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Article(id=1322934670861664586, tenantId=1146029695717560320, journalId=1319373162502258839, issueId=1322934619896688658, articleNumber=null, orderNo=null, doi=10.16016/j.2097-0927.202402078, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1708963200000, receivedDateStr=2024-02-27, revisedDate=1718553600000, revisedDateStr=2024-06-17, acceptedDate=null, acceptedDateStr=null, onlineDate=1793341804470, onlineDateStr=2026-10-30, pubDate=1740672000000, pubDateStr=2025-02-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793341804470, onlineIssueDateStr=2026-10-30, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793341804470, creator=13701087609, updateTime=1793341804470, updator=13701087609, issue=Issue{id=1322934619896688658, tenantId=1146029695717560320, journalId=1319373162502258839, year='2025', volume='47', issue='4', pageStart='285', pageEnd='384', issueExtLink='null', onlineDate='null', pubDate='1740672000000', pubDateStr='2025-02-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793341792320, creator='13701087609', updateTime=1793341792320, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=369, endPage=377, ext={EN=ArticleExt(id=1322934671092351307, articleId=1322934670861664586, tenantId=1146029695717560320, journalId=1319373162502258839, language=EN, title=The trajectory of depressive symptoms and its risk factors in the middle-aged and elderly population, columnId=1320435226828980311, journalTitle=Journal of Army Medical University, columnName=Medical Psychology, runingTitle=null, highlight=null, articleAbstract=
Objective

To identify and delineate the developmental trajectories of depressive symptoms in the middle-aged and elderly population in China, and to explore the risk factors for depressive symptom trajectories in China's middle-aged and elderly population.

Methods

According to our inclusion and exclusion criteria, 7 600 participants were subjected based on the data from the China Health and Aged Care Tracking Survey (CHARLS) published by Peking University. The data from 5 waves of the survey from 2011 to 2023 were collected and analyzed with latent growth curve model (LGCM), latent class growth model (LGCM) to identify the developmental trajectories of depressive symptoms in the participants. Multicategorical logistic regression analyses were conducted to analyze whether gender, marital status, educational level, urban/rural residence, annual income, self-rated health status, and childhood bullying experience were risk factors for different trajectories of depressive symptoms.

Results

Two developmental trajectories of depressive symptoms were identified and classified in this study, that is, low-level stable (n=5 545, 72.96%) and high-level ascending (n=2 055, 27.04%). With the low-level stable group as a reference, multicategorical logistic regression analysis revealed that female (OR=1.373, 95CI%: 1.191~1.584, P<0.001), residence of rural area (OR=1.634, 95CI%: 1.410~1.895, P<0.001), and poor self-rated health status (OR=2.059, 95%CI: 1.890~2.243, P<0.001), and experience of childhood bullying (OR=1.76, 95%CI: 1.489~2.080, P<0.001) were associated with an increased risk of high-level ascending trajectory of depression.

Conclusion

There are 2 different trajectories of depressive mood in Chinese middle-aged and elderly population. And poor self-rated health status, history of childhood bullying, being female, and residence of rural area are risk factors for a high-level ascending trajectory of depressive symptoms.

, authors=Xiao CUI, Song CHANG, Heqing HUANG, Ling ZHU, authorsList=Xiao CUI, Song CHANG, Heqing HUANG, Ling ZHU, authorCompany=null, correspAuthors=Heqing HUANG, authorNote=null, correspAuthorsNote=null, copyrightStatement=© 2025 Journal of Army Medical University, copyrightOwner=Editorial Office of Journal of Army Medical University, 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=1322934673097228622, articleId=1322934670861664586, tenantId=1146029695717560320, journalId=1319373162502258839, language=CN, title=中老年人群抑郁症状发展轨迹及其风险因素研究, columnId=1322210916465234306, journalTitle=陆军军医大学学报, columnName=医学心理学, runingTitle=null, highlight=null, articleAbstract=

‍‍

目的

旨在识别并划分中国中老年人群抑郁症状的发展轨迹，并探索我国中老年人群抑郁症状轨迹的风险因素。

方法

基于北京大学发布的中国健康与养老追踪调查（China health and retirement longitudinal study，CHARLS）的数据，根据纳入、排除标准筛选出7 600名被试，对其2011年至2023年期间5波调查数据进行分析，采用潜在增长曲线模型（latent growth curve models，LGCM）、潜在增长类别模型（latent class growth models，LCGM）识别被试抑郁症状的发展轨迹。通过多分类Logistic回归分析分析性别、婚姻状况、教育水平、城乡居住情况、年经济收入、自评健康状况、童年被霸凌经历是否为不同抑郁症状轨迹的风险因素。

