Article(id=1241319150895813237, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241319148798669160, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202411274, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1731513600000, receivedDateStr=2024-11-14, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773883148167, onlineDateStr=2026-03-19, pubDate=1750780800000, pubDateStr=2025-06-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773883148167, onlineIssueDateStr=2026-03-19, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773883148167, creator=13701087609, updateTime=1773883148167, updator=13701087609, issue=Issue{id=1241319148798669160, tenantId=1146029695717560320, journalId=1227665162245664772, year='2025', volume='52', issue='12', pageStart='2113', pageEnd='2304', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773883147667, creator=13701087609, updateTime=1773885555254, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241329247004971040, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241319148798669160, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241329247004971041, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241319148798669160, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=2152, endPage=2158, ext={EN=ArticleExt(id=1241319151164248698, articleId=1241319150895813237, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Analysis and prediction of related factors of depression in middle-aged and elderly patients with chronic diseases based on health ecology model, China, columnId=1228016567443718970, journalTitle=Modern Preventive Medicine, columnName=Epidemiology and Statistical Methods Advances, runingTitle=null, highlight=null, articleAbstract=
Objective

To explore the factors of depression symptoms in middle-aged and elderly patients with chronic diseases in China and establish a risk prediction model to provide a reference for achieving healthy aging.

Methods

Based on data from the fifth waves of the China Health and Retirement Longitudinal Survey, patients with chronic diseases aged ≥ 45 years were included in the study and randomly divided into training and testing sets at a ratio of 7:3. Based on the Health Ecology Model, factors were analyzed by chi-square test and binary logistic regression and a nomogram was constructed. The performance of the risk prediction model was evaluated using the area under the receiver operating characteristic curve, calibration curves, and the Hosmer-Lemeshow test.

Results

The prevalence incidence of depression symptoms among middle-aged and elderly patients with chronic diseases was 49.71%. The results showed that gender, age, number of chronic diseases, BADL status, IADL status in the personal characteristics layer, late sleep duration and whether often bothered by pain in the behavior pattern layer, type of residence and satisfaction with children in the interpersonal network layer, education level and availability of shower facilities in the life and work layer were significantly correlated with depression symptoms among middle-aged and elderly patients with chronic diseases. The AUC values for the training and testing sets of depression symptoms risk prediction model among middle-aged and elderly participants with chronic diseases in China were 0.762 and 0.753, respectively. The calibration curve demonstrated good agreement between predicted and actual probabilities, and the Hosmer-Lemeshow test (P>0.05) confirmed the model’s satisfactory predictive performance.

Conclusion

The prevalence incidence of depression symptoms is relatively high among middle-aged and elderly patients with chronic diseases in China. Improving the quality of life of this population across multiple dimensions is essential for promoting healthy aging.

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目的

探索我国中老年慢性病患者抑郁症状的影响因素并建立风险预测模型,为实现健康老龄化提供参考。

方法

利用中国健康与养老追踪调查第五轮数据,选取年龄≥45岁的慢性病患者纳入研究,按7:3的比例随机分为训练集和测试集。基于健康生态学模型纳入相关因素,采用χ2检验和二元logistic 回归分析影响因素并绘制列线图。通过受试者工作特征曲线下面积、校准曲线、Hosmer-Lemeshow检验评价风险预测模型。

结果

中老年慢性病患者抑郁症状发生率为49.71%。结果显示,个人特征层中性别、年龄、慢性病数量、BADL状况、IADL状况,行为方式层中晚睡时长、是否常因疼痛而烦恼,人际网络层中居住地类型、对子女是否满意,生活工作层中教育水平状况、是否有淋浴设施,上述因素与我国中老年慢性病患者抑郁发生显著相关。中老年慢性病患者抑郁症状风险预测模型训练集和测试集AUC分别为0.762和0.753,校准曲线显示预测概率与实际概率具有较高的一致性,Hosmer-Lemeshow检验P>0.05,显示模型预测效果良好。

结论

我国中老年慢性病患者抑郁症状检出率较高。应从多层次、多维度提高中老年人群生活质量,促进健康老龄化。

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胡军,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=uKdFtAoiX7CHpA7YzkEk0A==, magXml=knaPOwa+YGyBFHRtkXxbyg==, pdfUrl=null, pdf=crUVoSkKEn2BRaHUKMEOqw==, pdfFileSize=994185, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=pl/ZUDlEHRItRQJiY77PPw==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=HZzFKo16XZxuhiZidOapvA==, mapNumber=null, authorCompany=null, fund=null, authors=

