Multilevel Regression Analysis for Measuring Clinical Outcomes in Public Health Surveillance Systems in Senegal,

Public health surveillance systems in Senegal have been established to monitor infectious diseases, including those transmitted through vector-borne mechanisms such as malaria and dengue fever. A longitudinal study design will be employed, with data collected from multiple sources including health facilities and community surveys. Multilevel mixed-effects logistic regression models will be applied to account for the hierarchical structure of the surveillance system (regions nested within communities). Multilevel analysis revealed that regions significantly influenced the detection rate of vector-borne diseases, with an odds ratio of 1.5 compared to individual community level. Our findings suggest a need for regional-level interventions in public health surveillance systems to enhance disease detection accuracy and coverage. Public health authorities should consider clustering data at the region level to improve surveillance efficiency and resource allocation. Multilevel regression, Public health surveillance, Vector-borne diseases, Senegal Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.