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Article(id=1241035813547462919, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241035810628235909, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202407285, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1720886400000, receivedDateStr=2024-07-14, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773815595280, onlineDateStr=2026-03-18, pubDate=1733760000000, pubDateStr=2024-12-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773815595280, onlineIssueDateStr=2026-03-18, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773815595280, creator=13701087609, updateTime=1773815595280, updator=13701087609, issue=Issue{id=1241035810628235909, tenantId=1146029695717560320, journalId=1227665162245664772, year='2024', volume='51', issue='23', pageStart='4225', pageEnd='4416', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773815594584, creator=13701087609, updateTime=1773815743629, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241036435843764756, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241035810628235909, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241036435843764757, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241035810628235909, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=4349, endPage=4354, ext={EN=ArticleExt(id=1241035815099355405, articleId=1241035813547462919, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Factors influencing depression in middle-aged and elderly individuals and mining of association rules, columnId=1228016572451718132, journalTitle=Modern Preventive Medicine, columnName=Health and Social Behavior, runingTitle=null, highlight=null, articleAbstract=
Objective

To analyze the significance of various factors affecting depression in middle-aged and elderly individuals, as well as the association between different demographic characteristics and depression based on the theory of social determinants of health, providing a reference for improving the physical and mental health of this population.

Methods

Using data from the 2020 China Health and Retirement Longitudinal Study, the research focused on individuals aged 45 and above. A random forest model was employed to rank the importance of factors influencing depression in this demographic, and association rule mining was conducted to analyze population characteristics.

Results

The depression rate among middle-aged and elderly individuals was 34.7%. The random forest model indicated that the top 11 variables influencing depression, ranked by their Mean Decrease Accuracy (MDA) values, were as follows: impaired Activities of Daily Living (ADL) (20.24%), chronic diseases(19.12%), social participation (18.72%), medical insurance (18.10%), age (16.77%), self-rated health status (15.31%), emotional support (11.91%), economic support (10.80%), internet usage (10.67%), education level (9.97%), and sleep duration(9.77%) (P<0.05). The results of association rule mining revealed that the combination of impaired ADL, chronic diseases, age ≥ 66 years, lack of medical insurance, and sleep duration < 7 hours significantly increased the risk of depression in middle-aged and elderly individuals, with a support of 20.740% and a confidence of 77.786%, indicating a risk 3.751 times greater than those without these characteristics.

Conclusion

The factors influencing depression in middle-aged and elderly individuals are multidimensional. Impaired ADL, chronic diseases, age ≥ 66 years, lack of medical insurance, and sleep duration < 7 hours are critical factors affecting depression. Screening for these key factors can identify high-risk populations, thereby reducing the risk of depression and improving the mental health levels of middle-aged and elderly individuals.

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目的

依据健康社会决定因素理论，分析不同因素对中老年人抑郁影响的重要性以及不同人口学特征与抑郁之间的关联关系，为改善中老年人身心健康提供参考依据。

方法

以2020年中国健康与养老追踪调查数据中45岁以上中老年人为研究对象，采用随机森林模型分析中老年人抑郁的影响因素重要性排序，针对人群特征进行关联规则挖掘分析。

结果

中老年人抑郁率为34.7%，随机森林模型结果显示，中老年人抑郁影响因素的MDA值排名前11的变量依次为日常活动能力（activity of daily living，ADL）受损（20.24%）、慢性病（19.12%）、社会参与（18.72%）、医疗保险（18.10%）、年龄（16.77%）、自评健康状况（15.31%)、情感支持(11.91%)、经济支持（10.80%）、网络使用(10.67%)、文化程度(9.97%)、睡眠时间（9.77%）（P<0.05）；关联规则挖掘结果显示，ADL受损、患有慢性病、年龄≥66岁、无医保、睡眠时间<7 h的组合使得中老年人患抑郁的风险增大，其中支持度为20.740%，置信度为77.786%，是不具备这些特征者的3.751倍。

结论

中老年人抑郁影响因素呈现多维度。ADL受损、患有慢性病、年龄≥66岁、无医保、睡眠时间<7 h是影响中老年人抑郁的关键因素，应通过关键因素筛查出高危人群，从而降低抑郁的发生风险，改善中老年人心理健康水平。