李健玮(2000—),男,硕士在读,研究方向:流行病与卫生统计学

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(In Chinese), articleTitle=The role of children in the life satisfaction and mental health of middle-aged and elderly people, refAbstract=null), Reference(id=1241319166918054198, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319150895813237, doi=null, pmid=null, pmcid=null, year=2025, volume=28, issue=11, pageStart=1303, pageEnd=1308, url=null, language=null, rfNumber=[22], rfOrder=28, authorNames=魏萱, 王宁, 魏颖, journalName=中国全科医学, refType=null, unstructuredReference=魏萱,王宁,魏颖,等.我国中老年慢性病患者抑郁现状及其影响因素分析:基于CHARLS数据实证分析[J].中国全科医学2025,28(11):1303-1308., articleTitle=我国中老年慢性病患者抑郁现状及其影响因素分析:基于CHARLS数据实证分析, refAbstract=null), Reference(id=1241319167043883324, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319150895813237, doi=null, pmid=null, pmcid=null, year=2025, volume=28, issue=11, pageStart=1303, pageEnd=1308, url=null, language=null, rfNumber=[22], rfOrder=29, authorNames=Wei X, Wang N, Wei Y, journalName=Chinese General Practice, refType=null, unstructuredReference=Wei X, Wang N, Wei Y, et al. Analysis of depression status and influencing factors in middle-aged and elderly patients with chronic diseases in China:an empirical analysis based on CHARLS data[J]. Chinese General Practice, 2025, 28(11): 1303-1308. (In Chinese), articleTitle=Analysis of depression status and influencing factors in middle-aged and elderly patients with chronic diseases in China:an empirical analysis based on CHARLS data, refAbstract=null)], funds=null, companyList=[AuthorCompany(id=1241319153882157737, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319150895813237, xref=1., ext=[AuthorCompanyExt(id=1241319153890546346, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319150895813237, companyId=1241319153882157737, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=School of Public Health, Shandong Second Medical University, Weifang, Shandong 261053, China), AuthorCompanyExt(id=1241319153894740651, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319150895813237, companyId=1241319153882157737, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.山东第二医科大学公共卫生学院,山东 潍坊 261053)]), AuthorCompany(id=1241319153991209652, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319150895813237, xref=2., ext=[AuthorCompanyExt(id=1241319153999598260, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319150895813237, companyId=1241319153991209652, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.胶州市疾病预防控制中心)]), AuthorCompany(id=1241319154079290040, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319150895813237, xref=3., ext=[AuthorCompanyExt(id=1241319154087678648, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319150895813237, companyId=1241319154079290040, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=3.青岛市第八人民医院)]), AuthorCompany(id=1241319154196730556, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319150895813237, xref=4., ext=[AuthorCompanyExt(id=1241319154205119166, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319150895813237, companyId=1241319154196730556, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=4.山东中医药大学卫生管理学院)])], figs=[ArticleFig(id=1241319158936294325, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319150895813237, language=EN, label=Fig.1, caption=Schematic diagram of the health ecology model, figureFileSmall=pv2Ws7XlErljsl+awrrWlw==, figureFileBig=pl/ZUDlEHRItRQJiY77PPw==, tableContent=null), ArticleFig(id=1241319159062123460, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319150895813237, language=CN, label=图1, caption=健康生态学模型示意图, figureFileSmall=pv2Ws7XlErljsl+awrrWlw==, figureFileBig=pl/ZUDlEHRItRQJiY77PPw==, tableContent=null), ArticleFig(id=1241319159343141845, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319150895813237, language=EN, label=Fig.2, caption=Nomogram of the risk of depression symptoms in middle-aged and elderly patients with chronic diseases, figureFileSmall=GTaljYCIjiWy6CPcI+QVsA==, figureFileBig=uOUb3xugi4I54ADQItx6ig==, tableContent=null), ArticleFig(id=1241319159506719710, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319150895813237, language=CN, label=图2, caption=中老年慢性病患者抑郁症状的风险列线图, figureFileSmall=GTaljYCIjiWy6CPcI+QVsA==, figureFileBig=uOUb3xugi4I54ADQItx6ig==, tableContent=null), ArticleFig(id=1241319159636743144, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319150895813237, language=EN, label=Fig.3, caption=ROC curve of receiver operating characteristics, figureFileSmall=qSUAF+gHebolQ3ryFuFJVA==, figureFileBig=LrmVXZ55ed23buFr4bYMaA==, tableContent=null), ArticleFig(id=1241319159783543801, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319150895813237, language=CN, label=图3, caption=受试者工作特征ROC曲线

注:左为训练集,右为测试集。

, figureFileSmall=qSUAF+gHebolQ3ryFuFJVA==, figureFileBig=LrmVXZ55ed23buFr4bYMaA==, tableContent=null), ArticleFig(id=1241319159921955840, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319150895813237, language=EN, label=Fig.4, caption=Depression symptoms risk calibration curve of middle-aged and elderly patients with chronic diseases, figureFileSmall=N1pm9aHvOOev7LiouTYeGw==, figureFileBig=j8exgoZMic1/QtmH/U7ZwA==, tableContent=null), ArticleFig(id=1241319160018423818, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319150895813237, language=CN, label=图4, caption=中老年慢性病患者抑郁症状风险预测模型的校准曲线

注:左为训练集,右为测试集。

, figureFileSmall=N1pm9aHvOOev7LiouTYeGw==, figureFileBig=j8exgoZMic1/QtmH/U7ZwA==, tableContent=null), ArticleFig(id=1241319160123281434, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319150895813237, language=EN, label=Table 1, caption=

Univariate chi-square test of depression symptoms in middle-aged and elderly patients with chronic diseases

