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Estimating Mortality from Census Data: A record linkage study in the - - PowerPoint PPT Presentation

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Estimating Mortality from Census Data: A record linkage study in the Nouna Demographic and Health Surveillance System in Burkina Faso Bruno Lankoande on behalf of the WP3 team Colloque DEMOSTAF Paris - October 16, 2019 Background Data and

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Estimating Mortality from Census Data: A record linkage study in the Nouna Demographic and Health Surveillance System in Burkina Faso

Bruno Lankoande on behalf of the WP3 team

Colloque DEMOSTAF

Paris - October 16, 2019

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Background Data and methods Key findings Conclusion

  • In Sub-Saharan Africa, because of the absence of full-fledged CRVS

system in most countries, mortality levels and trends are largely derived from large-scale surveys and censuses ;

  • Surveys have collected birth and sibling histories ;
  • Censuses have included questions on the survival of children,

parents, and recent household members ;

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Background Data and methods Key findings Conclusion

  • A complete life table can be obtained from data on the number of

deaths in each household preceding the enumeration ;

  • But these data are affected by various errors including :
  • Underreporting of deaths ;
  • Transfers outside the reference period ;
  • Age mistatement
  • Under enumeration of some specific populations

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Background Data and methods Key findings Conclusion

  • The magnitude and direction of these errors are difficult to assess in

the absence of a mortality gold standard ;

  • Estimates have sometimes been evaluated in simulated

environments ;

  • Few attempts to compare them to high quality data from Health

and Demographic Surveillance Systems (HDSSs) except in Senegal ;

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Background Data and methods Key findings Conclusion

Using the Nouna HDSS as the reference, we evaluate the reliability

  • f mortality indicators derived from the last national census of

Burkina Faso, conducted in 2006

  • Capture the magnitude of mortality underestimation in the census

and their variation by age group and sex ;

  • Link individual records to evaluate the quality of ages and their

impact on mortality estimates ;

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Background Data and methods Key findings Conclusion Data Methods

  • Data collected in the Nouna HDSS since 1992.
  • Extract of Individual-level data of the population under

surveillance in the HDSS from the census database.

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Background Data and methods Key findings Conclusion Data Methods

Comparisons at the aggregate level based on the names of villages

  • Relying on the same methodology to compare summary

indices of mortality between census and HDSS estimates.

  • Decomposition of the differences in life expectancies at birth

into contributions of the major age groups.

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Background Data and methods Key findings Conclusion Data Methods

Record linkages

  • Automatic search based on first and last names was

performed using Jaro-Winkler distance.

  • Manual search based on kinship graphs derived from the

census and the HDSS.

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Background Data and methods Key findings Conclusion Data Methods

Analysis at individual level

  • Logistic regressions on the probability to be matched using

socio-demographic characteristics.

  • Comparing ages of the surviving population as well as of the

deceased in 2006 across data sources.

  • Computing a life table from the census using ages reported in

the HDSS.

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Background Data and methods Key findings Conclusion Comparisons at the aggregate level Individual-level analysis

Figure 1 : Population pyramid in 2006 according to the HDSS and the Census ‘The male population is

  • nly 2% larger in the

HDSS the female population is 7% larger in the HDSS, as compared to the census

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Background Data and methods Key findings Conclusion Comparisons at the aggregate level Individual-level analysis

Figure 2 : Number of deaths reported by month in 2006 in Nouna according to the HDSS and the census, by age group

5 10 15 20 25 30 35 5 10 15 20 25 30 35 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov

1-4 5-59 60+ Census HDSS Number of deaths Month of death

‘18% fewer deaths were collected in men and 29.6% in women Fewer deaths were particularly collected below 15 and above 60

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Background Data and methods Key findings Conclusion Comparisons at the aggregate level Individual-level analysis

Figure 3 : Age specific mortality rates (ASMR) inferred from the census and the HDSS data, Nouna, 2006

0.001 0.01 0.1

1-4 5-9 10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80-84 85+ 1-4 5-9 10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80-84 85+

Females Males Census HDSS Log mortality rates Age group 12 / 20

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Background Data and methods Key findings Conclusion Comparisons at the aggregate level Individual-level analysis

Table 1 : Direct estimates of mortality in Nouna according to the HDSS and the reporting of deaths that occurred in households during the last 12 months in 2006 census Males Females Indices Census HDSS

  • Rela. diff

Contri. Census HDSS

  • Rela. diff

Contri.

