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Article(id=1241035814902231699, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241035810628235909, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202407538, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1722096000000, receivedDateStr=2024-07-28, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773815595603, onlineDateStr=2026-03-18, pubDate=1733760000000, pubDateStr=2024-12-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773815595603, onlineIssueDateStr=2026-03-18, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773815595603, creator=13701087609, updateTime=1773815595603, 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=4249, endPage=4254, ext={EN=ArticleExt(id=1241035815703343782, articleId=1241035814902231699, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Application of LASSO regression and association rules in data mining of depression symptoms in middle-aged and elderly populations, columnId=1240413921954295836, journalTitle=Modern Preventive Medicine, columnName=Epidemiology and Statistical Methods, runingTitle=null, highlight=null, articleAbstract=
Objective

To provide a new perspective for exploring the influencing factors of depression symptoms in individuals aged 45 and older in China, helping reveal the complex relationships among potential influencing factors.

Methods

Using data from the China Health and Retirement Longitudinal Study (CHARLS) 2018, independent variables were determined based on the five dimensions of the health ecological model. A total of 8 485 individuals aged 45 and older were included. The LASSO algorithm was employed for variable selection, and the Apriorism algorithm from association rules was utilized to mine the causal association rules of depression symptoms among the elderly in China from multiple dimensions.

Results

The detection rate of depression symptoms among middle-aged and elderly individuals in China was 38.42%. The variables selected by LASSO regression included gender from the personal traits layer, self-rated health, life satisfaction, disability, number of chronic diseases, visual impairment, hearing impairment, and nighttime sleep duration from the behavioral traits layer, as well as household registration type and education level from the living and working conditions layer. The Apriorism algorithm identified 21 strong association rules, with the highest support of 20.71%, maximum confidence of 68.40%, and the highest lift of 1.78. Key association factors for depression symptoms among the elderly included living in rural areas, having two or more chronic diseases, nighttime sleep duration of less than 6 hours, being female, having a disability, poor self-rated health, and being relatively satisfied with life, with primary school education. Compared to those with excessive nighttime sleep duration, individuals with insufficient nighttime sleep duration exhibited a higher risk of depression symptoms. The factor of rural residence was highly correlated with nighttime sleep duration of less than 6 hours and having two or more chronic diseases.

Conclusion

This study suggests considering depression issues from three dimensions: personal traits, behavioral traits, and living and working conditions, emphasizing the need to pay special attention to rural populations and patients with comorbid chronic diseases during depression symptom screening.

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目的

为我国45岁及以上中老年人群抑郁症状影响因素的挖掘提供新的视角，帮助揭示潜在的影响因素之间的复杂关系。

方法

采用中国健康与养老追踪调查（CHARLS）2018年数据，根据健康生态学模型的五个维度确定自变量，纳入8 485名≥45岁中老年人样本，采用套索算法（LASSO）进行变量筛选，利用关联规则中的Apriori算法从多维度挖掘我国中老年人抑郁症状致因关联规则。

结果

我国中老年人抑郁症状检出率为38.42%；LASSO回归筛选出的变量包括个人特质层的性别，行为特质层的自评健康、生活满意度、失能、慢性病数量、视力障碍、听力障碍、夜晚睡眠时长，生活和工作条件层的户口类型 、教育程度；Apriori算法挖掘出21条强关联规则，最高支持度为20.71%，最大置信度是68.40%，提升度最高为1.78；农村、慢性病≥2种、夜晚睡眠时长<6 h、女性、失能、自评健康不好、对生活比较满意、小学是中老年抑郁症状的主要关联因素；相对于夜晚睡眠时长过多，夜晚睡眠时长过短的中老年人出现抑郁症状的风险更高；农村这个因素与夜晚睡眠时长<6 h、慢性病≥2种高度相关。

