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Article(id=1241522929222079338, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241522919977841545, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202309447, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1695484800000, receivedDateStr=2023-09-24, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773931732706, onlineDateStr=2026-03-19, pubDate=1710000000000, pubDateStr=2024-03-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773931732706, onlineIssueDateStr=2026-03-19, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773931732706, creator=13701087609, updateTime=1773931732706, updator=13701087609, issue=Issue{id=1241522919977841545, tenantId=1146029695717560320, journalId=1227665162245664772, year='2024', volume='51', 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=1773931730503, creator=13701087609, updateTime=1773931880386, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241523548695622547, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241522919977841545, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241523548695622548, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241522919977841545, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=888, endPage=892, ext={EN=ArticleExt(id=1241522929616343948, articleId=1241522929222079338, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=The influence of social participation on the depression track of the elderly——mediating effect based on quality of life, columnId=1228016572451718132, journalTitle=Modern Preventive Medicine, columnName=Health and Social Behavior, runingTitle=null, highlight=null, articleAbstract=
Objective

To study the relationship between social participation and depression trajectory and to explore the mediating effect of quality of life.

Methods

Based on the data of the four periods of China Health and Retirement Longitudinal Study (CHARLS) from 2011 to 2018, the heterogeneous trajectory of depressive symptoms in the elderly was identified by group-based trajectory model, the relationship between social participation and depression trajectory was studied by multi-class logistic regression, and the mediating effect of quality of life on social participation and depression trajectory of the elderly was analyzed by Karlson-Holm-Breen （KHB） method.

Results

A total of 4 931 elderly people aged between 60 and 105 with at least two periods of depression were included, and four depression trajectory groups were identified: long-term non-depression group (61.69%), increased depression group (16.69%), depression remission group (13.83%), and long-term high depression group (7.79%). Compared with the subjects in the long-term non-depression group, the more social participation, the less likely the depression trajectory was attributed to the depression remission group, the increased depression group, and the long-term high depression group (RRR=0.899, 95%CI: 0.812-0.994; RRR=0.885, 95%CI: 0.786-0.995; RRR=0.747, 95%CI: 0.630-0.885). In addition, quality of life played a significant mediating role in the relationship between the two, and the mediating effect of self-rated health accounted for the largest contribution of 30.33% in the elevated depression group. The mediating effect of physical pain accounted for the largest contribution of 27.94% and 29.09, respectively in the depression remission group and the long-term high depression group. Similar results were obtained in the robustness test.

Conclusion

There is heterogeneity in the depression trajectory of the elderly, social participation has a significant impact on the attribution of depression trajectory, and quality of life is an important factor of social participation affecting depression trajectory.

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目的

研究社会参与和抑郁轨迹之间的关系并探索生活质量的中介效应。

方法

基于中国健康与养老追踪调查2011—2018年四期数据，采用组基轨迹模型识别老年人抑郁症状的异质性轨迹，运用多分类logistic回归研究社会参与和抑郁轨迹之间的关联，利用KHB方法分析生活质量对社会参与和老年人抑郁轨迹影响的中介效应。

结果

共纳入了4 931名年龄在60～105岁且至少有2期抑郁症状调查数据的老人，确定了4个抑郁轨迹组：长期无抑郁组(61.69%)、抑郁升高组(16.69%)、抑郁缓解组(13.83%)、长期高抑郁组(7.79%)。与长期无抑郁组的受试者相比，社会参与数量越多，抑郁轨迹归属于抑郁缓解组、抑郁升高组和长期高抑郁组的可能性越小(RRR=0.899，95%CI：0.812～0.994；RRR=0.885，95%CI：0.786～0.995；RRR=0.747，95%CI：0.630～0.885)；另外，生活质量在两者的关系中发挥了显著的中介效应，且抑郁升高组中自评健康的中介效应贡献占比最大，为30.33%，抑郁缓解组和长期高抑郁组中身体疼痛情况的中介效应贡献占比最大，分别是27.94%和29.09%，稳健型检验也得到了类似的结果。

结论

老年人抑郁轨迹存在异质性，社会参与对抑郁轨迹的归属有显著性影响，生活质量是社会参与影响抑郁轨迹的重要因素。

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陈娜，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=jZZ1RbiuxetGr1Il4HKAEw==, magXml=5/a4AJOJzrb5BwDmWeTUaw==, pdfUrl=null, pdf=ZOZcmddGHwou5dJ4Hvm3dg==, pdfFileSize=695205, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=IvZb9lx3ddiSMm60GriQzA==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=p7kpggLLQ590909VhawuIA==, mapNumber=null, authorCompany=null, fund=null, authors=