结果

共识别并划分出低水平稳定型（5 545，72.96%）和高水平上升型（2 055，27.04%）两种抑郁症状发展轨迹。以低水平稳定组为参照，多分类logistic回归分析发现，女性（OR=1.373，95CI%=1.191~1.584，P<0.001）、居住在乡村（OR：1.634，95CI%：1.410~1.895，P<0.001）、自评健康状况不好（OR：2.059，95%CI：1.890~2.243，P<0.001）、童年期被霸凌经历（OR：1.76，95%CI：1.489~2.080，P<0.001）与高水平上升型抑郁轨迹风险增加有关。

结论

我国中老年人群抑郁情绪存在两种不同的发展轨迹，自评健康状况不好、童年期被霸凌、女性、居住在乡村是抑郁症状高水平上升型轨迹的风险因素。

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HUANG Heqing，E-mail：
黄河清，E-mail：
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Int J Public Health，2009，54（Suppl 2）：225-234., articleTitle=Cross-national time trends in bullying behaviour 1994-2006：findings from Europe and North America, refAbstract=null)], funds=null, companyList=[AuthorCompany(id=1322934673344692559, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322934670861664586, xref=1, ext=[AuthorCompanyExt(id=1322934673357275472, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322934670861664586, companyId=1322934673344692559, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1Department of Medical Psychology，First Affiliated Hospital，Army Medical University，Chongqing，China), AuthorCompanyExt(id=1322934673365664081, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322934670861664586, companyId=1322934673344692559, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1陆军军医大学（第三军医大学）第一附属医院医学心理科，重庆)])], figs=[ArticleFig(id=1322934675177603436, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322934670861664586, language=EN, label=null, caption=null, figureFileSmall=YVCA83cKvI3wlma//Qki6Q==, figureFileBig=kpr/jPyEwYwnbSRL3IjnCw==, tableContent=null), ArticleFig(id=1322934675253100909, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322934670861664586, language=CN, label=图1, caption=抑郁症状轨迹分析图, figureFileSmall=YVCA83cKvI3wlma//Qki6Q==, figureFileBig=kpr/jPyEwYwnbSRL3IjnCw==, tableContent=null), ArticleFig(id=1322934675454427502, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322934670861664586, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
指标人数占比/%
性别
男3 50546.12
女4 09553.88
教育
无正规教育1 76823.26
初中及以下4 93464.92
高中及以上8 9811.82
婚姻状况
已婚638984.06
离婚650.86
丧偶110814.58
未婚380.50
城乡居住情况
居住在城市2 56633.76
居住在乡村5 03466.24
年经济收入/元3 683.1326 705.99
), ArticleFig(id=1322934675529924975, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322934670861664586, language=CN, label=表1, caption=

研究对象人口学信息（n=7 600）

, figureFileSmall=null, figureFileBig=null, tableContent=
指标人数占比/%
性别
男3 50546.12
女4 09553.88
教育
无正规教育1 76823.26
初中及以下4 93464.92
高中及以上8 9811.82
婚姻状况
已婚638984.06
离婚650.86
丧偶110814.58
未婚380.50
城乡居住情况
居住在城市2 56633.76
居住在乡村5 03466.24
年经济收入/元3 683.1326 705.99
), ArticleFig(id=1322934675622199664, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322934670861664586, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
模型AICBICSSA-BICCFITLIRMSEA （95% CI ）
截距模型233 586.752233 690.791233 643.1240.9720.9680.034（0.031~0.037）
线性模型233 295.834233 441.488233 374.7540.9930.9910.018（0.015~0.021）
二次模型233 256.778233 450.984233 362.0050.9960.9940.015（0.011~0.018）
), ArticleFig(id=1322934677459304817, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322934670861664586, language=CN, label=表3, caption=

抑郁症状的潜在成长曲线分析结果 （n=7 600）

, figureFileSmall=null, figureFileBig=null, tableContent=
模型AICBICSSA-BICCFITLIRMSEA （95% CI ）
截距模型233 586.752233 690.791233 643.1240.9720.9680.034（0.031~0.037）
线性模型233 295.834233 441.488233 374.7540.9930.9910.018（0.015~0.021）
二次模型233 256.778233 450.984233 362.0050.9960.9940.015（0.011~0.018）
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模型AICBICSSA-BICEntropy各类别样本量LMR - LRT
P-value		BLRT
P-value
单类别244 530.417244 655.263244 598.0631.0007 600
双类别234 914.33323 5059.987234 993.2530.8435 5452 055<0.001<0.001
三类别232 916.267233 082.729233 006.4620.7842 5377274 336<0.001<0.001
四类别232 467.288232 654.558232 568.7570.7591 0271 3786934 502<0.05<0.001
五类别232 018.601232 226.679232 131.3450.7046493 1088054302 608<0.001<0.001
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LCGM模型拟合信息 （n=7 600）

