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Activity Number: 595 - Global Estimates of Morbidity and Mortality
Type: Invited
Date/Time: Thursday, August 1, 2019 : 8:30 AM to 10:20 AM
Sponsor: Health Policy Statistics Section
Abstract #300343
Title: Monitoring Maternal Mortality by the United Nations (UN MMEIG): Improved Estimates of Levels, Trends and Reporting Errors Through Bayesian Multilevel Temporal Regression Modeling
Author(s): Leontine Alkema* and Emily Peterson and Doris Chou and Ann Beth Moller and Lale Say
Companies: University of Massachusetts Amherst and University of Massachusetts Amherst and World Health Organization and World Health Organization and World Health Organization
Keywords: Bayesian multilevel temporal regression model; United Nations Sustainable Development Goals; maternal mortality ratio; bias adjustment; missing data

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). Estimates are constructed using a Bayesian multilevel temporal regression model (BMat) that captures accelerations and decelerations in the rate of change in the MMR and accounts for varying reporting and data quality issues. In this presentation, we summarize BMat and highlight recent work to account for misclassification biases in the reporting of maternal mortality in vital registration systems. We highlight methodological considerations in model development to provide estimates in diverse contexts and limited data settings.

Authors who are presenting talks have a * after their name.

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