Methodological Evaluation of Urban Primary Care Networks in Tanzania Using Multilevel Regression Analysis for Clinical Outcomes Assessment

Urban primary care networks in Tanzania face challenges related to clinical outcomes assessment, necessitating methodological improvements. A multilevel regression model was employed to analyse data from urban primary care networks in Tanzania. The model included fixed effects for network-level variables and random intercepts for individual patient outcomes. The multilevel regression analysis revealed that the proportion of patients achieving clinical improvement varied significantly across different urban primary care networks (p < 0.05). Multilevel regression analysis provided a nuanced understanding of factors influencing clinical outcomes in urban primary care settings. Further research should focus on implementing targeted interventions within identified network-specific areas to enhance patient outcomes. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.