit
less than the wealthy from collectively funded health services and suffer a disease burden
as great or greater (Bustreo, Harding and Axelsson 2003; Castro-Leal et al. 2000;
Gwatkin, Bhuiya and Victora 2004; Palmer et al. 2002; Prata et al. 2005). In situations
where private providers are unable or unwilling to satisfy healthcare needs of the poor,
governments may decide to use public funding to subsidize access. When governments
decide to provide healthcare, they can either supply medical staff and facilities or
purchase health services from qualified professionals and institutions (Preker, Harding
and Travis 2000).
Low-income countries, with donor support, have largely engaged in supply side
healthcare production (i.e. building, stocking, and staffing health facilities). In some
cases, donor support of healthcare supply has resulted in measurable improvements in
health services management and health outcomes; however, there are many examples of
supply side donor-funded programs that were poorly planned, improperly managed and
unable to demonstrate links between expenditures and improvements in healthcare
management or health outcomes (Ensor and Ronoh 2005). Although the application of
private sector models to stimulate demand for public health goods and services has been
successfully piloted (Grant and Walford 2004, p 13), the majority of these models have
utilized supply side financing (Ensor and Ronoh 2005).
53
What finance strategies are available to donors and governments?
Governments seeking to improve efficiency in healthcare delivery or equity in
health may decide to increase access to health goods and services via one or more of the
following options: 1) unrestricted cash payments or vouchers (economic ‘gifts’), 2)
conditional demand subsidies to patients for health goods or services, 3) competitive
purchasing of goods or services on behalf of consumers, or 4) monopoly, often
government, production of health goods or services for consumers (Posner et al. 2000).
Conditional government payments can be channeled through demand-side
subsidies that the user, armed with a voucher, can use to shop for a provider among those
approved and willing to accept the voucher. Janssen and colleagues identified
competitive vouchers, flat-rate subsidies and direct cash subsidies as typical demand-side
tools (2004). Unlike direct cash subsidies, which do not restrict consumer choice on how
the cash is spent, a voucher limits the bearer to a specific set of goods and services at a
fixed reimbursement amount (Janssen et al. 2004; Steuerle 2000). The voucher is
essentially conditional cash paid before the service, while a conditional cash transfer is
paid only after the service has been provided or the condition is met.
Alternatively, conditional payments can fund the supply of health goods or
services (e.g. paying a performance bonus to health workers or organizations), which is
more common in the infrastructure sectors where natural monopoly characteristics may
exist (World Bank 2006). On the supply side, performance contracts are useful tools for
enforcing delivery standards and service quality (Eichler 2001; Logie, Rowson and
Ndagije 2008).
54
What is output-based aid (OBA)?
The use of vouchers in combination with results-based contracting can stimulate
consumer demand for and increase the supply of competitively contracted healthcare
goods and services. The use of vouchers and results-based contracting is generally known
as output-based aid (OBA) (Gorter et al. 2003; Janisch and Potts 2005; Sandiford et al.
2002). In salaried positions not linked to performance, staff may have little incentive to
raise their productivity or be concerned with patient perceptions of health care quality
(Robinson 2001). Under an OBA contract, however, incentives are created to increase the
number of patients seen. A voucher empowers the patient to choose his or her health
provider. Informed patient choice has the potential to induce providers to improve the
quality of their services.
Donors and governments are interested in a variety of reforms utilizing output
based models, including “quasi-contracts” between government agencies;
commercializing public agencies; contracting out specific services to the private sector;
transferring responsibility for providing services to the private sector through concessions
or outright privatization; and providing demand subsidies directly to consumers (Brook
and Petrie 2001). In contrast with more traditional supply side financing approaches,
these schemes seek to define objectives and specify expected performance in terms of
outputs rather than inputs (Brook and Petrie 2001). Under quasi-contracting, for instance,
public health staff may receive a regular salary, but bonuses are conditional on meeting
performance targets; an example of quasi-contracting is the Rwanda OBA program (Rusa
and Fritsche 2006). In Rwanda, there is no explicit competition between providers for
patients; however, there are competitive incentives for contracted facilities to improve the
55
quality of care. In this situation, results-based contracting emphasizes improvements to
an organization’s ability to deliver public services (Brook and Petrie 2001). The pay-for-
performance incentives are largely contained to institutional management, such as
staffing quality, supply chain, buildings and other inputs (Eichler 2001; Logie et al.
2008). This focus on incentives for better management is a useful mechanism for
improving service quality; however, it does not link patient utilization to performance
payments as is done with demand side subsidies.
Output-based models can also be used to finance demand side interventions. It is
possible to transfer power to the patient and remunerate providers according to the
number of patients they are able to attract, as reflected in Figure III-1 (Bhatia and Gorter
2007). The feasibility of using targeted vouchers in demand side finance has been
demonstrated in several regional projects (Bellows, Mulogo and Bagenda 2008; Ensor
2004; Gorter et al. 2003; Grant and Walford 2004). Vouchers deliver a conditional
economic subsidy to recipients, ideally giving the bearer the ability to choose from a
selected set of goods and services at approved providers, who compete for the voucher.
OBA links performance-based contracting and demand stimulation
Brook and Petrie (2001) identified a basic choice when deciding whether to
provide a service in a competitive market or through what might be called supply-side
results contracting, what they call “monopolistic supply arrangements”. Where healthcare
providers are many and easily accessed, vouchers targeted to consumers can give patients
a choice and create incentives for provider efficiency and attention to patient satisfaction.
Where there are few providers, the absence of a potential market or concern with weak
healthcare supply might lead program planners to focus on linking reimbursement to
56
improvements in provider service delivery, without any attempt to link subsidies to
patient choice. The decision whether to subsidize demand or supply requires
consideration of the contract’s length, how many facilities should be contracted, how to
monitor quality, and how best to ensure that the service provider has incentives to be
efficient. Competitive bidding for short term contracts can provide a useful incentive
structure. Programs linking provider performance with contractual payments have been
implemented in a number of settings (Logie et al. 2008; World Bank 2006). Other
demand-side programs, using cash transfers or vouchers, give purchasing power to the
consumer and pay providers according to the number of patients they are able to attract
(Bhatia and Gorter 2007). Demand side financing is increasingly being implemented in
an effort to improve access to reproductive and health services in low-income countries
(Behrman and Knowles 1998) (DfID 2006; Sandiford et al. 2005).
Why implement OBA?
OBA voucher schemes have four aims: to improve provider quality; stimulate
utilization of selected services; target services to high-priority populations; and contain
costs (Mushi et al. 2003; Steuerle 2000). Examples of high priority populations include
poor youth at high risk of HIV infection, women suffering from domestic violence,
pregnant women or mothers of very young children, and administrative districts with a
high incidence of STIs.
Voucher schemes may induce clinic-level improvements without any competitive
pressure placed on service providers, although it is commonly assumed that voucher
programs introduce greater competition (Gorter et al. 2003). Competition may occur
57
during the initial clinic accreditation and later as contracted clinics compete for voucher-
bearing patients.
OBA vouchers are combined with contracts in which service suppliers or provider
networks agree to service delivery standards (Grant and Walford 2004 p 32). It may not
be necessary to give a physical voucher to consumers. Providers can be contracted to
deliver healthcare to properly screened patients (e.g. poor or high risk patients) without
requiring the patients to bring a physical voucher for each visit (Bradford and Shaviro
2000).
Utilization
Some services that have weak incentives (i.e. male circumcisions for HIV
prevention) or high utilization costs (i.e. facility-based maternal delivery) are good
candidates for OBA voucher programs (Griffith, Bellows and Potts 2007; Janisch and
Potts 2005). Salaried staff providing these services have little incentive to raise their
productivity or to be concerned with patient perceptions of health care quality (Bhatia
and Gorter 2007; Robinson 2001). Linking payments to performance and giving patients
their choice of provider introduces incentives for providers to treat more patients and to
treat them well.
In a voucher program in Nicaragua, a voucher program for treatment of STIs
among youth in Managua was credited with a 10% increase in service utilization
compared to the routine care in the absence of the vouchers (Borghi et al. 2005). In a
voucher program for STI treatment in Uganda, utilization at seven contracted clinics
increased on average 200% in the first 12 months of the program, compared to the 12
months prior to the program launch (Lowe and Bellows 2007).
58
Quality
It is assumed that in a voucher program, providers will maintain high quality
medical services to keep patient satisfaction high. The assumption is that there are no
market failures; that consumers have multiple options when seeking care. In reality,
providers may be contracted in areas with few or no alternatives.
A study of providers in a voucher program in Nicaragua used simulated patients
to measure provider adherence to protocols (Meuwissen et al. 2006a). Nineteen clinics
were contracted to accept vouchers from youth seeking sexual and reproductive health
services, principally contraception. Sixteen clinics were visited by the simulated patients
before the study (three clinics were not visited) and only eight of the simulated patients
were provided treatment according to the guidelines. One month after the voucher
program began, the 16 providers treated all simulated patients according to guidelines
(Meuwissen et al. 2006a).
Distance to care
Voucher programs can be expected to improve population health outcomes and
healthcare utilization if healthcare is sufficiently high quality and the targeted population
can reach the service providers.
Although it has long been recognized that proximity to health services is
associated with increased utilization (Dear 1977; Jarvis 1850), there has been no evidence
that distance from home to clinic is a barrier to healthcare utilization in voucher
programs. The correlation between healthcare utilization and distance from a patient’s
home to clinic likely varies with the specific service. For some health services in which
59
anonymity might be highly valued, greater distance from home may be preferred as fewer
people would recognize the patient.
Studies from sub-Saharan Africa indicate that distance is one of several critical
factors in accessing reproductive and sexual healthcare (Mills et al. 2006; Molesworth
2007; Thaddeus and Maine 1994). For example, a study in South Africa found that
demand for private insurance decreased as the distance from home to service providers
increased (Söderlund and Hansl 2003). In several studies of adherence to HIV
antiretroviral therapy (ART) in Africa, distance was consistently recognized as an
important barrier to adherence to treatment (Rosen et al. 2007; Uzochukwu et al. 2009).
Research objectives
The primary objective of this study was to evaluate the impact of the OBA
program in southwestern Uganda. We hypothesized that the combination of social
marketing and the economic subsidy of the OBA program would result in the following
outcomes:
1. an increase in knowledge of STI symptoms as a result of extensive social
marketing and health education efforts from the OBA program across the entire
study area between 2006 and 2007;
2. an increase in utilization of STI treatment services among respondents with one or
more STI symptoms by subsidizing cost and increasing patient demand for care
among individuals living close to contracted clinics and less so among individuals
living farther away from the clinics;
3. a decrease in the prevalence of syphilis due to treating more patients and treating
patients more appropriately under the program’s quality service delivery
60
guidelines, with the decrease being greater among individuals living close to
contracted clinics than among individuals living farther away;
4. among the respondents who had one more STI symptoms, an increase in the
proportion who utilized STI treatment between 2006 and 2007
5. among all respondents who submitted to the VDRL test, a decrease in the
prevalence of syphilis between 2006 and 2007
6. among the poor, an increase in the knowledge of STI symptoms between 2006
and 2007;
7. among the poor with one or more STI symptoms, an increase in the proportion
who used STI treatment between 2006 and 2007;
8. among the poor, a decrease in the prevalence of syphilis between 2006 and 2007.
Figure III-2 presents a directed acyclic graph of the effect of distance from place
of residence to contracted STI clinics on the proportion of respondents who know two or
more STI symptoms, the proportion of respondents with one or more STI symptoms who
seek any STI treatment, and the prevalence of syphilis among all respondents.
Methods
Three data sets were used: two surveys of the population, conducted in 2006 and
2007; a voucher claims management database; and a spatial dataset for the region
indicating administrative boundaries, clinic locations and roads.
61
Population-based surveys dataset
The first dataset was from cross sectional surveys conducted by Mbarara
University of Science and Technology (MUST) in 2006 and 2007, in which respondents
in the same 82 villages were targeted in each survey. In each survey, study participants
were asked about household assets, food insecurity, alcohol use, general healthcare
utilization in the previous six months, knowledge of STI symptoms, STI behavioral risks
(i.e. number of partners and condom use), STI treatment utilization in the previous six
months, and respondent’s social capital.
Study participants were also screened for syphilis, gonorrhea and trichomoniasis
(women only). After informed consent was obtained and the interview completed, a
blood sample and vaginal or urethral swab were collected. The samples, stored in
transport media, were returned to Mbarara University on a daily basis for processing in
the laboratory.
Survey team leaders also took the coordinates of the approximate center of each
village using a handheld Garmin eTrex GPS unit. Village coordinates were then added to
the spatial dataset, described below, using ArcMap software (version 9.2 Build 1500,
Redlands, CA).
Voucher claims dataset
The second dataset came from an ongoing voucher program launched in July
2006 to subsidize treatment of STIs at contracted clinics in the region. Claims submitted
for reimbursement were entered into a database at the management agency. For the
period July 2006 to April 2008, there were 14,989 voucher records that documented
62
patient sex, age, home village, STI services received, and cost of STI treatment at the
clinic.
Local areas spatial dataset
The third dataset was a spatial dataset from the Uganda Bureau of Statistics. It
contained administrative areas down to local village areas, primary roads, and population
density. Spatial data were accessed using ArcMap software (version 9.2 Build 1500,
Redlands, CA). The coordinates for each contracted clinic were measured on site with a
handheld Garmin eTrex GPS unit and then added to the spatial dataset. We used the
spatial data to estimate distances to nearest clinics from our surveyed villages (e.g. first
dataset above) and from the voucher patients' home villages (e.g. second dataset above).
