Modelling child survival in malaria-endemic regions of Kenya using Bayesian Generalized Log Logistic models

Abstract

Child mortality remains a major public health challenge in malaria-endemic regions, particularly in sub-Saharan Africa, where children under five face disproportionately high risks. In Kenya, the burden is amplified by factors such as limited healthcare access, socioeconomic disparities, and insufficient malaria prevention. This study develops and applies a Bayesian Generalized Log-Logistic (GLL) survival model to examine how maternal health practices, regional and household-level socioeconomic conditions influence child survival in malaria endemic regions. Using nationally representative data from the 2020 Kenya Demographic and Health Survey (KDHS) and Malaria Indicator Survey (MIS), the model captures a wide range of hazard shapes, including both increasing and decreasing risk patterns. To strengthen the model’s theoretical foundation, its asymptotic properties were derived to ensure consistency and efficiency in parameter estimation under large-sample conditions. The Bayesian framework further enables robust inference by incorporating prior information and quantifying uncertainty. Posterior predictive checks demonstrated good model fit, confirming the model’s capacity to reflect the observed survival dynamics. Key predictors of child survival included antenatal care utilization, household wealth, regional malaria endemicity, and malaria prevention behaviors. The study concludes that the Bayesian GLL model is a robust and flexible tool for understanding child mortality risk and can inform the design of targeted public health interventions in high-burden settings like Kenya.