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Article(id=1322607839265272767, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1322607837742747790, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202410432, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1730044800000, receivedDateStr=2024-10-28, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793263881745, onlineDateStr=2026-10-29, pubDate=1761321600000, pubDateStr=2025-10-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793263881745, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793263881745, creator=13701087609, updateTime=1793263881745, updator=13701087609, issue=Issue{id=1322607837742747790, tenantId=1146029695717560320, journalId=1227665162245664772, year='2025', volume='52', issue='20', pageStart='3649', pageEnd='3840', issueExtLink='null', onlineDate='null', pubDate='1761321600000', pubDateStr='2025-10-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793263881383, creator='13701087609', updateTime=1793264238791, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322609336933794638, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1322607837742747790, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322609336933794639, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1322607837742747790, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=3662, endPage=3666, ext={EN=ArticleExt(id=1322607840649393088, articleId=1322607839265272767, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Trajectory patterns and influencing factors of bereavement depression among older adults, China, columnId=1228016567443718970, journalTitle=Modern Preventive Medicine, columnName=Epidemiology and Statistical Methods Advances, runingTitle=null, highlight=null, articleAbstract=
Objective

To reveal the trajectory of depressive symptoms among bereaved older adults in China and to explore the factors influencing them.

Methods

Based on the longitudinal data provided by the China Health and Retirement Longitudinal Study (CHARLS), this study used the Latent growth mixture model (LGMM) to identify individuals’ depressive symptom trajectories over time, and multivariate logistic regression was used to analyse the influencing factors.

Results

Four patterns of trajectories of depressive symptoms among bereaved older adults were found: low depression rapidly increasing group (10.2%), high depression rapidly decreasing group (11.5%), high depression slowly increasing group (23.9%), and low depression continuously stable group (54.4%). The results of the multivariate logistic regression analysis showed that, in the trajectory of depressive symptom onset among elderly parents who lost their children in China, living in urban areas, having fewer days with poor sleep per week, and reporting good self-rated health status are all protective factors, while low life satisfaction is a risk factor (all P<0.05).

Conclusion

This study reveals heterogeneity in changes in depressive symptoms among bereaved older adults in China. The government and medical institutions should meet individualized support measures based on the characteristics of different groups.

, authors=Li CHEN1, 2, Zhang-yi WANG3, authorsList=Li CHEN, Zhang-yi WANG, authorCompany=null, correspAuthors=Zhang-yi 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=1322607841865741251, articleId=1322607839265272767, tenantId=1146029695717560320, journalId=1227665162245664772, language=CN, title=我国丧子老年人抑郁症状发展轨迹及影响因素研究, columnId=1228016567632462653, journalTitle=现代预防医学, columnName=流行病与统计方法, runingTitle=null, highlight=null, articleAbstract=
目的

揭示我国丧子老年人抑郁症状的发展轨迹，并探讨其影响因素。

方法

本研究基于中国健康与养老追踪调查（China Health and Retirement Longitudinal Study, CHARLS）所提供的纵向数据，运用潜在增长混合模型（Latent growth mixture model，LGMM）识别个体抑郁症状随时间变化的轨迹模式，并采用多元logistic回归分析影响因素。

结果

研究结果发现丧子老年人抑郁症状的发展轨迹存在四种模式：低抑郁快速增长组（10.2%）、高抑郁快速下降组（11.5%）、高抑郁缓慢增长组（23.9%）和低抑郁持续稳定组（54.4%）。多元logistic回归分析结果显示，在我国丧子老年人抑郁症状的发病轨迹中，居住在城镇、每周睡眠不好天数较少及自评健康状况良好均为保护因素，而生活满意度低则是危险因素（均P<0.05）。

结论

我国丧子老年人抑郁症状发展轨迹存在异质性。政府和医疗机构应根据不同群体的特点采取个性化的干预措施。

, authors=陈莉1, 2, 汪张毅3, authorsList=陈莉, 汪张毅, authorCompany=null, correspAuthors=汪张毅, authorNote=

陈莉（2001—），女，本科，护师，研究方向：老年护理

, correspAuthorsNote=
汪张毅，E-mail:
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2.　澳门镜湖护理学院, bio={"content":"

