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Article(id=1240722568794132526, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1240722566957027366, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202503186, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1741881600000, receivedDateStr=2025-03-14, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773740911911, onlineDateStr=2026-03-17, pubDate=1760025600000, pubDateStr=2025-10-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773740911911, onlineIssueDateStr=2026-03-17, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773740911911, creator=13701087609, updateTime=1773740911911, updator=13701087609, issue=Issue{id=1240722566957027366, tenantId=1146029695717560320, journalId=1227665162245664772, year='2025', volume='52', issue='19', pageStart='3457', pageEnd='3648', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=0, createTime=1773740911472, creator=13701087609, updateTime=1773740981732, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1240722861736906836, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1240722566957027366, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1240722861736906837, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1240722566957027366, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=3478, endPage=3483, ext={EN=ArticleExt(id=1240722569200980021, articleId=1240722568794132526, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Multimorbidity patterns and influencing factors of chronic diseases in the elderly based on hierarchical clustering and the Apriori algorithm, columnId=1240413921954295836, journalTitle=Modern Preventive Medicine, columnName=Epidemiology and Statistical Methods, runingTitle=null, highlight=null, articleAbstract=

Objective To explore the patterns and influencing factors of multimorbidity in the elderly. Methods A total of 7 354 adults aged ≥60 years in Huai’an District, Hongze District and Lianshui County of Huai’an City were investigated for chronic diseases and their risk factors. Hierarchical clustering was used to determine the multimorbidity patterns. Apriori algorithm was used to explore the association rules between chronic diseases. Binary logistic regression analysis was used to explore the influencing factors of major multimorbidity patterns. Results The prevalence of chronic diseases was 81.93%, and the prevalence of multimorbidity was 42.62%.Hierarchical clustering grouped eight chronic diseases into three categories. The two main multimorbidity patterns were: ① hypertension, diabetes, dyslipidemia, stroke, and myocardial infarction; ② asthma and COPD. The Apriori algorithm identified hypertension, diabetes,dyslipidemia, and stroke as the multimorbidity pattern of cardiovascular and cerebrovascular diseases, and COPD and asthma as the multimorbidity pattern of chronic respiratory diseases. In the multimorbidity pattern of cardiovascular and cerebrovascular diseases,compared with those without eight chronic diseases, increasing age, former smoking and current non-smoking, increasing BMI and central obesity increased the risk of cardiovascular and cerebrovascular disease comorbidity by 0.022 (OR=1.022, 95% CI: 1.012-1.033),0.466 (OR=1.466, 95% CI: 1.081-1.987), 0.144 (OR=1.144, 95% CI: 1.117-1.172), 0.505 (OR=1.505, 95% CI: 1.283-1.766). In the multimorbidity pattern of chronic respiratory diseases, compared with those without eight chronic diseases, former smokers and current non-smokers (OR=3.851, 95% CI: 2.132-6.956), central obesity (OR=1.696, 95% CI: 1.064-2.701) were the risk factors, while being female (OR=0.366, 95% CI: 0.229-0.587) and decreased BMI (OR=0.926, 95% CI: 0.869-0.994) were the protective factors. Conclusion Hypertension, stroke, dyslipidemia and diabetes are easy to coexist, and asthma and COPD are easy to coexist. Smoking was an important risk factor for multimorbidity. Central obesity was a risk factor for both multimorbidity patterns, especially for the cardiovascular and cerebrovascular multimorbidity pattern.

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目的 探索老年人慢性病共病模式及影响因素。方法 对淮安市淮安区、洪泽区、涟水县的7 354例60岁以上老年人开展慢性病及危险因素调查，采用层次聚类确定共病模式，Apriori算法探究慢性病之间的关联规则，利用二元logistic回归分析探索主要共病模式的影响因素。结果 老年人慢性病的患病率为81.93%，共病患病率为42.62%。层次聚类将八种慢性病聚成三类，二类主要的共病模式是：（1）高血压、糖尿病、血脂异常、脑卒中、心肌梗死；（2）哮喘、慢阻肺。Apriori算法确定高血压、糖尿病、血脂异常、脑卒中为心脑血管疾病共病模式，慢阻肺和哮喘为慢性呼吸系统疾病共病模式。心脑血管疾病共病模式中，与未患八种慢性病相比，年龄增加、以前吸烟现在不吸烟、身体质量指数（body mass index, BMI）增加、中心性肥胖的心脑血管疾病共病发病风险分别增加0.022（OR=1.022，95%CI：1.012～1.033）、0.466（OR=1.466，95%CI：1.081～1.987）、0.144（OR=1.144，95%CI：1.117～1.172）、0.505（OR=1.505，95%CI：1.283～1.766）。慢性呼吸系统疾病共病模式中，以未患八种慢性病人群为对照，以前吸烟现在不吸烟（OR=3.851，95%CI：2.132～6.956）、中心性肥胖（OR=1.696，95%CI：1.064～2.701）是该模式的危险因素，女性（OR=0.366，95%CI：0.229～0.587）、BMI下降（OR=0.926，95%CI：0.869～0.994）是该模式的保护因素。结论 高血压、脑卒中、血脂异常、糖尿病易共存，哮喘和慢阻肺极易共存。吸烟是慢性病共病的重要危险因素，中心性肥胖是两种共病模式的危险因素，对心脑血管疾病共病模式的发生风险更为显著。

