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.
BMC Public Health. 2024 Feb 9;24(1):423. doi: 10.1186/s12889-024-17950-y.
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.
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).
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.
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.
确保全民健康覆盖和公平获得医疗服务需要全面了解医疗资源的时空异质性，尤其是在小区域内。现有的研究中缺乏结构化的时空评估框架，这启发我们提出了一个包含三个视角的概念框架：时空不平等、热点和决定因素。
为了展示我们的三视角概念框架，我们采用了三种最先进的方法，并分析了中国县级医院床位数据的十年数据。首先，我们通过空间基尼系数描绘了省内医院床位的空间不平等及其时间不平等。接下来，我们使用新兴的热点分析（Getis-Ord Gi* 统计量）在县级水平上识别不同类型的时空热点和冷点。最后，我们使用贝叶斯时空变系数（STVC）模型探索社会经济和环境因素对医院床位的时空异质性影响，并使用时空方差分解指数（STVPI）量化因素的时空可解释百分比。
空间不平等图揭示了医院床位的显著差异，随着时间的推移，21 个省份的医院床位逐渐得到改善。在 24.78%的县中，有七种类型的热点和冷点突出表明了高、低水平医院床位县中区域马太效应的持续存在。社会经济因素对县级医院床位的贡献为 36.85%（95%可信区间 [CI]：31.84-42.50%），而环境因素占 59.12%（53.80-63.83%）。因素的空间尺度变化解释了 75.71%（68.94-81.55%），而时间尺度变化贡献了 20.25%（14.14-27.36%）。此外，确定了六个因素（GDP、第一产业产出、地方一般预算收入、道路、河流和坡度）为时空决定因素，它们共同解释了超过 84%的变异。
三视角框架使全球政策制定者和利益相关者能够在微观层面上识别卫生服务差距，确定需要针对性干预的地区，并制定与各自独特时空决定因素相一致的差异化战略，这对实现可持续医疗保健发展具有重要意义。
