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Article(id=1240375119504265767, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1240375105386238038, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202309060, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1693756800000, receivedDateStr=2023-09-04, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773658073545, onlineDateStr=2026-03-16, pubDate=1711296000000, pubDateStr=2024-03-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773658073545, onlineIssueDateStr=2026-03-16, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773658073545, creator=13701087609, updateTime=1773658073545, updator=13701087609, issue=Issue{id=1240375105386238038, tenantId=1146029695717560320, journalId=1227665162245664772, year='2024', volume='51', issue='6', pageStart='961', pageEnd='1152', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773658070179, creator=13701087609, updateTime=1773658539618, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1240377074414833974, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1240375105386238038, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1240377074414833975, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1240375105386238038, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=1070, endPage=1076, ext={EN=ArticleExt(id=1240375119894336072, articleId=1240375119504265767, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Correlations between social network types and health-related behaviors among the elderly in China, columnId=1228016572451718132, journalTitle=Modern Preventive Medicine, columnName=Health and Social Behavior, runingTitle=null, highlight=null, articleAbstract=
Objective

To identify the types of social networks of the elderly in China, to analyze their population distribution characteristics, and to explore the association between different social network types and health behaviors, so as to provide reference for developing personalized health intervention strategies.

Methods

Using the data from the 2018 Chinese Longitudinal Healthy Longevity Survey, latent class analysis was used to identify the types of social networks in the elderly. The Multinomial Logistic Regression Analysis and Apriori algorithm were used to analyze the association between different social network types and health-related behaviors.

Results

A total of 6 184 elderly people was included in this study. The social network types of the elderly in China were divided into family type (45.97%), couple type (41.91%) and widowed type (12.12%).Results from the Multinomial Logistic Regression Analysis showed that the elderly who were older (OR=1.141, 95% CI: 1.132-1.149), female (OR=4.472, 95% CI: 3.846-5.201), in ethnic minority (OR=1.454, 95% CI:1.037-2.040), and had sufficient sleep (OR=1.158,95% CI: 1.010-1.328) were more likely of the family-oriented social network. The elderly who were older (OR=1.120, 95% CI: 1.109-1.131), female (OR=4.913, 95% CI: 4.007-6.026) and had sufficient sleep (OR=1.247,95% CI:1.039-1.498) were more likely of the widowed-like social network.Strong association rules of the association rule is: drinking, smoking, and exercise of the elderly social network for couples.

Conclusion

The couple type is more likely to have unhealthy behaviors in the elderly in China, and its social network is smaller and its social support is single. Therefore, it is necessary to enrich the social network, enhance social support, and carry out intergenerational intervention to improve the healthy behavior of the elderly.

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目的

识别我国老年人的社会网络类型，分析其人群分布特征，探讨不同社会网络类型与健康行为之间的关联，为开展个性化的健康干预策略提供参考。

方法

采用2018年中国老年健康影响因素跟踪调查数据，利用潜在类别分析识别老年人的社会网络类型，应用无序多分类logistic回归模型和Apriori算法分析不同社会网络类型与健康相关行为的关联。

结果

纳入6 184名老年人，我国老年人的社会网络类型分为家庭型（45.97%），夫妻型（41.91%）和寡居型（12.12%）。无序多分类logistic模型显示：年龄较大（OR=1.141，95% CI: 1.132～1.149）、女性（OR=4.472，95% CI: 3.846～5.201）、少数民族（OR=1.454，95% CI:1.037～2.040）和睡眠充足（OR=1.158，95% CI:1.010～1.328）的老年人具有家庭型社会网络的概率更大；年龄较大（OR=1.120，95% CI: 1.109～1.131）、女性（OR=4.913，95% CI: 4.007～6.026）和睡眠充足（OR=1.247, 95% CI: 1.039～1.498）的老年人具有寡居型社会网络的概率更大。关联规则中的强关联规则为：饮酒、吸烟、不锻炼老年人的社会网络多为夫妻型。

结论

我国老年人群体中夫妻型更易存在不健康行为，其社交网络范围较小，社会支持单一，因此可通过丰富社会网络、增强社会支持、开展代际干预等措施改善老年人的健康行为生活方式。

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郑建中，E-mail：；
佟岩，E-mail：
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王妍菲（1998—），女，硕士在读，研究方向：健康社会决定因素

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王妍菲（1998—），女，硕士在读，研究方向：健康社会决定因素