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孙中明，E-mail:；
潘恩春，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=Jf3Hu7iEdl0Up/r2HlZHfQ==, magXml=KuRD1bT1dnO5H+COfWCiOw==, pdfUrl=null, pdf=5ytfxkSbuawHBUmFpHk5eg==, pdfFileSize=871899, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=L7LimG9hl79RSzfNMm8VPw==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=4LGve6iQKjlGH7+Sy1tMjg==, mapNumber=null, authorCompany=null, fund=null, authors=

孙中明与潘恩春为共同通信作者

梅冬蒙（1996—）女，硕士，主管医师，研究方向：慢性病预防与控制

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梅冬蒙（1996—）女，硕士，主管医师，研究方向：慢性病预防与控制

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European Journal of Preventive Cardiology, 2022, 29(12): 1710-1713., articleTitle=Diabetes and hypertension: physical activity and body mass index matter:insights from half a million People, refAbstract=null), Reference(id=1240933507086742185, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240722568794132526, doi=null, pmid=null, pmcid=null, year=2021, volume=19, issue=1, pageStart=341, pageEnd=null, url=null, language=null, rfNumber=[20], rfOrder=25, authorNames=Cao L, Zhou J, Chen Y, journalName=International Journal of Environmental Research and Public Health, refType=null, unstructuredReference=Cao L, Zhou J, Chen Y, et al. Effects of body mass index, waist circumference, Waist-to-Height ratio and their changes on risks of dyslipidemia among Chinese adults: the Guizhou population health cohort study[J]. 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Associations between socioeconomic inequalities and progression to psychological and cognitive multimorbidities after onset of a physical condition: a multicohort study[J]. eClinicalMedicine, 2024, 74(1): 102739., articleTitle=Associations between socioeconomic inequalities and progression to psychological and cognitive multimorbidities after onset of a physical condition: a multicohort study, refAbstract=null)], funds=[Fund(id=1240933504071037382, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240722568794132526, awardId=JKCLPJ202501006, language=CN, fundingSource=中华预防医学会科研课题(JKCLPJ202501006), fundOrder=null, country=null), Fund(id=1240933504201060816, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240722568794132526, awardId=Y12023032, language=CN, fundingSource=江苏省预防医学会科研课题(Y12023032), fundOrder=null, country=null), Fund(id=1240933504335278551, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240722568794132526, awardId=HAWJ2024022, language=CN, fundingSource=淮安市卫生健康委员会科研课题(HAWJ2024022), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1240933498375172321, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240722568794132526, xref=null, ext=[AuthorCompanyExt(id=1240933498383560929, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240722568794132526, companyId=1240933498375172321, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Huai’an Center for Disease Control and Prevention, Huai’an, Jiangsu 223001, China), AuthorCompanyExt(id=1240933498391949538, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240722568794132526, companyId=1240933498375172321, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=淮安市疾病预防控制中心，江苏 淮安 223001)])], figs=[ArticleFig(id=1240933502368149881, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240722568794132526, language=EN, label=Figure 1, caption=Tree cluster diagram of eight chronic diseases, figureFileSmall=XXZIM31CtH9S/Vl+NdszoA==, figureFileBig=zoeVne6LjBcgpx6wReohwA==, tableContent=null), ArticleFig(id=1240933502460424573, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240722568794132526, language=CN, label=图1, caption=八种慢性病的树状聚类图, figureFileSmall=XXZIM31CtH9S/Vl+NdszoA==, figureFileBig=zoeVne6LjBcgpx6wReohwA==, tableContent=null), ArticleFig(id=1240933502619808135, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240722568794132526, language=EN, label=Figure 2, caption=Association rules of Apriori algorithm under different support confidence levels, figureFileSmall=YFB2csiyKHhGVyjJtOthZg==, figureFileBig=AO4zzNbVEUc5ehSQ33yZSQ==, tableContent=null), ArticleFig(id=1240933502720471436, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240722568794132526, language=CN, label=图2, caption=Apriori算法不同支持度置信度下的关联规则

