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Article(id=1240375272730579319, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1240375270163673092, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202312138, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1701964800000, receivedDateStr=2023-12-08, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773658110077, onlineDateStr=2026-03-16, pubDate=1713974400000, pubDateStr=2024-04-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773658110077, onlineIssueDateStr=2026-03-16, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773658110077, creator=13701087609, updateTime=1773658110077, updator=13701087609, issue=Issue{id=1240375270163673092, tenantId=1146029695717560320, journalId=1227665162245664772, year='2024', volume='51', issue='8', pageStart='1345', pageEnd='1536', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773658109465, creator=13701087609, updateTime=1773658579758, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1240377242795176417, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1240375270163673092, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1240377242795176418, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1240375270163673092, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=1453, endPage=1459, ext={EN=ArticleExt(id=1240375274538324391, articleId=1240375272730579319, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Spatio-temporal differentiation of medical resource mismatch in China and its enhancement paths, columnId=1228016567846367388, journalTitle=Modern Preventive Medicine, columnName=Health Policy and Management, runingTitle=null, highlight=null, articleAbstract=
Objective

To measure China’s medical resources mismatch index, to analyze the differences in the spatial distribution of medical resources mismatch, and to clarify the specific path to improve the mismatch of medical resources.

Methods

Based on the 2009-2021 data, China’s medical capital and labor mismatch indices were measured, the spatial and temporal evolution characteristics and sources of differences were analyzed using Dagum’s Gini coefficient and its decomposition method, and the fsQCA method was used to analyze the conditional groupings of high or low medical resource mismatches.

Results

There were different degrees of capital mismatch or labor mismatch in each region. The overall Gini coefficient showed an "inverted V" evolution trend, the overall Gini coefficient within the region showed a fluctuating downward trend, and the overall Gini coefficient between regions was more complicated. Inter-region differences were the most important factor in the differences in the spatial allocation of medical resources. There were two types of pathways that lead to a high medical resource mismatch, i.e., labor-labor mismatch index and labor-labor mismatch index. There were 2 types of paths, i.e., labor force-hospital structure dual-driven (H1) and service level auxiliary-driven (H2), and 2 types of paths leading to low medical resource mismatch, i.e., urbanization-hospital structure-service level linkage-restricted (L1, L2) and internal and external linkage-restricted (L3, L4).

Conclusion

It’s supposed to play themacro-controlrole for the government to promote the rational allocation of regional medical resources, take the medical association as an important hand to play the leading role of tertiary hospitals, and fully implement the development strategy of new type of urbanization and lower the threshold of population settlement.

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目的

测算我国医疗资源错配指数，分析医疗资源错配空间分布差异，明确改善医疗资源错配的具体路径。

方法

基于2009年至2021年数据，测算我国医疗资本和劳动力错配指数，利用Dagum基尼系数及其分解方法分析时空演化特征及差异来源，运用fsQCA方法分析高或低医疗资源错配的条件组态。

结果

各区域存在不同程度资本错配或劳动力错配现象；总体基尼系数呈“倒V”演变趋势，区域内总体基尼系数呈波动下降趋势，区域间总体基尼系数变化较为复杂；区域间差异是医疗资源空间配置差异的最主要因素；导致高医疗资源错配路径共有2种，即劳动力-医院结构双重驱动型（H1）和服务水平辅助驱动型（H2），导致低医疗资源错配的路径有2种，即城市化-医院结构-服务水平联动限制型（L1、L2）和内外联动限制型（L3、L4）。

结论

发挥政府宏观调控作用，促进区域医疗资源合理配置；以医联体为重要抓手，发挥三级医院引领作用；充分落实新型城镇化发展战略，降低人口落户门槛。

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舒燕；E-mail：
, copyrightStatement=本刊刊出的所有文章不代表中华预防医学会和本刊编委会的观点，除非特别声明。, copyrightOwner=中华预防医学会和四川大学华西公共卫生学院, extLink=null, articleAbsUrl=null, sourceXml=/MEMWH2V0jVXjIWod/5r3Q==, magXml=oUO80L+yRVyDwAkWm3gzMQ==, pdfUrl=null, pdf=xSj2x9pKKzcvzQ93uZcWEw==, pdfFileSize=1003345, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=RgLrKfr31L3lI1gAsR1SNA==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=IR/porTU9rtZ/Zyg6/KBJA==, mapNumber=null, authorCompany=null, fund=null, authors=

