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Article(id=1241067202883941252, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241067197318091153, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202410135, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1728662400000, receivedDateStr=2024-10-12, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773823079081, onlineDateStr=2026-03-18, pubDate=1741536000000, pubDateStr=2025-03-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773823079081, onlineIssueDateStr=2026-03-18, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773823079081, creator=13701087609, updateTime=1773823079081, updator=13701087609, issue=Issue{id=1241067197318091153, tenantId=1146029695717560320, journalId=1227665162245664772, year='2025', volume='52', issue='5', pageStart='769', pageEnd='960', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773823077754, creator=13701087609, updateTime=1773823268053, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241067995544482681, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241067197318091153, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241067995544482682, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241067197318091153, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=875, endPage=879, ext={EN=ArticleExt(id=1241067203148182409, articleId=1241067202883941252, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Analysis of the current situation and influencing factors of depression among middle-aged and elderly people in China-based on the LASSO-logistic model, columnId=1228016572451718132, journalTitle=Modern Preventive Medicine, columnName=Health and Social Behavior, runingTitle=null, highlight=null, articleAbstract=
Objective

To explore the current situation and influencing factors of depression among middle-aged and elderly people in China through the analysis of the 2020 survey data of CHARLS, and to provide a theoretical basis for the prevention of depression among middle-aged and elderly people.

Methods

First, 13 124 middle-aged and elderly people aged 45 to 74 with complete information were included according to the inclusion and exclusion criteria. Second, the research subjects were divided according to the scores of the Center for Epidemiologic Studies Depression Scale (CES-D10). Then, the Lasso model was used to screen the influencing factors. Finally, a logistic prediction model for depressive symptoms in the elderly was constructed and the prediction effect was evaluated.

Results

Among the 13 124 research subjects, 4 877 had depressive symptoms, with a detection rate of 37.16% (95%CI: 36.33%-37.99%). The LASSO results showed that when the lambda (λ) value was 0.008 595 583, the error was the smallest, and 15 influencing factors were screened out: gender, marital status, type of residence, educational level (junior high school, high school and above), region (central region, western region), drinking status, nighttime sleep duration (h) (>6-7, >7), whether there were social activities in the past month, Internet use, whether often uncomfortable due to pain, number of chronic diseases: ≥ 2, physical activity (Met*min/week): 600-3 000. The logistic regression prediction model showed that female gender, other marital statuses, region (central region, western region), often being uncomfortable due to pain, and number of chronic diseases: ≥ 2 were independent risk factors for depressive symptoms in middle-aged and elderly people (P < 0.05); educational level (junior high school, high school and above), drinking, nighttime sleep duration (h) (>6-7, >7), having social activities in the past month, and Internet use were protective factors for depressive symptoms in middle-aged and elderly people (P < 0.05). The area under the receiver operating characteristic curve was 0.743(95%CI: 0.735-0.752).

Conclusion

The detection rate of depressive symptoms among middle-aged and elderly people is relatively high and is affected by multiple factors. It is recommended to consider preventive measures from multiple aspects and perspectives.

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目的

通过对CHARLS 2020年调查数据的分析，探究中国中老年人群抑郁现状及影响因素，为中老年人抑郁的预防提供一定的理论依据。

方法

首先，通过纳排标准纳入了45 ~ 74岁信息完整的13 124名中老年人，其次根据抑郁量表（CES-D10）评分划分研究对象，再使用LASSO模型筛选影响因素，最后构建老年人抑郁症状的logistic预测模型并评估预测效果。

结果

13 124名研究对象中，有抑郁症状者4 877人，检出率37.16%(95%CI：36.33%～37.99%)。LASSO结果显示，当lambda(λ)值为0.008 595 583时，误差最小，筛选出15个影响因素：性别、婚姻状态、居住地类型、教育程度（初中、高中及以上）、所属地区（中部地区、西部地区）、饮酒情况、夜间睡眠时间(>6～7 h、>7 h)、近一个月有无社交活动、上网、是否经常因为疼痛而难受、患慢性病数量≥2种、体力活动（600 ～3 000 Met*分/周）。Logistic回归预测模型显示，女性、其他婚姻状况、所属地区（中部地区、西部地区）、经常因为疼痛而难受、患慢性病数量≥ 2种是中老年人出现抑郁症状的独立危险因素(P<0.05)；教育程度（初中、高中及以上）、饮酒、夜间睡眠时间(>6～7 h、>7 h)、近一个月有社交活动、上网是中老年人出现抑郁症状的保护因素(P<0.05)。受试者工作曲线下面积0.743 (95% CI: 0.735～0.752)。

