MATHEMATICS AND STATISTICS COLLOQUIUM: Improving the delivery of healthcare to pregnant women in sub-Saharan Africa with statistics and data science
Isabel Fulcher, Postdoctoral Fellow, Harvard Data Science Initiative
Monday, December 2, 4:00 PM, Olin 1
Refreshments at 3:30 PM, Davis 2nd floor
Nearly 250 million women of childbearing age live in sub-Saharan Africa and face significant obstacles in receiving vital health care. Despite significant improvements over the past two decades, pregnant women in this region still lack access to quality care during pregnancy, including skilled birth attendants during childbirth. Specifically, an estimated 1 in 38 women die of a pregnancy-related complication, a rate 50 times higher than the United States. Almost all of these deaths are preventable. In response to these alarming rates, a wide variety of health interventions have been implemented in many regions of sub-Saharan Africa. However, the efficacy of these interventions has often been left unqualified, leaving many pressing questions, including: Does a particular intervention improve health outcomes among pregnant women? Are there certain populations of women not benefiting from the intervention? How can we fine-tune the intervention to accelerate improvements? Statistics and data science approaches, specifically the application of causal inference and machine learning methods, are needed to provide answers to these open questions. As anecdotes of unique challenges that arise in such contexts, I will discuss my ongoing work with two very different interventions – a large-scale digital maternal health program in Zanzibar and the introduction of ultrasound machines at rural health clinics in Rwanda. The goal of this talk is to elucidate some of the exciting ways that applied statistics can be used to inform medical and policy decisions in global health contexts.