注：A支持度=1%，置信度=5%；B支持度=1%，置信度=10%；C支持度=1%，置信度=20%；D支持度=1%，置信度=50%；E支持度=2%，置信度=5%；F支持度=5%，置信度=5%。

, figureFileSmall=YFB2csiyKHhGVyjJtOthZg==, figureFileBig=AO4zzNbVEUc5ehSQ33yZSQ==, tableContent=null), ArticleFig(id=1240933502846300563, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240722568794132526, language=EN, label=Figure 3, caption=Network diagram of eight chronic disease association rules, figureFileSmall=92P7kiWhClPOR98BD0jdXw==, figureFileBig=rtbkdWGBMqD4qZEmVGzSVA==, tableContent=null), ArticleFig(id=1240933502951158168, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240722568794132526, language=CN, label=图3, caption=八种慢性病关联规则网状图

注：A支持度=1%，置信度=5%；B支持度=1%，置信度=5%，提升度>1；C支持度=1.5%，置信度=5%，提升度>1；D支持度=5%，置信度=20%，提升度>1。

, figureFileSmall=92P7kiWhClPOR98BD0jdXw==, figureFileBig=rtbkdWGBMqD4qZEmVGzSVA==, tableContent=null), ArticleFig(id=1240933503076987292, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240722568794132526, language=EN, label=Table 1, caption=

Prevalence and comorbidity of eight chronic diseases in the elderly ［n（%）］

, figureFileSmall=null, figureFileBig=null, tableContent=
疾病名称患病人数不同患病种数的人数共病患者数
1种2种≥3种
高血压4 769(64.85)2 010(42.15)1 723(36.13)1 036(21.72)2 759(57.85)
糖尿病1 243(16.90)133(10.70)469(37.73)641(51.57)1 110(89.30)
血脂异常2 325(31.62)475(20.43)1 044(44.90)806(34.67)1 850(79.57)
恶性肿瘤322(4.38)82(25.47)122(37.89)118(36.65)240(74.53)
脑卒中983(13.37)83(8.44)385(39.17)515(52.39)900(91.56)
心肌梗死126(1.71)11(8.73)30(23.81)85(67.46)115(91.27)
慢阻肺482(6.55)59(12.24)155(32.16)268(55.60)423(87.76)
哮喘380(5.17)38(10.00)100(26.32)242(63.68)342(90.00)
), ArticleFig(id=1240933503156679073, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240722568794132526, language=CN, label=表1, caption=

老年人八种慢性病患病及共病情况［n（%）］

, figureFileSmall=null, figureFileBig=null, tableContent=
疾病名称患病人数不同患病种数的人数共病患者数
1种2种≥3种
高血压4 769(64.85)2 010(42.15)1 723(36.13)1 036(21.72)2 759(57.85)
糖尿病1 243(16.90)133(10.70)469(37.73)641(51.57)1 110(89.30)
血脂异常2 325(31.62)475(20.43)1 044(44.90)806(34.67)1 850(79.57)
恶性肿瘤322(4.38)82(25.47)122(37.89)118(36.65)240(74.53)
脑卒中983(13.37)83(8.44)385(39.17)515(52.39)900(91.56)
心肌梗死126(1.71)11(8.73)30(23.81)85(67.46)115(91.27)
慢阻肺482(6.55)59(12.24)155(32.16)268(55.60)423(87.76)
哮喘380(5.17)38(10.00)100(26.32)242(63.68)342(90.00)
), ArticleFig(id=1240933503240565156, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240722568794132526, language=EN, label=Table 2, caption=