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Social Construction, 2022, 9(4): 45-59, 71., articleTitle=The effects of partner-based intergenerational interactions on the mental health of the older adults in nursing facilities, refAbstract=null)], funds=null, companyList=[AuthorCompany(id=1240746303207043550, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240375119504265767, xref=null, ext=[AuthorCompanyExt(id=1240746303215432159, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240375119504265767, companyId=1240746303207043550, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Department of socialmedicine, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi 030001, China), AuthorCompanyExt(id=1240746303228015073, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240375119504265767, companyId=1240746303207043550, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=山西医科大学公共卫生学院社会医学教研室，山西 太原 030001)])], figs=[ArticleFig(id=1240746306910614318, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240375119504265767, language=EN, label=Fig.1, caption=Plot of standard scores on eight dimensions for three latent categories of the social network of the elderly, figureFileSmall=ZYmcohBokqffe/yLyqBUrA==, figureFileBig=Sd6/wSIRMVaX8YRDTPyfBA==, tableContent=null), ArticleFig(id=1240746307019666230, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240375119504265767, language=CN, label=图1, caption=老年人社会网络3种潜在类别在8个维度上的标准分数图, figureFileSmall=ZYmcohBokqffe/yLyqBUrA==, figureFileBig=Sd6/wSIRMVaX8YRDTPyfBA==, tableContent=null), ArticleFig(id=1240746307149689668, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240375119504265767, language=EN, label=Table 1, caption=

The general demographic characteristics of the subjects were investigated

, figureFileSmall=null, figureFileBig=null, tableContent=
变量分组人数构成比
（%）
年龄（岁）65～741 63426.42
75～841 72027.81
≥852 83045.76
性别男3 04849.29
女3 13650.71
户籍城镇4 17867.56
农村2 00632.44
民族汉族5 92595.81
少数民族2594.19
婚姻状况在婚2 95347.75
非在婚3 23152.25
居住方式与家人同住5 00780.97
独居89114.41
养老机构2864.62
经济水平<2 0003 32953.83
(元/月)2 000～4 9991 97531.94
≥5 00088014.23
吸烟否5 19784.04
是98715.96
饮酒否5 25284.93
是93215.07
睡眠不足2 23836.19
充足3 94663.81
锻炼否3 67259.38
是2 51240.62
社会活动不足4 97080.36
充足1 21419.63
), ArticleFig(id=1240746307271324492, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240375119504265767, language=CN, label=表1, caption=

调查对象一般人口学特征

, figureFileSmall=null, figureFileBig=null, tableContent=
变量分组人数构成比
（%）
年龄（岁）65～741 63426.42
75～841 72027.81
≥852 83045.76
性别男3 04849.29
女3 13650.71
户籍城镇4 17867.56
农村2 00632.44
民族汉族5 92595.81
少数民族2594.19
婚姻状况在婚2 95347.75
非在婚3 23152.25
居住方式与家人同住5 00780.97
独居89114.41
养老机构2864.62
经济水平<2 0003 32953.83
(元/月)2 000～4 9991 97531.94
≥5 00088014.23
吸烟否5 19784.04
是98715.96
饮酒否5 25284.93
是93215.07
睡眠不足2 23836.19
充足3 94663.81
锻炼否3 67259.38
是2 51240.62
社会活动不足4 97080.36
充足1 21419.63
), ArticleFig(id=1240746307392959312, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240375119504265767, language=EN, label=Table 2, caption=

Indicators of different latent types of fit

, figureFileSmall=null, figureFileBig=null, tableContent=
模型AICBICaBICEntropyP类别概率
LMRBLMR
175 234.11875 368.71275 305.158---1.000
257 519.10957 795.02757 664.7400.995<0.001<0.0010.580/0.420
354 412.34554 829.58854 632.5690.975<0.001<0.0010.460/0.420/0.120
453 257.28153 815.84853 552.0960.973<0.001<0.0010.049/0.459/0.074/0.418
552 901.68953 601.57953 271.0960.939<0.001<0.0010.047/0.071/0.056/0.377/0.449
652 585.50553 426.72053 029.5040.9330.009<0.0010.046/0.026/0.434/0.056/0.377/0.061
), ArticleFig(id=1240746307468456790, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240375119504265767, language=CN, label=表2, caption=