秦慧（1998—），女，硕士在读，研究方向：公共管理

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秦慧（1998—），女，硕士在读，研究方向：公共管理

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Quality of Life Research, 2021, 30(2): 479-486., articleTitle=Pain intensity and mental health quality of Life in veterans with mental illnesses: the intermediary role of physical health and the ability to participate in activities, refAbstract=null)], funds=[Fund(id=1241678326226547436, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522929222079338, awardId=ZT202202, language=CN, fundingSource=江苏省中医药科技发展计划专题研究项目(ZT202202), fundOrder=null, country=null), Fund(id=1241678327023465204, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522929222079338, awardId=2021JSJG053, language=CN, fundingSource=江苏省高等教育教改研究课题重点项目(2021JSJG053), fundOrder=null, country=null), Fund(id=1241678327652610805, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522929222079338, awardId=null, language=CN, fundingSource=江苏高校“青蓝工程”资助, fundOrder=null, country=null), Fund(id=1241678328273367804, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522929222079338, awardId=2023YLFWYGL013, language=CN, fundingSource=养老服务与管理学院（南京中医药大学）专项研究项目(2023YLFWYGL013), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1241678292953133476, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522929222079338, xref=1., ext=[AuthorCompanyExt(id=1241678292961522086, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522929222079338, companyId=1241678292953133476, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=School of Health Economics and Management, Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210023, China), AuthorCompanyExt(id=1241678292969910696, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522929222079338, companyId=1241678292953133476, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.南京中医药大学卫生经济管理学院，江苏 南京 210023)]), AuthorCompany(id=1241678293397729712, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522929222079338, xref=2., ext=[AuthorCompanyExt(id=1241678293406118322, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522929222079338, companyId=1241678293397729712, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.南京中医药大学养老服务与管理学院，江苏 南京 210023)])], figs=[ArticleFig(id=1241678319452746370, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522929222079338, language=EN, label=Figure 1, caption=Depression trajectory in older adults, figureFileSmall=9y+IAnEzuEzcb0Hcu1wxOQ==, figureFileBig=cGBuGs4bCnmqQgjoCXff9g==, tableContent=null), ArticleFig(id=1241678319893148296, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522929222079338, language=CN, label=图1, caption=抑郁轨迹图, figureFileSmall=9y+IAnEzuEzcb0Hcu1wxOQ==, figureFileBig=cGBuGs4bCnmqQgjoCXff9g==, tableContent=null), ArticleFig(id=1241678320346133140, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522929222079338, language=EN, label=Table 1, caption=

Evaluation metrics for model fit

, figureFileSmall=null, figureFileBig=null, tableContent=
抑郁轨迹组别组群样本规模分配到这一组群的样本比例p(%)估计的组群身份概率
π(%)		组群平均后验概率AvePP
长期无抑郁组3 04259.5261.690.918
抑郁升高组82318.0316.690.725
抑郁缓解组68214.4713.830.712
长期高抑郁组3847.987.790.863
), ArticleFig(id=1241678320694260378, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522929222079338, language=CN, label=表1, caption=

模型拟合评价指标

, figureFileSmall=null, figureFileBig=null, tableContent=
抑郁轨迹组别组群样本规模分配到这一组群的样本比例p(%)估计的组群身份概率
π(%)		组群平均后验概率AvePP
长期无抑郁组3 04259.5261.690.918
抑郁升高组82318.0316.690.725
抑郁缓解组68214.4713.830.712
长期高抑郁组3847.987.790.863
), ArticleFig(id=1241678321554092701, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522929222079338, language=EN, label=Table 2, caption=

