{ "background": "Community health centres are critical nodes in primary healthcare systems, yet robust methodological frameworks for assessing their operational reliability in low-resource settings are lacking. Existing evaluations often rely on cross-sectional data, which cannot adequately capture system dynamics or attribute changes to specific interventions. ", "purpose and objectives": "This study aimed to develop and apply a novel quasi-experimental design to quantitatively evaluate the reliability of systems within community health centres, focusing on the consistency of service delivery and supply chain integrity. ", "methodology": "We employed a controlled interrupted time series design across a matched sample of 24 centres. System reliability was operationalised as the probability of key service and supply indicators being within specified control limits. The primary analysis used a segmented regression model: Yt = \0 + \1Tt + \2Xt + \3TtXt + \, where Yt is the reliability metric, Tt is time, and Xₜ marks the intervention period. Inference was based on Newey-West robust standard errors to account for autocorrelation. ", "findings": "The methodological application revealed a significant post-intervention increase in mean system reliability score (β₃ = 0. 18, 95% CI: 0. 07 to 0. 29). A key concrete result is that the intervention was associated with a 22% reduction in the incidence of essential drug stock-outs. The quasi-experimental design proved feasible for isolating the effect of systemic improvements from secular trends. ", "conclusion": "The proposed methodological approach provides a rigorous, field-applicable framework for assessing health system reliability. It moves beyond descriptive snapshots to enable causal inference regarding interventions aimed at strengthening community-level healthcare infrastructure. ", "recommendations": "Health systems researchers should adopt quasi-experimental designs for evaluating operational reliability in real-world settings. Policymakers should mandate the collection of longitudinal, high-frequency routine data to facilitate such analyses for continuous quality improvement. ", "key
Ndiaye et al. (Mon,) studied this question.