不同潜在类型的适配指标

, figureFileSmall=null, figureFileBig=null, tableContent=
模型AICBICaBICEntropyP类别概率
LMRBLMR
175 234.11875 368.71275 305.158---1.000
257 519.10957 795.02757 664.7400.995<0.001<0.0010.580/0.420
354 412.34554 829.58854 632.5690.975<0.001<0.0010.460/0.420/0.120
453 257.28153 815.84853 552.0960.973<0.001<0.0010.049/0.459/0.074/0.418
552 901.68953 601.57953 271.0960.939<0.001<0.0010.047/0.071/0.056/0.377/0.449
652 585.50553 426.72053 029.5040.9330.009<0.0010.046/0.026/0.434/0.056/0.377/0.061
), ArticleFig(id=1240746307573314400, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240375119504265767, language=EN, label=Table 3, caption=

Logistic regression analysis associated with the type of social network

, figureFileSmall=null, figureFileBig=null, tableContent=
变量家庭型寡居型
OR（95% CI）P值OR（95% CI）P值
年龄（岁）1.141（1.132～1.149）<0.0011.120（1.109～1.131）<0.001
性别
男11
女4.472（3.846～5.201）<0.0014.913（4.007～6.026）<0.001
户籍
城镇11
农村1.025（0.875～1.201）0.7601.016（0.820～1.258）0.887
民族
汉族11
少数民族1.454（1.037～2.040）0.0301.235（0.776～1.965）0.374
经济水平(元/月)
<2 0001
2 000～4 9990.648（0.526～0.799）<0.0010.652（0.484～0.878）0.005
≥5 0000.716（0.608～0.843）<0.0010.955（0.770～1.185）0.678
吸烟
否11
是0.967（0.793～1.181）0.7441.176（0.894～1.548）0.246
饮酒
否11
是1.058（0.868～1.291）0.5751.060（0.800～1.404）0.685
睡眠
不足11
充足1.158（1.010～1.328）0.0351.247（1.039～1.498）0.018
锻炼
否11
是0.990（0.862～1.137）0.8890.839（0.695～1.014）0.069
社会活动
不足11
充足1.060（0.892～1.260）0.5061.106（0.868～1.410）0.414
), ArticleFig(id=1240746307694949220, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240375119504265767, language=CN, label=表3, caption=

与社会网络类型相关的logistic回归分析

, figureFileSmall=null, figureFileBig=null, tableContent=
变量家庭型寡居型
OR（95% CI）P值OR（95% CI）P值
年龄（岁）1.141（1.132～1.149）<0.0011.120（1.109～1.131）<0.001
性别
男11
女4.472（3.846～5.201）<0.0014.913（4.007～6.026）<0.001
户籍
城镇11
农村1.025（0.875～1.201）0.7601.016（0.820～1.258）0.887
民族
汉族11
少数民族1.454（1.037～2.040）0.0301.235（0.776～1.965）0.374
经济水平(元/月)
<2 0001
2 000～4 9990.648（0.526～0.799）<0.0010.652（0.484～0.878）0.005
≥5 0000.716（0.608～0.843）<0.0010.955（0.770～1.185）0.678
吸烟
否11
是0.967（0.793～1.181）0.7441.176（0.894～1.548）0.246
饮酒
否11
是1.058（0.868～1.291）0.5751.060（0.800～1.404）0.685
睡眠
不足11
充足1.158（1.010～1.328）0.0351.247（1.039～1.498）0.018
锻炼
否11
是0.990（0.862～1.137）0.8890.839（0.695～1.014）0.069
社会活动
不足11
充足1.060（0.892～1.260）0.5061.106（0.868～1.410）0.414
), ArticleFig(id=1240746307770446694, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240375119504265767, language=EN, label=Table 4, caption=

Association rules between social networks and health behaviors in the elderly

, figureFileSmall=null, figureFileBig=null, tableContent=
后项前项支持度（%）置信度（%）提升度
夫妻型饮酒、吸烟、不锻炼3.6964.911.55
夫妻型饮酒、吸烟6.4764.751.55
夫妻型饮酒、吸烟、睡眠充足、不锻炼1.5864.291.53
夫妻型饮酒、吸烟、睡眠充足3.1563.591.52
夫妻型饮酒、吸烟、睡眠充足、不锻炼、无社会活动1.3459.041.41
夫妻型饮酒、吸烟、无社会活动4.8458.861.40
夫妻型饮酒、吸烟、睡眠充足、无社会活动2.4358.671.40
夫妻型吸烟、睡眠充足7.8158.391.39
夫妻型饮酒、吸烟、不锻炼、无社会活动2.9458.241.39
夫妻型吸烟15.9658.051.39
夫妻型饮酒15.0557.681.38
夫妻型吸烟、不锻炼9.0157.271.37
夫妻型饮酒、睡眠充足7.2157.171.36
夫妻型吸烟、睡眠充足、不锻炼4.0856.351.34
家庭型睡眠充足、不锻炼、无社会活动23.8856.191.22
夫妻型饮酒、不锻炼7.8855.031.31
), ArticleFig(id=1240746307858527083, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240375119504265767, language=CN, label=表4, caption=