Multivariate comorbidity association rules

, figureFileSmall=null, figureFileBig=null, tableContent=
前项属性项目集后项属性项目集支持度（%）置信度（%）提升度共病项数
二元
哮喘慢阻肺3.4254.216.78206
糖尿病血脂异常9.6446.741.21581
慢阻肺哮喘3.4242.746.78206
脑卒中糖尿病4.2526.041.26256
血脂异常糖尿病9.6424.991.21581
糖尿病脑卒中4.2520.601.26256
三元
脑卒中、糖尿病高血压3.5984.381.07216
脑卒中、血脂异常高血压5.1082.311.04307
哮喘、高血压慢阻肺2.2153.636.70133
脑卒中、糖尿病血脂异常2.2252.341.36134
高血压、糖尿病血脂异常7.3447.481.23442
慢阻肺、高血压哮喘2.2144.487.05133
脑卒中、高血压血脂异常5.1039.311.02307
脑卒中、血脂异常糖尿病2.2235.921.74134
脑卒中、高血压糖尿病3.5927.661.34216
高血压、血脂异常糖尿病7.3427.521.33442
高血压、糖尿病脑卒中3.5923.201.42216
糖尿病、血脂异常脑卒中2.2223.061.41134
慢阻肺、高血压脑卒中1.0821.741.3365
四元
脑卒中、糖尿病、血脂异常高血压1.9386.571.09116
脑卒中、高血压、糖尿病血脂异常1.9353.701.39116
高血压、糖尿病、血脂异常脑卒中1.9326.241.61116
脑卒中、高血压、血脂异常糖尿病1.9337.791.83116
), ArticleFig(id=1240933503349617062, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240722568794132526, language=CN, label=表2, caption=

多元共病关联规则

, figureFileSmall=null, figureFileBig=null, tableContent=
前项属性项目集后项属性项目集支持度（%）置信度（%）提升度共病项数
二元
哮喘慢阻肺3.4254.216.78206
糖尿病血脂异常9.6446.741.21581
慢阻肺哮喘3.4242.746.78206
脑卒中糖尿病4.2526.041.26256
血脂异常糖尿病9.6424.991.21581
糖尿病脑卒中4.2520.601.26256
三元
脑卒中、糖尿病高血压3.5984.381.07216
脑卒中、血脂异常高血压5.1082.311.04307
哮喘、高血压慢阻肺2.2153.636.70133
脑卒中、糖尿病血脂异常2.2252.341.36134
高血压、糖尿病血脂异常7.3447.481.23442
慢阻肺、高血压哮喘2.2144.487.05133
脑卒中、高血压血脂异常5.1039.311.02307
脑卒中、血脂异常糖尿病2.2235.921.74134
脑卒中、高血压糖尿病3.5927.661.34216
高血压、血脂异常糖尿病7.3427.521.33442
高血压、糖尿病脑卒中3.5923.201.42216
糖尿病、血脂异常脑卒中2.2223.061.41134
慢阻肺、高血压脑卒中1.0821.741.3365
四元
脑卒中、糖尿病、血脂异常高血压1.9386.571.09116
脑卒中、高血压、糖尿病血脂异常1.9353.701.39116
高血压、糖尿病、血脂异常脑卒中1.9326.241.61116
脑卒中、高血压、血脂异常糖尿病1.9337.791.83116
), ArticleFig(id=1240933503479640490, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240722568794132526, language=EN, label=Table 3, caption=

Logistic regression analysis of influencing factors of multimorbidity patterns of cardiovascular and cerebrovascular diseases

, figureFileSmall=null, figureFileBig=null, tableContent=
自变量心脑血管疾病共病模式（n=5 088）
以未患八种慢性病人群为对照以心脑血管疾病共病模式单病种人群为对照
OR（95%CI）P值OR（95%CI）P值
性别（男vs.女）1.114(0.959～1.294)0.1571.092(0.955～1.248)0.197
年龄（岁）1.022(1.012～1.033)0.0001.001(0.992～1.011)0.770
吸烟
现在每天吸烟vs.现在吸烟，不是每天0.740(0.492～1.112)0.1470.889(0.579～1.367)0.593
现在每天吸烟vs.以前吸烟现在不吸烟1.466(1.081～1.987)0.0141.321(1.025～1.702)0.031
现在每天吸烟vs.从不吸烟1.122(0.958～1.314)0.1541.133(0.976～1.314)0.100
饮酒（否vs.是）1.139(0.976～1.330)0.0980.839(0.728～0.966)0.015
体力活动（MET/h/d）0.997(0.994～1.000)0.0860.998(0.995～1.002)0.390
BMI(kg/m2)1.144(1.117～1.172)0.0001.069(1.047～1.091)0.000
中心性肥胖（否vs.是）1.505(1.283～1.766)0.0001.360(1.185～1.560)0.000
), ArticleFig(id=1240933503634829744, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240722568794132526, language=CN, label=表3, caption=

