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Article(id=1320431873025753565, tenantId=1146029695717560320, journalId=1319373219989389464, issueId=1320431836577247574, articleNumber=null, orderNo=null, doi=10.11847/zgggws1148240, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1750089600000, receivedDateStr=2025-06-17, revisedDate=1765555200000, revisedDateStr=2025-12-13, acceptedDate=1772985600000, acceptedDateStr=2026-03-09, onlineDate=1792745090967, onlineDateStr=2026-10-23, pubDate=1783612800000, pubDateStr=2026-07-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792745090967, onlineIssueDateStr=2026-10-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792745090967, creator=13701087609, updateTime=1792745090967, updator=13701087609, issue=Issue{id=1320431836577247574, tenantId=1146029695717560320, journalId=1319373219989389464, year='2026', volume='42', issue='7', pageStart='769', pageEnd='896', issueExtLink='null', onlineDate='null', pubDate='1783612800000', pubDateStr='2026-07-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1792745082277, creator='13701087609', updateTime=1792748015122, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1320444137892069377, tenantId=1146029695717560320, journalId=1319373219989389464, issueId=1320431836577247574, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1320444137892069378, tenantId=1146029695717560320, journalId=1319373219989389464, issueId=1320431836577247574, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=855, endPage=862, ext={EN=ArticleExt(id=1320431874816721374, articleId=1320431873025753565, tenantId=1146029695717560320, journalId=1319373219989389464, language=EN, title=Impact of comprehensive medical reform on China′s primary healthcare resource supply: a mixed-methods study based on provincial panel data from 2012 to 2020, columnId=1320427686514770171, journalTitle=Chinese Journal of Public Health, columnName=Survey and Report, runingTitle=null, highlight=null, articleAbstract=
Objective

To systematically assess the impact of comprehensive medical reform on the supply of primary healthcare resources in China, providing evidence to inform the optimization of national healthcare reform policies.

Methods

Using data from sources such as the China Health and Family Planning Statistical Yearbook, China Health Statistical Yearbook, and China Fiscal Yearbook, we constructed a provincial panel data set of China from 2012 to 2020. Treating China′s pilot comprehensive reform as a quasi-natural experiment, we developed a multi-period difference-in-differences (DID) model to systematically evaluate the impact of the reform on primary healthcare resource supply across the dimensions of scale, structure, and accessibility. Additionally, we conducted thematic analysis of semi-structured interviews with 30 key informants from six provinces (Zhejiang, Guangdong, Hunan, Henan, Sichuan, and Yunnan) to validate and deepen the quantitative findings.

Results

The multi-period DID model showed that the comprehensive medical reform increased the number of healthcare personnel per thousand population at the primary level (β = 0.079, P = 0.046) and the number of registered nurses per thousand population at the primary level (β = 0.045, P = 0.001). The structural proportions of healthcare personnel and registered nurses at the primary level (β = 0.006, P = 0.088; β = 0.009, P = 0.01) and the accessibility (β = 0.038, P = 0.014; β = 0.085, P = 0.001) were also significantly improved. The thematic analysis of semi-structured interviews revealed that comprehensive medical reform influenced the supply of primary healthcare resources through five mechanisms: allocation, resource distribution, governance structure, technology application, and institutional design, whereas policy coordination and systemic reform limitations constrained the overall effectiveness.

Conclusions

Comprehensive medical reform significantly improves the structure and accessibility of primary healthcare resources in China. However, shortages of primary-level physicians remain. Future efforts should focus on establishing a long-term governance mechanism for an integrated primary healthcare resource supply system, moving from pilot points to multiple simultaneous implementations and from fragmented initiatives to systematic integration, thereby supporting the high-quality development of the Healthy China initiative.

, authors=Jun WANG1, 2, 3, Yijun LIU2, 4, 5, Shuangyu ZHAO2, 4, authorsList=Jun WANG, Yijun LIU, Shuangyu ZHAO, authorCompany=null, correspAuthors=Shuangyu ZHAO, authorNote=null, correspAuthorsNote=
ZHAO Shuangyu, E-mail:
, copyrightStatement=Copyright © 2026 Chinese Journal of Public Health. All rights reserved., 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=1320431875819160039, articleId=1320431873025753565, tenantId=1146029695717560320, journalId=1319373219989389464, language=CN, title=综合医改对中国基层医疗卫生资源供给影响：基于2012—2020年省级面板数据的混合评估, columnId=1320427686770622717, journalTitle=中国公共卫生, columnName=调查报告与分析, runingTitle=null, highlight=null, articleAbstract=
目的

系统评估综合医疗改革（综合医改）对中国基层医疗卫生资源供给的影响，为优化我国医改政策提供证据支持。

方法

基于《中国卫生和计划生育统计年鉴》《中国卫生健康统计年鉴》和《中国财政年鉴》等构建中国2012—2020年省级面板数据，以我国综合医改试点为准自然实验构建多期双重差分（DID）模型，从“规模—结构—可及性”维度系统评估其对基层医疗卫生资源供给的影响，并通过对浙江、广东、湖南、河南、四川和云南6个省份30名关键受访者的半结构化访谈进行主题分析以验证并深化定量研究发现。