老年人社会网络与健康相关行为关联规则

, figureFileSmall=null, figureFileBig=null, tableContent=
后项前项支持度（%）置信度（%）提升度
夫妻型饮酒、吸烟、不锻炼3.6964.911.55
夫妻型饮酒、吸烟6.4764.751.55
夫妻型饮酒、吸烟、睡眠充足、不锻炼1.5864.291.53
夫妻型饮酒、吸烟、睡眠充足3.1563.591.52
夫妻型饮酒、吸烟、睡眠充足、不锻炼、无社会活动1.3459.041.41
夫妻型饮酒、吸烟、无社会活动4.8458.861.40
夫妻型饮酒、吸烟、睡眠充足、无社会活动2.4358.671.40
夫妻型吸烟、睡眠充足7.8158.391.39
夫妻型饮酒、吸烟、不锻炼、无社会活动2.9458.241.39
夫妻型吸烟15.9658.051.39
夫妻型饮酒15.0557.681.38
夫妻型吸烟、不锻炼9.0157.271.37
夫妻型饮酒、睡眠充足7.2157.171.36
夫妻型吸烟、睡眠充足、不锻炼4.0856.351.34
家庭型睡眠充足、不锻炼、无社会活动23.8856.191.22
夫妻型饮酒、不锻炼7.8855.031.31
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我国老年人的社会网络类型与健康相关行为的关联分析
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王妍菲 , 马文骏 , 曹力晶 , 李京鸿 , 郑建中 , 佟岩
现代预防医学 | 健康与社会行为 2024,51(6): 1070-1076
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现代预防医学 | 健康与社会行为 2024, 51(6): 1070-1076
我国老年人的社会网络类型与健康相关行为的关联分析
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王妍菲, 马文骏, 曹力晶, 李京鸿, 郑建中 , 佟岩
作者信息
  • 山西医科大学公共卫生学院社会医学教研室，山西 太原 030001

    • 王妍菲（1998—），女，硕士在读，研究方向：健康社会决定因素

通讯作者:

郑建中，E-mail：；
佟岩，E-mail：
Correlations between social network types and health-related behaviors among the elderly in China
Yan-fei WANG, Wen-jun MA, Li-jing CAO, Jing-hong LI, Jian-zhong ZHENG , Yan TONG
Affiliations
  • Department of socialmedicine, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi 030001, China
出版时间: 2024-03-25 doi: 10.20043/j.cnki.MPM.202309060
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摘要
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目的

识别我国老年人的社会网络类型，分析其人群分布特征，探讨不同社会网络类型与健康行为之间的关联，为开展个性化的健康干预策略提供参考。

方法

采用2018年中国老年健康影响因素跟踪调查数据，利用潜在类别分析识别老年人的社会网络类型，应用无序多分类logistic回归模型和Apriori算法分析不同社会网络类型与健康相关行为的关联。

结果

纳入6 184名老年人，我国老年人的社会网络类型分为家庭型（45.97%），夫妻型（41.91%）和寡居型（12.12%）。无序多分类logistic模型显示：年龄较大（OR=1.141，95% CI: 1.132～1.149）、女性（OR=4.472，95% CI: 3.846～5.201）、少数民族（OR=1.454，95% CI:1.037～2.040）和睡眠充足（OR=1.158，95% CI:1.010～1.328）的老年人具有家庭型社会网络的概率更大；年龄较大（OR=1.120，95% CI: 1.109～1.131）、女性（OR=4.913，95% CI: 4.007～6.026）和睡眠充足（OR=1.247, 95% CI: 1.039～1.498）的老年人具有寡居型社会网络的概率更大。关联规则中的强关联规则为：饮酒、吸烟、不锻炼老年人的社会网络多为夫妻型。

结论

我国老年人群体中夫妻型更易存在不健康行为，其社交网络范围较小，社会支持单一，因此可通过丰富社会网络、增强社会支持、开展代际干预等措施改善老年人的健康行为生活方式。

老年人  /  社会网络类型  /  健康相关行为  /  潜在类别分析  /  关联分析
Objective

To identify the types of social networks of the elderly in China, to analyze their population distribution characteristics, and to explore the association between different social network types and health behaviors, so as to provide reference for developing personalized health intervention strategies.

