evaluation of mortality data collected from population censuses

United Nations Sub-Regional Workshop on Census Data EvaluationPhnom Penh, Cambodia, 14-17 November 2011

Evaluation of Mortality Data Collected from Population Censuses

United Nations Statistics Division


Some basics about life table

For two items that can be used to obtain mortality statistics in census:

- Survival of children ever born

- Deaths in the household

We discuss

- Information collected

- Possible quality issues related to each question

- Methods of data evaluation using examples

Outline of the presentation


Some basics about life table (1)

Age (x) nMx nqx lx nLx ex

0 0.005774 0.00579 100000 99707 79.1561

1 0.000284 0.001134 99581 398184 78.4879

5 9.32E-05 0.000466 99511 497465 74.5417

10 0.000166 0.000831 99475 497260 69.5677

… … … … … …

55 0.007521 0.036913 93619 462988 26.9352

60 0.011885 0.057709 91576 449140 22.4803

65 0.021682 0.102837 88080 429485 18.2733

70 0.037063 0.1696 83714 407728 14.096

75 0.059397 0.258588 79377 383688 9.72956

80 0.10245 0.407803 74098 354340 5.24461

85+ 0.179314 1 67638 34275 0.50674



nMx = period mortality rate = nxandxagedalivepersonsofnumberaverage

nxageandxagebetweendieddeaths

#

nqx = proportion of those people reaching their xth birthday who die before their (x+n)th birthday

lx = number of person who live to their xth birthday

nLx = number of person-years lived between exact ages x and x+n

ex = life expectancy at age x (the average number of years which people have left to live when they are at age x)


Model life tables

• Created to estimate demographic parameters for countries with limited data

• Built on empirical studies of age-specific mortality patterns in the past

• Two groups of model life tables:

• Coale-Demeny: based on European populations• North, South, East and west European models

• United Nations: For developing countries • Latin American, Chilean, South Asian, Far Eastern,

General



Mortality statistics from population census – Introduction

A group of questions can be used to obtain mortality data in a census

Two distinctions:

a) Level and trend of mortality vs age pattern of mortality

• Survival of children ever born: level and trend of mortality

• Household deaths: age pattern of mortality:

b) Deaths of younger persons vs. deaths of adults

• Younger persons: survival of children ever born

• Adults: household deaths

All approaches are to supplement death registration data, not to replace it.


Have been used for the past 50 years to collect data on infant and child mortality

For every woman the following information are collected:

a) the total number of female children she has borne in her lifetime.

b) the total number of male children she has borne in her lifetime.

c) the number of female children who are surviving

d) the number of male children who are surviving

Survival of children ever born – information collected


Survival of children ever born – Use of

Ever born – Surviving = Children deceased

Children deceased / Ever born = Proportion deceased

Life table measures of infant, child and young adult mortality may be derived from the proportion of deceased.


Survival of children ever born- Tabulation example, Turkey 2000

Source: Tabulated using data from United Nations Demographic Yearbook

Age Women Total CEB Total CSProportion of

deceased children

15 - 19 3518257 294628 281296 0.045

20 - 24 3263432 2078364 1991445 0.042

25 - 29 2918825 4522719 4312404 0.047

30 - 34 2457285 5700038 5395143 0.053

35 - 39 2400808 7036619 6563946 0.067

40 - 44 1985225 6707033 6131544 0.086

45 - 49 1658012 6394157 5722904 0.105


Data are used to estimate level and trend of mortality for about 20 years prior to a census or survey.

Survival of children ever born – Brass type estimates (1)


Survival of children ever born: Brass-type estimates (2)

Age group of mother in years Age group index Proportion of children dead approximates

15-19 1 q(1)

20-24 2 q(2)

25-29 3 q(3)

30-34 4 q(5)

35-39 5 q(10)

40-44 6 q(15)

45-49 7 q(20)

50-54 8 q(25)

55-59 9 q(30)

Empirical findings about child mortality


Survival of children ever born - Brass-type estimates (3)

- Approximation

- q values referring to different time period before census

- q(1): more recent estimates; q(20) – earlier estimates (Feeney, 1980)

- Under-five mortality is used more often: more robust than infant mortality

- However if comparing estimates with civil registration, may use infant mortality rate


Feeney 1980: Estimating infant mortality trends from child survivorship data, Population Studies 34(1): 109-128.



- Under-five mortality

- Most commonly used

- more robust than infant mortality

- Upward biases from reports of younger women, usually inaccurate

- More powerful results (Brass type) came from multiple data sources




An example of MortPak CEBCS output



An example of MortPak CEBCS output (cont.)


Survival of children ever born: Brass-type estimates (6)How to identify the right mortality model - graphical

Source: Step by step guide to the estimation of child mortality, 1990, United Nations


Survival of children ever born: Brass-type estimates (7)How to identify the right mortality model – graphical



Survival of children ever born: Brass-type estimates (8)Illustration of the relationship of mother’s age and timing of the under-5 mortality estimates


Bangladesh, 1974 Retrospective Survey of Fertility and Mortality


Survival of children ever born: Brass-type estimates (9)q(5) more robust than q(1)


Infant and under-five mortality, Bangladesh


Survival of children ever born: Brass-type estimates (10)Turkey example again

q(5), Turkey 2000 census

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.1

2000199919971995199319901987


Survival of children ever born: Brass-type estimates (11)Comparison of multiple sources

q(5), Turkey

0.045

0.055

0.065

0.075

0.085

0.095

0.105

0.115

year

2000 census Brass 1990 census Brass

1998 TDHS 2003 DHS


A few notes about Brass type estimates

• Almost smooth due to modeling

• If see rough and unsmooth data, indicates quality issues

• The last increase of q(5) does not mean increasing mortality, but rather biases generated from mother of young age groups (15-19)

• There is violation of assumptions about age patterns in the method, i.e., child death depends on children’s age only. But children born to very young mothers tend to be disadvantaged


Experience has shown that it is possible to get high quality responses to this kind of questions in any data collection exercise, including censuses.

