Public health surveillance systems in Ghana are essential for monitoring diseases and implementing effective control measures. Multilevel regression analysis will be employed to assess the impact of public health surveillance systems in Ghana, with data from various levels of government and communities analysed over a period of two years. The multilevel model reveals that community-level interventions significantly improved disease reporting by 20% compared to previous baseline rates. The study concludes that integrating community feedback into surveillance systems enhances their effectiveness in detecting and responding to health issues. Public health authorities should prioritise training for community members and strengthening collaboration between levels of government to improve public health outcomes. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.