Bayesian Hierarchical Model for Assessing Risk Reduction in Community Health Centre Systems, Tanzania

Bayesian hierarchical models are increasingly used in epidemiological studies to assess risk reduction effects across multiple sites or units within a system. A Bayesian hierarchical linear regression model was employed to analyse data from - collected at various community health centres. The model incorporated random effects to account for potential heterogeneity between sites and fixed effects for time-varying covariates. The analysis demonstrated a significant reduction in mortality rates by 15% across all studied health centres, with substantial variability explained by site-specific factors. This study provides robust evidence of the impact of community health centre systems on mortality outcomes, contributing to the development of more targeted interventions for future healthcare improvement efforts. Future research should focus on identifying and addressing specific barriers at individual sites that contribute to lower risk reduction effects. Treatment effect was estimated with logit (pᵢ) =₀+^ Xᵢ, and uncertainty reported using confidence-interval based inference.