卫生资源配置效率及其影响因素研究——基于超效率 SBM-Tobit 模型的实证分析。
The efficiency of health resource allocation and its influencing factors: evidence from the super efficiency slack based model-Tobit model.
机构信息
Department of Hospital Management, Tsing Hua University, Shenzhen Campus, Shenzhen 518000, China.
Department of Health Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, USA.
出版信息
Int Health. 2023 May 2;15(3):326-334. doi: 10.1093/inthealth/ihac054.
BACKGROUND
This study aims to analyze the health resource allocation efficiency in Sichuan Province from 2010 to 2018 and provide other countries with China's experience.
METHODS
We used the super efficiency slack based model (SBM) model and Malmquist index to analyze the super efficiency and inter-period efficiency of health resource allocation in 19 cities in Sichuan Province from 2010 to 2018 and propose the input-output optimization scheme of health resource allocation in 2018. Finally, the Tobit model was used to estimate the influencing factors of health resource allocation efficiency.
RESULTS
The total allocation of health resources in Sichuan Province was increasing in addition to the total number of visits from 2010 to 2018. The super efficiency SBM results identified that the sample's average score was between 0.651 and 3.244, with an average of 1.041, of which 15 cities had not reached data envelopment analysis effectiveness. According to the Malmquist index, the average total factor productivity index of Sichuan Province was 0.930, which showed an imbalance in resource input, and its fluctuation was mainly related to the technological progress index and scale efficiency. The efficiency score was affected by the average annual income of residents, population density and education level.
CONCLUSIONS
The amount of health resource allocation in Sichuan Province had shown an overall upward trend since 2010. However, resource allocation efficiency was not high, and there were problems such as significant regional differences, insufficient technological innovation capabilities and unscientific allocation of resource scale. To optimize the resource allocation structure, we suggest that the relevant departments pay attention to the impact of natural disasters, the average annual income of residents, population density and education level on efficiency to allocate health resources scientifically.
背景
本研究旨在分析 2010 年至 2018 年四川省卫生资源配置效率，并为其他国家提供中国的经验。
方法
我们使用超效率松弛基模型（SBM）和 Malmquist 指数分析了 2010 年至 2018 年四川省 19 个城市的卫生资源配置超效率和跨期效率，并提出了 2018 年卫生资源配置的投入产出优化方案。最后，采用 Tobit 模型估计卫生资源配置效率的影响因素。
结果
除了 2010 年至 2018 年的总就诊人数外，四川省的卫生资源总配置也在增加。超效率 SBM 结果表明，样本的平均得分为 0.651 至 3.244，平均得分为 1.041，其中 15 个城市未达到数据包络分析效率。根据 Malmquist 指数，四川省的平均全要素生产率指数为 0.930，表明资源投入不平衡，其波动主要与技术进步指数和规模效率有关。效率得分受到居民平均年收入、人口密度和教育水平的影响。
结论
自 2010 年以来，四川省的卫生资源配置量呈整体上升趋势。然而，资源配置效率不高，存在区域差异显著、技术创新能力不足、资源规模配置不合理等问题。为了优化资源配置结构，建议相关部门关注自然灾害、居民平均年收入、人口密度和教育水平对效率的影响，科学配置卫生资源。
Zotero 插件