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Civil registration vital statistics (CRVS) data are used to produce nationalestimates of maternal mortality, but are often subject to substantial reportingerrors due to misclassification of maternal deaths. The accuracy of CRVSsystems can be assessed by comparing CRVS-based counts of maternal andnon-maternal deaths to those obtained from specialized studies, which arerigorous assessments of maternal mortality for a given country-period. Wedeveloped a Bayesian bivariate random walk model to estimate sensitivity andspecificity of the reporting on maternal mortality in CRVS data, and associatedCRVS adjustment factors. The model was fitted to a global data set of CRVS andspecialized study data. Validation exercises suggest that the model performswell in terms of predicting CRVS-based proportions of maternal deaths forcountry-periods without specialized studies. The new model is used by the UNMaternal Mortality Inter-Agency Group to account for misclassification errorswhen estimating maternal mortality using CRVS data.

Estimating maternal mortality using data from national civil  registration vital statistics systems: A Bayesian hierarchical bivariate  random walk model to estimate sensitivity and specificity of reporting