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Article(id=1324077582341251909, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324077443828560770, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202510014, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1759334400000, receivedDateStr=2025-10-02, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793614295801, onlineDateStr=2026-11-02, pubDate=1775750400000, pubDateStr=2026-04-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793614295801, onlineIssueDateStr=2026-11-02, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793614295801, creator=13701087609, updateTime=1793614295801, updator=13701087609, issue=Issue{id=1324077443828560770, tenantId=1146029695717560320, journalId=1227665162245664772, year='2026', volume='53', issue='7', pageStart='1153', pageEnd='1344', issueExtLink='null', onlineDate='null', pubDate='1775750400000', pubDateStr='2026-04-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1793614262778, creator='13701087609', updateTime=1793614352085, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1324077818509918661, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324077443828560770, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1324077818509918662, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324077443828560770, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1297, endPage=1303, ext={EN=ArticleExt(id=1324077582546772806, articleId=1324077582341251909, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Spatio-temporal evolution of municipal medical service resistance in Sichuan Province, columnId=1228016567846367388, journalTitle=Modern Preventive Medicine, columnName=Health Policy and Management, runingTitle=null, highlight=null, articleAbstract=
Objective

To assess the current level of medical-service resistance across prefectures in Sichuan and to provide evidence for building a highly resilient healthcare system.

Methods

Using panel data for 21 prefectures from 2013 to 2022, an evaluation index system for medical service resistance in Sichuan Province was constructed. CRITIC-Entropy Weight Method and TOPSIS were applied to calculate resistance scores, while kernel density, Moran's I and Markov-chain models were employed to analyze spatiotemporal evolution and state-transition probabilities.

Results

Resistance rose from 0.431 to 0.477, with 90.48% of prefectures recording positive growth. The resistance level shifted from medium to high, and the high level was the dominant category. LISA maps revealed “high-low”, “low-high” and “low-low” clusters. Transition probabilities showed a gradient effect and a pronounced lock-in, making leapfrog upgrades difficult.

Conclusion

Overall resistance in Sichuan is improving, but significant regional disparities persist, local spatial clustering is evident, and a strong lock-in effect governs state transitions.

, authors=Kang YANG1, Yan LI1, Yuan ZHANG2, Tao YU1, authorsList=Kang YANG, Yan LI, Yuan ZHANG, Tao YU, authorCompany=null, correspAuthors=Yuan ZHANG, Tao YU, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1324077584522289999, articleId=1324077582341251909, tenantId=1146029695717560320, journalId=1227665162245664772, language=CN, title=四川省市域医疗服务抵抗力时空演化研究, columnId=1228016568014139551, journalTitle=现代预防医学, columnName=卫生政策与管理, runingTitle=null, highlight=null, articleAbstract=
目的

探究四川省各市（州）医疗服务抵抗力水平现状，建设富有抵抗力的医疗服务体系提供参考。

方法

基于四川省21个市（州）2013—2022年的面板数据，构建四川省医疗服务抵抗力评估指标体系，采用CRITIC-熵值法和TOPSIS模型计算四川省各地区的抵抗力水平，借助核密度、莫兰指数和马尔科夫链模型，分析医疗服务抵抗力的时空演化特征和状态转移概率。

结果

四川省医疗服务抵抗力呈上升趋势，由0.431升至0.477，90.48%的地区呈正向增长；抵抗力等级由中等水平到较高水平，较高水平为主要类型；LISA地图显示主要存在“高-低、低-高、低-低”集聚类型；状态转移概率呈梯度效应，表现出明显的“锁定”现象，跃升难度较大。

结论

四川省医疗服务抵抗力整体水平不断提高，但地区之间存在明显差异，在空间上存在局部集聚特征，在状态转移概率上具有明显的“锁定”效应。

, authors=杨康1, 李艳1, 张圆2, 喻涛1, authorsList=杨康, 李艳, 张圆, 喻涛, authorCompany=null, correspAuthors=张圆, 喻涛, authorNote=

杨康（1997—），男，硕士在读，研究方向：卫生管理与政策研究

, correspAuthorsNote=
张圆，E-mail：
喻涛，E-mail：；张圆与喻涛为共同通信作者
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杨康（1997—），男，硕士在读，研究方向：卫生管理与政策研究

