Bayesian hierarchical models are increasingly used in evaluating adoption rates across various systems, including healthcare settings. Bayesian hierarchical modelling will be employed to analyse data from multiple studies, integrating uncertainty through credible intervals. The analysis revealed a significant variation in adoption rates across different districts, with urban areas showing higher adoption compared to rural regions. This review emphasizes the importance of considering contextual factors and regional variations when applying Bayesian hierarchical models for adoption rate measurement. Future research should focus on longitudinal studies to better understand changes over time in district hospitals systems. Treatment effect was estimated with logit (pᵢ) =₀+^ Xᵢ, and uncertainty reported using confidence-interval based inference.
Mwangi et al. (Wed,) studied this question.