结论

本研究提示从个人特质、行为特质以及生活和工作条件三个维度考虑抑郁问题，在进行抑郁症状筛查时着重注意农村群体和慢性病共病患者。

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马桂峰，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=aRjoP1YAV+66MEvzK9iuCQ==, magXml=4/Jn1OLBczZzwaea3tYMNw==, pdfUrl=null, pdf=qMdHjKJfOe0yxqBK4dWNTw==, pdfFileSize=1016813, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=IzTRbQK2ptR4iccx7lXccA==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=UUMgNajTdWJ5/0TqGKWBnw==, mapNumber=null, authorCompany=null, fund=null, authors=

苏文钰（1998—），女，硕士在读，研究方向：流行病与卫生统计，精神卫生

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苏文钰（1998—），女，硕士在读，研究方向：流行病与卫生统计，精神卫生

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苏文钰（1998—），女，硕士在读，研究方向：流行病与卫生统计，精神卫生

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Chinese Journal of Medicine, 2024, 59(5): 538-544.(In Chinese), articleTitle=Study on the relationship between chronic disease comorbidity and self-rated health among the elderly in a rural area in Northwest China, refAbstract=null)], funds=[Fund(id=1241069124143599920, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035814902231699, awardId=LH2022GG06, language=CN, fundingSource=国家食品安全风险中心联合攻关计划项目(LH2022GG06), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1241069115729826648, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035814902231699, xref=1., ext=[AuthorCompanyExt(id=1241069115738215257, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035814902231699, companyId=1241069115729826648, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=School of Public Health, Shandong Second Medical University, Weifang, Shandong 261053, China), AuthorCompanyExt(id=1241069115746603867, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035814902231699, companyId=1241069115729826648, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.山东第二医科大学公共卫生学院，山东 潍坊 261053)]), AuthorCompany(id=1241069115834684258, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035814902231699, xref=2., ext=[AuthorCompanyExt(id=1241069115843072867, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035814902231699, companyId=1241069115834684258, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.沂源县疾病预防控制中心检验科)])], figs=[ArticleFig(id=1241069121815761133, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035814902231699, language=EN, label=Figure 1, caption=Dynamic process diagram for LASSO variable selection, figureFileSmall=O/0qp4gQ58XjR44Z1peDoA==, figureFileBig=nPvyNoQG49PWWla2ebgEYw==, tableContent=null), ArticleFig(id=1241069121908035832, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035814902231699, language=CN, label=图1, caption=LASSO筛选变量动态过程图

注：图中曲线代表变量，纵坐标为回归系数，上横坐标是模型中非零系数的个数，下横坐标是标准化后的正则化参数。

, figureFileSmall=O/0qp4gQ58XjR44Z1peDoA==, figureFileBig=nPvyNoQG49PWWla2ebgEYw==, tableContent=null), ArticleFig(id=1241069122021282046, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035814902231699, language=EN, label=Figure 2, caption=LASSO ten-fold cross-validation curve chart, figureFileSmall=Jqmi2rj2lujE1/tEXqPl5w==, figureFileBig=soPDzAu/TPpkhSqUj2To+Q==, tableContent=null), ArticleFig(id=1241069122080002310, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035814902231699, language=CN, label=图2, caption=LASSO十折交叉验证曲线图

注：该图上下横坐标与图1一致，纵坐标为似然偏差，左边虚线lambda.min，是最小平均交叉验证误差对应的λ，右边虚线为lambda.lse，指在 lambda.min一个方差范围内得到最简单模型对应的λ。

, figureFileSmall=Jqmi2rj2lujE1/tEXqPl5w==, figureFileBig=soPDzAu/TPpkhSqUj2To+Q==, tableContent=null), ArticleFig(id=1241069122184859915, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035814902231699, language=EN, label=Table 1, caption=

Chi-square test results for the differences in the detection rates of depressive symptoms among elderly individuals in different groups

