Public health surveillance systems in Nigeria are crucial for monitoring infectious diseases such as malaria and tuberculosis (TB). However, their effectiveness varies across different regions. A mixed-method approach was employed, including both quantitative data analysis and qualitative interviews with stakeholders. The study utilised a difference-in-differences (DiD) regression model for evaluating the impact of system improvements over time. The DiD model revealed a significant increase in reported cases following system enhancements, particularly in urban areas where TB surveillance saw a 20% reduction in reporting errors compared to rural regions. This study provides robust evidence supporting the efficacy of systematic interventions in improving public health surveillance outcomes. The findings can inform policy makers on best practices for enhancing disease monitoring systems. Implementing standardised training programmes and regular system audits are recommended to sustain improved reporting accuracy and ensure consistent performance across different regions. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.