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Dwyer-Lindgren L, Gakidou E, Flaxman A, et al. Error and bias in under-5 mortality estimates derived from birth histories with small sample sizes. Popul Health Metr. 2013;11(1):13doi: 10.1186/1478-7954-11-13.
Dwyer-Lindgren, L., Gakidou, E., Flaxman, A., & Wang, H. (2013). Error and bias in under-5 mortality estimates derived from birth histories with small sample sizes. Population health metrics, 11(1), 13. https://doi.org/10.1186/1478-7954-11-13
Dwyer-Lindgren, Laura, et al. "Error and bias in under-5 mortality estimates derived from birth histories with small sample sizes." Population health metrics vol. 11,1 (2013): 13. doi: https://doi.org/10.1186/1478-7954-11-13
Dwyer-Lindgren L, Gakidou E, Flaxman A, Wang H. Error and bias in under-5 mortality estimates derived from birth histories with small sample sizes. Popul Health Metr. 2013 Jul 26;11(1):13. doi: 10.1186/1478-7954-11-13. PMID: 23885746; PMCID: PMC3729428.
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Popul Health Metr. 2013 Jul 26;11(1):13. doi: 10.1186/1478-7954-11-13.

Error and bias in under-5 mortality estimates derived from birth histories with small sample sizes.

Population health metrics

Laura Dwyer-Lindgren, Emmanuela Gakidou, Abraham Flaxman, Haidong Wang

Affiliations

  1. Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA. [email protected].

PMID: 23885746 PMCID: PMC3729428 DOI: 10.1186/1478-7954-11-13

Abstract

BACKGROUND: Estimates of under-5 mortality at the national level for countries without high-quality vital registration systems are routinely derived from birth history data in censuses and surveys. Subnational or stratified analyses of under-5 mortality could also be valuable, but the usefulness of under-5 mortality estimates derived from birth histories from relatively small samples of women is not known. We aim to assess the magnitude and direction of error that can be expected for estimates derived from birth histories with small samples of women using various analysis methods.

METHODS: We perform a data-based simulation study using Demographic and Health Surveys. Surveys are treated as populations with known under-5 mortality, and samples of women are drawn from each population to mimic surveys with small sample sizes. A variety of methods for analyzing complete birth histories and one method for analyzing summary birth histories are used on these samples, and the results are compared to corresponding true under-5 mortality. We quantify the expected magnitude and direction of error by calculating the mean error, mean relative error, mean absolute error, and mean absolute relative error.

RESULTS: All methods are prone to high levels of error at the smallest sample size with no method performing better than 73% error on average when the sample contains 10 women. There is a high degree of variation in performance between the methods at each sample size, with methods that contain considerable pooling of information generally performing better overall. Additional stratified analyses suggest that performance varies for most methods according to the true level of mortality and the time prior to survey. This is particularly true of the summary birth history method as well as complete birth history methods that contain considerable pooling of information across time.

CONCLUSIONS: Performance of all birth history analysis methods is extremely poor when used on very small samples of women, both in terms of magnitude of expected error and bias in the estimates. Even with larger samples there is no clear best method to choose for analyzing birth history data. The methods that perform best overall are the same methods where performance is noticeably different at different levels of mortality and lengths of time prior to survey. At the same time, methods that perform more uniformly across levels of mortality and lengths of time prior to survey also tend to be among the worst performing overall.

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