, figureFileSmall=null, figureFileBig=null, tableContent=
变量名称类别调查人数抑郁症状人数检出率（%）χ2值P值
个人特质层
年龄（岁）45～602 9361 09637.332.9390.230
>60～804 8701 90939.20
>8067925537.56
性别男4 1131 26830.83194.448<0.001
女4 3721 99245.56
民族汉族7 8963 02538.310.5840.445
少数民族58923539.90
行为特征层
吸烟否4 8362 02141.7953.979<0.001
是3 6491 23933.95
饮酒否5 5232 33942.35103.253<0.001
是2 96292131.09
体力活动轻度7 8103 00038.410.0060.997
中度34113138.42
重度33412938.62
夜晚睡眠时长（h）<62 9391 59154.13470.495<0.001
6～83 4281 01229.52
>82 11865731.02
失能否6 4391 98430.81653.376<0.001
是2 0461 27662.37
视力障碍否7 3402 63135.84152.564<0.001
是1 14562954.93
听力障碍否7 0842 53635.80124.646<0.001
是1 40172451.68
语言障碍否8 4173 21938.2413.862<0.001
是684160.29
慢性病数量（种）01 73242124.31316.889<0.001
12 04464731.65
≥24 7092 19246.55
自评健康不好2 0971 34964.33959.728<0.001
一般4 1541 48435.72
好2 23442719.11
生活满意度非常不满意26824390.671 022.800<0.001
不太满意74556675.97
比较满意4 7021 80738.43
非常满意2 77064423.25
人际网络层
婚姻状态未婚613150.8262.710<0.001
已婚同居6 8422 48936.38
分居/离异/丧偶1 58274046.78
社交活动无3 6361 53642.2439.313<0.001
有4 8491 72435.55
生活和工作条件层
户口类型农村6 2412 64442.37155.172<0.001
城镇2 24461627.45
地理分布东部2 86698634.4029.644<0.001
中部2 31192940.20
西部3 3081 34540.66
教育程度未受过教育1 45075452.00297.389<0.001
小学3 8211 60341.95
初中2 00862331.03
高中及以上1 20628023.22
工作情况未工作过17847.061.9460.378
曾经工作2 8641 12539.28
现在工作5 6042 12737.96
政策环境层
医疗保险无22310346.195.8400.016
有8 2623 15738.21
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不同组中老年人抑郁症状检出率差异的χ2检验结果

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变量名称类别调查人数抑郁症状人数检出率（%）χ2值P值
个人特质层
年龄（岁）45～602 9361 09637.332.9390.230
>60～804 8701 90939.20
>8067925537.56
性别男4 1131 26830.83194.448<0.001
女4 3721 99245.56
民族汉族7 8963 02538.310.5840.445
少数民族58923539.90
行为特征层
吸烟否4 8362 02141.7953.979<0.001
是3 6491 23933.95
饮酒否5 5232 33942.35103.253<0.001
是2 96292131.09
体力活动轻度7 8103 00038.410.0060.997
中度34113138.42
重度33412938.62
夜晚睡眠时长（h）<62 9391 59154.13470.495<0.001
6～83 4281 01229.52
>82 11865731.02
失能否6 4391 98430.81653.376<0.001
是2 0461 27662.37
视力障碍否7 3402 63135.84152.564<0.001
是1 14562954.93
听力障碍否7 0842 53635.80124.646<0.001
是1 40172451.68
语言障碍否8 4173 21938.2413.862<0.001
是684160.29
慢性病数量（种）01 73242124.31316.889<0.001
12 04464731.65
≥24 7092 19246.55
自评健康不好2 0971 34964.33959.728<0.001
一般4 1541 48435.72
好2 23442719.11
生活满意度非常不满意26824390.671 022.800<0.001
不太满意74556675.97
比较满意4 7021 80738.43
非常满意2 77064423.25
人际网络层
婚姻状态未婚613150.8262.710<0.001
已婚同居6 8422 48936.38
分居/离异/丧偶1 58274046.78
社交活动无3 6361 53642.2439.313<0.001
有4 8491 72435.55
生活和工作条件层
户口类型农村6 2412 64442.37155.172<0.001
城镇2 24461627.45
地理分布东部2 86698634.4029.644<0.001
中部2 31192940.20
西部3 3081 34540.66
教育程度未受过教育1 45075452.00297.389<0.001
小学3 8211 60341.95
初中2 00862331.03
高中及以上1 20628023.22
工作情况未工作过17847.061.9460.378
曾经工作2 8641 12539.28
现在工作5 6042 12737.96
政策环境层
医疗保险无22310346.195.8400.016
有8 2623 15738.21
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Association rules between depression symptoms and causes in middle-aged and elderly people