结果

多期DID模型分析结果显示，综合医改可显著提升基层卫生人员千人拥有量（β=0.079，P=0.046）和基层注册护士千人拥有量（β=0.045，P=0.001），卫生人员和注册护士这2种基层医疗资源的结构占比（β=0.006，P=0.088；β=0.009，P=0.010）和可及性（β=0.038，P=0.014；β=0.085，P=0.001）均显著提高；基于半结构化访谈的主题分析结果显示，综合医改通过分配机制、资源配置、治理结构、技术应用和制度设计五大机制影响我国基层医疗卫生资源供给，而政策衔接与体制机制改革不足则制约了整体成效。

结论

综合医改显著改善中国基层医疗资源的结构、可及性维度，但基层医师仍然紧缺，未来应着力构建整合型基层医疗资源供给体系的长效治理机制，从单点试行到多点并行、从单一零散到系统整合，助力健康中国的高质量发展。

, authors=王俊1, 2, 3, 刘怡均2, 4, 5, 赵双雨2, 4, *, *, authorsList=王俊, 刘怡均, 赵双雨, authorCompany=null, correspAuthors=赵双雨, authorNote=

王俊（1976—），教授，博士，研究方向：卫生政策和卫生经济微观评价、医院运行机制分析

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赵双雨，E-mail：
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王俊（1976—），教授，博士，研究方向：卫生政策和卫生经济微观评价、医院运行机制分析

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王俊（1976—），教授，博士，研究方向：卫生政策和卫生经济微观评价、医院运行机制分析

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city=null, postcode=null, companyName=null, departmentName=null, remark=5美国约翰霍普金斯大学彭博公共卫生学院，马里兰州 巴尔的摩 21202)])], figs=[ArticleFig(id=1320431880009269789, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1320431873025753565, language=EN, label=Fig.1, caption=Parallel trends test, figureFileSmall=WicoCpu+UYvlSQaZZK7sog==, figureFileBig=ALI7ezN9SEPDeUoSGFQsPQ==, tableContent=null), ArticleFig(id=1320431880080572958, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1320431873025753565, language=CN, label=图1, caption=平行趋势检验, figureFileSmall=WicoCpu+UYvlSQaZZK7sog==, figureFileBig=ALI7ezN9SEPDeUoSGFQsPQ==, tableContent=null), ArticleFig(id=1320431880298676767, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1320431873025753565, language=EN, label=Tab.1, caption=

Descriptive statistical analysis of key variables

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变量分类指标MinMaxMeanSD
解释变量规模基层人员千人拥有量/（人/千人）1.6285.5972.7630.583
基层执业（助理）医师千人拥有量/（人/千人）0.3761.5340.8480.191
基层注册护士千人拥有量/（人/千人）0.1131.0960.5290.172
基层床位千人拥有量/（张/千人）0.2011.8691.0070.358
结构基层人员的结构占比/%21.10064.20033.5006.400
基层医师的结构占比/%26.10048.60035.5004.300
基层护士的结构占比/%12.20031.60020.4003.500
基层床位的结构占比/%3.70031.40018.8006.100
可及性基层人员–人口集聚度0.6441.9351.0150.201
基层医师–人口集聚度0.5061.4590.9780.176
基层护士–人口集聚度0.2911.6060.9910.241
基层床位–人口集聚度0.1841.6000.9470.329
被解释变量省份t是否在第i年推行了综合医改0.0001.000
控制变量经济发展省份国内生产总值/亿元701.030110 760.9402.7630.583
调整后人均国内生产总值的对数值/元9.86211.8300.8480.191
人口规模年末人口数的对数值/万人5.7539.4430.5290.172
老龄人口占比/%0.0500.1761.0070.358
人口出生率/%4.66017.8900.3350.064
人口死亡率/%4.2608.0700.3550.043
社会结构城镇化率/%22.75089.6000.2040.035
国内专利申请受理量/项170.000967 204.0000.1880.061
城市绿地面积/万公顷0.34052.5501.0150.201
普通高等学校数/个6.000167.0000.9780.176
行政区划地级区划数/个0.00021.0000.9910.241
县级区划数/个16.000183.0000.9470.329
财政收支财政收入占全国财政收入比重/%0.1007.1000.2110.409
财政支出占全国财政支出比重/%0.6007.4002.7630.583
本级财政收入占地方财政收入比重/%0.10012.9000.8480.191
本级财政支出占地方财政支出比重/%0.7008.7000.5290.172
公共投入地方财政教育支出/亿元94.4843 510.5571.0070.358
地方财政科学技术支出/亿元4.1651 168.7930.3350.064
地方财政文化体育与传媒支出/亿元14.444417.2210.3550.043
地方财政社会保障和就业支出/亿元65.5371 998.6740.2040.035
地方财政城乡社区事务支出/亿元31.5502 413.8430.1880.061
医疗卫生地方财政医疗卫生支出/地方财政一般预算支出/%0.0400.1211.0150.201
地方财政医疗卫生支出/地方财政一般公共服务支出/%0.2231.4790.9780.176
医疗卫生机构数/个4 140.00086 939.0000.9910.241
医院病床使用率/%48.30099.3000.9470.329
), ArticleFig(id=1320431880374174240, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1320431873025753565, language=CN, label=表1, caption=

