Article(id=1324077597600121030, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324077560820282023, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202509479, 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=1793614299439, onlineDateStr=2026-11-02, pubDate=1774368000000, pubDateStr=2026-03-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793614299439, onlineIssueDateStr=2026-11-02, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793614299439, creator=13701087609, updateTime=1793614299439, 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=986, endPage=991, ext={EN=ArticleExt(id=1324077597868556487, articleId=1324077597600121030, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Comparative analysis of factors associated with depressive symptoms among the urban and rural elderly, China, columnId=1228016567443718970, journalTitle=Modern Preventive Medicine, columnName=Epidemiology and Statistical Methods Advances, runingTitle=null, highlight=null, articleAbstract=
Background

With the acceleration of population aging in China, the problem of depressive symptoms in the elderly is prominent, and there is health inequity between urban and rural elderly.

Objective

To investigate the influencing factors of depressive symptoms among Chinese elderly population and their urban-rural differences, so as to provide references for preventing depressive symptoms in the elderly.

Methods

Relevant data of 4 449 elderly individuals aged ≥ 60 years were collected from the 2022 China Family Panel Studies (CFPS). The chi-square test was used to compare the differences in the detection rate of depressive symptoms between urban and rural areas. Binary logistic regression was adopted to screen for the main influencing factors, and the random forest model was applied to rank these factors by their importance.

Results

The detection rate of depressive symptoms among Chinese elderly was 25.20%, with rural elderly showing a higher rate (30.58%) than urban elderly (19.80%) (χ2=68.542, P<0.05).

Results

of the regression analysis indicated that gender, chronic disease, self-rated health, napping, nighttime sleep time, interpersonal relationships, education level, retirement status, regional distribution, and income level are common influencing factors of depressive symptoms among the urban and rural elderly populations. The random forest model showed that, for the urban elderly population, the factors ranked in descending order of influence on depressive symptoms are good self-rated health (OR=0.396, 95% CI: 0.312-0.503), secondary school (OR=0.639, 95% CI: 0.473-0.862), and being retired (OR=0.726, 95% CI: 0.541-0.975). For the rural elderly population, the factors are good self-rated health (OR=0.472, 95% CI: 0.383-0.581), having chronic diseases (OR=1.918, 95% CI: 1.552-2.370), and nighttime sleep time <7h (OR=1.697, 95% CI: 1.365-2.110).

Conclusion

The higher detection rate of depressive symptoms among rural elderly warrants special attention to self-rated health, with particular focus on education level and retirement status for urban elderly, and chronic disease and nighttime sleep time for rural elderly, so as to formulate differentiated intervention strategies.

, authors=Ying ZHANG1, Xin-tong JIANG1, Yun-kai YOU1, Xing-han XIE2, Ping-yu WANG1, authorsList=Ying ZHANG, Xin-tong JIANG, Yun-kai YOU, Xing-han XIE, Ping-yu WANG, authorCompany=null, correspAuthors=Ping-yu WANG, 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=1324077600171229386, articleId=1324077597600121030, tenantId=1146029695717560320, journalId=1227665162245664772, language=CN, title=中国城乡老年人群抑郁症状影响因素比较分析, columnId=1228016567632462653, journalTitle=现代预防医学, columnName=流行病与统计方法, runingTitle=null, highlight=null, articleAbstract=
背景

我国人口老龄化加速,老年抑郁症状问题突出,城乡老年人群存在健康不公平现象。

目的

了解中国老年人群抑郁症状影响因素及其城乡差异,为预防抑郁症状的发生提供参考依据。

方法

收集2022年中国家庭追踪调查(CFPS)4 449名≥60岁老年人的相关数据,采用χ2检验比较城乡抑郁症状检出率差异,通过二元logistic回归筛选主要影响因素,运用随机森林模型排序。

结果

中国老年人群抑郁症状检出率为25.20%,其中农村老年人群(30.58%)高于城镇老年人群(19.80%)(χ2=68.542,P<0.05)。回归分析结果显示,性别、慢性病状况、自评健康状况、午休、夜间睡眠时长、人际关系、文化程度、退休状况、地区分布、收入水平是城乡老年人群抑郁症状的共同影响因素。随机森林模型显示,对城镇老年人群抑郁症状的影响程度从高到低依次为自评健康状况好(OR=0.396,95% CI:0.312~0.503)、中学文化程度(OR=0.639,95% CI:0.473~0.862)、已退休(OR=0.726,95% CI:0.541~0.975);农村地区依次为自评健康状况好(OR=0.472,95% CI:0.383~0.581)、患慢性病(OR=1.918,95% CI:1.552~2.370)、夜间睡眠<7 h(OR=1.697,95% CI:1.365~2.110)。

