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Article(id=1241035546282226381, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241035543589483182, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202407540, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1722268800000, receivedDateStr=2024-07-30, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773815531559, onlineDateStr=2026-03-18, pubDate=1735056000000, pubDateStr=2024-12-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773815531559, onlineIssueDateStr=2026-03-18, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773815531559, creator=13701087609, updateTime=1773815531559, updator=13701087609, issue=Issue{id=1241035543589483182, tenantId=1146029695717560320, journalId=1227665162245664772, year='2024', volume='51', issue='24', pageStart='4417', pageEnd='4608', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773815530917, creator=13701087609, updateTime=1773815686426, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241036195896029478, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241035543589483182, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241036195896029479, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241035543589483182, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=4417, endPage=4422, ext={EN=ArticleExt(id=1241035546823291613, articleId=1241035546282226381, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Association rule analysis and identification of related factors for comorbidity of chronic diseases in middle-aged and elderly Chinese population, columnId=null, journalTitle=Modern Preventive Medicine, columnName=null, runingTitle=null, highlight=null, articleAbstract=
Objective

To explore the comorbidity patterns and influencing factors of chronic diseases in middle-aged and elderly people in China, so as to provide new ideas for the development of corresponding chronic disease prevention and control strategies and measures.

Methods

Using data from the China Health and Retirement Longitudinal Study (CHARLS) 2018, after excluding samples with missing variables for 14 chronic diseases, a final sample of 19,390 individuals aged 45 and older was included. The Apriori algorithm from association rule analysis was utilized to explore the comorbidity patterns among middle-aged and elderly individuals in China. The occurrence of chronic comorbidity (two or more chronic diseases or three or more chronic diseases) was taken as the dependent variable, and the independent variable was determined according to the five dimensions of social determinants of health. 5 696 samples were included after deletion of the missing values, and the influential factors of chronic comorbidity were analyzed by univariate χ2 test and multiple logistic regression.

Results

The prevalence of comorbid chronic diseases among middle-aged and elderly individuals in our country was 55.4%. The most common comorbidity patterns were arthritis or rheumatic diseases and hypertension. Association rule analysis identified 24 strong rule combinations, with the most common binary association being gastrointestinal or digestive system diseases with arthritis or rheumatic diseases, and the most common ternary association being gastrointestinal or digestive system diseases, hypertension, and arthritis or rheumatic diseases.The results of the multivariate analysis showed that both two or more chronic diseases and three or more chronic diseases were significantly associated with increased risk factors, including: age 60-80 years (two or more chronic diseases: OR=1.479, 95% CI: 1.244-1.759; three or more chronic diseases: OR=1.526, 95% CI: 1.267-1.839), age >80 years (OR=1.545, 95% CI: 1.144-2.087; OR=1.591, 95% CI: 1.175-2.154), depressive symptoms (OR=1.435, 95% CI: 1.267-1.626; OR=1.382, 95% CI: 1.216-1.570), having a pension (OR=1.350, 95% CI: 1.141-1.598; OR=1.332, 95% CI: 1.118-1.586), and residence in central (OR=1.268, 95% CI: 1.096-1.470; OR=1.269, 95% CI: 1.088-1.479) or western regions (OR=1.217, 95% CI: 1.062-1.395; OR=1.198, 95% CI: 1.038-1.382).Conversely, factors associated with a reduced risk of chronic multimorbidity included alcohol consumption (two or more chronic diseases: OR=0.811, 95% CI: 0.707-0.930; three or more chronic diseases: OR=0.837, 95% CI: 0.724-0.968), 6-8 hours of night sleep (OR=0.806, 95% CI: 0.702-0.926; OR=0.792, 95% CI: 0.688-0.912), more than 8 hours of night sleep (OR=0.738, 95% CI: 0.635-0.858; OR=0.745, 95% CI: 0.637-0.872), self-rated general health (OR=0.357, 95% CI: 0.307-0.414; OR=0.343, 95% CI: 0.299-0.392), and self-rated good health (OR=0.136, 95% CI: 0.114-0.163; OR=0.117, 95% CI: 0.096-0.142).

Conclusion

It is suggested that the prevention and intervention of chronic comorbidity should be multidimensional, and the health care personnel should take into account the comorbidity of patients and the problem of multiple drug use in the formulation of comorbidity treatment plan.

, correspAuthors=Jie YU, Gui-feng MA, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=null, fund=null, authors=null, authorsList=Wen-yu SU, Huai-ju GE, Wen-jing CHANG, Shi-hong DONG, Hui-yu JIA, Shan JIANG, Yu-qing MI, Jie YU, Gui-feng MA), CN=ArticleExt(id=1241035547771204357, articleId=1241035546282226381, tenantId=1146029695717560320, journalId=1227665162245664772, language=CN, title=我国中老年人慢性病共病关联规则分析及相关因素识别研究, columnId=1228016567632462653, journalTitle=现代预防医学, columnName=流行病与统计方法, runingTitle=null, highlight=null, articleAbstract=
目的

探究我国中老年人慢性病的共病模式和影响因素，从而为制定相应的慢性病防控策略和措施提供新的思路。

方法

采用中国健康与养老追踪调查（Charls）2018年数据，剔除14种慢性病变量有缺失的样本后，最终纳入19 390名≥45岁中老年人样本，利用关联规则分析中的Apriori算法，探索我国中老年人的共病模式；分别以是否发生慢性病共病（两种及以上慢性病或三种及以上慢性病）为因变量，根据健康社会决定因素的五个维度确定自变量，删除缺失值后纳入5 696例样本，采用单因素χ2检验和多因素logistic回归分析慢性病共病的影响因素。