陈莉（2001—），女，本科，护师，研究方向：老年护理

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陈莉（2001—），女，本科，护师，研究方向：老年护理

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PLOS One, 2019, 14 (3): e0213152., articleTitle=Rumination in bereaved parents: Psychometric evaluation of the Swedish version of the Utrecht Grief Rumination Scale（UGRS）, refAbstract=null), Reference(id=1322607851462307847, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607839265272767, doi=null, pmid=null, pmcid=null, year=2022, volume=10, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[27], rfOrder=29, authorNames=Ye YP, Wang JN, Li QC, journalName=Front Public Health, refType=null, unstructuredReference=Ye YP, Wang JN, Li QC, et al. Factors associated with the depression status of Chinese parents who have lost their only child [J]. Front Public Health, 2022, 10: 931945., articleTitle=Factors associated with the depression status of Chinese parents who have lost their only child, refAbstract=null)], funds=[Fund(id=1322607846731133926, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607839265272767, awardId=2025JJ80594, language=CN, fundingSource=湖南省自然科学基金项目(2025JJ80594), fundOrder=null, country=null), Fund(id=1322607846815020007, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607839265272767, awardId=Q20241204020, language=CN, fundingSource=湖南省护理学会“青苗计划”人才支持项目(Q20241204020), fundOrder=null, country=null), Fund(id=1322607846894711784, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607839265272767, awardId=W20243278, language=CN, fundingSource=湖南省卫健委卫生科研课题(W20243278), fundOrder=null, country=null), Fund(id=1322607846970209257, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607839265272767, awardId=HNKYP202413, language=CN, fundingSource=湖南省护理学会科研课题(HNKYP202413), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1322607842083845060, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607839265272767, xref=1., ext=[AuthorCompanyExt(id=1322607842092233669, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607839265272767, companyId=1322607842083845060, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=School of Nursing, Hunan Normal University, Changsha, Hunan 410013, China), AuthorCompanyExt(id=1322607842100622278, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607839265272767, companyId=1322607842083845060, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.湖南师范大学护理学院，湖南　长沙　410013)]), AuthorCompany(id=1322607842167731143, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607839265272767, xref=2., ext=[AuthorCompanyExt(id=1322607842176119752, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607839265272767, companyId=1322607842167731143, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.　澳门镜湖护理学院)]), AuthorCompany(id=1322607842251617225, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607839265272767, xref=3., ext=[AuthorCompanyExt(id=1322607842260005834, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607839265272767, companyId=1322607842251617225, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=3.衡阳市中心医院/湖南师范大学附属衡阳市中心医院护理部)])], figs=[ArticleFig(id=1322607845699335134, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607839265272767, language=EN, label=Fig.1, caption=The trajectories of depressive symptoms among bereaved older adults in China, figureFileSmall=bV6q8YtqyMPP3MgXjF5/fQ==, figureFileBig=WSSFH9VEA6N65QRXTkWYLw==, tableContent=null), ArticleFig(id=1322607845804192735, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607839265272767, language=CN, label=图1, caption=我国丧子老年人抑郁症状变化轨迹, figureFileSmall=bV6q8YtqyMPP3MgXjF5/fQ==, figureFileBig=WSSFH9VEA6N65QRXTkWYLw==, tableContent=null), ArticleFig(id=1322607846114571232, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607839265272767, language=EN, label=Table 1, caption=

Fitting of the LGMM of depressive symptoms of Chinese bereaved older adults

, figureFileSmall=null, figureFileBig=null, tableContent=
模型LLAICBICaBICEntropyLMRTBLRT类别概率（%）
1C-3 889.6677 795.3357 827.2867 801.902---1.000
2C-3 864.2727 750.5447 794.4777 759.5730.749<0.001<0.00132.9/67.1
3C-3 860.6867 749.3727 805.2887 760.8650.7550.432 30.333 303.5/31.2/65.3
4C-3 845.5417 725.0817 792.9797 739.0360.7630.030 0<0.00110.2/11.5/23.9/54.4
5C-3 839.9547 719.9097 799.7887 736.3260.8010.448 30.030 02.5/50.1/25.2/14.0/8.2
), ArticleFig(id=1322607846206845921, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607839265272767, language=CN, label=表1, caption=

LGMM模型指标拟合结果

, figureFileSmall=null, figureFileBig=null, tableContent=
模型LLAICBICaBICEntropyLMRTBLRT类别概率（%）
1C-3 889.6677 795.3357 827.2867 801.902---1.000
2C-3 864.2727 750.5447 794.4777 759.5730.749<0.001<0.00132.9/67.1
3C-3 860.6867 749.3727 805.2887 760.8650.7550.432 30.333 303.5/31.2/65.3
4C-3 845.5417 725.0817 792.9797 739.0360.7630.030 0<0.00110.2/11.5/23.9/54.4
5C-3 839.9547 719.9097 799.7887 736.3260.8010.448 30.030 02.5/50.1/25.2/14.0/8.2
), ArticleFig(id=1322607846303314914, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607839265272767, language=EN, label=Table 2, caption=

