Estimating misclassification errors in the reporting of maternal mortality in national civil registration vital statistics systems: A Bayesian hierarchical bivariate random walk model to estimate sensitivity and specificity for multiple countries and years with missing data

• Type of Material: Article
• Authors: Emily Peterson, Emory University; Doris Chou, World Health Organization, Geneva; Ann-Beth Moller, World Health Organization, Geneva; Alison Gemmill, Johns Hopkins University; Lale Say, World Health Organization; Leontine Alkema, University of Massachusetts Amherst
• Language: English
• Date: 2022-06-30
• Publisher: Wiley
• Publication Version: Final Published Version
• Copyright Statement: © 2022 World Health Organization; licensed by John Wiley & Sons Ltd. Statistics in Medicine published by John Wiley & Sons Ltd.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1002/sim.9335
• Title of Journal or Parent Work: STATISTICS IN MEDICINE
• Volume: 41
• Issue: 14
• Start Page: 2483
• End Page: 2496
• Grant/Funding Information: WHO, USAID, University of Massachusetts Amherst
• Keywords: Bayesian hierarchical models; misclassification; maternal mortality
• Research Categories: Biology, Biostatistics

Abstract

Civil registration vital statistics (CRVS) systems provide data on maternal mortality that can be used for monitoring trends and to inform policies and programs. However, CRVS maternal mortality data may be subject to substantial reporting errors due to misclassification of maternal deaths. Information on misclassification is available for selected countries and periods only. We developed a Bayesian hierarchical bivariate random walk model to estimate sensitivity and specificity for multiple populations and years and used the model to estimate misclassification errors in the reporting of maternal mortality in CRVS systems. The proposed Bayesian misclassification (BMis) model captures differences in sensitivity and specificity across populations and over time, allows for extrapolations to periods with missing data, and includes an exact likelihood function for data provided in aggregated form. Validation exercises using maternal mortality data suggest that BMis is reasonably well calibrated and improves upon the CRVS‐adjustment approach used until 2018 by the UN Maternal Mortality Inter‐Agency Group (UN‐MMEIG) to account for bias in CRVS data resulting from misclassification error. Since 2019, BMis is used by the UN‐MMEIG to account for misclassification errors when estimating maternal mortality using CRVS data.