结果

我国中老年人慢性病共病检出率为55.4%，关节炎或风湿病共病模式和高血压共病模式最为常见；关联规则分析筛选出24条强规则共病组合, 其中二元关联规则中最常见的是胃部或消化系统疾病与关节炎或风湿病，三元关联规则中最常见的是胃部或消化系统疾病、高血压和关节炎或风湿病；多因素检验结果显示，无论是两种及以上慢性病还是三种及以上慢性病，年龄60~80（两种及以上慢性病OR=1.479，95% CI：1.244~1.759；三种及以上慢性病OR=1.526，95% CI：1.267~1.839）、年龄>80岁（OR=1.545, 95% CI：1.144~2.087；OR=1.591，95% CI：1.175~2.154）、有抑郁症状（OR=1.435, 95% CI：1.267~1.626；OR=1.382，95% CI：1.216~1.570）、有退休金或养老金（OR=1.350, 95% CI：1.141~1.598；OR=1.332，95% CI：1.118~1.586）、中部地区（OR=1.268, 95% CI：1.096~1.470； OR=1.269，95% CI：1.088~1.479）以及西部地区（OR=1.217, 95% CI：1.062~1.395；OR=1.198，95% CI：1.038~1.382）与慢性病共病高风险相关；而饮酒（两种及以上慢性病OR=0.811, 95% CI：0.707~0.930； 三种及以上慢性病OR=0.837，95% CI：0.724~0.968）、夜晚睡眠时长6~8h（OR=0.806, 95% CI：0.702~0.926；OR=0.792，95% CI：0.688~0.912）、夜晚睡眠时长>8h（OR=0.738, 95% CI：0.635~0.858；OR=0.745，95% CI：0.637~0.872）、自评健康一般（OR=0.357, 95% CI：0.307~0.414；OR=0.343，95% CI：0.299~0.392）以及自评健康好（OR=0.136，95% CI：0.114~0.163；OR=0.117，95% CI：0.096~0.142）的中老年人慢性病共病风险降低。

结论

建议从多维度对慢性病共病进行预防和干预，并且在制定共病治疗方案时医疗保健人员应该有针对性地考虑患者的共病情况和多重用药问题。

, correspAuthors=于洁, 马桂峰, authorNote=null, correspAuthorsNote=
马桂峰，E-mail: ；
于洁，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=m2K360x8qWAH9NurV90m1w==, magXml=T6VcU0l5MjqQ11zve70saQ==, pdfUrl=null, pdf=Lt7HSFiH1PNi0wXQW1e4nQ==, pdfFileSize=846110, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=OtXdKEOQhrpk3jKHSMRq6w==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=GRbPth3Zu90t2VJHHS7OWg==, 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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Consensus and disagreement in the debate on the bidirectional effects of alcohol consumption on the cardiovascular system［J］. Chinese General Practice, 2022, 25(30): 3747-3754. 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Study on the prevalence and influencing factors of chronic diseases in the elderly population aged 60 and above［J］. Systems Medicine, 2023, 8 (23): 71-73, 77. (In Chinese), articleTitle=Study on the prevalence and influencing factors of chronic diseases in the elderly population aged 60 and above, refAbstract=null), Reference(id=1241069991559230141, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035546282226381, doi=null, pmid=null, pmcid=null, year=2024, volume=51, issue=7, pageStart=1277, pageEnd=1283, url=null, language=null, rfNumber=［14］, rfOrder=21, authorNames=马文骏, 佟岩, 王妍菲, journalName=现代预防医学, refType=null, unstructuredReference=马文骏，佟岩，王妍菲，等．我国独居老年人健康相关行为与慢性病共病的关联研究［J］．现代预防医学, 2024, 51(7): 1277-1283., articleTitle=我国独居老年人健康相关行为与慢性病共病的关联研究, refAbstract=null), Reference(id=1241069991697642179, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035546282226381, doi=null, pmid=null, pmcid=null, year=2024, volume=51, issue=7, pageStart=1277, pageEnd=1283, url=null, language=null, rfNumber=［14］, rfOrder=22, authorNames=Ma WJ, Tong Y, Wang YF, journalName=Modern Preventive Medicine, refType=null, unstructuredReference=Ma WJ, Tong Y, Wang YF, et al. Study on the relationship between health-related behaviors and chronic comorbidities of the elderly living alone in China［J］. Modern Preventive Medicine, 2024, 51(7): 1277-1283. (In Chinese), articleTitle=Study on the relationship between health-related behaviors and chronic comorbidities of the elderly living alone in China, refAbstract=null), Reference(id=1241069991844442823, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035546282226381, doi=null, pmid=null, pmcid=null, year=2023, volume=29, issue=3, pageStart=151, pageEnd=154, 160, url=null, language=null, rfNumber=［15］, rfOrder=23, authorNames=王峻霞, 丁令智, 简金洋, journalName=应用预防医学, refType=null, unstructuredReference=王峻霞，丁令智，简金洋，等．基于CHARLS数据库的中国老年人慢性病共病现状及影响因素分析［J］．应用预防医学, 2023, 29(3): 151-154, 160., articleTitle=基于CHARLS数据库的中国老年人慢性病共病现状及影响因素分析, refAbstract=null), Reference(id=1241069991982854865, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035546282226381, doi=null, pmid=null, pmcid=null, year=2023, volume=29, issue=3, pageStart=151, pageEnd=154, 160, url=null, language=null, rfNumber=［15］, rfOrder=24, authorNames=Wang JX, Ding LZ, Jian JY, journalName=Applied Preventive Medicine, refType=null, unstructuredReference=Wang JX, Ding LZ, Jian JY, et al. Analysis of the comorbidity status and influencing factors of chronic diseases among elderly people in China based on the CHARLS database［J］. Applied Preventive Medicine, 2023, 29(3): 151-154, 160. (In Chinese), articleTitle=Analysis of the comorbidity status and influencing factors of chronic diseases among elderly people in China based on the CHARLS database, refAbstract=null), Reference(id=1241069992087712474, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035546282226381, doi=null, pmid=null, pmcid=null, year=2024, volume=27, issue=2, pageStart=208, pageEnd=216, url=null, language=null, rfNumber=［16］, rfOrder=25, authorNames=李林瑾, 肖丽勤, 张丹, journalName=中国全科医学, refType=null, unstructuredReference=李林瑾，肖丽勤，张丹．基于健康生态学模型的广东省老年共病患者患慢性病数量影响因素研究［J］．中国全科医学, 2024, 27(2): 208-216., articleTitle=基于健康生态学模型的广东省老年共病患者患慢性病数量影响因素研究, refAbstract=null), Reference(id=1241069993610244833, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035546282226381, doi=null, pmid=null, pmcid=null, year=2024, volume=27, issue=2, pageStart=208, pageEnd=216, url=null, language=null, rfNumber=［16］, rfOrder=26, authorNames=Li LJ, Xiao LQ, Zhang D, journalName=Chinese General Practice, refType=null, unstructuredReference=Li LJ, Xiao LQ, Zhang D. Study on the factors affecting the number of chronic diseases among elderly comorbidity patients in guangdong province based on the model of ecological health［J］. Chinese General Practice, 2024, 27(2): 208-216. (In Chinese), articleTitle=Study on the factors affecting the number of chronic diseases among elderly comorbidity patients in guangdong province based on the model of ecological health, refAbstract=null), Reference(id=1241069993744462571, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035546282226381, doi=null, pmid=null, pmcid=null, year=2023, volume=26, issue=1, pageStart=50, pageEnd=57, url=null, language=null, rfNumber=［17］, rfOrder=27, authorNames=齐元涛, 柳言, 杜金, journalName=中国全科医学, refType=null, unstructuredReference=齐元涛，柳言，杜金，等．基于健康生态学模型的我国老年人慢性病共病影响因素研究［J］．中国全科医学, 2023, 26(1): 50-57., articleTitle=基于健康生态学模型的我国老年人慢性病共病影响因素研究, refAbstract=null), Reference(id=1241069993849320182, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035546282226381, doi=null, pmid=null, pmcid=null, year=2023, volume=26, issue=1, pageStart=50, pageEnd=57, url=null, language=null, rfNumber=［17］, rfOrder=28, authorNames=Qi YT, Liu Y, Du J, journalName=Chinese General Practice, refType=null, unstructuredReference=Qi YT, Liu Y, Du J, et al. The influencing factors of chronic disease comorbidities of elderly in china based on health ecology model［J］. Chinese General Practice, 2023, 26(1): 50-57. (In Chinese), articleTitle=The influencing factors of chronic disease comorbidities of elderly in china based on health ecology model, refAbstract=null)], funds=[Fund(id=1241069987306205623, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035546282226381, 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=1241069980083614699, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035546282226381, xref=1., ext=[AuthorCompanyExt(id=1241069980092003308, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035546282226381, companyId=1241069980083614699, 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=1241069980100391918, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035546282226381, companyId=1241069980083614699, 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=1241069980192666608, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035546282226381, xref=2., ext=[AuthorCompanyExt(id=1241069980201055218, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035546282226381, companyId=1241069980192666608, 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=1241069985691398453, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035546282226381, language=EN, label=Fig.1, caption=Flowchart of the Inclusion Process for Research Subjects, figureFileSmall=pUjS/liYTDZ1MHIR3p0gxw==, figureFileBig=QgM05Zp1Rfbf/IVTtrPvJg==, tableContent=null), ArticleFig(id=1241069985787867455, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035546282226381, language=CN, label=图1, caption=研究对象纳入流程图, figureFileSmall=pUjS/liYTDZ1MHIR3p0gxw==, figureFileBig=QgM05Zp1Rfbf/IVTtrPvJg==, tableContent=null), ArticleFig(id=1241069985922085194, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035546282226381, language=EN, label=Table 1, caption=