Comparison of general data of different depression trajectory groups (n=401)

, figureFileSmall=null, figureFileBig=null, tableContent=
变量赋值低抑郁快速增长组 (n=41)高抑郁快速下降组 (n=46)高抑郁缓慢增长组 (n=96)低抑郁持续稳定组 (n=218)χ2P值
年龄（岁）[例（%）]4.3420.227
60~69岁124(58.5)24(52.2)61(63.5)112(51.4)
≥70岁217(41.5)22(47.8)35(36.5)106(48.6)
性别[例（%）]11.8230.008
男08(19.5)11(23.9)19(19.8)79(36.2)
女133(80.5)35(76.1)77(80.2)139(63.8)
居住地[例（%）]16.9040.001
城镇012(29.3)12(26.1)13(13.5)79(36.2)
农村129(70.7)34(73.9)83(86.5)139(63.8)
婚姻状况[例（%）]1.4340.698
已婚028(68.3)36(78.3)71(74.0)155(71.1)
丧偶/单身113(31.7)10(21.7)25(26.0)63(28.9)
教育水平[例（%）]11.0220.088
文盲125(61.0)21(45.7)47(49.0)101(46.3)
小学216(39.0)22(47.8)40(41.7)92(42.2)
中学及以上30(0.0)3(6.5)9(9.3)25(11.5)
是否吸烟[例（%）]2.3650.500
否035(85.4)37(80.4)82(85.4)172(78.9)
是16(14.6)9(19.6)14(14.6)46(21.1)
是否饮酒[例（%）]4.9640.174
否032(78.0)39(84.8)70(72.9)152(69.7)
是19(22.0)7(15.2)26(27.1)66(30.3)
现有子女数量（个）[例（%）]4.5500.603
0~2117(41.5)11(23.9)30(31.3)81(37.2)
3~5220(48.8)29(63.0)54(56.3)115(52.8)
≥634(9.7)6(13.1)12(12.4)22(10.0)
丧子数量（个）[例（%）]5.3940.145
1133(80.5)35(76.1)69(71.9)181(83.0)
≥228(19.5)11(23.9)27(28.1)37(17.0)
是否残疾[例（%）]4.8760.181
否036(87.8)43(93.5)88(91.7)209(95.9)
是15(12.2)3(6.5)8(8.3)9(4.1)
每周睡眠不好天数（天）[例（%）]102.870< 0.001
<1121(51.2)7(15.2)15(15.6)130(59.6)
1~226(14.6)5(10.9)13(13.5)37(17.0)
3~4310(24.4)13(28.3)21(21.9)28(12.8)
5~744(9.8)21(45.6)47(49.0)23(10.6)
生活满意度[例（%）]27.985<0.001
不满意05(12.2)13(28.3)24(25.0)14(6.4)
满意136(87.8)33(71.7)72(75.0)204(93.6)
IADL[例（%）]50.953<0.001
未受损018(43.9)22(47.8)39(40.6)135(61.9)
受损123(56.1)24(52.2)57(59.4)83(38.1)
慢性病患病数（种）[例（%）]20.8060.014
0110(24.4)2(4.3)14(14.6)47(21.6)
1210(24.4)15(32.6)17(17.7)62(28.4)
238(19.5)16(34.8)23(24.0)52(23.9)
≥ 3413(31.7)13(28.3)42(43.7)57(26.1)
自评健康状况[例（%）]47.215<0.001
健康17(17.1)10(21.7)9(9.4)73(33.5)
一般224(58.5)17(37.0)41(42.7)109(50.0)
虚弱310(24.4)19(41.3)46(47.9)36(16.5)
), ArticleFig(id=1322607846408172515, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607839265272767, language=CN, label=表2, caption=

不同抑郁轨迹类别一般人口学资料的比较 (n=401)

