{ "background": "Public health surveillance systems in Ghana often rely on aggregated, facility-level data, which can mask localised variations in clinical outcomes and hinder targeted interventions. This methodological limitation reduces the precision of health system evaluations.", "purpose and objectives": "This study aimed to develop and evaluate a novel Bayesian hierarchical modelling intervention designed to improve the measurement and spatial understanding of clinical outcomes within the nation's public health infrastructure.", "methodology": "We implemented an intervention applying a Bayesian hierarchical model to routine health management information system data. The core model was $y{it} \\sim \\text{Binomial}(n{it}, p{it})$, $\\text{logit}(p{it}) = \\alpha + \\beta X{it} + ui + vi + \\gammat$, where $ui$ and $vi$ represent structured and unstructured spatial random effects for district $i$, and $\\gamma_t$ is a temporal effect. Model performance was assessed using Watanabe-Akaike information criterion and posterior predictive checks against conventional aggregation methods.", "findings": "The intervention model provided superior fit, reducing deviance by 32% compared to standard aggregation. It revealed substantial sub-national heterogeneity, with the posterior probability of a district-level anaemia reduction rate exceeding the national target being below 0.3 in over a quarter of districts, highlighting priority areas.", "conclusion": "The Bayesian hierarchical modelling intervention offers a robust methodological advance for public health surveillance, enabling more nuanced, spatially precise inferences on clinical outcomes from existing data streams.", "recommendations": "National health authorities should integrate hierarchical modelling techniques into surveillance analytics to enable data-driven, sub-national prioritisation. Capacity building in spatial statistics is required for sustained implementation.", "key words": "Bayesian hierarchical model, public health surveillance, spatial epidemiology, health systems strengthening, clinical outcomes, Ghana", "contribution statement": "This paper provides a novel methodological framework for extracting spatially granular performance estimates from aggregated national health data, demonstrating its utility for identifying