A Bayesian Hierarchical Model for Evaluating the Adoption of Public Health Surveillance Systems in Nigeria, 2000–2026

{ "background": "Public health surveillance systems are critical for disease control, yet their adoption across diverse administrative regions in Nigeria remains uneven and poorly quantified. Existing evaluations often lack a formal statistical framework to integrate multi-level data and account for spatial and temporal heterogeneity.", "purpose and objectives": "This study aimed to develop and apply a novel Bayesian hierarchical model to evaluate the adoption rates of integrated disease surveillance and response (IDSR) systems across Nigerian states, and to project future trajectories under current policy conditions.", "methodology": "We constructed a Bayesian hierarchical logistic model using state-level panel data on surveillance system implementation. The core model is specified as (pit) = \α + \β Xit + \ + \ + \εit, where pit is the probability of full adoption in state i at time t, Xit are covariates, \ captures temporal trends, and \δᵢ are state-level random effects. Parameters were estimated using Hamiltonian Monte Carlo.", "findings": "Model projections indicate a median national adoption rate of 67% (95% credible interval: 61–73%) by the end of the projection period, with substantial inter-state variation. The posterior probability that the adoption rate in northern states lags behind southern states exceeded 0.85.", "conclusion": "The methodological approach provides a robust, probabilistic framework for evaluating public health system adoption. Findings reveal that, without intervention, significant geographical inequities in surveillance capacity will persist.", "recommendations": "Policy should prioritise targeted, data-driven support for low-adoption regions. Future evaluations of health systems should employ similar hierarchical models to formally incorporate uncertainty and heterogeneity.", "key words": "Bayesian statistics, hierarchical model, public health surveillance, health systems, adoption, Nigeria", "contribution statement": "This paper introduces a novel Bayesian hierarchical modelling framework for the spatio-temporal analysis of health system adoption