We then used the “distance to clinic” measurements for the voucher patients to
test whether utilization was inversely correlated with distance to contracted clinics. If
such a relationship was found, we could use distance as a “treatment” in the cross
sectional surveys and test whether living in a village near to contracted clinics was
associated with a greater change in the proportion of STI utilization and the prevalence of
syphilis between 2006 and 2007, compared to living in a village far from contracted
clinics.
Control of confounding
In this study, we calculated the prevalence of syphilis (VDRL results) and
proportion of individuals with one or more STI symptoms who used STI treatment in
both surveys. To control for potential confounding when making comparisons of
outcomes between surveys, we used a non-parametric search algorithm to match
63
respondents on ten variables likely to be associated with the three outcomes of interest
(knowledge of STI symptoms, utilization of STI treatment, and prevalence of syphilis)
and the exposures of interest (survey year, distance to care, and poverty).
Common analytic methods to adjust for potential confounding include
stratification and multivariable regression (Greenland and Morgenstern 2001).
Stratification is the simpler method to implement; however, when a sufficiently large
number of variables are stratified, it can result in cells with sparse data and lead to
imprecise estimates of association (i.e. “sparse-data problem”) (Greenland and
Morgenstern 2001; Greenland, Robins and Pearl 1999). The most common method to
avoid the sparse-data problem is multivariable regression, which examines the potential
effect of an exposure of interest, while simultaneously holding constant any statistical
association between other factors (confounders) and the outcome of interest (Grimes and
Schulz 2002).
Although there are advantages to multivariable regression analysis, there are
limitations in its ability to control confounding. Confounding covariates are controlled for
one regressor at a time; regression does not attempt to balance the joint distribution of the
confounders independent of the exposure of interest. The common regression analysis is
often inadequate to measure the full complexity of the interaction between confounding
covariates, treatment, and outcome. Given the constraints of standard regression, Sekhon
(2008) developed a non-parametric matching method to achieve balance of the joint
distribution across all levels of treatment to control for confounding.
64
GenMatch package in R
The Matching package in R (version 2.8.1 ) (abbreviated as GenMatch) provides
matching functions for propensity score, Mahalanobis, inverse variance and a genetic
search algorithm for optimal balance of the joint distribution of any variable set (Sekhon
2008). As a generalization of propensity score and Mahalanobis distance matching,
GenMatch optimizes the balance of the joint distribution of observed covariates between
treated and control groups (Mebane and Sekhon 1998; Sekhon and Mebane Jr. 1998).
Although a propensity score is not necessary, the nonparametric GenMatch algorithm is
improved when a propensity score is added. Sekhon demonstrated that GenMatch is able
to find good balance of the joint distribution of covariates in the treatment and control
populations and to reliably reproduce experimental outcomes from non-experimental
matching designs (Diamond and Sekhon 2006; Sekhon 2008). GenMatch optimizes the
joint distribution of observed variables by using a genetic search algorithm that
determines the best weight for each variable. By default GenMatch matches 1-to-1 with
replacement and estimates the average treatment effect among the treated (ATT).
For our analysis, observations were matched with replacement. Observations were
matched exactly on the following variables: parish of residence, sex, age in years, the
number of sex partners in the previous six months, the number of health facilities in the
respondent’s village, whether the respondent had any unprotected sex in the previous six
months, and binary variables to indicate missingness for respondent sex, age in years,
number of sex partners, and unprotected sex. Because the match was restricted to
respondents from the same parish, the universe of potential pairings with the same sex,
65
age in years, number of sex partners, unprotected sex and same pattern of missingness
was limited to a range of 50 to 72 respondents for each parish.
GenMatch was set to optimize the genetic algorithm in samples of 1000
observations each generation (pop.size=1000), the number of maximum generations was
set to 50 (max.generations=50) and the number of generations to continue calculating
after optimization was reached was set to four (wait.generations=4).
“Wait.generations=4” determines that if there is no improvement in optimization of
matches within four generations, the matching process stops.
Model selection
Matched observations from GenMatch were exported from R to Stata. Multilevel
mixed-effects logistic regression models were run in Stata (“xtmelogit” command in
version 10.1 for Windows) for the following dichotomous outcomes: knowledge of two
or more STI symptoms among all respondents who completed the survey interview, use
of STI treatment services among study participants who reported one or more STI
symptoms in the previous six months, and prevalence of syphilis among all study
participants who submitted samples for VDRL syphilis screening.
Different names are used in the literature: contextual models, hierarchical linear
model, hierarchical linear regression, random coefficient model, and hierarchical mixed
model, among others (Diprete and Forristal 1994), however, they share a common intent
to carry out a simultaneous multivariable analysis of effects at micro (e.g. respondent)
and macro (e.g. group) levels (Diprete and Forristal 1994; Duncan, Jones and Moon
1998; Krieger 2001).
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Diez-Roux (2000) summarized the differences between multilevel analysis and
other modeling approaches in epidemiology. Multilevel analysis allows for the
simultaneous examination of the effects of group-level and individual-level predictors,
accounts for the non-independence of observations within groups, treats groups as
sampled from a larger unobserved population of groups, inter-individual and intergroup
variation can be examined (as well as the contributions of individual-level and group-
level variables to those variations) (Diez-Roux 2000).
In this study, we used multilevel mixed-effects logistic models to estimate effects
that we assumed had hierarchical variance structures at respondent level, at the level of
pairs across survey years, and at parish level. Under random effects we consider
individual differences as random disturbances drawn from a distribution specified in the
model. The random effects model has the advantage of using fewer degrees of freedom,
and that individual differences are considered random rather than fixed and estimable.
Poverty assessment
As explained elsewhere, poverty was a multidimensional concept measured
separately by four variables in the study. The four poverty variables were dichotomous:
median household monthly expenditures, median number of household assets,
educational level completed, and median food insecurity score. The change in
proportions of knowledge of STI symptoms, STI treatment utilization, and prevalence of
syphilis was compared between 2006 and 2007 among the four alternate definitions of
the poor.
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Results
Distance as a barrier to STI treatment
Distance to clinic from patient village was inversely correlated with use of STI
voucher treatment services. Using the voucher claims data and the spatial dataset, we
found that the majority of voucher patients (54%) traveled fewer than ten kilometers.
Generally, distance was inversely correlated with use of STI treatment services at
contracted clinics (r= -0.78).
When voucher patients had a choice of clinics, they chose the nearest clinic 87%
of the time. Table III-5 presents the distance, in five kilometer increments, from each
patient’s home village to the clinic they visited. In the left-hand column is the distance
from each patient’s home village to the nearest contracted clinic. Every row of the table
indicates, in five kilometer increments, the distance to the nearest clinic from each
patient’s village and the distance from each patient’s village to the clinic he or she
actually visited. For instance, the first row of the table shows that among the 5,741
patients who lived within five kilometers of the nearest clinic, 5,467 patients (95%)
visited a clinic within five kilometers of their village of residence. The remaining 274
patients (5%) of 5,741 patients ≤5 kilometers of a contracted clinic traveled farther than
the nearest clinic: 193 patients (3.3%) traveled to another clinic 5-10 kilometers from
home, 12 patients (<1%) traveled to another clinic 10-15 kilometers from home, and 35
(<1%) patients traveled to another clinic that was 20-25 kilometers from home.
Considering the choice of clinics made by patients, we see in the bottom row in
Table III-1 (i.e. the marginal values of “Total”) that as the actual distance from village of
residence to contracted clinic increased from five to 10 kilometers, and 10 to 15
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kilometers and so forth, the number of patients visiting clinics at greater distances
decreased. 40% of patients traveled five kilometers or fewer to visit a voucher clinic.
20% of patients traveled between 5 and 9.9 kilometers to visit a voucher clinic; 10% of
patients traveled between 10 and 14.9 kilometers and 11% traveled between 15 and 19.9
kilometers to visit a voucher clinic. The remaining 19% traveled more than 20 kilometers
to visit a clinic (see Table III-1).
GenMatch cohort is balanced on potential confounders
Matching on the joint distribution of ten potential confounders, the GenMatch
function in R generated a dataset of 3438 observations sampled from the original
unmatched dataset, with some observations matched multiple times. The match was done
without considering the outcomes of interest. The match criteria specified exact match on
the joint distribution of the ten variables: parish of residence, age in years, sex, number of
sex partners in the previous six months (0, 1, 2, or 3 or more), having unprotected sex in
the previous six months, having any health facility in the village and missingness
indicators for sex, age in years, number of sex partners, and having unprotected sex. As a
result, the after-match balance was perfect between respondents from the two survey
years (see Table III-6).
Knowledge of STI symptoms increased after the voucher program began
The proportion of respondents in the surveys who could recognize two or more
STI symptoms increased between July 2006 and November 2007. In this 16-month
period, the voucher program ran extensive social marketing and health education
programs on the radio and in community presentations throughout the region. When
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respondents were asked which medium they trusted the most for STI information, radio
was the highest ranked. In 2006, over 65 percent of respondents indicated “radio” as their
preferred means to learn about STIs and in 2007 more than 70 percent named “radio” as
their preferred source for STI information. In both surveys, “friends and peers” (10%)
and “government clinic staff” (7%) were distant second and third options as sources of
STI information.
In 2006, 49% of respondents (716/1470) could name two or more STI symptoms;
at the time of the second survey in 2007, 58% of respondents (892/1528) correctly named
two or more symptoms (aOR=1.43, 95% CI=1.22-1.68) (see Table III-3). Increases in
STI knowledge did not vary significantly by distance to a contracted clinic.
STI treatment utilization increased after the voucher program began
When analyzing utilization among those having had one or more STI symptoms
without considering distance from village of residence to clinics, the odds of respondents
having used any STI treatment service in the previous six months had a non-significant
increase between 2006 and 2007 (27% to 31% between 2006 and 2007, aOR=1.14, 95%
CI=0.89-1.47) (see Table III-7). Utilization of STI treatment did not distinguish between
types of clinics visited (public or private) or whether the respondent used a voucher.
When distance was taken into consideration, a much higher proportion of
respondents who lived <11 kilometers from the contracted clinics and who reported
having STI symptoms used STI treatment, compared to respondents who lived ≥11
kilometers from the contracted clinics. Respondents who reported STI symptoms and
lived <11 kilometers from a contracted clinic had a 48% increase in STI treatment
utilization (from 29% in 2006 to 43% in 2007) compared to a <1% increase among
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respondents with STI symptoms who lived ≥11 kilometers from a contracted clinic (23%
in 2006 and 24% in 2007, see Table III-8).
Prevalence of syphilis decreased after the voucher program began
The prevalence of syphilis (positive VDRL) was lower in 2007 than 2006
(aOR=0.62 95% CI=0.44-0.93) (see Table III-3). The decrease in the prevalence of a
positive VDRL test between 2006 and 2007 was greater among study participants who
lived <11 kilometers from a contracted clinic (57% decrease) than among participants
who lived ≥11 kilometers from a contracted clinic (20% decrease) (Table III-9).
Increase in knowledge of STI symptoms among the poor
The poverty measures were not highly correlated; the highest Pearson pairwise
correlation coefficient was 0.28. Regardless of the approach used to classify poverty, the
proportion of poor respondents who could name two or more STI symptoms significantly
increased between the launch of the voucher program in 2006 and the follow-up survey in
2007 (low monthly household expenditures 51% to 57%, aOR=1.37; few household
assets 46% to 57%, aOR=1.68, low food security score 48% to 61%, aOR=1.82, low
education level 47% to 54%, aOR=1.34; see Table III-10).
Increase in STI utilization among the poor
As mentioned previously, the four different poverty measures were not highly
correlated. In the four separate models that restricted the dataset to observations that met
alternate definitions of the poor, among those who reported having one or more STI
symptoms in the previous six months there was some evidence that utilization of STI
treatment services increased between 2006 and 2007. Among the poor defined by two
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alternate measures of poverty, there was a significant increase in the proportion of STI
treatment used between 2006 and 2007 for those reporting one or more STI symptoms
(low food security 25% to 38%, aOR=2.08; low education level 22% to 28%, aOR=1.70;
see Table III-6). In two other alternate measures of poverty (i.e. low household monthly
expenditure and few household assets), there was a non-significant increase in the
proportion of STI treatment utilized by the poor who reported one or more STI
symptoms.
Decrease in prevalence of syphilis among the poor
The prevalence of syphilis decreased among the poor after the launch of the
voucher program. Among the poor defined by three alternate measures of poverty, there
was a significant decrease in the prevalence of syphilis (VDRL) between 2006 and 2007
(low education level 6.1% to 1.8%, aOR=0.25; low food security score 5.6% to 1.9%,
aOR=0.23, few household assets 6.9% to 4.3%, aOR=0.62).
Discussion
As expected, we found an inverse correlation in clinic utilization with distance
from village of residence to contracted clinic. There were limitations in the way distance
was measured. We used a direct line measurement (i.e. “as the crow flies” distance),
when in reality, patients travel non-linear routes from home to clinic. Linear distance to
clinic very likely underestimates the true distance to clinic. We would expect, but have no
way to correct for in the current study, local differences in non-linear routes. For
example, patients in a certain village are relatively close to clinic but the only available
route has many curves and, as a result, represents a great distance to travel. In contrast,
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patients from another village may have a longer linear distance to clinic, but their route to
clinic is also straight. In our spatial table it would appear that patients from the more
distant village visited the clinic in greater numbers when in reality, the route followed by
patients from the near village had a greater distance to travel. This would reduce the
magnitude of the inverse correlation between distance and healthcare utilization by
misclassifying patients near to clinics as far and patient far from clinics as near.
