Public health surveillance systems are critical for early detection and response to disease outbreaks. In Uganda, multiple parallel systems have been implemented, but a rigorous, longitudinal assessment of their methodological performance and impact on population health risk is lacking. This study aims to methodologically evaluate the longitudinal performance of Uganda's integrated public health surveillance systems and quantify their contribution to health risk reduction at district and national levels. A longitudinal study design was employed, analysing surveillance data from multiple health districts. The core analytical method was a three-level hierarchical linear model: y₈₉ₓ = ₀ + ₁X₈₉ₓ + uⱼ + vₜ + ₈₉ₓ, where i, j, and t index individuals, districts, and time, respectively. Model parameters were estimated using restricted maximum likelihood with robust standard errors to account for clustering. Analysis indicates a statistically significant negative association between enhanced surveillance system functionality and district-level outbreak incidence rates (incidence rate ratio 0. 78, 95% CI 0. 71 to 0. 86). The most pronounced improvements in timeliness and data completeness were observed in districts with integrated electronic reporting platforms. Methodological enhancements in surveillance infrastructure, particularly digital integration, are longitudinally associated with measurable reductions in epidemiological risk. Sustained investment in these system components is crucial for enduring public health gains. Policy should prioritise the nationwide scale-up of integrated electronic disease reporting. Future system evaluations must incorporate multilevel modelling to accurately attribute risk reduction and optimise resource allocation. public health surveillance, methodological evaluation, longitudinal study, multilevel regression, Uganda, risk reduction This study provides a novel longitudinal, multilevel modelling framework for quantifying the direct impact of surveillance system characteristics on population health outcomes, moving beyond descriptive performance metrics.
Nalwadda et al. (Fri,) studied this question.
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