, figureFileSmall=null, figureFileBig=null, tableContent=
变量赋值低抑郁快速增长组 (n=41)高抑郁快速下降组 (n=46)高抑郁缓慢增长组 (n=96)低抑郁持续稳定组 (n=218)χ2P值
年龄（岁）[例（%）]4.3420.227
60~69岁124(58.5)24(52.2)61(63.5)112(51.4)
≥70岁217(41.5)22(47.8)35(36.5)106(48.6)
性别[例（%）]11.8230.008
男08(19.5)11(23.9)19(19.8)79(36.2)
女133(80.5)35(76.1)77(80.2)139(63.8)
居住地[例（%）]16.9040.001
城镇012(29.3)12(26.1)13(13.5)79(36.2)
农村129(70.7)34(73.9)83(86.5)139(63.8)
婚姻状况[例（%）]1.4340.698
已婚028(68.3)36(78.3)71(74.0)155(71.1)
丧偶/单身113(31.7)10(21.7)25(26.0)63(28.9)
教育水平[例（%）]11.0220.088
文盲125(61.0)21(45.7)47(49.0)101(46.3)
小学216(39.0)22(47.8)40(41.7)92(42.2)
中学及以上30(0.0)3(6.5)9(9.3)25(11.5)
是否吸烟[例（%）]2.3650.500
否035(85.4)37(80.4)82(85.4)172(78.9)
是16(14.6)9(19.6)14(14.6)46(21.1)
是否饮酒[例（%）]4.9640.174
否032(78.0)39(84.8)70(72.9)152(69.7)
是19(22.0)7(15.2)26(27.1)66(30.3)
现有子女数量（个）[例（%）]4.5500.603
0~2117(41.5)11(23.9)30(31.3)81(37.2)
3~5220(48.8)29(63.0)54(56.3)115(52.8)
≥634(9.7)6(13.1)12(12.4)22(10.0)
丧子数量（个）[例（%）]5.3940.145
1133(80.5)35(76.1)69(71.9)181(83.0)
≥228(19.5)11(23.9)27(28.1)37(17.0)
是否残疾[例（%）]4.8760.181
否036(87.8)43(93.5)88(91.7)209(95.9)
是15(12.2)3(6.5)8(8.3)9(4.1)
每周睡眠不好天数（天）[例（%）]102.870< 0.001
<1121(51.2)7(15.2)15(15.6)130(59.6)
1~226(14.6)5(10.9)13(13.5)37(17.0)
3~4310(24.4)13(28.3)21(21.9)28(12.8)
5~744(9.8)21(45.6)47(49.0)23(10.6)
生活满意度[例（%）]27.985<0.001
不满意05(12.2)13(28.3)24(25.0)14(6.4)
满意136(87.8)33(71.7)72(75.0)204(93.6)
IADL[例（%）]50.953<0.001
未受损018(43.9)22(47.8)39(40.6)135(61.9)
受损123(56.1)24(52.2)57(59.4)83(38.1)
慢性病患病数（种）[例（%）]20.8060.014
0110(24.4)2(4.3)14(14.6)47(21.6)
1210(24.4)15(32.6)17(17.7)62(28.4)
238(19.5)16(34.8)23(24.0)52(23.9)
≥ 3413(31.7)13(28.3)42(43.7)57(26.1)
自评健康状况[例（%）]47.215<0.001
健康17(17.1)10(21.7)9(9.4)73(33.5)
一般224(58.5)17(37.0)41(42.7)109(50.0)
虚弱310(24.4)19(41.3)46(47.9)36(16.5)
), ArticleFig(id=1322607846508835812, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607839265272767, language=EN, label=Table 3, caption=

Multi-classification logistic regression analysis of the trajectory of depressive symptoms of bereaved older adults (n=401)

, figureFileSmall=null, figureFileBig=null, tableContent=
因变量自变量βSEWald χ2P值OR值(95% CI）
低抑郁快速增长组vs高抑郁缓慢增长组城镇1.5350.5079.1590.0024.643（1.718~12.549）
每周睡眠不好天数（<1天）3.0240.65321.442<0.00120.582（5.722~74.037）
每周睡眠不好天数（1~2天）1.6840.7375.2190.0225.387（1.270~22.844）
每周睡眠不好天数（3~4天）2.0630.6779.3000.0027.871（2.090~29.639）
高抑郁快速下降组vs高抑郁缓慢增长组城镇0.9450.4743.9810.0462.574（1.017~6.514）
低抑郁持续稳定组vs高抑郁缓慢增长组城镇1.8590.40321.321<0.0016.419（2.916~14.133）
每周睡眠不好天数（<1）2.9470.43346.312<0.00119.054（8.153~44.527）
每周睡眠不好天数（1~2天）1.6560.46012.976<0.0015.236（2.127~12.890）
每周睡眠不好天数（3~4天）1.3890.43710.0870.0014.012（1.702~9.457）
生活满意度（不满意）-1.3210.4428.9340.0030.267（0.112~0.635）
自评健康状况（健康）1.7040.50911.1980.0015.496（2.026~14.909）
), ArticleFig(id=1322607846596916197, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607839265272767, language=CN, label=表3, caption=

对丧子老年人抑郁症状轨迹的多元logistic回归分析 (n=401)