In addition to the non-linearity of most travel routes, patients also have a varying
ability to pay out of pocket for transport, and the type of transport they can afford affects
the practical distance they can expect to travel. We expect that poverty modifies the
relationship between distance from home to clinic and utilization of that clinic. At higher
levels of income, patients can travel farther and can exercise more choice in selecting a
healthcare provider.
The STI treatment utilization observed in this study is not a direct measure of
utilization due to vouchers per se. The increase in STI utilization could have been driven
by the social marketing campaign carried out during this study period, by an increase in
incidence of an STI (likely not syphilis), by the economic incentive of the voucher
subsidy, or by some other unknown factor(s).
In the matched dataset, 27 of 214 respondents in 2007 reported using a voucher
yet we observed a 15% increase in utilization of STI treatment compared to 2006. The
social marketing of vouchers and health education on radio and in communities may have
had a role in increasing treatment seeking at non-voucher and voucher facilities alike.
Considered the importance that respondents placed on radio as a medium for health
information, it is possible that respondents were motivated to seek STI treatment
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regardless of whether they had a voucher. Additional research on the impact of marketing
on the purchase and utilization of vouchers, including longitudinal studies of marketing
events and utilization trends, ought to be considered.
The poverty measures were not highly correlated suggesting that the measures
reflected different dimensions of poverty. Regardless of which measure was used to
define poverty, knowledge of STI symptoms increased between 2006 and 2007 among
the poor. In three alternate measures of poverty, there was a significant decrease in the
prevalence of syphilis among the poor. Among the poor in two of the four poverty
measures, there was a significant increase in utilization of any STI treatment services
between 2006 and 2007. Regardless of which measure was used to define poverty, none
of the models indicated a significant decrease in utilization between 2006 and 2007 or a
significant increase in the prevalence of syphilis between 2006 and 2007. The evidence is
not strong; however, there is some evidence that, among the poor, there was an increase
in the proportion of respondents using STI treatment services and a decrease in the
prevalence of syphilis between 2006 and 2007.
Matching on the joint distribution of potential confounders did remove the
potential for confounding from the matched variables. However, there was a cost; the loss
of many unmatched observations (n=3676) represented a large loss of information. There
are limitations to creating a synthetic cohort, even a cohort with strict matching
requirements. It is not possible to claim that the matched observations are observations on
the same “synthetic individual”.
Matching on the joint distribution resulted in the inclusion of many observations
from 2007 with missing values in sex, age, number of sex partners in previous six
74
months, and having unprotected sex in previous six months. Observations were matched
on value of their joint distribution, not the values of each variable. As a result, it was
possible to match individuals with different values in some of their 10 matching variables
as long as the value of the joint distribution was the same. The matched dataset had a
greater proportion of missing values in the matched variables in 2007 (10-11% of
observations) compared to the unmatched 2007 dataset in which 2-5% of observations
were missing values for those matched variables (see Table III-6). The implications are
unknown; however, if missingness is correlated with the outcomes of interest,
missingness could act as a statistical confounder by differentially excluding observations
from 2007, as the statistical software drops observations with missing values from
multivariable models. The issue warrants further analysis.
Alternative analytic methods might control for confounding more efficiently than
GenMatch in this study of the impact of vouchers. These methods include machine model
selection using an algorithm like Deletion/Substitution/Addition (DSA) for counterfactual
causal estimation in g-computation (Petersen et al. 2006; Sinisi and van der Laan 2004).
Comparing the efficiency gains in g-computation to GenMatch would be a useful
investigation in future analysis.
We observed local area effects on utilization STI treatment and the prevalence of
syphilis; this was expected as we assumed that the new voucher services would attract
new patients and likely be of higher quality. Our study suggests that output-based aid
voucher programs, like the Uganda STI treatment program, can have multiple positive
health impacts in local populations.
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Figures and Tables
Figure III-1: Direction of financial flows under supply-side and demand-side strategies
76
Figure III-2: A directed acyclic graph of the effect of distance to contracted STI clinics on 1) knowledge of STI symptoms, 2) any STI treatment seeking, and 3) the prevalence of syphilis
OUTCOMES
1. Knowledge of STI symptoms
2. Prevalence of syphilis
3. Utilization of STI treatment in previous six months
W3. SEXUAL RISK: Any unprotected
sex previous six months
Number of symptoms previous six months
Partner disclosure of past STIs
Knowledge of STI symptoms
V1. ECONOMIC RISK: Food insecurity Household assets Education Household
expenditure
W1. DEMOGRAPHIC FACTORS: Age Sex Marital status
A. Access to treatment
Distance to clinic from
village
UNSEEN: Voucher use in sex networks
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Table III-1: Distance between village of residence and contracted clinics for patients using vouchers in Mbarara region 2006-2008
Distance to nearest clinic
distance to clinic actually visited (km)
5 10 15 20 25 30 35 40 45 50 miss. Total
5km 5,467 193 12 4 35 30 0 0 0 0 0 5,741
10km - 2,380 99 58 59 648 0 0 0 0 0 3,244
15km - - 1,218 261 68 35 0 0 0 0 0 1,582
20km - - 0 1,192 37 264 0 0 0 0 0 1,493
25km - - - - 152 11 0 0 0 0 0 163
30km - - - - - 70 0 0 0 0 0 70
35km - - - - - - 229 0 0 0 0 229
40km - - - - - - - 208 0 0 0 208
45km - - - - - - - - 103 0 0 103
50km - - - - - - - - - 596 0 596
miss. - - - - - - - - - - 1532 1,532
Total 5,467 2,573 1,329 1,515 351 1,058 229 208 103 596 1532 14,961
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Table III-2: GenMatch balance on the means of matched variable after matching*
Parish Before Matching After Matching
mean treatment 20.841 21.161
mean control 21.043 21.161
std mean diff -1.7058 0
Sex Before Matching After Matching
mean treatment 0.8045 0.4928
mean control 0.50474 0.4928
std mean diff 18.544 0
Age Before Matching After Matching
mean treatment 66.404 48.026
mean control 54.379 48.026
std mean diff 6.504 0
Number of sex partners in 6 months Before Matching After Matching
mean treatment 1.5053 1.1772
mean control 1.2046 1.1772
std mean diff 15.668 0
Number of health facilities in village Before Matching After Matching
mean treatment 0.9456 0.71326
mean control 0.86055 0.71326
std mean diff 6.053 0
Any unprotected sex in 6 months (NA=2) Before Matching After Matching
mean treatment 2.8770 1.7450
mean control 2.1197 1.7450
std mean diff 21.723 0
*GenMatch also balanced on “missingness” for sex, age, number of sex partners, and unprotected sex.
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Table III-3: Knowledge of STI symptoms, use of STI treatment and the prevalence of syphilis in the Mbarara study population 2006 and 2007
Respondent knew two or more STI symptoms
Respondent sought any STI treatment+ in past 6 months
Respondent had a reactive VDRL result
Total N 2998 1642 3201
2006 participants 716/1470 (49%) 256/948 (27%) 95/1527 (6.2%)
2007 participants 892/1528 (58%) 214/694 (31%) 60/1674 (3.6%)
Bivariate OR 1.51*** 1.27 0.54***
Bivariate 95% CI 1.30-1.75 0.99-1.61 0.38-0.76
Multivariate aOR^ 1.43*** 1.14 0.63*
Multivariate 95% CI
1.22-1.68 0.89-1.47 0.44-0.93
*p<0.05, **p<0.01, ***p<0.001
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Table III-4: Among poor respondents in four alternate definitions of poverty, knowledge of STI symptoms, use of STI treatment and the prevalence of syphilis in the Mbarara study population 2006 and 2007
Respondent knows 2+ STI symptoms
Respondent seeks any STI treatment in previous 6 months
Respondent has a reactive VDRL result
Low household monthly expenditures
Total N 1611 859 1536
2006 participants 409/810 (51%) 123/497 (25%) 32/768 (4.2%)
2007 participants 458/801 (57%) 104/362 (29%) 44/768 (5.7%)
Bivariate OR 1.39** 1.25 1.52
Bivariate 95% CI 1.13-1.72 0.88-1.77 0.91-2.45
Multivariate aOR^ 1.37** 1.22 1.57
Multivariate 95% CI 1.10-1.70 0.84-1.77 0.93-2.65
Low household asset score
Total N 1800 962 1669
2006 participants 426/931 (46%) 145/560 (26%) 56/814 (6.9%)
2007 participants 498/869 (57%) 103/402 (26%) 37/855 (4.3%)
Bivariate OR 1.67*** 1.04 0.59*
Bivariate 95% CI 1.37-2.04 0.75-1.46 0.37-0.92
Multivariate aOR^ 1.68*** 1.04 0.62*
Multivariate 95% CI 1.36-2.06 0.73-1.47 0.39-0.99
Low household food insecurity in the past 30 days
Total N 1399 776 1336
2006 participants 208/655 (48%) 106/429 (25%) 31/559 (5.6%)
2007 participants 487/794 (61%) 132/347 (38%) 15/777 (1.9%)
Bivariate OR 1.83*** 2.10*** 0.26***
Bivariate 95% CI 1.46-2.29 1.47-3.01 0.13-0.51
Multivariate aOR^ 1.82*** 2.08*** 0.23***
Multivariate 95% CI 1.44-2.30 1.40-3.08 0.11-0.46
Low education score
Total N 1464 874 1416
2006 participants 366/779 (47%) 116/531 (22%) 45/743 (6.1%)
2007 participants 368/685 (54%) 95/343 (28%) 12/673 (1.8%)
Bivariate OR 1.33* 1.62** 0.24***
Bivariate 95% CI 1.07-1.66 1.14-2.32 0.12-0.48
Multivariate aOR^ 1.34* 1.70** 0.25***
Multivariate 95% CI 1.07-1.68 1.18-2.47 0.12-0.49
*p<0.05, **p<0.01, ***p<0.001
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Table III-5: Prevalence of syphilis by distance to a contracted clinic in the Mbarara study population
VDRL results 2006 survey 2007 survey Percent change
2 tail T-test
Near to clinic (<11km)
58/789 (7.3%)95% CI=5.5-9.2%
27/931 (2.9%)95% CI=1.8-3.9%
57% p<0.001
Far from clinic (≥11km)
37/738 (5.0%) 95% CI=3.4-6.6%
33/743 (4.4%)95% CI=2.9-5.9%
20% p=0.604
STI treatment utilizationNear to clinic (<11km)
143/472 (30%)95% CI=26-35%
127/325(39%)95% CI=33-44%
30% p=0.011
Far from clinic (≥11km)
113/476 (24%)95% CI =20-28%
87/369 (24%) 95% CI=19-28%
0% p<0.001
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Table III-6: Distributions of matching variables in the unmatched and matched datasetsPopulation Unmatched (n=5395) GenMatch (n= 3438)
Parishes 41 41
Sex Women =2,830 (53%)Men =2,443 (45%)Missing =122 (2%)
Women =2,288 (67%)Men =794 (23%)Missing =356 (10%)
Age Mean =31 (SD=8.9)Median =29 (IQR 24-37)Missing =166 (3%)
Mean =29 (SD=6.9)Median =28 (IQR 25-32)Missing =364 (11%)
Sex partners previous six months
0 =867 (16%)1 =3,439 (64%)2 =514 (10%)3 =387 (7%)Missing =188 (4%)
0 =156 (5%)1 =2,786 (81%)2 =88 (3%)3 =52 (2%)Missing =356 (10%)
Health facilities in respondent’s village
0 =3,182 (59%)1 =884 (16%)2 =652 (12%)3 =202 (4%)4 =291 (5%)5 =184 (3%)Missing =0
0 =2,248 (65%)1 =622 (18%)2 =212 (6%)3 =92 (3%)4 =134 (4%)5 =130 (4%)Missing =0
Unprotected sex 0 =1,425 (26%)1 =3,691 (68%)Missing =279 (5%)
0 =244 (7%)1 =2,834 (82%)Missing =360 (11%)
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Chapter IV: Social capital and health - testing the reliability and validity of a social capital instrument in southwestern Uganda using item response theory
Abstract
Rationale: There is emerging evidence that social capital, defined as the cognitive
and structural features of social organization such as networks, norms, and social trust,
has a significant beneficial association with general well-being, mental health, healthcare
utilization, and infectious and chronic disease outcomes in high-income countries.
However, limited work has been done on social capital in low-income countries. A better
understanding of how social capital affects health and health behaviors in developing
countries could have important implications for improving health care interventions.
Objectives: The primary objective of this survey is to develop two measures of
social capital (cognitive and structural) based on a population survey in southwestern
Uganda. Additional objectives are to test the reliability and validity of the social capital
measures and to determine whether social capital is related to important health behaviors
or health outcomes, particularly sexually transmitted infections.
Methods: An 18-item instrument measured cognitive and structural social capital
in two cross sectional population surveys conducted in 2006 and 2007. Using item
response models, 15 items that measured cognitive social capital (CSC) were tested for
item fit, reliability and validity. Three items that measured structural social capital (SSC)
were tested for reliability and validity.
Results: The 15 CSC items fit non-overlapping response curves and the mean
square fit statistic was normal. CSC items’ Cronbach’s α=0.81 and SSC items
Cronbach’s α=0.96. Items analysis found that in 14 of 15 CSC items, respondents
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exhibited incrementally greater cognitive social capital as response categories increased.
