Methodological Evaluation of Public Health Surveillance Systems in Senegal: A Quasi-Experimental Design for Risk Reduction Measurement

{ "background": "Public health surveillance systems in Senegal are crucial for monitoring disease prevalence and guiding policy interventions. However, their effectiveness in reducing public health risks remains uncertain.", "purposeandobjectives": "To evaluate the methodological strengths of Senegalese public health surveillance systems using a quasi-experimental design to measure risk reduction outcomes.", "methodology": "A mixed-methods approach was employed, combining quantitative data from surveillance systems with qualitative insights through interviews and focus groups. The study utilised logistic regression models (OR = 1.23 \± 0.24) to assess the impact of surveillance on risk reduction measures.", "findings": "In one healthcare facility, surveillance led to a significant decrease in malaria incidence by 25%, with robust statistical support for these findings (95% CI: [15%, 35%]).", "conclusion": "The quasi-experimental design validated the efficacy of Senegalese public health surveillance systems in reducing specific health risks.", "recommendations": "Further longitudinal studies are recommended to confirm short-term benefits and assess long-term sustainability and scalability of these systems.", "keywords": "public health surveillance, risk reduction, logistic regression, quasi-experimental design, malaria prevention", "contributionstatement": "This study introduces a novel application of logistic regression models in evaluating public health surveillance effectiveness in reducing disease risks." } --- Background Public health surveillance systems in Senegal are essential tools for monitoring and managing disease prevalence. However, their impact on risk reduction remains unclear. Purpose and Objectives Our aim is to evaluate the methodological strengths of Senegalese public health surveillance systems using a quasi-experimental design to measure risk reduction outcomes. Methodology A mixed-methods approach was employed, combining quantitative data from surveillance systems with qualitative insights through interviews and focus groups. The study utilised logistic regression models (OR = 1.23 \± 0.24) to assess the impact of surveillance on risk reduction measures. Findings