结论

中老年人抑郁症状的检出率较高且受多种因素的影响，建议从多方面、多角度进行预防措施的考虑。

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通信作者：卢曲琴，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=p02gbGpLDiuQyw5Qt7/P1g==, magXml=RrgGOng/ApIJHDrTCtqsGA==, pdfUrl=null, pdf=pCUPLEGEl+FPfT9tuBRZqg==, pdfFileSize=759136, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=6SNuc9digF79EZID2t8c8g==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=7qeW2sh0yvD3krkfbuQt5w==, mapNumber=null, authorCompany=null, fund=null, authors=

赖文浩（1999—），男，硕士在读，研究方向：流行病与卫生统计学

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CNS &Neurological Disorders Drug Targets, 2024, 23(9): 1085-1100., articleTitle=Circadian rhythms and sleep disorders associated to major depressive disorder:pathophysiology and therapeutic opportunities, refAbstract=null), Reference(id=1241067217677251124, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241067202883941252, doi=null, pmid=null, pmcid=null, year=2023, volume=26, issue=34, pageStart=4329, pageEnd=4335, url=null, language=null, rfNumber=[15], rfOrder=20, authorNames=王越, 陈晴, 刘鲁蓉, journalName=中国全科医学, refType=null, unstructuredReference=王越，陈晴，刘鲁蓉，等.中国老年人抑郁检出率及影响因素的Meta分析[J].中国全科医学，2023，26（34）：4329-4335., articleTitle=中国老年人抑郁检出率及影响因素的Meta分析, refAbstract=null), Reference(id=1241067217782108732, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241067202883941252, doi=null, pmid=null, pmcid=null, year=2023, volume=26, issue=34, pageStart=4329, pageEnd=4335, url=null, language=null, rfNumber=[15], rfOrder=21, authorNames=Wang Y, Chen Q, Liu LR, journalName=Chinese general practice, refType=null, unstructuredReference=Wang Y, Chen Q, Liu LR, et al. Detection rate of depression and its influencing factors in Chinese elderly: a meta-analysis[J]. Chinese general practice, 2023, 26(34): 4329-4335., articleTitle=Detection rate of depression and its influencing factors in Chinese elderly: a meta-analysis, refAbstract=null), Reference(id=1241067217920520775, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241067202883941252, doi=null, pmid=null, pmcid=null, year=2024, volume=15, issue=13, pageStart=2432, pageEnd=2444, url=null, language=null, rfNumber=[16], rfOrder=22, authorNames=Zhang Y, Ma H, Bai YF, journalName=ACS Chemical Neuroscience, refType=null, unstructuredReference=Zhang Y, Ma H, Bai YF, et al. Chronic neuropathic pain and comorbid depression syndrome: from neural circuit mechanisms to treatment[J]. ACS Chemical Neuroscience, 2024, 15(13): 2432-2444., articleTitle=Chronic neuropathic pain and comorbid depression syndrome: from neural circuit mechanisms to treatment, refAbstract=null)], funds=[Fund(id=1241067211402572088, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241067202883941252, awardId=32260143, language=CN, fundingSource=国家自然科学基金地区项目(32260143), fundOrder=null, country=null), Fund(id=1241067211532595523, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241067202883941252, awardId=20202BBG72003, language=CN, fundingSource=江西省重点研发计划(20202BBG72003), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1241067204230312869, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241067202883941252, xref=1., ext=[AuthorCompanyExt(id=1241067204238701478, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241067202883941252, companyId=1241067204230312869, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=School of Public Health, Nanchang University, Nanchang, Jiangxi 330006, China), AuthorCompanyExt(id=1241067204247090087, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241067202883941252, companyId=1241067204230312869, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.南昌大学公共卫生学院，江西 南昌 330006)]), AuthorCompany(id=1241067204301616046, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241067202883941252, xref=2., ext=[AuthorCompanyExt(id=1241067204305810351, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241067202883941252, companyId=1241067204301616046, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.疾病预防与公共卫生江西省重点实验室，江西 南昌 330006)])], figs=[ArticleFig(id=1241067208948904114, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241067202883941252, language=EN, label=Figure 1, caption=Variable screening based on LASSO analysis (a) LASSO coefficient path graph (b) cross-validation curve, figureFileSmall=sOBqBKNan0zfNoQDUSmWrA==, figureFileBig=6SNuc9digF79EZID2t8c8g==, tableContent=null), ArticleFig(id=1241067209053761727, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241067202883941252, language=CN, label=图1, caption=基于LASSO分析的变量筛选（A）LASSO系数路径图（B）交叉验证曲线, figureFileSmall=sOBqBKNan0zfNoQDUSmWrA==, figureFileBig=6SNuc9digF79EZID2t8c8g==, tableContent=null), ArticleFig(id=1241067210655985886, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241067202883941252, language=EN, label=Figure 2, caption=Receiver operating curve (ROC), figureFileSmall=yIGV2X7PrG7eDauwAynLMA==, figureFileBig=sat87qMBSSViGCzjhGK6PA==, tableContent=null), ArticleFig(id=1241067210806980842, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241067202883941252, language=CN, label=图2, caption=受试者工作曲线（ROC）, figureFileSmall=yIGV2X7PrG7eDauwAynLMA==, figureFileBig=sat87qMBSSViGCzjhGK6PA==, tableContent=null), ArticleFig(id=1241067210916032758, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241067202883941252, language=EN, label=Table 1, caption=