The social capital measures were examined for their relationships with health behaviors
and health outcomes in multivariable logistic models. There were significant associations
between increased cognitive social capital and decreased odds of male aggression,
decreased odds of having ≥2 sex partners, increased odds of unprotected sex, increased
odds of HIV+ disclosure to a partner, and increased odds of disclosure of a genital sore to
a partner. No multivariable associations were found between structural social capital and
selected health-related behaviors, including aggression, number of sex partners,
unprotected sex, and HIV+ disclosure.
Conclusions: Social capital in southwestern Uganda appears to be nested within
respondents’ psyches (cognitive social capital) rather than present as a community level
construct (structural social capital). Although the evidence is preliminary and additional
research is needed, the findings suggest that programs to improve social capital should
consider giving preference to cognitive interventions that build trust over interventions
that shape social structure.
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Background
Social capital is defined as the cognitive and structural features of social
organization, such as networks, norms, and social trust that make cooperation possible
within and between groups. Two schools of thought have developed from this general
definition. Putnam views social capital as an inherently relational property of the
‘collective’ whereas Bourdieu juxtaposes social capital against other forms of capital
(e.g. economic and human) and suggests it is something that can be conceptualized at the
individual level and accessed through their agency (Bourdieu 1986; Putnam 1993, 1995).
To put Putnam’s concept in the simplest terms, social capital is a resource that groups,
not individuals, can access. Acknowledging that social capital is inherently relational,
Bourdieu’s approach is more relevant to a discussion of individuals accessing resources
from social networks that can then be translated in material capital.
Social capital has both cognitive and contextual or structural dimensions
(Harpham, Grant and Thomas 2002; Islam et al. 2006; Portes 1998). The cognitive
component includes norms, values, attitudes and beliefs and refers to individuals’
perceptions of others’ trustworthiness, reciprocity, mutual obligation, and social
interaction (Bourdieu 1986; Islam et al. 2006; Narayan and Cassidy 2001; Putnam 1993,
1995).
The structural component of social capital refers to the observable aspects of
social organizations, such as savings groups, parent-teacher associations, and local
governance committees, which foster social exchange and reify trust. Structural social
capital is often manifest in the density and intensity of associational links and social
activity, the presence of social institutions and organizations or alternatively patterns of
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engagement within civil society (Grootaert and van Bastelaer 2002; Harpham et al. 2002;
Islam et al. 2006; Narayan and Cassidy 2001).
Structural social capital can be categorized as horizontal, reflecting ties that exist
among social peers, or vertical, reflecting ties that exist between distinct socio-economic
classes or patron-client relationships with differences in power and resource bases
(Cullen 2000). Peer linkages, including intra-group trust and neighborliness, cement
group identities and foster collective action. Vertical ties coordinate action across
horizontal group identities. Vertical social capital in healthcare, for instance, is manifest
in the doctor-patient relationship and more broadly in a patient’s trust of health systems
(Gilson 2003). In some studies, “vertical social capital” is labeled “linking social capital”
and refers to communities’ ability to leverage resources, ideas, and information from
formal institutions, such as the health sector or village leadership structures (Szreter and
Woolcock 2004).
Horizontal structural social capital reflects ties among social or economic equals,
identified based on one or more common characteristics such as a profession, trade,
income level, social standing, or institutional membership. These ties between social
equals can be found within and between groups – leading to social capital ties that bind or
bridge groups. Bonding social capital refers to relationships within homogeneous groups,
whereas bridging social capital refers to ties that link groups who are unlike each other
(e.g. different ethnicity, educational level, occupation, or other characteristics) (Putnam
1995). In other words, different dimensions of social capital operate on different levels:
within groups (i.e. bonding), across groups (i.e. bridging or horizontal) and through ties
to public institutions or formal associations (i.e. linking or vertical) (Saegert, Thompson
87
and Warren 2001; Szreter and Woolcock 2004). Present in different quantities in a
population, the relative concentrations of these three forms of social capital can lead to
strikingly different outcomes (Colletta and Cullen 2000).
Social capital shares elements with social control. Strong in-group cohesion
(bonding social capital) and weak between-group connections (bridging social capital)
can result in horrific social pathologies such as the Chinese Cultural Revolution and the
Rwanda and Cambodia genocides (Colletta and Cullen 2000). Less dramatically, strong
in-group patriarchal cohesion was the motivating power behind ‘witch killings’ in local
Ugandan communities of the late 1980s, as patriarchal traditions were reasserted in
response to the social disarray from a long-running civil war and an emergent HIV
epidemic (Allen and Heald 2004).
A sharp distinction can be made between social capital and social control. Social
capital may contain elements of social compulsion or control, but it also provides a safety
valve on social pressures by bridging across horizontal and vertically aligned groups.
Although debate continues about the nuances of the nature of social capital, an
extensive and rapidly growing literature has found social capital to be associated with
health behaviors and outcomes. Researchers have found that social capital is generally
associated with greater self-reported health (Kawachi, Kennedy and Glass 1999;
Subramanian, Kim and Kawachi 2002).
Berkman and Glass summarized these relationships through a theoretical
construct of the Social Network Theory. They identified multiple psychosocial
mechanisms through which social capital might influence health outcomes (Berkman and
Glass 2000; Bury and Gabe 2004). The major psychosocial mechanisms are:
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Social support: The structure of network ties influences health through the various
types of social support that network members can access. Emotional support
relates to the sharing of love, caring, and sympathy within a network.
Instrumental support refers to assistance with tangible needs – aid in kind, money
or labor. Appraisal support suggests help in decision-making, feedback, or in
deciding which course of action to take. Knowledge transfer within networks or
the provision of informational support is also presented as a form of social
support (Berkman and Glass 2000).
Social influence: “People obtain normative guidance by comparing their attitudes
with those of a reference group of similar others” (Berkman and Glass 2000)
Social engagement: This includes the opportunities provided by social networks
for companionship and sociability, which provide coherence and belonging, and
help to define and reinforce social roles and identity (Berkman and Glass 2000).
Person-to-person contact: Networks also influence disease by restricting or
promoting exposure to infectious disease agents. In this context, disease
transmission is not random but rather based on shared social networks (HIV being
one important example) (Berkman and Glass 2000).
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Access to material resource: Social networks and the ties between actors create
social and economic opportunities for improving access to material resources,
either directly or indirectly (Berkman and Glass 2000).
The relative size and efficiency of these mechanisms affect the rate of diffusion of
health information and the likelihood that health-related behaviors are adopted –
behaviors such as seeking healthcare at modern facilities and completing a full course of
tuberculosis therapy. Cohesive communities may be better advocates for improving the
conditions of all through collective support and stigma reduction. In addition, strong ties
within communities are a source of self-esteem and mutual respect, even among
marginalized groups, such as those living with HIV (Pronyk 2009).
To summarize, social capital is defined as the cognitive and structural features of
social organization such as networks, norms, and social trust. Structural features include
close bonding ties within a proximate group and more distal bridging links across
multiple groups and networks. There is emerging evidence that social capital has a
significant, and generally positive, association with well-being, mental health, healthcare
utilization, and infectious and chronic diseases in high-income countries. Social capital
measures such as civic engagement, organizational membership, and trust in others have
been associated with lower all-cause mortality, lower rates of self-reported poor health,
better mental health status, and decreased violence (Kawachi, Kim and Subramanian
2004; Putnam 1995). In a review of 42 studies from OECD countries in North America,
Europe and Australia, social capital has been most frequently measured in a combination
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of cognitive (mainly trust and reciprocity) and structural (participation and civic
engagement) dimensions (Islam et al. 2006).
Epidemiologists find social capital conceptually useful because it facilitates the
measurement of complex socio-economic processes in coherent ways that in turn help to
explain variations in the health status of individuals and communities (Kawachi et al.
1997). Theories of social capital come to public health by way of sociology and political
science and have been incorporated into social epidemiology only recently. Social capital
has increasingly been seen as a predictor of observed differences in population health
outcomes for individuals, communities, countries and even regions (Kawachi 1999;
McKenzie, Whitley and Weich 2002; Pilkington 2002).
Social capital in Africa
Little is currently known about whether social capital is a valid and meaningful
construct in the African region. Additionally, little research has explored how social
capital constructs may relate to health in resource scarce African settings. What is known
about social capital in Africa is based largely on qualitative research. An ethnographic
study in Mbarara, Uganda; Dar es Salaam, Tanzania; and Jos, Nigeria found that social
capital was a locally meaningful concept and had implications for health maintenance in
HIV-infected patient populations (Ware et al. 2009). The researchers found that among
individuals on HIV therapy, those with greater social networks could call on more
resources to support their ongoing HIV treatment and felt more socially obligated to take
drugs on schedule. The study generalized that with additional social capital, there were
greater resources; however, there were behavioral constraints placed on individuals with
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greater access to social capital. Whether these behavioral constraints were a health deficit
or asset depended on the behavior.
Another ethnographic study in Malawi found that the accrual of both greater
resources and greater constraints accruing to individuals with higher levels of social
capital was very important in low-income societies, in which informal ties were often the
exclusive source of support (Swidler and Watkins, 2007).
The World Bank’s Social Capital Initiative (SCI) conducted a thorough review of
measures in various case studies of social capital in low-income countries and concluded
that social capital could be measured by three types of proxy indicators: membership in
local associations and networks, trust and adherence to norms, and levels of collective
action (Grootaert and van Bastelaer 2002).
One survey conducted by the World Bank in Ghana and Uganda measured social
capital and how it varied by group structure and network size, subjective well-being (not
health status), political engagement, sociability, community activities, violence and
crime, and communications (Narayan and Cassidy 2001). The researchers found that
social capital was multi-dimensional and that certain dimensions were consistently salient
at different levels of group aggregation in both countries. The dimensions were: network
characteristics and membership frequency, generalized norms of reciprocity, togetherness
(community solidarity), everyday sociability, neighborhood connections, volunteerism
and trust (Narayan and Cassidy 2001). No measures of health status were collected.
The ability of social capital to both constrain individual choice and reward
individuals for group-normative behavior can help explain the wide range of HIV
prevalence and incidence in different social groups across southern Africa. The evidence
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suggests that social capital is neither good nor bad; it simply acts as a mechanism to
realize group norms.
In cross sectional studies from Zimbabwe and South Africa, membership in some
social groups was associated with low HIV prevalence while membership in other social
groups was associated with increases in the risk of HIV infection (Campbell and
MacPhail 2002; Gregson et al. 2004). In the South Africa study, Campbell and colleagues
measured civic participation, as one aspect of social capital, to understand community
influences on HIV infection. They found that participation in organizations like churches,
sports clubs, and youth groups was protective against HIV infection, while membership in
social groups with high levels of social drinking was associated with an increased risk of
HIV infection (Campbell, Williams and Gilgen 2002).
Pronyk et al. (2006; 2008b) conducted a randomized cluster intervention in South
Africa to test whether cognitive and structural social capital were associated with a lower
risk of acquiring HIV infection. Pronyk et al. distinguished cognitive social capital (CSC)
from structural social capital (SSC), defining CSC as respondents’ established norms and
psychological trust, and SSC as social membership and participation in social institutions.
They found that the male respondents in households with greater levels of CSC had a
lower prevalence of HIV infection and higher levels of reported condom use. Among
female respondents, similar relationships with CSC were observed. However, while
greater SSC was associated with protective psychosocial attributes and risk behaviors, it
was also associated with a higher prevalence of HIV infection among female respondents
(Pronyk et al. 2008b).
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In summary, the social capital literature is limited in the African context. More
research is needed to determine the extent to which social capital is a valid concept in
African communities and whether it is related to health outcomes in Africa. The limited
evidence to date suggests that not all social capital promotes health, and that identifying
and selectively encouraging the creation of social capital that maintains health is critical
to informing health interventions and health promotion efforts.
New Contribution
This study aspires to contribute to the understanding of social capital in Africa by
measuring social capital in a survey of individuals in southwestern Uganda. There are
three main objectives to this research. The first objective is to create two indexed
measures of social capital using data from the survey, a cognitive social capital measure
and a structural social capital measure. Next, it is important to evaluate the indices in
terms of their reliability and validity. The third objective is to examine how the social
capital indices are associated with health behaviors and health outcomes, particularly
those relevant to STIs.
Methods
Description of the item set
Twenty-six questions related to social capital were asked in the 2006 survey and
28 questions were asked in the 2007 survey. For the five category questions, respondents
were shown an image of five water glasses ordered from “completely full” to “empty”
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before answering these questions to demonstrate the concept of “degrees of agreement”
for a population potentially unaccustomed to Likert scale responses.
For this study, cognitive social capital was developed largely as a function of an
individual’s trust in other persons in their community. Fifteen questions common to both
surveys assessed cognitive social capital by asking about respondents’ trust of others and
whether they view themselves as a community member. Three questions assessed
respondents’ structural social capital as measured by engagement in community groups.
The first 14 questions (“Trust different groups”) common to 2006 and in 2007
asked respondents to rate their trust of professionals and institutions in their community
on a five category Likert scale. These questions were drawn from the instrument
developed by a World Bank project led by Narayan et al. in Ghana and Uganda (2001).
There were seven trust questions unique to the 2006 survey and nine trust
questions unique to the 2007 survey. Six of the questions asked only in 2006 concerned
the community’s shared values, as the respondent views them. These questions were also
drawn from Narayan and Cassidy (2001). The seventh question asked only in 2006
concerned whether the community’s level of trust varies from other communities in the
region. Four of the nine unique questions in the 2007 survey explored the community’s
shared values in slightly different ways from questions asked in the 2006 survey. The
remaining five questions from 2007 (“Informal Social Control”) asked about the level of
collective response to specific non-normative behaviors in the community. The final
section was drawn from the validated instrument in Sampson et al study of social capital
in Chicago (1997) and then modified in consultation with an anthropologist at Mbarara
University of Science and Technology in Uganda.