陈浩斌(2002—),男,本科在读，研究方向：卫生经济

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Variable calibration of the healthcare resource mismatch index

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变量类型变量维度变量名称计算方法
结果变量医疗资源错配指数医疗资源错配指数（资本错配指数绝对值+劳动力错配指数绝对值）/2
条件变量城市化水平年末城镇人口占比城镇人口/年末常住人口
政府投入医疗卫生支出占比地方财政医疗卫生支出/地方财政一般预算支出
医院结构三级医院占比总医院数三级医院数量/总医院数量
医院服务水平平均住院日出院者占用总床日数/出院人数
劳动力资源每万口人口卫生技术人员医疗卫生机构数/人口数×10 000
), ArticleFig(id=1240748858888745878, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240375272730579319, language=CN, label=表1, caption=

医疗资源错配指数的变量标定

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变量类型变量维度变量名称计算方法
结果变量医疗资源错配指数医疗资源错配指数（资本错配指数绝对值+劳动力错配指数绝对值）/2
条件变量城市化水平年末城镇人口占比城镇人口/年末常住人口
政府投入医疗卫生支出占比地方财政医疗卫生支出/地方财政一般预算支出
医院结构三级医院占比总医院数三级医院数量/总医院数量
医院服务水平平均住院日出院者占用总床日数/出院人数
劳动力资源每万口人口卫生技术人员医疗卫生机构数/人口数×10 000
), ArticleFig(id=1240748858985214877, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240375272730579319, language=EN, label=Table 2, caption=

Spatial gaps in the degree of resource mismatch and their decomposition results

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年份Dagum基尼系数Dagum基尼系数差异分解结果
总体东部中部西部东部&中部东部&西部中部&西部
20090.3730.4030.3170.2560.4350.4050.298
20100.3880.4370.3060.2230.4670.4310.286
20110.3970.4190.3510.2630.4580.4340.323
20120.4020.4330.3430.2500.4710.4410.314
20130.3960.4320.3220.2270.4740.4420.294
20140.3960.4290.3050.2320.4620.4500.289
20150.3710.3990.2980.2180.4400.4190.276
20160.3680.4030.3060.2170.4440.4060.272
20170.3700.4180.2770.2320.4500.4040.272
20180.3640.4230.2720.2320.4380.3950.266
20190.3550.4280.2300.2260.4320.3880.250
20200.3140.3630.1870.1980.3740.3680.199
20210.3380.3920.2830.2720.3730.3540.290
), ArticleFig(id=1240748859157181351, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240375272730579319, language=CN, label=表2, caption=

资源错配程度空间差距及其分解结果

, figureFileSmall=null, figureFileBig=null, tableContent=
年份Dagum基尼系数Dagum基尼系数差异分解结果
总体东部中部西部东部&中部东部&西部中部&西部
20090.3730.4030.3170.2560.4350.4050.298
20100.3880.4370.3060.2230.4670.4310.286
20110.3970.4190.3510.2630.4580.4340.323
20120.4020.4330.3430.2500.4710.4410.314
20130.3960.4320.3220.2270.4740.4420.294
20140.3960.4290.3050.2320.4620.4500.289
20150.3710.3990.2980.2180.4400.4190.276
20160.3680.4030.3060.2170.4440.4060.272
20170.3700.4180.2770.2320.4500.4040.272
20180.3640.4230.2720.2320.4380.3950.266
20190.3550.4280.2300.2260.4320.3880.250
20200.3140.3630.1870.1980.3740.3680.199
20210.3380.3920.2830.2720.3730.3540.290
), ArticleFig(id=1240748859320759216, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240375272730579319, language=EN, label=Table 3, caption=

