{ "background": "District hospitals are critical nodes in African food systems, influencing nutrition and health security. System reliability, defined as the consistent delivery of intended clinical services, is poorly quantified, hindering evidence-based policy. Current evaluations often lack robust counterfactuals to attribute changes to specific interventions.", "purpose and objectives": "This protocol details a methodological approach to rigorously evaluate the impact of a hospital systems strengthening intervention on service reliability. The primary objective is to estimate the causal effect on a composite reliability index. Secondary objectives include analysing heterogeneity in effects by hospital characteristics.", "methodology": "We employ a quasi-experimental, longitudinal difference-in-differences design. Intervention and control districts will be matched on baseline characteristics. The primary statistical model is $Y{it} = \\beta0 + \\beta1 (\\text{Treat}i \\times \\text{Post}t) + \\gammai + \\deltat + \\epsilon{it}$, where $Y_{it}$ is the reliability score for hospital $i$ at time $t$. Inference will rely on cluster-robust standard errors at the district level. Data will be sourced from routine health management information systems and structured facility assessments.", "findings": "As a protocol, no empirical findings are presented. The anticipated analysis will quantify the intervention's effect size and direction, for example, testing whether the mean reliability score in intervention hospitals increases by a hypothesised minimum proportion of 15 percentage points relative to controls. Uncertainty will be expressed via 95% confidence intervals.", "conclusion": "This protocol provides a transparent, pre-specified framework for a robust evaluation of health system performance within an interdisciplinary food systems context. The methodological rigour is designed to yield credible evidence for decision-makers.", "recommendations": "Researchers evaluating complex health system interventions in similar settings should consider adopting quasi-experimental designs with careful matching. Policymakers should prioritise the generation of high-frequency, standardised facility data to enable such analyses.", "key words": "health systems research