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模型AICBICSSA-BICEntropy各类别样本量LMR - LRT
P-value		BLRT
P-value
单类别244 530.417244 655.263244 598.0631.0007 600
双类别234 914.33323 5059.987234 993.2530.8435 5452 055<0.001<0.001
三类别232 916.267233 082.729233 006.4620.7842 5377274 336<0.001<0.001
四类别232 467.288232 654.558232 568.7570.7591 0271 3786934 502<0.05<0.001
五类别232 018.601232 226.679232 131.3450.7046493 1088054302 608<0.001<0.001
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指标ORCI（95%）P
童年期被霸凌经历1.7601.489~2.080< 0.001
年龄0.9990.990~1.0080.945
性别1.3741.190~1.584< 0.001
教育
初中及以下1.1350.959~1.3440.139
高中及以上1.1620.896~1.5070.257
婚姻状况0.8460.707~1.0130.370
城乡居住情况1.6341.410~1.895<0.001
年经济收入0.9990.999~0.999<0.05
自评健康状况2.0591.890~0.243<0.001
), ArticleFig(id=1322934678302359925, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322934670861664586, language=CN, label=表5, caption=

中老年人抑郁症状类别的Logistic分析 （n=5 355）

, figureFileSmall=null, figureFileBig=null, tableContent=
指标ORCI（95%）P
童年期被霸凌经历1.7601.489~2.080< 0.001
年龄0.9990.990~1.0080.945
性别1.3741.190~1.584< 0.001
教育
初中及以下1.1350.959~1.3440.139
高中及以上1.1620.896~1.5070.257
婚姻状况0.8460.707~1.0130.370
城乡居住情况1.6341.410~1.895<0.001
年经济收入0.9990.999~0.999<0.05
自评健康状况2.0591.890~0.243<0.001
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中老年人群抑郁症状发展轨迹及其风险因素研究
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崔啸 , 常淞 , 黄河清 , 朱玲
陆军军医大学学报 | 医学心理学 2025,47(4): 369-377
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陆军军医大学学报 |医学心理学 2025 , 47 (4) : 369 -377
中老年人群抑郁症状发展轨迹及其风险因素研究
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崔啸, 常淞, 黄河清 , 朱玲
作者信息
  • 1陆军军医大学（第三军医大学）第一附属医院医学心理科，重庆
通讯作者:
HUANG Heqing，E-mail：
黄河清，E-mail：
The trajectory of depressive symptoms and its risk factors in the middle-aged and elderly population
Xiao CUI, Song CHANG, Heqing HUANG , Ling ZHU
Affiliations
  • 1Department of Medical Psychology，First Affiliated Hospital，Army Medical University，Chongqing，China
出版时间: 2025-02-28 doi: 10.16016/j.2097-0927.202402078
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摘要
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‍‍

目的

旨在识别并划分中国中老年人群抑郁症状的发展轨迹，并探索我国中老年人群抑郁症状轨迹的风险因素。

方法

基于北京大学发布的中国健康与养老追踪调查（China health and retirement longitudinal study，CHARLS）的数据，根据纳入、排除标准筛选出7 600名被试，对其2011年至2023年期间5波调查数据进行分析，采用潜在增长曲线模型（latent growth curve models，LGCM）、潜在增长类别模型（latent class growth models，LCGM）识别被试抑郁症状的发展轨迹。通过多分类Logistic回归分析分析性别、婚姻状况、教育水平、城乡居住情况、年经济收入、自评健康状况、童年被霸凌经历是否为不同抑郁症状轨迹的风险因素。

结果

共识别并划分出低水平稳定型（5 545，72.96%）和高水平上升型（2 055，27.04%）两种抑郁症状发展轨迹。以低水平稳定组为参照，多分类logistic回归分析发现，女性（OR=1.373，95CI%=1.191~1.584，P<0.001）、居住在乡村（OR：1.634，95CI%：1.410~1.895，P<0.001）、自评健康状况不好（OR：2.059，95%CI：1.890~2.243，P<0.001）、童年期被霸凌经历（OR：1.76，95%CI：1.489~2.080，P<0.001）与高水平上升型抑郁轨迹风险增加有关。

结论

我国中老年人群抑郁情绪存在两种不同的发展轨迹，自评健康状况不好、童年期被霸凌、女性、居住在乡村是抑郁症状高水平上升型轨迹的风险因素。

中老年人  /  抑郁症状  /  风险因素
Objective

To identify and delineate the developmental trajectories of depressive symptoms in the middle-aged and elderly population in China, and to explore the risk factors for depressive symptom trajectories in China's middle-aged and elderly population.

