{ "background": "Evaluating the cost-effectiveness of district hospital systems in low-resource settings is critical for health systems strengthening, yet robust methodological approaches are scarce. Existing analyses often lack counterfactual comparisons, limiting causal inference for policy.", "purpose and objectives": "This case study presents a methodological framework for conducting a quasi-experimental cost-effectiveness analysis of district hospital systems. It aims to detail the design, implementation, and analytical challenges encountered in a real-world evaluation context.", "methodology": "A difference-in-differences design was employed, comparing intervention and control districts before and after a systems-strengthening initiative. Cost data were collected from hospital financial records, and effectiveness was measured using disability-adjusted life years averted. The primary analysis estimated the average treatment effect on the treated using a two-way fixed effects model: $Y{it} = \\beta0 + \\beta1 (Treati \\times Postt) + \\alphai + \\gammat + \\epsilon{it}$, where robust standard errors were clustered at the district level.", "findings": "The methodological application revealed that the intervention was associated with a reduction in cost per DALY averted, though with considerable uncertainty (95% CI: -15% to +5%). A key theme was the critical challenge of securing comparable control districts, which required extensive propensity score matching. Detailed procedural insights on cost data harmonisation across disparate hospital accounting systems are provided.", "conclusion": "Quasi-experimental designs are feasible for health systems cost-effectiveness analysis in complex, real-world settings, but require meticulous design to address threats to validity, particularly selection bias and data quality.", "recommendations": "Future evaluations should invest in pre-intervention baseline data collection and develop standardised protocols for extracting economic data from heterogeneous hospital records. Sensitivity analyses, including placebo tests, are essential to assess the robustness of findings.", "key words": "health economics, quasi-experiment, difference-in-differences, health systems research, economic evaluation, sub-Saharan Africa",