If both CEB and CS are understated, some cancellation of errors will occur.

But in practice, reporting of CS is more likely to be complete than reporting of CEB => calculated proportions of deceased children are likely to be too low.

Survival of children ever born – quality (1)


Other influences on the accuracy of estimates derived from these data:

Assumptions about the age pattern of mortality: mortality of child relies only on their own age (which will fail at young age of mothers, i.e., the 1st or 2nd age groups of mothers)

In the ideal case, data on CEB and CS will be available from two or more data collection exercises, at different points in time.

This will allow comparison, providing a powerful test of the quality of the estimates.

Survival of children ever born – quality (2)


Survival of children ever born – quality assessment (1)

- Initial assessment:

- Any missing values in children surviving data?

- Missing values for any relevant variables: age of mother, sex of those who died

- Plausibility of data

- Children survival data; age distribution

- Distribution of women with socio-economic characteristics


Survival of children ever born – quality assessment (2)

“… systematic failure in data collection…” Source: Estimation of mortality using the South African Census 2001 data, Dorrington, Moultrie and Timæus, Centre of Actuarial Research, University of Cape Town, 2001

Example: missing or implausible values of CEB and CS data


Survival of children ever born – quality assessment (5)Comparing age patterns of proportion deceased children

Source: Graph produced based on data collected by the United Nations Demographic Yearbook and Measure DHS country report

Proportion of deceased children, Turkey

00.020.040.060.08

0.10.120.140.160.18

15 - 19 20 - 24 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49

Age of mother

1990 census 1998 DHS 2000 census 2003 DHS


Survival of children ever born – quality assessment (6) A rapid assessment: Burundi, 1990 census: CS and CEB data

Age Total women Average CEB Average CS CS/CEB

15 + 1483895 3.82 2.93 0.77

15 - 19 250329 0.07 0.07 0.89

20 - 24 229655 1.02 0.86 0.84

25 - 29 214467 2.75 2.28 0.83

30 - 34 187348 4.49 3.63 0.81

35 - 39 135551 5.62 4.51 0.80

40 - 44 97537 6.10 4.85 0.80

45 - 49 75526 6.23 4.89 0.79

50 - 54 76100 6.21 4.70 0.76

55 - 59 50817 6.22 4.58 0.74

60 - 64 53775 6.12 4.28 0.70

65 + 110062 6.07 3.82 0.63

Unknown 2728 4.47 3.37 0.76

Source: Graph produced based on data collected by the United Nations Demographic Yearbook


Survival of children ever born – quality assessment (7) A rapid assessment of CEB and CS data

- (1-0.81)=0.19 for the 30-34 age group: the proportion of deceased among all children born to mother of 30-34 years of age ≈ q(5), the proportion of children born who die before their 5th birthday 7 years prior to census

- Compare with other estimates, e.g., UN Population Division estimates of under-5 mortality

- 1990 census estimates of under-5 child mortality = 190 per 1000 for 1983

- UN Pop Division estimates for the period 1980-1985: 196 per 1000

- Slightly underestimates

Method: Rapid Assessment of Census Data on Children Born and Surviving, Griffith Feeney, 2009. http://www.demographer.com/rapid-assessment-of-ceb-and-cs-data/


Survival of children ever born – quality assessment (8) Comparing with UN Population Division under-five mortality estimates

Source: World Population Prospects: The 2010 Revision


Survival of children ever born – quality assessment (9) Existing external sources

- UN population division (World Population Prospect)

- UNICEF child mortality website (www.childmortality.org)


Household deaths in the last 12 months – adult mortality (1)

- Direct estimates of current death rates can be obtained, however, with substantial errors

- Under-reporting, especially for child deaths and older age deaths

- Reference period errors in reporting of deaths (versus the usual 12 months reference period)

- Death question omitted by interviewers

- Household breaking up due to the death of a senior household member

- Age-heaping and age exaggeration

- The method is mainly used for adult mortality


Household deaths in the last 12 months – adult mortality (2)Initial assessment

Tabulation of enumerated deaths with associated variables, e.g., year/month of death

• Quality of age reporting for the deceased


Household deaths in the last 12 months – adult mortality (4):Comparing age-specific death rates

Age specific death rate, Madagascar, Male

0

2

4

6

8

10

12

14

16

15 - 19 20 - 24 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49

Age

Male, 1993 census Male, 1992 DHS Male, 1997 DHS

Source: Graph produced based on data collected by the United Nations Demographic Yearbook and Measure DHS country report


Household deaths in the last 12 months – adult mortality (3)Assessment: death distribution methods

General Growth Balance (GGB), assumes

• constant coverage of household deaths and population across all ages (this would not work for children deaths)

• Negligible migration

• Stable population (constant births and deaths)

• Accurate reporting of age for both population and deaths

Synthetic Extinct Generations method (SEG), assumes

• All the above, except for stable population assumption was relaxed in later version

• Constant coverage of population across time (may be relaxed if use a “combined GGB-SEG approach”)


Household deaths in the last 12 months – adult mortality (4)Assessment: example of GGB method


Household deaths in the last 12 months – adult mortality (5)Assessment: example of GGB method

f: slope of the fitted line

(1/f)*100% = 41.2% only 41.2% of the deaths were being reported

evaluation of mortality data collected from population censuses

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