{ "background": "Evaluating health system efficiency in low-resource settings is critical for policy, yet robust causal evidence is scarce. Quasi-experimental designs (QEDs) have been increasingly employed to assess efficiency gains from interventions in district-level hospital systems, but their methodological rigour and comparability have not been systematically appraised.", "purpose and objectives": "This meta-analysis aims to methodologically evaluate the application of QEDs in measuring efficiency gains within district hospital systems, assessing design quality, common biases, and the consistency of effect estimates across studies.", "methodology": "We systematically identified peer-reviewed and grey literature studies employing QEDs, including difference-in-differences, interrupted time series, and regression discontinuity designs. Study quality was assessed using a modified Cochrane ROBINS-I tool. A random-effects meta-regression model, $\\hat{\\theta}i = \\mu + \\beta Xi + \\nui + \\epsiloni$, where $\\nu_i$ is the study variance, was used to synthesise standardised effect sizes and examine associations with methodological covariates. Inference was based on 95% confidence intervals and robust standard errors.", "findings": "Of 42 included studies, only 38% adequately addressed confounding and selection bias. The pooled effect size for reported efficiency gains was positive but exhibited high heterogeneity (I² = 87%). A key theme was that studies employing propensity score matching within a difference-in-differences framework produced more conservative, and likely more reliable, estimates of efficiency improvements.", "conclusion": "While QEDs are valuable for evaluating health system efficiency, their current application is methodologically inconsistent, often overstating gains. The field requires more rigorous design implementation and transparent reporting.", "recommendations": "Future research should pre-register analysis plans, prioritise designs that control for time-varying confounders, and utilise linked administrative data to enhance validity. Funders should mandate stronger methodological standards.", "key words": "health systems research, causal inference,