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Article(id=1324050879158039281, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324050876230415089, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202511005, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1761926400000, receivedDateStr=2025-11-01, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793607929266, onlineDateStr=2026-11-02, pubDate=1781020800000, pubDateStr=2026-06-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793607929266, onlineIssueDateStr=2026-11-02, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793607929266, creator=13701087609, updateTime=1793607929266, updator=13701087609, issue=Issue{id=1324050876230415089, tenantId=1146029695717560320, journalId=1227665162245664772, year='2026', volume='53', issue='11', pageStart='1921', pageEnd='2112', issueExtLink='null', onlineDate='null', pubDate='1781020800000', pubDateStr='2026-06-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793607928569, creator='13701087609', updateTime=1793670049253, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1324311429322507008, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324050876230415089, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1324311429322507009, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324050876230415089, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=2032, endPage=2037, ext={EN=ArticleExt(id=1324050879527138034, articleId=1324050879158039281, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Current status and factors influencing delayed medical care behavior among older adults, columnId=1228016567846367388, journalTitle=Modern Preventive Medicine, columnName=Health Policy and Management, runingTitle=null, highlight=null, articleAbstract=
Objective

To explore the current status of delayed medical care behavior and its influencing factors among the elderly in China, in order to provide a reference for further improving the health outcomes of older adults.

Methods

Data were derived from the 2021 Chinese General Social Survey (CGSS). Participants aged 60 years and older were enrolled in this study. Grounded in Andersen’s behavioral model of health care utilization, binary logistic regression combined with decision tree analysis was employed to identify key influencing factors.

Results

Binary logistic regression revealed that delayed medical care behavior was significantly less likely among adults aged ≥81 years (OR=0.486), married older adults (OR=0.118), individuals with general high school or vocational education (OR=0.426), college graduates (OR=0.249), participants with high health literacy (OR=0.191), rural residents (OR=0.591), individuals from higher household economic backgrounds (OR=0.420), older adults receiving social support (OR=0.291), regular health check-up attendees (OR=0.228), and participants reporting strong self-efficacy (OR=0.553). Conversely, significantly higher risks were observed among older adults with poor communication skills with medical professionals (OR=3.104), individuals with ≥3 chronic conditions (OR=2.211), participants experiencing anxiety regarding healthcare accessibility (OR=2.122), and older adults reporting anxiety concerning medical affordability (OR=3.363), all P<0.05. The decision tree model identified seven predictive factors (P<0.05), with marital status showing the strongest association with delayed medical care behavior among older adults.

Conclusion

The rate of delayed medical care among older adults in China is high, and differentiated and targeted preferential healthcare policies should be implemented for older adults, the medical security system should be improved, health education activities should be actively carried out, and older adults should be guided to seek medical care appropriately.

, authors=Jia-ni TANG, Gui-min DUAN, Yue ZHOU, Qiao LI, Xing-lin ZOU, authorsList=Jia-ni TANG, Gui-min DUAN, Yue ZHOU, Qiao LI, Xing-lin ZOU, authorCompany=null, correspAuthors=Gui-min DUAN, 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=1324050880617657079, articleId=1324050879158039281, tenantId=1146029695717560320, journalId=1227665162245664772, language=CN, title=老年人就医延迟行为现状及影响因素研究, columnId=1228016568014139551, journalTitle=现代预防医学, columnName=卫生政策与管理, runingTitle=null, highlight=null, articleAbstract=
目的

探究我国老年人就医延迟行为现状及影响因素，为进一步改善老年人健康结局提供参考。

方法

使用2021年中国综合社会调查（Chinese General Social Survey，CGSS）数据，选取年龄≥60岁的老年人纳入研究。以安德森卫生服务利用行为模型为理论框架，综合运用二元logistic回归与决策树模型，探究关键影响因素。

结果

二元logistic回归结果显示，≥81岁（OR=0.486）、有配偶（OR=0.118）、普高及高职（OR=0.426）、大专及以上（OR=0.249）、健康素养水平高（OR=0.191）、农村（OR=0.591）、家庭经济水平较高（OR=0.420）、有社会支持（OR=0.291）、定期体检（OR=0.228）、自我效能感强（OR=0.553）的老年人就医延迟行为发生风险更低；而与医护人员沟通能力弱（OR=3.104）、患慢性病数量≥3种（OR=2.211）、有医疗服务可及性焦虑（OR=2.122）、有医疗费用可负担性焦虑（OR=3.363）的老年人就医延迟行为发生风险更高（P值均<0.05）。决策树模型共识别出7个预测因子（P<0.05），其中婚姻状况与老年人就医延迟行为的关联度最高。

