Estimating causes of maternal death in data-sparse contexts

TL;DR

This paper introduces a Bayesian hierarchical model to estimate global, regional, and country-specific maternal death causes, effectively integrating diverse data sources and handling data gaps to inform health policies.

Contribution

It presents a novel Bayesian framework that combines multiple data sources to estimate maternal death causes in data-sparse contexts, accounting for data quality and missing causes.

Findings

Model provides cause-specific estimates for countries with limited data

Framework effectively integrates diverse data sources

Case studies demonstrate model adaptability to different data scenarios

Abstract

Understanding the underlying causes of maternal death across all regions of the world is essential to inform policies and resource allocation to reduce the mortality burden. However, in many countries there exists very little data on the causes of maternal death, and data that do exist do not capture the entire population at risk. In this paper, we present a Bayesian hierarchical multinomial model to estimate maternal cause of death distributions globally, regionally, and for all countries worldwide. The framework combines data from various sources to inform estimates, including data from civil registration and vital systems, smaller-scale surveys and studies, and high-quality data from confidential enquiries and surveillance systems. The framework accounts for varying data quality and coverage, and allows for situations where one or more causes of death are missing. We illustrate the results of the model on three case-study countries that have different data availability situations.