District hospital systems in sub-Saharan Africa face persistent challenges in patient safety and operational risk. Methodological rigour in evaluating systemic interventions over time remains underdeveloped, limiting evidence-based policy. This study aims to methodologically evaluate the longitudinal impact of a structured hospital systems intervention on composite risk scores, employing a multilevel modelling framework to account for hierarchical data structures. A longitudinal cohort design was used, with repeated annual measurements from multiple hospital units. The primary analysis used a three-level linear mixed model: $Y_{tij} = \beta_0 + \beta_1T_{tij} + u_{i} + v_{ij} + \epsilon_{tij}$, where $u_i$ and $v_{ij}$ are random intercepts for district and hospital, respectively. Inference was based on robust standard errors. Preliminary analysis of a subset indicates a significant reduction in median composite risk score, with an estimated coefficient of -0.18 (95% CI: -0.31 to -0.05) per intervention year. The multilevel structure accounted for 22% of the variance at the district level. The methodological approach demonstrates utility for isolating system-level effects from contextual noise. The intervention shows a statistically significant association with reduced risk. Implement the multilevel regression methodology for national monitoring and evaluation frameworks. Scale the systems intervention to further facilities, contingent on final full-cohort analysis. health systems research, multilevel modelling, patient safety, longitudinal data, sub-Saharan Africa, medical statistics This paper provides a novel methodological application of longitudinal multilevel regression for health systems evaluation in a low-resource setting, generating a new evidence base for district-level policy.