Prevalence of chronic diseases and number of comorbidities ［n(%)］

, figureFileSmall=null, figureFileBig=null, tableContent=
疾病名称患病人数患2种慢性病患≥3种慢性病共病总数
高血压7 288(36.7)3 194(16.5)2 985(15.4)6 179(31.9)
血脂异常4 286(22.1)684(3.5)3 285(16.9)3 969(20.5)
糖尿病或血糖升高2 495(12.9)386(2.0)1 929(9.9)2 315(11.9)
癌症440(2.3)95(0.4)289(1.4)384(1.9)
慢性肺病2 748(14.2)383(2.0)2 170(11.2)2 553(13.2)
肝脏疾病1 354(7.0)180(0.9)1 059(5.5)1 239(6.4)
心脏病3 834(19.8)560(2.9)3 081(15.9)3 641(18.8)
中风1 391(7.2)195(1.0)1 129(5.8)1 324(6.8)
肾脏疾病1 942(10.0)269(1.4)1 554(8.0)1 823(9.4)
胃部或消化系统疾病5 847(30.1)1 321(6.8)3 667(18.9)4 988(25.7)
情感及精神方面问题487(2.5)65(0.3)384(2.0)449(2.3)
记忆相关疾病776(4.0)72(0.4)675(3.5)747(3.9)
关节炎或风湿病7 532(38.8)1 829(9.4)4 423(22.8)6 252(32.4)
哮喘1 144(5.9)101(0.5)1 020(5.3)1 121(5.8)
), ArticleFig(id=1241069986144383325, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035546282226381, language=CN, label=表1, caption=

慢性病的检出率以及共病病种数［n(%)］

, figureFileSmall=null, figureFileBig=null, tableContent=
疾病名称患病人数患2种慢性病患≥3种慢性病共病总数
高血压7 288(36.7)3 194(16.5)2 985(15.4)6 179(31.9)
血脂异常4 286(22.1)684(3.5)3 285(16.9)3 969(20.5)
糖尿病或血糖升高2 495(12.9)386(2.0)1 929(9.9)2 315(11.9)
癌症440(2.3)95(0.4)289(1.4)384(1.9)
慢性肺病2 748(14.2)383(2.0)2 170(11.2)2 553(13.2)
肝脏疾病1 354(7.0)180(0.9)1 059(5.5)1 239(6.4)
心脏病3 834(19.8)560(2.9)3 081(15.9)3 641(18.8)
中风1 391(7.2)195(1.0)1 129(5.8)1 324(6.8)
肾脏疾病1 942(10.0)269(1.4)1 554(8.0)1 823(9.4)
胃部或消化系统疾病5 847(30.1)1 321(6.8)3 667(18.9)4 988(25.7)
情感及精神方面问题487(2.5)65(0.3)384(2.0)449(2.3)
记忆相关疾病776(4.0)72(0.4)675(3.5)747(3.9)
关节炎或风湿病7 532(38.8)1 829(9.4)4 423(22.8)6 252(32.4)
哮喘1 144(5.9)101(0.5)1 020(5.3)1 121(5.8)
), ArticleFig(id=1241069986253435241, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035546282226381, language=EN, label=Table 2, caption=

