Bayesian Hierarchical Model for Evaluating Clinical Outcomes in Kenyan Community Health Centres Systems

Community health centers (CHCs) in Kenya are critical for delivering healthcare services to underserved populations. However, evaluating their effectiveness is challenging due to variability across different settings and time periods. A Bayesian hierarchical linear regression model was employed to analyse data from multiple CHCs across different regions. The model incorporated region-specific intercepts and slopes to capture regional differences in clinical outcomes. The model revealed significant variability in treatment success rates among regions, with a notable difference of 15% between the highest- and lowest-performing regions. This study demonstrated the utility of Bayesian hierarchical models for understanding complex healthcare systems, providing insights into how CHCs can be optimised to improve patient outcomes. The findings should inform policy decisions aimed at enhancing resource allocation in CHC networks across Kenya. Bayesian hierarchical model, clinical outcomes, community health centers, Kenyan healthcare system Treatment effect was estimated with logit (pᵢ) =₀+^ Xᵢ, and uncertainty reported using confidence-interval based inference.