Methods

Using the data from the 2018 Chinese Longitudinal Healthy Longevity Survey, latent class analysis was used to identify the types of social networks in the elderly. The Multinomial Logistic Regression Analysis and Apriori algorithm were used to analyze the association between different social network types and health-related behaviors.

Results

A total of 6 184 elderly people was included in this study. The social network types of the elderly in China were divided into family type (45.97%), couple type (41.91%) and widowed type (12.12%).Results from the Multinomial Logistic Regression Analysis showed that the elderly who were older (OR=1.141, 95% CI: 1.132-1.149), female (OR=4.472, 95% CI: 3.846-5.201), in ethnic minority (OR=1.454, 95% CI:1.037-2.040), and had sufficient sleep (OR=1.158,95% CI: 1.010-1.328) were more likely of the family-oriented social network. The elderly who were older (OR=1.120, 95% CI: 1.109-1.131), female (OR=4.913, 95% CI: 4.007-6.026) and had sufficient sleep (OR=1.247,95% CI:1.039-1.498) were more likely of the widowed-like social network.Strong association rules of the association rule is: drinking, smoking, and exercise of the elderly social network for couples.

Conclusion

The couple type is more likely to have unhealthy behaviors in the elderly in China, and its social network is smaller and its social support is single. Therefore, it is necessary to enrich the social network, enhance social support, and carry out intergenerational intervention to improve the healthy behavior of the elderly.