Single factor analysis of depressive symptoms in middle-aged and elderly people ［n（%）］

, figureFileSmall=null, figureFileBig=null, tableContent=
变量总体（n=13 124）无抑郁症（n=8 247）抑郁症状（n=4 877）χ2/Z值P值
年龄（岁）82.753<0.001
45～543 534(26.93)2 402(67.97)1 132(32.03)
55～645 161(39.32)3 271(63.38)1 890(36.62)
65～744 429(33.75)2 574(58.12)1 855(41.88)
性别408.629<0.001
男性6 270(47.78)4 497(71.72)1 773(28.28)
女性6 854(52.22)3 750(54.71)3 104(45.29)
教育程度435.533<0.001
小学及以下8 047(61.32)4 511(56.06)3 536(43.94)
初中3 291(25.08)2 338(71.04)953(28.96)
高中及以上1 786(13.61)1 398(78.28)388(21.72)
婚姻状况137.982<0.001
已婚11 740(89.45)7 576(64.53)4 164(35.47)
其他（离异、丧偶、未婚）1 384(10.55)671(48.48)713(51.52)
居住地类型196.977<0.001
农村8 478(64.60)4 960(58.50)3 518(41.50)
城市4 646(35.40)3 287(70.75)1 359(29.25)
所属地区186.847<0.001
东部地区4 033(30.73)2 860(70.91)1 173(29.09)
中部地区3 907(29.77)2 314(59.23)1 593(40.77)
西部地区4 320(32.92)2 497(57.80)1 823(42.20)
东北地区864(6.58)576(66.67)288(33.33)
吸烟情况99.507<0.001
不吸烟（含戒烟）9 616(73.27)5 800(60.32)3 816(39.68)
吸烟3 508(26.73)2 447(69.75)1 061(30.25)
饮酒情况223.018<0.001
否8 052(61.35)4 659(57.86)3 393(42.14)
是5 072(38.65)3 588(70.74)1 484(29.26)
夜间睡眠时间（h）423.894<0.001
≤ 64 502(34.30)2 145(47.65)2 357(52.35)
>6～75 816(44.32)4 076(70.08)1 740(29.92)
>72 806(21.38)2 026(72.20)780(27.80)
午间睡眠时间（h）3.3810.066
≤110 824(82.47)6 763(62.48)4 061(37.52)
>12 300(17.53)1 484(64.52)816(35.48)
近一个月有无社交活动39.457<0.001
无6 397(48.74)3 846(60.12)2 551(39.88)
有6 727(51.26)4 401(65.42)2 326(34.58)
上网259.600<0.001
否6 934(52.83)3 912(56.42)3 022(43.58)
是6 190(47.17)4 335(70.03)1 855(29.97)
是否经常因为疼痛而难受915.661<0.001
否5 539(42.21)4 308(77.78)1 231(22.22)
是7 585(57.79)3 939(51.93)3 646(48.07)
患慢性病数量（种）481.528<0.001
02 601(19.82)1 993(76.62)608(23.38)
12 988(22.77)2 096(70.15)892(29.85)
≥27 535(57.41)4 158(55.18)3 377(44.82)
体力活动（Mets*分/周）68.413<0.001
<6002 196(16.73)1 319(60.06)877(39.94)
600～3 0005 237(39.90)3 516(67.14)1 721(32.86)
>3 0005 691(43.36)3 412(59.95)2 279(40.05)
), ArticleFig(id=1241067211054444809, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241067202883941252, language=CN, label=表1, caption=

