Public health surveillance systems are crucial for monitoring disease prevalence in resource-limited settings such as Tanzania. However, their effectiveness and cost-effectiveness vary across different regions. The study will employ multilevel regression models with robust standard errors to analyse data from multiple levels including national, regional, and local. Data sources include administrative records and surveys. Initial findings suggest that the surveillance system at district level has a moderate impact on disease reporting (r² = 0. 35, p < 0. 05). Multilevel regression analysis provides a robust framework for understanding cost-effectiveness in public health surveillance systems. Future studies should consider expanding the scope of data collection to include more variables and longitudinal trends. Treatment effect was estimated with logit (pᵢ) =₀+^ Xᵢ, and uncertainty reported using confidence-interval based inference.
Katuna et al. (Thu,) studied this question.
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