Public health surveillance systems are crucial for monitoring disease prevalence and guiding public health interventions in South Africa. A comprehensive search strategy was employed to identify relevant studies. Multilevel logistic regression models were used to analyse the data, accounting for the nested nature of the data (level-1: surveillance units; level-2: regions). Multilevel analysis revealed that adoption rates varied significantly by region, with a proportion of 65% in urban areas compared to 40% in rural settings. The multilevel regression model provided robust estimates for the factors influencing surveillance system adoption, including funding and infrastructure availability. Strategies should be developed to enhance adoption rates in underserved regions by addressing identified barriers such as limited resources and inadequate infrastructure. Treatment effect was estimated with logit (pᵢ) =₀+^ Xᵢ, and uncertainty reported using confidence-interval based inference.
Ditsha et al. (2008) studied this question.