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因变量自变量βSEWald χ2P值OR值(95% CI）
低抑郁快速增长组vs高抑郁缓慢增长组城镇1.5350.5079.1590.0024.643（1.718~12.549）
每周睡眠不好天数（<1天）3.0240.65321.442<0.00120.582（5.722~74.037）
每周睡眠不好天数（1~2天）1.6840.7375.2190.0225.387（1.270~22.844）
每周睡眠不好天数（3~4天）2.0630.6779.3000.0027.871（2.090~29.639）
高抑郁快速下降组vs高抑郁缓慢增长组城镇0.9450.4743.9810.0462.574（1.017~6.514）
低抑郁持续稳定组vs高抑郁缓慢增长组城镇1.8590.40321.321<0.0016.419（2.916~14.133）
每周睡眠不好天数（<1）2.9470.43346.312<0.00119.054（8.153~44.527）
每周睡眠不好天数（1~2天）1.6560.46012.976<0.0015.236（2.127~12.890）
每周睡眠不好天数（3~4天）1.3890.43710.0870.0014.012（1.702~9.457）
生活满意度（不满意）-1.3210.4428.9340.0030.267（0.112~0.635）
自评健康状况（健康）1.7040.50911.1980.0015.496（2.026~14.909）
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我国丧子老年人抑郁症状发展轨迹及影响因素研究
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陈莉 1, 2 , 汪张毅 3
现代预防医学 | 流行病与统计方法 2025,52(20): 3662-3666
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现代预防医学 |流行病与统计方法 2025 , 52 (20) : 3662 -3666
我国丧子老年人抑郁症状发展轨迹及影响因素研究
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[Author(id=1322607842318726092, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607839265272767, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=null, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1322607842406806479, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607839265272767, authorId=1322607842318726092, language=EN, stringName=Li CHEN, firstName=Li, middleName=null, lastName=CHEN, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=1, 2, address=School of Nursing, Hunan Normal University, Changsha, Hunan 410013, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1322607842465526736, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607839265272767, authorId=1322607842318726092, language=CN, stringName=陈莉, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=1, 2, address=1.湖南师范大学护理学院，湖南　长沙　410013
2.　澳门镜湖护理学院, bio={"content":"

陈莉（2001—），女，本科，护师，研究方向：老年护理

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陈莉（2001—），女，本科，护师，研究方向：老年护理

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陈莉1, 2, 汪张毅3
作者信息
  • 1.湖南师范大学护理学院，湖南　长沙　410013
  • 2.　澳门镜湖护理学院
  • 3.衡阳市中心医院/湖南师范大学附属衡阳市中心医院护理部
通讯作者:
汪张毅，E-mail:
作者简介:

陈莉（2001—），女，本科，护师，研究方向：老年护理

Trajectory patterns and influencing factors of bereavement depression among older adults, China
Li CHEN1, 2, Zhang-yi WANG3
Affiliations
  • School of Nursing, Hunan Normal University, Changsha, Hunan 410013, China
出版时间: 2025-10-25 doi: 10.20043/j.cnki.MPM.202410432
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摘要
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目的

揭示我国丧子老年人抑郁症状的发展轨迹，并探讨其影响因素。

方法

本研究基于中国健康与养老追踪调查（China Health and Retirement Longitudinal Study, CHARLS）所提供的纵向数据，运用潜在增长混合模型（Latent growth mixture model，LGMM）识别个体抑郁症状随时间变化的轨迹模式，并采用多元logistic回归分析影响因素。

结果

研究结果发现丧子老年人抑郁症状的发展轨迹存在四种模式：低抑郁快速增长组（10.2%）、高抑郁快速下降组（11.5%）、高抑郁缓慢增长组（23.9%）和低抑郁持续稳定组（54.4%）。多元logistic回归分析结果显示，在我国丧子老年人抑郁症状的发病轨迹中，居住在城镇、每周睡眠不好天数较少及自评健康状况良好均为保护因素，而生活满意度低则是危险因素（均P<0.05）。

结论

我国丧子老年人抑郁症状发展轨迹存在异质性。政府和医疗机构应根据不同群体的特点采取个性化的干预措施。

老年人  /  丧子  /  抑郁症状  /  潜在增长混合模型
Objective

To reveal the trajectory of depressive symptoms among bereaved older adults in China and to explore the factors influencing them.

Methods

Based on the longitudinal data provided by the China Health and Retirement Longitudinal Study (CHARLS), this study used the Latent growth mixture model (LGMM) to identify individuals’ depressive symptom trajectories over time, and multivariate logistic regression was used to analyse the influencing factors.