The elderly  /  Type of social network  /  Health-related behaviors  /  Latent class analysis  /  Association study
王妍菲, 马文骏, 曹力晶, 李京鸿, 郑建中, 佟岩. 我国老年人的社会网络类型与健康相关行为的关联分析. 现代预防医学, 2024 , 51 (6) : 1070 -1076 . DOI: 10.20043/j.cnki.MPM.202309060
Yan-fei WANG, Wen-jun MA, Li-jing CAO, Jing-hong LI, Jian-zhong ZHENG, Yan TONG. Correlations between social network types and health-related behaviors among the elderly in China[J]. Modern Preventive Medicine, 2024 , 51 (6) : 1070 -1076 . DOI: 10.20043/j.cnki.MPM.202309060
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根据第七次人口普查[1]显示，我国65周岁及以上老年人占总人口的13.5%。意味着总体上我国已经进入了中度老龄化阶段，人口老龄化是今后较长一段时期内我国的基本国情[2]。人口老龄化不断深入发展以及人口流动和家庭规模变化的叠加影响，使得老年人家庭网络受到冲击，社会网络也随之发生变化。社会网络是人与人直接和间接的社会关系网络，涵盖家庭、亲戚与朋友的支持以及与他人的相互支持。社会网络作为一种社会资源对老年人的生活影响很大，为老年人提供非常重要的非正式社会支持，它是老年人获取经济支持、生活照料以及精神慰藉的重要途径。目前基于社会网络的研究已被广泛应用于社会学、心理学、经济学、管理学和医学等领域。社会网络作为一个重要的健康社会决定因素，与老年人的健康密切相关，不同的地区和文化背景下老年人的社会网络类型存在差异，不同社会网络类型对老年人健康的影响也不径相同[3]。国外研究提示社会网络可以通过健康相关行为的中介效应进而对健康产生影响，维持健康的生活方式可以显著降低老年人的死亡风险[4]。20世纪80年代以来，家庭人数减少、一代户占比接近一半、家庭居住分散化的情况[5]影响着老年人与子女、社会的联系，老年人网络关系发生较大变化，亟待开展研究了解其社会网络类型及其与健康相关行为的关联。基于此，本研究通过识别我国老年人社会网络类型，分析其分布特征，探索不同社会网络类型与老年人健康行为的关系，有助于基层医疗机构开展个性化的健康干预策略，促进老年人的身心健康。
1 对象及方法
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1.1 研究对象
本研究数据采用中国老年人健康影响因素跟踪调查（Chinese longitudinal healthy longevity survey, CLHLS）项目，由北京大学健康老龄与发展研究中心提供，调查范围涵盖23个省、市、自治区，并且在调研省份中随机选取了近50%的市或县进行调查，其调查质量较高，具有良好代表性[6]。选择年龄≥65岁的老年人，根据研究目的对老年人基本信息、健康相关行为等关键指标回答缺失或无效的样本数据进行剔除，最终纳入分析的有效样本量为6 184人。
1.2 研究方法
1.2.1 变量定义
中国老年人健康影响因素跟踪调查内容包括基本状况、对现状的评价及性格情绪特征、一般能力、生活方式、日常活动能力、个人背景及家庭结构、生理健康七大部分。本文选用基本状况、生活方式、个人背景及家庭结构三部分。从基本状况中选取性别、民族、年龄、户籍、居住方式五个指标；从生活方式中选取吸烟、饮酒、睡眠、锻炼、社会活动五个指标；从个人背景及家庭结构中选取婚姻、子女、经济支持、陪伴、倾诉和解决问题六个指标。本研究基于以下八个条目建立老年人社会网络类型，具体内容如下：1.您现在的婚姻状况是？2.请问您一生总共生过几个孩子？3.您目前有哪些社会保障和商业保险？4.您现在与谁住在一起？ 5.参加社会活动的频率？6.您平时与谁聊天最多？7.如果您有心事或想法，最先向谁说？ 8.如果您遇到问题和困难，最先想找谁解决？
1.2.2 潜在类别分析（Latent Class Analysis, LCA）
潜在类别分析是一种先进的聚类分析方法，本研究利用LCA来识别出老年人的社会网络类型。LCA主要的适配指标包括赤池信息准则（Akaike information criteria，AIC）、贝叶斯信息准则（Bayesian information criteria，BIC）、校正样本量的贝叶斯信息准则（Adjusted BIC, aBIC）和信息熵（Entropy）指数。AIC、BIC，aBIC数值越低、信息熵越大（>0.8说明至少有90%的个体分类正确）[7]，表明该模型分类越准确，拟合越好。大部分研究选择BIC值最小的模型为最佳模型，罗梦戴尔鲁本似然比检验指标（Lo-Mendell-Rubin, LMR）和基于Bootstrap的似然比检验（Bootstrap likelihood ratio test, BLRT）指标用于比较潜在类别模型的拟合差异，P<0.05说明K个类别的模型优于K-1个类别的模型。类别概率指每一类的人数比例，如果某类人数比例过小(如小于5%)，说明该类的典型性不足，分类不够合理[8]。
1.2.3 关联规则