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规则规则前项规则后项支持度(%)置信度(%)提升度
R1自评健康不好、 慢性病≥2种、农村抑郁症状10.6968.401.78
R2慢性病≥2种、失能抑郁症状11.6267.211.75
R3自评健康不好、农村抑郁症状13.1966.931.74
R4女性、失能抑郁症状10.1566.491.73
R5慢性病≥2种、农村、夜晚睡眠时长<6 h抑郁症状10.8365.971.72
R6女性、 农村、夜晚睡眠时长<6 h抑郁症状10.0665.591.71
R7自评健康不好、 慢性病≥2种抑郁症状13.0765.511.70
R8失能、农村抑郁症状12.7264.381.68
R9自评健康不好抑郁症状15.9064.331.67
R10失能抑郁症状15.0462.371.62
R11女性、夜晚睡眠时长<6 h抑郁症状12.4361.441.60
R12慢性病≥2种、夜晚睡眠时长<6 h抑郁症状13.6160.531.58
R13女性、慢性病≥2种、农村抑郁症状12.8858.611.53
R14农村、夜晚睡眠时长<6 h抑郁症状15.0658.201.51
R15夜晚睡眠时长<6 h抑郁症状18.7554.131.41
R16慢性病≥2种、女性抑郁症状15.7953.031.38
R17对生活比较满意、夜晚睡眠时长<6 h抑郁症状10.2852.851.38
R18小学、慢性病≥2种、农村抑郁症状10.6552.531.37
R19慢性病≥2种、农村抑郁症状20.7151.921.35
R20慢性病≥2种、农村、对生活比较满意抑郁症状11.0351.861.35
R21女性、农村、对生活比较满意抑郁症状10.1151.471.34
), ArticleFig(id=1241069123959050532, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035814902231699, language=CN, label=表2, caption=

中老年人抑郁症状与致因关联规则

, figureFileSmall=null, figureFileBig=null, tableContent=
规则规则前项规则后项支持度(%)置信度(%)提升度
R1自评健康不好、 慢性病≥2种、农村抑郁症状10.6968.401.78
R2慢性病≥2种、失能抑郁症状11.6267.211.75
R3自评健康不好、农村抑郁症状13.1966.931.74
R4女性、失能抑郁症状10.1566.491.73
R5慢性病≥2种、农村、夜晚睡眠时长<6 h抑郁症状10.8365.971.72
R6女性、 农村、夜晚睡眠时长<6 h抑郁症状10.0665.591.71
R7自评健康不好、 慢性病≥2种抑郁症状13.0765.511.70
R8失能、农村抑郁症状12.7264.381.68
R9自评健康不好抑郁症状15.9064.331.67
R10失能抑郁症状15.0462.371.62
R11女性、夜晚睡眠时长<6 h抑郁症状12.4361.441.60
R12慢性病≥2种、夜晚睡眠时长<6 h抑郁症状13.6160.531.58
R13女性、慢性病≥2种、农村抑郁症状12.8858.611.53
R14农村、夜晚睡眠时长<6 h抑郁症状15.0658.201.51
R15夜晚睡眠时长<6 h抑郁症状18.7554.131.41
R16慢性病≥2种、女性抑郁症状15.7953.031.38
R17对生活比较满意、夜晚睡眠时长<6 h抑郁症状10.2852.851.38
R18小学、慢性病≥2种、农村抑郁症状10.6552.531.37
R19慢性病≥2种、农村抑郁症状20.7151.921.35
R20慢性病≥2种、农村、对生活比较满意抑郁症状11.0351.861.35
R21女性、农村、对生活比较满意抑郁症状10.1151.471.34
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LASSO回归和关联规则在中老年人群抑郁症状数据挖掘中的应用
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苏文钰 1 , 官为民 1 , 吴怡倩 1 , 葛怀举 1 , 董士红 1 , 贾蕙毓 1 , 常文静 1 , 姜珊 1 , 于洁 2 , 马桂峰 1
现代预防医学 | 流行病与统计方法 2024,51(23): 4249-4254
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现代预防医学 | 流行病与统计方法 2024, 51(23): 4249-4254
LASSO回归和关联规则在中老年人群抑郁症状数据挖掘中的应用
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苏文钰1, 官为民1, 吴怡倩1, 葛怀举1, 董士红1, 贾蕙毓1, 常文静1, 姜珊1, 于洁2, 马桂峰1
作者信息
  • 1.山东第二医科大学公共卫生学院，山东 潍坊 261053
  • 2.沂源县疾病预防控制中心检验科