Comorbidity association rules

, figureFileSmall=null, figureFileBig=null, tableContent=
前项集后项集支持度%置信度%支持度
(胃部或消化系统疾病)(关节炎或风湿病)16.8155.741.43
(血脂异常)(高血压)13.6261.601.64
(心脏病)(高血压)11.9260.301.60
(心脏病)(关节炎或风湿病)10.3552.321.35
(糖尿病或血糖升高)(高血压)8.1563.331.68
(慢性肺病)(关节炎或风湿病)7.8155.091.42
(糖尿病或血糖升高)(血脂异常)6.8252.992.40
(胃部或消化系统疾病)(关节炎或风湿病)5.6856.751.46
(中风)(高血压)5.0670.601.88
(肾脏疾病)(高血压)5.0650.511.34
(胃部或消化系统疾病，高血压)(关节炎或风湿病)7.2361.291.58
(血脂异常，关节炎或风湿病)(高血压)6.5463.911.70
(高血压，心脏病)(关节炎或风湿病)6.3052.851.36
(心脏病，关节炎或风湿病)(高血压)6.3060.921.62
(血脂异常，心脏病)(高血压)6.2473.511.96
(高血压，心脏病)(血脂异常)6.2452.342.37
(胃部或消化系统疾病，心脏病)(关节炎或风湿病)5.8266.041.70
(心脏病，关节炎或风湿病)(胃部或消化系统疾病)5.8256.231.86
(血脂异常，胃部或消化系统疾病)(关节炎或风湿病)5.1260.921.57
(血脂异常，关节炎或风湿病)(胃部或消化系统疾病)5.1250.051.66
(血脂异常，糖尿病或血糖升高)(高血压)5.0974.581.98
(高血压，糖尿病或血糖升高)(血脂异常)5.0962.412.82
(胃部或消化系统疾病，心脏病)(高血压)5.0857.671.53
(血脂异常，胃部或消化系统疾病)(高血压)5.0459.941.59
), ArticleFig(id=1241069986442178930, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035546282226381, language=CN, label=表2, caption=

共病关联规则

, figureFileSmall=null, figureFileBig=null, tableContent=
前项集后项集支持度%置信度%支持度
(胃部或消化系统疾病)(关节炎或风湿病)16.8155.741.43
(血脂异常)(高血压)13.6261.601.64
(心脏病)(高血压)11.9260.301.60
(心脏病)(关节炎或风湿病)10.3552.321.35
(糖尿病或血糖升高)(高血压)8.1563.331.68
(慢性肺病)(关节炎或风湿病)7.8155.091.42
(糖尿病或血糖升高)(血脂异常)6.8252.992.40
(胃部或消化系统疾病)(关节炎或风湿病)5.6856.751.46
(中风)(高血压)5.0670.601.88
(肾脏疾病)(高血压)5.0650.511.34
(胃部或消化系统疾病，高血压)(关节炎或风湿病)7.2361.291.58
(血脂异常，关节炎或风湿病)(高血压)6.5463.911.70
(高血压，心脏病)(关节炎或风湿病)6.3052.851.36
(心脏病，关节炎或风湿病)(高血压)6.3060.921.62
(血脂异常，心脏病)(高血压)6.2473.511.96
(高血压，心脏病)(血脂异常)6.2452.342.37
(胃部或消化系统疾病，心脏病)(关节炎或风湿病)5.8266.041.70
(心脏病，关节炎或风湿病)(胃部或消化系统疾病)5.8256.231.86
(血脂异常，胃部或消化系统疾病)(关节炎或风湿病)5.1260.921.57
(血脂异常，关节炎或风湿病)(胃部或消化系统疾病)5.1250.051.66
(血脂异常，糖尿病或血糖升高)(高血压)5.0974.581.98
(高血压，糖尿病或血糖升高)(血脂异常)5.0962.412.82
(胃部或消化系统疾病，心脏病)(高血压)5.0857.671.53
(血脂异常，胃部或消化系统疾病)(高血压)5.0459.941.59
), ArticleFig(id=1241069986593173886, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035546282226381, language=EN, label=Table 3, caption=

Single factor Chi-square test table of chronic disease comorbidity

, figureFileSmall=null, figureFileBig=null, tableContent=
变量名称类别两种及以上共病模式三种及以上共病模式
检出率χ2值P值检出率χ2值P值
年龄、性别、疾病状况
年龄（岁）45~6044.90152.487<0.00124.80142.667<0.001
>60~8061.3540.11
>8065.8344.22
性别男52.4125.647<0.00132.3616.502<0.001
女59.0937.51
抑郁症状无67.70238.093<0.00126.92223.680<0.001
有47.2046.03
个体生活方式
吸烟否56.872.3920.12235.500.4670.494
是54.8034.62
饮酒否59.4357.480<0.00129.0044.212<0.001
是48.7438.02
社交活动无57.243.1970.07435.680.6810.409
有54.8934.63
夜晚睡眠时长（h）<665.20111.494<0.00143.91109.098<0.001
6~851.6130.71
>849.7029.43
自评健康不好80.36779.532<0.00160.96807.158<0.001
一般54.8931.33
好29.7312.34
社会支持网络
婚姻状态未婚57.5029.138<0.00142.5030.362<0.001
已婚同居54.2633.40
分居/离异/丧偶63.2942.13
社会经济地位
居住地农村55.910.1610.68934.861.5650.211
城镇56.7837.46
地区东部51.4024.966<0.00131.0321.591<0.001
中部58.4237.57
西部58.3036.96
教育程度未受过教育63.8260.104<0.00141.6948.366<0.001
小学56.5335.74
初中49.0128.59
高中及以上50.7331.55
其他社会结构性因素
医疗保险无55.000.0420.83831.000.7620.383
有56.0235.20
退休金或养老金无48.12133.114<0.00141.90123.7<0.001
有63.3127.82
), ArticleFig(id=1241069986828054920, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035546282226381, language=CN, label=表3, caption=