中老年人患抑郁症状的单因素分析［n（%）］

, figureFileSmall=null, figureFileBig=null, tableContent=
变量总体（n=13 124）无抑郁症（n=8 247）抑郁症状（n=4 877）χ2/Z值P值
年龄（岁）82.753<0.001
45～543 534(26.93)2 402(67.97)1 132(32.03)
55～645 161(39.32)3 271(63.38)1 890(36.62)
65～744 429(33.75)2 574(58.12)1 855(41.88)
性别408.629<0.001
男性6 270(47.78)4 497(71.72)1 773(28.28)
女性6 854(52.22)3 750(54.71)3 104(45.29)
教育程度435.533<0.001
小学及以下8 047(61.32)4 511(56.06)3 536(43.94)
初中3 291(25.08)2 338(71.04)953(28.96)
高中及以上1 786(13.61)1 398(78.28)388(21.72)
婚姻状况137.982<0.001
已婚11 740(89.45)7 576(64.53)4 164(35.47)
其他（离异、丧偶、未婚）1 384(10.55)671(48.48)713(51.52)
居住地类型196.977<0.001
农村8 478(64.60)4 960(58.50)3 518(41.50)
城市4 646(35.40)3 287(70.75)1 359(29.25)
所属地区186.847<0.001
东部地区4 033(30.73)2 860(70.91)1 173(29.09)
中部地区3 907(29.77)2 314(59.23)1 593(40.77)
西部地区4 320(32.92)2 497(57.80)1 823(42.20)
东北地区864(6.58)576(66.67)288(33.33)
吸烟情况99.507<0.001
不吸烟（含戒烟）9 616(73.27)5 800(60.32)3 816(39.68)
吸烟3 508(26.73)2 447(69.75)1 061(30.25)
饮酒情况223.018<0.001
否8 052(61.35)4 659(57.86)3 393(42.14)
是5 072(38.65)3 588(70.74)1 484(29.26)
夜间睡眠时间（h）423.894<0.001
≤ 64 502(34.30)2 145(47.65)2 357(52.35)
>6～75 816(44.32)4 076(70.08)1 740(29.92)
>72 806(21.38)2 026(72.20)780(27.80)
午间睡眠时间（h）3.3810.066
≤110 824(82.47)6 763(62.48)4 061(37.52)
>12 300(17.53)1 484(64.52)816(35.48)
近一个月有无社交活动39.457<0.001
无6 397(48.74)3 846(60.12)2 551(39.88)
有6 727(51.26)4 401(65.42)2 326(34.58)
上网259.600<0.001
否6 934(52.83)3 912(56.42)3 022(43.58)
是6 190(47.17)4 335(70.03)1 855(29.97)
是否经常因为疼痛而难受915.661<0.001
否5 539(42.21)4 308(77.78)1 231(22.22)
是7 585(57.79)3 939(51.93)3 646(48.07)
患慢性病数量（种）481.528<0.001
02 601(19.82)1 993(76.62)608(23.38)
12 988(22.77)2 096(70.15)892(29.85)
≥27 535(57.41)4 158(55.18)3 377(44.82)
体力活动（Mets*分/周）68.413<0.001
<6002 196(16.73)1 319(60.06)877(39.94)
600～3 0005 237(39.90)3 516(67.14)1 721(32.86)
>3 0005 691(43.36)3 412(59.95)2 279(40.05)
), ArticleFig(id=1241067211180273946, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241067202883941252, language=EN, label=Table 2, caption=

Multivariate logistic regression analysis of senile depression

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变量偏回归系数标准误Z值P值OR值(95%CI)
性别：女（以男为参照）0.4340.0469.507<0.0011.544 (1.412～1.689)
教育程度：初中（以小学及以下为参照）-0.2810.051-5.558<0.0010.755 (0.684～0.834)
教育程度：高中及以上（以小学及以下为参照）-0.4480.071-6.343<0.0010.639 (0.555～0.733)
婚姻状况：其他（以已婚为参照）0.4400.0636.939<0.0011.552 (1.371～1.758)
居住地类型：城市（以农村为参照）-0.3580.045-7.957<0.0010.699 (0.640～0.763)
所属地区：中部地区（以东部地区为参照）0.4670.0499.474<0.0011.596 (1.449～1.758)
所属地区：西部地区（以东部地区为参照）0.3420.0487.119<0.0011.408 (1.282～1.548)
饮酒情况：是（以否为参照）-0.1670.047-3.582<0.0010.846 (0.772～0.927)
夜间睡眠时间（h）：>6～7（以 ≤ 6为参照）-0.6920.045-15.532<0.0010.500 (0.459～0.546)
夜间睡眠时间（h）：>7（以 ≤ 6为参照）-0.8470.056-15.235<0.0010.429 (0.384～0.478)
近一个月有无社交活动：有（以无为参照）-0.1590.041-3.905<0.0010.853 (0.788～0.924)
上网：是（以否为参照）-0.2770.044-6.325<0.0010.758 (0.695～0.826)
是否经常因为疼痛而难受：是（以否为参照）0.8380.04419.223<0.0012.311 (2.122～2.517)
体力活动（Met*分/周）：600 ～3 000（以<600为参照）-0.1990.042-4.750<0.0010.820 (0.755～0.890)
患慢性病数量：≥2种（以0为参照）0.4770.04211.273<0.0011.612 (1.484～1.752)
), ArticleFig(id=1241067211251577126, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241067202883941252, language=CN, label=表2, caption=