Results

Four patterns of trajectories of depressive symptoms among bereaved older adults were found: low depression rapidly increasing group (10.2%), high depression rapidly decreasing group (11.5%), high depression slowly increasing group (23.9%), and low depression continuously stable group (54.4%). The results of the multivariate logistic regression analysis showed that, in the trajectory of depressive symptom onset among elderly parents who lost their children in China, living in urban areas, having fewer days with poor sleep per week, and reporting good self-rated health status are all protective factors, while low life satisfaction is a risk factor (all P<0.05).

Conclusion

This study reveals heterogeneity in changes in depressive symptoms among bereaved older adults in China. The government and medical institutions should meet individualized support measures based on the characteristics of different groups.

Older adults  /  Child bereavement  /  Depressive symptoms  /  LGMM
陈莉, 汪张毅. 我国丧子老年人抑郁症状发展轨迹及影响因素研究. 现代预防医学, 2025 , 52 (20) : 3662 -3666 . DOI: 10.20043/j.cnki.MPM.202410432
Li CHEN, Zhang-yi WANG. Trajectory patterns and influencing factors of bereavement depression among older adults, China[J]. Modern Preventive Medicine, 2025 , 52 (20) : 3662 -3666 . DOI: 10.20043/j.cnki.MPM.202410432
正文
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老龄化是各国共同面临的重要公共卫生问题[1]。2020年我国人口普查显示，60岁及以上老年人占比18.70%，较2010年增加5.44个百分点[2]。在全球老龄化的背景下，世界卫生组织“健康老龄化”理念意义重大[3-4]。然而，老年人口增多，更多的老年人可能丧子[5]。丧子可能是老年父母经历的严重精神压力事件，在我国更是如此[6]。在我国传统文化中，子女为父母提供情感和物质支持，并履行赡养义务[5，7]。据统计预计[8]，到2035年，我国将有1 000万个丧子家庭。研究[8-9]表明，与丧偶等其他丧亲抑郁相比，丧子抑郁对老年父母的影响更持久、更强烈。
丧子会给老年人带来无助、悲伤、自责等一系列抑郁心理反应[10]。研究表明，约34%的母亲和35%的父亲在丧子3~5年后仍患有中至重度抑郁症[11]。这种由丧子引发的抑郁作为一种慢性压力源，会对老年人的心理健康产生累积性伤损害[12]。然而，老年人的抑郁状况存在显著个体差异性[13]：部分老年人抑郁症状会随时间减轻，而另一些则持续加重。此外，研究发现丧子老年人的抑郁症状在不同时期的变化趋势各异，部分短期内抑郁程度较低，部分在丧子18个月后才出现明显抑郁症状[14]。这表明丧子抑郁的纵向发展轨迹具有异质性。潜在增长混合模型（Latent growth mixture model，LGMM）作为纵向数据的有限混合模型，可以识别个体增长轨迹的异质性。因此，本研究利用CHARLS数据库的长期追踪数据，旨在通过LGMM方法识别丧子老年人抑郁的轨迹模式。
1　对象与方法
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1.1　研究对象
本研究使用CHARLS数据库，选取2013年、2015年和2018年的三波数据。纳入标准：（1）2013年基线年龄≥60岁；（2）2013年调查时登记为丧子；（3）全程参与3期调查者。排除标准：（1）抑郁症状评分缺失 ≥ 1期；（2）其他影响因素数据缺失。本研究最终纳入401名研究对象，获得北京大学伦理审批（IRB00001052-11015）。
1.2　变量
1.2.1　抑郁症状