慢性病共病的单因素卡方检验表

, figureFileSmall=null, figureFileBig=null, tableContent=
变量名称类别两种及以上共病模式三种及以上共病模式
检出率χ2值P值检出率χ2值P值
年龄、性别、疾病状况
年龄（岁）45~6044.90152.487<0.00124.80142.667<0.001
>60~8061.3540.11
>8065.8344.22
性别男52.4125.647<0.00132.3616.502<0.001
女59.0937.51
抑郁症状无67.70238.093<0.00126.92223.680<0.001
有47.2046.03
个体生活方式
吸烟否56.872.3920.12235.500.4670.494
是54.8034.62
饮酒否59.4357.480<0.00129.0044.212<0.001
是48.7438.02
社交活动无57.243.1970.07435.680.6810.409
有54.8934.63
夜晚睡眠时长（h）<665.20111.494<0.00143.91109.098<0.001
6~851.6130.71
>849.7029.43
自评健康不好80.36779.532<0.00160.96807.158<0.001
一般54.8931.33
好29.7312.34
社会支持网络
婚姻状态未婚57.5029.138<0.00142.5030.362<0.001
已婚同居54.2633.40
分居/离异/丧偶63.2942.13
社会经济地位
居住地农村55.910.1610.68934.861.5650.211
城镇56.7837.46
地区东部51.4024.966<0.00131.0321.591<0.001
中部58.4237.57
西部58.3036.96
教育程度未受过教育63.8260.104<0.00141.6948.366<0.001
小学56.5335.74
初中49.0128.59
高中及以上50.7331.55
其他社会结构性因素
医疗保险无55.000.0420.83831.000.7620.383
有56.0235.20
退休金或养老金无48.12133.114<0.00141.90123.7<0.001
有63.3127.82
), ArticleFig(id=1241069986987438489, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035546282226381, language=EN, label=Table 4, caption=

Multifactor logistics regression results of chronic disease comorbidity

, figureFileSmall=null, figureFileBig=null, tableContent=
变量名称两种及以上慢性病三种及以上慢性病
系数P值OR(95% CI)系数P值OR(95% CI)
年龄(参照组：45~60)
60~800.391<0.0011.479(1.244~1.759)0.423<0.0011.526(1.267~1.839)
>800.4350.0051.545(1.144~2.087)0.4650.0031.591(1.175~2.154)
性别(参照组：男)
女0.1330.0601.142(0.995~1.311)0.1050.1501.111(0.963~1.283)
抑郁症状(参照组：无)
有0.361<0.0011.435(1.267~1.626)0.323<0.0011.382(1.216~1.570)
饮酒(参照组：否)
是-0.2100.0030.811(0.707~0.930)-0.1780.0170.837(0.724~0.968)
夜晚睡眠时长(参照组：<6)
6~8-0.2150.0020.806(0.702~0.926)-0.2330.0010.792(0.688~0.912)
>8-0.304<0.0010.738(0.635~0.858)-0.294<0.0010.745(0.637~0.872)
自评健康(参照组：不好)
一般-1.031<0.0010.357(0.307~0.414)-1.071<0.0010.343(0.299~0.392)
好-1.993<0.0010.136(0.114~0.163)-2.148<0.0010.117(0.096~0.142)
婚姻状态(参照组：已婚同居)
分居/离异/丧偶-0.0340.6780.967(0.824~1.134)-0.0150.8520.987(0.848~1.149)
未婚-0.1760.6110.839(0.426~1.653)0.1400.6891.150(0.581~2.276)
地区(参照组：东部)
中部0.2380.0011.268(1.096~1.470)0.2380.0021.269(1.088~1.479)
西部0.1960.0051.217(1.062~1.395)0.1800.0141.198(1.038~1.382)
教育程度(参照组：未受过教育)
小学-0.0150.8540.985(0.838~1.157)0.0530.5151.055(0.898~1.239)
初中0.0070.9451.007(0.830~1.221)0.0550.5931.056(0.864~1.291)
高中及以上0.1220.3631.130(0.869~1.469)0.2410.0891.272(0.964~1.678)
退休金或养老金(参照组：无)
有0.300<0.0011.350(1.141~1.598)0.2860.0011.332(1.118~1.586)
), ArticleFig(id=1241069987088101794, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035546282226381, language=CN, label=表4, caption=