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Analysis and model building methods
Social epidemiology deals in the social distribution and social determinants of
health (Berkman and Kawachi 2000; Krieger 2001). When measuring latent
characteristics, it is common to use composite scores as an observable proxy. For
instance, a study of violence and neighborhood cohesion in Chicago scored local social
cohesion and the community’s ability to respond to threats like the loss of social services
and the perceived threat of youth on the street. The composite score became a measure of
the latent “collective efficacy” of the community (Sampson et al. 1997).
“Collective efficacy” is a group’s belief in its ability to act together and is
conceptualized as the combination of group cohesion and agency (the ability of a group
to come together and then work together). The concept is not directly observable; it is
hidden or latent, yet it was possible for Sampson and colleagues to measure collective
efficacy using ten questions about example behaviors with a theoretical foundation in
collective efficacy. In Sampson’s study the “collective efficacy” score helped to explain
why some Chicago neighborhoods had a lower incidence of violence.
When designing an instrument to measure a latent variable, there are two aspects
to consider: instrument reliability and validity. Reliability is the repeatability or
variability in measurement of a latent variable. A reliable instrument will consistently
produce similar results, with little variance, following repeated measurement of
individuals. Validity is the ability of an instrument to distinguish ‘truth’ from
measurement noise.
Two schools of measurement theory, classical test theory and item response
modeling, offer methods for estimating instrument reliability. Under classical test theory,
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psychometricans assume an instrument’s composite score (X) for any characteristic (e.g.
social cohesion, math skill, or socio-economic status) is the sum of the respondent’s
‘true’ value (T) plus random error (E), irrespective of respondent or item characteristics
(Wilson, Allen and Li 2006a).
X= T+Ε
In this method, each respondent’s score on the instrument is assumed to be drawn
from a probability distribution of responses for the true score. By definition, the
expectation of this probability distribution is the true value for the population sample.
To determine instrument reliability, the ratio of the mean true score variance to
the mean observed score variance is calculated in the sample population. It is impossible
under this framework to measure the reliability of a single respondent’s score on the
instrument, as it is assumed the respondent’s score (T) is true with zero variance, so a
ratio of the respondent’s true score variance to the observed score variance (i.e.
respondent’s reliability) is also zero. Researchers have proposed solutions, including the
use of repeated sampling of the respondent, or parallel testing, but the fundamental
problem remains in instances when respondents are sampled only once (Wilson et al.
2006a).
One weakness in classical test theory is that the relative contribution of a survey
instrument item’s difficulty and the respondent’s ability cannot be separated, which could
lead a researcher to incorrectly assume the result of the respondent’s math score or social
capital value is ‘true’, even when the score may be the result of the instrument design
(Wilson et al. 2006a).
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Item response models are built on a less stringent assumption that a respondent’s
answer to any question is a probability based on two unknowns: the respondent’s inherent
ability to agree with the instrument’s items (commonly designated as θ) and each item’s
inherent difficulty (commonly denoted as δ), regardless of the population of respondents
surveyed. For example, a respondent may believe an item that asks about ‘willingness to
lend’ is more difficult to agree with than an item that asks about ‘trust of community
leaders’. Item difficulty is expressed on the same scale as respondent ability. In the IRM
literature, both item difficulty and respondent ability are presented as logarithmic
transformations of odds of an event (logits). There are three potential relationships
between respondent ability and item difficulty (Wilson, Allen and Li 2006b):
1. Θ = δ. When respondent ability and item difficulty are the same, the probability of
response for any two choices (yes/no, 1 or 2, 3 or 4) is 0.5. For instance, if we are
measuring respondent’s trust of others as a yes/no option and the item asks about
trust of lending to others, a moderately trusting respondent has a 50% probability
of choosing “yes” (Wilson et al. 2006b).
2. Θ > δ. When respondent ability (in our example, a high level of trust) exceeds
item difficulty (asking about trust of lending to others), the probability of
responding “yes” is greater than 50% (Wilson et al. 2006b).
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3. Θ < δ. When respondent ability (in our example, a low level of trust) is lower than
item difficulty (asking about trust of lending to others), the probability of
responding “yes” is less than 50% (Wilson et al. 2006b).
The basic relationship between respondent ability (θ) and item difficulty (δi) is
expressed in the following model:
Pr(Xi =1| θ, δi) = f (θ - δi)
The general function can be arranged as a logistic Rasch model that forms the
basis for a family of item response models (Wilson et al. 2006a). Modeling both
respondents and the items necessitates a “multi-level” approach, as such models consider
the probability of any response a function of the survey item difficulty and a respondent’s
ability to answer the item. Because reliability and validity testing can be conducted at the
level of both the respondent and the item, item-response models have the ability to
identify item sets with optimal fit that reduce measurement error and improve the
potential for generalizability of findings (Wilson et al. 2006b).
Item fit
Item Response Function
The probability of response for all categories can be plotted on a curve commonly
called an item response function (IRF). For clarity’s sake, the item – “trust of
pharmacies” will be used as an example. In Figure IV-1, the locations for all respondents
on the continuum of the latent construct are plotted on the horizontal axis (imagine, for
example, all respondents arrayed along a continuum from low to high trust). The vertical
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axis displays the probability of answering any level for a given item (Wilson et al.
2006b).
This type of figure is customarily called an item response function, IRF, (other
common terms are item characteristic curve and cumulative probability curve). The IRF
depends on the respondents’ level of latent characteristic (θ) and item difficulty (δ). In
this example, respondents’ trust of pharmacies is plotted along four response boundaries:
the probability of responding either 0 or 1 (from “very unlikely” to “unlikely”), 1 or 2
(“unlikely” to “indifferent”), 2 or 3 (“indifferent” to “likely”), and 3 or 4 (“likely” to
“very likely”).
Figure IV-1 also provides the logit locations or “thresholds” where the item
response curves indicate a 50% probability of responding on each pair of responses. In
the example plot, the logit location of 50% probability response for 0 or 1 is -1.23 (see
list of 4 threshold values in lower left corner of Figure IV-1). The exponential of the log-
odds (-1.23) is an odds of 0.29. The odds of 0.29 (a ratio of the probability of respondent
ability over one minus the probability of respondent ability) indicates that the
probabilities are not equal and there is a great deal of error in the estimated location. In
contrast, the 50% probability of choosing either 2 or 3 (“indifferent” to “likely” trust
local pharmacies) is located at -0.13 logits (an odds of 0.87) and is a more reliable
estimated location. Items with “better fit” will have non-overlapping item response
functions with 50% probability thresholds located closer to 0 logits.
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Mean Square Fit Statistic
Item fit can also be determined from the residual between the estimated item
parameters (the four smooth curves in Figure IV-1) and the observed curves (indicated by
dashed lines and dots).
For any respondent, the difference between the observed and the expected
response on any item is estimated by the following:
Yin = Xin - Ein
Where Yin, Xin, and Ein are the residual, observed response, and expected response for
person n responding to item i.
Each respondent’s expected response (Ein) is characterized by the following
probability:
Where Ki is the number of response categories for the item (a measure of difficulty), δ is
a vector of the parameters for item i, and k is the probability of observing a response in
each category. For example, on a 5 point Likert scale asking a respondent’s trust of the
police, k is the probability of observing a response in each category given two unknowns:
the respondent’s inherent trust of others and the difficulty of the specific question about
police.
The measurement model is the researcher’s best estimate of the theoretical
construct’s functional form. To determine how well the data fit the measurement model,
it is possible to calculate the ratio of observed mean residuals and the expected mean
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residuals for any item i. This is called the mean square (MS) fit statistic, as described by
Wilson (2004).
For any respondent, the expected squared residual for any item i is the summation
of the squared difference between the observed response (k) and the expected response
(Ein).
The average of these expected squared residuals (expected variance) across all
respondents is:
The mean of the squares of the observed residuals (observed variance) across all
respondents is:
The mean square fit statistic, or simply the weighted mean square, is the ratio of
observed variance to expected variance:
It is generally agreed that a ‘good’ weighted mean square value is bounded at 0.75
and 1.33 (Adams and Khoo 1996) cited in (Wilson 2004). Wilson (2004) notes that
another fit index, the weighted t, can attempt to transform the weighted mean square into
a normal distribution and be used to apply Student’s t test of normality. The test,
however, is likely to be significant for many items with a large sample size. Combining
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the t-test and weighted mean square fit statistic to determine item fit is a more cautious
approach than either test alone (Wilson 2004).
If there is evidence of misfit, a decision must be made whether to remove the
item, collapse response categories, or try fitting a different model. If the instrument
testing is early enough to allow another round of interviews, it should be considered.
In summary, item fit is an important iterative diagnostic process in which item
response functions are plotted and the mean square fit statistic is used to determine
whether additional work is needed on item design. Careful attention to item fit is a useful
preliminary process before measuring the instrument for reliability and validity.
Reliability
Reliability refers to the consistency of measurements that describe a latent
characteristic of a population through a survey instrument. Item response theory
commonly uses two indicators to determine internal reliability: the standard error of
measurement and a ratio of variances similar to Cronbach’s alpha.
Standard error of measurement (SEM)
Item response theory and classical test theory alike are interested in consistently
scoring respondents’ latent characteristics. The portion of the measurement that is
inconsistent is the residual error. This measurement error is a function of the each
respondent’s ability to answer the items, the conditions of the interviews, the instrument
design, and the interviewer’s ability to properly score responses.
The standard error of measurement (SEM) is a function of the respondent location
on the construct continuum and the standard deviation of the raw scores. The more the
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respondent’s ability, θ, approaches equality with an item’s difficulty, δ, the more likely
his or her response reflects the unobserved truth. Respondents whose ability to answer
does not match the items’ difficulty will be placed on the construct continuum with
greater error than well matched respondents and items.
When item or full test scores are plotted against the standard error, a parabola is
formed. Figure IV-2 displays the information curve for the item ‘trust of pharmacies’.
The information curve is the reciprocal of the square of the SEM (Wilson 2004).
Reliability in measurement is usually assessed in a single instrument because one
instrument is what the researcher used in his or her study. However, it is possible to
consider measuring consistency between alternate forms of the instrument or repeated
tests of the same instrument. In the Uganda study we did not run these alternate forms of
reliability tests.
Ratio of variances (Cronbach’s alpha)
In classical test theory, the internal consistency or reliability coefficient
commonly used for polytomous data is Cronbach’s α (Cronbach 1990). In item response
theory, an equivalent coefficient is calculated as a ratio of variances from the marginal
maximum likelihood (MML) estimation algorithm (Daniel 1999; Wilson et al. 2006a).
Among other applications, this reliability value can be used to predict the effect on
reliability of reducing or increasing the number of items using the Spearman–Brown
formula (Cronbach 1990; Wilson et al. 2006a). It can also be used to compare the
reliability of any given item set against the conventional threshold for a reliable
instrument of α= 0.65 (Wilson et al. 2006b).
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Validity
In the item response modeling literature, the term “validity” covers three areas of
instrument consistency: internal or construct validity, content validity, and external
validity (Wilson et al. 2006b). Each of these types of validity refers to a body of evidence
based on appropriate statistical tests. Evidence is gathered from these areas to determine
how valid an instrument might be. Three common validity tests to measure an
instrument’s performance – internal structure, items analysis, and external correlation –
can address internal, content validity, and external validity (Wilson et al. 2006a).
Internal validity
Testing for validity based on internal structure (construct hierarchy) can
determine whether the observed data are correlated with a priori expectations based on
theory. Spearman’s rho is a common method to test the rank order of items in each
construct. However, in the case of the social capital measures, no such a priori
expectations for item order were developed (i.e. there was no belief that trust of
healthcare providers would have a higher mean score than trust of extended family).
Content validity
The second area of validity testing is content validity. Here, the interest is in the
structure of the data. Do they have a meaningful, conceptually valid structure? One
common content validity test in item response modeling is what Wilson (2004) terms an
“item analysis”, or a check of the relationship between respondents’ mean location at
each category in each item. In this test, the order of mean locations of the respondent
groups on each item is compared against the ordered response categories. Are
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respondents with a weaker or lower ability giving low responses as expected? If the item
categories are structured properly, there should be an incremental progression as
respondents with higher ability answer more difficult categories in each item. For
instance, we assumed that only respondents with high levels of unobserved cognitive
social capital (CSC) will, on average, score highly on items asking about trust.
External validity
External validity is evidence that the construct measurements are related as
expected with respondent characteristics, behaviors or outcomes. The relationships
usually are tested by means of bivariate correlations and multivariate models of
association.
For this study, the estimated a posteriori (EAP) values for the continuous
cognitive social capital score were imported into Stata (version 10.1 for Windows,
College Station, TX). The continuous cognitive social capital (CSC) score was grouped
into low, medium, and high tertiles to aid in understanding the score. It was hard to
understand what a change of 0.5 logits of CSC would mean, whereas a move from low to
medium CSC could be qualitatively understood. A dichotomous version of CSC was also
tested but considered not as conceptually useful, as the individuals with lowest CSC were
grouped with those of moderate social capital, and this study was most interested in
identifying health-related behaviors associated with low social capital.