, figureFileSmall=null, figureFileBig=null, tableContent=
健康生态学维度变量人数
[n (%)]
有抑郁症状组
[n (%)]
无抑郁症状组
[n (%)]
χ2P
个人特质性别171.231<0.001
2 618(46.28)1 562(54.90)1 056(37.55)
3 039(53.72)1 283(45.10)1 756(62.45)
年龄(岁)8.8690.012
45~592 396(42.35)1 259(44.25)1 137(40.43)
60~742 663(47.07)1 288(45.27)1 375(48.90)
≥75598(10.58)298(10.47)300(10.67)
慢性病数量(种)97.079<0.001
13 719(65.74)2 043(71.81)1 676(59.60)
2~31 718(30.37)724(25.45)994(35.35)
≥4220(3.89)78(2.74)142(5.05)
BADL状况122.022<0.001
受损369(6.52)83(2.92)286(10.17)
未受损5 288(93.48)2 762(97.08)2 526(89.83)
IADL状况280.641<0.001
受损974(17.22)252(8.86)922(25.68)
未受损4 683(82.78)2 593(91.14)2 090(74.32)
行为特征吸烟36.874<0.001
1 414(25.00)810(28.47)604(21.48)
4 323(75.00)2 035(71.53)2 208(78.52)
喝酒79.203<0.001
2 008(35.50)1 170(41.12)838(29.80)
3 649(64.50)1 675(58.88)1 974(70.20)
晚睡时长(h)366.356<0.001
<62 241(39.61)775(27.24)1 466(52.13)
6~83 054(53.99)1 852(65.10)1 202(42.75)
>8362(6.40)218(7.66)144(5.12)
是否午睡27.277<0.001
3 665(64.79)1 937(68.08)1 728(61.45)
1 992(35.21)908(31.92)1 084(38.55)
是否常因疼痛而难受504.893<0.001
完全没有1 799(31.80)1 222(42.95)577(20.52)
较少2 454(43.38)1 231(43.27)1 223(43.49)
较多1 404(24.82)392(13.78)1 012(35.99)
人际网络居住地类型102.426<0.001
城镇1 484(26.24)876(30.79)608(21.64)
城乡结合730(12.90)423(14.87)307(10.93)
乡村3 443(60.86)1 546(54.34)1 895(67.44)
是否有配偶或伴侣29.902<0.001
4 807(84.97)2 491(87.56)2 316(82.36)
850(15.03)354(12.44)496(17.64)
对子女是否满意118.056<0.001
满意5 333(94.27)2 777(97.61)2 556(90.90)
不满意或无子女324(5.73)68(2.39)256(9.10)
社交活动状况31.548<0.001
2 694(47.62)1 272(44.71)1 422(50.47)
1~3项2 827(49.97)1 481(52.06)1 346(47.87)
≥4项136(2.41)92(3.23)44(1.56)
子女数量36.647<0.001
05(0.09)3(0.11)2(0.07)
1~34 661(82.39)2 430(85.41)2 231(79.34)
≥4991(17.52)412(14.48)579(20.59)
生活与工作教育水平状况149.821<0.001
小学及以下3 578(63.25)1 593(55.99)1 985(70.59)
初中或高中1 780(31.46)1 036(36.41)744(26.46)
中专及以上299(5.29)216(7.59)83(2.95)
在过去一年有收入57.798<0.001
1 298(22.95)773(27.17)525(18.67)
4 359(77.05)2 072(72.83)2 287(81.33)
是否办理退休手续0.3020.582
3 587(63.41)1 794(63.06)1 793(63.76)
2 070(36.59)1 051(36.94)1 019(36.24)
是否有电0.4510.502
5 617(99.29)2 827(99.37)2 790(99.22)
40(0.71)18(0.63)22(0.78)
是否有水10.3710.001
5 136(90.79)2 618(92.02)2 518(89.54)
521(9.21)294(7.98)227(10.46)
是否有洗澡设施106.028<0.001
4 081(72.14)2 226(78.24)1 855(65.97)
1 576(27.86)619(21.76)957(34.03)
政策环境社会养老保险0.75880.384
4 883(86.32)2 467(86.71)2 416(85.92)
774(13.68)378(13.29)396(14.08)
社会医疗保险0.7960.372
5 423(95.86)2 734(96.10)2 689(95.63)
234(4.14)111(3.90)123(4.37)
), ArticleFig(id=1241319160286859306, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319150895813237, language=CN, label=表1, caption=