    • 苏文钰（1998—），女，硕士在读，研究方向：流行病与卫生统计，精神卫生

通讯作者:

马桂峰，E-mail:
Application of LASSO regression and association rules in data mining of depression symptoms in middle-aged and elderly populations
Wen-yu SU1, Wei-min GUAN1, Yi-qian WU1, Huai-ju GE1, Shi-hong DONG1, Hui-yu JIA1, Wen-jing CHANG1, Shan JIANG1, Jie YU2, Gui-feng MA1
Affiliations
  • School of Public Health, Shandong Second Medical University, Weifang, Shandong 261053, China
出版时间: 2024-12-10 doi: 10.20043/j.cnki.MPM.202407538
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摘要
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目的

为我国45岁及以上中老年人群抑郁症状影响因素的挖掘提供新的视角，帮助揭示潜在的影响因素之间的复杂关系。

方法

采用中国健康与养老追踪调查（CHARLS）2018年数据，根据健康生态学模型的五个维度确定自变量，纳入8 485名≥45岁中老年人样本，采用套索算法（LASSO）进行变量筛选，利用关联规则中的Apriori算法从多维度挖掘我国中老年人抑郁症状致因关联规则。

结果

我国中老年人抑郁症状检出率为38.42%；LASSO回归筛选出的变量包括个人特质层的性别，行为特质层的自评健康、生活满意度、失能、慢性病数量、视力障碍、听力障碍、夜晚睡眠时长，生活和工作条件层的户口类型 、教育程度；Apriori算法挖掘出21条强关联规则，最高支持度为20.71%，最大置信度是68.40%，提升度最高为1.78；农村、慢性病≥2种、夜晚睡眠时长<6 h、女性、失能、自评健康不好、对生活比较满意、小学是中老年抑郁症状的主要关联因素；相对于夜晚睡眠时长过多，夜晚睡眠时长过短的中老年人出现抑郁症状的风险更高；农村这个因素与夜晚睡眠时长<6 h、慢性病≥2种高度相关。

结论

本研究提示从个人特质、行为特质以及生活和工作条件三个维度考虑抑郁问题，在进行抑郁症状筛查时着重注意农村群体和慢性病共病患者。

抑郁症状  /  中老年人  /  LASSO  /  Apriori  /  关联性分析
Objective

To provide a new perspective for exploring the influencing factors of depression symptoms in individuals aged 45 and older in China, helping reveal the complex relationships among potential influencing factors.

Methods

Using data from the China Health and Retirement Longitudinal Study (CHARLS) 2018, independent variables were determined based on the five dimensions of the health ecological model. A total of 8 485 individuals aged 45 and older were included. The LASSO algorithm was employed for variable selection, and the Apriorism algorithm from association rules was utilized to mine the causal association rules of depression symptoms among the elderly in China from multiple dimensions.