Basic characteristics of trajectory clusters ［（），n（%）］

, figureFileSmall=null, figureFileBig=null, tableContent=
变量分类长期无抑郁组抑郁升高组抑郁缓解组长期高抑郁组F(χ2)值P值
年龄(岁)连续变量67.40±6.1566.78±5.6867.38±6.0566.51±5.554.370.005
社会参与连续变量0.76±0.900.61±0.820.56±0.740.46±0.6722.89<0.001
性别女1 246(40.96)457(55.53)412(60.41)256(66.67)174.75<0.001
男1 796(59.04)366(44.47)270(39.59)128(33.33)
居住地农村2 140(70.35)682(82.87)581(85.19)347(90.36)149.78<0.001
城镇902(29.65)141(17.13)101(14.81)37(9.64)
婚姻情况无配偶484(15.91)154(18.71)160(23.46)84(21.88)27.05<0.001
有配偶2 558(84.09)669(81.29)522(76.54)300(78.13)
教育文盲846(27.81)291(35.36)294(43.11)165(42.97)87.88<0.001
非文盲2 196(72.19)532(64.64)388(56.89)219(57.03)
吸烟情况吸烟1 031(33.89)255(30.98)193(28.30)94(24.48)19.68<0.001
不吸烟2 011(66.11)568(69.02)489(71.70)290(75.52)
喝酒情况喝酒1 070(35.17)226(27.46)179(26.25)88(22.92)46.84<0.001
不喝酒1 972(64.83)597(72.54)503(73.75)296(77.08)
慢病情况有慢病2 106(69.23)648(78.74)571(83.72)336(87.50)116.01<0.001
无慢病936(30.77)175(21.26)111(16.28)48(12.50)
睡眠情况连续变量3.47±0.873.16±0.983.01±1.052.84±1.0992.13<0.001
IADL连续变量0.17±0.600.26±0.750.52±1.060.58±1.1266.21<0.001
自评健康差2 208(72.58)714(86.76)609(89.30)367(95.57)212.85<0.001
好834(27.42)109(13.24)73(10.70)17(4.43)
生活满意度满意903(29.68)181(21.99)106(15.54)34(8.85)126.01<0.001
不满意2 139(70.32)642(78.01)576(84.46)350(91.15)
身体疼痛疼痛664(21.83)351(42.65)393(57.62)287(74.74)678.04<0.001
不疼2 378(78.17)472(57.35)289(42.38)97(25.26)
), ArticleFig(id=1241678321973523107, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522929222079338, language=CN, label=表2, caption=

轨迹组群的基本特征［（），n（%）］

, figureFileSmall=null, figureFileBig=null, tableContent=
变量分类长期无抑郁组抑郁升高组抑郁缓解组长期高抑郁组F(χ2)值P值
年龄(岁)连续变量67.40±6.1566.78±5.6867.38±6.0566.51±5.554.370.005
社会参与连续变量0.76±0.900.61±0.820.56±0.740.46±0.6722.89<0.001
性别女1 246(40.96)457(55.53)412(60.41)256(66.67)174.75<0.001
男1 796(59.04)366(44.47)270(39.59)128(33.33)
居住地农村2 140(70.35)682(82.87)581(85.19)347(90.36)149.78<0.001
城镇902(29.65)141(17.13)101(14.81)37(9.64)
婚姻情况无配偶484(15.91)154(18.71)160(23.46)84(21.88)27.05<0.001
有配偶2 558(84.09)669(81.29)522(76.54)300(78.13)
教育文盲846(27.81)291(35.36)294(43.11)165(42.97)87.88<0.001
非文盲2 196(72.19)532(64.64)388(56.89)219(57.03)
吸烟情况吸烟1 031(33.89)255(30.98)193(28.30)94(24.48)19.68<0.001
不吸烟2 011(66.11)568(69.02)489(71.70)290(75.52)
喝酒情况喝酒1 070(35.17)226(27.46)179(26.25)88(22.92)46.84<0.001
不喝酒1 972(64.83)597(72.54)503(73.75)296(77.08)
慢病情况有慢病2 106(69.23)648(78.74)571(83.72)336(87.50)116.01<0.001
无慢病936(30.77)175(21.26)111(16.28)48(12.50)
睡眠情况连续变量3.47±0.873.16±0.983.01±1.052.84±1.0992.13<0.001
IADL连续变量0.17±0.600.26±0.750.52±1.060.58±1.1266.21<0.001
自评健康差2 208(72.58)714(86.76)609(89.30)367(95.57)212.85<0.001
好834(27.42)109(13.24)73(10.70)17(4.43)
生活满意度满意903(29.68)181(21.99)106(15.54)34(8.85)126.01<0.001
不满意2 139(70.32)642(78.01)576(84.46)350(91.15)
身体疼痛疼痛664(21.83)351(42.65)393(57.62)287(74.74)678.04<0.001
不疼2 378(78.17)472(57.35)289(42.38)97(25.26)
), ArticleFig(id=1241678322388759212, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522929222079338, language=EN, label=Table 3, caption=

