Abstract
{ "background": "Community health centres are pivotal for primary care delivery in sub-Saharan Africa, yet robust longitudinal evidence on their systemic impact on population health risk is limited. Existing evaluations often lack rigorous counterfactual frameworks to isolate the effect of health system interventions from secular trends.", "purpose and objectives": "This study aims to methodologically evaluate the impact of a nationwide scale-up of integrated community health centre systems on key health risk indicators. The primary objective is to estimate causal risk reduction using a quasi-experimental, longitudinal design.", "methodology": "A longitudinal, controlled study was conducted using district-level panel data. The core analytical framework is a difference-in-differences model, $Y{dt} = \\beta0 + \\beta1 (Treatd \\times Postt) + \\gammad + \\deltat + \\epsilon{dt}$, where $Y_{dt}$ is the outcome for district $d$ in period $t$. Treatment districts received enhanced health centre systems, while control districts did not. Inference is based on cluster-robust standard errors.", "findings": "The analysis indicates a statistically significant reduction in composite health risk scores in treatment districts relative to controls following the intervention. The estimated average treatment effect was a 15.2% reduction (95% CI: 9.8% to 20.6%). The most pronounced improvements were observed in maternal health and infectious disease screening metrics.", "conclusion": "The scaled-up community health centre system had a significant, positive effect on reducing population health risks. The difference-in-differences approach provided a robust methodological framework for causal inference in a real-world, non-randomised setting.", "recommendations": "Policy should prioritise sustained investment in integrated community health systems. Future research should apply this methodological model to other food system and health interventions to build a comparative evidence base.", "key words": "difference-in-differences, health systems evaluation, longitudinal study, causal inference, primary health care, sub-Saharan Africa", "contribution