Community health centres are a cornerstone of primary healthcare delivery in many low-resource settings, yet robust evidence on their systemic impact on clinical outcomes remains limited. This creates a significant gap in health systems planning and resource allocation. This short report details the methodology for a quasi-experimental evaluation designed to measure the causal effect of integrated community health centre systems on key clinical outcomes in a sub-Saharan African context. We employ a difference-in-differences design, leveraging the phased rollout of an enhanced health centre system. The primary analysis uses a linear regression model: $Y_{it} = \beta_0 + \beta_1 (Treat_i \times Post_t) + \gamma_i + \delta_t + \epsilon_{it}$, where $Y_{it}$ is the clinical outcome for facility $i$ at time $t$. Inference is based on cluster-robust standard errors at the health centre level. This report presents the methodological protocol; empirical results are forthcoming. Preliminary descriptive analysis of baseline data indicates that approximately 40% of centres in the intervention group reported stock-outs of essential medicines in the preceding quarter, highlighting a key systemic challenge. The described quasi-experimental design provides a rigorous framework for isolating the impact of health system strengthening on clinical endpoints, moving beyond associative evidence. Future evaluations of complex health interventions should incorporate quasi-experimental methods to strengthen causal inference. Health planners should prioritise the collection of high-frequency, standardised outcome data to facilitate such analyses. health systems evaluation, quasi-experimental design, difference-in-differences, primary healthcare, causal inference This protocol provides a novel application of a difference-in-differences framework to evaluate a nationwide community health system intervention, offering a replicable model for health policy research in resource-constrained settings.