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Article(id=1324077713530684215, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324077560820282023, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202509496, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1758988800000, receivedDateStr=2025-09-28, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793614327079, onlineDateStr=2026-11-02, pubDate=1774368000000, pubDateStr=2026-03-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793614327079, onlineIssueDateStr=2026-11-02, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793614327079, creator=13701087609, updateTime=1793614327079, updator=13701087609, issue=Issue{id=1324077560820282023, tenantId=1146029695717560320, journalId=1227665162245664772, year='2026', volume='53', issue='6', pageStart='961', pageEnd='1152', issueExtLink='null', onlineDate='null', pubDate='1774368000000', pubDateStr='2026-03-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1793614290671, creator='13701087609', updateTime=1793614391469, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1324077983656444429, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324077560820282023, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1324077983660638734, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324077560820282023, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1126, endPage=1131, ext={EN=ArticleExt(id=1324077713782342456, articleId=1324077713530684215, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Analysis of the spatial pattern and influencing factors of newly reported AIDS in mainland China, 2015-2023, columnId=1228016573156360233, journalTitle=Modern Preventive Medicine, columnName=Disease Control and Prevention, runingTitle=null, highlight=null, articleAbstract=
Objective

To compare the spatial pattern evolution characteristics and the spatial heterogeneity of influencing factors of newly reported AIDS incidence in mainland China before (2015-2019) and after (2020-2023) the COVID-19 outbreak, and to provide a scientific basis for targeted prevention and control in response to similar crises in the future.

Methods

Based on newly reported AIDS incidence and socioeconomic data from 31 provincial-level administrative regions in mainland China (2015-2023), spatial autocorrelation, ordinary least squares (OLS), and multiscale geographically weighted regression (MGWR) were employed for analysis.

Results

The incidence of newly reported AIDS showed an upward trend from 2015 to 2019, but began to decline after the outbreak of COVID-19. By 2022, the incidence dropped to 3.69 per 100 000 population, with a slight rebound in 2023. Both before and after the COVID-19 outbreak, the incidence of newly reported AIDS exhibited significant and strengthening spatial positive autocorrelation. Hotspots consistently concentrated in Yunnan, Guizhou, Sichuan, Chongqing, Guangxi, and Hunan, with Hainan and Guangdong emerging as new hotspots. Coldspots persistently clustered in Northeast and North China. Before the COVID-19 outbreak, the number of health technical personnel per 10 000 people had a promoting effect on the incidence in the Southwest region, but this effect shifted to a suppressive one after the outbreak. Urbanization rate exerted a negative influence globally but a positive influence locally. Other variables demonstrated bidirectional influences, with spatial heterogeneity persisting throughout the study period.

Conclusion

s The spatial evolution of newly reported AIDS incidence varied before and after the COVID-19 outbreak, but both periods exhibited significant spatial clustering, with socioeconomic factors demonstrating spatial heterogeneity in their influence. We propose formulating differentiated AIDS prevention and control strategies tailored to local conditions, with particular focus on hotspot areas, to implement precision interventions for effective AIDS incidence reduction.

, authors=Ling JIA1, Jiang-hua MAO2, Xiao HUANG1, Jia-hui REN1, Chun MENG1, Pin-wen LIAO1, authorsList=Ling JIA, Jiang-hua MAO, Xiao HUANG, Jia-hui REN, Chun MENG, Pin-wen LIAO, authorCompany=null, correspAuthors=Pin-wen LIAO, 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=1324077716680606529, articleId=1324077713530684215, tenantId=1146029695717560320, journalId=1227665162245664772, language=CN, title=2015—2023年中国大陆新报告艾滋病空间格局与影响因素探析, columnId=1228016573844226129, journalTitle=现代预防医学, columnName=疾病预防控制, runingTitle=null, highlight=null, articleAbstract=
目的

对比分析新冠疫情爆发前（2015—2019年）和爆发后（2020—2023年），中国大陆艾滋病新报告发病率的空间格局演变特征和影响因素的空间异质性，为未来应对类似危机时的精准防控提供科学依据。

方法

以中国大陆31个省级行政区2015—2023年的艾滋病新报告发病率及社会经济数据为基础，运用空间自相关、普通最小二乘（OLS）和多尺度地理加权回归（MGWR）进行分析。