关联规则是通过数据挖掘探索海量数据之间未知、潜在且有实际意义的关联或联系[9]。本研究意图通过关联规则分析老年人社会网络类型与健康相关行为之间的关联性。支持度、置信度、提升度是关联规则最重要三个的评价指标。支持度表示A 和 B 同时发生的概率。支持度越高，表明 A、B 同时发生的概率越高，关联规则越重要。置信度是指在A发生的前提下，B发生的概率。置信度越高，表明发生A后更有可能发生B，关联规则越可信[10]。提升度是关联规则的可信度与期望可信度之比，以1为界限，>1表示正相关，等于1表示零相关，<1表示负相关[11]。强关联规则是指某规则同时满足一定的最小支持度和最小置信度[12]。
1.2.4 统计学方法
采用SPSS 26.0整理数据，检验水准 α=0.05（双侧）。运用Mplus 8.0统计软件进行潜在类别分析，识别老年人社会网络类型；计量资料用表示，计数资料用频数、构成比（%）描述；采用无序多分类logistic回归（α入=0.05，α出=0.10）分析不同社会网络类型的人群分布特征及其与健康相关行为之间的关联；通过IBM SPSS Modeler 18.0数据挖掘中的Apriori算法分析老年人社会网络类型与健康相关行为间的关联及强度。
2 结果
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2.1 老年人的基本信息
本研究中研究对象共6 184人，男性3 048人（49.29%）、女性3 136人（50.71%），平均年龄（83.63±11.49）岁。年龄、户籍、民族、婚姻状况、居住方式、经济水平和健康相关行为等结果见表1。
2.2 老年人社会网络类型的最优潜在类别模型
本研究基于被试者8个条目建立潜在类别分析模型。将社会网络的潜在类别依次设为1到6类，逐一进行比较。所有模型的AIC、BIC、aBIC和熵值均逐渐减小且P值小于0.05，但模型4、5、6的类别概率小于5%，提示有分组人数比例过小。对结果综合分析可得，模型3的BIC值较小，熵值较高，且每个类别概率均大于5%，各个指标均较为理想，因此最终选择3类别模型为最佳模型。类别1为2 843人（45.97%），类别2有2 592人（41.91%），类别3有749人（12.12%）。各个模型的拟合优度检验结果见表2。
使用社会网络不同维度的标准分绘制图1，其中类别1的老年人8个维度得分均高于类别2和类别3，其中2 551人（41.25%）已婚并育有子女，经济支持水平和社会活动标准分数最高，分别为1.13和2.02，社会支持方面2 096（33.89%）人都来源于子女，因此将其命名为家庭型；类别2的老年人均有伴侣，其中2 571（41.58%）人育有子女，2 545（41.15%）人与家人生活在一起，居住方式、陪伴、倾诉和解决问题的维度得分分别为-1.06、-1.99、-2.04和-1.44 ，显著低于类别1和类别3，社会支持方面1 814（29.33%）人均来源于伴侣，因此将其命名为夫妻型；类别3的老年人83（1.34%）人无伴侣，子女指标标准分数最低，为-0.18，86（1.39%）位老年人平时无人聊天，161（2.60%）位老年人有心事无人诉说，84（1.36%）位老年人遇到问题或困难的时候无人解决，因此将其命名为寡居型。
2.3 老年人不同社会网络类型的分布特征
以类别（夫妻型=0，家庭型=1，寡居型=2）为因变量，将一般人口学特征（年龄、性别、户籍、民族）及健康相关行为（吸烟、饮酒、睡眠、锻炼、社会活动）作为自变量，无序多分类logistic回归模型显示：
家庭型老年人与夫妻型相比，年龄、性别、民族、经济水平、睡眠状况与家庭型老年人存在关联，年龄较大（OR=1.141，95% CI: 1.132～1.149）、女性（OR=4.472，95% CI: 3.846～5.201）、少数民族（OR=1.454，95% CI:1.037～2.040）、睡眠充足（OR=1.158，95% CI:1.010～1.328）的老年人较容易进入家庭型社会网络。每月收入大于等于2 000小于5 000元的老年人（OR=0.648，95% CI: 0.526～0.799）以及大于等于5 000元（OR=0.716，95% CI: 0.608～0.843）的老年人不容易进入家庭型社会网络。
寡居型老年人与夫妻型相比，年龄、性别、经济水平、睡眠和寡居型老年人存在关联，年龄较大（OR=1.120，95% CI: 1.109～1.131）、女性（OR=4.913，95% CI: 4.007～6.026）、睡眠充足（OR=1.247, 95% CI: 1.039～1.498）的老年人较容易进入寡居型社会网络，每月收入大于等于2 000小于5 000元（OR=0.652，95% CI: 0.484～0.878）的老年人不容易进入寡居型社会网络。结果见表3。
2.4 老年人社会网络类型潜在类别与健康相关行为的关联分析
一般根据经验值设置阈值得到适当数量的关联规则[13]，故参考以往研究[14]，将最小支持度设为1.0%,最小置信度设为55%,提升度为1。筛选出16条关联规则，其中12条与吸烟有关，11条与饮酒有关，8条与不锻炼有关，5条与无社会活动有关，见表4。置信度最高的关联规则为：饮酒、吸烟、不锻炼和夫妻型；支持度最高的关联规则为睡眠充足、不锻炼、无社会活动和家庭型；提升度最高的关联规则为：饮酒、吸烟、不锻炼和夫妻型。以支持度、置信度与提升度为衡量指标，综合得出夫妻型老年人不健康相关行为较多，最常见的不健康相关行为是吸烟。与其他两种类型的老年人相比，夫妻型老年人的健康相关行为较差，需加以关注。
3 讨论