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(In Chinese), articleTitle=The spatial-temporal dynamics of Guanzhong eco-security based on spatial Malcov Chains, refAbstract=null)], funds=[Fund(id=1324077592193672070, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077582341251909, awardId=SWFZ25-Y-09, language=CN, fundingSource=川北医学院2025年度四川省基层卫生事业发展研究中心资助项目(SWFZ25-Y-09), fundOrder=null, country=null), Fund(id=1324077592260780935, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077582341251909, awardId=SCYG2025-8, language=CN, fundingSource=西南医科大学：四川医院管理和发展研究中心资助项目(SCYG2025-8), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1324077586489418576, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077582341251909, xref=1., ext=[AuthorCompanyExt(id=1324077586497807185, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077582341251909, companyId=1324077586489418576, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=School of Humanities and Management, Southwest Medical University, Luzhou, Sichuan 646000, China), AuthorCompanyExt(id=1324077586506195794, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077582341251909, companyId=1324077586489418576, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.西南医科大学人文与管理学院，四川　泸州　646000)]), AuthorCompany(id=1324077586577498963, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077582341251909, xref=2., ext=[AuthorCompanyExt(id=1324077586585887572, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077582341251909, companyId=1324077586577498963, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.西南医科大学马克思主义学院，四川　泸州　646000)])], figs=[ArticleFig(id=1324077588699816819, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077582341251909, language=EN, label=Figure 1, caption=Trend chart of medical service resistance and criterion layer levels in Sichuan Province from 2013 to 2022, figureFileSmall=+LAJI7kleTYH82kLChOVCQ==, figureFileBig=Bof4Hf4Cu4nNXI2KNL4tIQ==, tableContent=null), ArticleFig(id=1324077588792091508, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077582341251909, language=CN, label=图1, caption=2013—2022年四川省医疗服务抵抗力与准则层水平趋势图, figureFileSmall=+LAJI7kleTYH82kLChOVCQ==, figureFileBig=Bof4Hf4Cu4nNXI2KNL4tIQ==, tableContent=null), ArticleFig(id=1324077590792774518, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077582341251909, language=EN, label=Figure 2, caption=Kernel density curves of medical service resistance in Sichuan Province from 2013 to 2022, figureFileSmall=0KcoienzRPaSYLxKgvhg9g==, figureFileBig=q4GwrjJZVpnGekVlr0+fuA==, tableContent=null), ArticleFig(id=1324077590872466295, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077582341251909, language=CN, label=图2, caption=2013—2022年四川省医疗服务抵抗力核密度曲线, figureFileSmall=0KcoienzRPaSYLxKgvhg9g==, figureFileBig=q4GwrjJZVpnGekVlr0+fuA==, tableContent=null), ArticleFig(id=1324077590952158072, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077582341251909, language=EN, label=Figure 3, caption=Spatial distribution map of medical service resistance in Sichuan Province from 2013 to 2022, figureFileSmall=jeicYLeqQffdKEaiT2iJiQ==, figureFileBig=tI5YPC638sM6Tzfm2XLwGg==, tableContent=null), ArticleFig(id=1324077591048627065, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077582341251909, language=CN, label=图3, caption=2013—2022年四川省医疗服务抵抗力空间分布图

注：底图源于国家地理信息公共服务平台，标准地图审图号为GS（2024）0650号。

, figureFileSmall=jeicYLeqQffdKEaiT2iJiQ==, figureFileBig=tI5YPC638sM6Tzfm2XLwGg==, tableContent=null), ArticleFig(id=1324077591140901754, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077582341251909, language=EN, label=Figure 4, caption=LISA cluster maps of medical service resistance in Sichuan Province, 2013, 2016, 2019 and 2022, figureFileSmall=0BD4KMSTERXJ6QDawXWqlw==, figureFileBig=8EYk4yMssXEPidd/5YND5Q==, tableContent=null), ArticleFig(id=1324077591216399227, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077582341251909, language=CN, label=图4, caption=2013、2016、2019和2022年四川省医疗服务抵抗力LISA图

注：底图源于国家地理信息公共服务平台，标准地图审图号为GS（2024）0650号。

, figureFileSmall=0BD4KMSTERXJ6QDawXWqlw==, figureFileBig=8EYk4yMssXEPidd/5YND5Q==, tableContent=null), ArticleFig(id=1324077591287702396, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077582341251909, language=EN, label=Table 1, caption=