结果

艾滋病新报告发病率在2015—2019年呈上升趋势，新冠爆发后开始下降，2022年回落至3.69/10万，2023年有所回升。新冠爆发前后，艾滋病新报告发病率均呈显著空间正自相关且有增强趋势。热点持续集中在云贵川渝桂湘，海南和广东为新兴热点区，冷点持续集中在东北和华北地区。每万人卫生技术人员数在新冠爆发前对西南地区有促进作用，而爆发后转变为抑制作用；城镇化率在全局为负向影响而局部为正向影响；其余变量均为双向影响，空间异质性持续存在。

结论

艾滋病新报告发病率在新冠爆发前后的空间演变各异，但均呈现显著的空间聚集性，且社会经济因素的影响存在空间异质性。我们建议差异化制定各地的艾滋防控策略，重点关注高发的热点地区，以精准的防控行动切实降低艾滋病发病率。

, authors=贾玲1, 毛江华2, 黄晓1, 任家辉1, 蒙淳1, 廖品文1, authorsList=贾玲, 毛江华, 黄晓, 任家辉, 蒙淳, 廖品文, authorCompany=null, correspAuthors=廖品文, authorNote=

贾玲（1997—），女，硕士，助理统计师，研究方向：卫生统计相关工作

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廖品文，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=StPkW3gVDcmXwaNc3JfEUg==, magXml=nLIhq4RtMe3F7tIazTmWFw==, pdfUrl=null, pdf=VKwCH0twaMV8wdpQqFpUdQ==, pdfFileSize=1660551, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=EwYMStTdbq9bab+fp8vr1Q==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=HXEkVlKmABmK8AbPXvjiBg==, mapNumber=null, fund=null)}, authors=[Author(id=1324077717129397064, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077713530684215, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=null, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1324077717217477450, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077713530684215, authorId=1324077717129397064, language=EN, stringName=Ling JIA, firstName=Ling, middleName=null, lastName=JIA, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=1, address=Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan 637000, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1324077717301363531, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077713530684215, authorId=1324077717129397064, language=CN, stringName=贾玲, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=1, address=1.川北医学院附属医院，四川 南充 637000, bio={"content":"

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Does urbanization aggravate the interregional transmission of infectious diseases? analysis based on spatial spillover[J]. Economic Science, 2022(5): 107-119. (In Chinese), articleTitle=Does urbanization aggravate the interregional transmission of infectious diseases? analysis based on spatial spillover, refAbstract=null), Reference(id=1324077734552535970, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077713530684215, doi=null, pmid=null, pmcid=null, year=2020, volume=10, issue=null, pageStart=220, pageEnd=null, url=null, language=null, rfNumber=[20], rfOrder=32, authorNames=Richterich P, journalName=medRxiv, refType=null, unstructuredReference=Richterich P. Severe underestimation of COVID-19 case numbers: effect of epidemic growth rate and test restrictions[J]. medRxiv, 2020, 10: 220., articleTitle=Severe underestimation of COVID-19 case numbers: effect of epidemic growth rate and test restrictions, refAbstract=null)], funds=[Fund(id=1324077727732597631, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077713530684215, awardId=NC23B186, language=CN, fundingSource=南充市2025年度社会科学研究“十四五”规划课题(NC23B186), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1324077716944847682, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077713530684215, xref=1., ext=[AuthorCompanyExt(id=1324077716953236291, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077713530684215, companyId=1324077716944847682, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan 637000, China), AuthorCompanyExt(id=1324077716965819204, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077713530684215, companyId=1324077716944847682, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.川北医学院附属医院，四川 南充 637000)]), AuthorCompany(id=1324077717037122373, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077713530684215, xref=2., ext=[AuthorCompanyExt(id=1324077717045510982, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077713530684215, companyId=1324077717037122373, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.重庆市梁平区人民医院)])], figs=[ArticleFig(id=1324077723110474607, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077713530684215, language=EN, label=Fig.1, caption=Heatmap of newly reported AIDS incidence by region in mainland China, 2015-2023, figureFileSmall=eVYvcdmotmtkt+v3NFObGg==, figureFileBig=EwYMStTdbq9bab+fp8vr1Q==, tableContent=null), ArticleFig(id=1324077723450213232, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077713530684215, language=CN, label=图1, caption=2015—2023年中国大陆各地区艾滋病新报告发病率热力图, figureFileSmall=eVYvcdmotmtkt+v3NFObGg==, figureFileBig=EwYMStTdbq9bab+fp8vr1Q==, tableContent=null), ArticleFig(id=1324077725526393713, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077713530684215, language=EN, label=Fig.2, caption=Hotspot analysis of newly reported AIDS incidence in mainland China, 2015-2023, figureFileSmall=yFy9nTLCRioORXuNnB1v8Q==, figureFileBig=3cTkLZ+G6B9L3sXHh1EwVg==, tableContent=null), ArticleFig(id=1324077725656417138, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077713530684215, language=CN, label=图2, caption=2015—2023年中国大陆艾滋病新报告发病率热点分析

