{ "background": "Public health surveillance systems in Uganda are essential for monitoring infectious diseases and ensuring timely interventions. However, their efficiency varies across different levels of governance.", "purposeandobjectives": "The purpose is to evaluate the efficiency of public health surveillance systems at various administrative levels within Uganda using multilevel regression analysis.", "methodology": "Multilevel regression models will be employed to account for hierarchical data structures and potential correlations between levels. The model equation is $Y{ijk} = \beta0 + \beta1X{1ijk} + \beta2X{2ijk} + b{i} + b{j} + e{ijk}$, where $Y$ represents surveillance effectiveness, $X1$ and $X2$ are explanatory variables, $bi$ and $bj$ are random effects for district ($i$) and subdistrict ($j$) levels respectively, and $e{ijk}$ is the error term.", "findings": "The analysis revealed significant variability in surveillance effectiveness across districts (effect size = 0.45), with some areas showing substantial gains in efficiency after implementing new monitoring tools.", "conclusion": "This study provides empirical evidence on the efficacy of public health surveillance systems at different administrative levels, contributing to policy recommendations for system optimization and resource allocation.", "recommendations": "Based on findings, targeted interventions should focus on underserved subdistricts where surveillance effectiveness is notably lower, suggesting a need for more focused capacity-building activities.", "keywords": "Public Health Surveillance Systems, Multilevel Regression Analysis, Efficiency Evaluation, Uganda", "contributionstatement": "This study introduces a rigorous multilevel regression framework to assess and compare the efficiency of public health surveillance systems across multiple levels in Uganda." } Multilevel regression analysis is applied to evaluate the efficiency of public health surveillance systems at various administrative levels within Uganda. The model equation $Y{ijk} = \beta0 + \beta1X_{1ijk