"background": "District hospitals in Uganda face persistent challenges in resource allocation and service delivery, constraining health system performance. Robust, quantitative methods are required to evaluate the efficiency impacts of systemic interventions, moving beyond descriptive assessments. ", "purpose and objectives": "This protocol details a methodological approach to quantify efficiency gains from a national health system strengthening initiative. The primary objective is to estimate the causal effect of the intervention on hospital-level technical efficiency, measured via a stochastic frontier analysis composite score. ", "methodology": "A quasi-experimental difference-in-differences design will be employed, using longitudinal administrative data from intervention and matched control hospitals. The core econometric model is: Y{it = \0 + \1 + \2 + \ (\) + \₈ₓ, where \ is the causal parameter of interest. Inference will rely on cluster-robust standard errors at the hospital level. ", "findings": "As a research protocol, this paper presents no empirical results. The anticipated analysis will test the hypothesis that the intervention yields a positive and statistically significant \ coefficient, indicating an improvement in efficiency scores for treated facilities relative to controls. A preliminary power calculation suggests the design can detect a minimum 15% relative improvement. ", "conclusion": "The proposed methodology provides a rigorous framework for causal inference in health systems research, directly applicable to policy evaluation in resource-limited settings. ", "recommendations": "Future health system evaluations should adopt quasi-experimental designs to strengthen evidence for scaling interventions. Policymakers should mandate the routine collection of standardised efficiency metrics to facilitate such analyses. ", "key words": "health systems research, efficiency, difference-in-differences, Uganda, district hospitals, quasi-experimental design", "contribution statement": "This protocol introduces a novel application of the difference-in-differences framework to stochastic frontier efficiency
Moses Kato (Wed,) studied this question.