A Bayesian approach to the global estimation of maternal mortality
Leontine Alkema, Sanqian Zhang, Doris Chou, Alison Gemmill, Ann-Beth, Moller, Doris Ma Fat, Lale Say, Colin Mathers, Daniel Hogan

TL;DR
This paper introduces a Bayesian maternal mortality estimation model that incorporates time series analysis and data quality adjustments to produce more accurate, data-driven country-specific MMR trends for global health monitoring.
Contribution
It extends the UN MMEIG multilevel regression model by integrating an ARIMA time series component and source-specific data models for improved maternal mortality estimates.
Findings
Enhanced data-driven MMR trend estimation.
Better handling of data quality and reporting issues.
Applicable for global maternal health monitoring.
Abstract
The maternal mortality ratio (MMR) is defined as the number of maternal deaths in a population per 100,000 live births. Country-specific MMR estimates are published on a regular basis by the United Nations Maternal Mortality Estimation Inter-agency Group (UN MMEIG) to track progress in reducing maternal deaths and to evaluate regional and national performance related to Millennium Development Goal (MDG) 5, which calls for a 75% reduction in the MMR between 1990 and 2015. Until 2014, the UN MMEIG used a multilevel regression model for producing estimates for countries without sufficient data from vital registration systems. While this model worked well in the past to assess MMR levels for countries with limited data, it was deemed unsatisfactory for final MDG 5 reporting for countries where longer time series of observations had become available because by construction, estimated…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