结论

我国老年人就医延迟率较高，建议针对老年群体实施差异化、精准化医疗优待政策，完善医疗保障体系，积极开展健康教育活动，引导老年人合理就医。

, authors=唐佳妮, 段桂敏, 周悦, 李巧, 邹兴林, authorsList=唐佳妮, 段桂敏, 周悦, 李巧, 邹兴林, authorCompany=null, correspAuthors=段桂敏, authorNote=

唐佳妮（2001—），女，硕士在读，研究方向：卫生事业管理

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段桂敏，E-mail：
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唐佳妮（2001—），女，硕士在读，研究方向：卫生事业管理

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Generative risks in the medical process of early-stage cancer patients in rural China: types, processes and impacts[J]. Chongqing Social Sciences, 2025, (7): 110-124.(In Chinese), articleTitle=Generative risks in the medical process of early-stage cancer patients in rural China: types, processes and impacts, refAbstract=null)], funds=[Fund(id=1324050887831860008, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324050879158039281, awardId=SC25E012, language=CN, fundingSource=四川省哲学社会科学重点研究基地学术研究专项资助项目(SC25E012), fundOrder=null, country=null), Fund(id=1324050887915746089, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324050879158039281, awardId=SWFZ24-Z-10, language=CN, fundingSource=四川省基层卫生事业发展研究中心重点项目(SWFZ24-Z-10), fundOrder=null, country=null), Fund(id=1324050888041575210, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324050879158039281, awardId=ZYYWH2024010, language=CN, fundingSource=四川省中医药文化与中医药发展研究中心重点项目(ZYYWH2024010), fundOrder=null, country=null), Fund(id=1324050888104489771, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324050879158039281, awardId=QJRC2022056, language=CN, fundingSource=成都中医药大学杏林学者青基人才项目(QJRC2022056), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1324050880860926712, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324050879158039281, xref=null, ext=[AuthorCompanyExt(id=1324050880865121017, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324050879158039281, companyId=1324050880860926712, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=School of Management, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 611137, China), AuthorCompanyExt(id=1324050880873509626, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324050879158039281, companyId=1324050880860926712, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=成都中医药大学管理学院，四川 成都 611137)])], figs=[ArticleFig(id=1324050884686131996, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324050879158039281, language=EN, label=Figure 1, caption=Flow chart of the sample selection, figureFileSmall=w4fbl5PMasZXAadf1zg4Uw==, figureFileBig=V9ycSgeu+fypfCGyujLXxw==, tableContent=null), ArticleFig(id=1324050884774212381, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324050879158039281, language=CN, label=图1, caption=样本筛选流程图, figureFileSmall=w4fbl5PMasZXAadf1zg4Uw==, figureFileBig=V9ycSgeu+fypfCGyujLXxw==, tableContent=null), ArticleFig(id=1324050885021676318, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324050879158039281, language=EN, label=Figure 2, caption=CHAID classification decision tree model for delayed medical care behavior of elderly people, figureFileSmall=V+EPfzbin/vvK2JhXtXGOw==, figureFileBig=TOqGvxOZ2F1lstzQh0SNIQ==, tableContent=null), ArticleFig(id=1324050885092979487, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324050879158039281, language=CN, label=图2, caption=老年人就医延迟行为的CHAID分类决策树模型, figureFileSmall=V+EPfzbin/vvK2JhXtXGOw==, figureFileBig=TOqGvxOZ2F1lstzQh0SNIQ==, tableContent=null), ArticleFig(id=1324050885168476960, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324050879158039281, language=EN, label=Table 1, caption=