For the structural social capital (SSC) construct, quartiles were estimated for each
of the three variables: number of groups the respondent belonged to, the amount of
money contributed to groups in an average month, and the number of days volunteered to
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each group in an average month. The quartiles were then summed and divided by three to
create a three-category variable of low, medium and high structural social capital.
The bivariate correlations were calculated using the social capital scores and ten
variables with a theoretical link to cognitive and structural social capital: men who hit
others in the previous 12 months, women hit by others in the previous 12 months, having
more than one sexual partner in the previous six months, having any unprotected sex in
the past six months, having sought healthcare when ill in the past six months, having
sought STI treatment in the previous six months, having ever disclosed a positive
gonorrhea test result to any partner, having ever disclosed a clinically confirmed genital
sore to any partner, having ever disclosed clinically confirmed genital discharge to any
partner, and having ever disclosed HIV positive status to any partner.
For multivariable modeling, the cognitive and structural social capital scores were
used as independent variables, and with other covariates of interest, tested for association
with the health-related behaviors. The covariates of interest included individual
demographic factors, household economic status, individual reproductive health
knowledge and behaviors, and community characteristics. The community characteristics
were commercial status of parish (whether it was rural or had a trading center) and the
distance from respondents’ village to the nearest contracted OBA clinic.
All of the explanatory covariates were categorical or binary: age group, sex,
marital status, education, above median monthly household expenditure, above median
household asset score, median household food security, and presence of a trading center
in the village. Age group was constructed from three categories: 15-24, 25-34, and 35-49
years of age. Sex was a dichotomous value of 0 for female and 1 for male respondents.
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Marital status was a dichotomous variable with single, widowed, or divorced set to 0 and
married or cohabitating set to 1. Education was a dichotomous variable with completed
primary school or fewer years of education set to 0, and some secondary education and
more years of education set to 1. Three economic variables were used: monthly
household expenditure, household asset index, and household food insecurity. A
dichotomous variable was created by dividing values above from those at or below the
median value. The variable for village with trading center was a dichotomous measure of
commercial activity in each survey village. Having a concentration of shops and
businesses likely introduced many unobserved differences in the local society compared
to rural areas with less commercial activity.
Results
Respondent characteristics
Of the 5,396 respondents from the 2006 and 2007 surveys, 214 respondents were
missing all values for the 18 social capital items and were excluded from the study.
Characteristics of 5,182 respondents (2558 from 2006, 2624 from 2007) with complete
values for social capital are presented in Table IV-2 by survey year.
There was a significant difference in proportions of the sexes between survey
years. There was also a significant difference in proportions of respondents by marital
status between survey years. The two surveys also differed significantly in terms of the
distribution of educational level of the respondents.Dichotomous measures of poverty
(e.g. median monthly household expenditure, median household asset index, and median
household food insecurity) were significiantly different between survey years. As the
same villages were surveyed both years, there was no significant difference between
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survey years in whether the village was a trading center or a rural community. Because of
differences in respondent characteristics, social capital scores were estimated by year and
then data from both years were combined and analyzed as a single cross-sectional survey.
Items selected for cognitive social capital
The 2006 and 2007 surveys shared 15 variables that measured cognitive social
capital by describing respondents’ trust of others and their own perceived trustworthiness.
Two variables (trust of NGO providers and trust of mission hospitals) were missing for
more than 10 percent of respondents (Table IV-3). Although there was concern about the
large number of missing values, those two variables were included in the cognitive social
capital (CSC) index. Three variables in both surveys measured structural social capital
(SSC) by asking respondents for the number of groups they belonged to, the number of
days they volunteered to work with those groups in an average month, and the amount of
money they contributed to those groups in an average month.
For the 18 variables, missing values were imputed using multiple imputation by
chain equation (MICE) under the assumption of missingness at random (the “ice” add-in
for Stata version 10.1).
Item fit results
There were two major operational steps to create a measure of the latent construct
using item response models. The first step was to choose and evaluate a measurement
model and the second step was to test the reliability and validity of the model results.
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As discussed above, the item response models were built from general
probabilistic measurement models in the family of polytomous Rasch models. Because of
its generality, a conditional maximum likelihood model was selected (referred to as a
“partial credit model” in the educational testing literature) and then evaluated for best fit.
As noted above, the item-response function can be plotted for each item. An
example of an IRF for the item “trust of pharmacies” is given in Figure IV-1. The
respondent locations, “Latent Trait (logits)”, were plotted on the horizontal axis, and the
probability of selecting response ‘1’ to a given item is shown on the vertical axis (Wilson
et al. 2006b).
On the original questionnaire, respondents were asked, on a scale of 0 (low) to 4
(high), to rank their trust of pharmacies in their community. In the IRF plotted in Figures
IV-1 and IV-3, the probability of moving from one level of social capital to another was
compared to respondents’ ability to answer. The evidence of item fit, in contrast to
respondent fit, was developed from information contained in the IRF plots like those in
Figures IV-1 and IV-3.
Reliability results
The Wright Map plots the logit location of each item-step. Each item has five
categories ranging from low cognitive social capital (0) to high cognitive social capital
(4). The item-step is the location where the probability of answering (0 or 1), (1 or 2), (2
or 3), (3 or 4) is balanced at 50 percent for each category in each item. The Wright Map
(Figure IV-4) indicates the item-step locations for each of the 15 cognitive social capital
items in the surveys. In each item, there are four steps between the five categories.
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The coefficient alpha (similar to Cronbach’s alpha) in the 2006 item set is 0.86
and in the 2007 item set is 0.72. Although there is no hard rule for evaluating the
coefficient alpha, the closer it approaches one, the better the items “hang together” and
likely represent a single latent variable. Generally, values above 0.7 are considered
acceptable and indicative of consistency (Wilson et al. 2006b). 0.86 is a better value and
is in keeping with generally better performance in the 2007 survey data.
Validity results
In the item response modeling literature two validity tests are commonly
recommended: internal content analysis of the items and external tests of correlation
(Wilson et al. 2006a). Testing for validity based on internal structure (construct
hierarchy) can determine whether the observed data are correlated with a priori
expectations based on theory. However, in the case of the social capital measures, no
such a priori expectations for item order were developed (i.e. there was no belief that
trust of healthcare providers would have a higher mean score than trust of extended
family).
Content validity
We expect at each categorical response level (in this case 0 to 4) to see the mean
location or mean respondent “ability” to increase. In a well designed instrument, more
difficult item categories are correlated with higher respondent ability. Table IV-4
contains the average logit value for respondents at each response category (0, 1, 2, 3, and
4) for each item of the instrument.
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Fourteen of 15 cognitive social capital items exhibited an increase in mean ability
across the response categories. In effect, respondents displayed incrementally greater
trust in 14 of the 15 items as response categories increased. This is evidence that the item
design was appropriate.
External validity: multivariable associations between cognitive social capital and
structural social capital and respondent characteristics
Before running separate multivariable models for cognitive social capital and
structural social capital and health-related behaviors, a pairwise Pearson’s test was
performed on all the explanatory variables. All correlations between explanatory
variables were well below the standard threshold of 0.8, indicating that multicollinearity
was not a serious concern (IV-5). The highest correlation was between median of
household goods and median monthly household expenditures (0.28).
In evaluating the external validity of the CSC and SSC, it was hypothesized that
these indices would be associated with positive health behaviors. Included in the surveys
were questions on HIV and other sexually transmitted infections, health care utilization,
and violence/aggression. A number of positive health behaviors were associated with
having higher CSC scores, including:
Among respondents who reported being HIV positive in 2007 (n=113, see Table
IV-6), those who had the highest CSC score had a significantly higher odds of
disclosing their HIV status to a partner (aOR=1.98, 95% CI 1.02-3.51),
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Among respondents who reported ever having a genital sore (n=107, Table IV-6),
those who had the highest CSC score had a significantly higher odds of telling a
partner about their genital sore (aOR=2.20, 95% CI=1.00-4.83),
Among respondents who reported their number of sex partners in the previous six
months (n=5142, Table IV-6), those with the highest CSC score had a lower odds
of having two or more partners (aOR=0.80 95% CI=0.69-0.93),
Among men who reported hitting others at least once since the age of 15 (n=769,
Table IV-6), men with the highest CSC had the lowest odds of having hit others in
the past year (aOR=0.65 95%, CI=0.53-0.81).
Among women who reported being hit by others at least once since the age of 15
(n=942) CSC was not associated with being hit in the previous 12 months;
however, the direction of effect was protective (aOR= 0.85, 95% CI=0.70-1.03).
One health behavior, seeking treatment for a STI symptom, had a statistically
significant result that was in the opposite direction of the original hypothesis. Among
respondents who reported having an STI symptom in the previous six months (n=769,
Table IV-6), those with highest CSC scores had a significantly lower odds of seeking STI
treatment (aOR=0.80, 95% CI=0.65-0.97). For this result, poverty acted an effect
modifier; among the poor, the relationship between high CSC and low odds of STI
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treatment was more pronounced (aOR=0.75, 95% CI=0.58-0.97). While among the not-
poor, high CSC and odds of STI treatment were not significantly associated.
The following health-related behaviors had no empirical relationship with CSC:
having unprotected sex in the previous six months; seeking any type of healthcare if sick
in previous six months; ever disclosing a positive test for syphilis; ever disclosing a
positive test for gonorrhea; and ever disclosing clinically confirmed genital discharge. No
health-related behaviors were associated with the SSC score (Table IV-7).
Discussion
Study limitations
There were several limitations to this study. The cognitive and structural social
capital scores were developed as measurements of latent social capital that, based on a
review of the literature, we posited was causally antecedent to observed health-related
behaviors. A strong case for causality could not be made, given the lack of an observed
temporal order between the proposed causes and effects. Longitudinal observation and in-
depth qualitative interviews are needed to establish whether the statistical associations
observed between variables hypothesized to be inter-related in our theoretical model
reflect the direction of causality implied by the model.
Data collection and entry was another study limitation. Data entry in 2006 was the
first time that the study coordinators and the lead investigator implemented the design.
Extensive data re-entry and cleaning were conducted, but there may have been error in
the data collection. The consistently higher reliability tests, including for example the
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coefficient alpha in 2007 (α=0.86) versus 2006 (α=0.72), could suggest that the 2007 data
may have been collected and entered in a more consistent manner than the 2006 data.
Another limitation was the surveyed population’s understanding of sexually
transmitted infections. In 1999, a study in the region noted a general misunderstanding of
STIs; study participants then named tuberculosis, leprosy, and skin fungal infections as
being sexually transmitted (Nuwaha et al. 1999). Anecdotally in and around Mbarara,
syphilis is a term used for a wide range of health complaints from rashes to backache.
Although interviewers were trained to use standard terms for STIs, trust, and other key
concepts in the survey, it is possible that the respondents failed to understand the medical
meaning of specific concepts like genital sores or syphilis.
Study significance
Social capital is little studied in the context of health outcomes in sub-Saharan
Africa. This study was one of the first to test, in a population-based sample, that cognitive
and structural social capital are: (1) valid constructs in rural and semi-urban Ugandan
settings and (2) determinants of several health-related behaviors, including partner-
disclosure of HIV status. Little prior work has been done in sub-Saharan Africa to
measure relationships between social capital and health-related behaviors.
Based on tests from item response theory, the 15 items on cognitive social capital
fit together and have strong reliability and validity. Interestingly, in the 2006 survey,
“trust of NGOs”, and in the 2007 survey, “feel like a full member of the community”, did
not demonstrate internal validity as the mean ability scores by category did not order
themselves by the category value; that is, for several higher categories the mean ability
scores were lower than the mean ability scores in lower categories. However, both
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problematic items were specific to a single survey year and the other measures of
reliability and validity indicated reasonably good performance, i.e. the coefficient
reliability was above 0.72 in both years.
Other studies on social capital and health have been validated using methods
drawn from classical test theory (Narayan and Cassidy 2001). As Wilson (2006a) noted,
there is a great deal of concordance in classical test theory and item response theory.
However, item response theory offers greater flexibility and utility in measuring latent
variables (Wilson et al. 2006a). One of the more helpful innovations in item response
modeling is the development of a common scale for respondents and items as
demonstrated on the Wright Map (Wilson et al. 2006a). Qualitative assessment of the
instrument is possible and allows for assessment of possible gaps in instrument coverage
of the latent variable space. As Wilson summarized “the item response modeling
approach can do all [his italics] that you can do in the classical approach when it comes
to assessing items and instruments, and it can do a great deal ‘more’.” (Wilson et al.
2006a)
One strength of this analysis was the careful attention given to the design and
validation of measures of cognitive and structural dimensions of social capital, including
their empirical relationship with health-related behaviors and the statistical effects of
potential confounders. Using the item response modeling approach, this study suggests
that in southwestern Uganda, CSC has an important relationship with several health-
related behaviors.
Higher CSC was associated with a higher odds of disclosing his or her HIV status
to a partner and higher odds of telling a partner about genital sores. Disclosure of HIV
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status and potential STIs can be a tool in prevention. Disclosure to sexual partners
accompanied by the choice to reduce risk by using condoms and reducing partners can be
effective in limiting STI infections, including HIV. Helen Epstein argued in her book,
Invisible Cure, that disclosure and frank discussion of HIV, mediated by a socially
cohesive population, was responsible for the dramatic reduction in HIV prevalence in
Uganda in the early 1990s (Epstein 2007).
Additionally, the current study found that respondents with two or more partners
are more likely to have lower CSC. Sexual networks with a high number of concurrent
partnerships is a well established risk factor for high incident HIV transmission.