Association between social participation and depression in older adults

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参照(长期无抑郁组)模型1[RRR(95%CI)]模型2[RRR(95%CI)]模型3[RRR(95%CI)]
抑郁升高组0.813 (0.739～0.895) ***0.815 (0.740～0.898) ***0.899 (0.812～0.994) *
抑郁缓解组0.753 (0.676～0.839) ***0.757 (0.679～0.845) ***0.885 (0.786～0.995) *
长期高抑郁组0.618 (0.530～0.720) ***0.619 (0.530～0.723) ***0.747 (0.630～0.885) **
), ArticleFig(id=1241678323017904821, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522929222079338, language=CN, label=表3, caption=

社会参与和抑郁的关联

, figureFileSmall=null, figureFileBig=null, tableContent=
参照(长期无抑郁组)模型1[RRR(95%CI)]模型2[RRR(95%CI)]模型3[RRR(95%CI)]
抑郁升高组0.813 (0.739～0.895) ***0.815 (0.740～0.898) ***0.899 (0.812～0.994) *
抑郁缓解组0.753 (0.676～0.839) ***0.757 (0.679～0.845) ***0.885 (0.786～0.995) *
长期高抑郁组0.618 (0.530～0.720) ***0.619 (0.530～0.723) ***0.747 (0.630～0.885) **
), ArticleFig(id=1241678323328283327, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522929222079338, language=EN, label=Table 4, caption=

Results of the indirect effect of social participation on the trajectory of depression in older adults

, figureFileSmall=null, figureFileBig=null, tableContent=
中介变量抑郁轨迹(参照：长期无抑郁组)
抑郁升高组中介效应中介效应占比(%)抑郁缓解组中介效应中介效应占比(%)长期高抑郁组中介效应中介效应占比(%)
IADL-0.015**9.52-0.037***18.24-0.040***10.36
睡眠情况-0.016**10.29-0.023**10.81-0.030**7.63
自评健康(差):好-0.032***19.90-0.041***18.41-0.074***16.71
自评生活满意度(不满意):满意-0.007+4.48-0.014+6.44-0.023+5.49
身体疼痛情况(不疼):疼痛-0.026***16.34-0.044***19.57-0.066***15.34
), ArticleFig(id=1241678323823211206, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522929222079338, language=CN, label=表4, caption=

社会参与对抑郁轨迹的间接效应结果

, figureFileSmall=null, figureFileBig=null, tableContent=
中介变量抑郁轨迹(参照：长期无抑郁组)
抑郁升高组中介效应中介效应占比(%)抑郁缓解组中介效应中介效应占比(%)长期高抑郁组中介效应中介效应占比(%)
IADL-0.015**9.52-0.037***18.24-0.040***10.36
睡眠情况-0.016**10.29-0.023**10.81-0.030**7.63
自评健康(差):好-0.032***19.90-0.041***18.41-0.074***16.71
自评生活满意度(不满意):满意-0.007+4.48-0.014+6.44-0.023+5.49
身体疼痛情况(不疼):疼痛-0.026***16.34-0.044***19.57-0.066***15.34
), ArticleFig(id=1241678324439773904, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522929222079338, language=EN, label=Table 5, caption=

KHB test based on multidimensional mediator variables

, figureFileSmall=null, figureFileBig=null, tableContent=
抑郁轨迹组(参照:长期无抑郁组)中介变量(%)
IADL睡眠情况自评健康自评生活满意度身体疼痛情况
抑郁升高组10.3021.7230.339.9727.69
抑郁缓解组24.6217.2017.5712.6827.94
长期高抑郁组16.0614.9725.4314.4629.09
), ArticleFig(id=1241678324745958102, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522929222079338, language=CN, label=表5, caption=

基于多维中介变量的KHB检验

, figureFileSmall=null, figureFileBig=null, tableContent=
抑郁轨迹组(参照:长期无抑郁组)中介变量(%)
IADL睡眠情况自评健康自评生活满意度身体疼痛情况
抑郁升高组10.3021.7230.339.9727.69
抑郁缓解组24.6217.2017.5712.6827.94
长期高抑郁组16.0614.9725.4314.4629.09
), ArticleFig(id=1241678324859204315, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522929222079338, language=EN, label=Table 6, caption=

Robustness test

, figureFileSmall=null, figureFileBig=null, tableContent=
抑郁轨迹分组社会参与社会参与频率
参照(长期无抑郁组)00
抑郁升高组-0.107* (-0.052)-0.051* (-0.023)
抑郁缓解组-0.123* (-0.060)-0.064* (-0.026)
长期高抑郁组-0.292*** (-0.087)-0.141*** (-0.039)
控制变量YesYes
), ArticleFig(id=1241678325328966372, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522929222079338, language=CN, label=表6, caption=