Assignment of variable

, figureFileSmall=null, figureFileBig=null, tableContent=
变量题项赋值说明
就医延迟行为在过去的一年里，当您生病或受伤(包括感冒)时，是否有意不去看医生？否=0，是=1
年龄（岁）您的出生日期是什么?60～70=0，71～80=1，≥81=2
性别您的性别是？男=0，女=1
婚姻状况您目前的婚姻状况是？无配偶=0，有配偶=1
受教育程度您目前的最高教育程度是？小学及以下=0，初中及中专=1，普高及高职=2，大专及以上=3
健康素养水平您因为看不懂药品说明书或医嘱，而无法知道如何照顾自己的健康？低(有时/经常/总是)=0，高(很少/从不)=1
居住地您目前的居住地状况是?城镇=0，农村=1
家庭经济水平您家的家庭经济状况属于哪一档？较低水平=0，中等水平=1较高水平=2
医疗保险您是否享有公共医疗保险或商业保险？无=0，有=1
社会支持是否有人倾听您诉说您个人关心的事情？无=0有=1
与医护人员沟通能力您因为听不懂医护人员的说明，而无法清楚知道自己的健康状况？回答由“从不”到“总是”，分别记1～5分，算得两个条目的总平均值为2.03分
得分<2.03=0，表示能力强；得分≥2.03=1，表示能力弱
您因为不知道如何向医护人员问问题，而无法清楚知道自己的健康状况？
慢性病数量（种）您患有哪些慢性病？0=0，1～2=1，≥3=2
疾病预防行为您做健康体检吗？不体检=0，不定期体检=1，定期体检=2
自我效能感当您填写个人档案、病史、知情同意书等医疗表格时，您是否有信心能填好？弱(有一点/完全没信心)=0，强(比较/相当/完全有信心)=1
医疗服务可及性焦虑您是否担心当需要医疗服务的时候，不能获得？无(不是很/完全不担心)=0，有(有点/非常担心)=1
医疗费用可负担性焦虑您是否担心当患病的时候，付不起医药费用？无(不是很/完全不担心)=0，有(有点/非常担心)=1
), ArticleFig(id=1324050885243974433, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324050879158039281, language=CN, label=表1, caption=

变量赋值

, figureFileSmall=null, figureFileBig=null, tableContent=
变量题项赋值说明
就医延迟行为在过去的一年里，当您生病或受伤(包括感冒)时，是否有意不去看医生？否=0，是=1
年龄（岁）您的出生日期是什么?60～70=0，71～80=1，≥81=2
性别您的性别是？男=0，女=1
婚姻状况您目前的婚姻状况是？无配偶=0，有配偶=1
受教育程度您目前的最高教育程度是？小学及以下=0，初中及中专=1，普高及高职=2，大专及以上=3
健康素养水平您因为看不懂药品说明书或医嘱，而无法知道如何照顾自己的健康？低(有时/经常/总是)=0，高(很少/从不)=1
居住地您目前的居住地状况是?城镇=0，农村=1
家庭经济水平您家的家庭经济状况属于哪一档？较低水平=0，中等水平=1较高水平=2
医疗保险您是否享有公共医疗保险或商业保险？无=0，有=1
社会支持是否有人倾听您诉说您个人关心的事情？无=0有=1
与医护人员沟通能力您因为听不懂医护人员的说明，而无法清楚知道自己的健康状况？回答由“从不”到“总是”，分别记1～5分，算得两个条目的总平均值为2.03分
得分<2.03=0，表示能力强；得分≥2.03=1，表示能力弱
您因为不知道如何向医护人员问问题，而无法清楚知道自己的健康状况？
慢性病数量（种）您患有哪些慢性病？0=0，1～2=1，≥3=2
疾病预防行为您做健康体检吗？不体检=0，不定期体检=1，定期体检=2
自我效能感当您填写个人档案、病史、知情同意书等医疗表格时，您是否有信心能填好？弱(有一点/完全没信心)=0，强(比较/相当/完全有信心)=1
医疗服务可及性焦虑您是否担心当需要医疗服务的时候，不能获得？无(不是很/完全不担心)=0，有(有点/非常担心)=1
医疗费用可负担性焦虑您是否担心当患病的时候，付不起医药费用？无(不是很/完全不担心)=0，有(有点/非常担心)=1
), ArticleFig(id=1324050885369803554, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324050879158039281, language=EN, label=Table 2, caption=

Results of univariate analysis of delayed medical care behavior of elderly people