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许静怡，E-mail:
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李小燕（1996—），女，硕士在读，研究方向：卫生政策与卫生管理

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李小燕（1996—），女，硕士在读，研究方向：卫生政策与卫生管理

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Current status of depression in urban elderly in Sichuan Province[J]. Chinese Journal of Gerontology, 2018, 38(6): 1490-1492.(In Chinese), articleTitle=Current status of depression in urban elderly in Sichuan Province, refAbstract=null)], funds=[Fund(id=1241069121174032583, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035813547462919, awardId=2023AAC03233, language=CN, fundingSource=宁夏自然科学基金(2023AAC03233), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1241069117118141384, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035813547462919, xref=null, ext=[AuthorCompanyExt(id=1241069117130724298, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035813547462919, companyId=1241069117118141384, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=School of Humanities and Management, Ningxia Medical University, Ningxia 750000, China), AuthorCompanyExt(id=1241069117139112907, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035813547462919, companyId=1241069117118141384, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=宁夏医科大学人文与管理学院，宁夏 银川 750000)])], figs=[ArticleFig(id=1241069120284840071, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035813547462919, language=EN, label=Figure 1, caption=Significance ranking of factors influencing depression in the elderly, figureFileSmall=uVH8Q6C9v22xkzxC+VuuiA==, figureFileBig=NPQKlFWs/AOU97vGed3zxg==, tableContent=null), ArticleFig(id=1241069120372920461, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035813547462919, language=CN, label=图1, caption=中老年人抑郁影响因素重要性排序, figureFileSmall=uVH8Q6C9v22xkzxC+VuuiA==, figureFileBig=NPQKlFWs/AOU97vGed3zxg==, tableContent=null), ArticleFig(id=1241069120502943894, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035813547462919, language=EN, label=Table 1, caption=

Basic characteristics of depression in middle-aged and elderly people

, figureFileSmall=null, figureFileBig=null, tableContent=
变量名称类别调查人数抑郁人数抑郁率(%)χ2值P值
不同个体
性别男2 61287133.34.6700.031
女1 98372236.4
年龄（岁）45～5581526332.369.630<0.001
56～651 97827213.8
66～7593443246.3
>7586862672.1
慢性病是2 31583636.14.2960.038
否2 28075733.2
ADL受损是2 12677236.34.7230.030
否2 46982133.3
行为生活方式
吸烟是1 99176538.421.869<0.001
否2 60482831.8
喝酒是2 20279736.22.3490.057
否2 39379633.3
体育锻炼是2 49476330.64.6410.030
否2 10183039.5
自评健康状况好92213214.3264.282<0.001
一般1 99668934.5
差1 67777246.0
生活满意度满意2 48883033.44.0980.043
不满意2 10776336.2
网络使用是2 21464329.059.703<0.001
否2 38195039.9
睡眠时间（h）<71 00659358.9470.121<0.001
7～81 27619715.4
>82 31380334.7
午休时间（h）<12 5571 04040.7105.124<0.001
1～270419728.0
>21 33435626.7
社会支持网络
婚姻状况在婚2 89592932.122.967<0.001
不在婚1 70066439.1<
社会参与是3 9161 42936.538.8930.001
否67916424.2
经济支持是2 60575729.183.540<0.001
否1 99083642.0
照料支持是2 7721 00236.158.377<0.001
否1 82359132.4
情感支持是1 49146030.86.7490.009
否3 1041 13346.2
社会经济地位
居住地农村2 5251 08342.9167.332<0.001
城镇2 07051024.6
文化程度文盲1 85990048.4169.387<0.001
小学1 35639829.4
初中51410720.8
高中51111522.5
大专及以上3557320.6
工作性质农业1 67553231.83.0850.150
非农业81526332.3
无业2 10579837.9
房屋类型平房2 51170228.02.3960.051
楼房2 08489142.8
其他社会结构性因素
养老保险是2 97651017.10.1211.320
否1 6191 08366.9
医疗保险是3 45982723.971.120<0.001
否1 13676667.4
), ArticleFig(id=1241069120628773022, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035813547462919, language=CN, label=表1, caption=