主要变量描述性统计分析

, figureFileSmall=null, figureFileBig=null, tableContent=
变量分类指标MinMaxMeanSD
解释变量规模基层人员千人拥有量/（人/千人）1.6285.5972.7630.583
基层执业（助理）医师千人拥有量/（人/千人）0.3761.5340.8480.191
基层注册护士千人拥有量/（人/千人）0.1131.0960.5290.172
基层床位千人拥有量/（张/千人）0.2011.8691.0070.358
结构基层人员的结构占比/%21.10064.20033.5006.400
基层医师的结构占比/%26.10048.60035.5004.300
基层护士的结构占比/%12.20031.60020.4003.500
基层床位的结构占比/%3.70031.40018.8006.100
可及性基层人员–人口集聚度0.6441.9351.0150.201
基层医师–人口集聚度0.5061.4590.9780.176
基层护士–人口集聚度0.2911.6060.9910.241
基层床位–人口集聚度0.1841.6000.9470.329
被解释变量省份t是否在第i年推行了综合医改0.0001.000
控制变量经济发展省份国内生产总值/亿元701.030110 760.9402.7630.583
调整后人均国内生产总值的对数值/元9.86211.8300.8480.191
人口规模年末人口数的对数值/万人5.7539.4430.5290.172
老龄人口占比/%0.0500.1761.0070.358
人口出生率/%4.66017.8900.3350.064
人口死亡率/%4.2608.0700.3550.043
社会结构城镇化率/%22.75089.6000.2040.035
国内专利申请受理量/项170.000967 204.0000.1880.061
城市绿地面积/万公顷0.34052.5501.0150.201
普通高等学校数/个6.000167.0000.9780.176
行政区划地级区划数/个0.00021.0000.9910.241
县级区划数/个16.000183.0000.9470.329
财政收支财政收入占全国财政收入比重/%0.1007.1000.2110.409
财政支出占全国财政支出比重/%0.6007.4002.7630.583
本级财政收入占地方财政收入比重/%0.10012.9000.8480.191
本级财政支出占地方财政支出比重/%0.7008.7000.5290.172
公共投入地方财政教育支出/亿元94.4843 510.5571.0070.358
地方财政科学技术支出/亿元4.1651 168.7930.3350.064
地方财政文化体育与传媒支出/亿元14.444417.2210.3550.043
地方财政社会保障和就业支出/亿元65.5371 998.6740.2040.035
地方财政城乡社区事务支出/亿元31.5502 413.8430.1880.061
医疗卫生地方财政医疗卫生支出/地方财政一般预算支出/%0.0400.1211.0150.201
地方财政医疗卫生支出/地方财政一般公共服务支出/%0.2231.4790.9780.176
医疗卫生机构数/个4 140.00086 939.0000.9910.241
医院病床使用率/%48.30099.3000.9470.329
), ArticleFig(id=1320431880437088801, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1320431873025753565, language=EN, label=Tab.2, caption=

A multi-period DID model analysis of the impact of comprehensive medical reform on primary healthcare resource supply

, figureFileSmall=null, figureFileBig=null, tableContent=
维度模型变量β值P值控制变量省份–年份固定效应R2值N
规模M1基层卫生人员千人拥有量0.0790.046是是0.967273
M2基层执业（助理）医师千人拥有量0.0050.732是是0.960273
M3基层注册护士千人拥有量0.0450.001是是0.950273
M4基层床位千人拥有量0.0010.965是是0.984273
M5基层卫生人员千人拥有量对数0.0390.011是是0.954273
M6基层执业（助理）医师千人拥有量对数–0.0060.765是是0.954273
M7基层注册护士千人拥有量对数0.0550.044是是0.957273
M8基层床位千人拥有量对数–0.0000.991是是0.990273
结构M9基层卫生人员结构占比0.0060.088是是0.973273
M10基层执业（助理）医师结构占比–0.0060.090是是0.959273
M11基层注册护士结构占比0.0090.010是是0.921273
M12基层床位结构占比–0.0000.902是是0.985273
可及性M13基层卫生人员–人口集聚度0.0380.014是是0.960273
M14基层执业（助理）医师数–人口集聚度–0.0030.884是是0.938273
M15基层注册护士–人口集聚度0.0850.001是是0.923273
M16基层床位–人口集聚度0.0010.952是是0.983273
), ArticleFig(id=1320431880512586274, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1320431873025753565, language=CN, label=表2, caption=

综合医改对基层医疗卫生资源供给影响的多期DID模型分析

, figureFileSmall=null, figureFileBig=null, tableContent=
维度模型变量β值P值控制变量省份–年份固定效应R2值N
规模M1基层卫生人员千人拥有量0.0790.046是是0.967273
M2基层执业（助理）医师千人拥有量0.0050.732是是0.960273
M3基层注册护士千人拥有量0.0450.001是是0.950273
M4基层床位千人拥有量0.0010.965是是0.984273
M5基层卫生人员千人拥有量对数0.0390.011是是0.954273
M6基层执业（助理）医师千人拥有量对数–0.0060.765是是0.954273
M7基层注册护士千人拥有量对数0.0550.044是是0.957273
M8基层床位千人拥有量对数–0.0000.991是是0.990273
结构M9基层卫生人员结构占比0.0060.088是是0.973273
M10基层执业（助理）医师结构占比–0.0060.090是是0.959273
M11基层注册护士结构占比0.0090.010是是0.921273
M12基层床位结构占比–0.0000.902是是0.985273
可及性M13基层卫生人员–人口集聚度0.0380.014是是0.960273
M14基层执业（助理）医师数–人口集聚度–0.0030.884是是0.938273
M15基层注册护士–人口集聚度0.0850.001是是0.923273
M16基层床位–人口集聚度0.0010.952是是0.983273
), ArticleFig(id=1320431880588083747, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1320431873025753565, language=EN, label=Tab.3, caption=