Individual variable conditional necessity analysis

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高医疗资源错配低医疗资源错配
变量一致性覆盖率变量一致性覆盖率
医疗卫生支出占比0.6210.613医疗卫生支出占比0.6410.747
~医疗卫生支出占比0.7440.637~医疗卫生支出占比0.6680.675
每万人口技术人员数0.7550.699每万人口技术人员数0.6040.660
~每万人口技术人员数0.6330.575~每万人口技术人员数0.7240.777
年末城镇人口占比0.7530.736年末城镇人口占比0.5740.663
~年末城镇人口占比0.6550.565~年末城镇人口占比0.7710.786
平均出院日0.7010.665平均出院日0.5920.663
~平均出院日0.6440.572~平均出院日0.7000.735
三甲医院占比0.7450.692三甲医院占比0.5680.623
~三甲医院占比0.5940.538~三甲医院占比0.7190.769
), ArticleFig(id=1240748859425616825, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240375272730579319, language=CN, label=表3, caption=

单个变量条件必要性分析

, figureFileSmall=null, figureFileBig=null, tableContent=
高医疗资源错配低医疗资源错配
变量一致性覆盖率变量一致性覆盖率
医疗卫生支出占比0.6210.613医疗卫生支出占比0.6410.747
~医疗卫生支出占比0.7440.637~医疗卫生支出占比0.6680.675
每万人口技术人员数0.7550.699每万人口技术人员数0.6040.660
~每万人口技术人员数0.6330.575~每万人口技术人员数0.7240.777
年末城镇人口占比0.7530.736年末城镇人口占比0.5740.663
~年末城镇人口占比0.6550.565~年末城镇人口占比0.7710.786
平均出院日0.7010.665平均出院日0.5920.663
~平均出院日0.6440.572~平均出院日0.7000.735
三甲医院占比0.7450.692三甲医院占比0.5680.623
~三甲医院占比0.5940.538~三甲医院占比0.7190.769
), ArticleFig(id=1240748859534668736, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240375272730579319, language=EN, label=Table 4, caption=

Resource mismatch high/low configuration

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条件组态高资源错配低资源错配
H1H2L1L2L3L4
医疗卫生支出占比▲◇▲
每万人口技术人员数◆◆◇◆▲○
年末城镇人口占比◇◇◆▲○▲
平均出院日◆▲◆▲
三甲医院占比◆◆▲○▲◆
一致性0.820.830.970.980.970.95
原始覆盖度0.490.420.340.270.430.32
唯一覆盖度0.100.040.070.020.120.08
总体一致性0.8160.942
总体覆盖度0.5240.629
), ArticleFig(id=1240748859673080775, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240375272730579319, language=CN, label=表4, caption=

资源错配高/低的组态

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条件组态高资源错配低资源错配
H1H2L1L2L3L4
医疗卫生支出占比▲◇▲
每万人口技术人员数◆◆◇◆▲○
年末城镇人口占比◇◇◆▲○▲
平均出院日◆▲◆▲
三甲医院占比◆◆▲○▲◆
一致性0.820.830.970.980.970.95
原始覆盖度0.490.420.340.270.430.32
唯一覆盖度0.100.040.070.020.120.08
总体一致性0.8160.942
总体覆盖度0.5240.629
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我国医疗资源错配的时空分异及其提升路径
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陈浩斌 1 , 林芷涵 2 , 王泽嘉毓 2 , 舒燕 2
现代预防医学 | 卫生政策与管理 2024,51(8): 1453-1459
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现代预防医学 | 卫生政策与管理 2024, 51(8): 1453-1459
我国医疗资源错配的时空分异及其提升路径
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陈浩斌1, 林芷涵2, 王泽嘉毓2, 舒燕2
作者信息
  • 1.广州中医药大学第八临床医学院，广东 广州 510006
  • 2.广州中医药大学公共卫生与管理学院

    • 陈浩斌(2002—),男,本科在读，研究方向：卫生经济

通讯作者:

舒燕；E-mail：
Spatio-temporal differentiation of medical resource mismatch in China and its enhancement paths
Hao-bin CHEN1, Zhi-han LIN2, WANG Ze-jia-yu2, Yan SHU2
Affiliations
  • The Eighth Clinical Medical School, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510006, China
出版时间: 2024-04-25 doi: 10.20043/j.cnki.MPM.202312138
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摘要
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目的

