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Demography

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01.04.2014

Reporting Errors in Siblings’ Survival Histories and Their Impact on Adult Mortality Estimates: Results From a Record Linkage Study in Senegal

verfasst von: Stéphane Helleringer, Gilles Pison, Almamy M. Kanté, Géraldine Duthé, Armelle Andro

Erschienen in: Demography | Ausgabe 2/2014

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Abstract

Estimates of adult mortality in countries with limited vital registration (e.g., sub-Saharan Africa) are often derived from information about the survival of a respondent’s siblings. We evaluated the completeness and accuracy of such data through a record linkage study conducted in Bandafassi, located in southeastern Senegal. We linked at the individual level retrospective siblings’ survival histories (SSH) reported by female respondents (n = 268) to prospective mortality data and genealogies collected through a health and demographic surveillance system (HDSS). Respondents often reported inaccurate lists of siblings. Additions to these lists were uncommon, but omissions were frequent: respondents omitted 3.8 % of their live sisters, 9.1 % of their deceased sisters, and 16.6 % of their sisters who had migrated out of the DSS area. Respondents underestimated the age at death of the siblings they reported during the interview, particularly among siblings who had died at older ages (≥45 years). Restricting SSH data to person-years and events having occurred during a recent reference period reduced list errors but not age and date errors. Overall, SSH data led to a 20 % underestimate of ₄₅ q ₁₅ relative to HDSS data. Our study suggests new quality improvement strategies for SSH data and demonstrates the potential use of HDSS data for the validation of “unconventional” demographic techniques.

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In such analyses, “adulthood” is most commonly defined as the age range 15–49 years or 15–59 years. The most commonly used indicators of adult mortality are thus ₃₅ q ₁₅ or ₄₅ q ₁₅.

The primary objectives of a Demographic and Health Survey often focus on estimating the total fertility rate and infant/child mortality rates in a given country. The sample size calculations for each DHS thus aim to achieve a given level of precision for these indicators. Births and infant/child deaths are significantly more common than adult deaths in sub-Saharan populations, however. As a result, even if a respondent reports about several adult siblings during SSH, SSH may still yield an insufficient number of reports of deaths to enable specific analyses.

For example, in a cohort of 20 women in which 10 deaths occur, all at age 35, the true death rate at adult ages ₄₅ M ₁₅ is 10 / [(10 × 20) + (10 × 45)] = 15.4 per 1,000. A list error in which a respondent omits one deceased sister would yield ₄₅ M ₁₅ = 9 / [(9 × 20) + (10 × 45)] = 14.3 per 1,000, whereas a vital status error in which a respondent misclassifies one deceased sister as alive would yield ₄₅ M ₁₅ = 9 / [(9 × 20) + (11 × 45)] = 13.3 per 1,000.

For example, in a cohort of 20 women in which 10 deaths occur, all at age 35, the “true” death rate at adult ages ₄₅ M ₁₅, obtained from a complete and accurate reference data set, is 10 / [(10 × 20) + (10 × 45)] = 15.4 per 1,000. If one of these women is omitted by respondents during SSH (i.e., list error), but respondents make no age errors, then the estimated death rate ₄₅ M ₁₅ would be 9 / [(9 × 20) + (10 + 45)] = 14.3 per 1,000. On the other hand, the estimated death rate ₄₅ M ₁₅ would also be 14.3 per 1,000 if respondents reported all their deceased sisters (i.e., no list errors) but overestimated the age at death of their sisters by an average of five years (age errors).

VR data are also collected prospectively in theory, but events may be registered late (e.g., children are registered when they reach school age) so that VR data are affected by significant age/date errors.

Several comparisons of SSH and HDSS data have already been conducted, but these have compared DSS data for small areas with SSH estimates from national data sets (Obermeyer et al. 2010). In these comparisons, differences in estimates of adult mortality between SSH and HDSS may have been due to idiosyncrasies of the local HDSS populations rather than issues with SSH data quality. Some comparisons were conducted within the same geographic areas but did not control for differences in sampling universe between HDSS and SSH data sets (e.g., Ngom et al. 1999). To the best of our knowledge, only one study comparing SSH and HDSS was based on a linkage design (Shahidullah 1995). This study, however, was limited to assessing the reporting of maternal deaths.

In oral genealogies, multiple informants are interviewed to confirm the existence and nature of kinship ties between population members. See, for example, Pison (1987), Quinlan and Hagen (2008), and Silagan (1986).

The Bandafassi HDSS differs from other HDSS in that respect given that oral genealogies are not necessarily conducted at the time of the census in each HDSS. In other HDSS, the data on maternal sibships of individuals who were already present in the population at the time of the initial census may be missing or may be obtained from one single informant. Lists of maternal siblings may thus be less complete/accurate in other HDSS.

The SSH survey took place before the 2010 HDSS data became available; as a result, the reference period and all results herein do not include deaths and person-years from 2010.

To trace migrants, we first conducted a short migration survey with a member of the last known HDSS residence of the migrant. This survey included information on destination, new address (if available), phone number, and other information (open-ended) that may be useful for migrant tracing.

The full questionnaire, in French, is available online (http://www.columbia.edu/~sh2813/BandafassiQuest.pdf).

Pairs of reviewers were randomly selected among the study’s coauthors. Each team member was asked to select an ID number from the HDSS files for each SSH-reported sibling based on the available information.

The concordance was assessed by comparing the ID number attributed by each member.

Some siblings were reported as adults by the SSH and recorded as children by the HDSS, and vice versa.

In Bandafassi, different persons report demographic events during demographic surveillance and SSH: for example, key informants may be health workers posted in the rural villages or heads of compounds (predominantly older males), whereas SSH respondents were women aged 15–59. However, the HDSS and SSH data may not be entirely independent because, for example, key informants may have heard about the death of an individual by relatives of the deceased who may be interviewed during SSH surveys. Similarly, some deaths may be less likely to be reported in both data sources because of the cause of death or the location where the death took place. For example, if a DSS resident seeks health care in a city and dies while away, his/her death may be less likely to be reported both in the DSS and in the SSH data sets. Capture-recapture estimates thus likely overestimate the completeness of each data source.

This is the case because multiple sisters were interviewed by sibship (see Table 1).

Sensitivity refers to the proportion of deaths among adult sisters recorded in the HDSS who are reported as deceased in the SSH survey.

Specificity refers to the proportion of alive sisters recorded in the HDSS who are reported as alive in the SSH survey.

It appears that in a few cases, interviewers mistakenly recorded the date of death in the space where the age at death should have been recorded, and vice versa. For example, if a respondent reported that her sibling died three years ago at age 37, the interviewer could have mistakenly recorded that the death occurred 37 years ago at age 3. This generates very large age and date errors that cannot be detected by data editors because they leave the reported birth intervals between siblings unaffected.

Sensitivity refers to the proportion of deaths observed during the reference period (2002–2009) that were reported by SSH respondents as having occurred during that time frame.

Specificity refers to the proportion of deaths observed before the reference period (before 2002) that were reported by SSH respondents as having occurred during that timeframe.

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Titel: Reporting Errors in Siblings’ Survival Histories and Their Impact on Adult Mortality Estimates: Results From a Record Linkage Study in Senegal
verfasst von: Stéphane Helleringer
Gilles Pison
Almamy M. Kanté
Géraldine Duthé
Armelle Andro
Publikationsdatum: 01.04.2014
Verlag: Springer US
Erschienen in: Demography / Ausgabe 2/2014
Print ISSN: 0070-3370
Elektronische ISSN: 1533-7790
DOI: https://doi.org/10.1007/s13524-013-0268-3

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