Public health surveillance systems are critical for disease control, yet their adoption across sub-Saharan Africa remains uneven and poorly quantified. In Uganda, despite policy emphasis, a systematic, longitudinal assessment of adoption drivers and rates is lacking. This study aimed to methodologically evaluate surveillance system performance and to estimate the determinants and temporal trajectory of their adoption across Ugandan districts. We constructed a novel district-level panel dataset from administrative records and health facility surveys. Adoption was measured using a composite index of system functionality. A two-way fixed effects model was estimated: Adoption₈ₓ = ₀ + ₁X₈ₓ + ᵢ + ₜ + ₈ₓ, where X₈ₓ includes health workforce density, funding, and infrastructure. Inference was based on cluster-robust standard errors. Health workforce density was the strongest predictor of adoption (coefficient = 0. 15, 95% CI: 0. 09, 0. 21). The model projects that, under current trends, national adoption will reach 65% by the end of the forecast period, with pronounced regional disparities persisting. Adoption of surveillance systems is increasing but remains inequitable and heavily contingent on human resource allocation. The methodological framework provides a robust tool for monitoring and evaluation. Policy should prioritise sustained investment in training and retention of surveillance officers. Future system deployment must incorporate equity-focused targeting to close regional gaps. health surveillance, panel data, adoption models, health systems, Uganda This paper provides a novel methodological framework for longitudinal analysis of surveillance system adoption and delivers the first district-level panel-data estimates for Uganda, identifying human resources as the critical leverage point.
Nakigozi et al. (Wed,) studied this question.
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