A Bayesian Approach to Estimate Maternal Mortality Globally Using National Civil Registration Vital Statistics Data Accounting for Reporting Errors

Abstract

AbstractEstimation and monitoring of mortality, across multiple population-periods, is vital to improving national and global health outcomes. Reducing maternal mortality is a key part of the Sustainable Development Goals (SGDs), which commit countries and international agencies to monitor progress toward improvement of global maternal health outcomes. Country-specific estimates of maternal mortality are used for monitoring country progress. Civil registration vital statistics (CRVS) data provide valuable information on maternal mortality but are often subject to substantial reporting errors due to misclassification of maternal deaths or incompleteness of CRVS-reporting on deaths to women of reproductive age. Motivated by the challenge of estimating maternal mortality using error-prone CRVS data, this article introduces a Bayesian two-stage approach to obtain country-specific trends in maternal mortality ratios (MMRs), accounting for CRVS-related misclassification errors. In the first stage, we produce country-specific trends of CRVS-related data quality metrics, in terms sensitivity and specificity, through a Bayesian hierarchical misclassification model. In the second stage, we produce data-driven country trends in MMRs and proportion maternal deaths out of all-cause deaths, which are constructed using a Bayesian maternal mortality estimation model, accounting for CRVS-related data quality assessed in Stage 1. We present the approach to estimating maternal mortality and illustrate its use for several country case studies. This approach is used by the United National Maternal Mortality Interagency Group (UN-MMEIG) to produce maternal mortality estimates for global monitoring.

Keywords

• Bayesian hierarchical models
• Maternal mortality
• Time series