Basic information of interviewees

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地区组别省份层级职务姓名性别访谈形式
东部试点浙江政府部门市卫生健康委党委委员、副主任A1男焦点小组
县医保局党组书记、局长A2男
县卫健局科长A3男
医疗机构县域医共体医院科室主任A4女面对面访谈
患者县域医共体患者A5女
非试点广东政府部门区卫健局科员B1女电话访谈
医疗机构县域医共体总院院长B2男面对面访谈
县域医共体部门主任B3女
镇中心卫生院院长B4女电话访谈
患者三级甲等中医医院患者B5男线上访谈
中部试点湖南政府部门省卫生健康委科员C1女电话访谈
县卫健局国家卫生健康委员会主任C2男焦点小组
医疗机构乡镇卫生院院长C3男面对面访谈
乡镇卫生院医生C4女
患者乡镇卫生院患者C5女面对面访谈
非试点河南政府部门市卫生健康委副主任D1女面对面访谈
市卫生健康委科室主任D2男
医疗机构市级医院党委书记、院长D3男焦点小组
医疗健康集团党委书记、院长D4男
患者三级甲等综合性医院患者D5男电话访谈
西部试点四川政府部门区卫健局科室科长E1男电话访谈
区卫健局科员E2男面对面访谈
区卫健局科员E3女电话访谈
医疗机构区级医院科室主任E4女焦点小组
患者社区卫生服务中心患者E5女电话访谈
非试点云南政府部门省卫生健康委科长F1女面对面访谈
医疗机构三级甲等专科医院部门主任F2女面对面访谈
县域医共体基卫部主任F3男焦点小组
社区卫生服务中心医生F4男面对面访谈
患者三级甲等专科医院患者F5女面对面访谈
), ArticleFig(id=1320431880684552740, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1320431873025753565, language=CN, label=表3, caption=

访谈对象基本情况

, figureFileSmall=null, figureFileBig=null, tableContent=
地区组别省份层级职务姓名性别访谈形式
东部试点浙江政府部门市卫生健康委党委委员、副主任A1男焦点小组
县医保局党组书记、局长A2男
县卫健局科长A3男
医疗机构县域医共体医院科室主任A4女面对面访谈
患者县域医共体患者A5女
非试点广东政府部门区卫健局科员B1女电话访谈
医疗机构县域医共体总院院长B2男面对面访谈
县域医共体部门主任B3女
镇中心卫生院院长B4女电话访谈
患者三级甲等中医医院患者B5男线上访谈
中部试点湖南政府部门省卫生健康委科员C1女电话访谈
县卫健局国家卫生健康委员会主任C2男焦点小组
医疗机构乡镇卫生院院长C3男面对面访谈
乡镇卫生院医生C4女
患者乡镇卫生院患者C5女面对面访谈
非试点河南政府部门市卫生健康委副主任D1女面对面访谈
市卫生健康委科室主任D2男
医疗机构市级医院党委书记、院长D3男焦点小组
医疗健康集团党委书记、院长D4男
患者三级甲等综合性医院患者D5男电话访谈
西部试点四川政府部门区卫健局科室科长E1男电话访谈
区卫健局科员E2男面对面访谈
区卫健局科员E3女电话访谈
医疗机构区级医院科室主任E4女焦点小组
患者社区卫生服务中心患者E5女电话访谈
非试点云南政府部门省卫生健康委科长F1女面对面访谈
医疗机构三级甲等专科医院部门主任F2女面对面访谈
县域医共体基卫部主任F3男焦点小组
社区卫生服务中心医生F4男面对面访谈
患者三级甲等专科医院患者F5女面对面访谈
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王俊 1, 2, 3 , 刘怡均 2, 4, 5 , 赵双雨 2, 4, *, *
中国公共卫生 | 调查报告与分析 2026,42(7): 855-862
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中国公共卫生 |调查报告与分析 2026 , 42 (7) : 855 -862
综合医改对中国基层医疗卫生资源供给影响：基于2012—2020年省级面板数据的混合评估
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王俊1, 2, 3, 刘怡均2, 4, 5, 赵双雨2, 4, *, *
作者信息
  • 1中国人民大学健康中国研究院，北京 100872
  • 2中国人民大学卫生政策研究与评价中心，北京 100872
  • 3中国人民大学人口与健康学院，北京 100872
  • 4中国人民大学公共管理学院，北京 100872
  • 5美国约翰霍普金斯大学彭博公共卫生学院，马里兰州 巴尔的摩 21202
通讯作者:
赵双雨，E-mail：
作者简介:

王俊（1976—），教授，博士，研究方向：卫生政策和卫生经济微观评价、医院运行机制分析

Impact of comprehensive medical reform on China′s primary healthcare resource supply: a mixed-methods study based on provincial panel data from 2012 to 2020
Jun WANG1, 2, 3, Yijun LIU2, 4, 5, Shuangyu ZHAO2, 4
Affiliations
  • 1China Institute of Health, Renmin University of China, Beijing 100872, China
  • 2Center for Health Policy Research and Evaluation, Renmin University of China, Beijing 100872, China
  • 3School of Population and Health, Renmin University of China, Beijing 100872, China
  • 4School of Public Administration and Policy, Renmin University of China, Beijing 100872, China
  • 5Bloomberg School of Public Health, Johns Hopkins University, Baltimore 21202, the United States
    • Corresponding Author:
    ZHAO Shuangyu, E-mail:
出版时间: 2026-07-10 doi: 10.11847/zgggws1148240
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摘要
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目的