结论

中国农村老年人群抑郁症状检出率高于城镇老年人群,应重点关注自评健康状况,特别关注城镇老年人群文化程度、退休状况等影响因素,以及农村老年人群慢性病状况、夜间睡眠时长等影响因素,制定差异化干预策略。

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张滢(2000—),女,硕士在读,研究方向:流行病与卫生统计学

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王萍玉,E-mail:
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Investigation on prevalence and associated factors of poor sleep quality among community-dwelling older People in China[J]. Chinese Journal of the Frontiers of Medical Science(Electronic Version), 2022, 14(3): 1-7. 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The association between nap duration, nighttime sleep, and depressive symptoms among elderly People in China: an empirical analysis based on 2020 CHARLS data[J]. Chinese General Practice, 2025, 28(5): 560-567. (In Chinese), articleTitle=The association between nap duration, nighttime sleep, and depressive symptoms among elderly People in China: an empirical analysis based on 2020 CHARLS data, refAbstract=null), Reference(id=1324077618114461991, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077597600121030, doi=null, pmid=null, pmcid=null, year=2023, volume=68, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[24], rfOrder=40, authorNames=Liu HX, Zhou ZL, Fan XJ, journalName=International Journal of Public Health, refType=null, unstructuredReference=Liu HX, Zhou ZL, Fan XJ, et al. Association between multiple chronic conditions and depressive symptoms among older adults in China: evidence from the China health and retirement longitudinal study (CHARLS)[J]. International Journal of Public Health, 2023, 68: 1605572., articleTitle=Association between multiple chronic conditions and depressive symptoms among older adults in China: evidence from the China health and retirement longitudinal study (CHARLS), refAbstract=null), Reference(id=1324077618227708200, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077597600121030, doi=null, pmid=null, pmcid=null, year=2023, volume=16, issue=9, pageStart=56, pageEnd=63, url=null, language=null, rfNumber=[25], rfOrder=41, authorNames=张航空, 马琦峰, 李慧慧, journalName=中国卫生政策研究, refType=null, unstructuredReference=张航空,马琦峰,李慧慧.城乡居民医保、老年人健康与医疗服务利用-来自五期CLHLS数据的证据[J].中国卫生政策研究,2023,16(9):56-63., articleTitle=城乡居民医保、老年人健康与医疗服务利用-来自五期CLHLS数据的证据, refAbstract=null), Reference(id=1324077618319982889, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077597600121030, doi=null, pmid=null, pmcid=null, year=2023, volume=16, issue=9, pageStart=56, pageEnd=63, url=null, language=null, rfNumber=[25], rfOrder=42, authorNames=Zhang HK, Ma QF, Li HH, journalName=Chinese Journal of Health Policy, refType=null, unstructuredReference=Zhang HK, Ma QF, Li HH. Medical insurance for urban and rural residents, health and utilization of medical services for the elderly: Evidence from CLHLS data[J]. Chinese Journal of Health Policy, 2023, 16(9): 56-63. (In Chinese), articleTitle=Medical insurance for urban and rural residents, health and utilization of medical services for the elderly: Evidence from CLHLS data, refAbstract=null)], funds=[Fund(id=1324077605879677179, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077597600121030, awardId=ZR2024MH228, language=CN, fundingSource=山东省自然科学基金项目(ZR2024MH228), fundOrder=null, country=null), Fund(id=1324077605950980348, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077597600121030, awardId=2024JCYJ058, language=CN, fundingSource=烟台市科技创新发展计划项目(2024JCYJ058), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1324077600460636363, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077597600121030, xref=1., ext=[AuthorCompanyExt(id=1324077600469024972, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077597600121030, companyId=1324077600460636363, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Department of Epidemiology, School of Public Health, Binzhou Medical University, Yantai, Shandong 264003, China), AuthorCompanyExt(id=1324077600477413581, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077597600121030, companyId=1324077600460636363, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.滨州医学院公共卫生学院流行病学教研室,山东 烟台 264003)]), AuthorCompany(id=1324077600540328142, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077597600121030, xref=2., ext=[AuthorCompanyExt(id=1324077600548716751, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077597600121030, companyId=1324077600540328142, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.滨州医学院临床医学院)])], figs=[ArticleFig(id=1324077604596220147, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077597600121030, language=EN, label=Fig.1, caption=Analysis of the importance of influencing factors for depressive symptoms in urban and rural elderly in China, figureFileSmall=Owp3vfh4a56QEdN+E8wbGw==, figureFileBig=bpIFgvskblQth7fLR3vOeQ==, tableContent=null), ArticleFig(id=1324077604738826484, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077597600121030, language=CN, label=图1, caption=中国城乡老年人群抑郁症状影响因素重要性分析, figureFileSmall=Owp3vfh4a56QEdN+E8wbGw==, figureFileBig=bpIFgvskblQth7fLR3vOeQ==, tableContent=null), ArticleFig(id=1324077604994679029, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077597600121030, language=EN, label=Table 1, caption=