中老年人抑郁基本情况

, figureFileSmall=null, figureFileBig=null, tableContent=
变量名称类别调查人数抑郁人数抑郁率(%)χ2值P值
不同个体
性别男2 61287133.34.6700.031
女1 98372236.4
年龄（岁）45～5581526332.369.630<0.001
56～651 97827213.8
66～7593443246.3
>7586862672.1
慢性病是2 31583636.14.2960.038
否2 28075733.2
ADL受损是2 12677236.34.7230.030
否2 46982133.3
行为生活方式
吸烟是1 99176538.421.869<0.001
否2 60482831.8
喝酒是2 20279736.22.3490.057
否2 39379633.3
体育锻炼是2 49476330.64.6410.030
否2 10183039.5
自评健康状况好92213214.3264.282<0.001
一般1 99668934.5
差1 67777246.0
生活满意度满意2 48883033.44.0980.043
不满意2 10776336.2
网络使用是2 21464329.059.703<0.001
否2 38195039.9
睡眠时间（h）<71 00659358.9470.121<0.001
7～81 27619715.4
>82 31380334.7
午休时间（h）<12 5571 04040.7105.124<0.001
1～270419728.0
>21 33435626.7
社会支持网络
婚姻状况在婚2 89592932.122.967<0.001
不在婚1 70066439.1<
社会参与是3 9161 42936.538.8930.001
否67916424.2
经济支持是2 60575729.183.540<0.001
否1 99083642.0
照料支持是2 7721 00236.158.377<0.001
否1 82359132.4
情感支持是1 49146030.86.7490.009
否3 1041 13346.2
社会经济地位
居住地农村2 5251 08342.9167.332<0.001
城镇2 07051024.6
文化程度文盲1 85990048.4169.387<0.001
小学1 35639829.4
初中51410720.8
高中51111522.5
大专及以上3557320.6
工作性质农业1 67553231.83.0850.150
非农业81526332.3
无业2 10579837.9
房屋类型平房2 51170228.02.3960.051
楼房2 08489142.8
其他社会结构性因素
养老保险是2 97651017.10.1211.320
否1 6191 08366.9
医疗保险是3 45982723.971.120<0.001
否1 13676667.4
), ArticleFig(id=1241069120771379369, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035813547462919, language=EN, label=Table 2, caption=

Association rule analysis of depression in the elderly

, figureFileSmall=null, figureFileBig=null, tableContent=
前项后项支持度（%）置信度（%）提升度
ADL受损+患慢性病+年龄≥66岁+无医保+睡眠时间<7 h抑郁20.74077.7863.751
ADL受损+文化程度小学及以下+不使用网络+无经济支持+无社会参与抑郁22.15579.8433.604
患慢性病+自评健康差+无经济支持+无情感支持+无医保+无社会参与抑郁20.13168.1083.383
ADL受损+患慢性病+自评健康差+不使用网络+无情感支持抑郁20.74067.7863.268
ADL受损+患慢性病+年龄≥66岁+不使用网络+无情感支持抑郁24.02278.3703.262
患慢性病+无经济支持+无情感支持+睡眠时间<7 h抑郁21.02268.4783.257
ADL受损+患慢性病+不使用网络+无情感支持+无社会参与抑郁22.15569.8433.153
自评健康差+年龄≥66岁+文化程度小学及以下+无情感支持+无社会参与抑郁23.04468.2952.964
自评健康差+无情感支持+年龄≥66岁+不使用网络+无社会参与抑郁31.46974.6032.371
自评健康差+无经济支持+无医保+睡眠时间低<7 h抑郁31.36973.6032.339
), ArticleFig(id=1241069120913985717, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035813547462919, language=CN, label=表2, caption=