系统评估综合医疗改革（综合医改）对中国基层医疗卫生资源供给的影响，为优化我国医改政策提供证据支持。

方法

基于《中国卫生和计划生育统计年鉴》《中国卫生健康统计年鉴》和《中国财政年鉴》等构建中国2012—2020年省级面板数据，以我国综合医改试点为准自然实验构建多期双重差分（DID）模型，从“规模—结构—可及性”维度系统评估其对基层医疗卫生资源供给的影响，并通过对浙江、广东、湖南、河南、四川和云南6个省份30名关键受访者的半结构化访谈进行主题分析以验证并深化定量研究发现。

结果

多期DID模型分析结果显示，综合医改可显著提升基层卫生人员千人拥有量（β=0.079，P=0.046）和基层注册护士千人拥有量（β=0.045，P=0.001），卫生人员和注册护士这2种基层医疗资源的结构占比（β=0.006，P=0.088；β=0.009，P=0.010）和可及性（β=0.038，P=0.014；β=0.085，P=0.001）均显著提高；基于半结构化访谈的主题分析结果显示，综合医改通过分配机制、资源配置、治理结构、技术应用和制度设计五大机制影响我国基层医疗卫生资源供给，而政策衔接与体制机制改革不足则制约了整体成效。

结论

综合医改显著改善中国基层医疗资源的结构、可及性维度，但基层医师仍然紧缺，未来应着力构建整合型基层医疗资源供给体系的长效治理机制，从单点试行到多点并行、从单一零散到系统整合，助力健康中国的高质量发展。

综合医疗改革（综合医改）  /  基层卫生  /  医疗资源  /  供给  /  影响  /  中国  /  省级面板数据  /  双重差分（DID）模型  /  半结构化访谈
Objective

To systematically assess the impact of comprehensive medical reform on the supply of primary healthcare resources in China, providing evidence to inform the optimization of national healthcare reform policies.

Methods

Using data from sources such as the China Health and Family Planning Statistical Yearbook, China Health Statistical Yearbook, and China Fiscal Yearbook, we constructed a provincial panel data set of China from 2012 to 2020. Treating China′s pilot comprehensive reform as a quasi-natural experiment, we developed a multi-period difference-in-differences (DID) model to systematically evaluate the impact of the reform on primary healthcare resource supply across the dimensions of scale, structure, and accessibility. Additionally, we conducted thematic analysis of semi-structured interviews with 30 key informants from six provinces (Zhejiang, Guangdong, Hunan, Henan, Sichuan, and Yunnan) to validate and deepen the quantitative findings.

Results

The multi-period DID model showed that the comprehensive medical reform increased the number of healthcare personnel per thousand population at the primary level (β = 0.079, P = 0.046) and the number of registered nurses per thousand population at the primary level (β = 0.045, P = 0.001). The structural proportions of healthcare personnel and registered nurses at the primary level (β = 0.006, P = 0.088; β = 0.009, P = 0.01) and the accessibility (β = 0.038, P = 0.014; β = 0.085, P = 0.001) were also significantly improved. The thematic analysis of semi-structured interviews revealed that comprehensive medical reform influenced the supply of primary healthcare resources through five mechanisms: allocation, resource distribution, governance structure, technology application, and institutional design, whereas policy coordination and systemic reform limitations constrained the overall effectiveness.

Conclusions

Comprehensive medical reform significantly improves the structure and accessibility of primary healthcare resources in China. However, shortages of primary-level physicians remain. Future efforts should focus on establishing a long-term governance mechanism for an integrated primary healthcare resource supply system, moving from pilot points to multiple simultaneous implementations and from fragmented initiatives to systematic integration, thereby supporting the high-quality development of the Healthy China initiative.