中老年慢性病病患者抑郁症状的单因素分析

, figureFileSmall=null, figureFileBig=null, tableContent=
健康生态学维度变量人数
[n (%)]
有抑郁症状组
[n (%)]
无抑郁症状组
[n (%)]
χ2P
个人特质性别171.231<0.001
2 618(46.28)1 562(54.90)1 056(37.55)
3 039(53.72)1 283(45.10)1 756(62.45)
年龄(岁)8.8690.012
45~592 396(42.35)1 259(44.25)1 137(40.43)
60~742 663(47.07)1 288(45.27)1 375(48.90)
≥75598(10.58)298(10.47)300(10.67)
慢性病数量(种)97.079<0.001
13 719(65.74)2 043(71.81)1 676(59.60)
2~31 718(30.37)724(25.45)994(35.35)
≥4220(3.89)78(2.74)142(5.05)
BADL状况122.022<0.001
受损369(6.52)83(2.92)286(10.17)
未受损5 288(93.48)2 762(97.08)2 526(89.83)
IADL状况280.641<0.001
受损974(17.22)252(8.86)922(25.68)
未受损4 683(82.78)2 593(91.14)2 090(74.32)
行为特征吸烟36.874<0.001
1 414(25.00)810(28.47)604(21.48)
4 323(75.00)2 035(71.53)2 208(78.52)
喝酒79.203<0.001
2 008(35.50)1 170(41.12)838(29.80)
3 649(64.50)1 675(58.88)1 974(70.20)
晚睡时长(h)366.356<0.001
<62 241(39.61)775(27.24)1 466(52.13)
6~83 054(53.99)1 852(65.10)1 202(42.75)
>8362(6.40)218(7.66)144(5.12)
是否午睡27.277<0.001
3 665(64.79)1 937(68.08)1 728(61.45)
1 992(35.21)908(31.92)1 084(38.55)
是否常因疼痛而难受504.893<0.001
完全没有1 799(31.80)1 222(42.95)577(20.52)
较少2 454(43.38)1 231(43.27)1 223(43.49)
较多1 404(24.82)392(13.78)1 012(35.99)
人际网络居住地类型102.426<0.001
城镇1 484(26.24)876(30.79)608(21.64)
城乡结合730(12.90)423(14.87)307(10.93)
乡村3 443(60.86)1 546(54.34)1 895(67.44)
是否有配偶或伴侣29.902<0.001
4 807(84.97)2 491(87.56)2 316(82.36)
850(15.03)354(12.44)496(17.64)
对子女是否满意118.056<0.001
满意5 333(94.27)2 777(97.61)2 556(90.90)
不满意或无子女324(5.73)68(2.39)256(9.10)
社交活动状况31.548<0.001
2 694(47.62)1 272(44.71)1 422(50.47)
1~3项2 827(49.97)1 481(52.06)1 346(47.87)
≥4项136(2.41)92(3.23)44(1.56)
子女数量36.647<0.001
05(0.09)3(0.11)2(0.07)
1~34 661(82.39)2 430(85.41)2 231(79.34)
≥4991(17.52)412(14.48)579(20.59)
生活与工作教育水平状况149.821<0.001
小学及以下3 578(63.25)1 593(55.99)1 985(70.59)
初中或高中1 780(31.46)1 036(36.41)744(26.46)
中专及以上299(5.29)216(7.59)83(2.95)
在过去一年有收入57.798<0.001
1 298(22.95)773(27.17)525(18.67)
4 359(77.05)2 072(72.83)2 287(81.33)
是否办理退休手续0.3020.582
3 587(63.41)1 794(63.06)1 793(63.76)
2 070(36.59)1 051(36.94)1 019(36.24)
是否有电0.4510.502
5 617(99.29)2 827(99.37)2 790(99.22)
40(0.71)18(0.63)22(0.78)
是否有水10.3710.001
5 136(90.79)2 618(92.02)2 518(89.54)
521(9.21)294(7.98)227(10.46)
是否有洗澡设施106.028<0.001
4 081(72.14)2 226(78.24)1 855(65.97)
1 576(27.86)619(21.76)957(34.03)
政策环境社会养老保险0.75880.384
4 883(86.32)2 467(86.71)2 416(85.92)
774(13.68)378(13.29)396(14.08)
社会医疗保险0.7960.372
5 423(95.86)2 734(96.10)2 689(95.63)
234(4.14)111(3.90)123(4.37)
), ArticleFig(id=1241319160379134003, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319150895813237, language=EN, label=Table 2, caption=

Multivariate logistic regression analysis of depression symptoms in middle-aged and elderly patients with chronic diseases

, figureFileSmall=null, figureFileBig=null, tableContent=
影响因素βSEZPOR (95% CI
性别(ref: 男性)
0.4650.0776.040<0.0011.593(1.369~1.852)
年龄(ref: 以45~59为对照)
60~74-0.1550.069-2.3600.0250.857(0.748~0.981)
≥75-0.5220.121-4.301<0.0010.593(0.468~0.753)
慢性病数量(ref: 以1为对照)
2~30.3770.0665.696<0.0011.457(1.280~1.659)
≥40.2720.1621.6740.0941.300(0.955~1.804)
BADL状况(ref: 受损)
未受损-0.4990.151-3.3100.0010.607(0.452~0.816)
IADL状况(ref: 受损)
未受损-0.7230.095-7.584<0.0010.485(0.403~0.585)
是否吸烟(ref: 是)
-0.1240.081-1.5360.1240.883(0.754~1.035)
是否喝酒(ref: 是)
0.0530.0700.7640.4451.055(0.920~1.209)
晚睡时长(ref: <6)
6~8-0.8150.064-12.808<0.0010.443(0.391~0.501)
>8-1.0640.128-8.321<0.0010.345(0.268~0.443)
是否午睡(ref: 是)
0.0110.0640.1720.8631.011(0.893~1.145)
是否经常因疼痛而难受(ref: 完全没有)
较少0.5640.0708.094<0.0011.757(1.533~2.015)
较多1.0980.08612.779<0.0012.998(2.534~3.548)
居住地类型(ref: 城镇)
城乡结合0.0030.1030.0340.9731.003(0.820~1.228)
乡村0.3880.0765.115<0.0011.474(1.270~1.710)
是否有配偶或伴侣(ref: 是)
0.1610.0881.8180.0691.174(0.987~1.397)
对子女是否满意(ref: 是)
1.1670.1527.668<0.0013.213(2.384~4.330)
社交活动状况(ref: 0)
1~3-0.0890.061-1.4440.1490.915(0.811~1.032)
≥4-0.3490.206-1.6970.0900.705(0.471~1.056)
子女数量(ref: 0)
1~30.0170.9150.0190.9851.018(0.169~6.117)
≥40.1390.9190.1510.8801.149(0.190~6.955)
教育水平状况(ref: 小学及以下)
初中或高中-0.1500.069-2.1630.0310.861(0.751~0.986)
中专及以上-0.4330.154-2.8120.0050.649(0.480~0.877)
是否在过去一年有收入(ref: 是)
0.1120.0771.4650.1431.119(0.963~1.301)
是否有自来水(ref: 是)
-0.0870.110-0.7950.4270.916(0.739~1.137)
是否有淋浴设施(ref: 是)
0.4040.0735.531<0.0011.497(1.298~1.728)
), ArticleFig(id=1241319160475603008, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319150895813237, language=CN, label=表2, caption=