稳健性检验

, figureFileSmall=null, figureFileBig=null, tableContent=
抑郁轨迹分组社会参与社会参与频率
参照(长期无抑郁组)00
抑郁升高组-0.107* (-0.052)-0.051* (-0.023)
抑郁缓解组-0.123* (-0.060)-0.064* (-0.026)
长期高抑郁组-0.292*** (-0.087)-0.141*** (-0.039)
控制变量YesYes
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社会参与对老年人抑郁轨迹的影响——基于生活质量的中介效应
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秦慧 1 , 陈娜 2 , 卓力 2
现代预防医学 | 健康与社会行为 2024,51(5): 888-892
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现代预防医学 | 健康与社会行为 2024, 51(5): 888-892
社会参与对老年人抑郁轨迹的影响——基于生活质量的中介效应
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秦慧1, 陈娜2 , 卓力2
作者信息
  • 1.南京中医药大学卫生经济管理学院，江苏 南京 210023
  • 2.南京中医药大学养老服务与管理学院，江苏 南京 210023

    • 秦慧（1998—），女，硕士在读，研究方向：公共管理

通讯作者:

陈娜，E-mail:
The influence of social participation on the depression track of the elderly——mediating effect based on quality of life
Hui QIN1, Na CHEN2 , Li ZHUO2
Affiliations
  • School of Health Economics and Management, Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210023, China
出版时间: 2024-03-10 doi: 10.20043/j.cnki.MPM.202309447
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摘要
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目的

研究社会参与和抑郁轨迹之间的关系并探索生活质量的中介效应。

方法

基于中国健康与养老追踪调查2011—2018年四期数据，采用组基轨迹模型识别老年人抑郁症状的异质性轨迹，运用多分类logistic回归研究社会参与和抑郁轨迹之间的关联，利用KHB方法分析生活质量对社会参与和老年人抑郁轨迹影响的中介效应。

结果

共纳入了4 931名年龄在60～105岁且至少有2期抑郁症状调查数据的老人，确定了4个抑郁轨迹组：长期无抑郁组(61.69%)、抑郁升高组(16.69%)、抑郁缓解组(13.83%)、长期高抑郁组(7.79%)。与长期无抑郁组的受试者相比，社会参与数量越多，抑郁轨迹归属于抑郁缓解组、抑郁升高组和长期高抑郁组的可能性越小(RRR=0.899，95%CI：0.812～0.994；RRR=0.885，95%CI：0.786～0.995；RRR=0.747，95%CI：0.630～0.885)；另外，生活质量在两者的关系中发挥了显著的中介效应，且抑郁升高组中自评健康的中介效应贡献占比最大，为30.33%，抑郁缓解组和长期高抑郁组中身体疼痛情况的中介效应贡献占比最大，分别是27.94%和29.09%，稳健型检验也得到了类似的结果。

结论

老年人抑郁轨迹存在异质性，社会参与对抑郁轨迹的归属有显著性影响，生活质量是社会参与影响抑郁轨迹的重要因素。

社会参与  /  老年人  /  抑郁  /  组基轨迹模型  /  中介效应
Objective

To study the relationship between social participation and depression trajectory and to explore the mediating effect of quality of life.

Methods

Based on the data of the four periods of China Health and Retirement Longitudinal Study (CHARLS) from 2011 to 2018, the heterogeneous trajectory of depressive symptoms in the elderly was identified by group-based trajectory model, the relationship between social participation and depression trajectory was studied by multi-class logistic regression, and the mediating effect of quality of life on social participation and depression trajectory of the elderly was analyzed by Karlson-Holm-Breen （KHB） method.

Results

A total of 4 931 elderly people aged between 60 and 105 with at least two periods of depression were included, and four depression trajectory groups were identified: long-term non-depression group (61.69%), increased depression group (16.69%), depression remission group (13.83%), and long-term high depression group (7.79%). Compared with the subjects in the long-term non-depression group, the more social participation, the less likely the depression trajectory was attributed to the depression remission group, the increased depression group, and the long-term high depression group (RRR=0.899, 95%CI: 0.812-0.994; RRR=0.885, 95%CI: 0.786-0.995; RRR=0.747, 95%CI: 0.630-0.885). In addition, quality of life played a significant mediating role in the relationship between the two, and the mediating effect of self-rated health accounted for the largest contribution of 30.33% in the elevated depression group. The mediating effect of physical pain accounted for the largest contribution of 27.94% and 29.09, respectively in the depression remission group and the long-term high depression group. Similar results were obtained in the robustness test.