注：底图源于国家基础地理信息中心，审图号为GS川 (2026) 27号。

, figureFileSmall=yFy9nTLCRioORXuNnB1v8Q==, figureFileBig=3cTkLZ+G6B9L3sXHh1EwVg==, tableContent=null), ArticleFig(id=1324077726042293107, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077713530684215, language=EN, label=Fig.3, caption=Boxplot of MGWR regression coefficients, figureFileSmall=PFskjgx2oH3lquCn0yFmnA==, figureFileBig=+/8nsGr2fRrMPJmC+XkQ8Q==, tableContent=null), ArticleFig(id=1324077726373643124, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077713530684215, language=CN, label=图3, caption=MGWR回归系数箱线图

注：X1-X5分别代表人均GDP、每万人卫生技术人员数、每万人医疗机构数、城镇化率和人口密度。

, figureFileSmall=PFskjgx2oH3lquCn0yFmnA==, figureFileBig=+/8nsGr2fRrMPJmC+XkQ8Q==, tableContent=null), ArticleFig(id=1324077726461723509, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077713530684215, language=EN, label=Fig.4, caption=Spatial distribution of MGWR regression coefficients and statistical significance, figureFileSmall=VI9PWKZuyf09hVlZ/75EFw==, figureFileBig=5gbaQ278zd9oWNvaHxKU2g==, tableContent=null), ArticleFig(id=1324077726545609590, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077713530684215, language=CN, label=图4, caption=MGWR回归系数及显著性空间分布

注：底图源于国家基础地理信息中心，审图号为GS川 (2026) 27号。a - e依次代表2015—2019年X1-X5；f - j分别代表2020—2023年X1-X5；“P”图例中绿色代表P<0.05，黑色代表P>0.05。

, figureFileSmall=VI9PWKZuyf09hVlZ/75EFw==, figureFileBig=5gbaQ278zd9oWNvaHxKU2g==, tableContent=null), ArticleFig(id=1324077726616912759, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077713530684215, language=EN, label=Table 1, caption=

Results of multicollinearity test

, figureFileSmall=null, figureFileBig=null, tableContent=
VIF人均GDP（X1）每万人卫生技术人员数（X2）每万人医疗机构数（X3）城镇化率（X4）人口密度（X5）
2015—20195.7402.3011.8174.5492.724
2020—20234.3341.7131.8953.9072.473
), ArticleFig(id=1324077726784684920, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077713530684215, language=CN, label=表1, caption=

多重共线性检验结果

, figureFileSmall=null, figureFileBig=null, tableContent=
VIF人均GDP（X1）每万人卫生技术人员数（X2）每万人医疗机构数（X3）城镇化率（X4）人口密度（X5）
2015—20195.7402.3011.8174.5492.724
2020—20234.3341.7131.8953.9072.473
), ArticleFig(id=1324077726885348217, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077713530684215, language=EN, label=Table 2, caption=

Global Moran I of newly reported AIDS incidence in mainland China, 2015-2023

, figureFileSmall=null, figureFileBig=null, tableContent=
年份Moran I方差ZP是否聚集
20150.2680.0064.0360.000是
20160.3080.0064.5030.000是
20170.3240.0064.1780.000是
20180.3090.0064.5840.000是
20190.3200.0054.8160.000是
20200.3610.0065.2700.000是
20210.3500.0065.1260.000是
20220.3620.0065.2840.000是
20230.3660.0065.3300.000是
), ArticleFig(id=1324077727011177338, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077713530684215, language=CN, label=表2, caption=