Methods

According to our inclusion and exclusion criteria, 7 600 participants were subjected based on the data from the China Health and Aged Care Tracking Survey (CHARLS) published by Peking University. The data from 5 waves of the survey from 2011 to 2023 were collected and analyzed with latent growth curve model (LGCM), latent class growth model (LGCM) to identify the developmental trajectories of depressive symptoms in the participants. Multicategorical logistic regression analyses were conducted to analyze whether gender, marital status, educational level, urban/rural residence, annual income, self-rated health status, and childhood bullying experience were risk factors for different trajectories of depressive symptoms.

Results

Two developmental trajectories of depressive symptoms were identified and classified in this study, that is, low-level stable (n=5 545, 72.96%) and high-level ascending (n=2 055, 27.04%). With the low-level stable group as a reference, multicategorical logistic regression analysis revealed that female (OR=1.373, 95CI%: 1.191~1.584, P<0.001), residence of rural area (OR=1.634, 95CI%: 1.410~1.895, P<0.001), and poor self-rated health status (OR=2.059, 95%CI: 1.890~2.243, P<0.001), and experience of childhood bullying (OR=1.76, 95%CI: 1.489~2.080, P<0.001) were associated with an increased risk of high-level ascending trajectory of depression.

Conclusion

There are 2 different trajectories of depressive mood in Chinese middle-aged and elderly population. And poor self-rated health status, history of childhood bullying, being female, and residence of rural area are risk factors for a high-level ascending trajectory of depressive symptoms.

