Methodological Evaluation of Urban Primary Care Networks in Rwanda: A Multilevel Regression Analysis of Clinical Outcomes

Urban primary care networks in Rwanda aim to improve access to healthcare services for urban populations. However, their effectiveness and sustainability require rigorous evaluation. A multilevel regression model was employed to analyse data from clinics within the network. The model accounts for both clinic-level (e.g., staff training, equipment) and patient-level factors influencing clinical outcomes. The multilevel regression analysis revealed that adequate staffing levels ($\text{Clinic Level} + \beta imes \text{Patient Level}$) significantly improved patient satisfaction scores by an average of 15% (95% CI: [8%, 23%]) compared to clinics with insufficient staff. The multilevel regression analysis provided insights into the impact of staffing levels on clinical outcomes in urban primary care networks, offering a robust framework for future evaluations and policy development. Clinic managers should prioritise regular training sessions for healthcare providers and ensure sufficient resources to meet patient needs effectively.