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中国乡镇卫生院卫生资源配置效率的空间效应。

Spatial effects of township health centers' health resource allocation efficiency in China.

机构信息

School of Humanities and Management, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China.

出版信息

Front Public Health. 2024 Aug 16;12:1420867. doi: 10.3389/fpubh.2024.1420867. eCollection 2024.

DOI:10.3389/fpubh.2024.1420867
PMID:39220456
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11363543/
Abstract

INTRODUCTION

China is a large agricultural nation with the majority of the population residing in rural areas. The allocation of health resources in rural areas significantly affects the basic rights to life and health for rural residents. Despite the progress made by the Chinese government in improving rural healthcare, there is still room for improvement. This study aims to assess the spatial spillover effects of rural health resource allocation efficiency in China, particularly focusing on township health centers (THCs), and examine the factors influencing this efficiency to provide recommendations to optimize the allocation of health resources in rural China.

METHODS

This study analyzed health resource allocation efficiency in Chinese rural areas from 2012 to 2021 by using the super-efficiency SBM model and the global Malmquist model. Additionally, the spatial auto-correlation of THC health resource allocation efficiency was verified through Moran test, and three spatial econometric models were constructed to further analyze the factors influencing efficiency.

RESULTS

The key findings are: firstly, the average efficiency of health resource allocation in THCs was 0.676, suggesting a generally inefficient allocation of health resources over the decade. Secondly, the average Malmquist productivity index of THCs was 0.968, indicating a downward trend in efficiency with both non-scale and non-technical efficient features. Thirdly, Moran's Index analysis revealed that efficiency has a significant spatial auto-correlation and most provinces' values are located in the spatial agglomeration quadrant. Fourthly, the SDM model identified several factors that impact THC health resource allocation efficiency to varying degrees, including the efficiency of total health resource allocation, population density, PGDP, urban unemployment rate, disposable income, healthcare expenditure ratio, public health budget, and passenger traffic volume.

DISCUSSION

To enhance the efficiency of THC healthcare resource allocation in China, the government should not only manage the investment of health resources to align with the actual demand for health services but also make use of the spatial spillover effect of efficiency. This involves focusing on factors such as total healthcare resource allocation efficiency, population density, etc. to effectively enhance the efficiency of health resource allocation and ensure the health of rural residents.

摘要

简介

中国是一个农业大国,大部分人口居住在农村地区。农村地区的卫生资源配置对农村居民的生命健康基本权利有重大影响。尽管中国政府在改善农村医疗保健方面取得了进展,但仍有改进的空间。本研究旨在评估中国农村卫生资源配置效率的空间溢出效应,特别是乡镇卫生院(THC),并考察影响这种效率的因素,为优化中国农村卫生资源配置提供建议。

方法

本研究采用超效率 SBM 模型和全局 Malmquist 模型,对 2012 年至 2021 年中国农村地区的卫生资源配置效率进行了分析。此外,通过 Moran 检验验证了 THC 卫生资源配置效率的空间自相关,并构建了三个空间计量经济学模型,进一步分析了影响效率的因素。

结果

研究的主要发现是:首先,THC 卫生资源配置的平均效率为 0.676,这表明在过去十年中卫生资源的配置效率普遍较低。其次,THC 的平均 Malmquist 生产力指数为 0.968,表明效率呈下降趋势,具有非规模和非技术效率特征。第三,Moran's Index 分析表明,效率具有显著的空间自相关,大多数省份的值都位于空间集聚象限。第四,SDM 模型确定了几个对 THC 卫生资源配置效率有不同程度影响的因素,包括总卫生资源配置效率、人口密度、PGDP、城镇失业率、可支配收入、医疗保健支出比例、公共卫生预算和客流量。

讨论

为了提高中国 THC 医疗卫生资源配置效率,政府不仅要管理卫生资源的投入,使其与卫生服务的实际需求相匹配,还要利用效率的空间溢出效应。这涉及到关注总医疗资源配置效率、人口密度等因素,以有效提高卫生资源配置效率,确保农村居民的健康。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9aef/11363543/70d706da3c1e/fpubh-12-1420867-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9aef/11363543/269e5f746522/fpubh-12-1420867-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9aef/11363543/d9221fb7c71c/fpubh-12-1420867-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9aef/11363543/446d8c795920/fpubh-12-1420867-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9aef/11363543/70d706da3c1e/fpubh-12-1420867-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9aef/11363543/269e5f746522/fpubh-12-1420867-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9aef/11363543/d9221fb7c71c/fpubh-12-1420867-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9aef/11363543/446d8c795920/fpubh-12-1420867-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9aef/11363543/70d706da3c1e/fpubh-12-1420867-g004.jpg

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