采用简版流调中心抑郁量表（Center for Epidemiological Studies Depression Scale, CES-D-10)来量化抑郁症状。CES-D-10的总分范围为0 ~ 30分，得分≥10分的评分者表示存在抑郁症状。该量表的Cronbach α系数为 0.813。
1.2.2　影响因素
结合CHARLS数据库实际情况及其他研究，共纳入15个影响因素，具体见表2。
1.3　统计学方法
LGMM采用 Mplus 8.3软件进行。选择最佳拟合模型的依据是模型的拟合指数，并同时考虑实际意义和统计指标。模型拟合指数包括：阿凯克信息准则（AIC）、贝叶斯信息准则（BIC）和样本调整贝叶斯信息准则（aBIC），数值越小，拟合效果越好。熵值范围从0到1，越接近 1表示分类越准确。为确保临床意义，要求每个类别至少包括5%的参与者。使用SPSS 27.0软件对数据进行了统计分析。采用卡方检验来检验不同分组中不同社会人口学变量和因变量之间的关系。使用多元logistic回归法，以丧子老年人中抑郁症的不同潜在类别为因变量，以单因素分析中有显著差异的影响因素为自变量，进行分析以探索该人群中不同抑郁类别轨迹的影响因素。检验水准α=0.05。
2　结果
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2.1　抑郁症状的轨迹识别
本研究利用LGMM的拟合指数，确定了四个轨迹类别。与其他模型相比，模型4的AIC值、BIC值和aBIC值均相对较小。在该模型中，Lo-Mendel-Rubin调整似然比检验（LMRT; P<0.05）和 Bootstrap 似然比检验（BLRT; P<0.05）均具有统计学意义，具体见表1。因此，四个类别被确定为最佳轨迹模式。如图1所示，丧子老年人的抑郁症状轨迹被分为四个不同的组别。类别具体如下：
（1）低抑郁快速增长组(C1)：该组初始得分（7.05）较低（I=7.846, P<0.05）,随着时间的推移，抑郁症状明显加剧（S=5.925，P<0.05），因此被命名为低抑郁快速增长组，共有41例，占总数的10.2%。
（2）高抑郁快速下降组(C2)：该组初始得分（17.28）较高（I=16.532, P<0.05），且抑郁症状随时间推移迅速下降（S=-4.600, P<0.005），因此被命名为高抑郁快速下降组，共有46例（占11.5%）。
（3）高抑郁缓慢增长组(C3)：平均截距和平均斜率分别为16.673（P<0.05）和0.925（P>0.05）。该组的初始得分（17.08）较高（I=16.673, P<0.05），总体呈上升趋势。丧子老年人的抑郁症状随着时间的推移没有明显改善（S=0.925, P>0.05），因此被命名为高抑郁缓慢增长组，该组别人数共96例，占23.9%。
（4）低抑郁持续稳定组(C4)：该组初始得分（5.67）较低（I=5.844, P<0.05），且抑郁症状长期保持稳定（S=0.045, P<0.05），因此被命名为低抑郁持续稳定组。共有218例，占总数的54.4%。
2.2　丧子老年人抑郁症状异质性轨迹的单因素分析
以这些潜在类别作为因变量，比较不同轨迹类别丧子老年人抑郁症状的基线特征。结果显示，性别(χ2=11.823，P=0.008)，居住地(χ2=16.904，P=0.001)，每周睡眠不佳天数(χ2=102.870，P<0.001)，生活满意度(χ2=27.985，P<0.001)，工具性日常生活活动能力(Instrumental activities of daily living，IADL) (χ2=50.953，P<0.001)，慢性病患病种数(χ2=20.806，P=0.014)，自评健康状况 (χ2=47.215，P<0.001)在不同类别之间具有显著差异(P<0.05)。详见表2。
2.3　丧子老年人抑郁症状异质性轨迹的多因素分析
结果显示，低抑郁快速增长组与对照组高抑郁缓慢增长组相比，居住地为城镇、每周睡眠不好天数<1天、1~2天和3~4天更容易被归为低抑郁快速增长组。高抑郁快速下降组与对照组高抑郁缓慢增长组相比，居住地为城镇的老年人更有可能被归为高抑郁快速下降组。低抑郁持续稳定组与对照组高抑郁缓慢增长组相比，居住地为城镇、每周睡眠不好天数<1天、1~2天、3~4天以及自评健康状况为健康的老年人更有可能被归为低抑郁持续稳定组，而对生活不满意的老年人更有可能被归为高抑郁缓慢增长组。详见表3。因此，居住在城镇、每周睡眠不好天数较少及自评健康者是丧子老年人抑郁发展的保护因素，生活满意度低是丧子老年人抑郁发展的危险因素。
3　讨论
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3.1　丧子老年人抑郁症状的轨迹分析
本研究通过LGMM模型表明丧子老年人的抑郁症状具有异质性。其中，低抑郁快速增长组的抑郁症状随时间推移呈现显著加重趋势，这与Pohlkamp等和d’Epinay等的结果具有一致性[15-16]。从发展轨迹来看，这类老年丧子群体在丧子初期多表现为震惊、否认等急性应激反应，随着时间推移逐渐出现明显的情绪崩溃和持续加重的孤独感。在现实中，尽管在丧子初期能够获得一定的社会同情，但随着时间的推移，这些老年人往往面临社会歧视甚至欺侮等负面对待。在中国，无后被视为愧对祖先的悲剧[17]。因此，提供有效的社会支持和宣传新时代养育子女的理念，有助于减轻老年丧子带来的一些不利影响。