测算我国医疗资源错配指数，分析医疗资源错配空间分布差异，明确改善医疗资源错配的具体路径。

方法

基于2009年至2021年数据，测算我国医疗资本和劳动力错配指数，利用Dagum基尼系数及其分解方法分析时空演化特征及差异来源，运用fsQCA方法分析高或低医疗资源错配的条件组态。

结果

各区域存在不同程度资本错配或劳动力错配现象；总体基尼系数呈“倒V”演变趋势，区域内总体基尼系数呈波动下降趋势，区域间总体基尼系数变化较为复杂；区域间差异是医疗资源空间配置差异的最主要因素；导致高医疗资源错配路径共有2种，即劳动力-医院结构双重驱动型（H1）和服务水平辅助驱动型（H2），导致低医疗资源错配的路径有2种，即城市化-医院结构-服务水平联动限制型（L1、L2）和内外联动限制型（L3、L4）。

结论

发挥政府宏观调控作用，促进区域医疗资源合理配置；以医联体为重要抓手，发挥三级医院引领作用；充分落实新型城镇化发展战略，降低人口落户门槛。

医疗资源错配指数  /  Dagum基尼系数  /  组态路径分析
Objective

To measure China’s medical resources mismatch index, to analyze the differences in the spatial distribution of medical resources mismatch, and to clarify the specific path to improve the mismatch of medical resources.

Methods

Based on the 2009-2021 data, China’s medical capital and labor mismatch indices were measured, the spatial and temporal evolution characteristics and sources of differences were analyzed using Dagum’s Gini coefficient and its decomposition method, and the fsQCA method was used to analyze the conditional groupings of high or low medical resource mismatches.

Results

There were different degrees of capital mismatch or labor mismatch in each region. The overall Gini coefficient showed an "inverted V" evolution trend, the overall Gini coefficient within the region showed a fluctuating downward trend, and the overall Gini coefficient between regions was more complicated. Inter-region differences were the most important factor in the differences in the spatial allocation of medical resources. There were two types of pathways that lead to a high medical resource mismatch, i.e., labor-labor mismatch index and labor-labor mismatch index. There were 2 types of paths, i.e., labor force-hospital structure dual-driven (H1) and service level auxiliary-driven (H2), and 2 types of paths leading to low medical resource mismatch, i.e., urbanization-hospital structure-service level linkage-restricted (L1, L2) and internal and external linkage-restricted (L3, L4).

Conclusion

It’s supposed to play themacro-controlrole for the government to promote the rational allocation of regional medical resources, take the medical association as an important hand to play the leading role of tertiary hospitals, and fully implement the development strategy of new type of urbanization and lower the threshold of population settlement.