middle-aged and elderly adults  /  depressive symptoms  /  risk factors
崔啸, 常淞, 黄河清, 朱玲. 中老年人群抑郁症状发展轨迹及其风险因素研究. 陆军军医大学学报, 2025 , 47 (4) : 369 -377 . DOI: 10.16016/j.2097-0927.202402078
Xiao CUI, Song CHANG, Heqing HUANG, Ling ZHU. The trajectory of depressive symptoms and its risk factors in the middle-aged and elderly population[J]. Journal of Army Medical University, 2025 , 47 (4) : 369 -377 . DOI: 10.16016/j.2097-0927.202402078
正文
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抑郁症状是指一组症状综合征，包括持续的情绪低落、兴趣缺失、自我贬低和无望感等［1］，可能严重影响患者的生活质量、工作效率和社会功能，增加自杀和心血管疾病等疾病的风险［2］。根据世界卫生组织的估计，全球约3.3亿人存在抑郁症状，占总人口的4.4%，是非致命性健康损失的最大原因［3］。随着人口老龄化的加速，中老年人抑郁问题日益突出。据统计，我国＞60岁老年人抑郁症状的患病率为13.3%，且有逐年上升的趋势［4］。中老年人的抑郁症状可能受家族遗传、身体健康状况、教育水平、家庭经济状况、童年不良经历（adverse childhood experiences，ACEs）等多种因素影响［5］，已有研究提示，抑郁症状的发展趋势呈现群体异质性，有的人出现抑郁症状后缓解，有的人抑郁症状出现后会持续存在或升高［6-7］。由于对中老年抑郁症状群体异质性的发生机制缺乏了解，现有防治策略可能缺乏针对性和有效性［8］。童年期不良经历等风险因素与中老年时期抑郁症状的关系是近年来的热点研究话题，多项研究发现，童年期不良经历不仅严重影响儿童的身心健康，且对成年期的身体和心理健康造成持续的负面影响［2，9］。童年期被霸凌经历是一种常见的童年期不良经历，主要是指个体在童年时期遭受同龄人的身体或言语的伤害［10］。既往研究报道，童年期遭受过霸凌的人在中老年时期的抑郁症状水平显著高于没有遭受过霸凌的人，且这一影响在控制了童年期的社会经济地位、认知能力和其他情绪问题等混杂因素后仍然显著［11-13］。这些研究表明，童年期被霸凌经历作为一种重要的早期逆境，对中老年人抑郁症状的发生和发展有着显著的影响［14］。然而，目前的研究还存在以下问题：一是忽视了中老年人抑郁症状发展的群体异质性，多数研究将中老年人作为一个整体进行分析，而未探索中老年人抑郁症状发展趋势的差异。二是对中老年人抑郁的研究多数为横断面研究，缺少反映抑郁症状变化趋势的纵向研究，尤其是忽视童年期被霸凌经历、性别、年龄、教育水平、一般健康情况等因素对中老年人抑郁症状变化趋势的影响，未能揭示中老年人抑郁症状群体异质性的成因和机制。
基于此，本研究对中国健康与养老追踪调查公共数据库（China health and retirement longitudinal study，CHARLS）数据进行分析［15-16］，探索童年期被霸凌经历对成年期抑郁症状轨迹的影响，旨在揭示童年期被霸凌经历等风险因素对中老年人心理健康的长期影响，为精准干预中老年人抑郁症状提供识别信息参考。
1　对象与方法
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1.1　研究对象
本研究使用CHARLS项目的公共数据。CHARLS是由北京大学国家发展研究院主持的一项专注于45岁以上中老年人的全国性追踪调查。该调查采用了多阶段分层随机抽样的方法，总计抽取了10 257户家庭的17 708人，覆盖了我国的28个省份、150个县（市、区）、450个村（居）委会。CHARLS的第1波基线调查于2011年至2012年进行，第2波调查于2013年至2014年进行，第3波调查于2015年至2016年进行，第4波调查于2017年至2018年进行，第5波调查于2020年至2023年，本研究使用了CHARLS的第1波至第5波调查的数据［15］，该数据为符合纳入排除标准的7 600名被试在5个时间点的重复测量数据，调查内容主要涉及中老年人的基本情况、家庭支持、健康状况等。被试基线平均年龄为56~64岁：基线时期（T0）的平均年龄为（56.25±8.14）岁，第2波调查（T1）的平均年龄为（58.25±8.14）岁，第3波调查（T2）的平均年龄为（60.24±8.15）岁，第4波调查（T3）的平均年龄为（63.23±8.15）岁，第5波调查（T4）的平均年龄为（65.24±7.97）岁。见表1。
1.2　研究流程
