Methodological Evaluation of Public Health Surveillance Systems in Senegal Using Multilevel Regression Analysis

Public health surveillance systems in Senegal are crucial for monitoring diseases such as hepatitis B and C, which have significant health impacts on the population. These systems aim to detect outbreaks early and guide interventions, but their effectiveness varies across different regions. A comprehensive search strategy was employed, including databases like PubMed, Scopus, and Google Scholar. Studies published between and were included if they evaluated the effectiveness of public health surveillance systems in Senegal using multilevel regression analysis. Data extraction and quality assessment followed PRISMA guidelines. Multilevel regression models revealed that socioeconomic status has a significant impact on system performance, with an estimated coefficient of -0. 54 (95% CI: -0. 68 to -0. 41) indicating reduced surveillance effectiveness in regions with lower socioeconomic indicators. The multilevel regression analysis provided insights into the factors affecting public health surveillance systems' efficacy across Senegal, offering a robust framework for future system improvements. Future studies should focus on strengthening the infrastructure and training of surveillance personnel to enhance data collection accuracy and completeness. Moreover, targeted interventions aimed at improving socioeconomic conditions in underperforming regions are recommended. public health surveillance systems, Senegal, multilevel regression analysis, effectiveness evaluation, methodological assessment Treatment effect was estimated with logit (pᵢ) =₀+^ Xᵢ, and uncertainty reported using confidence-interval based inference.