Medical resources mismatch index  /  Dagum Gini coefficient  /  Group path analysis
陈浩斌, 林芷涵, 王泽嘉毓, 舒燕. 我国医疗资源错配的时空分异及其提升路径. 现代预防医学, 2024 , 51 (8) : 1453 -1459 . DOI: 10.20043/j.cnki.MPM.202312138
Hao-bin CHEN, Zhi-han LIN, WANG Ze-jia-yu, Yan SHU. Spatio-temporal differentiation of medical resource mismatch in China and its enhancement paths[J]. Modern Preventive Medicine, 2024 , 51 (8) : 1453 -1459 . DOI: 10.20043/j.cnki.MPM.202312138
正文
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党的“二十大”报告指出，“推动优质医疗资源扩容和区域均衡布局是构建优质高效医疗卫生服务体系的重要内容”，指明医疗卫生体制改革未来发展方向。纵观我国医疗卫生事业，我国的医疗资源配置在不同发展阶段、不同省份和区域间呈现出不同的时空差异特征[1]。深入研究我国资源错配区域差异影响因素，关注区域间的差异状况和发展差异的变化，有利于打造高质量发展的公共卫生体系，解决医疗卫生领域的重大难题，推动医疗资源均衡化发展。本文通过计算医疗资源错配指数，运用Dagum基尼系数揭示了全国医疗资源错配指数区域差异程度，以fsQCA作为研究方法，将定性分析与定量分析有效联结，探寻影响我国医疗资源错配区域差异因素，优化我国医疗资源分配。
1 资料来源及方法
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1.1 资料来源
研究数据来源于历年《中国卫生健康统计年鉴》、《中国统计年鉴》，以2009—2021年为研究时间跨度，由于西藏数据资料有所缺失，为考虑数据可得性及连续性，本文以全国30个省、直辖市、自治区（除西藏、港澳台地区）作为研究样本。
1.2 研究方法
1.2.1 资本错配指数与劳动力错配指数测算
借鉴陈永伟[2]、张虎[3]等的研究，通过测试资本错配指数（tk）和劳动错配指数（tl）以考察医疗资源各地区资本和劳动的错配情况。计算公式如下：，γK、γL为地区要素价格的绝对扭曲，表示资源在相对未扭曲时的加成情况，tk（tl）大于0说明相对于整个经济而言，该地区实际配置的生产要素低于有效配置时的理论比例，资源配置不足；反之，表示资源配置过度[4]。
1.2.2 Dagum基尼系数及其分解方法
用医疗资本错配指数和劳动力错配指数绝对值的平均值表示医疗资源错配指数[5]，依照国家统计局区域划分方式将我国分成东、中、西三个区域，参照Dagum提出的基尼系数及子群分解方法相关研究[6-8]，计算整体、区域内和区域间的基尼系数，并且将基尼系数分解为三个部分，即区域内差距贡献（Gw）、区域间净值差距贡献（Gnb）、超变密度贡献（Gt），从而充分考察医疗资源错配空间非均衡性差异及其差异来源。
1.2.3 模糊集定性比较分析法（fsQCA）
定性比较分析法（QCA）从整体视角探究变量间的组态效应，分析组态与结果间的因果关系[9]。本研究选取fsQCA方法主要是基于其操作具有“二性”并有机结合定性分析方法与定量分析方法[10]，对除了西藏、港澳台地区以外的30个省、直辖市、自治区的医疗资源状况进行比较。
在fsQCA的组态分析中，结果变量为医疗资源错配指数；选取可能影响医疗资源错配的五种变量维度，如表1所示。
根据上述条件变量的选取，构建医疗资源错配指数理论模型，如图1所示。
校准条件变量与结果变量，将样本数据95%、50%和5%分位数分别作为完全隶属阈值、交叉点以及完全不隶属的校准锚点，转换样本数据为集合模糊隶属度，使结果具有可解释性。
2 结果
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2.1 医疗资源空间错配差异分析
2.1.1 医疗资源错配指数测算
依据全国各省份2009—2021年的资本错配指数与劳动力错配指数均值符号[11]，分别对2009年、2013年、2017年和2021年的资本错配指数和劳动力错配指数作数据可视化处理。由图2可见，东部地区总体资本错配指数趋近于0，但北京、上海常年保持高资本错配指数，而海南则保持低资本错配指数；中部地区大多数常年稳定在-0.200~0.200之间，但山西、吉林、黑龙江存在资本错配指数持续降低趋势；西部地区总体保持低资本错配指数，而广西、重庆、陕西表现为向理想资本错配趋势发展。东部地区大多数区域常年保持高劳动力错配水平，而河北、辽宁、山东、海南呈现向理想劳动力错配水平发展趋势；中部地区以及西部地区劳动力错配水平总体偏低，只有少部分地区如湖北、重庆、云南劳动力错配指数常年接近于0。
