Measuring the Causes and Circumstances of Deaths beyond 42 Days Postpartum in Kenya, The Gambia, Malawi, and South Africa: Implications for Global Monitoring and Postpartum Care

Our previous research demonstrated that mortality remains elevated until four months postpartum in sub-Saharan Africa, but little is known about the causes of pregnancy-related deaths (PRD) beyond the standard 42-day postpartum period. This evidence gap is severe in sub-Saharan Africa, where cause of death information largely fails to meet international standards. We are now investigating the causes of deaths associated with this prolonged risk and the implications for measurement and clinical care.

Which First Trimester Risk-Estimation Method for Pre-eclampsia Is Most Suitable? A Model-Based Impact Study

Low-dose aspirin treatment reduces the risk of pre-eclampsia (PE) among high-risk pregnant women. Internationally, several risk-calculation strategies are available in the first trimester. The objective of this study was to assess costs and benefits of different first-trimester PE risk estimation algorithms - EXPECT (an algorithmic prediction model based on maternal characteristics), NICE (a checklist of risk factors), and the Fetal Medicine Foundation (a prediction model using additional uterine artery Doppler measurement and lab testing) - coupled with low-dose aspirin treatment, in comparison to no-screening, in nulliparous pregnant women.

Development of Risk-Prediction Models for Maternal and Neonatal Complications Using Machine Learning across the Continuum of Care in a Resource-Constrained Environment

To improve maternal and neonatal outcomes, health systems must identify individuals at risk to make informed decisions about priority clinical interventions and resource allocation. Our objective was to design risk stratification algorithms across the continuum of care.

Algorithms to Predict Newborn Complications in the First 28 days of Life in Eastern Uganda (N-COP Study)

Complications following preterm birth cause morbidity and mortality. Globally, newborn complications account for approximately 28% of neonatal deaths. Preemies are between six and 26 times more likely to die during the neonatal period than term newborns. Mathematical and statistical algorithms can be used to predict the risk of development of complications and adverse outcomes among preemies and can be used to drive proactive measures to anticipate, prevent, prepare management, and improve survival in the short and long run. We developed algorithms to predict newborn complications and estimate outcomes within the first 28 days of life.