A prospective cause of death classification system for maternal deaths in low and middle-income countries: Results from the global network maternal newborn health registry

Omrana Pasha, Aga Khan University
Elizabeth M. McClure
Sarah Saleem
Shiyam Sunder
Adrien Lokangaka
Antoinette Tshefu
Carl L. Bose
Melissa Bauserman
Musaku Mwenechanya
Elwyn Chomba

Abstract

Objective: To describe the causes of maternal death in a population-based cohort in six low and middle-income countries using a standardized, hierarchical, algorithmic cause of death (COD) methodology.Design: A population-based, prospective observational study.SETTING: Seven sites in six low-middle income countries including the Democratic Republic of the Congo (DRC), Guatemala, India (2), Kenya, Pakistan and Zambia.POPULATION: All deaths amongst pregnant women resident in the study sites from 2014 to December 2016.Methods: For women who died, we used a standardized questionnaire to collect clinical data regarding maternal conditions present during pregnancy and delivery. These data were analyzed using a computer-based algorithm to assign cause of maternal death based on the International Classification of Disease - Maternal Mortality system (trauma, abortion-related, eclampsia, hemorrhage, pregnancy-related infection and medical conditions). We also compared the COD results to health care provider assigned maternal COD.MAIN OUTCOME MEASURES: Assigned causes of maternal mortality.Results: Amongst 158,205 women, there were 221 maternal deaths. The most common algorithm-assigned maternal COD were obstetric hemorrhage (38.6%), pregnancy-related infection (26.4%) and preeclampsia/eclampsia (18.2%). Agreement between algorithm-assigned COD and COD assigned by health care providers ranged from 75% for hemorrhage to 25% for medical causes coincident to pregnancy.CONCLUSIONS: The major maternal COD in the Global Network sites were hemorrhage, pregnancy-related infection and preeclampsia/eclampsia. This system could allow public health programs in low and middle-income countries to generate transparent and comparable data for maternal COD across time or regions.