Bayesian Hierarchical Model for Measuring Adoption Rates in Community Health Centres Systems in Uganda

Community health centres in Uganda have been established to improve access to healthcare services. However, their effectiveness varies over time due to factors such as resource availability and community engagement. A longitudinal study design was employed, collecting data on service utilization and provider feedback from various Ugandan health centres over one year. A Bayesian hierarchical linear regression model was applied to estimate adoption rates, incorporating uncertainty through credible intervals. The model estimated an average adoption rate of 75% for essential healthcare services across all communities studied, with significant variability observed between regions. This study provides a robust framework for understanding and improving the integration of community health centres within Uganda’s healthcare system. Further research should focus on identifying key drivers of adoption rates and exploring strategies to enhance service uptake in underserved areas. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.