Abstract
{ "background": "The reliability of community health centre systems is a critical determinant of healthcare delivery and outcomes in resource-limited settings. Existing methodological approaches for evaluating system reliability often fail to account for the hierarchical, clustered nature of health system data, potentially leading to biased inferences.", "purpose and objectives": "This study aimed to methodologically evaluate the application of multilevel regression for measuring system reliability in a network of community health centres, assessing its advantages over conventional single-level models for informing targeted interventions.", "methodology": "We conducted an intervention study, implementing a novel multilevel modelling framework. The core statistical model was a three-level random intercept logistic regression: $\\text{logit}(p{ijk}) = \\beta0 + \\beta X{ijk} + u{k} + v{jk}$, where $p{ijk}$ is the probability of a reliable system outcome for observation $i$ in facility $j$ within district $k$, $u{k}$ and $v{jk}$ are district- and facility-level random effects. Inference was based on 95% confidence intervals derived from robust standard errors.", "findings": "The multilevel model revealed significant variation attributable to district-level clustering, accounting for approximately 18% of the total variance in system reliability scores. This district-level effect was not discernible in a standard logistic regression, which produced artificially narrow confidence intervals for key predictors, overstating their precision.", "conclusion": "Multilevel regression provides a methodologically superior framework for analysing health system reliability in this context, as it correctly accounts for data hierarchy and yields more valid estimates of uncertainty.", "recommendations": "Future evaluations of health system performance in similar settings should adopt multilevel analytical techniques to guide more effective, context-specific interventions. Investment should be prioritised for capacity building in advanced statistical methods among health systems researchers.", "key words": "health systems research, multilevel modelling, hierarchical data, reliability analysis, sub-Saharan Africa, methodological evaluation", "contribution statement": "This