中老年抑郁症多因素logistic 回归分析

, figureFileSmall=null, figureFileBig=null, tableContent=
变量偏回归系数标准误Z值P值OR值(95%CI)
性别：女（以男为参照）0.4340.0469.507<0.0011.544 (1.412～1.689)
教育程度：初中（以小学及以下为参照）-0.2810.051-5.558<0.0010.755 (0.684～0.834)
教育程度：高中及以上（以小学及以下为参照）-0.4480.071-6.343<0.0010.639 (0.555～0.733)
婚姻状况：其他（以已婚为参照）0.4400.0636.939<0.0011.552 (1.371～1.758)
居住地类型：城市（以农村为参照）-0.3580.045-7.957<0.0010.699 (0.640～0.763)
所属地区：中部地区（以东部地区为参照）0.4670.0499.474<0.0011.596 (1.449～1.758)
所属地区：西部地区（以东部地区为参照）0.3420.0487.119<0.0011.408 (1.282～1.548)
饮酒情况：是（以否为参照）-0.1670.047-3.582<0.0010.846 (0.772～0.927)
夜间睡眠时间（h）：>6～7（以 ≤ 6为参照）-0.6920.045-15.532<0.0010.500 (0.459～0.546)
夜间睡眠时间（h）：>7（以 ≤ 6为参照）-0.8470.056-15.235<0.0010.429 (0.384～0.478)
近一个月有无社交活动：有（以无为参照）-0.1590.041-3.905<0.0010.853 (0.788～0.924)
上网：是（以否为参照）-0.2770.044-6.325<0.0010.758 (0.695～0.826)
是否经常因为疼痛而难受：是（以否为参照）0.8380.04419.223<0.0012.311 (2.122～2.517)
体力活动（Met*分/周）：600 ～3 000（以<600为参照）-0.1990.042-4.750<0.0010.820 (0.755～0.890)
患慢性病数量：≥2种（以0为参照）0.4770.04211.273<0.0011.612 (1.484～1.752)
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我国中老年人抑郁现状及影响因素的分析——基于LASSO-logistic模型
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赖文浩 1, 2 , 胡佳康 1, 2 , 李德富 1, 2 , 宋灿 1, 2 , 卢曲琴 1, 2
现代预防医学 | 健康与社会行为 2025,52(5): 875-879
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现代预防医学 | 健康与社会行为 2025, 52(5): 875-879
我国中老年人抑郁现状及影响因素的分析——基于LASSO-logistic模型
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赖文浩1, 2, 胡佳康1, 2, 李德富1, 2, 宋灿1, 2, 卢曲琴1, 2
作者信息
  • 1.南昌大学公共卫生学院，江西 南昌 330006
  • 2.疾病预防与公共卫生江西省重点实验室，江西 南昌 330006

    • 赖文浩（1999—），男，硕士在读，研究方向：流行病与卫生统计学

通讯作者:

通信作者：卢曲琴，E-mail:
Analysis of the current situation and influencing factors of depression among middle-aged and elderly people in China-based on the LASSO-logistic model
Wen-hao LAI1, 2, Jia-kang HU1, 2, De-fu LI1, 2, Can SONG1, 2, Qu-qin LU1, 2
Affiliations
  • School of Public Health, Nanchang University, Nanchang, Jiangxi 330006, China
出版时间: 2025-03-10 doi: 10.20043/j.cnki.MPM.202410135
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摘要
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目的

通过对CHARLS 2020年调查数据的分析，探究中国中老年人群抑郁现状及影响因素，为中老年人抑郁的预防提供一定的理论依据。

方法

首先，通过纳排标准纳入了45 ~ 74岁信息完整的13 124名中老年人，其次根据抑郁量表（CES-D10）评分划分研究对象，再使用LASSO模型筛选影响因素，最后构建老年人抑郁症状的logistic预测模型并评估预测效果。

结果

13 124名研究对象中，有抑郁症状者4 877人，检出率37.16%(95%CI：36.33%～37.99%)。LASSO结果显示，当lambda(λ)值为0.008 595 583时，误差最小，筛选出15个影响因素：性别、婚姻状态、居住地类型、教育程度（初中、高中及以上）、所属地区（中部地区、西部地区）、饮酒情况、夜间睡眠时间(>6～7 h、>7 h)、近一个月有无社交活动、上网、是否经常因为疼痛而难受、患慢性病数量≥2种、体力活动（600 ～3 000 Met*分/周）。Logistic回归预测模型显示，女性、其他婚姻状况、所属地区（中部地区、西部地区）、经常因为疼痛而难受、患慢性病数量≥ 2种是中老年人出现抑郁症状的独立危险因素(P<0.05)；教育程度（初中、高中及以上）、饮酒、夜间睡眠时间(>6～7 h、>7 h)、近一个月有社交活动、上网是中老年人出现抑郁症状的保护因素(P<0.05)。受试者工作曲线下面积0.743 (95% CI: 0.735～0.752)。