各区域都存在不同程度的资本错配或劳动力错配现象，东部地区长期存在资本与劳动力配置不足现象；中部地区致力于扩张床位并更新设备，引入优质医疗资源，进一步优化资本与劳动力资源配置[12]；西部地区通过国家政策扶持，针对性制订卫生劳动力资源发展计划，优化医疗资源总量流动机制[13]，但新疆在2021年出现新疆资本配置不足，可能受到疫情影响，资金投入失误。
2.1.2 区域医疗资源配置空间分异解析
如表2所示，我国2009—2021年总体基尼系数呈“倒V”演变趋势，由2009年的0.373升至2012年的0.402，并以2012年为“分水岭”，波动降至2020年的0.314，但在2021年升至0.338，我国医疗资源总体空间分异程度可呈扩大后缩小的趋势，但存在再扩大可能性。
区域内总体基尼系数呈波动下降趋势，以东部地区总体基尼系数相对最大，中部地区与西部地区总体基尼系数相对最小。西部地区内部差异与云贵地区的“塌陷”地位有关，东部地区内部差异则受到长三角省域“极化”位势的影响[14]，三个区域内医疗资源配置空间分异呈缩小趋势，尤以中、西部地区为甚，但东部地区分异程度仍相对较大。
地区间总体基尼系数变化复杂，“东部-中部”基尼系数最大，“中部-西部”基尼系数最小，“东部-中部”呈波动性先升后降的趋势；“东部-西部”同样呈先升后降趋势但无波动性；“中部-西部”呈下降趋势但波动性较大，但在2021年突升。东部与西部之间分异较大，而中部与西部之间分异较小，“东部-中部”和“东部-西部”分异大，但年趋于缩小，可见地区间医疗资源配置体系在逐步完善；“中部-西部”空间分异也逐年缩小，可见西部与东部、中部地区医疗资源错配程度差异在年均缩小，这可能得益于“新医改”后西部卫生事业发展迅速，优质医疗资源配置效率提高[15]，但中西部在2021年出现医疗资源配置失衡现象。
2.1.3 区域总体差异来源分解及贡献率
由图3可见，区域间差异为医疗资源空间分异主要因素，稳定在35%以上的贡献率，而超变密度与其相反，稳定在30%以下，二者在2009—2020年基本呈镜像对称变化趋势，区域间差异贡献率呈波动性上升，而超变密度贡献率呈波动性下降，但在2021年区域间贡献率出现明显降幅，而超变密度出现明显增幅，成为空间分异主要来源；区域内贡献率稳定在30%之间。区域间差异是我国医疗资源空间配置差异的最主要因素，但超变密度于2021年突然成为差异来源的最主要因素，表明区域间交叉重叠现象对医疗资源空间分异占主导作用。
2.2 fsQCA结果分析
2.2.1 条件变量必要性分析
依据Verweij[16]研究，将达到必要条件的阈值设定为0.9。对高医疗资源错配与低医疗资源错配作单个条件变量必要性分析，分析结果如表3所示。所有单条件变量一致性水平低于0.9，故可见不存在导致高资源错配与低资源错配的必要条件，单个条件变量对医疗资源错配解释力弱，因此对单个变量进行组合分析。
2.2.2 条件组态分析
将与结果变量存在一定关系的五个条件变量进行条件组态分析，设置一致性阈值和案例频数阈值为0.8和1。由表4可见，高资源错配存在2种组态（H1~H2），低资源错配存在4种组态（L1~L4），且该6种组态路径一致性皆大于0.8，总体覆盖度都大于0.5，则可视为该6种条件组态对高（低）资源错配的具备充分解释力。
（1）高资源错配组态路径分析
路径1：劳动力-医院结构双重驱动型（H1）。高资源错配主要依赖于区域劳动力资源聚集程度及三级医院建设情况，即使区域财政支出在卫生领域投入较少，但只要卫生劳动力资源及三级医院存在高集聚现象，则可导致高医疗资源错配的产生。代表样本区域有北京、上海、浙江，卫生教育资源丰富，能够培养出大量优质的卫生人力资源，并且高经济发展水平会对周围城市产生虹吸，加剧卫生人力资源的区域差异[17]。
路径2：服务水平辅助驱动型（H2）。当卫生人力资源过多集中及建成较多三级医院，医疗服务水平也相对较高时，同样会产生高资源错配现象，代表样本区域为北京、上海。高水平医疗服务水平更多是医疗资源过多聚集而产生的结果，建议从整体考虑区域卫生服务体系，按照人口密度、社会基本健康需求等情况合理配置卫生人力资源，引导医疗资源合理利用，从而提高卫生服务水平[18]。
（2）低资源错配组态路径分析：