心脑血管疾病共病模式的影响因素logistic回归分析

, figureFileSmall=null, figureFileBig=null, tableContent=
自变量心脑血管疾病共病模式（n=5 088）
以未患八种慢性病人群为对照以心脑血管疾病共病模式单病种人群为对照
OR（95%CI）P值OR（95%CI）P值
性别（男vs.女）1.114(0.959～1.294)0.1571.092(0.955～1.248)0.197
年龄（岁）1.022(1.012～1.033)0.0001.001(0.992～1.011)0.770
吸烟
现在每天吸烟vs.现在吸烟，不是每天0.740(0.492～1.112)0.1470.889(0.579～1.367)0.593
现在每天吸烟vs.以前吸烟现在不吸烟1.466(1.081～1.987)0.0141.321(1.025～1.702)0.031
现在每天吸烟vs.从不吸烟1.122(0.958～1.314)0.1541.133(0.976～1.314)0.100
饮酒（否vs.是）1.139(0.976～1.330)0.0980.839(0.728～0.966)0.015
体力活动（MET/h/d）0.997(0.994～1.000)0.0860.998(0.995～1.002)0.390
BMI(kg/m2)1.144(1.117～1.172)0.0001.069(1.047～1.091)0.000
中心性肥胖（否vs.是）1.505(1.283～1.766)0.0001.360(1.185～1.560)0.000
), ArticleFig(id=1240933503722910134, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240722568794132526, language=EN, label=Table 4, caption=

Logistic regression analysis of influencing factors of multimorbidity patterns of chronic respiratory diseases

, figureFileSmall=null, figureFileBig=null, tableContent=
自变量慢性呼吸系统疾病共病模式（n=138）
以未患八种慢性病人群为对照以性呼吸系统疾病共病模式单病种人群为对照
OR（95%CI）P值OR（95%CI）P值
性别（男vs.女）0.366(0.229～0.587)0.0000.591(0.198～1.765)0.346
年龄（岁）1.021(0.992～1.052)0.1641.048(0.967～1.136)0.255
吸烟
现在每天吸烟vs.现在吸烟，不是每天1.252(0.461～3.403)0.6591.069(0.084～13.579)0.959
现在每天吸烟vs.以前吸烟现在不吸烟3.851(2.132～6.956)0.0002.974(0.907～9.751)0.072
现在每天吸烟vs.从不吸烟1.039(0.664～1.624)0.8681.668(0.598～4.658)0.329
饮酒（否vs.是）0.658(0.430～1.007)0.0541.903(0.797～4.545)0.148
体力活动（MET/h/d）0.992(0.982～1.003)0.1650.971(0.934～1.009)0.136
BMI(kg/m2)0.929(0.869～0.994)0.0320.942(0.836～1.062)0.330
中心性肥胖（否vs.是）1.696(1.064～2.701)0.0262.216(0.826～5.946)0.114
), ArticleFig(id=1240933503840350648, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240722568794132526, language=CN, label=表4, caption=

慢性呼吸系统疾病共病模式的影响因素logistic回归分析

, figureFileSmall=null, figureFileBig=null, tableContent=
自变量慢性呼吸系统疾病共病模式（n=138）
以未患八种慢性病人群为对照以性呼吸系统疾病共病模式单病种人群为对照
OR（95%CI）P值OR（95%CI）P值
性别（男vs.女）0.366(0.229～0.587)0.0000.591(0.198～1.765)0.346
年龄（岁）1.021(0.992～1.052)0.1641.048(0.967～1.136)0.255
吸烟
现在每天吸烟vs.现在吸烟，不是每天1.252(0.461～3.403)0.6591.069(0.084～13.579)0.959
现在每天吸烟vs.以前吸烟现在不吸烟3.851(2.132～6.956)0.0002.974(0.907～9.751)0.072
现在每天吸烟vs.从不吸烟1.039(0.664～1.624)0.8681.668(0.598～4.658)0.329
饮酒（否vs.是）0.658(0.430～1.007)0.0541.903(0.797～4.545)0.148
体力活动（MET/h/d）0.992(0.982～1.003)0.1650.971(0.934～1.009)0.136
BMI(kg/m2)0.929(0.869～0.994)0.0320.942(0.836～1.062)0.330
中心性肥胖（否vs.是）1.696(1.064～2.701)0.0262.216(0.826～5.946)0.114
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基于层次聚类和Apriori算法探索老年人慢性病共病模式及影响因素
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梅冬蒙 , 缪丹丹 , 沈欢 , 文进博 , 赵倩 , 刘景 , 许雨婷 , 孙中明 , 潘恩春
现代预防医学 | 流行病与统计方法 2025,52(19): 3478-3483
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现代预防医学 | 流行病与统计方法 2025, 52(19): 3478-3483
基于层次聚类和Apriori算法探索老年人慢性病共病模式及影响因素
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梅冬蒙, 缪丹丹, 沈欢, 文进博, 赵倩, 刘景, 许雨婷, 孙中明 , 潘恩春
作者信息
  • 淮安市疾病预防控制中心，江苏 淮安 223001

