Musarandega, ReubenNystrom, LennarthMurewanhema, GrantGwanzura, ChipoNgwenya, SolwayoPattinson, Robert CliveMachekano, RhoderickMunjanja, Stephen PeterZimbabwe Maternal and Perinatal Mortality Study Group2025-04-102025-04-102024-12Musarandega, R., Nystrom, L., Murewanhema, G. et al. 2024, 'Incompleteness and misclassification of maternal deaths in Zimbabwe : data from two reproductive age mortality surveys, 2007-2008 and 2018-2019', Journal of Epidemiology and Global Health, vol. 14, pp. 1642-1649. https://DOI.org/10.1007/s44197-024-00318-1.2465-755710.1007/s44197-024-00318-1http://hdl.handle.net/2263/102001DATA AVAILABILITY : Data will be made available on a reasonable request.INTRODUCTION : We implemented two cross-sectional reproductive age mortality surveys in 2007–2008 and 2018–2019 to assess changes in the MMR and causes of death in Zimbabwe. We collected data from health institutions, civil registration and vital statistics, the community, and surveillance. This paper analyses missingness and misclassification of deaths in the two surveys. METHODS : We compared proportions of missed and misclassified deaths in the surveys using Chi-square or Fisher’s exact tests. Using log-linear regression models, we calculated and compared risk ratios of missed deaths in the data sources. We assessed the effect on MMRs of misclassifying deaths and analysed the sensitivity and specificity of identifying deaths in the surveys using the six-box method and risk ratios calculated through Binomial exact tests. RESULTS : All data sources missed and misclassified the deaths. The community survey was seven times [RR 7.1 (5.1–9.7)] and CRVS three times [RR 3.4 (2.4–4.7)] more likely to identify maternal deaths than health records in 2007–08. In 2018–19, CRVS [RR 0.8 (0.7–0.9)] and surveillance [RR 0.7 (0.6–0.9)] were less likely to identify maternal deaths than health records. Misclassification of causes of death significantly reduced MMRs in health records [RR 1.4 (1.2–1.5)]; CRVS [RR 1.3 (1.1– 1.5)] and the community survey/surveillance [RR 1.4 (1.2–1.6)]. CONCLUSION : Incompleteness and misclassification of maternal deaths are still high in Zimbabwe. Maternal mortality studies must triangulate data sources to improve the completeness of data while efforts to reduce misclassification of deaths continue.en© The Author(s) 2024. Open access. This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.Pregnancy-related deathsMaternal deathsMaternal mortalityMissingnessIncompletenessMisclassificationSDG-03: Good health and well-beingMaternal mortality ratios (MMR)Article