Our cross sectional findings support the general thesis that greater CSC is
associated with lower violence. In our study, we found that among male respondents who
reported hitting others since 15 years of age, the odds of having hit someone in the
previous 12 months was lower among men with a higher cognitive social capital score.
However, unlike the findings of Pronyk and colleagues, our study found that among
women who reported being hit by others since 15 years of age, there was no association
between being hit in the previous 12 months and CSC. The comparability of our findings
to those of Pronyk et al. is limited. First, their cohort did not include men. Also they
measured incident intimate partner violence prospectively; we measured the prevalence
of a specific form of violence, physical hitting, in the previous 12 months, without
restricting it to intimate partnerships.
One surprising finding in our study was that respondents with high CSC and who
reported having one or more STIs were less likely to use STI treatment. Further research
should explore this question, to determine if this finding holds up in other surveys and
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contexts. Focus groups or open ended interviews could provide new insights into the
nature of trust and STI treatment. At this point, we only can surmise that there may have
been stigma associated with reporting or using STI treatment and that individuals with
high levels of CSC were more sensitive to community opinion. Alternatively, individuals
with high levels of CSC may have trusted their partners more and disregarded signs of
infection. Future research should explore the relationship between CSC and various types
of healthcare utilization.
Another surprising finding was that SSC was not significantly associated with any
health behavior. Previous research had suggested that structural interventions can
produce significant reductions in domestic partner and intimate partner violence (Merson,
Dayton and O’Reilly 2000; Sumartojo et al. 2000; Waldo and Coates 2000). Pronyk and
colleagues observed in their South African study that, while higher levels of SSC were
associated with protective psychosocial attributes and risk behavior, SSC was also
associated with higher rates of HIV infection (Pronyk et al. 2008b). Although the SSC
instrument in our study was reliable, the lack of any empirical relationship to external
health behaviors indicates a need to conduct new qualitative research on the meaning and
importance of group membership and consider new items for the instrument.
An important implication of our results is the need to explore the causal
relationship between social capital and health behaviors and health outcomes, through
further study with observation over time in longitudinal studies. If future research
indicates that higher CSC yields safer health behaviors, there are clear policy
implications. Those designing health interventions in Africa may want to consider adding
elements to their interventions that enhance social capital to individuals. How to build
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CSC effectively is not clear. One approach that was piloted in South Africa involved
participation in HIV education skills classes and a microfinance program (Pronyk et al.
2008a). Microfinance programs are appealing because, at their core, they are about
enabling exchange of funds, which requires trust and, as Pronyk demonstrated,
microfinance can foster social capital.
Another approach to building social capital in the context of STI treatment
services is social marketing. Previous studies have shown that trust plays a role in the
usefulness of information provided in social marketing (Thiede 2005). Simply put, health
information must be trusted to be effective. Building trust and social capital can improve
the effectiveness of social marketing, which in turn could increase utilization of STI
treatment services.
Future work needs to focus on exploring the relationship between CSC and STI
treatment seeking. If future studies confirm the finding that those with high CSC are less
likely to seek treatment, there will be a need to consider this a complicating factor in the
delivery of STI services.
Of course, future research should shed light on the appropriate courses that social
marketing campaigns can and should take. What the present study has shown, however, is
that CSC can be adequately measured within a population and that this measure is
associated with important health behaviors.
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Tables and Figures
Table IV-1: Social capital items
PART 1: TRUST DIFFERENT GROUPS
These questions are only to learn more about the community, not individuals’ opinions or gossip. Questions are about trust and we are interested to know how you see trust in the community. On a scale from 1 to 5, where 1 means ‘to a very small extent’ and 5 means ‘to a very large extent’, how much do you feel you can trust the people in each of the following groups? Please show the respondent the drawing of glasses of water to communicate the idea of a scale from 1 to 5, where a glass is drawn with no water (1), a little water (2), half full of water (3), mostly full (4), and completely full of water (5).
2006 2007
1. People in your tribe?
2. People of your religion?
3. People in other tribes?
4. People of other religions?
5. People in your village/neighborhood?
6. The business owners and traders you buy things from or do business with?
7. People in your extended family?
8. Local/municipal government?
9. Judges/courts/police?
10. Providers at for-profit private healthcare facilities?
11. Providers at not-for-profit or NGO healthcare facilities?
12. Providers at mission hospitals?
13. Sellers at drug shops and chemists?
14. Providers at government/public healthcare facilities?
1. People in your tribe/ ethnic or cultural group?
2. People of your religion?
3. People in other tribes / ethnic or cultural groups?
4. People of other religions?
5. ASKED IN #17 FOR 2007
6. The business owners and traders you buy things from or do business with?
7. People in your extended family?
8. Local/municipal government?
9. Judges/courts/police?
10. Providers at for-profit private healthcare facilities?
11. Providers at not-for-profit or NGO healthcare facilities?
12. Providers at mission hospitals?
13. Sellers at drug shops?
14. Providers at government healthcare facilities?
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Table IV-1: Social capital items, cont.
Part 2: SHARED VALUES
How likely do you agree with each of the following statements? Please phrase your agreement as very likely, likely, neither likely nor unlikely, unlikely, or very unlikely.
2006 2007
Think of a scale from 1 to 5. At number 1 you feel that people in the community cannot be trusted. At the number 5 you feel people in the community can generally be trusted.
15. On that scale from 1 to 5, where would you rank the trustworthiness of people in the community?
16. Would you say that most of the time people in the community are just looking out for themselves, or they are trying to be helpful? (1= looking out for self, 5= helpful)
17. Do you think that most people in the community would try to take advantage of you if they got the chance, or would they try to be fair? (1=take advantage, 5= be fair)
18. On a scale from 1 to 5, where 1 is very unlikely and 5 is very likely, how likely is it that you would ask your neighbors to take care of your children for a few hours if you were sick?
19. How likely is it that you would ask your neighbors for help if you were sick? (1= not likely, 5=very likely)
20. Do people in this community generally trust one another in matters of lending and borrowing? (4 point scale: 1=trust a great deal, 2=trust somewhat, 3=distrust somewhat, 4=distrust a great deal)
Think of a scale from 1 to 5. At number 1 you feel that people in the community cannot be trusted. At the number 5 you feel people in the community can generally be trusted.
15. People around here are willing to help their neighbors
16. This is a cohesive neighborhood or village, that is, it is a community with a great deal of togetherness
17. People in this neighborhood or village can be trusted
18. People in this neighborhood or village generally do not get al.ong with each other
19. People in this neighborhood or village do not share the same values
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Table IV-1: Social capital items, cont.
Part 3: GROUP MEMBERSHIP
2006 2007
21. On a scale of 1 to 5, where 1 is disagree completely and 5 is agree completely, how do you feel about the following statement.
“I feel accepted as a full member of this village/neighborhood.”
22. How many groups or organizations do you belong to? These could be religious groups, sports teams, clan groups, or just groups of people who get together regularly to do an activity or tasks.
23. In an average month, how much money, if any, do you contribute to the groups to which you belong?
24. In an average month, how many days do you participate in the activities of the groups to which you belong?
20. On a scale of 1 to 5, where 1 is disagree completely and 5 is agree completely, how do you feel about the following statement.
“I feel accepted as a full member of this village/ neighborhood.”
21. How many groups or organizations do you belong to? These could be religious groups, sports teams, clan groups, or just groups of people who get together regularly to do an activity or tasks.
22. In an average month, how much money, if any, do you contribute to the groups to which you belong?
23. In an average month, how many days do you participate in the activities of the groups to which you belong?
Part 4: CHANGE IN TRUST ACROSS SPACE & TIME
2006 2007
In the last year, has the level of trust improved, worsened, or stayed the same?
Compared with other villages in the district, how much do people in this community trust each other in matters of lending and borrowing? (3 point scale: More than other communities, the same as other communities, or less than other communities)
In the last year, has the level of trust in the community improved, worsened, or stayed the same?
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Table IV-1: Social capital items, cont.
Part 5: INFORMAL SOCIAL CONTROL
Would you say it is very likely, likely, neither likely nor unlikely, unlikely, or very unlikely that your neighbors and fellow community members would intervene in the following scenarios in some way
2006 2007
NONE
25. How likely would your neighbors and fellow community members intervene if children were skipping school and loitering in the towns
26. How likely would your neighbors and fellow community members intervene if children were damaging /dirtying houses, cars, property or people?
27. How likely would your neighbors and fellow community members intervene if children were showing disrespect to an adult in form of teasing and abusing?
28. How likely would your neighbors and fellow community members intervene if a fight broke out in front of their house?
29. How likely would your neighbors and fellow community members intervene if an institution/service center (i.e. health unit, community hall, sports ground) closest to their homes was threatened with closure?
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Table IV-2: Description of respondents’ demographic factors, economic status, and household characteristics by survey year
2006 Survey 2007 Survey Pearson X2
Sex Female Male
59% (n=1503)41% (n=1054)
48% (n=1272)52% (n=1352)
55.3***
Age Females’ mean age (years) Males’ mean age (years)
30.5 (n=1466)32.4 (n=1029)
(n=1261)30.6 (n=1338)
55.3***
Marital status Single, widowed, or divorced Married or cohabitating
26% (n=651)74% (n=1860)
34% (n=874)66% (n=1716)
37.2***
Monthly household expenditure (Ugandan shillings)
Mean: 84,800(SD 139,000)Median: 50,000(IQR 30,000 – 100,000)n=2508
Mean: 100,700(SD 250,000)Median: 50,000(IQR 30,000 – 100,000)n=2551
10.9***
Household food insecurity score (0-27)
Mean: 7 (SD 6)Median: 6 (IQR 3-10)n=2475
Mean 8 (SD 5)Median 8 (IQR 5-11)n=2564
3.9*
Household assets index (7 common goods)
Mean: 2 (SD 2)Median: 2 (IQR 1-3)n=2434
Mean: 3 (SD 2)Median 2 (IQR 1-3)n=2591
45.6***
Education level No formal education Some primary Completed primary Some secondary Completed secondary
14% (n=356)35% (n=888)9% (n=221)20% (n=502)22% (n=547)
11% (n=286)34% (n=871)25% (n=649)23% (n=589)8% (n=204)
380.6***
Urban vs. RuralVillage Rural Urban (w/ trading centers)
57% (n=1468)43% (n=1090)
55% (n=1437)45% (n=1187)
3.6+
+ p> 0.05* p≤0.05** p<0.01*** p<0.001
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Table IV-3: Percent missing values for each of the social capital items
Variable # Missing % Missing
Cognitive social capital
1 SC600_trust_ur_tribe 260 4.8
2 SC601_trust_ur_religion 272 5.0
3 SC613_trust_public_provider 303 5.6
4 SC604_trust_comm_2006 306 5.7
5 SC605_trust_business 316 5.9
6 SC612_trust_drug_sellers 325 6.0
7 SC617C_full_member 329 6.1
8 SC609_trust_private_provider 341 6.3
9 SC606_trust_extend_family 373 6.9
10 SC603_trust_other_rel 386 7.2
11 SC607_trust_local_govt 395 7.3
12 SC602_trust_other_tribe 424 7.9
13 SC608_trust_judges 474 8.8
14 SC610_trust_NGO_providers 851 15.8
15 SC611_trust_mission_hospital 1054 19.5
Structural social capital
1 SC618A_How_many_groups 180 3.5
2 SC618B_How_much_money 227 4.4
3 SC618C_How_many_days 254 4.9
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Table IV-4: Average logits for respondent ability at each response category
2006 2007
Item Mean ability Mean ability
0 1 2 3 4 0 1 2 3 4
1 Trust same ethnicity -0.14 -0.13 0.31 0.61 1.32 -0.08 0.10 0.36 0.72 0.95
2 Trust same religion -0.18 -0.18 0.19 0.60 1.24 -0.16 0.16 0.40 0.71 0.96
3 Trust other ethnicity 0.12 0.30 0.70 1.07 2.05 0.24 0.35 0.61 0.89 1.12
4 Trust other religions 0.13 0.38 0.70 1.10 2.10 0.25 0.36 0.63 0.89 1.18
5 Trust community -0.19 0.05 0.41 0.77 1.55 0.48 0.54 0.62 0.79 0.95
6 Trust business owners -0.05 0.20 0.57 0.91 1.70 0.22 0.44 0.61 0.81 1.13
7 Trust extended family -0.07 0.04 0.39 0.63 1.30 0.09 0.25 0.54 0.79 0.91
8 Trust local govt 0.06 0.11 0.56 0.77 1.44 0.30 0.38 0.62 0.80 0.97
9 Trust judges 0.42 0.47 0.72 0.95 1.73 0.47 0.60 0.69 0.87 1.17
10 Trust private providers 0.04 0.05 0.52 0.86 1.64 -0.03 0.27 0.56 0.77 1.05
11 Trust NGO providers -0.13 -0.02 0.45 0.80 1.50 -0.01 0.26 0.51 0.75 1.06
12 Trust mission providers -0.09 0.04 0.47 0.71 1.35 0.19 0.33 0.55 0.74 1.06
13 Trust drug sellers -0.09 0.18 0.57 0.90 1.69 0.24 0.42 0.63 0.83 1.19
14 Trust public clinics -0.17 -0.11 0.23 0.64 1.24 -0.05 0.23 0.56 0.74 0.93
15 Feel full member of community
-0.11 -0.07 0.30 0.64 0.98 0.56 0.00 0.45 0.72 0.74
Shaded cells indicate items with non-ordinal item-step means
126
Table IV-5: Correlation matrix between explanatory variables
Age Sex Marital Educ Rural/
Urban
HH Exp HH Goods
Food Sec
Age 1.0000
Sex 0.0627 1.0000
Marital 0.2548 -0.0382 1.0000
Educ -0.1729 0.1123 -0.0524 1.0000
Rural/