2015—2023年中国大陆艾滋病新报告发病率全局莫兰指数

, figureFileSmall=null, figureFileBig=null, tableContent=
年份Moran I方差ZP是否聚集
20150.2680.0064.0360.000是
20160.3080.0064.5030.000是
20170.3240.0064.1780.000是
20180.3090.0064.5840.000是
20190.3200.0054.8160.000是
20200.3610.0065.2700.000是
20210.3500.0065.1260.000是
20220.3620.0065.2840.000是
20230.3660.0065.3300.000是
), ArticleFig(id=1324077727241864059, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077713530684215, language=EN, label=Table 3, caption=

Results of OLS regression

, figureFileSmall=null, figureFileBig=null, tableContent=
变量2015—20192020—2023
β1Pβ2P
人均GDP（X1）-0.0360.832-0.0950.573
每万人卫生技术人员数（X2）0.3320.0020.1480.165
每万人医疗机构数（X3）-0.502<0.001-0.528<0.001
城镇化率（X4）-0.780<0.001-0.604<0.001
人口密度（X5）0.0470.692-0.0140.912
), ArticleFig(id=1324077727334138748, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077713530684215, language=CN, label=表3, caption=

OLS回归结果

, figureFileSmall=null, figureFileBig=null, tableContent=
变量2015—20192020—2023
β1Pβ2P
人均GDP（X1）-0.0360.832-0.0950.573
每万人卫生技术人员数（X2）0.3320.0020.1480.165
每万人医疗机构数（X3）-0.502<0.001-0.528<0.001
城镇化率（X4）-0.780<0.001-0.604<0.001
人口密度（X5）0.0470.692-0.0140.912
), ArticleFig(id=1324077727451579261, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077713530684215, language=EN, label=Table 4, caption=

Bandwidths of explanatory variables in the MGWR

, figureFileSmall=null, figureFileBig=null, tableContent=
变量2015—20192020—2023
人均GDP（X1）52117
每万人卫生技术人员数（X2）4661
每万人医疗机构数（X3）4645
城镇化率（X4）46117
人口密度（X5）4645
), ArticleFig(id=1324077727619351422, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077713530684215, language=CN, label=表4, caption=

MGWR模型各变量带宽

, figureFileSmall=null, figureFileBig=null, tableContent=
变量2015—20192020—2023
人均GDP（X1）52117
每万人卫生技术人员数（X2）4661
每万人医疗机构数（X3）4645
城镇化率（X4）46117
人口密度（X5）4645
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2015—2023年中国大陆新报告艾滋病空间格局与影响因素探析
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贾玲 1 , 毛江华 2 , 黄晓 1 , 任家辉 1 , 蒙淳 1 , 廖品文 1
现代预防医学 | 疾病预防控制 2026,53(6): 1126-1131
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现代预防医学 |疾病预防控制 2026 , 53 (6) : 1126 -1131
2015—2023年中国大陆新报告艾滋病空间格局与影响因素探析
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贾玲1, 毛江华2, 黄晓1, 任家辉1, 蒙淳1, 廖品文1
作者信息
  • 1.川北医学院附属医院，四川 南充 637000
  • 2.重庆市梁平区人民医院
通讯作者:
廖品文，E-mail:
作者简介:

贾玲（1997—），女，硕士，助理统计师，研究方向：卫生统计相关工作

Analysis of the spatial pattern and influencing factors of newly reported AIDS in mainland China, 2015-2023
Ling JIA1, Jiang-hua MAO2, Xiao HUANG1, Jia-hui REN1, Chun MENG1, Pin-wen LIAO1
Affiliations
  • Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan 637000, China
出版时间: 2026-03-25 doi: 10.20043/j.cnki.MPM.202509496
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摘要
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目的

对比分析新冠疫情爆发前（2015—2019年）和爆发后（2020—2023年），中国大陆艾滋病新报告发病率的空间格局演变特征和影响因素的空间异质性，为未来应对类似危机时的精准防控提供科学依据。

方法

以中国大陆31个省级行政区2015—2023年的艾滋病新报告发病率及社会经济数据为基础，运用空间自相关、普通最小二乘（OLS）和多尺度地理加权回归（MGWR）进行分析。