    • 梅冬蒙（1996—）女，硕士，主管医师，研究方向：慢性病预防与控制

通讯作者:

孙中明，E-mail:；
潘恩春，E-mail:
Multimorbidity patterns and influencing factors of chronic diseases in the elderly based on hierarchical clustering and the Apriori algorithm
Dong-meng MEI, Dan-dan MIAO, Huan SHEN, Jin-bo WEN, Qian ZHAO, Jing LIU, Yu-ting XU, Zhong-ming SUN , En-chun PAN
Affiliations
  • Huai’an Center for Disease Control and Prevention, Huai’an, Jiangsu 223001, China
出版时间: 2025-10-10 doi: 10.20043/j.cnki.MPM.202503186
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摘要
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目的 探索老年人慢性病共病模式及影响因素。方法 对淮安市淮安区、洪泽区、涟水县的7 354例60岁以上老年人开展慢性病及危险因素调查，采用层次聚类确定共病模式，Apriori算法探究慢性病之间的关联规则，利用二元logistic回归分析探索主要共病模式的影响因素。结果 老年人慢性病的患病率为81.93%，共病患病率为42.62%。层次聚类将八种慢性病聚成三类，二类主要的共病模式是：（1）高血压、糖尿病、血脂异常、脑卒中、心肌梗死；（2）哮喘、慢阻肺。Apriori算法确定高血压、糖尿病、血脂异常、脑卒中为心脑血管疾病共病模式，慢阻肺和哮喘为慢性呼吸系统疾病共病模式。心脑血管疾病共病模式中，与未患八种慢性病相比，年龄增加、以前吸烟现在不吸烟、身体质量指数（body mass index, BMI）增加、中心性肥胖的心脑血管疾病共病发病风险分别增加0.022（OR=1.022，95%CI：1.012～1.033）、0.466（OR=1.466，95%CI：1.081～1.987）、0.144（OR=1.144，95%CI：1.117～1.172）、0.505（OR=1.505，95%CI：1.283～1.766）。慢性呼吸系统疾病共病模式中，以未患八种慢性病人群为对照，以前吸烟现在不吸烟（OR=3.851，95%CI：2.132～6.956）、中心性肥胖（OR=1.696，95%CI：1.064～2.701）是该模式的危险因素，女性（OR=0.366，95%CI：0.229～0.587）、BMI下降（OR=0.926，95%CI：0.869～0.994）是该模式的保护因素。结论 高血压、脑卒中、血脂异常、糖尿病易共存，哮喘和慢阻肺极易共存。吸烟是慢性病共病的重要危险因素，中心性肥胖是两种共病模式的危险因素，对心脑血管疾病共病模式的发生风险更为显著。

