Bayesian Hierarchical Model for Measuring Clinical Outcomes in Urban Primary Care Networks in Tanzania: An Evaluation

Urban primary care networks in Tanzania are essential for addressing healthcare needs efficiently. However, evaluating their effectiveness requires robust methods to measure clinical outcomes accurately. A Bayesian hierarchical model was employed to analyse data from multiple clinics across different areas of Tanzania. The model accounts for variability within and between clinics, using prior knowledge about treatment efficacy and patient characteristics. The analysis revealed significant heterogeneity in clinical outcomes among the clinics, with some showing substantial improvement over others. This study demonstrates the utility of Bayesian hierarchical models in assessing urban primary care networks' performance in Tanzania. The findings highlight areas needing further attention to enhance service quality and patient satisfaction. Future research should consider expanding the model to include more clinics or different clinical outcomes, ensuring broader applicability and reliability of results. Bayesian hierarchical models, urban primary care networks, clinical outcomes, Tanzania, system evaluation Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.