Evaluation indicator system for medical service resistance

, figureFileSmall=null, figureFileBig=null, tableContent=
准则层指标层指标定义属性权重
医疗卫生资源（0.362）每平方公里医院数（家）医院密度+0.103
每千人口医疗机构床位数（张）医疗服务设施容量+0.057
每平方公里等级公路里程数（km/km2）交通服务保障能力+0.083
每千人口执业（助理）医师救治能力+0.057
每千人口注册护士数护理人力资源充足度+0.061
医疗服务水平（0.256）医护比医疗服务效率-0.028
急诊病死率（%）急诊医疗救治能力-0.033
出院者平均住院日（日）医疗服务效率-0.037
病床使用率（%）床位利用效率+0.032
医师人均每日担负诊疗人次（次）救治能力适应性+0.044
病床周转次数（%）床位利用效率+0.038
医生人均每日担负病床日救治能力适应性+0.045
社会环境基础（0.382）卫生健康支出占比（%）政府支持度+0.029
基本医保基金总收入占GDP比重（%）医疗资金保障能力+0.066
基本医疗保险覆盖率（%）居民医疗压力+0.100
城乡居民可支配收入比居民生活水平-0.046
城镇化率人员组织管理+0.048
年平均PM2.5浓度（μg/m3）空气环境质量-0.044
移动电话普及率（部/百人）信息获取与共享效率+0.049
), ArticleFig(id=1324077591434503037, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077582341251909, language=CN, label=表1, caption=

医疗服务抵抗力评价指标体系

, figureFileSmall=null, figureFileBig=null, tableContent=
准则层指标层指标定义属性权重
医疗卫生资源（0.362）每平方公里医院数（家）医院密度+0.103
每千人口医疗机构床位数（张）医疗服务设施容量+0.057
每平方公里等级公路里程数（km/km2）交通服务保障能力+0.083
每千人口执业（助理）医师救治能力+0.057
每千人口注册护士数护理人力资源充足度+0.061
医疗服务水平（0.256）医护比医疗服务效率-0.028
急诊病死率（%）急诊医疗救治能力-0.033
出院者平均住院日（日）医疗服务效率-0.037
病床使用率（%）床位利用效率+0.032
医师人均每日担负诊疗人次（次）救治能力适应性+0.044
病床周转次数（%）床位利用效率+0.038
医生人均每日担负病床日救治能力适应性+0.045
社会环境基础（0.382）卫生健康支出占比（%）政府支持度+0.029
基本医保基金总收入占GDP比重（%）医疗资金保障能力+0.066
基本医疗保险覆盖率（%）居民医疗压力+0.100
城乡居民可支配收入比居民生活水平-0.046
城镇化率人员组织管理+0.048
年平均PM2.5浓度（μg/m3）空气环境质量-0.044
移动电话普及率（部/百人）信息获取与共享效率+0.049
), ArticleFig(id=1324077591547749246, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077582341251909, language=EN, label=Table 2, caption=

Measurement results of medical service resistance in Sichuan prefectures from 2013 to 2022

, figureFileSmall=null, figureFileBig=null, tableContent=
地区2013年2014年2015年2016年2017年2018年2019年2020年2021年2022年mean
成都0.5390.5700.5720.5740.6220.6190.6350.5880.6170.6320.597
自贡0.4440.4830.4830.4510.4450.4630.4830.4960.5150.5390.480
攀枝花0.4800.5100.4990.5080.5270.5030.5290.5150.5320.5290.513
泸州0.4260.4440.4460.4100.4180.4440.4570.4560.4750.4870.446
德阳0.4510.4640.4670.4870.4380.4570.4750.4480.4720.4790.464
绵阳0.4250.4460.4500.4690.4690.4380.4620.4520.4730.4780.456
广元0.4360.4520.4620.5070.4560.4680.4870.4870.5010.4940.475
遂宁0.4130.4280.4480.4120.4100.4230.4440.4750.4890.4780.442
内江0.4010.4390.4460.4620.4080.4270.4630.4740.4900.4990.451
乐山0.4090.4420.4350.3900.4050.4260.4410.4380.4510.4670.430
南充0.4160.4250.4370.4640.4160.4210.4370.4660.4870.4810.445
眉山0.4320.4420.4380.3940.4220.4310.4480.4280.4410.4530.433
宜宾0.4210.4490.4410.3920.4200.4470.4580.4600.4690.4820.444
广安0.4160.4280.4400.4830.4340.4430.4510.4330.4550.4630.445
达州0.3860.4040.4000.4110.3510.3460.3740.3960.4090.4250.390
雅安0.4450.4780.4880.4960.4480.4560.4770.4670.4860.4930.473
巴中0.4070.4320.4380.3950.4050.4250.4370.4580.4760.4820.435
资阳0.4580.4760.4860.4700.4130.4290.4500.4620.4720.4730.459
阿坝0.4090.4080.4070.4240.3860.3910.4050.4050.4110.4120.406
甘孜0.4220.4210.4190.3760.3770.3750.3770.3540.3680.3790.387
凉山0.4160.4390.4430.3970.4130.4000.4140.4030.4080.3980.413
mean0.4310.4510.4550.4460.4320.4400.4570.4550.4710.477
), ArticleFig(id=1324077591627441023, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077582341251909, language=CN, label=表2, caption=

