A Systematic Review of Methodological Approaches for Evaluating Community Health Centre Systems in Nigeria: A Difference-in-Differences Framework for Assessing Adoption, 2000–2026

{ "background": "Community health centres are a cornerstone of primary healthcare delivery in Nigeria, yet robust methodological frameworks for evaluating their systemic adoption and impact remain underdeveloped. Existing literature often lacks rigorous quasi-experimental designs to isolate the effect of specific interventions or policy changes.", "purpose and objectives": "This systematic review aims to critically appraise methodological approaches used to evaluate community health centre systems, with a specific focus on the application and suitability of the difference-in-differences framework for assessing adoption rates of these systems.", "methodology": "A systematic search of peer-reviewed literature and grey sources was conducted following PRISMA guidelines. Studies were screened for inclusion based on pre-defined criteria focusing on evaluation methodologies. The difference-in-differences model was specified as Yit = \β0 + \β1 + \β2 + \β3 ( \× ) + \εit, where inference relied on cluster-robust standard errors to account for intra-class correlation.", "findings": "The review identified a predominant reliance on cross-sectional and descriptive designs, with only a limited subset employing quasi-experimental methods. Among these, the difference-in-differences approach was applied in fewer than 15% of eligible evaluations. A key thematic finding was that studies employing this framework more consistently accounted for time-varying confounders and secular trends.", "conclusion": "The application of robust quasi-experimental methodologies, particularly difference-in-differences, is notably scarce in the evaluation of community health centre systems in Nigeria, limiting the strength of causal inference regarding adoption and effectiveness.", "recommendations": "Future evaluations should prioritise the use of quasi-experimental designs with appropriate counterfactuals. Researchers must explicitly report model assumptions, conduct parallel trends tests, and utilise robust error estimation to improve the credibility of impact estimates.", "key words": "health systems evaluation, quasi-experimental design,