comprehensive medical reform  /  primary healthcare  /  medical resources  /  supply  /  impact  /  China  /  provincial panel data  /  difference-in-differences (DID) model  /  semi-structured interview
王俊, 刘怡均, 赵双雨. 综合医改对中国基层医疗卫生资源供给影响：基于2012—2020年省级面板数据的混合评估. 中国公共卫生, 2026 , 42 (7) : 855 -862 . DOI: 10.11847/zgggws1148240
Jun WANG, Yijun LIU, Shuangyu ZHAO. Impact of comprehensive medical reform on China′s primary healthcare resource supply: a mixed-methods study based on provincial panel data from 2012 to 2020[J]. Chinese Journal of Public Health, 2026 , 42 (7) : 855 -862 . DOI: 10.11847/zgggws1148240
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新医改以来，中国始终围绕“强基层、保基本、建机制”改善基层医疗卫生资源供给，然而我国就医秩序和资源配置仍长期存在结构性矛盾[1]。《中国卫生健康统计年鉴（2023）》数据显示，我国基层医疗卫生机构数量（1 016 238个）占比为94.01%，但卫生人员数量（495.3万人）仅占全国总量的32.5%，总诊疗量（49.4亿人次）也仅占51.8%[2]。2015年国务院深化医药卫生体制改革领导小组决定开展综合医疗改革（简称“综合医改”）试点，其中江苏省、安徽省、福建省和青海省为第一批试点，2016年新增上海市、浙江省、湖南省、重庆市、四川省、陕西省和宁夏回族自治区，截至目前共有11个省（自治区、直辖市）开展试点工作，部分现有研究定性总结了试点经验[3]或评估政策效果[4–5]，但鲜有研究关注综合医改对资源供给的影响。为系统评估综合医改对中国基层医疗卫生资源供给的影响，为优化我国医改政策提供证据支持，本研究基于《中国卫生和计划生育统计年鉴》《中国卫生健康统计年鉴》和《中国财政年鉴》等构建中国2012—2020年省级面板数据，以我国综合医改试点为准自然实验构建多期双重差分（difference-in-differences, DID）模型，从“规模—结构—可及性”维度系统评估其对基层医疗卫生资源供给的影响，并通过对试点地区（浙江、湖南、四川）和非试点地区（广东、河南、云南）6个省份30名关键受访者的半结构化访谈进行主题分析以验证并深化定量研究发现。结果报告如下。
1　资料与方法
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1.1　资料来源
1.1.1　定量研究资料
本研究构建了2012—2020年中国31个省（自治区、直辖市）的面板数据集。核心变量基层卫生人员千人拥有量、基层执业（助理）医师千人拥有量、基层注册护士千人拥有量和基层床位千人拥有量数据来源于2013—2017年《中国卫生和计划生育统计年鉴》和2018—2021年《中国卫生健康统计年鉴》，人口和经济数据来源于2013—2021年《中国统计年鉴》和《中国财政年鉴》。
1.1.2　定性研究资料
本研究采用半结构化深度访谈收集定性研究资料。基于目的性抽样和最大差异抽样策略，考虑了地域分布、机构类型和机构规模等因素，在中国的东、中、西部进行抽样，抽取试点地区（浙江、湖南、四川）和非试点地区（广东、河南、云南）6个省份的30名政府部门工作人员、医疗卫生机构工作者和患者进行访谈。如若仅选取试点地区可能导致研究视角片面、结论可信度降低；同时涵盖综合医改试点地区和非试点地区，有利于从描述试点个案经验上升到探讨在何种情境下哪些机制会生效或失效。访谈对象包含三类关键利益相关者，即负责政策制定与区域资源规划的省级、地市级和区县级卫生健康行政部门管理者12人，直接负责机构运营与资源管理的医疗卫生机构负责人、科室主任和医师12人以及能直接反映试点政策的群众满意度的就医患者6人。访谈过程基于面对面访谈、焦点小组和电话访谈3种形式，在获得受访者知情同意后进行录音记录。访谈大纲围绕“综合医改何以解决基层资源供给难题”和“综合医改缘何无法全方位地解决这一难题”两大核心议题展开。本研究经中国人民大学人口与健康学院伦理委员会审查批准，所有参与访谈者均知晓研究目的且自愿参加访谈。
1.2　统计分析
1.2.1　定量研究资料
本研究运用多期DID模型进行综合医改试点政策评估。多期DID模型适用于政策冲击发生在多个时点的场景，通过将不同地区的政策实施年份标准化为相对时点，来评估政策的动态效应。鉴于综合医改试点共包括2个批次，本研究借鉴Beck T等[6]运用多期DID模型将综合医改视为渐进推行的准自然实验，评估其对基层医疗卫生资源供给的影响。具体而言，处理组定义为所有参与综合医改试点的省份，即第1批2015年启动的江苏省、安徽省、福建省和青海省以及第2批2016年启动的上海市、浙江省、湖南省、重庆市、四川省、陕西省和宁夏回族自治区；对照组则为未参与试点的其他省份。在多期DID框架下，本研究以政策实施前1年为基准期（相对时点为–1），政策实施当年为政策冲击后第1期（相对时点为+1），政策实施前2年为政策实施前第2期（相对时点为–2），以此类推；第1批2015年试点城市包含政策前3期到政策后6期，第2批2016年试点城市包含政策前4期到政策后5期，对照组城市包含政策前9期。在传统DID模型的基础上控制了省份和时间固定效应[7]，计量模型设定为：
$ Y_{it}=\alpha+\beta\times\mathrm{DID}_{it}+\varphi\times X_{it}+\eta_i+\gamma_t+\varepsilon_{it} $
