{ "background": "Urban primary care networks in sub-Saharan Africa are critical for health system performance, yet robust methodological frameworks for evaluating their clinical outcomes longitudinally are lacking. Existing assessments often rely on cross-sectional data, which fail to capture temporal dynamics and system responsiveness.", "purpose and objectives": "This meta-analysis aims to methodologically evaluate the performance measurement systems within Senegal's urban primary care networks and to develop a validated time-series forecasting model for key clinical outcomes to inform proactive management.", "methodology": "A systematic search identified relevant studies and grey literature reporting on clinical outcomes and system performance metrics. Methodological quality was appraised using a modified Cochrane tool. We synthesised data to fit a Seasonal Autoregressive Integrated Moving Average (SARIMA) model, specified as $\\phi(B)\\Phi(B^s)(1-B)^d(1-B^s)^D Yt = \\theta(B)\\Theta(B^s)\\epsilont$, where $Y_t$ represents the clinical outcome time series. Model forecasting accuracy was assessed using mean absolute percentage error (MAPE).", "findings": "The methodological review revealed that 68% of included studies utilised inadequate statistical controls for confounding temporal trends. The fitted SARIMA model for antenatal care coverage demonstrated a MAPE of 4.7% (95% CI: 3.1, 6.3) in out-of-sample forecasts, indicating high predictive precision. Forecasts suggest a stable but sub-optimal trajectory for hypertension control rates without intervention.", "conclusion": "Current evaluation methodologies for primary care networks exhibit significant limitations in addressing time-dependent confounding. The implemented forecasting model provides a technically robust tool for predicting clinical outcomes, enabling evidence-based resource allocation.", "recommendations": "Health authorities should integrate time-series forecasting into routine health management information systems. Future research must prioritise longitudinal study designs and the development of context-specific leading indicators for clinical performance.", "key words": "health systems research, forecasting models, primary health care, urban health, Senegal, time-series analysis