, figureFileSmall=null, figureFileBig=null, tableContent=
变量总人数就医延迟人数就医延迟人数占比(%)χ2值P值
倾向特征
年龄(岁)6.2460.044
60～7041416539.86
71～8031610432.91
≥811133329.20
性别0.7930.373
男40715237.35
女43615034.40
婚姻状况156.389<0.001
无配偶37822258.73
有配偶4658017.20
受教育情况79.121<0.001
小学及以下31116151.77
初中及中专2469136.99
普高及高职2133717.37
大专及以上731317.81
健康素养水平41.610<0.001
低36717647.96
高47612626.47
使能资源
居住地13.611<0.001
城镇52821440.53
农村3158827.94
家庭经济水平71.172<0.001
较低水平37218048.39
中等水平38810426.80
较高水平831821.79
医疗保险26.861<0.001
无26913048.33
有57417229.97
社会支持54.616<0.001
无51023345.69
有3336920.72
与医护人员沟通能力67.104<0.001
强48411724.17
弱35918551.53
需要因素
慢性病数量（种）11.9550.003
02858931.23
1～242614934.98
≥31326448.48
行为心理因素
疾病预防行为53.348<0.001
不体检25113252.59
不定期体检2689535.45
定期体检3247523.15
自我效能感25.785<0.001
弱48320843.06
强3609426.11
医疗服务可及性焦虑130.321<0.001
无4207116.90
有42323154.61
医疗费用可负担性焦虑137.537<0.001
无4568217.98
有38722056.85
), ArticleFig(id=1324050885453689635, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324050879158039281, language=CN, label=表2, caption=

老年人就医延迟行为单因素分析结果

, figureFileSmall=null, figureFileBig=null, tableContent=
变量总人数就医延迟人数就医延迟人数占比(%)χ2值P值
倾向特征
年龄(岁)6.2460.044
60～7041416539.86
71～8031610432.91
≥811133329.20
性别0.7930.373
男40715237.35
女43615034.40
婚姻状况156.389<0.001
无配偶37822258.73
有配偶4658017.20
受教育情况79.121<0.001
小学及以下31116151.77
初中及中专2469136.99
普高及高职2133717.37
大专及以上731317.81
健康素养水平41.610<0.001
低36717647.96
高47612626.47
使能资源
居住地13.611<0.001
城镇52821440.53
农村3158827.94
家庭经济水平71.172<0.001
较低水平37218048.39
中等水平38810426.80
较高水平831821.79
医疗保险26.861<0.001
无26913048.33
有57417229.97
社会支持54.616<0.001
无51023345.69
有3336920.72
与医护人员沟通能力67.104<0.001
强48411724.17
弱35918551.53
需要因素
慢性病数量（种）11.9550.003
02858931.23
1～242614934.98
≥31326448.48
行为心理因素
疾病预防行为53.348<0.001
不体检25113252.59
不定期体检2689535.45
定期体检3247523.15
自我效能感25.785<0.001
弱48320843.06
强3609426.11
医疗服务可及性焦虑130.321<0.001
无4207116.90
有42323154.61
医疗费用可负担性焦虑137.537<0.001
无4568217.98
有38722056.85
), ArticleFig(id=1324050885831176996, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324050879158039281, language=EN, label=Table 3, caption=

Binary logistic regression analysis of factors influencing delayed medical care behavior of elderly people

, figureFileSmall=null, figureFileBig=null, tableContent=
变量分组参照组βP值OR(95%CI)
倾向特征
年龄(岁)71～8060～70-0.2210.3450.801(0.506～1.269)
≥81-0.7220.0410.486(0.243～0.972)
婚姻状况有配偶无配偶-2.139<0.0010.118(0.075～0.186)
受教育情况初中及中专小学及以下0.1380.6021.148(0.682～1.933)
普高及高职-0.8540.0050.426(0.235～0.770)
大专及以上-1.3890.0020.249(0.103～0.603)
健康素养水平高低-1.657<0.0010.191(0.118～0.309)
使能资源
居住地农村城镇-0.5260.0310.591(0.367～0.952)
家庭经济水平中等水平较低水平-0.0270.9420.973(0.463～2.044)
较高水平-0.868<0.0010.420(0.266～0.663)
医疗保险有无-0.3750.1000.687(0.439～1.075)
社会支持有无-1.233<0.0010.291(0.184～0.460)
与医护人员沟通能力弱强1.133<0.0013.104(1.924～5.007)
需要因素
慢性病数量（种）1～200.4420.0681.556(0.968～2.501)
≥30.7940.0122.211(1.194～4.096)
行为心理因素
疾病预防行为不定期体检不体检-0.7130.0050.490(0.298～0.807)
定期体检-1.480<0.0010.228(0.131～0.395)
自我效能感强弱-0.5920.0140.553(0.345～0.887)
医疗服务可及性焦虑有无0.7520.0012.122(1.354～3.325)
医疗费用可负担性焦虑有无1.213<0.0013.363(2.114～5.349)
), ArticleFig(id=1324050887391458085, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324050879158039281, language=CN, label=表3, caption=

