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 of the world there exists very little data on the causes of maternal death, and data that do exist do not capture the entire population of 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 surveillence systems. The framework accounts of 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: Canada, Nigeria and the United States. ∗monica.alexander@utoronto.ca. The authors would like to thank Jenny Cresswell, Doris Chou and Ann-Beth Moller from Department of Sexual and Reproductive Health and Research of the World Health Organization for their assistance with this project. The authors also sincerely thank Daniel Simpson for his assistance and input in the earlier stages of this project. Finally, valuable feedback on model and estimates was given by members of the Technical Advisory Group on Causes of Maternal Death. 1 ar X iv :2 10 1. 05 24 0v 1 [ st at .A P] 1 3 Ja n 20 21