5q0

124 128

  • 3%

0.2 97 115

  • 16%

1.4

10q5

19 24

  • 18%

0.2 16 21

  • 24%
  • 0. 3

45q15

291 306

  • 5%

0.3 166 218

  • 24%

1.2

20q60

532 652

  • 18%

2.5 417 584

  • 29%

3.7 e0 Diff. Diff. 61.0 57.8 6% 3.2 68.4 61.8 11% 6.6

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Background Data and methods Key findings Conclusion Comparisons at the aggregate level Individual-level analysis

Table 2 : Effects of age misstatement in the census on mortality indicators

Variables Survivors Deceased Matching rates 58% 36% Sex Males Ref. Males Ref. Females 0.977 Females 0.86 Age group 0-4 Ref. 0-4 Ref. 5-14 0.765*** 5-14 1.335 15-29 0.553*** 15-59 0.918 30-39 0.722*** 60-79 1.223 40-49 0.847*** 80+ 0.913 50-59 0.775*** 60-69 0.721*** 70-79 0.696*** 80+ 0.787*** Observations 71,706 589

*Statistical significance : *** p < 0.01 ; ** p < 0,05 ; * p < 0.1

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Background Data and methods Key findings Conclusion Comparisons at the aggregate level Individual-level analysis

Figure 4 : Age differences in men and women between the census and the HDSS in 2006 using the HDSS as a reference

  • 40
  • 20

20

0-4 5-14 15-29 30-39 40-49 50-59 60-69 70-79 80+ HDSS Age group 0-4 5-14 15-29 30-39 40-49 50-59 60-69 70-79 80+ HDSS Age group

Men Women

Age Census-Age HDSS

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Background Data and methods Key findings Conclusion Comparisons at the aggregate level Individual-level analysis

Figure 5 : : Age differences of deceased persons between the census and the HDSS in 2006 using the HDSS as a reference

  • 10
  • 5

5 10

Age Census-Age HDSS <5y 5-14y 15-59y 80y+ HDSS Age group

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Background Data and methods Key findings Conclusion Comparisons at the aggregate level Individual-level analysis

Table 3 : Effects of age misstatement in the census on mortality indicators in men Indices Census

  • Cen. corrected

HDSS

  • Rela. Diff1
  • Rela. Diff2

5q0 124 124 128

  • 3%
  • 3%

10q5 19 21 24

  • 21%
  • 12%

45q15 291 300 306

  • 5%
  • 2%

20q60 532 540 652

  • 18%
  • 17%

e0 61.0 60.2 57.8 6% 4%

(1) Relative difference, uncorrected estimates vs HDSS (2) Relative difference, corrected estimates vs HDSS 17 / 20

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Background Data and methods Key findings Conclusion Comparisons at the aggregate level Individual-level analysis

Table 4 : Effects of age misstatement in the census on mortality indicators in women Indices Census

  • Cen. corrected

HDSS

  • Rela. Diff1
  • Rela. Diff2

5q0 97 96 115

  • 16%
  • 16%

10q5 16 16 21

  • 24%
  • 26%

45q15 166 222 218

  • 24%

2% 20q60 477 462 584

  • 29%
  • 21%

e0 68.4 67.7 61.8 11% 10%

(1) Relative difference uncorrected estimates vs HDSS (2) Relative difference corrected estimates vs HDSS 18 / 20

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Background Data and methods Key findings Conclusion

Some limitations

  • Age misreporting may affect some groups of individuals in the

HDSS : migrants, Enumarated population.

  • Age errors mat be larger among individuals we failed to

matched compared to those who were successfully linked

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Background Data and methods Key findings Conclusion

  • It is likely that mortality rates underestimated in the 2006

census, particularly in elderly and women

  • Omissions of deaths play a larger role than age errors in

explaining the gaps.

  • There is a crucial need to develop innovative ways to improve

the reporting of demographic events.

  • Comparisons in other HDSSs sites of SSA may be a starting

point to inform adjustements made to census estimates.

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