Urban
-0.0940 -0.0198 -0.1187 0.1962 1.0000
HH Exp 0.0682 0.0667 0.0807 0.1529 0.1997 1.0000
HH Goods
0.0090 0.0282 0.0360 0.2651 0.2416 0.2842 1.0000
Food Sec
0.0268 -0.0032 0.0305 -0.1457 -0.0991 -0.1436 -0.195 1.0000
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Table IV-6: Cognitive social capital and health-related behaviors
Outcomes Low CSC
Mid. CSC
High CSC
Bivariate OR
Bivariate 95% CI
Multivariate
aOR
Multivariate 95% CI
Men who hit othersi
33% 22% 20% 0.69*** 0.57-0.85 0.65*** 0.53-0.81
Women hit by othersi
30% 18% 26% 0.85 0.71-1.03 0.85 0.70-1.03
2 or more sex partnersi
20% 16% 16% 0.88** 0.81-0.97 0.90* 0.82-0.98
Unprotected sex with any partneri
82% 89% 91% 1.00 0.84-1.19 0.96 0.80-1.15
Ever disclose HIV if result was positive++
51% 62% 76% 1.85* 1.17-2.92 1.89* 1.04-3.43
Ever disclose gonorrhea if result was reactive ++
64% 71% 78% 1.39 0.80-2.40 1.25 0.72-2.17
Ever disclose genital sore if provider confirmed STI++
78% 81% 93% 1.72* 1.05-2.83 2.20* 1.00-4.83
Ever disclose STI discharge if provider confirmed STI++
82% 60% 90% 1.22 0.67-2.23 1.14 0.52-2.48
Ever disclose syphilis if result was reactive++
83% 76% 84% 1.07 0.82-1.41 1.09 0.78-1.52
Seek any healthcare past six monthsi
84% 84% 88% 1.15 0.93-1.41 1.10 0.89-1.36
Seek STI care in past six monthsi
41% 32% 35% 0.76** 0.63-0.93 0.80* 0.65-0.97
i Multivariable model adjusted for age group, marital status, sex, education (completed primary or not), above median monthly HH expenditure, above median HH asset score, median HH food security, village commercial activity, year of survey, and the variance of respondents clustered by village * p<0.05** p<0.01*** p<0.001++ Mulitvariable model adjusted for age group, marital status, sex, education (completed primary or not), village commercial activity and the variance of respondents clustered by village
128
Table IV-7: Structural social capital and health-related behaviors
Outcomes Low SSC
Mid. SSC
High SSC
Bivariate OR
Bivariate 95% CI
Multivariate (aOR)
Multivariate 95% CI
Men who hit othersi
22% 24% 28% 0.97 0.74-1.28 0.94 0.68-1.29
Women hit by othersi
26% 26% 24% 1.05 0.75-1.49 1.06 0.72-1.59
2 or more sex partnersi
18% 15% 18% 0.93 0.83-1.06 0.93 0.82-1.07
Unprotected sex with any partneri
90% 88% 85% 0.98 0.80-1.20 0.97 0.80-1.18
Ever disclose HIV if result was positive++
64% 67% 62% 1.01 0.64-1.60 0.70 0.39-1.26
Ever disclose gonorrhea if result was reactive ++
72% 81% 65% 1.06 0.64-1.75 0.67 0.34-1.30
Ever disclose genital sore if provider confirmed STI++
93% 84% 79% 1.67 0.84-3.34 1.54 0.72-3.26
Ever disclose STI discharge if provider confirmed STI++
90% 79% 74% 1.55 0.75-3.21 1.36 0.62-3.01
Ever disclose syphilis if result was reactive++
82% 89% 76% 1.40 0.97-2.03 1.15 0.81-1.64
Seek any healthcare past six monthsi
83% 87% 85% 0.91 0.74-1.13 0.97 0.79-1.20
Seek STI care in past six monthsi
40% 35% 35% 1.06 0.91-1.23 1.02 0.86-1.20
i Multivariable model adjusted for age group, marital status, sex, education (completed primary or not), above median monthly HH expenditure, above median HH asset score, median HH food security, village commercial activity, year of survey, and the variance of respondents clustered by village * p<0.05** p<0.01*** p<0.001++ Multivariable model adjusted for age group, marital status, sex, education (completed primary or not), village commercial activity and the variance of respondents clustered by village
129
Figure IV-1: Item response curve for “trust of pharmacies” in 2006 survey
130
Figure IV-2: Information curve for the item ‘trust of pharmacies’ from the 2006 survey
131
Figure IV-3: Item response curve for “trust of pharmacies” in 2007
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Figure IV-4: Wright Map of respondents on cognitive social capital items from 2006 survey
Wright Map (EAP) Variable: Cognitive social capital IRT Categories Map of person estimates and response model parameter estimates ===================================================================== respondents Thurstonian Thresholds (Recoded) --------------------------------------------------------------------- | | 3 | | | |3.4 4.4 9.4 | XX| | X|6.4 13.4 2 | X|8.4 10.4 | XX|5.4 11.4 | X|4.3 7.4 9.3 12.4 | XX|1.4 3.3 14.4 1 | XXXXXXXXXXXX|2.4 6.3 13.3 | XXXXXXX|10.3 | ------------XXXXXXXX|4.2 5.3 8.3 9.2 11.3 0 | XXXXXXXXXXXXXXXXXXXX|3.2 7.3 12.3 | XXXXXXXXXXXXX|1.3 6.2 13.2 14.3 | XXX|2.3 10.2 | XXX|5.2 8.2 11.2 -1 | XXXXX|4.1 7.2 9.1 12.2 | X|1.2 3.1 14.2 | X|2.2 | X|6.1 10.1 13.1 -2 | |5.1 8.1 11.1 | X|7.1 12.1 | |1.1 14.1 | |2.1 -3 | | | | ===================================================================== Each X represents 35 respondents, each row is 0.255 logits Model Specifications:Measurement Model = Modified Rating ScaleProficiency Estimation Method = EAP Maximum Logit = 6.00Minimum Logit = -6.00Integration Method = QuadratureQuadrature Points = 15EM convergence criteria = 0.001
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Chapter V: Summary and conclusions
The three studies included in this dissertation explored social and economic
antecedents to healthcare utilization in southwestern Uganda. This concluding chapter
summarizes the main findings of this dissertation, the potential policy implications, and
areas for future research.
Main findings
In Chapter II, regardless of the four binary poverty measures used, we found that
there was a greater prevalence of STI symptoms among the poor than among the non-
poor. We also found that the poor were also more likely to not use STI treatment.
Additionally, the poor and non-poor appeared to use STI treatment services more
frequently at private providers than at public providers.
In Chapter III, we found that between the baseline survey in 2006 and the 16-
month follow-up survey in 2007 that the level of knowledge of STI symptoms improved,
the use of any STI treatment increased, and the prevalence of syphilis decreased in the
surveyed population. One important factor in utilization of STI treatment was distance
from village of residence to healthcare provider. Distance from village of residence to
clinic was inversely correlated with utilization (r= -0.78). Among respondents <11
kilometers from contracted clinics, there was a significant increase in the proportion of
respondents using STI treatment services between 2006 and 2007 while there was no
significant increase in the proportion of respondents ≥11 kilometers (30% increase versus
0% increase). We also found that distance was associated with a greater reduction in the
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prevalence of syphilis among respondents who lived <11 kilometers of a contracted
facility between 2006 and 2007 compared to respondents who lived ≥11 kilometers from
a contracted clinic (57% decrease versus 20% decrease).
In Chapter IV, two indices for measuring social capital cognitive (CSC) and
structural (SSC) were determined to be reliable and valid. As part of the validity testing,
the social capital measures were examined for any association with health behaviors and
health outcomes. There were significant multivariable associations between increased
CSC and decreased odds of male aggression, decreased odds of having two or more sex
partners, increased odds of unprotected sex, increased odds of HIV+ disclosure to
partner, and increased odds of disclosure of a genital sore to partner.
Policy implications
Applications and limitations of OBA programs
The findings from this dissertation suggest that output-based aid voucher
programs, structured like the Uganda STI treatment program, are an appropriate strategy
that can have multiple positive health impacts in local populations. However, there are
limitations to the OBA approach. OBA voucher programs require a responsive
management agency, a credible accreditation process, a transparent claims process and a
pool of competent healthcare providers.
The OBA approach may not work as well in areas where there are few providers
available to contract and similarly in lightly populated rural areas where few people
would likely use the voucher services in sufficient numbers to generate significant
revenue for providers. Conversely, OBA does well when providers compete to enter the
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system on contract. OBA as implemented in Uganda was launched in a region where the
local economy was generally doing well compared to other parts of the country and there
were many private providers available to contract. We know from our study that the
private sector was a significant source of STI treatment and that the poor used private
facilities as much, if not more, than they used public facilities. Yet, if the purpose of
OBA is to serve the poor, were the poor in the Mbarara region Uganda’s truly destitute?
There were certainly destitute individuals in the rural areas, but compared to the northern
region, the Mbarara region overall was doing well. Using the 2002 census measure of
households in poverty, the Mbarara region (Ibanda, Isingiro, Kiruhura, and Mbarara
districts) was in the top quintile of wealth as measured by the proportion of poor
households among all households in each district; 7.0% of Mbarara households were poor
compared to the national median of 10.1% (IQR= 7.2%-17%) (See Figure V-1).
One of the limitations of OBA as practiced in Uganda is that it is a peri-urban
strategy; the available clinics are usually located within short distances to concentrated
populations in trading centers and towns. There are alternatives; in Kenya, a UNICEF
voucher program functions within public facilities and is able to serve disbursed rural
populations where there is little cash economy and no private providers. According to
UNICEF, the percentage of women seeking a skilled attendant for delivery in the
province rose from 8 percent to 25 percent after the new voucher program was launched
(Sittoni 2009).
Another limitation of OBA is the need for significant management capacity. The
management agency ought to have the knowledge, the flexibility and the authority to
identify quickly provider fraud, respond to provider incompetence, improve patient
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satisfaction, and conduct a transparent financial and medical review of all claims. At
successful facilities, healthcare providers are busy and have little time to devote to claims
entry. The management agency ought to balance a judicious review of financial and
medical components on each claim with a need to reimburse providers quickly. In the
Uganda STI program, several providers dropped out after extended delays in
reimbursement; providers grew to distrust the management team when payments failed to
be processed on time. In the Bangladesh voucher program, there were lengthy
reimbursement schedules for specific services - from transport to food supplements
following delivery (Koehlmoos et al. 2008). Already overworked district health staff
were delayed making reimbursements to beneficiaries (Begum et al. 2008).
When appropriate to the local healthcare context, OBA is an appealing and viable
financing strategy: it can target the poor in economically diverse populations; it can
engage and improve private and public sector services; and it create incentives for high
quality healthcare provision.
Social capital, OBA, and STIs
Social capital is an underutilized framework to better understand why health
interventions succeed or fail in low-income African countries. Social capital is a measure
of one’s ability to convert social contacts and networks into other resources. The social
milieu in which health programs are implemented can have an important effect on the
success of a health program. In a simple example, a poor patient with many friends is
better off than the same patient without many friends. Social contacts can be sources of
emotional support, economic security, and provide a sense of security and purpose. These
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social resources are not well understood and poorly measured in Africa yet may be
important factors in healthcare delivery in OBA strategies.
If the findings from this research on the relationship between cognitive social
capital and health behaviors are verified by future research, it has challenging policy
implications. Building stocks of social capital is a potentially difficult intervention and
the benefits to health are not easy to estimate. Trust is a significant element in cognitive
social capital and exposure to economic and health information exchange has been
demonstrated to increase CSC (Pronyk et al. 2008a). Do the health information
campaigns in the Uganda OBA program have the potential to not only increase
knowledge of the program, but also improve the levels of social capital by building trust
in the voucher?
Pronyk and colleagues (2008a) showed that a microfinance intervention could
contribute to greater levels of social capital. Microfinance can increase both CSC and
SSC as greater trust is earned from repeated economic interaction and associations form
as people benefit from proximity to functional microfinance entities. In such settings,
increases in economic exchange and levels of social capital could also lead to increases in
healthcare utilization.
Future directions
Looking forward, there are two primary areas for future research. First, more
research is needed on the meaning of social capital and how it relates to population health
and health services in Africa. In particular, there is significant uncertainty about the
magnitude of the relationship and how it may change over time in relation to health care
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interventions. There is significant uncertainty also about how to build social capital.
Second, more evaluation of OBA programs are needed to understand where they
can be successful geographically, which services ought to be promoted, whether the
targeted population is served, and how successful programs can be scaled. All OBA
voucher programs to date have been regionally discrete pilots at best serving tens of
thousands of patients per year. The OBA model, however, could potentially serve
national populations. However, to offer delivery services to all poor Ugandan women, for
instance, would require the Ministry of Health to make the strategy a top priority and
shift major financial resources to the program.
The OBA programs are a significant improvement in the management of
healthcare in low-income regions of the world. Healthcare delivery ought to be more
responsive to patient needs and the OBA strategy is an important step in the direction of
more accountable and cost-effective health systems.
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Figure V-1: Percent of households below poverty line by subcounty (National Census 2002)
Note: Subcounties in the Mbarara region (Ibanda, Isingiro, Kiruhura, and Mbarara districts) are outlined in black
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REFERENCES
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