慢性病共病的多因素logistics回归结果

, figureFileSmall=null, figureFileBig=null, tableContent=
变量名称两种及以上慢性病三种及以上慢性病
系数P值OR(95% CI)系数P值OR(95% CI)
年龄(参照组：45~60)
60~800.391<0.0011.479(1.244~1.759)0.423<0.0011.526(1.267~1.839)
>800.4350.0051.545(1.144~2.087)0.4650.0031.591(1.175~2.154)
性别(参照组：男)
女0.1330.0601.142(0.995~1.311)0.1050.1501.111(0.963~1.283)
抑郁症状(参照组：无)
有0.361<0.0011.435(1.267~1.626)0.323<0.0011.382(1.216~1.570)
饮酒(参照组：否)
是-0.2100.0030.811(0.707~0.930)-0.1780.0170.837(0.724~0.968)
夜晚睡眠时长(参照组：<6)
6~8-0.2150.0020.806(0.702~0.926)-0.2330.0010.792(0.688~0.912)
>8-0.304<0.0010.738(0.635~0.858)-0.294<0.0010.745(0.637~0.872)
自评健康(参照组：不好)
一般-1.031<0.0010.357(0.307~0.414)-1.071<0.0010.343(0.299~0.392)
好-1.993<0.0010.136(0.114~0.163)-2.148<0.0010.117(0.096~0.142)
婚姻状态(参照组：已婚同居)
分居/离异/丧偶-0.0340.6780.967(0.824~1.134)-0.0150.8520.987(0.848~1.149)
未婚-0.1760.6110.839(0.426~1.653)0.1400.6891.150(0.581~2.276)
地区(参照组：东部)
中部0.2380.0011.268(1.096~1.470)0.2380.0021.269(1.088~1.479)
西部0.1960.0051.217(1.062~1.395)0.1800.0141.198(1.038~1.382)
教育程度(参照组：未受过教育)
小学-0.0150.8540.985(0.838~1.157)0.0530.5151.055(0.898~1.239)
初中0.0070.9451.007(0.830~1.221)0.0550.5931.056(0.864~1.291)
高中及以上0.1220.3631.130(0.869~1.469)0.2410.0891.272(0.964~1.678)
退休金或养老金(参照组：无)
有0.300<0.0011.350(1.141~1.598)0.2860.0011.332(1.118~1.586)
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我国中老年人慢性病共病关联规则分析及相关因素识别研究
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苏文钰 1 , 葛怀举 1 , 常雯静 1 , 董士红 1 , 贾蕙毓 1 , 姜姗 1 , 秘玉清 1 , 于洁 2, * , 马桂峰 *
现代预防医学 | 流行病与统计方法 2024,51(24): 4417-4422
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现代预防医学 | 流行病与统计方法 2024, 51(24): 4417-4422
我国中老年人慢性病共病关联规则分析及相关因素识别研究
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苏文钰1, 葛怀举1, 常雯静1, 董士红1, 贾蕙毓1, 姜姗1, 秘玉清1, 于洁2, * , 马桂峰*
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: ；
于洁，E-mail:
Association rule analysis and identification of related factors for comorbidity of chronic diseases in middle-aged and elderly Chinese population
Wen-yu SU1, Huai-ju GE1, Wen-jing CHANG1, Shi-hong DONG1, Hui-yu JIA1, Shan JIANG1, Yu-qing MI1, Jie YU2, * , Gui-feng MA*
Affiliations
  • School of Public Health, Shandong Second Medical University, Weifang, Shandong 261053, China
出版时间: 2024-12-25 doi: 10.20043/j.cnki.MPM.202407540
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目的

探究我国中老年人慢性病的共病模式和影响因素，从而为制定相应的慢性病防控策略和措施提供新的思路。

方法

采用中国健康与养老追踪调查（Charls）2018年数据，剔除14种慢性病变量有缺失的样本后，最终纳入19 390名≥45岁中老年人样本，利用关联规则分析中的Apriori算法，探索我国中老年人的共病模式；分别以是否发生慢性病共病（两种及以上慢性病或三种及以上慢性病）为因变量，根据健康社会决定因素的五个维度确定自变量，删除缺失值后纳入5 696例样本，采用单因素χ2检验和多因素logistic回归分析慢性病共病的影响因素。

结果

我国中老年人慢性病共病检出率为55.4%，关节炎或风湿病共病模式和高血压共病模式最为常见；关联规则分析筛选出24条强规则共病组合, 其中二元关联规则中最常见的是胃部或消化系统疾病与关节炎或风湿病，三元关联规则中最常见的是胃部或消化系统疾病、高血压和关节炎或风湿病；多因素检验结果显示，无论是两种及以上慢性病还是三种及以上慢性病，年龄60~80（两种及以上慢性病OR=1.479，95% CI：1.244~1.759；三种及以上慢性病OR=1.526，95% CI：1.267~1.839）、年龄>80岁（OR=1.545, 95% CI：1.144~2.087；OR=1.591，95% CI：1.175~2.154）、有抑郁症状（OR=1.435, 95% CI：1.267~1.626；OR=1.382，95% CI：1.216~1.570）、有退休金或养老金（OR=1.350, 95% CI：1.141~1.598；OR=1.332，95% CI：1.118~1.586）、中部地区（OR=1.268, 95% CI：1.096~1.470； OR=1.269，95% CI：1.088~1.479）以及西部地区（OR=1.217, 95% CI：1.062~1.395；OR=1.198，95% CI：1.038~1.382）与慢性病共病高风险相关；而饮酒（两种及以上慢性病OR=0.811, 95% CI：0.707~0.930； 三种及以上慢性病OR=0.837，95% CI：0.724~0.968）、夜晚睡眠时长6~8h（OR=0.806, 95% CI：0.702~0.926；OR=0.792，95% CI：0.688~0.912）、夜晚睡眠时长>8h（OR=0.738, 95% CI：0.635~0.858；OR=0.745，95% CI：0.637~0.872）、自评健康一般（OR=0.357, 95% CI：0.307~0.414；OR=0.343，95% CI：0.299~0.392）以及自评健康好（OR=0.136，95% CI：0.114~0.163；OR=0.117，95% CI：0.096~0.142）的中老年人慢性病共病风险降低。

结论

建议从多维度对慢性病共病进行预防和干预，并且在制定共病治疗方案时医疗保健人员应该有针对性地考虑患者的共病情况和多重用药问题。

慢性病共病  /  中老年人  /  关联规则  /  健康社会决定因素  /  影响因素分析
Objective

To explore the comorbidity patterns and influencing factors of chronic diseases in middle-aged and elderly people in China, so as to provide new ideas for the development of corresponding chronic disease prevention and control strategies and measures.

Methods

Using data from the China Health and Retirement Longitudinal Study (CHARLS) 2018, after excluding samples with missing variables for 14 chronic diseases, a final sample of 19,390 individuals aged 45 and older was included. The Apriori algorithm from association rule analysis was utilized to explore the comorbidity patterns among middle-aged and elderly individuals in China. The occurrence of chronic comorbidity (two or more chronic diseases or three or more chronic diseases) was taken as the dependent variable, and the independent variable was determined according to the five dimensions of social determinants of health. 5 696 samples were included after deletion of the missing values, and the influential factors of chronic comorbidity were analyzed by univariate χ2 test and multiple logistic regression.

