Bayesian Hierarchical Model for Assessing Risk Reduction in District Hospitals Systems in Uganda,

In Uganda, district hospitals play a critical role in healthcare delivery, but their performance varies widely. Previous studies have aimed to enhance these systems through various interventions, yet few have employed rigorous statistical methods to evaluate risk reduction. A Bayesian hierarchical model was developed to analyse data from multiple hospitals across different districts. This model accounts for both within-hospital variability and district differences, providing robust estimates of risk reduction across the system. The analysis revealed a significant reduction in patient wait times by approximately 15% (95% CI: -20%, -8%) after implementing targeted interventions. This finding underscores the model's ability to capture complex hospital performance variability. The Bayesian hierarchical model demonstrated its efficacy in evaluating risk reduction within district hospitals, offering a methodological advancement for future studies and policy-making. Further research should validate these findings through replication and longitudinal data collection. Policy makers could use this approach to guide the allocation of resources and interventions to maximise system performance. Treatment effect was estimated with logit (pᵢ) =₀+^ Xᵢ, and uncertainty reported using confidence-interval based inference.