Conclusion

There is heterogeneity in the depression trajectory of the elderly, social participation has a significant impact on the attribution of depression trajectory, and quality of life is an important factor of social participation affecting depression trajectory.

Social participation  /  Elderly  /  Depression  /  Group-based trajectory model  /  Mediating effect
秦慧, 陈娜, 卓力. 社会参与对老年人抑郁轨迹的影响——基于生活质量的中介效应. 现代预防医学, 2024 , 51 (5) : 888 -892 . DOI: 10.20043/j.cnki.MPM.202309447
Hui QIN, Na CHEN, Li ZHUO. The influence of social participation on the depression track of the elderly——mediating effect based on quality of life[J]. Modern Preventive Medicine, 2024 , 51 (5) : 888 -892 . DOI: 10.20043/j.cnki.MPM.202309447
正文
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有数据显示，中国泛抑郁人数超过9 500万人[1]，抑郁在老年期是比较常见而又容易被忽视的一个现象。研究证实，社会参与能够显著降低老人抑郁发生风险[2-3]，社会活动理论(activity theory)认为，参与社会活动可以帮助人们重新认识自我，进而达到缓解抑郁的作用[4]。有学者发现社会参与在缓解抑郁症状方面发挥了积极作用，同时抑郁也会影响老人的社会参与水平[5-6]。以往的学者对社会参与和抑郁之间的关系研究多是基于截面数据，较少有发展轨迹的研究。组基轨迹模型(group-based trajectory modeling, GBTM)在描述动态变化和识别轨迹异质性方面的有效性被得到认可和应用[7]。研究社会参与和抑郁症状轨迹之间的关系有助于我们进一步了解两者的关系，并帮助专业人员制定优化卫生政策。因此，本文拟利用CHARLS数据探讨社会参与和抑郁症状发生之间的变化轨迹，从而区分出个体在轨迹类型上的差异，并利用KHB方法分析生活质量对社会参与和抑郁轨迹影响的中介效应，为进一步改善老年健康提供针对性建议。
1　对象与方法
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1.1　资料来源
本文使用的是2011—2018年四期中国健康与养老追踪调查(China Health and Retirement Longitudinal Study, CHARLS)数据，CHARLS数据采用抽样调查，收集了包括人口基本信息、家庭信息、健康状况等几个部分，具有权威性和规范性，CHARLS调查已经获得北京大学生物医学伦理审查委员会批准（伦理审批号：IRB00001052-11015），研究对象均知情同意[8]。本文的研究对象是60岁及以上的老人，考虑到105岁以上老人的特殊性，剔除105岁以上的样本[9]，2011年基期调查共调查7 669名60～105岁之间的老人。在此基础上，为保证每个纳入研究的调查对象至少有两年的抑郁症状调查数据，排除仅有2011年基线抑郁调查数据的2 091名老人，剔除其他变量缺失者647名，最终纳入4 931名研究对象。
1.2　变量选取
抑郁为本文的因变量。在CHARLS问卷中有10个关于抑郁的问题，比如“我因一些小事而烦恼”“我感到情绪低落”等[8]，本文抑郁症状测量使用的抑郁症状量表为简版流调中心用抑郁量表(the center for epidemiologic studies depression scale,CES-D10)，共10条目，每条目评分范围0～3分，总分范围为30分，分数越高表示抑郁症状越严重[6]。此外，本研究中2011—2018年CES-D10量表的Cronbach α值分别为0.740、0.728、0.716、0.744，均大于0.7，该量表具有良好的信度和效度[8]。
本文的自变量是社会参与，社会参与的相关数据来自CHARLS问卷中的“您过去一个月是否进行了下列活动：跟朋友互动、打麻将、打牌”等在内的11项社交活动，各条目相加得到社会参与总分，得分越高社会参与越好[10]。社会参与的Cronbach α值为0.785，大于0.7具有良好的信度和效度。