Prevalence of depressive symptoms in the elderly in urban and rural areas of China

, figureFileSmall=null, figureFileBig=null, tableContent=
变量城镇农村
抑郁症状人数(检出率,%)χ2值P值抑郁症状人数(检出率,%)χ2值P值
年龄(岁)
60~69263(19.28)4.3110.116452(31.59)5.5910.061
70~79162(21.63)191(27.56)
≥8015(13.76)38(36.89)
性别
男164(14.67)37.332<0.001320(26.21)24.329<0.001
女276(25.00)361(35.88)
慢性病状况
否250(16.45)34.093<0.001369(24.47)82.134<0.001
是190(27.07)312(43.39)
BMI
低体重26(29.89)9.7010.02179(39.30)8.8880.031
正常体重213(19.93)365(30.54)
超重148(17.66)178(28.39)
肥胖53(23.25)59(28.92)
自评健康状况
不好270(32.07)128.412<0.001395(43.94)126.730<0.001
好170(12.32)286(21.54)
吸烟状况
否351(20.54)2.5270.112502(32.02)5.1480.023
是89(17.35)179(27.16)
饮酒状况
否388(20.51)3.9920.046608(31.95)11.5700.001
是52(15.76)73(22.53)
每周锻炼次数
0266(23.46)19.8560.001519(32.30)11.7150.020
1~215(16.48)22(27.85)
3~416(18.39)18(37.50)
5~66(15.38)4(20.00)
≥7137(15.73)118(24.95)
午休
否166(24.23)8.4080.003242(34.13)6.1870.013
是303(18.84)439(28.92)
夜间睡眠时长(h)
<7202(24.28)19.148<0.001249(38.54)28.055<0.001
7~9213(16.64)372(26.92)
>925(22.73)60(30.15)
每周看电视时间(h)
<7183(22.21)4.7770.092326(33.03)5.0390.080
7~14136(18.38)244(28.77)
>14121(18.39)111(28.32)
每周和家人吃饭次数
<7123(23.93)7.1750.007159(38.69)15.604<0.001
≥7317(18.56)522(28.74)
婚姻状况
无配偶112(28.43)22.431<0.001145(43.94)32.574<0.001
有配偶328(17.94)536(28.26)
家庭成员人数
144(25.99)4.4610.10753(40.15)6.0600.048
2153(18.84)238(29.90)
≥3243(19.60)390(30.02)
人际关系(分)
0~5136(25.66)18.548<0.001227(39.96)38.785<0.001
6~791(21.06)128(32.82)
8~10213(16.90)326(25.69)
是否使用互联网
否287(22.89)17.248<0.001533(32.17)7.6840.006
是153(15.81)148(25.96)
文化程度
文盲158(28.26)47.194<0.001348(37.06)39.632<0.001
小学103(22.84)163(29.32)
中学159(14.85)169(23.57)
大专及以上20(14.18)1(6.67)
退休状况
否198(25.32)23.133<0.001462(32.79)8.8250.003
是242(16.81)219(26.77)
地区分布
西部119(29.31)31.233<0.001236(35.65)11.4710.003
中部141(19.67)205(28.12)
东部180(16.38)240(28.71)
是否有医疗保险
否319(17.95)19.130<0.001364(28.44)6.5040.011
是121(27.19)317(33.47)
收入水平
低169(29.19)43.466<0.001225(40.11)32.208<0.001
中等167(16.62)240(27.78)
高104(16.30)216(26.93)
), ArticleFig(id=1324077605271503094, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077597600121030, language=CN, label=表1, caption=