Results

The prevalence of comorbid chronic diseases among middle-aged and elderly individuals in our country was 55.4%. The most common comorbidity patterns were arthritis or rheumatic diseases and hypertension. Association rule analysis identified 24 strong rule combinations, with the most common binary association being gastrointestinal or digestive system diseases with arthritis or rheumatic diseases, and the most common ternary association being gastrointestinal or digestive system diseases, hypertension, and arthritis or rheumatic diseases.The results of the multivariate analysis showed that both two or more chronic diseases and three or more chronic diseases were significantly associated with increased risk factors, including: age 60-80 years (two or more chronic diseases: OR=1.479, 95% CI: 1.244-1.759; three or more chronic diseases: OR=1.526, 95% CI: 1.267-1.839), age >80 years (OR=1.545, 95% CI: 1.144-2.087; OR=1.591, 95% CI: 1.175-2.154), depressive symptoms (OR=1.435, 95% CI: 1.267-1.626; OR=1.382, 95% CI: 1.216-1.570), having a pension (OR=1.350, 95% CI: 1.141-1.598; OR=1.332, 95% CI: 1.118-1.586), and residence in central (OR=1.268, 95% CI: 1.096-1.470; OR=1.269, 95% CI: 1.088-1.479) or western regions (OR=1.217, 95% CI: 1.062-1.395; OR=1.198, 95% CI: 1.038-1.382).Conversely, factors associated with a reduced risk of chronic multimorbidity included alcohol consumption (two or more chronic diseases: OR=0.811, 95% CI: 0.707-0.930; three or more chronic diseases: OR=0.837, 95% CI: 0.724-0.968), 6-8 hours of night sleep (OR=0.806, 95% CI: 0.702-0.926; OR=0.792, 95% CI: 0.688-0.912), more than 8 hours of night sleep (OR=0.738, 95% CI: 0.635-0.858; OR=0.745, 95% CI: 0.637-0.872), self-rated general health (OR=0.357, 95% CI: 0.307-0.414; OR=0.343, 95% CI: 0.299-0.392), and self-rated good health (OR=0.136, 95% CI: 0.114-0.163; OR=0.117, 95% CI: 0.096-0.142).

Conclusion

It is suggested that the prevention and intervention of chronic comorbidity should be multidimensional, and the health care personnel should take into account the comorbidity of patients and the problem of multiple drug use in the formulation of comorbidity treatment plan.