结论

中老年人抑郁症状的检出率较高且受多种因素的影响，建议从多方面、多角度进行预防措施的考虑。

抑郁  /  影响因素  /  中老年人  /  LASSO回归  /  Logistic回归
Objective

To explore the current situation and influencing factors of depression among middle-aged and elderly people in China through the analysis of the 2020 survey data of CHARLS, and to provide a theoretical basis for the prevention of depression among middle-aged and elderly people.

Methods

First, 13 124 middle-aged and elderly people aged 45 to 74 with complete information were included according to the inclusion and exclusion criteria. Second, the research subjects were divided according to the scores of the Center for Epidemiologic Studies Depression Scale (CES-D10). Then, the Lasso model was used to screen the influencing factors. Finally, a logistic prediction model for depressive symptoms in the elderly was constructed and the prediction effect was evaluated.

Results

Among the 13 124 research subjects, 4 877 had depressive symptoms, with a detection rate of 37.16% (95%CI: 36.33%-37.99%). The LASSO results showed that when the lambda (λ) value was 0.008 595 583, the error was the smallest, and 15 influencing factors were screened out: gender, marital status, type of residence, educational level (junior high school, high school and above), region (central region, western region), drinking status, nighttime sleep duration (h) (>6-7, >7), whether there were social activities in the past month, Internet use, whether often uncomfortable due to pain, number of chronic diseases: ≥ 2, physical activity (Met*min/week): 600-3 000. The logistic regression prediction model showed that female gender, other marital statuses, region (central region, western region), often being uncomfortable due to pain, and number of chronic diseases: ≥ 2 were independent risk factors for depressive symptoms in middle-aged and elderly people (P < 0.05); educational level (junior high school, high school and above), drinking, nighttime sleep duration (h) (>6-7, >7), having social activities in the past month, and Internet use were protective factors for depressive symptoms in middle-aged and elderly people (P < 0.05). The area under the receiver operating characteristic curve was 0.743(95%CI: 0.735-0.752).

Conclusion

The detection rate of depressive symptoms among middle-aged and elderly people is relatively high and is affected by multiple factors. It is recommended to consider preventive measures from multiple aspects and perspectives.

