Precision identification of maternal health vulnerability in sub-Saharan African conurbations using advanced geospatial approaches

Sub-Saharan African (SSA) countries have the highest maternal mortality ratios globally, despite most deaths being preventable through equitable and timely access to high-quality healthcare during pregnancy and childbirth. This is due to factors (obstacles) which make women vulnerable: environmental (pollution, climate change), socio-demographic (poverty, lack of education), disease burden, poor access to and low-quality healthcare, suboptimal health-seeking, and dysfunctional health systems. The majority of the SSA population now live in urban areas, yet we do not fully understand how these factors operate and interact in urban settings to produce multidimensional vulnerabilities to poor outcomes. Examining these factors within a novel multidimensional index at high spatial and temporal resolution can help accurately pinpoint areas with women at the highest risk.

 

I will examine the variation of ~30 factors across 20 SSA conurbations and construct a maternal-health vulnerability index to identify areas with concurrent disadvantages across space and time (2000-2024). I will use satellite imagery, national household surveys, and socio-demographic data within a suite of advanced geospatial methods with robust validation. Such a combination of methods, datasets, and context has not been done before. I will disseminate outputs to the scientific community and to policymaking stakeholders to inform policies and actions to reduce maternal deaths in urban SSA.