Bayesian Hierarchical Model for Measuring Adoption Rates in Senegal's District Hospital Systems

The adoption rates of healthcare innovations in Senegal's district hospitals have been assessed through traditional methods with varying levels of success. A Bayesian hierarchical model was developed to analyse data from multiple districts, accounting for both local variations and overall trends. The model incorporates prior knowledge about healthcare practices to refine estimates of adoption rates across the system. The analysis revealed a significant variation in adoption rates among different hospital types (p < 0.05), with primary health centers showing higher adoption compared to district hospitals, indicating the need for tailored interventions. This study validates the Bayesian hierarchical model as an effective tool for assessing and understanding adoption dynamics within Senegalese healthcare systems, thereby informing policy adjustments aimed at improving resource allocation and service delivery. Healthcare policymakers should prioritise the implementation of evidence-based strategies in primary health centers to promote broader system-wide adoption rates and improve overall patient outcomes. Adoption Rates, Bayesian Hierarchical Model, District Hospitals, Senegal Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.