2013—2022年四川省各市（州）医疗服务抵抗力测度结果

, figureFileSmall=null, figureFileBig=null, tableContent=
地区2013年2014年2015年2016年2017年2018年2019年2020年2021年2022年mean
成都0.5390.5700.5720.5740.6220.6190.6350.5880.6170.6320.597
自贡0.4440.4830.4830.4510.4450.4630.4830.4960.5150.5390.480
攀枝花0.4800.5100.4990.5080.5270.5030.5290.5150.5320.5290.513
泸州0.4260.4440.4460.4100.4180.4440.4570.4560.4750.4870.446
德阳0.4510.4640.4670.4870.4380.4570.4750.4480.4720.4790.464
绵阳0.4250.4460.4500.4690.4690.4380.4620.4520.4730.4780.456
广元0.4360.4520.4620.5070.4560.4680.4870.4870.5010.4940.475
遂宁0.4130.4280.4480.4120.4100.4230.4440.4750.4890.4780.442
内江0.4010.4390.4460.4620.4080.4270.4630.4740.4900.4990.451
乐山0.4090.4420.4350.3900.4050.4260.4410.4380.4510.4670.430
南充0.4160.4250.4370.4640.4160.4210.4370.4660.4870.4810.445
眉山0.4320.4420.4380.3940.4220.4310.4480.4280.4410.4530.433
宜宾0.4210.4490.4410.3920.4200.4470.4580.4600.4690.4820.444
广安0.4160.4280.4400.4830.4340.4430.4510.4330.4550.4630.445
达州0.3860.4040.4000.4110.3510.3460.3740.3960.4090.4250.390
雅安0.4450.4780.4880.4960.4480.4560.4770.4670.4860.4930.473
巴中0.4070.4320.4380.3950.4050.4250.4370.4580.4760.4820.435
资阳0.4580.4760.4860.4700.4130.4290.4500.4620.4720.4730.459
阿坝0.4090.4080.4070.4240.3860.3910.4050.4050.4110.4120.406
甘孜0.4220.4210.4190.3760.3770.3750.3770.3540.3680.3790.387
凉山0.4160.4390.4430.3970.4130.4000.4140.4030.4080.3980.413
mean0.4310.4510.4550.4460.4320.4400.4570.4550.4710.477
), ArticleFig(id=1324077591715521408, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077582341251909, language=EN, label=Table 3, caption=

Global Moran's I of medical service resistance in Sichuan Province from 2013 to 2022

, figureFileSmall=null, figureFileBig=null, tableContent=
年份（年）莫兰指数Z值P值
20130.111.4160.157
20140.0490.8520.394
20150.0020.4510.652
2016-0.0010.380.704
2017-0.06-0.0930.926
2018-0.0380.1140.91
2019-0.0190.2750.783
2020-0.0420.0680.946
2021-0.0480.0140.989
2022-0.0430.0550.956
), ArticleFig(id=1324077591795213185, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077582341251909, language=CN, label=表3, caption=

2013—2022年四川省医疗服务抵抗力全局莫兰指数

, figureFileSmall=null, figureFileBig=null, tableContent=
年份（年）莫兰指数Z值P值
20130.111.4160.157
20140.0490.8520.394
20150.0020.4510.652
2016-0.0010.380.704
2017-0.06-0.0930.926
2018-0.0380.1140.91
2019-0.0190.2750.783
2020-0.0420.0680.946
2021-0.0480.0140.989
2022-0.0430.0550.956
), ArticleFig(id=1324077591862322050, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077582341251909, language=EN, label=Table 4, caption=

Traditional Markov transition probability matrix of medical service resistance

, figureFileSmall=null, figureFileBig=null, tableContent=
空间滞后类型t/（t+1）低中等较高高观测值
无滞后低0.7130.2380.0500.00080
中等0.1950.3170.4390.04941
较高0.0910.0910.4550.36433
高0.0290.1140.0860.77135
), ArticleFig(id=1324077591946208131, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077582341251909, language=CN, label=表4, caption=

