Bayesian Hierarchical Model for Measuring Adoption Rates in Nigerian District Hospitals Systems

Adoption rates of new healthcare technologies in Nigerian district hospitals are often underreported and misinterpreted due to variability across different facilities. A Bayesian hierarchical model was constructed to account for both facility-specific and system-level factors influencing technology adoption. The model incorporates uncertainty through robust standard errors and credible intervals. The model estimated an average adoption rate of 45% across all district hospitals, with significant variability between urban and rural facilities (urban: 60%, rural: 30%). The Bayesian hierarchical model provides a nuanced understanding of technology adoption dynamics in Nigerian hospital systems. Policy makers should consider both facility-specific and system-level interventions to enhance technology uptake across different geographic regions. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.