"background": "Public health surveillance is a cornerstone of effective disease control, yet the methodological rigour and economic efficiency of such systems in resource-limited settings are not well synthesised. Uganda's diverse surveillance infrastructure, developed over decades, presents a critical case for evaluating analytical approaches and their associated costs. ", "purpose and objectives": "This meta-analysis aims to systematically evaluate methodological approaches used in assessing the country's public health surveillance systems and to estimate their cost-effectiveness using panel-data econometric techniques. ", "methodology": "We conducted a systematic review and meta-analysis of peer-reviewed and grey literature. Eligible studies were synthesised, and a bespoke panel-data model was constructed for cost-effectiveness estimation. The primary model specification was a two-way fixed effects regression: Y{it = \ + \1CEit + \2Mit + \ + \ +, where Yit is a surveillance performance outcome, CEit denotes cost-effectiveness metrics, Mit represents methodological covariates, and \ and \ₜ are entity and time fixed effects. Inference was based on cluster-robust standard errors. ", "findings": "The synthesis identified a predominant reliance on cross-sectional designs (approximately 65% of included studies), which correlated with higher variability in cost-effectiveness estimates. The panel-data estimation revealed that integrated disease surveillance and response (IDSR) strategies showed significantly superior cost-effectiveness ratios compared to vertical programmes, with a likelihood exceeding 95% that the difference is not due to chance. ", "conclusion": "Methodological choices, particularly the use of longitudinal data structures, substantially influence the measured cost-effectiveness of surveillance systems. The evidence indicates that integrated, multi-disease approaches represent a more economically efficient model for sustained surveillance. ", "recommendations": "Future surveillance evaluations should prioritise longitudinal, panel-data designs to generate more reliable economic metrics. Policymakers should advocate for and fund the integration of
Nakimuli et al. (Mon,) studied this question.