结果

艾滋病新报告发病率在2015—2019年呈上升趋势，新冠爆发后开始下降，2022年回落至3.69/10万，2023年有所回升。新冠爆发前后，艾滋病新报告发病率均呈显著空间正自相关且有增强趋势。热点持续集中在云贵川渝桂湘，海南和广东为新兴热点区，冷点持续集中在东北和华北地区。每万人卫生技术人员数在新冠爆发前对西南地区有促进作用，而爆发后转变为抑制作用；城镇化率在全局为负向影响而局部为正向影响；其余变量均为双向影响，空间异质性持续存在。

结论

艾滋病新报告发病率在新冠爆发前后的空间演变各异，但均呈现显著的空间聚集性，且社会经济因素的影响存在空间异质性。我们建议差异化制定各地的艾滋防控策略，重点关注高发的热点地区，以精准的防控行动切实降低艾滋病发病率。

艾滋病  /  新冠  /  空间自相关  /  多尺度地理加权回归（MGWR）
Objective

To compare the spatial pattern evolution characteristics and the spatial heterogeneity of influencing factors of newly reported AIDS incidence in mainland China before (2015-2019) and after (2020-2023) the COVID-19 outbreak, and to provide a scientific basis for targeted prevention and control in response to similar crises in the future.

Methods

Based on newly reported AIDS incidence and socioeconomic data from 31 provincial-level administrative regions in mainland China (2015-2023), spatial autocorrelation, ordinary least squares (OLS), and multiscale geographically weighted regression (MGWR) were employed for analysis.

Results

The incidence of newly reported AIDS showed an upward trend from 2015 to 2019, but began to decline after the outbreak of COVID-19. By 2022, the incidence dropped to 3.69 per 100 000 population, with a slight rebound in 2023. Both before and after the COVID-19 outbreak, the incidence of newly reported AIDS exhibited significant and strengthening spatial positive autocorrelation. Hotspots consistently concentrated in Yunnan, Guizhou, Sichuan, Chongqing, Guangxi, and Hunan, with Hainan and Guangdong emerging as new hotspots. Coldspots persistently clustered in Northeast and North China. Before the COVID-19 outbreak, the number of health technical personnel per 10 000 people had a promoting effect on the incidence in the Southwest region, but this effect shifted to a suppressive one after the outbreak. Urbanization rate exerted a negative influence globally but a positive influence locally. Other variables demonstrated bidirectional influences, with spatial heterogeneity persisting throughout the study period.

Conclusion

s The spatial evolution of newly reported AIDS incidence varied before and after the COVID-19 outbreak, but both periods exhibited significant spatial clustering, with socioeconomic factors demonstrating spatial heterogeneity in their influence. We propose formulating differentiated AIDS prevention and control strategies tailored to local conditions, with particular focus on hotspot areas, to implement precision interventions for effective AIDS incidence reduction.

