Methodological Assessment of Public Health Surveillance Systems in Ghana Through Multilevel Regression Analysis

Public health surveillance systems are crucial for monitoring diseases in Ghana, yet their effectiveness varies across different regions. Multilevel regression analysis was employed to assess the impact of various factors on disease prevalence data collected from different regions. The model includes random intercepts for geographical areas and fixed effects for demographic variables. In Ghana, multilevel models revealed significant variations in disease prevalence between urban and rural settings (p < 0.05), suggesting that local health interventions need to be tailored accordingly. The study highlights the importance of considering geographical differences when analysing public health surveillance data. Public health authorities should prioritise targeted intervention strategies for underserved regions identified through this analysis. Treatment effect was estimated with logit(pᵢ)=β₀+β^ Xᵢ, and uncertainty reported using confidence-interval based inference.