Incompleteness and Misclassification of Maternal Deaths in Zimbabwe: Data from Two Reproductive Age Mortality Surveys, 2007–2008 and 2018–2019 - Reuben Musarandega; Lennarth Nystrom; Grant Murewanhema; Chipo Gwanzura; Solwayo Ngwenya; Robert Pattinson; Rhoderick Machekano; Stephen Peter Munjanja; and the Zimbabwe Maternal and Perinatal Mortality Study Group (Journal of Epidemiology and Global Health)
Abstract 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.