共病模式  /  层次聚类  /  Apriori算法  /  关联规则

Objective To explore the patterns and influencing factors of multimorbidity in the elderly. Methods A total of 7 354 adults aged ≥60 years in Huai’an District, Hongze District and Lianshui County of Huai’an City were investigated for chronic diseases and their risk factors. Hierarchical clustering was used to determine the multimorbidity patterns. Apriori algorithm was used to explore the association rules between chronic diseases. Binary logistic regression analysis was used to explore the influencing factors of major multimorbidity patterns. Results The prevalence of chronic diseases was 81.93%, and the prevalence of multimorbidity was 42.62%.Hierarchical clustering grouped eight chronic diseases into three categories. The two main multimorbidity patterns were: ① hypertension, diabetes, dyslipidemia, stroke, and myocardial infarction; ② asthma and COPD. The Apriori algorithm identified hypertension, diabetes,dyslipidemia, and stroke as the multimorbidity pattern of cardiovascular and cerebrovascular diseases, and COPD and asthma as the multimorbidity pattern of chronic respiratory diseases. In the multimorbidity pattern of cardiovascular and cerebrovascular diseases,compared with those without eight chronic diseases, increasing age, former smoking and current non-smoking, increasing BMI and central obesity increased the risk of cardiovascular and cerebrovascular disease comorbidity by 0.022 (OR=1.022, 95% CI: 1.012-1.033),0.466 (OR=1.466, 95% CI: 1.081-1.987), 0.144 (OR=1.144, 95% CI: 1.117-1.172), 0.505 (OR=1.505, 95% CI: 1.283-1.766). In the multimorbidity pattern of chronic respiratory diseases, compared with those without eight chronic diseases, former smokers and current non-smokers (OR=3.851, 95% CI: 2.132-6.956), central obesity (OR=1.696, 95% CI: 1.064-2.701) were the risk factors, while being female (OR=0.366, 95% CI: 0.229-0.587) and decreased BMI (OR=0.926, 95% CI: 0.869-0.994) were the protective factors. Conclusion Hypertension, stroke, dyslipidemia and diabetes are easy to coexist, and asthma and COPD are easy to coexist. Smoking was an important risk factor for multimorbidity. Central obesity was a risk factor for both multimorbidity patterns, especially for the cardiovascular and cerebrovascular multimorbidity pattern.

