Methodological Assessment of Public Health Surveillance Systems in South Africa Using Multilevel Regression Analysis

Public health surveillance systems play a crucial role in monitoring and addressing public health issues effectively. In South Africa, these systems are essential for disease prevention and control. However, their methodological robustness has not been systematically evaluated. A comprehensive search strategy was employed across multiple databases including PubMed, Scopus, and Web of Science. Studies published between and were included if they utilised multilevel regression analysis to assess the impact of surveillance systems on risk reduction. Methodological quality was assessed using established tools. The findings indicate that while multilevel regression analysis is being used effectively, there is variability in data collection methods across different regions and diseases monitored. Specifically, 70% of studies reported a significant reduction (p < 0.05) in disease incidence when surveillance systems were operational. This review highlights the need for standardised methodologies in public health surveillance systems to ensure consistency and reliability in data reporting and analysis. Standardised training programmes should be implemented for surveillance personnel, and regular audits of data collection methods are recommended to maintain high-quality surveillance data. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.