Estimating causes of maternal death in data‐sparse contexts

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 captur...

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Bibliographic Details
Published in:Statistics in medicine 2024-10, Vol.43 (24), p.4702-4735
Main Authors: Chong, Michael Y. C., Pejchinovska, Marija, Alexander, Monica
Format: Article
Language:English
Subjects:
Bayesian modeling
cause of death
global health
maternal mortality
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Summary: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 article, 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.
ISSN:0277-6715
1097-0258
1097-0258
DOI:10.1002/sim.10199