中国城乡老年人群的抑郁症状情况

, figureFileSmall=null, figureFileBig=null, tableContent=
变量城镇农村
抑郁症状人数(检出率,%)χ2值P值抑郁症状人数(检出率,%)χ2值P值
年龄(岁)
60~69263(19.28)4.3110.116452(31.59)5.5910.061
70~79162(21.63)191(27.56)
≥8015(13.76)38(36.89)
性别
男164(14.67)37.332<0.001320(26.21)24.329<0.001
女276(25.00)361(35.88)
慢性病状况
否250(16.45)34.093<0.001369(24.47)82.134<0.001
是190(27.07)312(43.39)
BMI
低体重26(29.89)9.7010.02179(39.30)8.8880.031
正常体重213(19.93)365(30.54)
超重148(17.66)178(28.39)
肥胖53(23.25)59(28.92)
自评健康状况
不好270(32.07)128.412<0.001395(43.94)126.730<0.001
好170(12.32)286(21.54)
吸烟状况
否351(20.54)2.5270.112502(32.02)5.1480.023
是89(17.35)179(27.16)
饮酒状况
否388(20.51)3.9920.046608(31.95)11.5700.001
是52(15.76)73(22.53)
每周锻炼次数
0266(23.46)19.8560.001519(32.30)11.7150.020
1~215(16.48)22(27.85)
3~416(18.39)18(37.50)
5~66(15.38)4(20.00)
≥7137(15.73)118(24.95)
午休
否166(24.23)8.4080.003242(34.13)6.1870.013
是303(18.84)439(28.92)
夜间睡眠时长(h)
<7202(24.28)19.148<0.001249(38.54)28.055<0.001
7~9213(16.64)372(26.92)
>925(22.73)60(30.15)
每周看电视时间(h)
<7183(22.21)4.7770.092326(33.03)5.0390.080
7~14136(18.38)244(28.77)
>14121(18.39)111(28.32)
每周和家人吃饭次数
<7123(23.93)7.1750.007159(38.69)15.604<0.001
≥7317(18.56)522(28.74)
婚姻状况
无配偶112(28.43)22.431<0.001145(43.94)32.574<0.001
有配偶328(17.94)536(28.26)
家庭成员人数
144(25.99)4.4610.10753(40.15)6.0600.048
2153(18.84)238(29.90)
≥3243(19.60)390(30.02)
人际关系(分)
0~5136(25.66)18.548<0.001227(39.96)38.785<0.001
6~791(21.06)128(32.82)
8~10213(16.90)326(25.69)
是否使用互联网
否287(22.89)17.248<0.001533(32.17)7.6840.006
是153(15.81)148(25.96)
文化程度
文盲158(28.26)47.194<0.001348(37.06)39.632<0.001
小学103(22.84)163(29.32)
中学159(14.85)169(23.57)
大专及以上20(14.18)1(6.67)
退休状况
否198(25.32)23.133<0.001462(32.79)8.8250.003
是242(16.81)219(26.77)
地区分布
西部119(29.31)31.233<0.001236(35.65)11.4710.003
中部141(19.67)205(28.12)
东部180(16.38)240(28.71)
是否有医疗保险
否319(17.95)19.130<0.001364(28.44)6.5040.011
是121(27.19)317(33.47)
收入水平
低169(29.19)43.466<0.001225(40.11)32.208<0.001
中等167(16.62)240(27.78)
高104(16.30)216(26.93)
), ArticleFig(id=1324077605393137911, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077597600121030, language=EN, label=Table 2, caption=

Binary logistic regression analysis was used to analyze the influencing factors of depressive symptoms in the urban elderly in China

, figureFileSmall=null, figureFileBig=null, tableContent=
变量对照OR(95%CI)P值
性别男
女1.664(1.290~2.148)<0.001
慢性病状况否
是1.520(1.194~1.936)0.001
自评健康状况不好
好0.396(0.312~0.503)<0.001
午休否
是0.758(0.595~0.966)0.025
夜间睡眠时长(h)7~9
<71.618(1.275~2.053)<0.001
>91.192(0.717~1.981)0.498
人际关系(分)0~5
6~70.939(0.674~1.307)0.708
8~100.647(0.495~0.846)0.001
文化程度文盲
小学0.956(0.697~1.312)0.781
中学0.639(0.473~0.862)0.003
大专及以上0.784(0.436~1.409)0.416
退休状况否
是0.726(0.541~0.975)0.033
地区分布西部
中部0.567(0.415~0.773)<0.001
东部0.495(0.369~0.665)<0.001
收入水平中等
低1.738(1.334~2.266)<0.001
高1.027(0.768~1.374)0.855
), ArticleFig(id=1324077605493801208, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077597600121030, language=CN, label=表2, caption=

中国城镇老年人群抑郁症状影响因素二分类logistic回归分析

, figureFileSmall=null, figureFileBig=null, tableContent=
变量对照OR(95%CI)P值
性别男
女1.664(1.290~2.148)<0.001
慢性病状况否
是1.520(1.194~1.936)0.001
自评健康状况不好
好0.396(0.312~0.503)<0.001
午休否
是0.758(0.595~0.966)0.025
夜间睡眠时长(h)7~9
<71.618(1.275~2.053)<0.001
>91.192(0.717~1.981)0.498
人际关系(分)0~5
6~70.939(0.674~1.307)0.708
8~100.647(0.495~0.846)0.001
文化程度文盲
小学0.956(0.697~1.312)0.781
中学0.639(0.473~0.862)0.003
大专及以上0.784(0.436~1.409)0.416
退休状况否
是0.726(0.541~0.975)0.033
地区分布西部
中部0.567(0.415~0.773)<0.001
东部0.495(0.369~0.665)<0.001
收入水平中等
低1.738(1.334~2.266)<0.001
高1.027(0.768~1.374)0.855
), ArticleFig(id=1324077605644796153, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077597600121030, language=EN, label=Table 3, caption=

