Abstract
{ "background": "Evaluating the impact of health systems interventions in resource-constrained settings requires robust methodologies. Quasi-experimental designs (QEDs) are increasingly employed in such contexts to estimate causal effects on clinical outcomes where randomised trials are impractical. Their application within Ghana's district hospital systems, a critical tier of care, necessitates a systematic assessment of methodological rigour.", "purpose and objectives": "This systematic review aims to critically appraise the application of quasi-experimental methodologies for evaluating clinical outcomes in Ghanaian district hospitals. It seeks to catalogue the designs used, assess their internal validity, and synthesise evidence on their implementation challenges and analytical approaches.", "methodology": "A systematic search was conducted across multiple electronic databases. Pre-defined inclusion criteria captured peer-reviewed studies employing QEDs (e.g., difference-in-differences, regression discontinuity, interrupted time series) to assess clinical outcomes within the defined hospital systems. Study selection, data extraction, and quality assessment using the ROBINS-I tool were performed by two independent reviewers.", "findings": "The review identified a limited but growing corpus of studies. Interrupted time series was the most frequently employed design. A key theme was the frequent omission of tests for core model assumptions, such as parallel trends in difference-in-differences models, often specified as $Y{it} = \\beta0 + \\beta1 Treati + \\beta2 Postt + \\beta3 (Treati \\times Postt) + \\epsilon{it}$. Fewer than 30% of studies reported using robust standard errors or other methods to account for clustering.", "conclusion": "While QEDs offer a viable approach for impact evaluation in this setting, their current application is often methodologically incomplete. This undermines the reliability of causal inferences drawn about interventions aimed at improving clinical outcomes.", "recommendations": "Future research must prioritise rigorous adherence to QED assumptions, explicitly report validation tests, and employ analytical techniques that account for complex data structures. Capacity building in