Fitting Cox proportional hazard models to identify mortality predictors during pregnancy and postpartum periods

Mahama, Tahiru (2025) Fitting Cox proportional hazard models to identify mortality predictors during pregnancy and postpartum periods. World Journal of Advanced Research and Reviews, 26 (3). 027-047. ISSN 2581-9615

Maternal mortality remains a major global health challenge, particularly in low- and middle-income countries, where pregnancy and postpartum periods are associated with elevated risks of death due to both obstetric and non-obstetric complications. To identify predictors of mortality during these critical periods, this study employs Cox proportional hazards models, a robust survival analysis technique suited for time-to-event data. The objective is to evaluate time-dependent risks and determine significant covariates that influence maternal survival from conception through the first six weeks postpartum. Using longitudinal data from national reproductive health surveillance systems and hospital-based cohorts, we assess various socio-demographic, clinical, and obstetric factors, including age, parity, antenatal care utilization, mode of delivery, pre-existing conditions, and obstetric complications. The Cox model accommodates censoring and permits the estimation of hazard ratios (HRs), quantifying the relative risk of mortality associated with each predictor while adjusting for confounders. Our findings highlight critical predictors of maternal mortality, including advanced maternal age, delayed antenatal care, hypertensive disorders, cesarean delivery, and postpartum hemorrhage. Interaction terms and stratified analyses further reveal context-specific risk amplifiers, such as rural residence and limited health facility access. Proportional hazard assumptions are verified using Schoenfeld residuals and time-varying covariate assessments to ensure model validity. By identifying and quantifying key mortality predictors, this research offers valuable insights for targeted clinical interventions and policy-level strategies aimed at reducing maternal deaths. The integration of survival modeling into maternal health research enhances our understanding of temporal risk dynamics and supports evidence-based improvements in perinatal care delivery systems.