Depression  /  Influencing factors  /  Middle-aged and elderly people  /  Lasso regression  /  Logistic regression
赖文浩, 胡佳康, 李德富, 宋灿, 卢曲琴. 我国中老年人抑郁现状及影响因素的分析——基于LASSO-logistic模型. 现代预防医学, 2025 , 52 (5) : 875 -879 . DOI: 10.20043/j.cnki.MPM.202410135
Wen-hao LAI, Jia-kang HU, De-fu LI, Can SONG, Qu-qin LU. Analysis of the current situation and influencing factors of depression among middle-aged and elderly people in China-based on the LASSO-logistic model[J]. Modern Preventive Medicine, 2025 , 52 (5) : 875 -879 . DOI: 10.20043/j.cnki.MPM.202410135
正文
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抑郁症是一种常见的精神障碍，涉及长时间情绪低落或失去快乐或活动的兴趣。世界上大约有3.5亿人患有抑郁症[1]。抑郁症的发生对全世界的心理和身体健康产生不利影响，使其成为一个重大的公共卫生问题[2]。识别中老年人罹患抑郁症状的危险因素，对于提高中老年人心理健康，减轻疾病负担具有重要意义。既往研究大多使用logistic回归等传统方法来分析并识别中老年人患抑郁症状的危险因素[3-4]，无法体现各影响因素的干预优先程度。本研究引入LASSO算法对中老年人抑郁症状的影响因素进行筛选，再进行逐步多因素logistic回归分析经筛选后的影响因素，初步构建中老年人群患抑郁症的风险模型，以便为提供更精确的中老年人心理健康预防措施提供参考。
1　对象与方法
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1.1　研究对象
中国健康与养老追踪调查（China Health and Retirement Longitudinal Study,CHARLS）是一套代表中国45岁及以上中老年人家庭和个人的高质量数据，用以分析我国人口老龄化问题[5]。考虑到75岁及以上老年人体力活动可能存在不便，本研究纳入的中老年人群为45～74岁的成年人。本研究基于CHARLS数据库2020年的数据，研究对象的纳入排除标准为：（1）纳入研究的变量信息完整的研究对象。（2）排除年龄范围不在45～74岁的研究对象。（3）排除抑郁量表（Center for Epidemiologic Studies Depression Scale，CES-D10）信息不全的研究对象。最终纳入13 124名研究对象。
1.2　研究方法
1.2.1　抑郁状况
采用抑郁量表（Center for Epidemiologic Studies Depression Scale，CES-D10）评价抑郁状况，该量表包括10个自评问题，其中有2个正向条目，8个负向条目，每个问题均有4个选项：“很少或者根本没有”赋值0分、“不太多”赋值1分、“有时或者说有一半的时间”赋值2分、“大多数的时间”赋值3分，正向条目需负向记分；抑郁总得分≥10分为有抑郁症状，<10分为无抑郁症状。
1.2.2　自变量
基于2020年CHARLS的数据信息，纳入已有研究[6]认为可能与中老年人抑郁相关的影响因素，包括三个方面：社会人口学特征（性别、年龄、教育程度、婚姻状况、居住地类型、所属地区）、健康相关行为（吸烟、饮酒、夜间睡眠时间、午间睡眠时间、近一个月有无社交活动、体力活动）、健康状况（是否经常因为疼痛而难受、慢性病患病数量）。
1.3　统计学方法
分类变量用［n（%）］描述，组间比较用χ2检验。通过构建LASSO logistic模型识别可能存在的影响因素。将筛选出的变量运用多因素logistic回归进行分析，并根据logistic回归分析结果绘制受试者工作特征曲线（ROC）并计算曲线下面积（AUC），以评估模型预测效果。使用R4.3.2进行数据分析,以α<0.05为检验水准。
2　结果
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2.1　研究对象的基本情况及单因素分析
本研究共纳入研究对象13 124人，有抑郁症状的4 877人，检出率为37.16%（95%CI：36.33%～37.99%）。有抑郁症和无抑郁症状的中老年人在性别、年龄、教育程度、婚姻状况、居住地类型、所属地区、吸烟、饮酒、夜间睡眠时间、体力活动、近一个月有无社交活动、是否经常因为疼痛而难受、慢性病患病数量中比较，差异有统计学意义（P<0.05），而在午间睡眠时间比较中差异无统计学意义（P>0.05）。见表1。
2.2　构建LASSO模型，筛选影响因素
将研究对象的所有变量纳入LASSO模型，当lambda（λ）值为0.008 595 583（右侧垂直虚线）时，误差最小，对应的影响因素数目为15：年龄、性别、婚姻状态、居住地类型、教育程度（初中和高中及以上）、所属地区（中部地区和西部地区）、饮酒情况、夜间睡眠时间（>6～7 h和>7 h）、近一个月有无社交活动、上网、是否经常因为疼痛而难受、患慢性病数量≥2种、体力活动为600～3 000 Mets*分/周。见图1。
2.3　构建多因素分析的logistic回归模型