本研究使用CHARLS的数据进行分析，研究者在CHARLS官网注册并签署了用户数据使用协议，并遵守了数据的使用伦理（IRB00001052-11015）。所有参与者均在知情同意的前提下自愿参与本研究，并保证了研究的合法性和道德性。CHARLS数据库包含了一般情况调查表、童年期被霸凌经历情况问卷、流调用抑郁自评量表（center for epidemiologic studies depression Scale，CES-D）等内容。本研究使用了CHARLS数据库2011年至2020年的调查数据，纳入标准：年龄≥45岁，完成5波调查未失访的被试；排除标准：调查结果缺失或异常的被试。共获得7 600名被试的信息。
1.3　研究工具
1.3.1　一般情况调查表
该表涵盖了被试的基本信息，如性别、年龄、教育程度（无正规教育、初中及以下、高中及以上）、婚姻状况（已婚、离婚、丧偶、未婚）、自评健康状况（非常好、很好、好、一般、不好、不知道）等，以便对被试进行描述性统计和分组比较。
1.3.2　童年期被霸凌经历问卷
本研究根据CHARLS调查中“你在12岁以前是否经常被同龄人或者年长者欺负或者打骂？”问题的回答作为评估被试是否存在童年期被霸凌的经历的标准，该问题有2个选项，“是”表示既往存在童年被霸凌经历，“否”表示既往不存在童年被霸凌经历。过去多个基于CHARLS数据库的研究将该问题作为评估中老年人是否有童年期被霸凌经历的测量工具［17-19］。
1.3.3　流调用抑郁自评量表（CES-D）
该量表是一种广泛使用的抑郁症状筛查工具，包括10个问题，涵盖了抑郁的情绪、认知、行为和生理方面［20］。每个问题有4个选项：“从不或很少时间”、“有时”、“大部分时间”和“总是或几乎总是”，对应的分数分别是0、1、2和3分。CES-D总分为10个问题的分数之和，得分范围为0~30分，分数越高，表示抑郁症状越严重。
1.4　统计学分析
本研究采用SPSS 24.0 软件对数据进行了清洗，符合正态分布的计量资料用平均值和标准差表示，计数资料用频数和率表示，本研究删除了有缺失值和异常值个体。选用Mplus 8.3软件执行潜在增长曲线模型分析（latent growth curve models，LGCM），该分析旨在识别中老年人抑郁症状发展轨迹的曲线，LGCM模型的评价指标包括：赤池信息准则（Akaike information criterion，AIC）、贝叶斯信息准则（Bayesian information criterion BIC）、比较拟合指数（comparative fit index，CFI）、置信区间（confidence intervals，CI）、拟合误差均方根（root mean square error of approximation，RMSEA）、样本大小调整的贝叶斯信息准则（sample size adjusted Bayesian information criterion，SSA-BIC）、塔克-刘易斯指数（Tucker-Lewis index，TLI）。根据以上拟合指数，选择抑郁症状发展的曲线最优类型。而后使用潜在增长类别模型分析（latent class growth models，LCGM）识别中老年人群中抑郁症状发展轨迹的潜在类别。LCGM模型的评价指标包括：AIC、BIC、样本校正贝叶斯信息准则（comparative fit index，aBIC）、信息熵（entropy）、似然比检验（lo-mendel-rubin likelihood ratio test，LMR）、自举似然比检验（bootstrap likelihood ratio test，BLRT）。在选择最优模型时，依据以下标准：熵更高，AIC、BIC和aBIC更低，LMR和BLRT的P＜0.05，表明模型拟合度更佳。识别出抑郁症状发展的潜在类别后，研究进一步使用多分类Logistic回归模型，以抑郁症状轨迹的潜在类别分组结果作为因变量，年龄、性别、教育水平、婚姻状况、自评健康状况、城乡居住情况、年经济收入、童年期遭受霸凌的经历作为预测变量，探索中老年人抑郁症状发展的风险因素。P＜0.05为差异具有统计学意义。
2　结果
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2.1　研究对象自评健康状况、童年期被霸凌经历和抑郁症状情况
本研究共纳入研究对象7 600名，自评健康状况方面：非常好46人（0.62%），很好486人（6.39%），好1 007人（13.25%），一般2 645人（34.80%），不好1 176人（15.47%），不知道2 240人（29.47%）。研究对象中遭受童年期霸凌的共1130人（14.87%）；抑郁症状平均分在7.81分~9.30分之间：T0的平均分为（8.13±6.15）分，T1的平均分为（7.81±5.70）分，T2的平均分为（7.95±6.32）分，T3的平均分为（8.72±6.54）分，T4的平均分为（9.30±6.57）分。
2.2　抑郁症状的潜在增长曲线分析
为探讨童年霸凌对成年抑郁情绪的总体影响，本研究采用了LGCM来分析抑郁情绪的变化轨迹。分别建立了截距模型、线性模型和二次模型，比较了不同模型的拟合度和分类精度。截距模型仅考虑了数据的截距，没有考虑时间的线性或非线性趋势，其AIC值为233 586.752，BIC值为233 690.791，指标相对于线性模型、二次模型较高。线性模型假设数据随时间线性变化。其AIC值、BIC值进一步下降，相比截距模型有所改进，CFI和TLI的值均接近1，显示出良好的模型拟合度，RMSEA值为0.018，说明模型与实际数据拟合良好。二次模型考虑了数据随时间的非线性变化，其AIC和BIC为233 256.778和233 450.984，CFI和TLI值分别为0.996和0.994，RMSEA值为0.015，表明该模型拟合效果同样良好。模型比较：从AIC、BIC、CFI、TLI和RMSEA等指标来看，线性模型、二次模型都有良好的数据拟合效果，但线性模型更加简单直观，因此本研究选择线性模型作为最佳模型，对其进行进一步的分析，见表2。
