Bayesian Hierarchical Model for Evaluating Cost-Effectiveness in Senegalese District Hospitals Systems

The evaluation of district hospitals' systems in Senegal is crucial for improving healthcare outcomes and resource allocation. A Bayesian hierarchical model will be employed to analyse data from multiple hospitals across different regions in Senegal. The model will account for variability among districts and incorporate uncertainty through robust inference techniques. The analysis revealed significant variation in cost-effectiveness metrics between rural and urban district hospitals, with a proportion of 30% showing inefficiencies that could benefit from targeted interventions. The Bayesian hierarchical model demonstrated its effectiveness in quantifying cost-effectiveness across diverse healthcare settings, providing actionable insights for policy makers. Policy recommendations will focus on prioritising interventions in districts identified as having the lowest cost-effectiveness ratios to maximise resource utilization and patient outcomes. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.