"background": "Public health surveillance systems are critical for disease control, yet their adoption across diverse health facilities in low-resource settings remains poorly quantified. Current evaluations often rely on binary metrics that fail to capture the complex, multi-level determinants of implementation success, limiting the utility of data for decision-making. ", "purpose and objectives": "This protocol outlines a methodological framework to evaluate the adoption of integrated disease surveillance and response (IDSR) systems in Uganda. The primary objective is to develop and validate a Bayesian hierarchical model to estimate facility-level adoption rates and identify key predictors of successful implementation. ", "methodology": "We will conduct a cross-sectional survey of a stratified random sample of health facilities. Data on structural, process, and outcome indicators of IDSR adoption will be collected. The core statistical model is a Bayesian hierarchical logistic regression: (p{ij) = \ + + \ Xij, where pij is the probability of full adoption for facility i in district j, \ N (0, \²) are district-level random effects, and Xij are facility-level covariates. Posterior distributions will be estimated using Markov chain Monte Carlo sampling. ", "findings": "As this is a protocol, no empirical findings are presented. The anticipated analysis will generate district-level posterior estimates of adoption rates, expected to show significant heterogeneity (e. g. , an interquartile range of 20-60%). The model will quantify the probability that specific factors, such as staff training completeness, increase the odds of adoption. ", "conclusion": "This protocol proposes a novel analytical approach for surveillance system evaluation. The model's output will provide a more nuanced, probabilistic understanding of adoption, moving beyond descriptive summaries to actionable inference for health system strengthening. ", "recommendations": "Future research should apply this modelling framework longitudinally to assess changes in adoption. Programme managers should utilise the probabilistic outputs to prioritise districts
Tumwesigye et al. (Fri,) studied this question.