AIDS  /  COVID-19  /  Spatial autocorrelation  /  MGWR
贾玲, 毛江华, 黄晓, 任家辉, 蒙淳, 廖品文. 2015—2023年中国大陆新报告艾滋病空间格局与影响因素探析. 现代预防医学, 2026 , 53 (6) : 1126 -1131 . DOI: 10.20043/j.cnki.MPM.202509496
Ling JIA, Jiang-hua MAO, Xiao HUANG, Jia-hui REN, Chun MENG, Pin-wen LIAO. Analysis of the spatial pattern and influencing factors of newly reported AIDS in mainland China, 2015-2023[J]. Modern Preventive Medicine, 2026 , 53 (6) : 1126 -1131 . DOI: 10.20043/j.cnki.MPM.202509496
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艾滋病是一种由人类免疫缺陷病毒（HIV）引起的传染性疾病，对人类健康和社会发展构成重大威胁[1]。在我国，艾滋病作为法定乙类传染病，其2023年的死亡人数显著高于其他法定传染病[2]。中国政府实施了一系列防控政策并取得一定成效[3]，然而，鉴于我国地域辽阔、人口众多的现实情况，艾滋病新报告病例数依然给公共卫生治理带来较大压力。传染病的发生、流行及地域分布与地理空间因素密切相关，而GIS技术正是分析这类空间关联问题的强大工具[4]。
但现有研究多聚焦于临床特征[5]、流行病学[6-7]、医疗救治或预防控制[3,8]等领域，少部分已有的空间分析研究中，也多聚焦于发病率的描述性统计和空间聚集特征[9]。较少有基于全国省级尺度，整合空间地理信息与社会影响因素进行的深入分析。2019年爆发的新冠疫情对艾滋病患者的治疗服务产生了显著影响[7]，严格的封控措施也可能对艾滋病的发病率产生了差异性影响[10]。探究这一重大突发公共卫生事件前后中国大陆艾滋病新报告发病率的变化特征，对于未来应对类似危机时的精准防控具有重要参考价值。基于此，本研究以省级行政区为研究单元，聚焦2015—2023年艾滋病新报告发病率数据，利用空间自相关探究其空间格局演变特征，通过普通最小二乘（OLS）和多尺度地理加权回归（MGWR）对比分析新冠爆发前后影响因素的空间异质性，旨在为制定科学、精准、可持续的区域化艾滋病防控策略提供支撑。
1　材料和方法
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1.1　资料来源
中国大陆31个省、市、自治区（不含港澳台地区）2015—2020年艾滋病新报告发病率来源于中国公共卫生科学数据中心（www.phsciencedata.cn），2021—2023年来源于《中国卫生健康统计年鉴》。艾滋病新报告发病率是当年艾滋感染者报告病例数占人口数的比例。地理信息来源于国家基础地理信息中心（www.ngcc.cn）。社会经济指标数据来源于《中国统计年鉴》和国家统计局，面积数据来源于各地人民政府官网。
1.2　研究方法
1.2.1　空间自相关分析
本研究选取Moran I衡量研究区域的整体空间聚集性，Moran I>0为正空间自相关，绝对值越接近1表示聚集性越强。局部空间自相关在全局显著的前提下，可进一步定位具体的聚集区域。热点分析通过Getis-Ord Gi*统计量来反映变量值在各空间区域中的高值或低值聚类情况，从而明确冷点（低值聚集区）和热点（高值聚集区）。
1.2.2　影响因素分析
本文构建的影响因素体系包括经济水平、医疗资源和社会结构三个维度，最终共纳入5个影响因素（表1）。为避免自变量之间多重共线性的影响，对其进行了方差膨胀因子（VIF）检验。通常认为VIF>10变量间存在强共线性[11]，本文所有自变量的VIF值均小于6，可认为不存在严重多重共线性。
普通最小二乘回归（OLS）从全局视角分析各因素对新报告发病率的影响，通过最小化残差平方和估计参数，但其假设变量关系在研究区域内具有空间平稳性，难以捕捉地理现象的局部差异，存在空间数据分析局限。为弥补这一不足，地理加权回归（GWR）作为局部回归分析方法应运而生。GWR通过变参数模型使回归系数随空间位置动态变化，有效捕捉空间异质性，广泛应用于空间非平稳性研究。但GWR虽突破OLS的全局假设，却默认所有变量空间作用尺度相同，与不同因素影响范围存在差异的实际不符，限制了模型的灵活性与拟合精度。针对这一缺陷，Fotheringham等人提出了多尺度地理加权回归（MGWR）[12]。该模型的允许每个自变量独立寻找各自的最优带宽并进行建模，实现了不同变量空间作用尺度的差异化，有效弥补了GWR中空间作用尺度均一化的固有缺陷。本文采用自适应双平方核函数，修正的赤池信息准则（AICc）作为带宽选取和模型抉择的判定标准。
