Urban primary care networks in Tanzania aim to improve access to healthcare services for underserved populations. However, there is a need for robust methods to assess clinical outcomes across these networks. A Bayesian hierarchical model was employed to analyse data from multiple urban primary care clinics in Tanzania, accounting for both clinic-specific and patient-level variability. The model accounts for uncertainty through robust standard errors and provides confidence intervals on estimated outcomes. The analysis revealed significant heterogeneity in clinical outcomes across different clinics, with some clinics showing better performance than others. The Bayesian hierarchical model offers a nuanced understanding of clinic-specific factors influencing clinical outcomes, facilitating targeted interventions to enhance service quality and patient care. Clinics identified as underperforming should prioritise resource allocation and training programmes to align with best practices observed in higher-performing clinics. Bayesian Hierarchical Model, Urban Primary Care Networks, Clinical Outcomes, Tanzania Treatment effect was estimated with logit (pᵢ) =₀+^ Xᵢ, and uncertainty reported using confidence-interval based inference.
Kasunga et al. (Wed,) studied this question.