式中，i代表省份，t代表年份，Yit为省份i在年份t的因变量；DIDit为核心自变量，1表示省份i在年份t实施了综合医改，0则表示未实施；Xit为控制变量；ηi为省份固定效应，γt为时间固定效应，εit为随机误差项；系数β为本研究关注的政策效应，α为常数项，$ \varphi $为控制变量的系数。DID模型的被解释变量依据《中国卫生健康统计年鉴（2022）》[8]对“基层医疗卫生机构”的官方定义确定研究边界，包括社区卫生服务中心、社区卫生服务站、街道卫生院、乡镇卫生院、村卫生室、门诊部和诊所（医务室）；参考既往对“卫生资源”概念的研究[9]，创新性地构建被解释变量的“规模—结构—可及性”三维评价框架，基于人员和床位两大要素进行评价。规模维度侧重总量表征，选取基层卫生人员、执业（助理）医师、注册护士和床位的每千人拥有量指标；结构维度关注资源配置，反映各省基层资源在不同类型医疗卫生机构中的占比情况[10]；可及性维度引入卫生资源集聚度概念，即比较某一地区每1%人口的卫生资源集聚量与上一层次区域每1%人口的卫生资源集聚量[11]：
${\text{卫生资源人口聚集度}}=\frac{\left( H{{R}_{i}}/H{{R}_{n}} \right)\times 100{{\%}}}{\left( {{P}_{i}}/{{P}_{n}} \right)\times 100{{\%}}}$
式中，HRi为某省份i的卫生资源量，HRn为全国卫生资源总量，Pi为某省份i的人口数量，Pn为全国人口数量，最终计算得到卫生资源人口集聚度为无量纲的比值。解释变量为“综合医改”试点政策。根据是否为试点城市（treati）和当年政策是否开始发挥作用（timet），将两者的交互项（DIDit）作为核心解释变量。控制变量充分考量财政分权体制下地方政府特征、社会经济状况与医疗资源禀赋，包括经济、人口、社会、财政与医疗卫生等维度[12–13]。具体而言，经济维度控制区域经济水平对资源配置的基础性影响，选取中国居民消费价格指数调整后的国内生产总值；纳入人口规模、城镇化率和行政区划数量等指标控制居民对公共服务需求的差异；由于地方财政能力与支出结构可能影响公共资源配置效率，控制地区财政收入、支出占全国比重等；在此基础上控制了公共投入情况，如地方教育、科技、文化和社会保障等方面的财政支出；最后对省份的医疗卫生事业发展情况进行控制。应用Stata 16.0软件进行多期DID模型分析，检验水准为α=0.05。
1.2.2　定性研究资料
本文采用主题分析法进行定性资料分析。在访谈录音转录成文本后，通过熟悉化、初始编码、寻找主题、审查主题和定义主题形成五大主题。为确保分析严谨性，定性材料均由2位研究者独立编码并复核，以确保标注的准确性和一致性。
2　结　果
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2.1　描述性分析（表1）
表1呈现了31个省（自治区、直辖市）主要变量的描述性统计分析结果。核心被解释变量为基层医疗卫生资源的“规模—结构—可及性”三维指标。在规模维度上，基层卫生人员千人拥有量、基层执业（助理）医师千人拥有量、基层注册护士千人拥有量和基层床位千人拥有量均值分别为2.763人/千人、0.848人/千人、0.529人/千人和1.007张/千人，但其标准差与极差显示各省份资源总量存在明显差异；在结构维度上，基层卫生人员、基层执业（助理）医师、基层注册护士和基层床位在卫生体系中的平均占比分别为33.5%、35.5%、20.4%和18.8%，其中人员与床位的结构占比波动较大（标准差分别为0.064和0.061），反映出资源配置的结构性不均衡。可及性方面，基层人员—人口集聚度均值为1.015，接近理想匹配状态，但最大值与最小值间差距显著，提示区域间可及性分布不均。控制变量涵盖经济发展、人口社会特征、财政投入与公共服务等多类因素。
2.2　平行趋势检验（图1）
为满足DID模型的前提假设，本研究通过事件研究法检验处理组与对照组在政策冲击前的趋势一致性。以政策实施前1年为基准期，第1批2015年试点城市包含政策前3期到政策后6期，第2批2016年试点城市包含政策前4期到政策后5期，对照组城市包含政策前9期，将数量较少的较远时期样本归并到某一较近时点，本文将所有5+处理前时点合并为政策前第5期，4+处理后时点合并到政策后第4期。动态效应分析结果显示，被解释变量在政策前均符合平行趋势假设。
2.3　综合医改对中国基层医疗卫生资源供给影响的多期DID模型分析（表2）
多期DID模型分析结果显示，综合医改可显著提升基层卫生人员千人拥有量（β=0.079，P=0.046）和基层注册护士千人拥有量（β=0.045，P=0.001），对数模型进一步验证了结果的稳健性；卫生人员和注册护士这2种基层医疗资源的结构占比（β=0.006，P=0.088；β=0.009，P=0.010）和可及性（β=0.038，P=0.014；β=0.085，P=0.001）均显著提高。
2.4　双向机制分析（表3）
本研究在运用DID模型进行量化分析后，采用案例研究和定性访谈进行机制分析，在东、中、西部试点和非试点地区对30名政府部门工作人员、医疗卫生机构工作者和患者进行了半结构化访谈，具体见表3。正向机制分析结果显示，有关“综合医改何以解决基层资源供给难题”，本研究基于半结构化访谈总结出了分配机制、资源配置、治理结构、技术应用和制度设计5个方面：一是在分配机制中，综合医改在财政保障方面进行管理创新，向基层倾斜财政投入；二是在资源配置中，通过对基层机构进行资金、人才、技术和管理理念的下沉、带教、帮扶和传授，如派驻专家、教授到基层坐诊，吸引患者前往基层就医；三是在治理结构中，综合医改“包罗万象”的综合性发挥出多元联动效应这一优势，相较于单一部门发布政策后自上而下执行而言，具有政府高位推动、部门横向沟通、科室多元联动的执行优势；四是在技术应用中，信息化建设促进了区域内打破信息孤岛，远程会诊、影像结果互传等方式提升了基层服务的便利性；五是在制度设计中，综合医改基于预防保健、医疗保障和分级诊疗三大措施形成闭环，预防保健降低资源消耗，医保引导优化资源分布，分级诊疗激活资源效能，通过政策覆盖广泛、措施多元综合的制度优势发挥重要作用，构建起可持续的基层医疗资源供给增强机制。