Abstract
BACKGROUND: Regional disparities in healthcare resource allocation across space and time present significant challenges to the global achievement of SDG 3, SDG 10, and SDG 11. To this end, we proposed a joint spatiotemporal evaluation framework to assess the synergistic efficiency of multiple healthcare resources. METHODS: Using China as a case study, we analyzed data from 365 cities (2000-2021) on three key healthcare resource indicators: hospitals, hospital beds, and physicians. A composite healthcare resource score was constructed using the entropy weight method. We developed a three-dimensional joint spatiotemporal evaluation framework incorporating spatial Gini coefficient, emerging hotspot analysis, and Bayesian spatiotemporally varying coefficients (BSTVC) model with spatiotemporal variance partitioning index (STVPI) to evaluate spatiotemporal equity, agglomeration, and influencing factors. Individual indicators were evaluated to validate the framework's robustness. RESULTS: (i) Spatiotemporal description: The composite indicator, weighted by hospitals (25%), hospital beds (46%), and physicians (29%), showed only a modest increase from 2000 to 2021, with persistently lower values in western and northern regions. (ii) Common spatiotemporal equity: The spatial Gini coefficient for the composite indicator increased annually by 0.34%, mirroring trends in hospital beds (0.34%) and physicians (0.26%) but contrasting with hospitals (-0.32%). This suggested that declining equity was mainly driven by hospital beds and physicians, partially offset by the more balanced distribution of hospitals. (iii) Common spatiotemporal agglomeration: Hotspot intensity for the composite indicator was lower than that for hospitals but higher than that for hospital beds and physicians. Cold spots were more concentrated for the composite indicator than for any individual indicator, with less than 10% overlap across the three indicators, indicating weak regional synergy. (iv) Common spatiotemporal drivers: BSTVC and STVPI methods revealed consistent patterns of explainable percentages across four healthcare resource indicators, with population density (37.96%, 95% CI: 30.05-43.05%) and employed population density (31.63%, 30.69-33.83%) emerging as dominant common drivers, supporting unified and coordinated policy interventions. DISCUSSION: We proposed a joint spatiotemporal evaluation framework to quantify both common and differentiated allocation patterns and driving factors across multiple healthcare resource indicators, highlighting the necessity for type-specific, temporally responsive, and spatially adaptive interventions to support dynamic monitoring and precise regulation of regional healthcare resource allocation globally.