UNIVERSITY OF WASHINGTON

Measuring adult mortality using sibling survival

A new analytical method and new results for 44 countries, 1974-2006

SURVEY DATA FOR ADULT MORTALITY

• Sibling histories• Yield high return of observations per respondent

• Included in the DHS as a way to measure maternal mortality

• Respondent asked to report on total number of siblings born to the same mother (“sibship”)

• Akin to a complete birth history

• Direct estimates from sibling history data are implausibly low

• Timaeus & Jasseh (2004)

• 26 surveys in sub-Saharan Africa

• Used model life tables to smooth sibling history data

• Modeled change in age pattern due to HIV

BIASES IN SIBLING HISTORY DATA

• Selection bias:

• Underrepresentation of high-mortality families

• Recall bias

• Deaths omitted from respondent report

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BASICS OF THE CSS METHOD

• Use the observed, generally consistent age patterns of mortality across contexts

• Consistent patterns in shape of log death rates between the ages of 15 and 60, regardless of level of mortality

• Use logistic regression to estimate the probability of dying for a given country, sex, age group, and time period

• Apply the regression model to multiple surveys pooled together

• Can be applied to:

– Single population with multiple surveys over time

– Any grouping of populations where at least some have multiple surveys over time

• Correct for known selection and recall biases

LOGISTIC REGRESSION MODEL

5

5

TiPSIIYLogit itaait 01

aitY = survival or death in age group a, in country i for a one-year period of time t

aI = dummy indicators for each age group, a

itI= a set of dummy indicators for country i in the five-

year period containing t

TiPS = a continuous variable representing the time prior to the survey

DIFFERING AGE PATTERNS

6

02

46

81

01

21

4

20 40 60 20 40 60

single single

females males

OR

of

de

ath

age

Model 1 - single

02

46

81

01

21

4

20 40 60 20 40 60

females males

0-1% 2-6% 7-11% 12+%

OR

of

de

ath

age

Model 2 - HIV

02

46

81

01

21

4

20 40 60 20 40 60

females males

war 5q0 low 5q0 med 5q0 high

OR

of

de

ath

age

Model 3 - war 5q00

24

68

10

12

14

20 40 60 20 40 60

females males

war HIV 5q0 low 5q0 high

OR

of

de

ath

age

Model 4 - war HIV 5q0

RECALL BIAS

• Empirical work suggests that respondents omit some sibling deaths

• TiPS (Time Prior to the Survey) variable

• Captures difference between deaths reported in the more recent periods of older surveys and the older periods of more recent surveys

• Exponentiated coefficient approximates the annual incremental reduction in observed probability of death due to omitted deaths

• Can only be estimated with sufficient overlap of observations from different surveys in the same country year

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RECALL BIAS: TiPS

8

15 years

15 years

SELECTION BIAS• Underrepresentation of high-mortality sibships

• Families with higher mortality are less likely to be sampled

• Empirically, larger families have higher mortality

• Higher death rates in larger sibships means that high mortality sibships are underrepresented and thus bias the estimated rates downward

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SELECTION BIAS: GK WEIGHTS

• Underrepresentation of high-mortality sibships

• “Upweight” observations from high-mortality families:

• Bf /Sf is the inverse of the probability of surviving to the time of the survey

• Multiply the survey sampling weight by the GK weight for final weight; use weights in logistic regression

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survey. theof time the tosurviving siblings ofnumber

and size, sibship original

weight,levelfamily a is where

f

f

f

f

ff

S

B

W

S

BW

RESULTS FROM PRACTICAL APPLICATION

• Demographic and Health Surveys: Sibling history module

• 85 surveys

• 44 countries

• Respondents: women aged 15-49

• Use sibling history data up to 15 years prior to the survey

• Combine with:

• 2008 UNAIDS historical HIV seroprevalence data

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RESULTS: CORRECTIONS, STEP BY STEP

0.2

.4.6

1985 1990 1995 2000 2005 1985 1990 1995 2000 2005

female male

Uncorrected data Survival and recall bias corrected

Survival bias corrected

45q

15

year

Tanzania

RESULTS: CORRECTIONS

• Effect of including GK weights – selection bias correction

• Raise estimated 45q15 by an average of 28%

• Maximum: 66%; minimum: 6%

• Effect of TiPS – correction for recall bias

• Males: Annual decrease of 2.1% per year prior to the survey

• Females: Annual decrease of 1.4% per year prior to the survey

• Country-specific TiPS not significantly different from average effect, but:

– Country-specific effects ranged from -0.8% (Mali females) to 7.7% (Madagascar males)

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DATA COVERAGE WITH DHS SIBLING HISTORIES

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CSS RESULTS FOR AFRICAFemales and Males, circa 1990

15

Legend

45q15

< 0.15

0.16 - 0.30

0.31 - 0.45

0.46 - 0.60

0.61 - 0.75

0.76 +

Females circa 1990 Males circa 1990

Females circa 2000 Males circa 2000

CSS RESULTS FOR AFRICAFemales and Males, circa 2000

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Legend

45q15

< 0.15

0.16 - 0.30

0.31 - 0.45

0.46 - 0.60

0.61 - 0.75

0.76 +

Females circa 1990 Males circa 1990

Females circa 2000 Males circa 2000

Legend

45q15

< 0.15

0.16 - 0.30

0.31 - 0.45

0.46 - 0.60

0.61 - 0.75

0.76 +

Females circa 1990 Males circa 1990

Females circa 2000 Males circa 2000

DISCUSSION: FURTHER RESEARCH

• More sibling history data mean more power to estimate and correct for context-specific recall bias

• TiPS assumes the recall pattern is consistent across settings and over time

– Gives estimate of “average” recall bias across all surveys

– Strong assumption

– From countries with multiple surveys, some evidence that it doesn’t always hold

• Broader respondent pool needed

• Male respondents

• Older ages

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