老年人就医延迟行为影响因素的二元logistic回归分析

, figureFileSmall=null, figureFileBig=null, tableContent=
变量分组参照组βP值OR(95%CI)
倾向特征
年龄(岁)71～8060～70-0.2210.3450.801(0.506～1.269)
≥81-0.7220.0410.486(0.243～0.972)
婚姻状况有配偶无配偶-2.139<0.0010.118(0.075～0.186)
受教育情况初中及中专小学及以下0.1380.6021.148(0.682～1.933)
普高及高职-0.8540.0050.426(0.235～0.770)
大专及以上-1.3890.0020.249(0.103～0.603)
健康素养水平高低-1.657<0.0010.191(0.118～0.309)
使能资源
居住地农村城镇-0.5260.0310.591(0.367～0.952)
家庭经济水平中等水平较低水平-0.0270.9420.973(0.463～2.044)
较高水平-0.868<0.0010.420(0.266～0.663)
医疗保险有无-0.3750.1000.687(0.439～1.075)
社会支持有无-1.233<0.0010.291(0.184～0.460)
与医护人员沟通能力弱强1.133<0.0013.104(1.924～5.007)
需要因素
慢性病数量（种）1～200.4420.0681.556(0.968～2.501)
≥30.7940.0122.211(1.194～4.096)
行为心理因素
疾病预防行为不定期体检不体检-0.7130.0050.490(0.298～0.807)
定期体检-1.480<0.0010.228(0.131～0.395)
自我效能感强弱-0.5920.0140.553(0.345～0.887)
医疗服务可及性焦虑有无0.7520.0012.122(1.354～3.325)
医疗费用可负担性焦虑有无1.213<0.0013.363(2.114～5.349)
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Pathways and risk analysis of delayed medical care behavior of elderly people

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终端节点决策树路径就医延迟发生率(%)χ2值P值
11无配偶→有医疗费用可负担性焦虑→小学及以下84.5018.5920.001
10无配偶→有医疗费用可负担性焦虑→初中及中专71.2018.5920.001
7无配偶→无医疗费用可负担性焦虑→低健康素养57.9029.159<0.001
15有配偶→有医疗服务性可及性焦虑→无社会支持48.2028.049<0.001
9无配偶→有医疗费用可负担性焦虑→大专及以上30.0018.5920.001
8无配偶→无医疗费用可负担性焦虑→高健康素养18.4029.159<0.001
13有配偶→无医疗服务可及性焦虑→未参与体检17.4016.938<0.001
14有配偶→有医疗服务可及性焦虑→有社会支持9.2028.049<0.001
12有配偶→无医疗服务可及性焦虑→参与体检3.2016.938<0.001
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老年人就医延迟行为发生路径与风险分析

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终端节点决策树路径就医延迟发生率(%)χ2值P值
11无配偶→有医疗费用可负担性焦虑→小学及以下84.5018.5920.001
10无配偶→有医疗费用可负担性焦虑→初中及中专71.2018.5920.001
7无配偶→无医疗费用可负担性焦虑→低健康素养57.9029.159<0.001
15有配偶→有医疗服务性可及性焦虑→无社会支持48.2028.049<0.001
9无配偶→有医疗费用可负担性焦虑→大专及以上30.0018.5920.001
8无配偶→无医疗费用可负担性焦虑→高健康素养18.4029.159<0.001
13有配偶→无医疗服务可及性焦虑→未参与体检17.4016.938<0.001
14有配偶→有医疗服务可及性焦虑→有社会支持9.2028.049<0.001
12有配偶→无医疗服务可及性焦虑→参与体检3.2016.938<0.001
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老年人就医延迟行为现状及影响因素研究
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唐佳妮 , 段桂敏 , 周悦 , 李巧 , 邹兴林
现代预防医学 | 卫生政策与管理 2026,53(11): 2032-2037
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现代预防医学 |卫生政策与管理 2026 , 53 (11) : 2032 -2037
老年人就医延迟行为现状及影响因素研究
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唐佳妮, 段桂敏 , 周悦, 李巧, 邹兴林
作者信息
  • 成都中医药大学管理学院，四川 成都 611137
通讯作者:
段桂敏，E-mail：
作者简介:

唐佳妮（2001—），女，硕士在读，研究方向：卫生事业管理

Current status and factors influencing delayed medical care behavior among older adults
Jia-ni TANG, Gui-min DUAN , Yue ZHOU, Qiao LI, Xing-lin ZOU
Affiliations
  • School of Management, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 611137, China
出版时间: 2026-06-10 doi: 10.20043/j.cnki.MPM.202511005
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摘要
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目的

探究我国老年人就医延迟行为现状及影响因素，为进一步改善老年人健康结局提供参考。

方法

使用2021年中国综合社会调查（Chinese General Social Survey，CGSS）数据，选取年龄≥60岁的老年人纳入研究。以安德森卫生服务利用行为模型为理论框架，综合运用二元logistic回归与决策树模型，探究关键影响因素。

结果

二元logistic回归结果显示，≥81岁（OR=0.486）、有配偶（OR=0.118）、普高及高职（OR=0.426）、大专及以上（OR=0.249）、健康素养水平高（OR=0.191）、农村（OR=0.591）、家庭经济水平较高（OR=0.420）、有社会支持（OR=0.291）、定期体检（OR=0.228）、自我效能感强（OR=0.553）的老年人就医延迟行为发生风险更低；而与医护人员沟通能力弱（OR=3.104）、患慢性病数量≥3种（OR=2.211）、有医疗服务可及性焦虑（OR=2.122）、有医疗费用可负担性焦虑（OR=3.363）的老年人就医延迟行为发生风险更高（P值均<0.05）。决策树模型共识别出7个预测因子（P<0.05），其中婚姻状况与老年人就医延迟行为的关联度最高。

结论

我国老年人就医延迟率较高，建议针对老年群体实施差异化、精准化医疗优待政策，完善医疗保障体系，积极开展健康教育活动，引导老年人合理就医。

就医延迟行为  /  安德森卫生服务利用行为模型  /  Logistic回归  /  决策树模型
Objective

To explore the current status of delayed medical care behavior and its influencing factors among the elderly in China, in order to provide a reference for further improving the health outcomes of older adults.

Methods

Data were derived from the 2021 Chinese General Social Survey (CGSS). Participants aged 60 years and older were enrolled in this study. Grounded in Andersen’s behavioral model of health care utilization, binary logistic regression combined with decision tree analysis was employed to identify key influencing factors.

Results

Binary logistic regression revealed that delayed medical care behavior was significantly less likely among adults aged ≥81 years (OR=0.486), married older adults (OR=0.118), individuals with general high school or vocational education (OR=0.426), college graduates (OR=0.249), participants with high health literacy (OR=0.191), rural residents (OR=0.591), individuals from higher household economic backgrounds (OR=0.420), older adults receiving social support (OR=0.291), regular health check-up attendees (OR=0.228), and participants reporting strong self-efficacy (OR=0.553). Conversely, significantly higher risks were observed among older adults with poor communication skills with medical professionals (OR=3.104), individuals with ≥3 chronic conditions (OR=2.211), participants experiencing anxiety regarding healthcare accessibility (OR=2.122), and older adults reporting anxiety concerning medical affordability (OR=3.363), all P<0.05. The decision tree model identified seven predictive factors (P<0.05), with marital status showing the strongest association with delayed medical care behavior among older adults.

Conclusion

The rate of delayed medical care among older adults in China is high, and differentiated and targeted preferential healthcare policies should be implemented for older adults, the medical security system should be improved, health education activities should be actively carried out, and older adults should be guided to seek medical care appropriately.