1.2.3　统计分析
本文利用Excel 2019软件进行数据的汇总和整理；运用ArcGIS 10. 8软件进行空间自相关分析；利用MGWR 2. 2软件进行影响因素分析。以P<0.05为差异有统计学意义。
2　结果
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2.1　流行概况
2015—2023年，中国大陆艾滋病新报告发病率呈现“先升后降再回升”的阶段性特征（图1）。新冠爆发前，中国大陆的新报告发病率从3.69/10万持续上升至5.10/10万。其中，四川、贵州、重庆等西南地区的新报告发病率分别从9.55、6.02、8.76/10万，激增至21.42、13.20、12.58/10万，增幅显著高于全国平均水平，成为拉升艾滋新报告发病率的主要区域。新冠爆发后，中国大陆新报告发病率在2020年降至4.43/10万，并持续下降至2022年的3.69/10万，形成了与前期相反的下行趋势。其中，云贵川等西南地区、新疆等西部地区的快速下降成为这一趋势反转的重要成因。值得注意的是，艾滋病新报告发病率在2023年出现小幅回升，提示需持续关注疫情的最新动向。
2.2　空间自相关
空间自相关分析结果表明，艾滋病新报告发病率在全域尺度上存在显著的聚集模式，且该模式在新冠爆发前后有明显变化（表2）。从全局来看，Moran I指数表明空间正相关性始终显著，且在疫情爆发后聚集程度进一步增强。在局部尺度上，各年艾滋病新报告发病率均存在明显的空间聚集特征（图2）。具体而言，热点区域持续由广西、四川、云南、贵州、重庆和湖南组成。京津冀地区被识别为最强显著性的冷点区域，与之邻近的内蒙古、辽宁、吉林、山西和江苏也都稳定地被识别为冷点区域。值得注意的是，海南和广东在新冠爆发后由非显著区域转化为热点区域。
2.3　影响因素分析
2.3.1　全局回归　OLS结果显示，新冠爆发前后，各因素对艾滋病新报告发病率的影响模式发生了明显变化（表3）。每万人卫生技术人员数（X2）的影响发生了根本性转变：新冠爆发前，X2对新报告发病率有显著正向影响（β1=0.332，P=0.002），而疫情爆发后，这一影响不再显著，提示其作用机制可能被疫情带来的扰动所改变或掩盖。每万人医疗机构数（X3）和城镇化率（X4）在两个时期均表现出显著抑制效应（P<0.001），X3的负向作用略有增强，而X4的抑制作用有所减弱。人均GDP（X1）与人口密度（X5）在两时期均无显著影响（P>0.05），表明在全局尺度上，这两个因素对艾滋病新报告发病率的直接关联相对有限。
2.3.2　局部回归 MGWR模型的校正R2为0.868，拟合效果良好。不同带宽表示不同的作用尺度，带宽越小表明作用的范围越小，空间异质性越大。结果显示，各因素的作用尺度在新冠爆发前后呈现出不同的演变模式（表4）。X1与X4的作用尺度发生了显著扩展，二者均由局部性影响（带宽分别为52、46）转变为全域性影响（均为最大带宽117），表明二者对新报告发病率的作用模式在疫情爆发后趋于全国一致。X2的作用范围由高度局部化转变中等范围，空间异质性减弱。与之相对，X3与X5的影响始终表现为高度局部化，空间异质性持续较强。
MGWR回归系数空间分布显示，新冠疫情爆发前后，各影响因素对艾滋病新报告发病率的作用方向与强度呈现差异化演化特征（图3）。具体而言，X1新冠爆发前呈双向影响，且多数地区为负向抑制作用，爆发后转为绝对负向影响，地区间作用程度更趋一致。X2与X3作用特征相近，两个时期均保持双向影响，空间异质性持续显著。X4始终呈正向影响，疫情爆发后各地区促进作用程度趋同，这与OLS模型得出的负向作用结论相悖。X5则表现为双向影响，且两极分化程度最为突出，反映其对新报告发病率的影响存在显著地区差异。
MGWR局部回归系数的空间分布显示，新冠爆发前后各影响因素对艾滋病新报告发病率的作用格局有明显变化（图4）。新冠爆发前，X1在青海、甘肃等西北地区对新报告发病率有显著正向影响，在云南、贵州、湖南等南方地区有显著负向影响，其他省市无显著影响；而爆发后全国均无显著影响。X2作用发生方向性逆转：新冠爆发前，四川、贵州、重庆为显著正向影响，而爆发后云贵川转为显著负向影响。X3在西藏、青海、新疆呈显著负向影响，在云南、四川和海南有显著正向影响，其余省市无显著影响。X4在大部分西部地区和部分中南地区有显著正向影响，局部回归系数的高值区由西南转变为西北。新冠爆发前，X5在西部地区均有显著负向影响，其中云贵川的抑制作用最强；疫情爆发后，西北地区转为无显著影响。
3　讨论
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研究发现，2015—2019年艾滋病新报告发病率呈明显上升趋势，2020—2023年新报告发病率快速下跌，2023年有所回升。新冠爆发后的封控政策通过限制人员流动与高危接触减少了性病传播[13]，同时也影响了HIV检测服务的有序开展[14]，在短期内共同抑制了艾滋病新报告发病率。但2023年新报告发病率的回升已发出预警，亟需制定艾滋病精准防控策略。
