Modeling High-Priority Interventions to Reduce Aggregate Maternal and Infant Mortality in India

Abstract

Though India has made rapid progress in reducing its maternal and infant mortality rates, the country is still burdened with more than a quarter of the world’s maternal deaths and is the world’s largest single contributor to infant deaths. The purpose of this thesis was to aggregate maternal and infant mortality into a single model to determine how the system would respond to various public health interventions. Using existing survey data and demographic modeling, baseline mortality rates were calculated for three different conditions: per pregnancy cohort, per cohort of non-pregnant women, and per lifetime cohort (that is, throughout the course of a cohort’s reproductive years). Intervention conditions were then simulated at different points within the pregnancy and infancy processes. A major finding of this thesis is that, in the absence of other interventions, programs to reduce maternal mortality can actually increase infant mortality, as mothers survive to give birth to more infants overall. Conversely, though stillbirth prevention leads to a decrease in the overall number of infant deaths, it increases the rate of mortality per 10,000 live births. Despite these findings, the strongest evidence base for these interventions falls to early infant mortality prevention, which faces the greatest burden of mortality.