Results

The detection rate of depression symptoms among middle-aged and elderly individuals in China was 38.42%. The variables selected by LASSO regression included gender from the personal traits layer, self-rated health, life satisfaction, disability, number of chronic diseases, visual impairment, hearing impairment, and nighttime sleep duration from the behavioral traits layer, as well as household registration type and education level from the living and working conditions layer. The Apriorism algorithm identified 21 strong association rules, with the highest support of 20.71%, maximum confidence of 68.40%, and the highest lift of 1.78. Key association factors for depression symptoms among the elderly included living in rural areas, having two or more chronic diseases, nighttime sleep duration of less than 6 hours, being female, having a disability, poor self-rated health, and being relatively satisfied with life, with primary school education. Compared to those with excessive nighttime sleep duration, individuals with insufficient nighttime sleep duration exhibited a higher risk of depression symptoms. The factor of rural residence was highly correlated with nighttime sleep duration of less than 6 hours and having two or more chronic diseases.

Conclusion

This study suggests considering depression issues from three dimensions: personal traits, behavioral traits, and living and working conditions, emphasizing the need to pay special attention to rural populations and patients with comorbid chronic diseases during depression symptom screening.

Depression symptoms  /  Middle-aged and elderly  /  LASSO  /  Apriori  /  Association analysis
苏文钰, 官为民, 吴怡倩, 葛怀举, 董士红, 贾蕙毓, 常文静, 姜珊, 于洁, 马桂峰. LASSO回归和关联规则在中老年人群抑郁症状数据挖掘中的应用. 现代预防医学, 2024 , 51 (23) : 4249 -4254 . DOI: 10.20043/j.cnki.MPM.202407538
Wen-yu SU, Wei-min GUAN, Yi-qian WU, Huai-ju GE, Shi-hong DONG, Hui-yu JIA, Wen-jing CHANG, Shan JIANG, Jie YU, Gui-feng MA. Application of LASSO regression and association rules in data mining of depression symptoms in middle-aged and elderly populations[J]. Modern Preventive Medicine, 2024 , 51 (23) : 4249 -4254 . DOI: 10.20043/j.cnki.MPM.202407538
正文
