Public health surveillance systems in South Africa play a crucial role in monitoring disease prevalence and guiding public health interventions. However, their reliability and effectiveness can be assessed through methodological evaluations. This study will employ a mixed-methods approach, combining quantitative data from existing surveillance databases with qualitative interviews of key stakeholders. A causal inference model will be used to estimate the impact of system operations on disease prevalence estimates. A preliminary analysis suggests that there is a significant positive correlation (p <. 05) between automated reporting systems and accurate incidence rate calculations, indicating high reliability in routine surveillance activities. The findings highlight the importance of continuous system validation to maintain data integrity and public health relevance. System administrators are advised to conduct regular audits and incorporate feedback mechanisms from surveillance users to ensure ongoing accuracy and responsiveness. Public Health Surveillance, Reliability Evaluation, Quasi-Experimental Design, South Africa Treatment effect was estimated with logit (pᵢ) =₀+^ Xᵢ, and uncertainty reported using confidence-interval based inference.
Dr Amy Wallace (2009) studied this question.