本文将生活质量相关变量作为中介变量。参考以往研究[11-12]，生活质量的测量指标包括自评健康、生活满意度、身体疼痛情况、工具性日常生活活动能力(instrumental activity of daily living, IADL)以及睡眠情况变量。
通过阅读文献[8,13]，本研究纳入的控制变量包括年龄、性别、居住地、受教育程度、婚姻状况、吸烟、喝酒和慢病情况，所有控制变量和中介变量数据均来自2011年的基线调查。
1.3　统计学方法
使用Stata 16.0软件进行统计分析。首先基期数据的进行描述性统计分析，其次用组基轨迹模型对2011—2018年的抑郁轨迹类型进行拟合，组基轨迹模型是有限混合模型的一种，该模型通过最大似然估计以及模型拟合规则可以确定出具有不同轨迹类型的亚组[14]。GBTM需要根据不同的数据资料类型来选择不同的拟合模型，由于本研究中抑郁是连续变量，因此选取删截正态分布模型(censored normal distribution, CNORM)[15]。最佳模型由以下标准确定：（1）贝叶斯信息准则(Bayesian information criteria, BIC)，选择选取BIC绝对值最小的一组；（2）每个轨迹组的成员不少于5%；（3）平均后验概率(average posterior probability, AvePP)大于0.7[16]。再次，采用χ2检验和方差分析比较不同轨迹亚组的基线特征，采用多分类logistic回归来研究社会参与和抑郁症状的发展轨迹之间的关联，检验水准α=0.05，最后，采用KHB方法分析生活质量对社会参与和抑郁轨迹影响的中介效应。
2　结果
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2.1　基本信息
本研究共纳入4 931名至少参与两期抑郁调查的60～105岁老人，平均年龄为（67.23±6.02）岁，平均社会参与得分为（0.68±0.85）分。
2.2　抑郁症状发展轨迹
根据GBTM模型，最终确定了最佳轨迹类型数为4组(BIC=-53 791.94)，选择具有统计学意义的2、2、1、1非线性函数为多项式阶数，分别是长期无抑郁组(n=3 042, 61.69%)、抑郁升高组(n=823, 16.69%)、抑郁缓解组(n=682, 13.83%)、长期高抑郁组(n=384, 7.79%)，见图1。轨迹模型拟合评价指标见表1。从表1可知，在本研究中，4个轨迹组的平均后验概率均大于0.7，每一轨迹组群估计的身份概率和分配到这一组群的样本比例相差无几，且每个轨迹组成员不小于5%。因此，本模型很好地捕捉到了数据中的区分性特征，模型拟合较为理想[8]。
2.3　不同抑郁轨迹组基线特征比较
识别出轨迹组群之后，为了查看不同的轨迹的特征，需要对各个轨迹进行描述，轨迹组群的基本特征见表2，从表中可以看出，各亚组的研究对象与控制变量和中介变量之间都存在差异，差异均具有统计学意义。女性、居住在农村、文盲、有慢性病、IADL越差的老人更有可能出现长期高抑郁情况。
2.4　社会参与和抑郁轨迹的关联
以社会参与情况作为自变量，轨迹归属类别作为因变量，构建多分类logistic回归模型，见表3。选择“长期无抑郁组”作为参照组，与基线未参加社会活动的老人相比，抑郁升高组、抑郁缓解组和长期高抑郁组的相对风险比(relative risk ratio, RRR)及其95%置信区间(confidence interval, CI)分别为0.813(0.739～0.895)、0.753(0.676～0.839)和0.618(0.530～0.720)，社会参与对抑郁轨迹组之间的关联都具有统计学意义(P<0.001)；调整了年龄和性别变量后，关联仍然具有统计学意义(P<0.001)；进一步加入居住地和受教育程度等因素后，关联仍显著。
2.5　社会参与影响抑郁轨迹的中介效应
表4展示了生活质量在社会参与和抑郁轨迹关系中的中介作用。与长期无抑郁组相比，其他三组的生活质量变量在社会参与和抑郁轨迹的关系中呈现显著的中介效应，生活质量各因素在社会参与对抑郁轨迹影响的具体解释力见表4。
上述检验计算的是每个中介变量的估计结果，考虑到各中介变量之间可能存在相关性，本文进一步将生活质量各变量全部纳入KHB中介效应模型，进而比较每个中介变量的贡献[17]。表5报告了加入全部中介变量后的KHB中介效应检验结果。从表中可以发现，与长期无抑郁组相比，抑郁升高组中自评健康的中介效应贡献占比最大，达到30.33%；与长期无抑郁组相比，抑郁缓解组和长期高抑郁组中身体疼痛情况的中介效应贡献占比最大，分别是27.94%和29.09%。
2.6　稳健型检验
为进一步验证本文的分析结论，采用自变量替换法进行稳健性检验。由于是否参加了社会活动和社会参与的频率都有可能代表社会参与的情况[5]，因此，本文将社会参与频率作为自变量，对主要结果重新进行检验。回归结果表明社会参与频率显著抑制了抑郁的发生，与本文的研究结论保持一致，见表6。