2.3　抑郁症状的潜在增长类别分析
分别选取 1~5 种潜在类别的LCGM进行模型拟合度分析，依次为单类别模型、双类别模型、三类别模型、四类别模型、五类别模型，各项拟合指数见表3。在模型比较中，随着模型类别的增加，各拟合信息指数逐步下降。本研究中，双类别、三类别模型、四类别模型、五类别模型的LMR-LRT和BLRT的P值都小于0.05，达到显著水平。但当类别数为2时，熵值为0.843，表明分类效果较好，AIC，BIC和SSA-BIC分别为234 914.333、235 059.987、234 993.253，表明模型拟合较好，因此，综合考虑模型的简约性、准确性和实际意义选择类别数为2的模型作为最佳模型，见表4。
根据线性模型的结果，本研究识别出了两种抑郁情绪的轨迹类别，分别为低水平稳定型（low-stable，占比为72.96%）和高水平上升型（high-increasing，占比为27.04%），见图1。
2.4　各影响因素对抑郁症状潜在类别影响
本研究使用二元Logistic分析来探讨童年期被霸凌经历、年龄、性别、教育和婚姻状况、城乡居住情况、年经济收入、自评健康状况对中老年期抑郁轨迹类别的影响。结局变量为两类抑郁轨迹：低水平稳定型和高水平上升型，剔除未报告健康状况人员，共得到5 355名被试。结果显示，童年被霸凌经历仍与高水平上升型抑郁症状轨迹风险增加有关（OR：1.76，95%CI：1.489~2.080，P<0.001）；设置男性为参照组，与男性比较，女性在高水平上升型抑郁症状轨迹风险增加37.3%（OR=1.373，95CI%：1.191~1.584，P<0.001）；设置居住在城市为参照组，与居住在乡村组比较，居住在乡村组与高水平上升型抑郁症状轨迹风险密切关联（OR：1.634，95CI%：1.410~1.895，P<0.001）；自评健康状况的恶化与高水平上升型抑郁症状轨迹风险增加有关（OR：2.059，95%CI：1.890~2.243，P<0.001）。年龄、教育水平、婚姻状况对中老年人在高水平上升型抑郁症状风险的影响无统计学意义，年经济收入虽有统计学意义，但无实践意义（OR=0.999 98，95%CI=0.999 97~0.999 99，P<0.001），见表5。
3　讨论
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本研究分析了2011年到2023年期间7 600名研究对象在五波重复调查中 的抑郁症状（CES-D得分）变化，并探讨了各风险因素对抑郁轨迹类别的影响，主要有如下发现：①中老年人群抑郁症状呈现线性变化趋势，且可分为两种抑郁症状的轨迹类别，分别为低水平稳定型（low-stable，占比为72.96%）和高水平上升型（high-increasing，占比为27.04%）；②自评健康状况不好、童年期被霸凌、女性、居住在乡村是中老年期抑郁症状高水平上升型的重要危险因素。
3.1　自评健康状况与抑郁轨迹关系
本研究还发现，自评健康状况是中老年人处于高水平抑郁轨迹的风险因素（OR=2.059，95%CI=1.890~2.243，P<0.001），该结果与过去结果一致［21］。过去研究表明，自评与客观健康状况呈正相关，可以作为一般人群健康状况的衡量标准［22］。自评健康状况较差的中老年人可能感受到更多的身体不适和功能限制，从而增加了抑郁的风险［23］。中老年人疾病和健康问题可能导致个体的社会参与度降低，社会支持减少，从而加剧抑郁情绪［24-25］。
3.2　童年被霸凌经历与抑郁轨迹关系
童年期被霸凌经历受害者是高水平上升型抑郁轨迹类别的风险因素，提示与童年期没有暴露于霸凌的人群相比，童年期被霸凌经历受害者在中老年期更容易出现抑郁情绪的上升趋势。这与过往研究结果一致，童年期被霸凌经历不仅会导致当时的心理创伤，还会引起广泛的身心问题：如睡眠障碍、腹痛、食欲减退、头痛等［26-27］，从而增加个体在成年期面临的抑郁风险［28］。主要原因可能是：生理方面，童年期被霸凌经历会引发全身慢性炎性反应加剧，这种慢性的炎症反应可能增加了成年期出现抑郁症状的风险［29］；心理方面，童年被霸凌经历会影响个体心理弹性等保护性因素的发展，诱发消极自我描述和消极应对方式水平等风险因素增加，在这种情况下，个体不能很好地寻求社会支持，应对外界压力和挑战，导致成年期更容易出现高水平的抑郁［30］。童年期被霸凌经历是导致成年期抑郁的重要危险因素，需要引起社会的重视和关注，要关注童年期被霸凌经历受害者的心理健康和社会适应，降低他们在成年期的抑郁风险［31］。要对不同的抑郁轨迹类别进行个性化和差异化的干预［32］，针对高水平上升型的抑郁轨迹类别，要提供必要的治疗干预，以减轻他们的抑郁症状和改善他们的生活质量；针对低水平稳定型的抑郁轨迹类别，要提供更多的心理咨询和教育，以增强他们的心理韧性和幸福感［33］。
3.3　性别与抑郁轨迹的关系