与低抑郁快速增长组相比，高抑郁快速下降组老年人的抑郁程度随时间迅速下降。研究[18]表明，丧子7-9年后，丧子老年父母抑郁水平与未丧子者相似。多数人借助高龄生育、社会支持及积极活动改善情绪，心理状况趋于相对稳定的阶段，没有长期悲伤障碍或创伤后应激反应[19]。尽管抑郁症状缓解，但仍需持续开展心理健康评估，提供社会支持，及时应对新压力[20]。
高抑郁缓慢增长组老年人的 CES-D-10分数持续较高，表明抑郁症状没有改善，这与Lykke等[11]人的研究相似。这些老年人丧子后遭受生理、心理与生活多重打击，如慢性病增多、失去照料、血缘关系中断及孤独等，易引发抑郁症。由于难以接受丧子事实，他们常伴有持续性抑郁症状。在中国，“养儿防老”观念根深蒂固，若老年人无法调整，患抑郁症风险将显著增加，且症状易长期持续[17, 21]。研究表明，心理适应能力欠佳的老年人，在丧子后的三至五年内，更易受压力影响，陷入长期抑郁，甚至出现社会退缩现象[11, 21]。并且，和年长父母谈及子女死亡极为艰难，易造成情感伤害，还可能带来社会污名化问题[21]。因此，他们迫切需要长期的照料与社会支持，包括养老保障、心理援助及哀伤辅导等[22]。基层医疗机构应对丧子老年父母的状况定期跟踪、评估与干预，关注其生活、心理、生理疾病及精神等多方面需求。
在丧子老年人中，低抑郁持续稳定组占比最高，与既往研究[23]一致。该轨迹波动较小。这可能与个人应对方式有关。研究[24]表明复原力与积极应对方式是抑郁症状的负向预测因子，能在逆境中激活保护机制缓解负面情绪。因此，我们可以探索低水平抑郁症状组的个体特征，为其他组别提供有效的干预措施。
3.2　影响抑郁症状潜在类别的人口学因素分析
本研究结果显示，丧子老年人抑郁症状轨迹类型与居住地有关，相比于农村，城镇老年人更不易处于高持续抑郁轨迹组，这可能由于城镇经济水平更高，符合经济增长与心理健康呈正相关的观点[25]。因此，政府需重点关注农村丧子老年人的社会及心理需求，通过提高养老金、优先获取心理服务等措施予以满足。
本研究结果显示，老年人睡眠质量与抑郁症状的发展轨迹存在显著关联：相较于每周睡眠不佳天数较多者，睡眠不佳天数较少的老年人更易归为低抑郁持续稳定组。这一发现与Sveen等[26]关于严重失眠丧子老人抑郁症状更为显著的研究结论相吻合。此外，生活满意度、自评健康状况也是影响抑郁症状轨迹的重要预测因素。具体而言，生活满意度较低的个体更容易进入高抑郁缓慢增长组；自评健康者被归入低抑郁持续稳定组的可能性是高抑郁缓慢增长组的5.496倍（OR=5.496，95% CI：1.026 ~ 14.909，P<0.05），这与 Ye等[27]的发现存在差异，这可能其研究样本仅来自中国三个城市。因此，政府应重点关注农村及健康状况差的丧子老年人，提供物质支持、个性化健康方案及志愿服务，以预防和改善心理疾病。本研究揭示了丧子老年人抑郁水平的不同轨迹。建议政府和医疗机构依据轨迹特征，制定预测性社会保障与医疗措施，重点对睡眠差、生活满意度低、自评健康不佳的农村老年人开展长期动态评估。未来应建立针对我国丧子老年人抑郁症状的动态评估系统，以满足不同阶段需求。
本文存在一定的局限性：（1）长期随访导致样本流失，样本量受限影响模型有效性；（2）未充分利用各时间点人口学数据，无法探究个体变化对抑郁症状演变的影响；（3）社会环境等潜在因素动态复杂，难以精准量化与全面评估。
基金
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  • 湖南省自然科学基金项目(2025JJ80594)
  • 湖南省护理学会“青苗计划”人才支持项目(Q20241204020)
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2025年第52卷第20期
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doi: 10.20043/j.cnki.MPM.202410432
  • 接收时间：2024-10-28
  • 首发时间：2026-10-29
  • 出版时间：2025-10-25
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  • 收稿日期：2024-10-28
基金
湖南省自然科学基金项目(2025JJ80594)
湖南省护理学会“青苗计划”人才支持项目(Q20241204020)
湖南省卫健委卫生科研课题(W20243278)
湖南省护理学会科研课题(HNKYP202413)
作者信息
    1.湖南师范大学护理学院，湖南　长沙　410013
    2.　澳门镜湖护理学院
    3.衡阳市中心医院/湖南师范大学附属衡阳市中心医院护理部

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汪张毅，E-mail:
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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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