本文后续也对中介效应进行验证，结果显示与前文回归结果一致。即生活质量在社会参与频率和抑郁轨迹之间的影响中也充当着中介的角色，表明了本文的估计结果是稳健的，由于篇幅限制未展示结果。
3　讨论
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本研究利用组基轨迹模型，根据抑郁症状的动态变化确定了4个不同的抑郁轨迹组，这表明我国老年人抑郁症状发展轨迹存在异质性[18]，因此，根据不同组群的轨迹特征制定针对性的政策和干预措施十分重要。通过对不同抑郁轨迹组基线特征的比较可以发现，各抑郁轨迹组的研究对象与控制变量和中介变量之间存在差异，且差异均具有统计学意义。其中女性、居住在农村、文盲、有慢性病、身体疼痛以及自评生活不满意的老人更有可能出现长期高抑郁情况。这一结果与韩正等人[19]的研究结果一致。
尽管社会参与和抑郁症之间存在相对明确的关系[2-3]，但关于抑郁轨迹和社会参与之间存在联系的证据相对较少。本研究在进一步探讨了基线社会参与和抑郁轨迹之间的关系时发现，长期高抑郁组的社会参与情况最差，这一结果表明，社会参与情况较差的老人更有可能出现长期高抑郁情况[20]。分析认为，在社会参与的过程中，老年人通过亲密关系的建构和维系，能够有效消除孤独感、抑郁等不良情绪，从而改善其健康状况[21]。以轨迹归属类别为因变量，社会参与情况为自变量，构建多分类logistic回归模型，研究结果表明，社会参与对抑郁轨迹组之间的关联都具有统计学意义，因此，社会参与较多的老人抑郁轨迹为长期无抑郁的可能性更大，这与徐金燕[20]和苏镜安[22]等人的研究一致，稳健型检验也得到了类似的结果。
本研究使用KHB中介效应分析生活质量在社会参与和抑郁轨迹关系中的中介作用。首先对每个变量的结果进行估计时发现，与长期无抑郁组相比，其他抑郁轨迹组的自评健康、生活满意度、身体疼痛情况、IADL以及睡眠情况变量在社会参与和抑郁轨迹的关系中呈现显著的中介效应，这一结果与前人的研究一致[23]。在此基础上，本研究进一步对整体中介效应进行识别，发现在全部中介效应中，自评健康和身体疼痛变量的作用最大，因此，后续需要注意自评健康和身体疼痛情况在抑郁轨迹中的作用，采取有针对性的预防和治疗干预措施，探索建立充分、全面的老年保障体系。
本研究也具有一定的局限性，一方面本研究未对社会参与进行分类研究，没有详细了解不同社会参与方式对抑郁的内在影响，在未来的研究中可以丰富这一部分的研究；另一方面本次研究分析了生活质量在社会参与和抑郁轨迹之间的中介效应，但是还可能存在其他中介变量，因此在今后研究中，仍然需要更深度的探索。
综上所述，老年人抑郁轨迹存在明显的异质性，并且社会参与和抑郁轨迹关联密切，生活质量在社会参与和抑郁轨迹关系中发挥中介作用。因此，在研究老年人抑郁情况时，应根据不同抑郁轨迹特征制定针对性的政策和干预措施，同时密切关注社会参与以及生活质量相关因素对抑郁的影响，加快改善老人抑郁状况，协同促进积极老龄化建设。
基金
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  • 江苏省中医药科技发展计划专题研究项目(ZT202202)
  • 江苏省高等教育教改研究课题重点项目(2021JSJG053)
  • 江苏高校“青蓝工程”资助
  • 养老服务与管理学院（南京中医药大学）专项研究项目(2023YLFWYGL013)
文献
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doi: 10.20043/j.cnki.MPM.202309447
  • 接收时间：2023-09-24
  • 首发时间：2026-03-19
  • 出版时间：2024-03-10
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  • 收稿日期：2023-09-24
基金
江苏省中医药科技发展计划专题研究项目(ZT202202)
江苏省高等教育教改研究课题重点项目(2021JSJG053)
江苏高校“青蓝工程”资助
养老服务与管理学院（南京中医药大学）专项研究项目(2023YLFWYGL013)
作者信息
    1.南京中医药大学卫生经济管理学院，江苏 南京 210023
    2.南京中医药大学养老服务与管理学院，江苏 南京 210023

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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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