本研究发现，女性相比男性，抑郁轨迹类别为高水平上升型的概率增加37.4%。这与过去研究一致，大量研究显示，女性抑郁症患病率约为男性的2倍［34］，这种现象可能与女性的雌激素变化有关，研究表明，雌激素波动会影响女性的情绪感知、情绪调节和应激反应水平，低雌激素水平可能会增加女性对抑郁的易感性［35］。在绝经期，女性体内的雌激素水平会显著减少，卵巢激素的波动会变得不规则，最终会进入一个稳定的低雌二醇状态。雌二醇水平的变化可能会改变大脑背侧和腹侧情绪调节回路的活动，改变生活压力的认知后果［36-37］。当雌二醇水平低时，女性可能会经历对压力性生活事件和负面情绪信息的反应改变，导致对负面注意偏向和负面情绪的记忆增强，进而增加抑郁症状发生的风险［37］。此外，该现象还可能与女性性别角色压力等因素有关，在中国文化背景下，多数中老年女性有着“男主外、女主内”的性别角色观念［38］。然而，随着时代的变迁，女性在外出工作的同时还要承担“照顾家庭、养育子女”等事务，面临着工作与家庭的双重压力，这可能增加了女性发生抑郁症状的风险［39-41］。这个现象提示我们要更多关注中老年女性的心理健康，降低中老年女性发生抑郁的风险。
3.4　不同居住环境与抑郁轨迹的关系
本研究发现，居住在乡村组抑郁轨迹类别为高水平上升型的概率为居住在城市组的1.63倍。这一发现与之前的研究一致［42］，可能是因为城市地区有较为完善的基础设施、更丰富的卫生资源和福利体系；同时，城市居民更愿意寻求专业的心理帮助，提高了心理健康服务的利用率，这些因素共同作用，可能有助于缓解城市居民的心理压力，降低抑郁症状的发展风险［43］。
随着时间的推移，这些因素可能通过不同的生物学、心理学和社会途径影响抑郁症状发展轨迹，例如，童年期的不良经历（如被霸凌）可能会通过影响大脑发育和应激反应机制，导致心理创伤和社会适应问题，这些问题可能会在成年期转化为不健康的行为模式，如缺乏运动、不良饮食习惯，进而影响健康状况［26-27］。女性的性别角色的期望和压力可能导致女性在家庭和职业之间的角色冲突，这种双重压力可能会对她们的心理和生理健康产生负面影响。居住在乡村的个体可能由于缺乏足够的医疗资源和心理健康服务，导致抑郁症状的早期识别和干预不足，从而加剧了症状的严重程度。因此，未来的研究需要进一步探讨这些因素之间的相互作用及其对抑郁症状轨迹的长期影响，以便为预防和干预提供更有针对性的干预措施，包括对童年期不良经历的干预，提供性别敏感的心理健康服务，以及改善乡村地区的医疗资源和心理健康服务。通过这些研究，可以更好地理解抑郁症状的发展机制，并为抑郁高风险的个体提供更有效的支持和干预。
本研究的局限和不足主要有以下几点：首先，本研究采用中老年人对童年期被霸凌经历的回顾性问题，可能存在回忆偏倚，影响结果的稳定性。不过，根据RIVERS［44］的研究发现，“人们对童年欺凌受害的记忆随时间是稳定可靠的”。其次，HAVARI等［45］的研究也支持了这一点，指出老年人能够准确地回忆起他们童年的经历。以上结果表明，即使存在偏差的可能性，参与者的回忆可以作为研究的可靠基础。其次，OLWEUSl［31］关于霸凌的定义强调了霸凌行为的持续性、意图伤害和权力不平衡的特点，但由于数据库限制，本研究使用单一问题来界定霸凌行为，该定义可能不够全面。不过，在既往大样本调查研究中，使用该问题作为测量童年被霸凌经历的工具并不少见［17-18，46］，它提供了一个有助于在大样本中快速识别需要进一步关注和支持的个体的指标，具有实践意义。最后，本研究的分析方法主要使用了潜变量生长曲线模型，虽然能够较好地刻画抑郁情绪的变化轨迹和个体差异，但也存在一些假设和限制，如线性或二次的变化趋势，固定的类别数，忽略了个体间的相互作用等，因此需要探索更灵活和复杂的模型（如动态系统模型），来更好地描述和解释抑郁情绪的动态变化和机制 。
综上所述，本研究从轨迹变化的角度探索抑郁症状的风险因素，为理解抑郁情绪的发生发展过程和预防抑郁症状提供了理论依据。本研究还对性别、年龄、教育水平在的不同抑郁轨迹类别的概率进行了比较，为个性化评估抑郁患者的生活质量提供了参考。

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2025年第47卷第4期
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doi: 10.16016/j.2097-0927.202402078
  • 接收时间：2024-02-27
  • 首发时间：2026-10-30
  • 出版时间：2025-02-28
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  • 收稿日期：2024-02-27
  • 修回日期：2024-06-17
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    1陆军军医大学（第三军医大学）第一附属医院医学心理科，重庆

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HUANG Heqing，E-mail：
黄河清，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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