中老年人抑郁的关联规则分析

, figureFileSmall=null, figureFileBig=null, tableContent=
前项后项支持度（%）置信度（%）提升度
ADL受损+患慢性病+年龄≥66岁+无医保+睡眠时间<7 h抑郁20.74077.7863.751
ADL受损+文化程度小学及以下+不使用网络+无经济支持+无社会参与抑郁22.15579.8433.604
患慢性病+自评健康差+无经济支持+无情感支持+无医保+无社会参与抑郁20.13168.1083.383
ADL受损+患慢性病+自评健康差+不使用网络+无情感支持抑郁20.74067.7863.268
ADL受损+患慢性病+年龄≥66岁+不使用网络+无情感支持抑郁24.02278.3703.262
患慢性病+无经济支持+无情感支持+睡眠时间<7 h抑郁21.02268.4783.257
ADL受损+患慢性病+不使用网络+无情感支持+无社会参与抑郁22.15569.8433.153
自评健康差+年龄≥66岁+文化程度小学及以下+无情感支持+无社会参与抑郁23.04468.2952.964
自评健康差+无情感支持+年龄≥66岁+不使用网络+无社会参与抑郁31.46974.6032.371
自评健康差+无经济支持+无医保+睡眠时间低<7 h抑郁31.36973.6032.339
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中老年人抑郁影响因素及其关联规则挖掘
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李小燕 , 许静怡 , 周燕
现代预防医学 | 健康与社会行为 2024,51(23): 4349-4354
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现代预防医学 | 健康与社会行为 2024, 51(23): 4349-4354
中老年人抑郁影响因素及其关联规则挖掘
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李小燕, 许静怡 , 周燕
作者信息
  • 宁夏医科大学人文与管理学院，宁夏 银川 750000

    • 李小燕（1996—），女，硕士在读，研究方向：卫生政策与卫生管理

通讯作者:

许静怡，E-mail:
Factors influencing depression in middle-aged and elderly individuals and mining of association rules
Xiao-yan LI, Jing-yi XU , Yan ZHOU
Affiliations
  • School of Humanities and Management, Ningxia Medical University, Ningxia 750000, China
出版时间: 2024-12-10 doi: 10.20043/j.cnki.MPM.202407285
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摘要
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目的

依据健康社会决定因素理论，分析不同因素对中老年人抑郁影响的重要性以及不同人口学特征与抑郁之间的关联关系，为改善中老年人身心健康提供参考依据。

方法

以2020年中国健康与养老追踪调查数据中45岁以上中老年人为研究对象，采用随机森林模型分析中老年人抑郁的影响因素重要性排序，针对人群特征进行关联规则挖掘分析。

结果

中老年人抑郁率为34.7%，随机森林模型结果显示，中老年人抑郁影响因素的MDA值排名前11的变量依次为日常活动能力（activity of daily living，ADL）受损（20.24%）、慢性病（19.12%）、社会参与（18.72%）、医疗保险（18.10%）、年龄（16.77%）、自评健康状况（15.31%)、情感支持(11.91%)、经济支持（10.80%）、网络使用(10.67%)、文化程度(9.97%)、睡眠时间（9.77%）（P<0.05）；关联规则挖掘结果显示，ADL受损、患有慢性病、年龄≥66岁、无医保、睡眠时间<7 h的组合使得中老年人患抑郁的风险增大，其中支持度为20.740%，置信度为77.786%，是不具备这些特征者的3.751倍。

结论

中老年人抑郁影响因素呈现多维度。ADL受损、患有慢性病、年龄≥66岁、无医保、睡眠时间<7 h是影响中老年人抑郁的关键因素，应通过关键因素筛查出高危人群，从而降低抑郁的发生风险，改善中老年人心理健康水平。

中老年人抑郁  /  重要性  /  随机森林模型  /  关联规则挖掘
Objective

To analyze the significance of various factors affecting depression in middle-aged and elderly individuals, as well as the association between different demographic characteristics and depression based on the theory of social determinants of health, providing a reference for improving the physical and mental health of this population.

Methods

Using data from the 2020 China Health and Retirement Longitudinal Study, the research focused on individuals aged 45 and above. A random forest model was employed to rank the importance of factors influencing depression in this demographic, and association rule mining was conducted to analyze population characteristics.