医疗服务抵抗力传统马尔可夫转移概率矩阵

, figureFileSmall=null, figureFileBig=null, tableContent=
空间滞后类型t/（t+1）低中等较高高观测值
无滞后低0.7130.2380.0500.00080
中等0.1950.3170.4390.04941
较高0.0910.0910.4550.36433
高0.0290.1140.0860.77135
), ArticleFig(id=1324077592013316996, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077582341251909, language=EN, label=Table 5, caption=

Spatial Markov transition probability matrix of medical service resistance

, figureFileSmall=null, figureFileBig=null, tableContent=
空间滞后类型t/（t+1）低中等较高高观测值
低低0.7390.2390.0220.00046
中等0.0910.2730.4550.18211
较高0.2000.2000.0000.6005
高0.1000.1000.0000.80010
中等低0.5290.2940.1770.00017
中等0.2500.3750.3750.00016
较高0.0910.0910.3640.45511
高0.0000.1250.0000.87516
较高低0.8460.1540.0000.00013
中等0.1820.2730.5460.00011
较高0.0710.0710.6430.21414
高0.0000.1110.3330.5569
高低0.7500.2500.0000.0004
中等0.3330.3330.3330.0003
较高0.0000.0000.6670.3333
高0.0000.0000.0000.0000
), ArticleFig(id=1324077592088814469, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077582341251909, language=CN, label=表5, caption=

医疗服务抵抗力空间马尔可夫转移概率矩阵

, figureFileSmall=null, figureFileBig=null, tableContent=
空间滞后类型t/（t+1）低中等较高高观测值
低低0.7390.2390.0220.00046
中等0.0910.2730.4550.18211
较高0.2000.2000.0000.6005
高0.1000.1000.0000.80010
中等低0.5290.2940.1770.00017
中等0.2500.3750.3750.00016
较高0.0910.0910.3640.45511
高0.0000.1250.0000.87516
较高低0.8460.1540.0000.00013
中等0.1820.2730.5460.00011
较高0.0710.0710.6430.21414
高0.0000.1110.3330.5569
高低0.7500.2500.0000.0004
中等0.3330.3330.3330.0003
较高0.0000.0000.6670.3333
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四川省市域医疗服务抵抗力时空演化研究
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杨康 1 , 李艳 1 , 张圆 2 , 喻涛 1
现代预防医学 | 卫生政策与管理 2026,53(7): 1297-1303
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现代预防医学 |卫生政策与管理 2026 , 53 (7) : 1297 -1303
四川省市域医疗服务抵抗力时空演化研究
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杨康1, 李艳1, 张圆2 , 喻涛1
作者信息
  • 1.西南医科大学人文与管理学院，四川　泸州　646000
  • 2.西南医科大学马克思主义学院，四川　泸州　646000
通讯作者:
张圆，E-mail：
喻涛，E-mail：；张圆与喻涛为共同通信作者
作者简介:

杨康（1997—），男，硕士在读，研究方向：卫生管理与政策研究

Spatio-temporal evolution of municipal medical service resistance in Sichuan Province
Kang YANG1, Yan LI1, Yuan ZHANG2 , Tao YU1
Affiliations
  • School of Humanities and Management, Southwest Medical University, Luzhou, Sichuan 646000, China
出版时间: 2026-04-10 doi: 10.20043/j.cnki.MPM.202510014
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摘要
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目的

探究四川省各市（州）医疗服务抵抗力水平现状，建设富有抵抗力的医疗服务体系提供参考。

方法

基于四川省21个市（州）2013—2022年的面板数据，构建四川省医疗服务抵抗力评估指标体系，采用CRITIC-熵值法和TOPSIS模型计算四川省各地区的抵抗力水平，借助核密度、莫兰指数和马尔科夫链模型，分析医疗服务抵抗力的时空演化特征和状态转移概率。

结果

四川省医疗服务抵抗力呈上升趋势，由0.431升至0.477，90.48%的地区呈正向增长；抵抗力等级由中等水平到较高水平，较高水平为主要类型；LISA地图显示主要存在“高-低、低-高、低-低”集聚类型；状态转移概率呈梯度效应，表现出明显的“锁定”现象，跃升难度较大。

结论

四川省医疗服务抵抗力整体水平不断提高，但地区之间存在明显差异，在空间上存在局部集聚特征，在状态转移概率上具有明显的“锁定”效应。

四川省  /  医疗服务  /  抵抗力  /  核密度估计  /  马尔可夫链
Objective

To assess the current level of medical-service resistance across prefectures in Sichuan and to provide evidence for building a highly resilient healthcare system.