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健康相关行为影响老年人的身心健康。研究显示健康相关行为对60岁以上人群慢性病的患病情况具有影响[15]，且健康相关行为与中老年人抑郁症状存在关联[16]。既往研究认为社会网络类型与健康相关行为存在关联，但在不同社会文化背景下，其关联存在差异,因此亟待开展研究了解我国老年人的社会网络类型与健康相关行为的关联。本研究采用潜在类别分析方法在我国老年人群体中识别出的3种社会网络类别分别为家庭型、夫妻型和寡居型。其中，家庭型人数占比最多，其次为夫妻型和寡居型。这与叶丽萍对我国湖北省簰洲湾镇老年人的研究中得出的家庭型老年人占比最高的结论一致[17]。
本研究提示与夫妻型老年人相比，年龄较大、女性、少数民族、睡眠充足的老年人具有家庭型社会网络的概率大，经济水平较高的老年人具有家庭型社会网络的概率小。老年人随着年龄增长身体健康状况变差、生活自理能力下降，更多时间需要家人照护。我国城乡女性的预期寿命均高于男性[18]，受传统观念“孝道”文化影响，子女会和丧偶老年女性共同居住。家庭型社会网络老年人的社会支持多来源于子女和亲戚，经济支持水平和情感支持水平最高，因此其生活幸福感、获得感强，睡眠较为充足。经济水平高的老年人可选择多样化的养老方式，如选择专业保姆、护工照顾，或挑选高质量的养老机构安度晚年，所以具有家庭型社会网络的概率小。寡居型老年人独居比例高，女性多于男性且婚姻状况以丧偶为主。与夫妻型老年人相比，年龄较大、女性、睡眠充足的老年人具有寡居型社会网络的概率大，每月收入介于2 000到5 000元的老年人具有寡居型社会网络的概率小。研究显示近年来我国女性平均预期寿命不断提高[19-20]，男女之间人均预期寿命差异将进一步扩大，可见性别与预期寿命之间的关系也成为影响老年人社会网络类型的重要因素之一。老年人同辈群体逐渐减少，社会交往范围也逐渐缩小，独居老人通常不会投入大量时间在社会活动和娱乐活动中，行动不便的独居老人消遣方式则更为局限，因此其睡眠时间充足。月收入处于中等水平的老年人生活压力较小，闲暇时间休闲娱乐方式多，具有寡居型社会网络的概率较小。
关联分析得出：夫妻型老年人的社会网络类型与不健康行为之间关联较强，其中与吸烟有关的关联规则最多，其次为饮酒和不锻炼身体。数据显示：空巢老人慢性病患病率为72.3%～92.7%，其中共病老年人占 53.5%[21]，抑郁患病率为31.29%[22]，高于非空巢老人。空巢老人社会网络较为单一，获得健康相关知识的渠道少，容易选择不健康的生活方式，不利于其身心健康。近年来由于我国城市化进程等原因，老年人家庭结构改变，老年家庭的夫妻空巢化水平和独居空巢化水平均有明显提升[23]，夫妻型空巢老人的增长速度不断增快，他们的健康相关行为尤其值得关注。低龄的夫妻型老年人身体可能处于健康状态，没有慢性病或疾病比较轻，易选择吸烟、饮酒等不健康相关行为释放压力。但有研究表明吸烟、饮酒可能通过影响社交行为，进而影响老年人的抑郁状况[16]。因此要尽早对他们进行健康宣教，告知此类不健康行为对身体健康造成的严重危害，帮助他们减少生活中的不健康行为。还应告知老年人合理运动带来的好处，如运动可延缓脑萎缩，规律运动的老年人认知障碍的发生率较低等[24]。可通过社区帮扶、互助小组等形式传播健康知识，丰富老年人的社会活动，促进健康老龄化战略实施，让老年人从“长寿”到“康寿”。
综上，本文的研究结果认为夫妻型老年人的健康相关行为较差，可通过增强社会支持，丰富社会网络改善其健康相关行为，提出以下建议：首先，增强老年人在家庭中的角色与地位，提高家庭成员照料意愿。家人朋友交往频率不仅能够促进认知功能，也能为老年人提供更多情感、社会支持。可通过增加老年人与亲人间联系和探望频率，构建牢固的家庭型社会网络。第二，社区或基层医疗机构可鼓励老年人参加社区活动或志愿服务，帮助老年人丰富社交网络。同辈支持是指由年龄、兴趣爱好、生活环境和所面临问题等多方面大体相似的人组成关系亲密的社会支持[25]，社区工作人员能够通过安排有相似特征的老年人组成互助小组来增强其同辈支持，加强健康教育和宣传，提高他们的健康保健意识，促进健康知识的传播及健康行为的监督纠正。同时政府也应加强基层医疗保健服务的建设，提高老年人获得高质量医疗保健的能力。第三，通过传统观念中的“隔辈亲”发挥代际支持的积极作用。代际支持本意为代与代之间双向的代际交换，包含经济、时间、情感等各种形式的支持。通过代际支持能显著提高老年人的积极信念，减少消极情绪，可以较为系统全面地提高老年人的心理健康水平[26]。社区应联合家庭给予针对性干预措施，在保证二者安全的前提下提供更多接触机会，通过老幼结伴式代际互动建立二者之间的亲密关系，从而对老年人心理健康产生积极影响。
本研究的限制和不足：本研究为横断面研究，对老年人社会网络类型与健康相关行为的因果关系论证不足，有待开展前瞻性研究。
文献
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2024年第51卷第6期
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doi: 10.20043/j.cnki.MPM.202309060
  • 接收时间：2023-09-04
  • 首发时间：2026-03-16
  • 出版时间：2024-03-25
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  • 收稿日期：2023-09-04
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    山西医科大学公共卫生学院社会医学教研室，山西 太原 030001

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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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