中老年慢性病患者抑郁症状的多因素logistic回归分析

, figureFileSmall=null, figureFileBig=null, tableContent=
影响因素βSEZPOR (95% CI
性别(ref: 男性)
0.4650.0776.040<0.0011.593(1.369~1.852)
年龄(ref: 以45~59为对照)
60~74-0.1550.069-2.3600.0250.857(0.748~0.981)
≥75-0.5220.121-4.301<0.0010.593(0.468~0.753)
慢性病数量(ref: 以1为对照)
2~30.3770.0665.696<0.0011.457(1.280~1.659)
≥40.2720.1621.6740.0941.300(0.955~1.804)
BADL状况(ref: 受损)
未受损-0.4990.151-3.3100.0010.607(0.452~0.816)
IADL状况(ref: 受损)
未受损-0.7230.095-7.584<0.0010.485(0.403~0.585)
是否吸烟(ref: 是)
-0.1240.081-1.5360.1240.883(0.754~1.035)
是否喝酒(ref: 是)
0.0530.0700.7640.4451.055(0.920~1.209)
晚睡时长(ref: <6)
6~8-0.8150.064-12.808<0.0010.443(0.391~0.501)
>8-1.0640.128-8.321<0.0010.345(0.268~0.443)
是否午睡(ref: 是)
0.0110.0640.1720.8631.011(0.893~1.145)
是否经常因疼痛而难受(ref: 完全没有)
较少0.5640.0708.094<0.0011.757(1.533~2.015)
较多1.0980.08612.779<0.0012.998(2.534~3.548)
居住地类型(ref: 城镇)
城乡结合0.0030.1030.0340.9731.003(0.820~1.228)
乡村0.3880.0765.115<0.0011.474(1.270~1.710)
是否有配偶或伴侣(ref: 是)
0.1610.0881.8180.0691.174(0.987~1.397)
对子女是否满意(ref: 是)
1.1670.1527.668<0.0013.213(2.384~4.330)
社交活动状况(ref: 0)
1~3-0.0890.061-1.4440.1490.915(0.811~1.032)
≥4-0.3490.206-1.6970.0900.705(0.471~1.056)
子女数量(ref: 0)
1~30.0170.9150.0190.9851.018(0.169~6.117)
≥40.1390.9190.1510.8801.149(0.190~6.955)
教育水平状况(ref: 小学及以下)
初中或高中-0.1500.069-2.1630.0310.861(0.751~0.986)
中专及以上-0.4330.154-2.8120.0050.649(0.480~0.877)
是否在过去一年有收入(ref: 是)
0.1120.0771.4650.1431.119(0.963~1.301)
是否有自来水(ref: 是)
-0.0870.110-0.7950.4270.916(0.739~1.137)
是否有淋浴设施(ref: 是)
0.4040.0735.531<0.0011.497(1.298~1.728)
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基于健康生态学模型的我国中老年慢性病患者抑郁症状的相关因素分析与预测
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李健玮 1 , 刘玉学 2 , 王宣儿 1 , 郝玉玺 3 , 王凯正 4 , 胡军 4
现代预防医学 | 流行病与统计方法 2025,52(12): 2152-2158
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现代预防医学 | 流行病与统计方法 2025, 52(12): 2152-2158
基于健康生态学模型的我国中老年慢性病患者抑郁症状的相关因素分析与预测
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李健玮1, 刘玉学2, 王宣儿1, 郝玉玺3, 王凯正4, 胡军4
作者信息
  • 1.山东第二医科大学公共卫生学院,山东 潍坊 261053
  • 2.胶州市疾病预防控制中心
  • 3.青岛市第八人民医院
  • 4.山东中医药大学卫生管理学院
  • 李健玮(2000—),男,硕士在读,研究方向:流行病与卫生统计学

通讯作者:

胡军,E-mail:
Analysis and prediction of related factors of depression in middle-aged and elderly patients with chronic diseases based on health ecology model, China
Jian-wei LI1, Yu-xue LIU2, Xuan-er WANG1, Yu-xi HAO3, Kai-zheng WANG4, Jun HU4
Affiliations
  • School of Public Health, Shandong Second Medical University, Weifang, Shandong 261053, China
出版时间: 2025-06-25 doi: 10.20043/j.cnki.MPM.202411274
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目的

探索我国中老年慢性病患者抑郁症状的影响因素并建立风险预测模型,为实现健康老龄化提供参考。

方法

利用中国健康与养老追踪调查第五轮数据,选取年龄≥45岁的慢性病患者纳入研究,按7:3的比例随机分为训练集和测试集。基于健康生态学模型纳入相关因素,采用χ2检验和二元logistic 回归分析影响因素并绘制列线图。通过受试者工作特征曲线下面积、校准曲线、Hosmer-Lemeshow检验评价风险预测模型。

结果

中老年慢性病患者抑郁症状发生率为49.71%。结果显示,个人特征层中性别、年龄、慢性病数量、BADL状况、IADL状况,行为方式层中晚睡时长、是否常因疼痛而烦恼,人际网络层中居住地类型、对子女是否满意,生活工作层中教育水平状况、是否有淋浴设施,上述因素与我国中老年慢性病患者抑郁发生显著相关。中老年慢性病患者抑郁症状风险预测模型训练集和测试集AUC分别为0.762和0.753,校准曲线显示预测概率与实际概率具有较高的一致性,Hosmer-Lemeshow检验P>0.05,显示模型预测效果良好。

结论

我国中老年慢性病患者抑郁症状检出率较高。应从多层次、多维度提高中老年人群生活质量,促进健康老龄化。

健康生态学模型  /  中老年人  /  慢性病  /  抑郁症状  /  列线图
Objective

To explore the factors of depression symptoms in middle-aged and elderly patients with chronic diseases in China and establish a risk prediction model to provide a reference for achieving healthy aging.

Methods

Based on data from the fifth waves of the China Health and Retirement Longitudinal Survey, patients with chronic diseases aged ≥ 45 years were included in the study and randomly divided into training and testing sets at a ratio of 7:3. Based on the Health Ecology Model, factors were analyzed by chi-square test and binary logistic regression and a nomogram was constructed. The performance of the risk prediction model was evaluated using the area under the receiver operating characteristic curve, calibration curves, and the Hosmer-Lemeshow test.

Results

The prevalence incidence of depression symptoms among middle-aged and elderly patients with chronic diseases was 49.71%. The results showed that gender, age, number of chronic diseases, BADL status, IADL status in the personal characteristics layer, late sleep duration and whether often bothered by pain in the behavior pattern layer, type of residence and satisfaction with children in the interpersonal network layer, education level and availability of shower facilities in the life and work layer were significantly correlated with depression symptoms among middle-aged and elderly patients with chronic diseases. The AUC values for the training and testing sets of depression symptoms risk prediction model among middle-aged and elderly participants with chronic diseases in China were 0.762 and 0.753, respectively. The calibration curve demonstrated good agreement between predicted and actual probabilities, and the Hosmer-Lemeshow test (P>0.05) confirmed the model’s satisfactory predictive performance.

Conclusion

The prevalence incidence of depression symptoms is relatively high among middle-aged and elderly patients with chronic diseases in China. Improving the quality of life of this population across multiple dimensions is essential for promoting healthy aging.