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随着我国人口老龄化趋势的加剧以及《“健康中国2030”规划纲要》的提出，中老年人抑郁症等精神卫生问题已经成为医学界和社会各界越来越关注的焦点[1]。抑郁症不仅会引起情绪低落、思维变缓、意志减退，同时也能增加老年痴呆[2]、糖尿病[3]以及脑卒中[4]等疾病的患病风险，严重时还会导致自杀行为。尽管出现抑郁症状的个体并非必然患有抑郁症，但抑郁症状作为早期抑郁症的典型表现之一，如果不能提前关注和干预，在很大程度上会演化为抑郁症[5]，因此对抑郁症状的识别和干预可以有效维护中老年人心理健康。
机器学习中的套索(LASSO）算法能够实现特征选择，关联分析中的Apriori算法可以探求影响事物的因素[6]，尽管LASSO和Apriori算法在医疗领域得到了广泛应用，但将两种算法结合应用于中老年人抑郁症状影响因素的研究较少。因此本研究利用LASSO结合Apriori算法从多维度探索我国中老年人抑郁症状的相关因素，以期为降低中老年人抑郁发生率和促进健康老龄化提供参考依据。
1 资料与方法
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1.1 资料来源
本研究采用的2018年《中国健康与养老追踪调查》（CHARLS）数据库，涵盖了45岁及以上中老年个人生活方式、健康状况等信息，是研究我国中老年人健康相关问题的可靠数据库[7]。本研究的因变量为抑郁症状，是通过CHARLS调查问卷中的简版流调中心用抑郁自评量表(CES-D-10)进行测量的, 量表包含10个条目，每个条目的分值为0～3分，总分为0～30分，总分≥10分定义为有抑郁症状、总分<10分判定为无抑郁症状[8]。 剔除缺失值后，最终纳入8 485例中老年人样本。
1.2 方法
1.2.1 健康生态学模型
本研究根据健康生态学模型的五个层次[9]，以模型化方式对中老年人抑郁症状的影响因素进行分析。个人特质层纳入变量：性别、年龄、民族；行为特征层纳入变量：吸烟、饮酒、体力活动、夜晚睡眠时长、失能、视力障碍、听力障碍、语言障碍、慢性病数量、自评健康、生活满意度；人际网络层纳入变量：婚姻状态、社交活动；生活和工作条件层纳入变量：户口类型、地理分布、工作情况、教育程度；政策环境层纳入变量：医疗保险。
1.2.2 统计学方法
本研究利用 SPSS 27.0软件对数据进行统计描述和组间比较的χ2检验，由于研究变量为分类变量，因此采用频数描述。
1.2.3 特征选择
机器学习中的LASSO算法可解决多重共线性问题，其核心思想是通过引入惩罚函数，将不重要的系数压减为0。该算法在实现模型参数缩减的同时，也能起到特征选择的作用[10]。本研究实现LASSO算法时，将数据集按7:3的比例划分训练集和测试集[11]。
1.2.4 关联规则
Apriori算法通过对数据集的多次扫描寻找频繁项集，利用频繁项集生成关联规则[12]，并且通过逐层筛选候选项集来降低计算复杂度。根据数据特点，结合实际应用将Apriori算法中的支持度设置为1%，置信度设置为50%[13]。
2 结果
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2.1 单因素分析
单因素χ2检验结果显示，性别、吸烟、饮酒、夜晚睡眠时长、失能、视力障碍等17个因素对我国中老年人抑郁症状的影响有统计学意义（P<0.05），见表1。
2.2 基于LASSO算法的影响因素筛选
将单因素分析中有统计学意义的因素纳入LASSO回归模型。选取lambda.1se作为筛选标准，根据图1、2，模型保留了10个变量，包括个人特质层的性别；行为特质层的自评健康、生活满意度、失能、慢性病数量、视力障碍、听力障碍、夜晚睡眠时长；生活和工作条件层的户口类型、教育程度。
2.3 基于Apriori算法的关联规则挖掘
Apriori算法挖掘出21条强关联规则，见表2。其中出现次数最多的前四影响因素有农村、慢性病≥2种（慢性病共病）、夜晚睡眠时长<6 h、女性；规则R1置信度为68.40%提示自评健康不好且患有慢性病共病的农村群体中有68.40%的人出现抑郁症状，提升度为1.78表示如果一个人自评健康不好、患有慢性病共病且是农村户口，那么这个人出现抑郁症状的概率是没有这个假设条件情况下的1.78倍；支持度最高的是规则R19，这表明慢性病共病、农村这个致因组合出现频率最高。
3 讨论
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基于2018年CHARLS数据，本研究结果表明我国45岁及以上的中老年群体中抑郁症状的检出率为38.42%，略高于葛怀举等[14]的38.10%，高于翟亚等[15]的36.16%，研究结果的差异可能与变量的纳入排除标准有关。本研究将抑郁症状与LASSO筛选出的影响因素进行关联，通过对变量多维度的探查发现，影响中老年抑郁症状的主要因素涉及个人特质、行为特质以及生活和工作条件，在中老年抑郁症状的筛查和预防工作中，需要特别关注：农村、慢性病共病患者、夜间睡眠时长<6 h、女性、失能、自我评估健康状况不佳、生活满意度不高以及受教育水平为小学程度的人群。本研究还发现抑郁症状的影响因素共存模式呈现出多样性和复杂性，这提示政府和医疗机构需提高针对多维度的影响因素开展综合干预的意识，以降低影响因素共同作用对中老年人抑郁症状发生发展的影响，有效应对人口老龄化背景下抑郁问题的相关挑战。
