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Song C, Fang L, Xie M, Tang Z, Zhang Y, Tian F, Wang X, Lin X, Liu Q, Xu S, Pan J. Revealing spatiotemporal inequalities, hotspots, and determinants in healthcare resource distribution: insights from hospital beds panel data in 2308 Chinese counties. BMC Public Health 2024; 24:423. [PMID: 38336709 PMCID: PMC11218403 DOI: 10.1186/s12889-024-17950-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 02/01/2024] [Indexed: 02/12/2024] Open
Abstract
BACKGROUND Ensuring universal health coverage and equitable access to health services requires a comprehensive understanding of spatiotemporal heterogeneity in healthcare resources, especially in small areas. The absence of a structured spatiotemporal evaluation framework in existing studies inspired us to propose a conceptual framework encompassing three perspectives: spatiotemporal inequalities, hotspots, and determinants. METHODS To demonstrate our three-perspective conceptual framework, we employed three state-of-the-art methods and analyzed 10 years' worth of Chinese county-level hospital bed data. First, we depicted spatial inequalities of hospital beds within provinces and their temporal inequalities through the spatial Gini coefficient. Next, we identified different types of spatiotemporal hotspots and coldspots at the county level using the emerging hot spot analysis (Getis-Ord Gi* statistics). Finally, we explored the spatiotemporally heterogeneous impacts of socioeconomic and environmental factors on hospital beds using the Bayesian spatiotemporally varying coefficients (STVC) model and quantified factors' spatiotemporal explainable percentages with the spatiotemporal variance partitioning index (STVPI). RESULTS Spatial inequalities map revealed significant disparities in hospital beds, with gradual improvements observed in 21 provinces over time. Seven types of hot and cold spots among 24.78% counties highlighted the persistent presence of the regional Matthew effect in both high- and low-level hospital bed counties. Socioeconomic factors contributed 36.85% (95% credible intervals [CIs]: 31.84-42.50%) of county-level hospital beds, while environmental factors accounted for 59.12% (53.80-63.83%). Factors' space-scale variation explained 75.71% (68.94-81.55%), whereas time-scale variation contributed 20.25% (14.14-27.36%). Additionally, six factors (GDP, first industrial output, local general budget revenue, road, river, and slope) were identified as the spatiotemporal determinants, collectively explaining over 84% of the variations. CONCLUSIONS Three-perspective framework enables global policymakers and stakeholders to identify health services disparities at the micro-level, pinpoint regions needing targeted interventions, and create differentiated strategies aligned with their unique spatiotemporal determinants, significantly aiding in achieving sustainable healthcare development.
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Affiliation(s)
- Chao Song
- HEOA Group, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
- Institute for Healthy Cities and West China Research Centre for Rural Health Development, Sichuan University, Chengdu, Sichuan, China
- West China-PUMC C.C. Chen Institute of Health, Sichuan University, Chengdu, Sichuan, China
| | - Lina Fang
- HEOA Group, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
- Institute for Healthy Cities and West China Research Centre for Rural Health Development, Sichuan University, Chengdu, Sichuan, China
| | - Mingyu Xie
- School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Zhangying Tang
- State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, School of Geoscience and Technology, Southwest Petroleum University, Chengdu, Sichuan, China
| | - Yumeng Zhang
- HEOA Group, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
- Institute for Healthy Cities and West China Research Centre for Rural Health Development, Sichuan University, Chengdu, Sichuan, China
| | - Fan Tian
- HEOA Group, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Xiuli Wang
- HEOA Group, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
- Institute for Healthy Cities and West China Research Centre for Rural Health Development, Sichuan University, Chengdu, Sichuan, China
| | - Xiaojun Lin
- HEOA Group, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
- Institute for Healthy Cities and West China Research Centre for Rural Health Development, Sichuan University, Chengdu, Sichuan, China
- West China-PUMC C.C. Chen Institute of Health, Sichuan University, Chengdu, Sichuan, China
| | - Qiaolan Liu
- HEOA Group, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Shixi Xu
- HEOA Group, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China.
| | - Jay Pan
- HEOA Group, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China.
- China Center for South Asian Studies, Sichuan University, Chengdu, Sichuan, China.
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