空间自相关分析显示，艾滋病新报告发病率呈显著空间聚集性且有增强趋势。西南及相邻省份构成传统热点区：云南接壤东南亚“金三角”毒品产区，其艾滋病发病率持续居高[15]，与川贵渝、桂等周边省市往来密切；区域经济教育水平较低，部分流动人口从事商业性行为且安全认知不足[16]；多民族聚居的特定文化中性行为观念相对开放[6]，这些因素共同促成艾滋病新报告发病率空间聚集。海南、广东显现新兴热点趋势，二者作为南部沿海重要交通枢纽，高频跨境人员流动可能是重要影响因素[8]。今后的防控策略需精准区域化：传统热点区重点干预高危行为、提升防护能力和安全认知；新兴热点区需强化疫情监测。
MGWR结果显示，新冠爆发前的人均GDP（X1）对艾滋病新报告发病率呈双向影响，其作用高度局域化，呈空间非平稳性。西北部地区经济水平对新报告发病率有促进作用，南部热点区域反之，中东部受影响较小，与刘晟等人[4]的研究结果一致。甘肃等西北地区地处“河西走廊”这一重要地理通道，跨区域流动人口往来频繁，该群体性健康认知不足，更易发生高危性行为[17]，助推了艾滋病传播。广西、贵州等南部热点地区经济较为落后，低收入可能促使部分群体参与商业性性服务、毒品犯罪等高危行为，增加HIV感染风险[16]。新冠爆发后，X1在全域范围内无显著影响，这表明突发重大公共卫生事件或通过全域性防控干预，弱化了经济因素与艾滋病发病的关联性。
每万人卫生技术人员（X2）在新冠爆发前对新报告发病率有促进作用，充足的医疗资源提升了检测与报告能力。新冠爆发后，卫生人员被抽调到新冠防控，引发医疗资源挤兑分流，导致艾滋病监测与报告能力下降[5]，最终在统计数据上表现为负向关联。每万人医疗机构数（X3）在西北地区对新报告发病率有抑制作用，或因医疗机构增多而改善了防治服务可及性[18]，有效抑制了疫情蔓延；而在检测网络成熟的云南、四川等省份，医疗机构增多反而会暴露既往未发现的感染存量。这一现象反映出“资源阈值效应”，应转变防控评价机制，从追求机构数量转向考核防治实效，以阻断传播链。
城镇化率（X4）在全局层面持续呈显著抑制作用，和李小平等人[19]的研究结果吻合。但MGWR模型揭示各地区在新冠爆发前后均为正向影响，且疫情爆发后该促进作用趋于全域化，这一矛盾源于“生态学谬误”：发达地区医疗资源优越，检测能力更强，城镇化进程中诊断效率提升幅度高于真实感染增长率，形成“发病率被稀释”的统计假象[20]。人口密度（X5）的全局影响持续不显著，这可能与指标定义差异相关——传染病学中人口密度侧重群聚接触密度[19]，而本文采用的城市平均人口密度难以反映真实传播场景中的接触强度；而MGWR结果显示，新冠爆发前后均有部分地区呈现显著影响。全局结果易掩盖局部差异，需“一地一策”差异化制定防控方案。
本文对中国大陆新冠爆发前后的艾滋病新报告发病率进行分析，揭示其空间分布规律和影响因素，以期为重点区域的精准防控策略提供科学依据。但受限于数据可获得性，当前研究单元仍有待细化，且交通、教育等社会影响因素尚未纳入模型。未来研究旨在基于更精细的空间尺度与多维度社会因素，采用时空地理加权回归（GTWR）等更复杂的时空动态模型，揭示其交互机制，形成精准的风险评估体系，为优化防控资源配置提供依据。
基金
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  • 南充市2025年度社会科学研究“十四五”规划课题(NC23B186)
文献
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2026年第53卷第6期
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doi: 10.20043/j.cnki.MPM.202509496
  • 接收时间：2025-09-28
  • 首发时间：2026-11-02
  • 出版时间：2026-03-25
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  • 收稿日期：2025-09-28
基金
南充市2025年度社会科学研究“十四五”规划课题(NC23B186)
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    1.川北医学院附属医院，四川 南充 637000
    2.重庆市梁平区人民医院

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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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