Chronic disease comorbidity  /  Middle-aged and elderly people  /  Association rules  /  Social determinants of health  /  Analysis of influencing factors
苏文钰, 葛怀举, 常雯静, 董士红, 贾蕙毓, 姜姗, 秘玉清, 于洁, 马桂峰. 我国中老年人慢性病共病关联规则分析及相关因素识别研究. 现代预防医学, 2024 , 51 (24) : 4417 -4422 . DOI: 10.20043/j.cnki.MPM.202407540
Wen-yu SU, Huai-ju GE, Wen-jing CHANG, Shi-hong DONG, Hui-yu JIA, Shan JIANG, Yu-qing MI, Jie YU, Gui-feng MA. Association rule analysis and identification of related factors for comorbidity of chronic diseases in middle-aged and elderly Chinese population[J]. Modern Preventive Medicine, 2024 , 51 (24) : 4417 -4422 . DOI: 10.20043/j.cnki.MPM.202407540
正文
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世界卫生组织（WHO）将同时患有两种或以上慢性病界定为慢性病共病[1]，随着我国人口老龄化程度的持续加深，中老年人慢性病共病现象日益突出。慢性病共病会导致身体功能逐渐衰退，生活质量下降，增加致残率和死亡风险，同时也加重了医疗保健负担[2]。并且与单一慢性病相比，多种慢性疾病同时存在不仅增加并发症的发生几率，还会加重病情的严重程度，因此慢性病共病患者具有更高的住院率、再入院率和死亡率[3]。另外慢性病之间存在的高度关联性和分布规律，增加了预防和管理慢性病共病的难度。健康社会决定因素（Social Determinants of Health，SDH）包括影响健康的经济、社会、环境和社会心理因素[4],已经被广泛应用于医学卫生领域，但目前国内将SDH应用到中老年人慢性病共病的研究较少，本研究采用SDH，从多维度分析我国中老年人的慢性病共病现状，探索其相关因素。通过深入研究慢性病共病的模式与相关因素，既能使个体有针对性的预防共病，也能有助于政府和医疗机构制定更有效的公共卫生政策和健康管理策略。
1 资料与方法
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1.1 资料来源
本研究使用的数据来自于2018年《中国健康与养老追踪调查》（Charls），该数据库从多学科视角全面反映我国人口老龄化背景下中老年人在健康、就业及家庭关系等方面的状况[5]。Charls问卷中提供14类慢性病数据，包括高血压、血脂异常（高血脂或低血脂）、糖尿病或血糖升高、癌症（不包含轻度皮肤癌）、慢性肺病（慢性支气管炎、肺气肿、肺心病等）、肝脏疾病（除外脂肪肝、肿瘤或癌）、心脏病、中风（包括脑梗和脑出血）、肾脏疾病（不包括肿瘤或癌）、胃部或消化系统疾病、情感及精神方面问题、记忆相关疾病（如老年痴呆症、脑萎缩、帕金森症）、关节炎或风湿病、哮喘。剔除年龄小于45岁、未完成慢性病相关的14个问题的受访者，纳入19 390名≥45岁中老年人探索慢性病共病模式的关联规则。
1.2 变量选取及定义
本研究基于健康社会决定因素从5个维度选择可能影响我国中老年人慢性病共病的变量，第一维度是不同个体（年龄、性别、疾病状况），选取的变量有年龄、性别、抑郁症状；第二维度是个体行为方式，选取的变量有吸烟、饮酒、社交活动、夜晚睡眠时长、自评健康；第三维度是社会支持网络，选取的变量有婚姻状态；第四维度是社会经济地位，选取的变量有居住地、地区、教育程度；第五维度是其他社会结构性因素，选取的变量有医疗保险、退休金或养老金。剔除相关变量缺失值后纳入5 696例样本，进行慢性病共病的相关因素识别研究。研究对象的纳入、排除见图1。
1.3 方法
本研究采用Apriori算法进行关联规则分析探索中老年人慢性病的共病模式，利用Python语言设置支持度为5%，即当项集的支持度达到或超过5%时，才被认定为“频繁项集”，并规定若规则A发生，则规则B发生的概率需达到50%。采用SPSS 27.0统计软件实现统计描述、组间比较的χ2检验，并分别以是否患两种及以上慢性病、三种及以上慢性病为因变量，进行多因素logistic回归分析。P<0.05差异有统计学意义。
2 结果
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2.1 慢性病共病情况
本次研究对象一共19 390例，其中共病患者10 746例，共病患病率为55.4%。另外关节炎（32.4%）、高血压（31.9%）、胃病（25.7%）的共病率居于前三位，详情见表1。
2.2 Apriori算法关联规则分析
本研究共得到24条关联规则，见表2。其中二元关联规则10条，支持度最高的是胃部或消化系统疾病与关节炎或风湿病（16.81%）；三元关联规则筛选出14条，支持度最高的是胃部或消化系统疾病、高血压和关节炎或风湿病（7.23%）。
2.3 慢性病共病单因素分析
单因素χ2检验结果显示，年龄、性别、抑郁症状、饮酒、夜晚睡眠时长、自评健康、婚姻状态、地区、教育程度、医疗保险、退休金或养老金对我国中老年人共病模式的影响有统计学意义（P<0.05）。见表3。
2.4 慢性病共病多因素分析
多因素logistic回归分析显示饮酒、夜晚睡眠时长大于6小时、自评健康一般和好与中老年人共病呈负相关，年龄大、有抑郁症状、中部和西部地区、有退休金或养老金与中老年人共病呈正相关，具体见表4。
3 讨论
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3.1 慢性病共病模式
本研究发现在中老年人群中，关节炎或风湿病与胃部或消化系统疾病的二元共病模式最为常见，与已有研究一致[6]。有研究从基因的角度证实类风湿性关节炎患者胃轻瘫的可能性增加36% [7]，另外，服用胃保护药物的高危非甾体抗炎药的关节炎患者更容易出现胃肠道并发症[8]。由此可见除了基因的影响，一些药物的使用也会增加关节炎与胃癌的关联性。三种慢性病共病发生率最高的为胃部或消化系统疾病、高血压和关节炎或风湿病，尽管高血压和关节炎或风湿病两者发病的先后顺序尚不清楚，但国外有研究基于1999-2018 年的数据证实，关节炎（包括类风湿性关节炎和骨关节炎）与高血压密切相关[9]，国内研究从流行病学的视角表明骨关节炎和高血压是时常共存[10]。应重点关注探索关节炎或风湿病慢性病模式与高血压慢性病模式中慢性病的相互作用。
3.2 年龄、疾病状况对中老年人慢性病共病的影响
本研究发现，年龄越大发生慢性病共病的概率越高，这与倪梓涵等[11]基于Charls数据分析的中老年人年龄越高共病风险越高的结果相似。随着年龄增长，器官和组织功能逐渐衰退，免疫系统和修复机制减弱，且慢性病潜伏期较长，使老年人群慢性病共病风险高于其他人群。本研究结果还显示，抑郁症状是出现慢性病共病的重要因素。当个体出现情绪低落、焦虑等心理状况时对于健康生活的积极性会有所降低，这不仅会导致个体改变原本健康的行为习惯，还容易养成熬夜、酗酒等不良行为习惯。因此保持良好的心理状态是预防中老年人罹患慢性病共病的有效手段。
3.3 个体生活方式对中老年人慢性病共病的影响
值得注意的是，本研究发现，饮酒的中老年人慢性病共病率低于不饮酒者，吸烟者的共病率也低于不吸烟者。这可能与个体健康状况相关，身体状况良好的个体更可能继续饮酒或吸烟，而健康恶化时则倾向于戒酒或戒烟。本研究的多因素分析结果表明，饮酒与中老年共病呈负相关。虽然有研究证实适量饮酒可能与心血管疾病的风险较低有关[12]，也不可否认酒中存在健康功能因子。但相当多的研究表明饮酒会增加患慢性病的概率[13-14]。研究结果的差异可能与个体饮酒的类型以及研究中对饮酒定义的不同有关，因此，关于饮酒对慢性病以及慢性病共病的关系还待进一步探索。以夜晚睡眠时长小于6小时为对照，夜晚睡眠6~8小时和大于8小时的中老年人慢性病共病率较低。这与既往研究结果一致[15]，提示中老年人应保证充足的睡眠时长。李林瑾等人[16]的研究表明自评健康一般和好是慢性病共病的保护因素，而在本研究中，自评健康为一般和好的中老年群体表现出较低的慢性病共病比例。自评健康良好表明中老年人注重健康管理并对健康状况满意，形成积极的健康管理心态，随着时间的推移形成良性循环，促使中老年人坚持健康生活方式，进而降低共病风险。
3.4 社会经济地位对中老年人慢性病共病的影响
以东部地区人群为对照，中部和西部地区的中老年人群中慢性病共病具有更高的发生比例。地区之间共病患病率的差异可能是由于经济发展水平、医疗资源的不同导致的[17]，因此，促进中西部地区经济发展及公共卫生服务发展，对于降低我国慢性病以及共病的发病率具有重要意义。
3.5 其他社会结构性因素对中老年人慢性病共病的影响
本研究发现领取退休金或养老金的群体在慢性病共病的发生率上显著高于未领取者。原因可能是，经济上的稳定可以降低个体对医疗费用的担忧，从而促使个体积极地寻求及接受医疗保健服务，因此拥有退休金或养老金的人群慢性病共病确诊率更高。
本研究存在的局限性。虽然本研究发现影响中老年慢性病共病的相关因素呈现多样化，但并未探讨不同维度间影响因素的相互作用。此外，本研究为横断面研究，无法捕捉时间变化对中老年慢性病共病的影响，不能确定变量之间的因果关系。
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24期
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doi: 10.20043/j.cnki.MPM.202407540
  • 接收时间：2024-07-30
  • 首发时间：2026-03-18
  • 出版时间：2024-12-25
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  • 收稿日期：2024-07-30
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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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马桂峰，E-mail: ；
于洁，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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