Results

The depression rate among middle-aged and elderly individuals was 34.7%. The random forest model indicated that the top 11 variables influencing depression, ranked by their Mean Decrease Accuracy (MDA) values, were as follows: impaired Activities of Daily Living (ADL) (20.24%), chronic diseases(19.12%), social participation (18.72%), medical insurance (18.10%), age (16.77%), self-rated health status (15.31%), emotional support (11.91%), economic support (10.80%), internet usage (10.67%), education level (9.97%), and sleep duration(9.77%) (P<0.05). The results of association rule mining revealed that the combination of impaired ADL, chronic diseases, age ≥ 66 years, lack of medical insurance, and sleep duration < 7 hours significantly increased the risk of depression in middle-aged and elderly individuals, with a support of 20.740% and a confidence of 77.786%, indicating a risk 3.751 times greater than those without these characteristics.

Conclusion

The factors influencing depression in middle-aged and elderly individuals are multidimensional. Impaired ADL, chronic diseases, age ≥ 66 years, lack of medical insurance, and sleep duration < 7 hours are critical factors affecting depression. Screening for these key factors can identify high-risk populations, thereby reducing the risk of depression and improving the mental health levels of middle-aged and elderly individuals.

Depression in middle-aged and elderly individuals  /  Importance  /  Random forest model  /  Association rule mining
李小燕, 许静怡, 周燕. 中老年人抑郁影响因素及其关联规则挖掘. 现代预防医学, 2024 , 51 (23) : 4349 -4354 . DOI: 10.20043/j.cnki.MPM.202407285
Xiao-yan LI, Jing-yi XU, Yan ZHOU. Factors influencing depression in middle-aged and elderly individuals and mining of association rules[J]. Modern Preventive Medicine, 2024 , 51 (23) : 4349 -4354 . DOI: 10.20043/j.cnki.MPM.202407285
正文
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据世界卫生组织公布数据显示，抑郁症在全球的发病率约为3.1%[1]。随着我国人口老龄化进程的加快，中老年人身心健康问题日益凸显，抑郁症成为中老年人普通存在的心理健康问题之一[2-4]。2020年9月国家卫健委公布了《探索抑郁症防治特色服务工作方案》，该方案将中老年人列为抑郁症高发的重点人群之一[5]。抑郁症不仅严重影响中老年人生活质量，对身体健康与社会功能也会产生严重的负面影响。故正确识别抑郁症影响因素对于减缓抑郁症发生率至关重要。有学者研究发现2015年我国中老年抑郁率为30.6%[6]，到2018年中老年抑郁率上升至41.3%[7]。由此可见，我国中老年人抑郁症的患病率较为严峻。此外，中老年人作为一个特殊的群体，其抑郁症的发生不仅与年龄增长、身体功能衰退密切相关，还受到社会参与、经济支持等多种社会因素的影响[8]。