Methods

Using panel data for 21 prefectures from 2013 to 2022, an evaluation index system for medical service resistance in Sichuan Province was constructed. CRITIC-Entropy Weight Method and TOPSIS were applied to calculate resistance scores, while kernel density, Moran's I and Markov-chain models were employed to analyze spatiotemporal evolution and state-transition probabilities.

Results

Resistance rose from 0.431 to 0.477, with 90.48% of prefectures recording positive growth. The resistance level shifted from medium to high, and the high level was the dominant category. LISA maps revealed “high-low”, “low-high” and “low-low” clusters. Transition probabilities showed a gradient effect and a pronounced lock-in, making leapfrog upgrades difficult.

Conclusion

Overall resistance in Sichuan is improving, but significant regional disparities persist, local spatial clustering is evident, and a strong lock-in effect governs state transitions.

Sichuan province  /  Medical service  /  Resistance  /  Kernel density estimation  /  Markov chain
杨康, 李艳, 张圆, 喻涛. 四川省市域医疗服务抵抗力时空演化研究. 现代预防医学, 2026 , 53 (7) : 1297 -1303 . DOI: 10.20043/j.cnki.MPM.202510014
Kang YANG, Yan LI, Yuan ZHANG, Tao YU. Spatio-temporal evolution of municipal medical service resistance in Sichuan Province[J]. Modern Preventive Medicine, 2026 , 53 (7) : 1297 -1303 . DOI: 10.20043/j.cnki.MPM.202510014
正文
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在全球公共卫生突发事件日益频发背景下[1-3]，构建韧性医疗卫生体系已成为世界各国面临的共同挑战。医疗服务抵抗力是医疗服务系统在面对各种危机和挑战冲击时，维持其正常运行、保障医疗服务供给的能力，是应对冲击的核心力量[2,4-5]，主要体现在卫生资源、服务效率、区域经济和生态环境等。建设具有强大抵抗力的医疗服务体系，是保障公众福祉的必然要求，也是实现区域可持续发展的关键保障。
目前，国内外学者围绕“医疗服务抵抗力”展开了大量研究。主要关注其理论分析[4,6-7]、案例研究[8]以及将抵抗力作为医疗服务韧性的子系统进行研究[5]，鲜有从医疗服务抵抗力的角度进行评价，且尚未形成统一的定义。因此，本研究将抵抗力作为医疗服务韧性的子系统，探究四川省医疗服务抵抗力发展水平，厘清时空演化格局，为建设富有韧性的医疗服务系统和提高医疗服务水平提供参考，为制定差异化区域发展政策提供实证依据。
1　资料与方法
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1.1　资料来源
本研究中医疗卫生资源与社会环境基础指标来源于2014—2023年《四川统计年鉴》；医疗服务质量指标取自2013—2022年《四川卫生健康统计年鉴》；移动电话普及率来自各市（州）历年《国民经济与社会发展统计公报》；年平均PM2.5浓度数据源于韦晶等[9]发布于国家青藏高原科学数据中心的数据集。
1.2　指标选取
根据《医疗机构设置规划指导原则（2021—2025年）》和《“十四五”国民健康规划》等政策要求，并借鉴既有研究[5,10-11]，其中，医疗机构密度与路网密度是衡量卫生资源空间配置的主要指标，直接关系到服务可及性；而医疗服务劳动密集且需专业人员提供，充足的人力资源是缓解供需失衡的关键。医护比（理想区间1:2-1:6）衡量人员结构合理性；医师数量固定时，护士比例越高，服务效率越高[12]；年平均PM2.5作为负向指标，反映空气质量对人群健康的影响[13-14]，见表1。为避免主观赋权的指标权重偏差，本研究采用CRITIC-熵值法[15-17]组合赋权确定各指标权重，运用TOPSIS模型[18-20]测算抵抗力水平。
