Abstract
{ "background": "The adoption of community health centre systems in sub-Saharan Africa is a critical component of health system strengthening, yet rigorous quantitative evaluations of their rollout and impact remain scarce. This creates an evidence gap for policymakers seeking to allocate resources efficiently and scale effective models.", "purpose and objectives": "This case study aims to provide a methodological evaluation of the adoption rates of a national community health centre system. Its primary objective is to demonstrate the application of a difference-in-differences model to isolate the system's effect from underlying trends, serving as a template for similar evaluations in food and health systems research.", "methodology": "We employ a quasi-experimental difference-in-differences design using longitudinal district-level data. The core statistical model is $Y{dt} = \\beta0 + \\beta1 (\\text{Treat}d \\times \\text{Post}t) + \\gammad + \\deltat + \\epsilon{dt}$, where $Y{dt}$ is the adoption rate in district $d$ at time $t$. The coefficient $\\beta1$ captures the causal effect. Inference is based on cluster-robust standard errors at the district level.", "findings": "The analysis indicates a positive and statistically significant effect of the system rollout on service adoption. The estimated average treatment effect on the treated districts was an 18-percentage-point increase in adoption rates (95% CI: 12 to 24). This effect manifested after an initial implementation lag, highlighting the importance of considering temporal dynamics in impact assessment.", "conclusion": "The difference-in-differences approach provides a robust framework for evaluating the phased introduction of complex health systems. The findings confirm the system's effectiveness in increasing service uptake, demonstrating the value of causal inference methods in operational research.", "recommendations": "Policymakers should integrate similar rigorous evaluation designs from the outset of programme rollout. Future research should apply this model to assess the system's indirect effects on related outcomes, such as nutritional status and household