路径1：城市化-医院结构-服务水平联动限制型（L1、L2）。城市化水平低下，三级医院创建规模小且不具备较高医疗服务水平，以及卫生劳动力资源聚集度低的情况下，区域医疗资源错配程度小。城镇化扩大了城市医疗卫生服务规模，但城市建成区土地规模快速扩张为特征的城镇化进程，难以提高医疗资源配置水平[19]，在城市化水平较低的区域医疗系统运行效率较为缓慢，但能够将有限的医疗资源利用率达到最大化。
路径2：内外联动限制型（L3、L4）。卫生人力资源不存在集聚，且三级医院创建规模小及医疗服务水平低下的区域，或政府投入少和城市化进程缓慢的区域，皆可造成低资源错配。对于城市化水平较低的区域，三级医院建制小，高素质卫生劳动力资源相对稀少，竞争机制缓和使医疗领域劳动力资源供求平衡，使劳动力资源错配程度小。充分利用有限的卫生劳动力资源达到基本的医疗服务水平，即可实现区域的总体就医目标。医疗劳动力市场的政府干预可能会进一步加剧劳动力市场分割和户籍歧视等问题，劳动力易出现低效配置现象[20]。
样本区域如河北省在2009年到2017年主要是依靠路径1维持低资源错配，但在2018年到2021年则主要依靠路径2；如山东省在2010年和2012年依赖路径1驱动低资源错配，而到2015年到2021年则主要依赖路径2。各区域应结合区域异质性，因时因地因人达到医疗资源高效配置[21]。
3 讨论
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3.1 发挥政府宏观调控作用，促进区域医疗资源合理配置
为提高区域间资源均衡配置，政府可通过财政手段，加大医疗资源配置力度，实现东中西部地区医疗资源优势互补，改善医疗资源配置效率。政府应注重各省医疗资源投入的公平性，做到因地制宜，根据当地的人口密度、地理分布情况和社会基本健康需求等情况，对投入相对冗余的地区建立资源冗余区与资源匮乏区点对点精准对接的长效机制，使冗余资源流向区域间、区域内相对薄弱地区。
3.2 以医联体为重要抓手，发挥三级医院引领作用
深化医疗体制改革，以医联体为重要抓手，建立分级诊疗制度。三级公立医院应主动发挥核心医院的人才和技术优势，开展“输出”与“反补”式紧密合作，制定医疗同质化管理制度，将规范的诊疗技术及时普及到基层，提升医联体内医疗同质化水平[22]。以三级公立医院为核心，发挥其人才和技术优势，带动基层医疗机构整体医疗水平提升，促进优质医疗资源共享。
3.3 充分落实新型城镇化发展战略，降低人口落户门槛
以新型城镇化作为发展战略主线，根据常住人口或城市特征，拥有较大人口规模的区域应重点扩大医疗卫生服务规模，拥有较小人口规模的区域应重点关注医疗资源高效配置，提升医疗服务水平；放开、放宽农村人口在城市的落户限制，逐步剥离依附在户籍中的各种居民权益，推动城乡社会保障供给均等化；并进一步加强城乡之间资源共享，推动城乡融合发展，实现全要素的自由流通。
基金
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  • 2021年度教育部人文社会科学研究规划基金(21YJAZH074)
  • 2022年广东省哲学社科规划课题项目(GD22XGL51)
  • 2022年广东省教育科学规划课题项目(2022GXJK061)
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2024年第51卷第8期
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doi: 10.20043/j.cnki.MPM.202312138
  • 接收时间：2023-12-08
  • 首发时间：2026-03-16
  • 出版时间：2024-04-25
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  • 收稿日期：2023-12-08
基金
2021年度教育部人文社会科学研究规划基金(21YJAZH074)
2022年广东省哲学社科规划课题项目(GD22XGL51)
2022年广东省教育科学规划课题项目(2022GXJK061)
作者信息
    1.广州中医药大学第八临床医学院，广东 广州 510006
    2.广州中医药大学公共卫生与管理学院

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2种不同金属材料的力学参数
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小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
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