Multilevel Regression Analysis to Evaluate Adoption Rates in Public Health Surveillance Systems in South Africa: A Methodological Protocol

Public health surveillance systems in South Africa aim to monitor disease incidence and implement targeted interventions. However, their adoption rates vary significantly across regions and healthcare facilities. The research will employ multilevel logistic regression models to analyse data from South African healthcare facilities. Hierarchical structures at both the regional (level 1) and national (level 2) levels will be incorporated, ensuring comprehensive coverage of adoption rates across different settings. We expect to find a significant proportion (e.g., 30%) variation in adoption rates attributed to factors such as funding availability and local healthcare facility capacity. Regional disparities will also be evident with some regions showing higher adoption rates than others. The findings from this study will contribute valuable insights into the effectiveness of public health surveillance systems in South Africa, particularly regarding regional differences and underlying determinants of system uptake. Based on our analysis, we recommend targeted interventions to enhance system adoption in regions with lower rates. Additionally, ongoing monitoring is essential to ensure sustained system functionality across all healthcare facilities. Public health surveillance systems, multilevel regression, adoption rates, South Africa Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.