Methodological Evaluation and Multilevel Regression Analysis for Efficiency Gains in South African Public Health Surveillance Systems, 2000–2026

{ "background": "Public health surveillance systems are critical for disease control and health policy, yet their methodological evaluation for efficiency remains underdeveloped. In South Africa, despite substantial investment, systematic assessments of how structural and operational factors influence system performance over time are lacking, hindering evidence-based optimisation.", "purpose and objectives": "This protocol details a methodological evaluation to measure efficiency gains within the nation's public health surveillance systems. The primary objective is to quantify the impact of multi-level factors—including governance, resource allocation, and data integration—on system output efficiency, while controlling for contextual confounders.", "methodology": "A longitudinal, quantitative analysis of surveillance system data will be conducted. Efficiency will be modelled using a three-level random intercept regression: $Y{ijt} = \\beta0 + \\beta X{ijt} + uj + vt + \\epsilon{ijt}$, where $i$, $j$, and $t$ denote facility, district, and time, respectively. Inference will be based on 95% confidence intervals derived from robust standard errors clustered at the district level. Sensitivity analyses will assess model assumptions.", "findings": "As a research protocol, this paper does not present empirical results. The anticipated analysis is designed to estimate the direction and magnitude of efficiency gains associated with specific interventions, such as the proportion of improvement attributable to integrated electronic reporting platforms.", "conclusion": "The proposed methodology provides a novel, rigorous framework for the longitudinal evaluation of surveillance system efficiency, moving beyond descriptive assessment towards causal inference.", "recommendations": "Future research should apply this model to generate actionable evidence for system redesign. Policymakers should prioritise investment in the structural covariates identified as significant efficiency drivers.", "key words": "health surveillance, efficiency analysis, multilevel modelling, health systems research, evaluation methodology", "contribution statement": "This protocol introduces a novel application of longitudinal multilevel regression to decompose efficiency gains in public health surveillance, offering a replicable model for health systems evaluation across diverse settings