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{ "background": "Community health centres are a cornerstone of primary healthcare delivery in many African nations, yet robust methodological frameworks for evaluating their systematic adoption are lacking. Existing assessments often rely on descriptive statistics, which limit causal inference regarding implementation efficacy.", "purpose and objectives": "This study aimed to develop and apply a novel quasi-experimental design to rigorously measure the adoption rates of integrated community health centre systems, focusing on methodological validity and the estimation of causal effects.", "methodology": "A difference-in-differences design was employed, comparing intervention districts (n=15) with matched control districts (n=15) over multiple observation periods. The primary adoption outcome was a composite index of system functionality. The core statistical model was specified as $Y{dt} = \\beta0 + \\beta1 (Treatd \\times Postt) + \\gammad + \\deltat + \\epsilon{dt}$, where robust standard errors were clustered at the district level.", "findings": "The quasi-experimental design yielded a precise, positive estimate for the intervention effect. System adoption, measured by the functionality index, increased by 18.7 percentage points (95% CI: 12.3, 25.1) in intervention districts relative to controls. The methodological approach successfully isolated the programme effect from secular trends.", "conclusion": "The applied quasi-experimental design provides a valid and powerful methodological framework for assessing health system adoption, demonstrating significant measurable improvements attributable to the community health centre model.", "recommendations": "Future evaluations of health system scale-up should employ robust quasi-experimental or experimental designs to strengthen causal claims. Policymakers should mandate such methodological rigour in programme assessments to inform resource allocation.", "key words": "quasi-experimental design, difference-in-differences, health systems evaluation, primary healthcare, adoption, implementation science, causal inference", "contribution statement": "This paper provides a novel methodological blueprint for the causal evaluation of health system adoption in resource-limited settings, demonstrating that a