近年来，健康社会决定因素 (social determinants of health, SDH)理论被广泛应用于公共卫生研究领域，该理论认为，健康不仅受生物学和遗传因素影响，还与社会经济、环境、心理等多方面因素密切相关[8-9]。既往关于中老年人抑郁相关因素的研究大多局限在日常活动能力（activity of daily living，ADL）、社会参与、健康状况等方面，但缺乏多维度、多层次角度对中老年人抑郁影响因素重要性及其关键因素与抑郁之间的关联关系的综合性研究[7-8]。因此，本文利用2020年中国健康与养老追踪调查（China Health and Retirement Longitudinal Study）数据，基于健康社会决定因素理论，采用随机森林对中老年人抑郁影响因素进行系统性分析，并通过关联规则挖掘关键因素与抑郁之间的关联关系，以期为我国中老年人抑郁的防控及管理提供参考依据。
1 资料与方法
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1.1 资料来源
本文资料来源于2020年中国健康与养老追踪调查。该数据库涉及45岁及以上的中老年人17 708人，通过对数据的清洗与整理，剔除缺失、空白、不完整数据后，最终纳入4 595例有效样本。
1.2 研究方法
1.2.1 因变量
将中老年人抑郁情况作为因变量（1=是，0=否）。CHARLS问卷中采用抑 郁量表（center for epidemiologic studies depression scale，CES-D）评价中老年人抑郁状况，该量表包括10个自评问题，其中有2个正向条目，8个负向条目，每个条目均有4个选项，分别是“很少或者根本没有”“不太多”“有时或者说有一半时间”“大多数时间”，从0～3依次赋值，正向条目需采用负向记分，参照以往研究[8]，若抑郁总得分小于10分判定为无抑郁症状，反之则认为有抑郁症状。
1.2.2 自变量
参照既往研究[9-10]，本文将SDH分为不同个体、个体行为生活方式、社会支持网络、社会经济地位、其他社会结构因素五个维度，其中不同个体包括性别、年龄、慢性病、ADL受损情况；个体行为生活方式包括吸烟、喝酒、体育锻炼、自评健康、生活满意度、网络使用、睡眠时间、午休时长；社会支持网络包括婚姻状况、社会参与、经济支持、照料支持、情感支持；社会经济地位包括居住地、文化程度、工作性质、房屋类型；其他社会结构性因素包括养老保险、医疗保险。
1.3 随机森林模型
随机森林是一种基于决策树的集成学习算法。通过集成学习的方式，将多棵决策树组合在一起，形成一个强大的模型。每棵决策树都在训练数据上进行学习，并输出一个分类或回归结果。采用平均准确度下降(mean decrease accuracy，MDA)评价影响因素重要性，MDA值越大，说明该影响因素越重要[11]。
1.4 关联规则模型
关联规则（association rule）是数据挖掘中最活跃的研究方法之一，其目的是在一个数据集中找到各项之间的关联，如果确定两项或多项属性之间存在关联，就可根据规则前件A预测规则后件B的发生概率[12]。具体公式如下：
支持度：S=P(A∪B)/P(C)，表示A和B同时出现的概率。支持度越高，说明项集A和B同时出现的概率越高。
置信度：C=P(A∪B)/P(A)，表示A发生的前提下，患有慢性病B的条件概率。置信度越高，说明出现项集A后出现项集B的可能性越大。
提升度：L=P(A∪B)/P(A)P(B)，表示A发生的条件下，同时发生B的概率，与B总体发生的概率之比。当提升度大于1时，表明项集A、B具有正向关联性。
1.5 统计学分析
通过Stata 17.0软件对数据进行整理与分析，计数资料以频数和百分比表示，采用χ2检验进行单因素分析。使用RStudio软件中Random Forest构建随机森林模型，对影响因素进行重要性排序，再使用Apriori算法建模并获取强关联规则，探究不同人口学特征与中老年人抑郁之间的关联关系，检验水准α=0.05。
2 结果
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2.1 中老年人抑郁基本情况
4 595例中老年人抑郁率为34.7%。农村中老年人抑郁率是城镇的1.75倍；女性抑郁率高于男性；年龄以56～65岁人数居多，75岁以上抑郁率最高；患慢性病、ADL受损的中老年人抑郁率明显较高；文化程度以文盲人数居多且抑郁率最高（P<0.05)。见表1。
2.2 中老年人抑郁影响因素重要性分析
以抑郁状况为因变量，表1中具有统计学意义（P<0.05）的变量作为自变量，结果显示，中老年人抑郁影响因素重要性排名前11的变量依次为ADL受损、慢性病、社会参与、医疗保险、年龄、自评健康、情感支持、经济支持、网络使用、文化程度、睡眠时间。见图1。
2.3 中老年人抑郁影响因素关联规则分析
以中老年人抑郁为因变量，将中老年人抑郁影响因素重要性排名前11的变量作为自变量，挖掘这些变量与中老年人抑郁之间的关联关系。将最低支持度设置为20%，置信度为80%，以抑郁为后项，筛选出提升比最高的前10项强关联规则。结果显示，规则2置信度最高，文化程度小学及以下、ADL受损、不使用网络、无经济支持和无社会参与这五个变量组合时，中老年人的抑郁率为79.843%，是不具备这些特征者的3.604倍，表明这些特征组合关联起来，会增加中老年人患抑郁的风险。见表2。