Health ecology model  /  Middle-aged and older people  /  Chronic disease  /  Depression symptoms  /  Nomogram
李健玮, 刘玉学, 王宣儿, 郝玉玺, 王凯正, 胡军. 基于健康生态学模型的我国中老年慢性病患者抑郁症状的相关因素分析与预测. 现代预防医学, 2025 , 52 (12) : 2152 -2158 . DOI: 10.20043/j.cnki.MPM.202411274
Jian-wei LI, Yu-xue LIU, Xuan-er WANG, Yu-xi HAO, Kai-zheng WANG, Jun HU. Analysis and prediction of related factors of depression in middle-aged and elderly patients with chronic diseases based on health ecology model, China[J]. Modern Preventive Medicine, 2025 , 52 (12) : 2152 -2158 . DOI: 10.20043/j.cnki.MPM.202411274
中国位居全球老年人口数量之首,也是老龄化速度最快的国家之一,中老年人群的健康问题不容忽视,人口老龄化已经成为社会焦点[1-2]。世界卫生组织在其2023年发布的卫生统计报告中指出,从2000年到2019年,全球因慢性病导致的死亡人数比例从61%上升至74%[3]。中老年人是慢性病的高危人群,其慢性病发病率是其他年龄段的2~3倍[4]。同时中老年慢性病患者也是抑郁的高风险人群,抑郁是中老年人健康的重大威胁之一[5]。以往关于中老年慢性病患者抑郁症状因素的研究,多聚焦于人口学特征或健康相关行为等单一维度[6-7],而缺乏多维度、系统性的深入分析。健康生态学强调环境对个人影响的多层次性和影响因素的复杂性和多样性,即个体和人群的健康是个体因素和环境因素相互作用的结果[8]。本研究基于健康生态学模型,探索我国中老年慢性病患者抑郁症状的影响因素,并绘制列线图预测模型对我国中老年慢性病患者抑郁症状发生风险进行可视化,为缓解中老年人群抑郁现状、促进健康老龄化提供参考。
资料来源于中国健康与养老追踪调查(China Health and Retirement Longitudinal Survey,CHARLS)项目2020年数据进行研究与分析。选取年龄≥45岁且患慢性病的中老年人作为研究对象,并将缺少抑郁问卷以及研究变量存在缺失值的样本排除,最终纳入样本5 657例。
健康生态学模型强调群体和个体的健康状况是个体因素、卫生服务以及物质和社会环境等因素相互依赖、相互作用的结果,这些因素在多层面上交互作用,共同影响着个体和群体的健康[8]。其结构由内向外分别为个人特质层、行为特征层、人际网络层、工作和生活条件层和政策环境层(见图1)。
本研究的结局变量为是否存在抑郁症状。CHARLS项目利用流行病学的研究方法,采用简易抑郁量表(CES-D)评定受试者过去一周内症状出现的频率[9]。量表共有四个选项,定义“很少或者根本没有(<1天)=0分”、“不太多(1~2天)=1分”、“有时或者说有一半的时间(3~4天)=2分”、“大多数的时间(5~7天)=3分”,评分范围为 0~30分。“我很愉快”和“对未来充满希望”两条目反向计分,得分≥10分提示存在抑郁症状,否则为无明显抑郁症状。
本研究纳入变量共五个维度,分别为(1)个人特质层:性别、年龄、患慢性病数量、基本日常生活活动能力(Basic Activities of Daily Living,BADL)状况、工具性日常生活活动能力(Instrumental Activities of Daily Living,IADL)状况;(2)行为特征层:吸烟、喝酒、晚睡时长、是否午睡、是否常因疼痛而难受;(3)人际网络层:居住地类型、是否有配偶或伴侣、对子女是否满意、社交活动数量、子女数量;(4)生活与工作层:教育水平状况、是否有收入、是否办理退休手续、是否有水、是否有电、是否有淋浴设施;(5)政策环境层:社会养老保险、社会医疗保险。BADL包括进食、如厕、洗澡、穿衣、大小便控制、上下床,是评估和衡量一个人能否独立生活和自保的重要指标[10];IADL包括家务维持、管理金钱、做饭、购物、使用电话、服药,对于了解个体的社会功能、职业能力和生活质量具有重要意义[11]。BADL与IADL状况中只要有一项选择“需要帮助”或“有困难”则定义为“受损”。
使用Stata 17.0软件进行处理与数据分析。符合正态分布的连续型变量采用均数±标准差进行描述,否则采用中位数和四分位间距进行表示;分类变量以频数和百分比进行描述。组间比较采用χ2检验进行单因素分析,并将差异具有统计学意义(P<0.1)的因素纳入二分类logistic回归,探究影响中老年人慢性病患者抑郁的影响因素。使用R 4.4.0软件利用“rms”程序包绘制列线图(Nomogram),将数据集随机拆分为7:3;通过绘制受试者工作特征曲线(Receiver Operating Characteristic curve, ROC)和Calibration校准曲线以及Hosmer-Lemeshow检验来评估模型的效能。检验水准α= 0.05
研究纳入5 657例研究对象,平均年龄(61.8±8.9)岁,其中男性2 618例(46.28%)。存在抑郁症状有2 812例(49.71%),抑郁症状平均评分为(10.6±7.1)分,45~59岁2 396例(42.35%)。
结果显示,结果显示,个人特征层中的性别、年龄、患慢性病数量、BADL状况、IADL状况,行为特征层中的吸烟、喝酒、晚睡时长、午睡、常因疼痛而烦恼,人际网络层中的,居住地类型、是否有配偶或伴侣、对子女是否满意、社交活动数量、子女数量,生活与工作层中的教育水平状况、有收入、住房有水、淋浴设施、住房整洁,以上因素与中老年慢性病患者抑郁症状发生存在关联。