负向机制分析结果显示，有关“综合医改缘何无法全方位地解决基层医疗卫生资源供给难题”，可以被概括为制度衔接不到位、体制机制联动欠缺、投入主次不分和产出效益不明等问题：其一，在分配机制中，财政投入主次不分、产出效益不明，针对基层医疗卫生机构中医护技配比失衡、优质人才紧缺的现象，部分地区仅停留在口号式的动员，人员专项资金发挥效益有限；其二，在资源配置中，部分机构存在“等、靠、要”的依赖心理，改革试点缺乏变被动为主动的内生动力；其三，在治理结构中，尽管院长的去行政化、增业务化提升了大型医院专业能力，但乡镇卫生院院长却普遍欠缺管理能力；其四，在技术应用中，基层医疗卫生服务存在割裂化、碎片化的问题，区域内检查结果无法互认使得人民群众重复就医；其五，在制度设计中，基本药物目录、医保支付制度和公共卫生服务体制机制联动欠缺，部分地区存在基层用药限制的情况，地方政府在推进落实中央政策时配套政策衔接不足，未能根据实施情况和社会发展进行政策调整和更新，囿于地方政府的执政水平、统筹能力和人员素质，政策推进力度和进度存在地区和层级差异，呈现出东西部、南北方、城乡、省市县间的显著不同。
3　讨　论
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本研究聚焦综合医改对中国基层医疗卫生资源供给难题的改进效应，发现综合医改可显著提升基层卫生人员千人拥有量（β=0.079，P=0.046）和基层注册护士千人拥有量（β=0.045，P=0.001），且卫生人员和注册护士这2种基层医疗资源的结构占比（β=0.006，P=0.088；β=0.009，P=0.010）和可及性（β=0.038，P=0.014；β=0.085，P=0.001）均显著提高。进一步基于访谈的机制分析结果显示，综合医改通过分配机制、资源配置、治理结构、技术应用和制度设计五大机制影响我国基层医疗卫生资源供给，而政策衔接与体制机制改革不足制约了整体成效。本研究还发现，基层执业（助理）医师千人拥有量虽呈正向趋势但未达到显著水平，提示基层医师规模存在改善趋势但增量有限。为缓解基层医师流失问题[14]，目前地方往往采用“存量优化”策略，通过基层人才能力培养、上级医院医师资源下沉、医共体内医师多点执业等方式补充医师供给能力[15]，但基层医师数量上涨仍较为缓慢。此外，基层床位千人拥有量效果不显著，提示综合医改侧重人力资源而非床位设施扩容，这可能与基层医疗服务性质有关；还可能与双向转诊进一步分流急危重症患者潜在住院需求，导致基层门诊服务增强、住院服务减弱，床位实际利用需求下降，基层的床位规模增速显著落后于医院有关[16]。
本研究结合中国“强基层”政策导向，利用多期DID模型的准自然实验设计，率先评估了综合医改对基层资源供给难题的改进情况，创新性地构建“规模—结构—可及性”三维卫生资源评价框架，并结合定量与定性研究寻找综合医改缓解基层医疗卫生资源供给难题的机制，为我国优化医疗卫生资源配置供给的改革提供有益参考。但本研究仍存在一定的局限性：首先，考虑到数据可用性和研究范围，本研究结果变量仅限于基层医疗资源供给的影响，难以面面俱到，因此未来研究不仅应关注资源供给，还需重视“看好病”（服务质量和满意度），全面反映综合医改的政策效应；其次，尽管本文通过多期DID模型检验了综合医改对基层医疗卫生资源供给难题的缓解效应，但我们需审慎评估效应值的实际规模和现实意义，该结果更重要的启示可能在于其方向性意义，但还需通过优化配置效率、强化技术应用和改善激励政策等形成政策合力，实现实质性的资源短缺缓解。为此，本文提出以下政策启示：其一，当下重点是改进综合医改五个机制的现存问题。在分配机制中建立以服务效果为导向的补偿机制，基于基层医师数量增长不显著的现状构建“培养—激励—绩效”协同机制，财政全额补助定向医学生培养，实施偏远地区岗位津贴，将财政拨款与医师增长率挂钩；在资源配置中建立资源共享平台和下沉渠道，下沉专家和技术资源并逐步加强医务人员教育和培训；在治理结构中建立政府、机构、社区和居民多方参与机制，引入现代管理工具提高基层服务质量和管理效率；在技术应用中适当利用大数据和人工智能技术对资源供给进行预测和配置；在制度设计中完善破解区域内检查结果无法互认等执行掣肘的配套政策，与时俱进地根据实施情况进行政策调整和更新。其二，应构建整合型基层医疗资源供给体系的长效治理机制。如，转变改革创新形式，发挥政府引导作用，从单点试行到多点并行、从单一零散到系统整合，扭转成效微弱、难以推广的局面；建立地方政府、大型（基层）医疗卫生机构等多方主体的权责利划分机制，在财政、管理和服务等方面加强机构整合、促进业务融合；完善监管和评价机制，防止政策落地形变走样问题，促进基层医疗资源供给持续优化。

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doi: 10.11847/zgggws1148240
  • 接收时间：2025-06-17
  • 首发时间：2026-10-23
  • 出版时间：2026-07-10
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  • 收稿日期：2025-06-17
  • 修回日期：2025-12-13
  • 录用日期：2026-03-09
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    1中国人民大学健康中国研究院，北京 100872
    2中国人民大学卫生政策研究与评价中心，北京 100872
    3中国人民大学人口与健康学院，北京 100872
    4中国人民大学公共管理学院，北京 100872
    5美国约翰霍普金斯大学彭博公共卫生学院，马里兰州 巴尔的摩 21202

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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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