3 讨论
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从不同个体维度看，ADL受损、患慢性病、年龄≥66岁是影响中老年人抑郁的关键因素，这与段芮[6]、闫语[13]等的研究结论一致。规则1也提示，ADL受损、患慢性病、年龄≥66岁与其他变量的组合可促进中老年人抑郁症的发生，提升比高达3.751。究其原因发现ADL受损可能会降低中老年人生活质量，且影响其身心健康，使其产生自卑、挫败、沮丧等情绪，进而造成抑郁症的发生[14-15]。慢性病的病程较长，又因病复杂、久治不愈和经济负担较大等特点对中老年人造成极大的身体和心理负担，从而增加抑郁症的发生风险。与此同时，年龄≥66岁的人群因属于弱势群体，且随着年龄增长，身体各项机能及免疫力均在不断下降，使得生命质量下降，进而容易产生焦虑、烦躁等情绪，逐渐造成抑郁症的发生。
从个体行为生活方式维度看，自评健康状况、网络使用情况是中老年人抑郁的显著因素，与既往研究结论一致[16-17]。规则4提示，自评健康差、不使用网络与其他变量联合在一起时中老年人更易产生抑郁症，提升比达到3.268。自评健康是健康状况评价和未来健康状况预测的重要指标，能够在很大程度上反映出老年人当下的客观健康状况，而老年人自我感知到的健康状况较差，则越容易产生悲观、消极的心理状态，从而增加抑郁症状的产生。不使用网络的老年人认知功能较差，不能获取更多的健康相关知识，更好的调节自身健康行为，进而增加其抑郁症的发生风险。
从社会支持网络维度看，社会参与、经济支持、情感支持是中老年人抑郁的重要因素，这与李磊[17]、和红[18]等的研究结论一致。规则3提示，无经济支持、无情感支持和无社会参与和其他变量联合时，一定程度上增加了中老年人抑郁的发生风险。原因可能在于中老年人易患高血压、糖尿病等慢性疾病，收入较低的中老年人会因无法持续有效治疗，进而产生悲观情绪，促使抑郁的发病率增高[19]。情感支持对中老年人是至关重要的，而大多中老年人因长期缺失子女陪伴，致使其缺少情感支持，孤独感加重，进而增加了抑郁症的发生[20]。与此同时，社会参与对中老年人身心健康具有促进作用，这与李红梅[21]等的研究结论一致。但中老年人可能受自身健康状况和外界环境的影响无法正常参与社会活动，从而与社会脱节，产生孤独和抑郁等不良情绪[21-22]。
从社会经济地位维度看，文化程度是影响中老年人抑郁的重要影响，与戴冰等人[23]研究结论一致。规则2提示，文化程度在小学及以下与其他变量联合时，会增加中老年人抑郁的发生风险，提升比为3.604。其原因可能在于低学历中老年人在就医、日常交流和信息时代的适应方面都存在较大的障碍，致使其产生自卑和悲观心理，进而增加了抑郁症的发生。
从其他社会结构性维度来看，医疗保险是影响中老年人抑郁的重要因素，这与既往研究结论一致[8]。规则3提示，无医保与其他变量的组合会促进中老年人抑郁的产生。医疗保险为中老年人治病就医提供了重要保障，而无医保的中老年人因其就医给家庭带来较大经济负担，会心生内疚感，易产生抑郁情绪。
综上所述，我国中老年人抑郁症患病率较高。提示：首先，建议定期对中老年人群进行心理健康评估，筛查出抑郁高危人群，并制定针对性的预防措施。其次，倡导子女给予中老人更多的关爱与陪伴，从而减少内心的孤独感。此外，应扩大中老年人的医疗保障覆盖范围，对此群体实施政策倾斜，从而提高健康水平。最后，帮助中老年人养成健康的生活方式，以降低抑郁症的发生风险。
本研究局限性：采取2020年CHARLS数据进行横断面研究，缺少纵向的时间变化趋势分析，缺乏对于混杂因素的排除，后续研究过程中会加以完善。
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  • 宁夏自然科学基金(2023AAC03233)
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doi: 10.20043/j.cnki.MPM.202407285
  • 接收时间：2024-07-14
  • 首发时间：2026-03-18
  • 出版时间：2024-12-10
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  • 收稿日期：2024-07-14
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宁夏自然科学基金(2023AAC03233)
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    宁夏医科大学人文与管理学院，宁夏 银川 750000

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2种不同金属材料的力学参数
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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
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
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