Gong Hanxiang, Zhang Tao, Wang Xi, Chen Baoxin, Wu Baoling, Zhao Shufang
Faculty of Humanities and Social Sciences, Macao Polytechnic University, Macao, 999078, People's Republic of China.
The Second Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong, 510260, People's Republic of China.
Risk Manag Healthc Policy. 2024 Nov 14;17:2811-2828. doi: 10.2147/RMHP.S479911. eCollection 2024.
This study explores regional differences, dynamic evolution, and influencing factors of medical service levels in Guangzhou under the Health China Strategy to provide a basis for improving service quality and reducing disparities.
An evaluation system was constructed using the entropy weight TOPSIS method. The Dagum Gini coefficient analyzed regional differences, Kernel density estimation assessed service levels' distribution, and Tobit regression explored influencing factors. Data were collected from the "Guangzhou Statistical Yearbook", Guangzhou Health Commission reports, and government work reports from 2017 to 2022.
The study shows that from 2017 to 2022, there were significant differences in medical service levels among different regions of Guangzhou, with higher service quality in central urban areas compared to remote and peripheral areas. The application of the entropy weight method revealed the importance of indicators such as medical business costs and the number of registered nurses per thousand population in evaluating service quality. According to the Dagum Gini coefficient decomposition method, regional differences in medical services in Guangzhou are the main factor causing uneven overall development quality. Kernel density estimation indicates a bimodal distribution of medical service quality, suggesting heterogeneity in service quality and an increasing trend in low-quality service areas. The Tobit model confirms that factors such as medical institution drug costs, bed occupancy rate, and medical human resources have a positive impact on improving service quality.
This study uniquely integrates the entropy weight TOPSIS method, Dagum Gini coefficient decomposition, and Kernel density estimation to dissect regional disparities in Guangzhou's medical services, offering a novel perspective on healthcare evolution under the Health China Strategy. The findings provide an innovative framework for optimizing resource allocation and enhancing service quality, guiding balanced development across regions.
本研究探讨健康中国战略背景下广州市医疗服务水平的区域差异、动态演变及影响因素，为提高服务质量和缩小差距提供依据。
采用熵权TOPSIS法构建评价体系。运用达古姆基尼系数分析区域差异，核密度估计评估服务水平分布，托宾回归探究影响因素。数据来源于《广州统计年鉴》、广州市卫生健康委员会报告以及2017年至2022年的政府工作报告。
研究表明，2017年至2022年，广州不同区域的医疗服务水平存在显著差异，中心城区的服务质量高于偏远和周边地区。熵权法的应用揭示了医疗业务成本、每千人口注册护士数等指标在评价服务质量中的重要性。根据达古姆基尼系数分解法，广州医疗服务的区域差异是导致整体发展质量不均衡的主要因素。核密度估计表明医疗服务质量呈双峰分布，表明服务质量存在异质性，且低质量服务地区有扩大趋势。托宾模型证实，医疗机构药品成本、床位使用率和医疗人力资源等因素对提高服务质量有正向影响。
本研究创新性地整合了熵权TOPSIS法、达古姆基尼系数分解法和核密度估计法，剖析了广州医疗服务的区域差异，为健康中国战略下的医疗发展提供了新视角。研究结果为优化资源配置和提高服务质量提供了创新框架，指导区域均衡发展。