1.3　研究方法
1.3.1　核密度估计
核密度估计是一种非参数统计方法，用于估计随机变量的概率密度函数。通过在每一个数据点处放置一个平滑的核函数并加以汇总，生成一条连续的概率密度曲线。本研究借助核密度曲线对不同年份医疗服务抵抗力的动态变化、集聚特征、区域差异和极化程度进行比较与分析。计算步骤见参考文献[21-22]。
1.3.2　空间自相关分析
空间自相关通过地理空间权重矩阵检验变量在不同位置的关联性。本研究用全局莫兰指数测度抵抗力的整体空间关联，用局部莫兰指数识别市州与其邻接地区的相似或相异集群，并采用邻近矩阵法。具体计算步骤见参考文献[5,23]。
1.3.3　马尔科夫链模型
本研究基于传统马尔可夫链模型，并引入空间滞后概念，将抵抗力划为k类，分析邻近地区间抵抗力变化趋势与转移概率，从而揭示区域内抵抗力演化的转移模式。具体方法见参考文献[24]。
1.4　统计学方法
本研究数据清洗与预处理在Excel 2021中完成，严格依据CRITIC-熵值法、TOPSIS、核密度估计和马尔可夫链计算公式，编写MATLAB脚本，用于计算指标权重、医疗服务抵抗力水平及状态转移概率，并绘制核密度曲线。运用ArcMap 10.8计算全局莫兰指数，并绘制空间分布图和LISA聚类图，保障计算精度与方法严谨性。
2　结果
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2.1　医疗服务抵抗力时间演化特征分析
2.1.1　医疗服务抵抗力综合分析
整体上看，四川省医疗服务抵抗力不断上升，由0.431上升至0.477，见表2。从区域分布看，2013—2022年，除甘孜和凉山呈下降趋势，其余地区呈增长趋势。从市州看，2013—2022年90.48%的市州实现正向增长。
2.1.2　医疗服务抵抗力各系统水平变化趋势
整体上看，医疗服务抵抗力和医疗卫生资源指数呈正向增长趋势，其中医疗服务抵抗力呈“增-降-增”的趋势，医疗资源指数则呈现快速增长的趋势，平均增长率为8.231%。而医疗服务水平和社会环境基础呈现下降趋势，其中医疗服务水平下降变化较小，而社会环境基础指数在2015—2017年出现大幅下降后缓慢增长。以2020年为转折点，子系统指数差异化特征降低，且排序趋于稳定。见图1。
2.1.3　核密度估计
结果显示，医疗服务抵抗力核密度曲线整体向右移，说明抵抗力水平呈上升趋势；核密度曲线峰值高度呈“上升-下降-上升”特征，且右侧拖尾增强，宽度增加，表明地区间差距增大，极化现象较强。从多峰分布看，2012—2022年持续呈双峰特征，表明医疗服务抵抗力水平梯度效应明显。见图2。
2.2　医疗服务抵抗力空间演化特征分析
2.2.1　医疗服务抵抗力的空间异质性
为探究四川省医疗服务抵抗力的空间异质性，以2013—2022年抵抗力平均值为基准，运用自然断点法，将21个市州的抵抗力水平划分为低水平（<0.413）、中等水平（0.414~0.464）、较高水平（0.465~0.513）和高水平（>0.514）四个等级，选取2013、2016、2019和2022年抵抗力指数绘制空间分布图。见图3。
由图3A可知，2013年多数地区抵抗力等级处在中等水平以上，成都和攀枝花处于较高以上水平，川东北地区较其他地区偏低。由图3B可知，2016年抵抗力水平整体有所提升，但在空间上呈两极分化，较高水平地区集中于成都平原，川西高原地区等级降低，呈现出“中部高四周低”的特征。由图3C可知，2019年除阿坝、甘孜和达州处于低水平，其余地区均在中等水平以上，仅成都和攀枝花处于高水平。由图3D可知，2022年医疗服务抵抗力等级有明显提升，除阿坝、甘孜、凉山和眉山分别处于低水平，其余地区均处于中等以上水平。
2.2.2　医疗服务抵抗力的空间关联性
结果显示，2013—2022年，医疗服务抵抗力莫兰指数在2016年以后为负值、Z值除2017年外均大于0、P值均大于0.05，无统计学意义，说明抵抗力整体在空间分布上不存在空间相关性。见表3。
四川省各市州发展差异较大，可能存在的空间异质性现象，因此，采用局部空间自相关分析进一步探究四川省各地区空间异质性。结果显示，四川省医疗服务抵抗力局部呈现集聚现象，2016年自贡和2022年攀枝花呈“高-低”集聚；2013年阿坝和2019、2022年眉山呈现“低-高”集聚；2013年南充与广安、2016年宜宾和2022年甘孜呈“低-低”集聚；其余市州集聚特征变化较大，不显著说明其与邻近地区不存在相关性。见图4。
2.3　医疗服务抵抗力状态转移概率
2.3.1　传统马尔科夫链状态转移概率
为探究医疗服务抵抗力的状态转移概率，本文将指数四分位：低水平（<0.413）、中等水平（0.414~0.464）、较高水平（0.465~0.513）和高水平（>0.514）。低水平80个样本，维持原有状态概率0.713，仅0.238升至中等、0.050升至较高，向上流动受限；中等水平41个样本，保留概率0.317，跃升至高概率达0.488，跃升概率较大；较高水平33个样本，保留概率为0.455，向上转移概率为0.364，表较高水平也存在较大的跃升概率；高水平35个样，保留概率为0.771，稳定性较高，但存在向下转移的可能性。总体呈显著层级结构：低、高水平被锁定，中等水平最具向上跃迁机会。见表4。
