A Time-Series Forecasting Model for Clinical Outcomes in South African Urban Primary Care Networks: A Methodological Evaluation, 2000–2026

{ "background": "Urban primary care networks are critical for health system resilience, yet robust tools for forecasting their clinical performance are lacking, particularly in resource-constrained settings. This gap impedes proactive resource allocation and strategic planning. ", "purpose and objectives": "This study aimed to methodologically evaluate a novel time-series forecasting model designed to predict key clinical outcomes within urban primary care networks. The objective was to assess its predictive accuracy and operational utility for health system managers. ", "methodology": "We conducted an intervention study applying a Seasonal AutoRegressive Integrated Moving Average with eXogenous factors (SARIMAX) model to longitudinal clinical data. The core model is defined as \ (B) \ (Bˢ) \ᵈ\D yt = \ (B) \ (Bˢ) \ + \ Xt, where Xₜ represents intervention covariates. Model fit was evaluated using rolling-origin forecast evaluation, with uncertainty quantified via 95% prediction intervals. ", "findings": "The model demonstrated clinically useful forecasting accuracy for hypertension control rates up to 12 months ahead. Forecasts indicated a stable but suboptimal trajectory, with a predicted marginal improvement of 2. 3 percentage points (95% PI: 0. 8 to 3. 7) over the forecast horizon, contingent on maintaining current intervention levels. ", "conclusion": "The evaluated SARIMAX model provides a statistically sound and operationally feasible tool for forecasting clinical outcomes in complex primary care systems. It offers a mechanism for data-driven stewardship. ", "recommendations": "Health authorities should integrate this forecasting methodology into routine performance dashboards. Future research should focus on embedding these models within real-time health information systems for dynamic scenario planning. ", "key words": "forecasting, primary health care, time-series analysis, clinical outcomes, health systems, South Africa", "contribution statement": "This paper provides the first application and validation of a SARIMAX forecasting framework for clinical outcomes in African urban primary care networks, demonstrating its