  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.