Binary logistic regression analysis was used to analyze the influencing factors of depressive symptoms in the rural elderly in China

, figureFileSmall=null, figureFileBig=null, tableContent=
变量对照OR(95%CI)P值
性别男
女1.285(1.004~1.644)0.046
慢性病状况否
是1.918(1.552~2.370)<0.001
自评健康状况不好
好0.472(0.383~0.581)<0.001
每周锻炼次数0
1~21.034(0.596~1.795)0.905
3~41.502(0.792~2.850)0.213
5~60.563(0.173~1.828)0.339
>70.735(0.569~0.948)0.018
午休否
是0.789(0.639~0.975)0.028
夜间睡眠时长(h)7~9
<71.697(1.365~2.110)<0.001
>90.895(0.626~1.277)0.540
每周和家人吃饭次数<7
≥70.665(0.493~0.896)0.007
婚姻状况无配偶
有配偶0.580(0.422~0.797)0.001
人际关系(分)0~5
6~70.760(0.567~1.020)0.067
8~100.553(0.440~0.695)<0.001
文化程度文盲
小学0.863(0.669~1.114)0.259
中学0.746(0.576~0.968)0.027
大专及以上0.240(0.029~1.970)0.184
退休状况否
是0.728(0.563~0.941)0.015
地区分布西部
中部0.673(0.521~0.868)0.002
东部0.756(0.587~0.974)0.030
收入水平中等
低1.449(1.134~1.852)0.003
高1.077(0.850~1.364)0.539
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中国农村老年人群抑郁症状影响因素二分类logistic回归分析

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变量对照OR(95%CI)P值
性别男
女1.285(1.004~1.644)0.046
慢性病状况否
是1.918(1.552~2.370)<0.001
自评健康状况不好
好0.472(0.383~0.581)<0.001
每周锻炼次数0
1~21.034(0.596~1.795)0.905
3~41.502(0.792~2.850)0.213
5~60.563(0.173~1.828)0.339
>70.735(0.569~0.948)0.018
午休否
是0.789(0.639~0.975)0.028
夜间睡眠时长(h)7~9
<71.697(1.365~2.110)<0.001
>90.895(0.626~1.277)0.540
每周和家人吃饭次数<7
≥70.665(0.493~0.896)0.007
婚姻状况无配偶
有配偶0.580(0.422~0.797)0.001
人际关系(分)0~5
6~70.760(0.567~1.020)0.067
8~100.553(0.440~0.695)<0.001
文化程度文盲
小学0.863(0.669~1.114)0.259
中学0.746(0.576~0.968)0.027
大专及以上0.240(0.029~1.970)0.184
退休状况否
是0.728(0.563~0.941)0.015
地区分布西部
中部0.673(0.521~0.868)0.002
东部0.756(0.587~0.974)0.030
收入水平中等
低1.449(1.134~1.852)0.003
高1.077(0.850~1.364)0.539
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中国城乡老年人群抑郁症状影响因素比较分析
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张滢 1 , 姜欣彤 1 , 游云开 1 , 谢幸含 2 , 王萍玉 1
现代预防医学 | 流行病与统计方法 2026,53(6): 986-991
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现代预防医学 |流行病与统计方法 2026 , 53 (6) : 986 -991
中国城乡老年人群抑郁症状影响因素比较分析
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张滢1, 姜欣彤1, 游云开1, 谢幸含2, 王萍玉1
作者信息
  • 1.滨州医学院公共卫生学院流行病学教研室,山东 烟台 264003
  • 2.滨州医学院临床医学院
通讯作者:
王萍玉,E-mail:
作者简介:

张滢(2000—),女,硕士在读,研究方向:流行病与卫生统计学

Comparative analysis of factors associated with depressive symptoms among the urban and rural elderly, China
Ying ZHANG1, Xin-tong JIANG1, Yun-kai YOU1, Xing-han XIE2, Ping-yu WANG1
Affiliations
  • Department of Epidemiology, School of Public Health, Binzhou Medical University, Yantai, Shandong 264003, China
出版时间: 2026-03-25 doi: 10.20043/j.cnki.MPM.202509479
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背景