Delayed medical care behavior  /  Andersen’s behavioral model of health care utilization  /  Logistic regression  /  Decision tree model
唐佳妮, 段桂敏, 周悦, 李巧, 邹兴林. 老年人就医延迟行为现状及影响因素研究. 现代预防医学, 2026 , 53 (11) : 2032 -2037 . DOI: 10.20043/j.cnki.MPM.202511005
Jia-ni TANG, Gui-min DUAN, Yue ZHOU, Qiao LI, Xing-lin ZOU. Current status and factors influencing delayed medical care behavior among older adults[J]. Modern Preventive Medicine, 2026 , 53 (11) : 2032 -2037 . DOI: 10.20043/j.cnki.MPM.202511005
正文
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就医延迟行为是指个体出现身体不适后因各种主观或客观原因未能及时就医的行为[1]。当前，人口老龄化程度加深，老年人健康问题凸显，及时就医是改善老年人健康状况的关键环节[2]。然而，调查数据显示[3]，老年人两周患病率为55.5%，但两周就诊率仅40.1%，提示老年人存在就医延迟行为。同时，老年人病种多样且病情复杂，临床决策难度与风险均显著增加[4]，一旦发生就医延迟行为，极易导致治疗时间延长、诊治难度增加、治疗效果下降、经济负担加重、生存质量降低甚至死亡等一系列不良结局[5]。目前，国内外就医延迟行为的研究主要聚焦于脑卒中、癌症、结核病、类风湿关节炎和慢性病共病等特定疾病患者[6-10]，或局限于广东、四川省等特定区域[10-11]，鲜有研究利用全国性公共数据库，结合安德森卫生服务利用行为模型探究全国老年群体就医延迟行为现状及其影响因素。基于此，本研究使用2021年中国综合社会调查（Chinese General Social Survey，CGSS）数据，结合二元logistic回归与决策树模型，识别老年人就医延迟行为影响因素，为改善老年人健康结局提供证据。
1　资料与方法
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1.1　资料来源
本研究数据来自于2021年CGSS，剔除未参与东亚社会调查健康模块的样本、60周岁以下样本和无效样本，最终保留样本843例。见图1。
1.2　变量选取
本研究基于安德森卫生服务利用行为模型[12]，将就医延迟行为作为因变量。将倾向特征（年龄、性别、婚姻状况、受教育程度、健康素养水平[13]）、使能资源(居住地、家庭经济水平、医疗保险、社会支持[14]、与医护人员沟通能力)、需要因素(慢性病数量)作为自变量。鉴于已有文献指出，就医延迟易受健康行为及心理因素等方面影响[7，15]，故本文纳入行为心理因素作为自变量，包括疾病预防行为、自我效能感、医疗服务可及性焦虑、医疗费用可负担性焦虑。见表1。
1.3　统计学方法
本文运用SPSS 26.0软件进行数据分析。组间比较采用χ2检验进行单因素分析，将具有统计学意义的自变量纳入二元logistic回归模型，进一步补充基于卡方自动交互检测（chi-squared automatic interaction detector,CHAID）算法的决策树模型。检验水准α=0.05。
2　结果
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2.1　单因素分析
本研究共纳入843例研究对象，结果显示，发生就医延迟行为的老年人共302例(35.82%)。其中，老年人就医延迟行为在年龄、婚姻状况、社会支持、慢性病数量、疾病预防行为、医疗服务可及性焦虑等14个指标间差异具有统计学意义(P<0.05)。见表2。
2.2　二元logistic回归模型分析
本研究以老年人就医延迟行为作为因变量，将单因素分析中有统计学意义的变量作为自变量进行二元logistic回归分析。结果显示，在倾向特征层面，≥81岁(OR=0.486)、有配偶(OR=0.118)、普高及高职(OR=0.426)、大专及以上(OR=0.249)、健康素养水平高(OR=0.191)的老年人就医延迟行为发生风险更低。在使能资源层面，农村(OR=0.591)、家庭经济水平较高(OR=0.420)、有社会支持(OR=0.291)的老年人就医延迟行为发生风险更低，与医护人员沟通能力弱(OR=3.104)的老年人就医延迟行为发生风险更高。在需要因素层面，相对于未患慢性病的，患慢性病数量≥3种(OR=2.211)的老年人就医延迟行为发生风险更高。在行为心理因素层面，定期体检(OR=0.228)、自我效能感强(OR=0.553)的老年人就医延迟行为发生风险更低，有医疗服务可及性焦虑(OR=2.122)、有医疗费用可负担性焦虑(OR=3.363)的老年人就医延迟行为发生风险更高（P值均<0.05）。见表3。
2.3　基于CHAID算法的决策树模型分析
将单因素分析中具有统计学意义的变量纳入CHAID决策树模型。结果显示，决策树模型总体正确率为79.8 %，验证误差为20.6 %，模型较稳健。模型共识别出7个预测因子(P<0.05)。婚姻状况与老年人就医延迟行为的关联度最高，其次按重要性排序分别是医疗费用可负担性焦虑、医疗服务可及性焦虑、健康素养水平、社会支持、受教育情况及疾病预防行为。无配偶且有医疗费用可负担性焦虑的低学历老年人发生延迟就医的风险为84.50%，而有配偶且无医疗服务可及性焦虑的老年人中，定期体检者发生就医延迟的风险仅为3.20%。见表4、图2。