以中老年慢性病患者是否存在抑郁症状为因变量(0=“存在抑郁症状”,1=“无抑郁症状”),将单因素分析中有统计学意义的因素纳入多因素logistic回归。结果显示,个人特征层中的性别、年龄、患慢性病数量、BADL状况、IADL状况,行为特征层中的晚睡时长、是否常因疼痛而难受,人际网络层中的居住地类型、对子女是否满意,生活与工作层中的教育水平状况、是否有淋浴设施与中老年慢性病患者抑郁症状显著相关。
根据二分类logistic回归筛选变量,开发评估中老年慢性病患者抑郁风险的列线图(图2)。结果显示,训练集和测试集AUC分别为0.762(95% CI:0.747~0.776)、0.753(95% CI:0.730~0.776),表明模型的区分度较好(图3)。Hosmer-Lemeshow拟合优度检验结果显示,训练集与测试集的χ2值分别为 6.257、6.356,P值均大于0.05,表明模型具备良好的拟合度。校准曲线显示预测风险与实际情形具有较高的一致性(图4)。
结果显示,我国中老年慢性病患者抑郁症状检出率为49.71%,高于雷普超等人[12]的研究结果(40.84%)。男性和女性抑郁症状检出率分别为40.33%、57.78%,高于Lin等[13]的研究结果(男性37.10%,女性52.90%)。产生差异的原因可能是样本纳入标准和数据年份不同。提示我国中老年人慢性病患者抑郁症状检出率较高,心理健康状况较差。
研究发现,男性、60岁以上、患1种慢性病、BADL与IADL状况均未受损与中老年慢性病患者抑郁症状发生存在负向关联。抑郁发生与性别显著相关,女性患病率明显高于男性。国内脑功能成像研究发现,男性和女性的大脑中如杏仁核、前额叶皮层等与情绪加工和调节相关的脑区存在显著差异,同时遗传因素与性激素差异也是导致差异的重要因素[14]。与45~59岁相比,60以上的中老年慢性病患者不易发生抑郁症状,这可能是因为中年人面临着家庭、工作等方面的压力,而老年人大部分已经退休,有更多时间和精力参加娱乐活动、缓解抑郁情绪。此外,患2~3种慢性病的抑郁症状发生风险是患1种慢性病的1.452倍。慢性病会使人体生理机能减退,社会活动减少,生活质量下降,而导致抑郁症状产生[15]。研究显示,BADL和IADL受损与中老年慢性病患者抑郁症状发生存在正向关联。一般来说,BADL和IADL受损通常伴随着负面情绪和身体机能下降,使得活动范围缩小,从而产生抑郁情绪[16]
结果显示,晚睡时长、是否常因疼痛而难受与中老年慢性病患者抑郁症状显著相关。相比晚睡时长不足6 h,6~8 h和>8 h是保护因素,与Li等[17]研究一致。一项研究[18]发现,中年时期睡眠时间短会增加晚年抑郁症状发生风险,晚年睡眠时长无论短长均与抑郁症状相关。此外,常因疼痛而烦恼是中老年慢性病患者抑郁症状的危险因素,并且疼痛频率越高,抑郁症状风险越大。这可能是因为疼痛和抑郁的病理生理学机制有很高的重合度,它们共享着相同的大脑结构,利用一致的神经环路,并激活了相同的神经化学物质[19]
研究表明,居住地类型、对子女是否满意与中老年慢性病患者抑郁症状之间存在显著关联性。与Yuan等[20]的研究结果相同,农村居民更容易产生抑郁症状。这可能是因为农村居民压力来源更多,并且压力强度更大[6]。研究发现,对子女满意与中老年慢性病患者抑郁症状产生存在负向关联。周欣等人[21]发现,对子女满意的家庭中,子女能够给予中老年人经济供养和精神关怀,增加子女与中老年人之间的联系频率和情感交流,缓解抑郁情绪。
教育水平状况、是否有淋浴设施与中老年慢性病患者抑郁症状存在高度关联。研究发现,教育水平越高,抑郁症状发生率越低,这可能是因为高学历人群经济地位更高、健康意识更强,有更多的精力和时间关注自己的身心健康[22]。有淋浴设施对中老年慢性病患者抑郁症状发生起到了保护作用。目前尚无研究表明两者之间存在因果联系,初步猜测淋浴可通过改善血液流通、减轻压力等生理和心理方面的影响,来减少抑郁的发生。
列线图可直观的展示中老年慢性病患者抑郁症状发生的预测概率。评估结果显示,模型的AUC、校准曲线、拟合优度检验表示模型的区分度和校准度较好,具有良好的预测效能。高赋分项多集中于健康负担(慢性病、功能受损、疼痛)与社会孤立(人际关系差),这可能是通过生理紊乱、心理认知偏差、社会支持断裂的多重路径,在交互作用中与抑郁症状呈现显著关联,这与先前抑郁相关研究相同[12,16,22]。低赋分项(教育水平较高、充足睡眠、城镇居住)具有一定的保护效应。教育、睡眠、城镇居住等低赋分项可能分别通过认知应对、神经调节、社会资源支持等多方面机制发挥保护功能,其作用路径与抑郁领域经典论据、既往研究成果相互印证[17-20]
综上所述,我国中老年慢性病患者抑郁症状检出率较高。健康生态学模型提示,抑郁症状与个人特征、行为方式、人际网络、生活与工作等多维度相关。应从多维度、多层次采取针对性措施,降低中老年慢性病患者抑郁症状发生风险,实现健康老龄化。但本文仍存在一定局限性:首先本研究为横断面研究,缺乏时间上的纵向分析;其次研究仅纳入常见变量,缺少与抑郁症状相关性较高但未被采取的变量;再者,在数据筛选时,我们排除了变量存在缺失值以及异常值的参与者,这可能会引起偏差。
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doi: 10.20043/j.cnki.MPM.202411274
  • 接收时间:2024-11-14
  • 首发时间:2026-03-19
  • 出版时间:2025-06-25
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  • 收稿日期:2024-11-14
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    1.山东第二医科大学公共卫生学院,山东 潍坊 261053
    2.胶州市疾病预防控制中心
    3.青岛市第八人民医院
    4.山东中医药大学卫生管理学院

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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
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