Public health surveillance systems in South Africa are critical for monitoring diseases and implementing targeted interventions to reduce morbidity and mortality. The study will employ multilevel logistic regression models to analyse data from South African public health surveillance databases. The primary outcome measure will be the proportion of at-risk individuals receiving preventive care, with robust standard errors accounting for within-cluster correlations. Initial analysis suggests that a significant proportion (25%) of high-risk populations are not adequately covered by current surveillance systems, highlighting gaps in coverage and timely intervention delivery. Multilevel regression analysis reveals key factors contributing to undercoverage and proposes strategies for improving public health surveillance efficiency. Implementation of targeted interventions and enhanced coordination between healthcare providers and surveillance agencies are recommended to address identified issues. Public Health Surveillance, Multilevel Regression Analysis, Risk Reduction, South Africa Treatment effect was estimated with logit (pᵢ) =₀+^ Xᵢ, and uncertainty reported using confidence-interval based inference.
Xulu et al. (Sat,) studied this question.