Exploring the spatial and temporal dynamics of the health index of Bangladesh using Bayesian framework

Health is vital for survival and societal well-being, with global goals focusing on improved access and achieving progress in public health. In Bangladesh, investigating health determinants and disparities is crucial for effective interventions.

This study investigates spatial and temporal variations in the influence of socioeconomic and health-related factors on the health index at a detailed district level in Bangladesh, aiming to discern disparities in health indicators among districts. Based on 21 years of secondary data across 64 districts in Bangladesh, this research deploys four Bayesian spatial-temporal models to analyze the health index and relevant variables.

The study identifies the optimal model by evaluating linear, analysis of variance, and autoregressive models of orders one and two. Comparative analysis utilizing the WAIC and DIC aids model selection. The MCMC method enhances precision in determining health index determinants, providing valuable insights for policymakers and researchers.

Our findings reveal that the AR (2) model demonstrated superior performance among all the spatiotemporal models. The health index demonstrated a consistent upward trend overall, but regional variations in residents' health indices were noted. Positive spatial autocorrelation was identified in the health index, emphasizing interconnected health outcomes. Spatiotemporal models underscored the significance of the income index, child mortality, life expectancy, and the proportion of the active population as crucial factors shaping the health index in Bangladesh.

This study provides actionable insights for targeted health interventions in districts with specific needs, emphasizing focused policies on poverty, education, and healthcare to enhance overall well-being in Bangladesh.