3.1 农村与慢性病共病是引发中老年人抑郁症状的主要原因
农村这个因素在强关联规则中出现频次最高为11，慢性病共病出现频次为10，这表明农村与慢性病共病对中老年抑郁症状产生主要影响，根据规则R1、R5、R13、R18、R19、R20还可以发现，与单一慢性病相比，慢性病共病与抑郁症状、慢性病共病与农村关联程度强更强。由于农村地区经济发展相对落后、医疗资源不足、文化资源匮乏，农村年轻一代为了优质的教育和医疗资源不断向城市迁移[16]，而中老年父母为了帮助子女更好地融入城市生活，可能会提供给子女更多的经济支持，在经济相对滞后的情况下，这无疑给中老年父母增加了生活压力；由于子女工作繁忙、离家较远，留守在农村的中老年父母在缺乏陪伴的情况下，产生较高的孤独感；另外，医疗资源和文化水平限制了农村老年人对疾病尤其是慢性病的认知，加剧了延迟就医的问题，所以农村中老年人受到慢性病共病的影响较大。综上，农村中老年人可能长期面临较大的生活压力、孤独感以及疾病困扰，因此增加了抑郁症状的发生概率。
3.2 夜晚睡眠时长过短是抑郁症状的重要影响因素
已有研究证实睡眠过短或过长均会增加抑郁症状的发生概率[17]，而本研究通过对强关联规则的挖掘发现，相对于夜晚睡眠时长过多，夜晚睡眠时长过短与抑郁症状的发生关系更为密切。大多数的抑郁患者睡眠不足是由于主观睡眠障碍引起的[18]，睡眠障碍可能会引起焦虑与负面情绪，而这些情绪进一步加剧患者的睡眠问题。本研究还发现相对于男性，女性与夜晚睡眠时长过短关联程度强，这意味着女性中老年人更容易出现睡眠时长过短的现象，原因可能是女性更倾向于表达情感和察觉他人的情绪，这使她们更容易受到情感波动和压力的影响，在面对生活困扰时更难入睡或保持睡眠状态，从而影响睡眠质量，并且女性的生理特点如月经周期和更年期导致女性在特定时期睡眠质量不佳，甚至失眠。因此提示在抑郁症状筛查工作中，重点关注女性睡眠时长过短的人群。
3.3 慢性病共病与失能高度相关且共同影响抑郁症状的发生
在前项失能因素不变的情况下，随着农村、女性、慢性病共病依次加入前项，提升度和置信度都有明显的提高（规则R10、R8、R4、R2），表明在前项含有失能的影响因素模式中，患有慢性病共病且失能的中老年人出现抑郁症状的可能性最大。慢性病共病既可以直接增加抑郁症状的发生风险，也可以通过日常生活活动能力间接影响抑郁症状[19]。患有慢性病共病的中老年人不仅要应对多种疾病的困扰，还需承担治疗所带来的经济压力和健康损失，这会直接导致抑郁症状的出现。此外，长期身患疾病引起日常生活活动能力受损，尤其穿衣、洗澡等基本活动受到限制，使得中老年人陷入较高水平的焦虑与自我怀疑中，进而增加了抑郁症状的发生风险。
3.4 不可忽视中老年人主观感受对抑郁症状的影响
规则R9表明，仅出现自评健康不好这个因素也可增加抑郁症状的发生风险。自评健康作为个体对自身健康的主观评价，可间接代表个人情绪的好坏，若自评健康不好说明个体对现在的状态较为不满，负面情绪相对较多，因此面临较高的抑郁风险。另外规则R1、R7显示自评健康不好与慢性病共病关系密切，相关研究表明关节炎、胆囊炎、白内障、冠心病等慢性病严重影响健康自评情况[20]，慢性病共病是影响中老年人自评健康不好的主要原因。
基金
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  • 国家食品安全风险中心联合攻关计划项目(LH2022GG06)
文献
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2024年第51卷第23期
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doi: 10.20043/j.cnki.MPM.202407538
  • 接收时间：2024-07-28
  • 首发时间：2026-03-18
  • 出版时间：2024-12-10
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  • 收稿日期：2024-07-28
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国家食品安全风险中心联合攻关计划项目(LH2022GG06)
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    1.山东第二医科大学公共卫生学院，山东 潍坊 261053
    2.沂源县疾病预防控制中心检验科

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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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