{ "background": "District hospitals in sub-Saharan Africa face persistent challenges in service delivery and output measurement. Robust quantitative methods to evaluate systemic performance improvements, particularly yield—defined as the volume of key clinical services delivered per unit of resource input—are lacking.", "purpose and objectives": "This study aimed to develop and apply a quasi-experimental difference-in-differences (DiD) model to rigorously assess the impact of a national health system strengthening initiative on district hospital yield in Tanzania.", "methodology": "We constructed a longitudinal panel dataset from routine health management information systems for all district hospitals. The core DiD model is specified as $Y{it} = \\beta0 + \\beta1 (Treati \\times Postt) + \\gammai + \\deltat + \\epsilon{it}$, where $Y_{it}$ is the yield outcome for hospital $i$ in period $t$. Hospitals were classified into treatment and comparison groups based on phased programme rollout. Inference was based on cluster-robust standard errors.", "findings": "The intervention was associated with a statistically significant 18.4% increase in aggregate service yield (95% CI: 12.7% to 24.1%) in treatment hospitals relative to comparison hospitals. The effect was most pronounced for outpatient and maternal health service yields.", "conclusion": "The applied DiD model provides a rigorous methodological framework for quantifying health systems performance improvements, demonstrating that the national initiative substantially increased district hospital output efficiency.", "recommendations": "Health policy planners should adopt quasi-experimental designs for routine programme evaluation. The yield metric and DiD approach should be integrated into national health management information systems to facilitate ongoing performance tracking.", "key words": "health systems strengthening, performance measurement, quasi-experimental design, efficiency, service delivery, sub-Saharan Africa", "contribution statement": "This paper provides a novel application of the difference-in-differences framework to the measurement of hospital yield, generating robust causal evidence on the effectiveness of a large