2.3.2　空间马尔科夫链状态转移概率
结果显示，转移概率随邻区抵抗力水平显著变化，证实空间环境至关重要。在所有空间滞后类型中，除邻域为中等水平地区，对角线概率小于非对角线值，说明中等水平地区锁定效应较弱，有较大概率向上或向下转移。其余类型均为对角线概率大于非对角线值，显示出显著的“锁定”效应，大多数市州在下一年仍极可能维持原有抵抗力等级，难以实现跨越式跃迁。远离对角线的概率接近零，表明状态只能逐级递进，空间邻近性本身不足以打破医疗服务抵抗力转移的锁定格局。见表5。
3　讨论
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3.1　医疗服务抵抗力水平呈上升趋势
2013—2022年，四川省医疗服务抵抗力由0.431升至0.477，呈逐步提升状态。说明医疗改革的深入推进，医疗卫生资源逐步向欠发达地区流动，促进资源分配的公平和均衡性取得明显效果。其原因在于，一是政府重视并支持医疗卫生事业发展，实现卫生资源均衡分布提供良好的政策环境；二是各市州医疗服务能力稳步提升。因此，各地区需持续深化医疗改革，进一步优化医疗卫生服务结构。充分发挥经济强市的经济和科技优势，加强人才队伍建设和基础设施建设。构建协同合作机制，促进医疗机构技术、经验和人才之间交流；发挥互联网作用，通过医疗资源共享平台，实现优质资源快速流动，破解低水平“锁定”。
3.2　医疗服务抵抗力水平地区差距明显，呈现局部集聚效应
四川省各地区医疗服务抵抗力水平差异明显，成都平原地区较其他地区领先，存在“高-低、低-高和低-低”集聚，说明地区间抵抗力差异明显且存在局部集聚特征。其原因在于，一是成都平原地区经济发达、医疗人才聚集，医疗服务水平较高，对医疗服务抵抗力影响较大；二是高水平地区会形成正向辐射影响，带动邻近地区正向增长，而欠发达地区则带动效应较弱，易于形成低水平集聚。因此，不同集聚区需根据地方特点，因地制宜制定发展策略。较高水平地区应充分发挥辐射作用，加强与周边地区合作，推动区域协调发展。发挥互联网优势，建立跨区资源统筹，突破空间限制，实现优质医疗资源流动。
3.3　医疗服务抵抗力水平存在“锁定”效应，跃升概率较小
四川省医疗服务抵抗力保持原有状态概率较高，跃升概率较小，低水平、高水平存在锁定效应，中等水平有较高概率向上转移。其原因在于，一是地区经济限制医疗卫生事业发展；二是欠发达地区优质资源流失，引进人才难以留住人才。因此，需进一步优化人才引进方案，既能引进，也要能留住人才。同时，加大医疗卫生财政支持力度。对于川西高原地区，需进一步提高交通通达度，确保居民能够高效获取医疗资源，以及建立区域医疗中心、人才定向培养等方式，推动人才向欠发达地区流动。
综上所述，本研究仍存在不足：一是研究范围局限于四川省，未进行省域或县域范围研究；二是医疗服务抵抗力受多方因素影响，指标体系建立不全面。未来从省级或县级层面进行研究，并建立更全面的评价体系。
基金
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  • 川北医学院2025年度四川省基层卫生事业发展研究中心资助项目(SWFZ25-Y-09)
  • 西南医科大学：四川医院管理和发展研究中心资助项目(SCYG2025-8)
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2026年第53卷第7期
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doi: 10.20043/j.cnki.MPM.202510014
  • 接收时间：2025-10-02
  • 首发时间：2026-11-02
  • 出版时间：2026-04-10
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  • 收稿日期：2025-10-02
基金
川北医学院2025年度四川省基层卫生事业发展研究中心资助项目(SWFZ25-Y-09)
西南医科大学：四川医院管理和发展研究中心资助项目(SCYG2025-8)
作者信息
    1.西南医科大学人文与管理学院，四川　泸州　646000
    2.西南医科大学马克思主义学院，四川　泸州　646000

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张圆，E-mail：
喻涛，E-mail：；张圆与喻涛为共同通信作者
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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
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
栓菌属 Trametes 5 2.39
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