"background": "Rural clinical systems in sub-Saharan Africa face significant operational challenges, yet robust methodological frameworks for evaluating their performance and impact on patient outcomes are lacking. Existing evaluations often fail to account for the hierarchical structure of health data, potentially leading to biased inferences. ", "purpose and objectives": "This study aimed to develop and apply a multilevel modelling framework to evaluate the performance of rural clinics, with the objective of quantifying the influence of clinic-level systemic factors on a key clinical outcome, specifically the rate of successful antenatal care completion. ", "methodology": "We conducted a retrospective analysis of anonymised patient records from a representative sample of rural clinics. A two-level random intercept logistic regression model was fitted, defined as \ (p{ij) = \0 + \ Xij + uj, where pij is the probability of successful outcome for patient i in clinic j, Xij are patient-level covariates, and uj \ N (0, \²u) represents the clinic-specific random effect. Inference was based on robust standard errors. ", "findings": "Clinic-level factors explained a substantial 22% of the variation in successful outcomes (ICC = 0. 22). After adjusting for patient covariates, clinics with integrated pharmacy and laboratory services had significantly higher odds of successful completion (OR: 1. 85, 95% CI: 1. 42 to 2. 41). ", "conclusion": "The methodological approach demonstrates that clinic-level systemic characteristics are major determinants of clinical outcomes, a finding obscured by conventional single-level analyses. ", "recommendations": "Health system evaluations should routinely employ multilevel models to appropriately attribute variance. Policy should prioritise integrated service delivery models in rural clinic strengthening programmes. ", "key words": "health systems research, multilevel modelling, rural health services, clinical outcomes, sub-Saharan Africa", "contribution statement": "This paper provides a novel methodological application of multilevel
Mwangi et al. (Sat,) studied this question.