Public health surveillance systems are crucial for monitoring disease outbreaks in Tanzania. However, their effectiveness varies across different regions and sectors. A multilevel logistic regression model was employed to analyse data from multiple sources including government records, community surveys, and hospital databases. The model accounts for both individual-level (e.g., healthcare provider attitudes) and area-level (e.g., infrastructure availability) influences on surveillance system adoption. The analysis revealed that the proportion of healthcare facilities adopting electronic health record systems was significantly higher in urban areas compared to rural regions, with a 60% adoption rate in cities versus 45% in villages. This suggests an uneven distribution affecting equitable access to public health interventions. Multilevel regression analysis provided insights into the adoption dynamics of surveillance systems in Tanzania, highlighting regional disparities that require targeted intervention strategies. Public health authorities should prioritise investment and training programmes in underserved rural areas to improve electronic health record system adoption and ensure comprehensive coverage across all regions. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.