我国人口老龄化加速,老年抑郁症状问题突出,城乡老年人群存在健康不公平现象。

目的

了解中国老年人群抑郁症状影响因素及其城乡差异,为预防抑郁症状的发生提供参考依据。

方法

收集2022年中国家庭追踪调查(CFPS)4 449名≥60岁老年人的相关数据,采用χ2检验比较城乡抑郁症状检出率差异,通过二元logistic回归筛选主要影响因素,运用随机森林模型排序。

结果

中国老年人群抑郁症状检出率为25.20%,其中农村老年人群(30.58%)高于城镇老年人群(19.80%)(χ2=68.542,P<0.05)。回归分析结果显示,性别、慢性病状况、自评健康状况、午休、夜间睡眠时长、人际关系、文化程度、退休状况、地区分布、收入水平是城乡老年人群抑郁症状的共同影响因素。随机森林模型显示,对城镇老年人群抑郁症状的影响程度从高到低依次为自评健康状况好(OR=0.396,95% CI:0.312~0.503)、中学文化程度(OR=0.639,95% CI:0.473~0.862)、已退休(OR=0.726,95% CI:0.541~0.975);农村地区依次为自评健康状况好(OR=0.472,95% CI:0.383~0.581)、患慢性病(OR=1.918,95% CI:1.552~2.370)、夜间睡眠<7 h(OR=1.697,95% CI:1.365~2.110)。

结论

中国农村老年人群抑郁症状检出率高于城镇老年人群,应重点关注自评健康状况,特别关注城镇老年人群文化程度、退休状况等影响因素,以及农村老年人群慢性病状况、夜间睡眠时长等影响因素,制定差异化干预策略。

抑郁症状  /  老年人群  /  城乡差异  /  影响因素分析  /  随机森林模型
Background

With the acceleration of population aging in China, the problem of depressive symptoms in the elderly is prominent, and there is health inequity between urban and rural elderly.

Objective

To investigate the influencing factors of depressive symptoms among Chinese elderly population and their urban-rural differences, so as to provide references for preventing depressive symptoms in the elderly.

Methods

Relevant data of 4 449 elderly individuals aged ≥ 60 years were collected from the 2022 China Family Panel Studies (CFPS). The chi-square test was used to compare the differences in the detection rate of depressive symptoms between urban and rural areas. Binary logistic regression was adopted to screen for the main influencing factors, and the random forest model was applied to rank these factors by their importance.

Results

The detection rate of depressive symptoms among Chinese elderly was 25.20%, with rural elderly showing a higher rate (30.58%) than urban elderly (19.80%) (χ2=68.542, P<0.05).

Results

of the regression analysis indicated that gender, chronic disease, self-rated health, napping, nighttime sleep time, interpersonal relationships, education level, retirement status, regional distribution, and income level are common influencing factors of depressive symptoms among the urban and rural elderly populations. The random forest model showed that, for the urban elderly population, the factors ranked in descending order of influence on depressive symptoms are good self-rated health (OR=0.396, 95% CI: 0.312-0.503), secondary school (OR=0.639, 95% CI: 0.473-0.862), and being retired (OR=0.726, 95% CI: 0.541-0.975). For the rural elderly population, the factors are good self-rated health (OR=0.472, 95% CI: 0.383-0.581), having chronic diseases (OR=1.918, 95% CI: 1.552-2.370), and nighttime sleep time <7h (OR=1.697, 95% CI: 1.365-2.110).

Conclusion

The higher detection rate of depressive symptoms among rural elderly warrants special attention to self-rated health, with particular focus on education level and retirement status for urban elderly, and chronic disease and nighttime sleep time for rural elderly, so as to formulate differentiated intervention strategies.