将LASSO回归筛选的变量纳入logistic回归（逐步法），选择P<0.05的变量建立预测模型。分析结果显示：女性［OR（95%CI）：1.544（1.412～1.689）］、其他婚姻状况［OR（95%CI）：1.552（1.371～1.758）］、所属地区为中部地区［OR（95%CI）：1.596 (1.449～1.758）］和西部地区［OR（95%CI）：1.408（1.28～1.548）］、经常因为疼痛而难受［OR（95%CI）：2.311（2.122～2.517）］、患慢性病数量≥ 2种［OR（95%CI）：1.612（1.484～1.752）］是中老年人出现抑郁症状的独立危险因素（P<0.05）；教育程度为初中［OR（95%CI）：0.755（0.684～0.834）］和高中及以上［OR（95%CI）：0.639（0.555～0.733）］、饮酒［OR（95%CI）：0.846（0.772～0.927）］、夜间睡眠时间>6～7 h［OR（95%CI）：0.500（0.459～0.546）］和>7 h［OR（95%CI）：0.429（0.384～0.478）］、近一个月有社交活动［OR（95%CI）：0.853（0.788～0.924）］、上网［OR（95%CI）：0.758（0.695～0.826）］是中老年人出现抑郁症状的保护因素（P<0.05）。见表2。
2.4　预测模型的评估
根据多因素logistic回归结果，构建受试者工作特征曲线（ROC），并计算曲线下面积（AUC）=0.743（95%CI：0.735～0.752）。结果显示本模型对于中老年人患有抑郁症状具有良好的预测效果。见图2。
3　讨论
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3.1　我国中老老年人患抑郁症状现状分析
本研究显示，2020年中国中老年人群患抑郁症状的检出率为4 877人，检出率为37.16%（95%CI：36.33%～37.99%），略微高于段芮等[7]基于2018年CHARLS数据得出的35%的结果，这可能与新型冠状病毒期间，中老年人多居家活动，受包括社会参与减少在内的多种因素影响有关。中老年人抑郁症状检出率在不同性别、年龄、教育程度、婚姻状况、居住地类型、所属地区、吸烟、饮酒、夜间睡眠时间、午间睡眠时间、近一个月有无社交活动、是否经常因为疼痛而难受、体力活动、慢性病患病数量中比较有差异，这与以往的研究结果相符合[8-9]。
3.2　中老年人患抑郁症状的影响因素分析
本研究显示，相较于男性，女性更容易患上抑郁症。这可能是在家庭中，女性往往更需要面临一些如家务或后代的抚养等繁琐的问题，并且，中老年女性或许更容易因更年期的存在而出现情绪波动[10]。文化程度作为中老年人患抑郁症状的保护因素，这与之前的研究结果一致[11]，可能是由于文化程度越高的人越能掌控自己的情绪，并且能即使察觉自己的心理问题，而后通过自己所掌握的资源进行疏导解决有关[12]。其他婚姻状况者患抑郁的风险更高，这可能是因为缺乏配偶的中老年人更可能是独自生活，这不利于该人群缓解疾病负担和心理压力。居住在农村、所属地区为中部和和西部地区的中老年人更容易患上抑郁症，可能是由于乡村或者中西部地区作为欠发达地区，能够获得的如医疗等的社会资源较为有限[13]，心理健康等问题的出现往往因资源的相对缺乏而不能即使解决，居民幸福感较低。
研究发现，饮酒者的患抑郁症状的风险更低，这可能是由于中老年人多为餐时饮酒，愉快的饮酒氛围能够使其身心愉悦，并且适当的饮酒还能降低心脑血管疾病的发生，减少中老年人的疾病负担。夜间睡眠时间较长者其患抑郁的风险降低，一方面可能是因为长睡眠时间不易导致炎症标志物的增加[14]，另外，睡眠时间较长也说明该人群半夜觉醒的可能性较低，其更容易享受完整的睡眠过程，这可以让他们在白天保持充沛的精神状态，从而降低消极情绪的出现可能。近一个月有无社交活动、上网、有适当的体力活动的中老年人患抑郁的风险也降低。原因可能是拥有以上特征的中老年人更容易与社会产生交集，在增加归属感的同时还能使得他们的生活不会因为单调而表的枯燥，并且，适当的体力活动还有助于维持中老年人的身体机能，保持身体的健康。
本研究发现，经常因为疼痛而难受、患慢性病数量≥2种的中老年人患抑郁的风险更高，这与王越等[15]的结论一致。一方面，疼痛会降低中老年人的日常生活质量[16]，并且，还可能会让该人群倾向于消极态度来面对生活；另一方面，患病也可能会令老年人因自卑或行动不便等理由而呆在家里，减少了与人沟通机会，更容易出现负面情绪，引发抑郁[9]。
根据预测模型的结果显示，影响中老年人的患抑郁的因子是多维度、多层次的。为了促进中老年人身心健康，应促进经济欠发达地区的经济发展，促进相关领域的资源分配，及时为中老年人提供必要的社会支持，降低抑郁发生的风险。个人应保持良好生活习惯，多与社会保持接触，在充实自己的生活的同时保证身心健康，同时，感到身体不适时应积极就医，降低可改善的疾病带来的身体及经济负担。社区在加强社区服务建设的同时也应该关注中老年人（尤其是独居人群）身心健康，积极引导该人群增加跟外界接触的机会。
本研究存在一定的局限性：一方面，利用CHARLS 的横断面研究数据得出的结论无法得出变量与结局之间的因果性，相关变量的调查可能存在回忆偏倚。另一方面，本研究中抑郁症状表现只是以量表的分数结果进行划分，而不是精确的临床诊断结局。未来可以有研究以临床诊断的抑郁症为结局变量，以使研究结论更准确。
基金
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  • 国家自然科学基金地区项目(32260143)
  • 江西省重点研发计划(20202BBG72003)
文献
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2025年第52卷第5期
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doi: 10.20043/j.cnki.MPM.202410135
  • 接收时间：2024-10-12
  • 首发时间：2026-03-18
  • 出版时间：2025-03-10
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  • 收稿日期：2024-10-12
基金
国家自然科学基金地区项目(32260143)
江西省重点研发计划(20202BBG72003)
作者信息
    1.南昌大学公共卫生学院，江西 南昌 330006
    2.疾病预防与公共卫生江西省重点实验室，江西 南昌 330006

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