Bayesian Hierarchical Model for Assessing Adoption Rates in Tanzanian Community Health Centers Systems

Community health centers in Tanzania have been established to improve access to healthcare services, but their effectiveness varies across different settings. A Bayesian hierarchical model was applied to analyse data from Tanzanian community health centers, accounting for variability at multiple levels (e.g., district and individual facilities). Adoption rates varied significantly across different districts, with some reaching up to 80% in certain areas. The Bayesian hierarchical model effectively captured the complexity of adoption dynamics within the Tanzanian healthcare system. Further research should explore interventions that could increase adoption rates and improve service delivery in underperforming centers. Treatment effect was estimated with logit(pᵢ)=β₀+β^ Xᵢ, and uncertainty reported using confidence-interval based inference.