Depressive symptoms  /  Elderly population  /  Urban-rural differences  /  Influence factor analysis  /  Random forest model
张滢, 姜欣彤, 游云开, 谢幸含, 王萍玉. 中国城乡老年人群抑郁症状影响因素比较分析. 现代预防医学, 2026 , 53 (6) : 986 -991 . DOI: 10.20043/j.cnki.MPM.202509479
Ying ZHANG, Xin-tong JIANG, Yun-kai YOU, Xing-han XIE, Ping-yu WANG. Comparative analysis of factors associated with depressive symptoms among the urban and rural elderly, China[J]. Modern Preventive Medicine, 2026 , 53 (6) : 986 -991 . DOI: 10.20043/j.cnki.MPM.202509479
在人口老龄化趋势日益显著的背景下[1],老年群体心理健康问题已逐步发展为公共卫生领域重要议题。抑郁症(major depressive disorder, MDD)作为老年人群中常见的慢性情感障碍,发病率随年龄增长显著上升其早期阶段包括持续性情绪低落、兴趣减退、睡眠障碍等症状,与认知功能下降、心血管疾病风险增加、自杀风险上升及社会功能受损等不良结局密切相关[2-3]。受城乡社会经济发展水平、生活环境、医疗卫生条件等因素的差异,我国城乡老年人群存在健康不公平的现象[4-5]。农村老年人群因经济保障体系不完善、医疗卫生资源配置稀缺、家庭照料功能不足等多重因素影响,抑郁症状更严重[6-7]。基于2022年中国家庭追踪调查(China Family Panel Studies, CFPS)数据,本研究选取≥60岁的4 449个样本探究抑郁症状影响因素的城乡差异,并利用随机森林模型评估影响因素的重要性,为实施精准预防和有效干预措施,改善老年群体抑郁状况提供科学参考。
本研究数据来源于北京大学中国社会科学调查中心(Institute of Social Science Survey, ISSS)主持开展的CFPS2022年的最新调研数据。本研究纳入≥60岁的研究对象(n=6 860),剔除抑郁症状、城乡状况及其他重要变量缺失样本(n=2 411),最终4 449人纳入分析。
运用抑郁自评量表(CESD-8)对抑郁症状予以评估。将总分≤15分定义为无抑郁症状,总分>15分定义为有抑郁症状[8]。
对部分变量定义:(1)身体质量指数(BMI):依据调查对象自行申报的身高与体重数据,按照BMI=体重(kg)/身高2(m2)计算。<18.5 kg/m2为低体重,18.5~23.9 kg/m2为正常体重,24.0~27.9 kg/m2为超重,≥28.0 kg/m2为肥胖[9];(2)自评健康状况:针对“您认为自己的健康状况如何”这一问题,将回答“非常健康”、“很健康”、“比较健康”、“一般”的定义为自评健康状况好;(3)吸烟状况:调查前一个月有吸烟行为;(4)饮酒状况:调查前一个月每周饮酒≥3次。(5)每周和家人吃饭次数:指“一般情况下,您每周有几个晚上会和家人一起吃饭”,“家人”指同住的家庭成员,不包括非同住的亲属。(6)人际关系:根据“您认为自己的人缘关系有多好?”进行评估(0~10分)。(7)收入水平:根据“您的收入在本地地位”(1~5分),1~2分为低,3分为中等,4~5分为高。
采用Stata 16.0软件进行一般描述性分析、χ2检验和二元logistic回归分析。采用RStudio软件Random Forest程序包对城乡老年人群抑郁症状的影响因素分别构建随机森林模型,以平均精确度减少(Mean Decrease Accuracy)评价影响因素的重要性,平均精确度减少值越大代表影响因素越重要。检验水准α=0.05。
4 449名≥60岁中国老年人中,城镇2 222人(49.94%),农村2 227人(50.06%);男性2 339人(52.57%),女性2 110人(47.43%);以60~69岁的老年人为主,占62.82%。
研究对象抑郁症状检出率为25.20%(1 121/4 449),其中,城镇老年人群19.80%(440/2 222),低于农村老年人群30.58%(681/2 227),差异有统计学意义(χ2=68.542,P<0.05)。城镇老年人群中,17个变量(性别、慢性病状况、BMI、自评健康状况、饮酒状况、每周锻炼次数、午休、夜间睡眠时长、每周和家人吃饭次数、婚姻状况、人际关系、是否使用互联网、文化程度、退休状态、地区分布、是否有医疗保险、收入水平)与抑郁症状分布呈现显著性差异(P<0.05)。农村老年人群中,除上述变量外,吸烟状况和家庭成员人数也与抑郁症状分布呈现显著性差异(P<0.05)。见表1。
将是否存在抑郁症状作为因变量(无抑郁症状=0,有抑郁症状=1),选取表1中城镇老年人群抑郁症状分布有显著性差异的指标作为自变量,构建二分类logistic回归模型。结果显示,女性、患慢性病、夜间失眠时长<7 h、收入水平低是城镇老年人群抑郁症状的危险因素,自评健康状况好、午休、人际关系8~10分、中学文化程度、已退休、中部和东部地区是城镇老年人群抑郁症状的保护因素。见表2。
将是否存在抑郁症状作为因变量(无抑郁症状=0,有抑郁症状=1),选取表1中农村老年人群抑郁症状分布有显著性差异的指标作为自变量,构建二分类logistic回归模型。结果显示,女性、患慢性病、夜间睡眠时长<7 h、收入水平低是农村老年人群抑郁症状的危险因素,自评健康状况好、每周锻炼≥7次、午休、每周和家人吃饭≥7次、有配偶、人际关系8~10分、中学文化程度、已退休、中部和东部地区是农村老年人群抑郁症状的保护因素。见表3。
