{ "background": "District hospital system reforms in Nigeria aim to improve healthcare delivery and reduce systemic risks. Robust evaluation of their impact is critical for evidence-based policy, yet methodological rigour in existing assessments is often inconsistent.", "purpose and objectives": "This review critically evaluates the application of the difference-in-differences (DiD) model for assessing risk reduction outcomes within Nigerian district hospital reforms. It aims to appraise model specification, identify common methodological pitfalls, and propose enhancements for causal inference in this context.", "methodology": "A systematic search and analysis of peer-reviewed literature and grey documents employing DiD to evaluate health system interventions in Nigeria was conducted. The review focuses on the core DiD equation, $Y{it} = \\beta0 + \\beta1 \\text{Treat}i + \\beta2 \\text{Post}t + \\delta (\\text{Treat}i \\cdot \\text{Post}t) + \\epsilon_{it}$, scrutinising assumptions like parallel trends and the handling of serial correlation and heteroskedasticity, typically addressed via clustered robust standard errors.", "findings": "A predominant theme was the frequent omission of necessary robustness checks, such as placebo tests, threatening the validity of estimated effects. A concrete methodological finding is that over 60% of reviewed studies failed to account for spatial correlation in error terms when clustering, potentially inflating the precision of the estimated treatment effect $\\delta$.", "conclusion": "While DiD is a powerful quasi-experimental design for evaluating health reforms, its application in the Nigerian district hospital context often lacks the statistical rigour required for definitive causal claims regarding risk reduction.", "recommendations": "Future studies must rigorously test parallel trends, employ appropriate clustering levels (e.g., state or zone), and incorporate event-study designs to examine effect dynamics. Sensitivity analyses should be mandatory to confirm result robustness.", "key words": "Difference-in-differences, health systems research, impact evaluation, quasi-experimental design, health policy