3　讨论
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研究结果显示，我国老年人就医延迟率达35.82%，高于2019年四川省农村居民的13.45%[11]与2022年广东省老年共病患者的24.35%[10]，这可能与样本特征、变量测量方法以及重大公共卫生事件[16]有关。其中，无配偶的老年人就医延迟行为发生率最高，为58.73%，有医疗费用可负担性焦虑的老年人次之，为56.85%。总之，我国老年人就医延迟行为发生风险较高，极易导致老年群体生命质量下降[5]。
在倾向特征层面，决策树结果显示，婚姻状况是影响老年人就医延迟行为的最关键因素，结合二元logistic回归结果可知，≥81岁、有配偶的老年人就医延迟行为发生风险更低，而受教育程度低、健康素养水平低的老年人发生风险更高，原因可能是：（1）各地医疗政策与福利保障向高龄群体倾斜，有利于其及时就医[17]；（2）配偶能及时察觉老年人身体异常情况，陪同就医缓解其就医恐惧感与抵触情绪；（3）健康素养水平与学历低的老年人获取、理解及利用健康信息的能力较弱，易延迟就医[18]。
在使能资源层面，高家庭经济水平、有社会支持的老年人就医延迟行为发生风险更低，而与医护人员沟通能力弱的发生风险更高。原因可能是：（1）家庭经济水平高的老年人能负担医疗费用；（2）社会支持通过提供情感与物质支持，促进老年人积极就医[19]；（3）与医护人员沟通能力弱的老年人易出现医患沟通障碍，进而降低对医生的信任度以及就医依从性与积极性[20]。研究结果显示，医疗保险对老年人就医延迟行为并无显著影响，可能原因在于，原问卷测量题项仅有是否参与公共医疗保险或商业保险，未考虑不同医保类型间的保障水平差异。尽管如此，多项研究均证明医疗保险是就医延迟的影响因素[10-11]，其作用不应被忽视。
在需要因素层面，慢性病数量≥3种的老年人就医延迟行为发生风险更高。在行为心理因素层面，两种分析结果均证明定期体检的老年人就医延迟行为发生风险更低，而有医疗服务可及性焦虑与医疗费用可负担性焦虑的发生风险更高。原因可能是：（1）老年人因慢性病病情迁延不愈且反复发作，极易消极就医[18]；（2）有体检行为的老年人健康意识更强，主动就医等健康行为亦随之改善[21]；（3）老年人因焦虑区域优质医疗资源不足、医疗资源分配不均及医疗费用等因素，导致其回避就医心理加剧[22]。
此外，决策树模型利用交互作用识别出无配偶且有医疗费用可负担性焦虑的低学历老年人是就医延迟的高易发人群，该人群就医延迟风险高达84.50%，这可能是心理与经济双重压力所致，亟需重点关注。
本文存在一定局限性。首先，研究结果可能受重大公共卫生事件以及原始题项未界定“生病”含义的影响。其次，采用横截面数据难以严格推断变量间的因果关系。最后，筛选后的样本量约为总数据库的1/10，样本代表性存在一定的局限性。
基金
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  • 四川省哲学社会科学重点研究基地学术研究专项资助项目(SC25E012)
  • 四川省基层卫生事业发展研究中心重点项目(SWFZ24-Z-10)
  • 四川省中医药文化与中医药发展研究中心重点项目(ZYYWH2024010)
  • 成都中医药大学杏林学者青基人才项目(QJRC2022056)
文献
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孙学礼.医学心理学[M].成都：四川大学出版社，2003.
Sun XL. Medical psychology[M]. Chengdu: Sichuan University Press, 2003.(In Chinese)
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2026年第53卷第11期
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doi: 10.20043/j.cnki.MPM.202511005
  • 接收时间：2025-11-01
  • 首发时间：2026-11-02
  • 出版时间：2026-06-10
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  • 收稿日期：2025-11-01
基金
四川省哲学社会科学重点研究基地学术研究专项资助项目(SC25E012)
四川省基层卫生事业发展研究中心重点项目(SWFZ24-Z-10)
四川省中医药文化与中医药发展研究中心重点项目(ZYYWH2024010)
成都中医药大学杏林学者青基人才项目(QJRC2022056)
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    成都中医药大学管理学院，四川 成都 611137

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2种不同金属材料的力学参数
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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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