将城乡老年人群是否存在抑郁症状作为因变量(无抑郁症状=0,有抑郁症状=1),纳入logistic回归结果中有统计学意义的变量构建随机森林模型。结果显示,城镇老年人群抑郁症状三个最重要的影响因素分别是自评健康状况、文化程度、退休状况;农村老年人群抑郁症状三个最重要的影响因素分别是自评健康状况、慢性病状况、夜间睡眠时长。见图1。
本研究结果表明,2022年我国60岁及以上老年人群抑郁症状检出率为25.20%,虽较2020年(42.82%)[10]、2021年(53.77%)[11]有所下降,但高于成年人群(10.6%)[12],这意味着老年人群抑郁防治工作仍需重视。本研究中,农村老年人群抑郁症状检出率(30.58%)高于城镇老年人群(19.80%),与既往研究结果一致[13]。这与城镇地区基础设施完善、医疗卫生资源丰富,文化活动多元等相关,而农村中青年劳动力向城镇迁移导致老年群体家庭情感支持不足,进而导致抑郁症状的检出率上升[14]。
随机森林模型发现,自评健康状况是城乡老年人群抑郁症状的首要影响因素,良好的自评健康状况通常与健康自信心、乐观心态及主观幸福感紧密关联,这一主观感知指标对心理健康具有重要意义[15]。
中学文化程度、已退休是城乡老年人群抑郁症状的共同保护因素,但在城镇老年人群中重要性排序更靠前。较高教育水平可提高心理调节能力[16],且城镇地区的优质教育资源使老年人群更易利用学习交流的机会提升认知,进一步降低了发生抑郁症状的风险[17]。城镇完善的养老配套让退休者能依托社区服务、老年大学等快速建立新的社交联结与生活节奏[18],同时稳定的退休金保障减少了经济焦虑[19],使其更易通过旅游、兴趣活动丰富精神生活而降低抑郁风险[20]。
患慢性病、夜间睡眠<7 h是城乡老年人群抑郁症状的共同危险因素,且在农村中重要性排序更靠前。农村地区公共卫生服务供给和医疗资源配备不足,心理干预措施和治疗手段的获取受限,慢性病患者难以缓解疾病引发的心理负荷[21]。同时,患慢性病可能会限制其活动能力,加剧了社交隔离程度,进而加重负面情绪[14]。农村老年人群普遍存在睡眠质量较差现象[22],睡眠不足不仅会直接干扰神经递质的平衡调节[23],削弱个体的情绪调节功能,还会加剧躯体疲劳感与不适感,使老年人群更易陷入焦虑、情绪低落的状态,最终增加抑郁症状发生风险。值得注意的是,本研究发现有医疗保险的城乡老年人群反而抑郁症状发生风险更高,可能由于该群体往往伴随慢性疾病、身体机能衰退、潜在心理困扰等[24],健康关注度和医疗接触频率会更高[25],更易筛查出抑郁症状。
本研究存在一定的局限性,一是调查问卷的内容范围有限,可能遗漏潜在的影响因素而造成混杂偏倚;二是部分变量的调查依赖于自我报告,可能存在信息偏倚;三是横断面调查设计难以明确各影响因素与城乡老年人群抑郁症状之间的因果关联。后续研究应主动推进相关实证研究的开展,为老年群体抑郁症状的防治策略提供更为扎实的科学依据。
综上所述,应重点关注自评健康状况不好的老年人群,针对城镇文化程度低、退休老年人群及农村慢性病患者、睡眠不足老年人群指定差异化策略。建议完善健康与心理评估机制,社区卫生服务中心联合专业医疗机构,定期为老人开展身心检查并运用数字化平台长期追踪。在城镇,加大面向退休群体的专业心理服务资源的投入,确保服务的可及性;同时为文化程度较低的老人提供通俗易懂的健康传播材料与适应性心理支持活动。在农村,提升基层慢性病综合管理能力,确保规范治疗、健康指导及必要的上门服务,普及睡眠健康知识,积极构建社区互助网络,并大力发展便于参与的集体文娱活动。
  • 山东省自然科学基金项目(ZR2024MH228)
  • 烟台市科技创新发展计划项目(2024JCYJ058)
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2026年第53卷第6期
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doi: 10.20043/j.cnki.MPM.202509479
  • 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
山东省自然科学基金项目(ZR2024MH228)
烟台市科技创新发展计划项目(2024JCYJ058)
作者信息
    1.滨州医学院公共卫生学院流行病学教研室,山东 烟台 264003
    2.滨州医学院临床医学院

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2种不同金属材料的力学参数
科
Family
属数
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genus
种数
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占总种数比例
Percentage of
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属
Genus
种数
Number of
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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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