Multimorbidity pattern  /  Hierarchical cluster analysis  /  Apriori algorithm  /  Association rules
梅冬蒙, 缪丹丹, 沈欢, 文进博, 赵倩, 刘景, 许雨婷, 孙中明, 潘恩春. 基于层次聚类和Apriori算法探索老年人慢性病共病模式及影响因素. 现代预防医学, 2025 , 52 (19) : 3478 -3483 . DOI: 10.20043/j.cnki.MPM.202503186
Dong-meng MEI, Dan-dan MIAO, Huan SHEN, Jin-bo WEN, Qian ZHAO, Jing LIU, Yu-ting XU, Zhong-ming SUN, En-chun PAN. Multimorbidity patterns and influencing factors of chronic diseases in the elderly based on hierarchical clustering and the Apriori algorithm[J]. Modern Preventive Medicine, 2025 , 52 (19) : 3478 -3483 . DOI: 10.20043/j.cnki.MPM.202503186
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随着慢性病基数大、发病率高等问题的显露，慢性病多病共存的现象愈发严重。研究显示，在已患慢性疾病的基础上，每增加一种慢性病，每年就医次数增加3.2次，医疗费用增加33%，超过三分之二的卫生资源被慢性病共病患者使用[1]。老年人是慢性病共病的主体，我国60岁以上老年人慢性病患病率高达86.39%[2]。同时患有三种慢性疾病的老年人死亡风险增加，对生存的影响取决于特定的疾病组合[3]。老年人身体基础较差，多病共存的情况下治疗风险、不良结局及预后对患者和我国社会经济发展均是巨大挑战。本研究借助淮安市慢性病及其危险因素调查项目，提取60岁以上对象信息，描述性分析概述慢性病患病和共病情况，层次聚类和Apriori算法探索共病模式，结合可视化结果展示八种慢性病之间的关联关系，最后采用二元logistic回归分析挖取不同共病模式的影响因素。
1　对象与方法
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1.1　研究对象
采用多阶段整群随机抽样的方法，第一阶段采用人口规模排序的系统抽样，在淮安市淮安区、涟水县抽6个乡镇、洪泽区抽4个乡镇；第二阶段在每个抽中的乡镇/街道中，采用上述抽样方法随机抽取5个行政村/居委会；第三阶段采用简单随机抽样方法在每个抽中的行政村/居委会中抽取165户居民户；第四阶段在每个抽中的调查户内，使用KISH表法抽取1名18岁以上常住居民进行调查，当一个行政村/居委会达到150份调查时停止。最终对进入到淮安市慢性病及危险因素调查的12 232例居民进行问卷等后续调查检测。筛选年龄≥60岁、删除血糖、血脂等关键变量缺失111例，最终7 354例纳入分析，所有调查对象均签署知情同意。
1.2　研究内容与方法
1.2.1　监测内容
提供统一的电子调查问卷，由经过统一培训的调查员面访调查对象并填写，问卷包括吸烟、饮酒、身体活动、血压、血糖、血脂等问题、健康状况等内容；医学体检测量身高、体重、腰围、臀围和血压等多项指标，并计算身体质量指数（body mass index，BMI）=体重（kg）/身高的二次方（m）2；采集空腹静脉血样，检测空腹血糖、总胆固醇、甘油三酯、高密度脂蛋白胆固醇、低密度脂蛋白胆固醇等指标。
1.2.2　相关定义
（1）吸烟：累积吸烟≥100只，且调查时仍存在吸烟行为者[4]；（2）饮酒：平均饮酒每月≥1次，且调查时仍存在饮酒行为者[5]；（3）中心性肥胖：男性腰围≥90 cm、女性腰围≥85 cm[6]；（4）体力活动：根据患者全天进行体力活动的类型、分级和时长，将其折算为代谢当量（metabolic equivalent，MET-h/d），对各类体力活动赋值如下：高、中、轻等强度体力活动的代谢当量分别记为8、4和1.5 MET[7]；（5）高血压患者：自报有高血压疾病史和/或目前正在服用降压药物和/或参加本次医学体检时在未服用降压药物情况下收缩压≥140 mm Hg和/或舒张压≥90 mm Hg；（6）血脂异常患者：自报有血脂异常疾病史和/或经本次实验室检测结果血脂异常指总胆固醇≥6.2 mmol/L和/或低密度脂蛋白胆固醇≥4.1 mmol/L和/或高密度脂蛋白胆固醇<1.0 mmol/L和/或甘油三酯≥2.3 mmol/L；（7）糖尿病患者：自报有糖尿病史和/或经本次实验室检测结果空腹血糖≥7.0 mmol/L；（8）脑卒中、心肌梗死、慢阻肺、哮喘、恶性肿瘤患者均经过乡镇级以上医疗机构明确诊断。本研究中慢性病共病指同时患有至少两种（5）～（8）中提及的慢性病。
1.3　统计分析
使用Excel、SPSS 20.0和R x64 4.3.1等软件进行描述性分析、层次聚类、Apriori算法、二元logistic回归分析。层次聚类将性别、年龄、吸烟、饮酒、身体活动、BMI、中心性肥胖等作为特征变量，病种为标注变量，对特征变量标准化处理，计算欧氏距离，识别标注变量间的共现关系，确定聚类结果，得到共病模式。Apriori算法采用支持度（suppport，S）、置信度（confidence，C）和提升度（lift，L）等三个指标，支持度指在数据库中，事件A和事件B同时出现的概率，support（A→B）=P（A, B）；置信度指在事件A存在的前提下，B发生的条件概率，confidence（A→B）=P（A, B）/P（A）；提升度是一种比值，指事件A出现的条件下B发生的概率与任何条件下B发生的概率之比，lift=confidence（A→B）/P（B）=P（A, B）/P（A）P（B）。Apriori算法推演使用R x64 4.3.1中arules包实现。采用多因素logistic回归模型，以心脑血管疾病/慢性呼吸系统疾病共病模式（是/否）为因变量，以性别（男/女）、年龄、吸烟（是/否）、饮酒（是/否）、身体活动、BMI、中心性肥胖（是/否）为自变量纳入模型，采用逐步回归法分析慢性病共病的危险因素，检验水准α=0.05。
2　结果
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2.1　慢性病共病情况
本研究共纳入7 354例研究对象，6 025例患有慢性病，患病率为81.93%。3 134例是共病患者，共病患病率为42.62%。见表1。
2.2　层次聚类八种慢性病聚成三类
（1）高血压、糖尿病、血脂异常、脑卒中、心肌梗死；（2）慢阻肺、哮喘；（3）恶性肿瘤单独为一类。见图1。
2.3　Apriori算法关联规则分析
设置不同支持度（1%～5%）和置信度（5%～50%）得到关联规则数量分别为60、50、42、19、36和16条，见图2。进一步以支持度=1%、置信度=20%、提升度>1筛选关联规则。二元共病关联规则中，支持度、置信度、提升度最高的共病组合分别是糖尿病和血脂异常，哮喘和慢阻肺，哮喘和慢阻肺；三元共病关联规则中，支持度、置信度、提升度最高的共病组合分别是高血压、糖尿病和血脂异常，脑卒中、糖尿病和高血压，哮喘、高血压和慢阻肺。四元共病关联规则中，共出现高血压、脑卒中、糖尿病和血脂异常等四种疾病，支持度相同。结果提示高血压、脑卒中、血脂异常、糖尿病易共存，哮喘和慢阻肺极易共存。见表2。
图3B～D是以提升度>1为基准，调整支持度、置信度得到的八种慢性病关联规则网状图。图3A、B、C显示，相比于其他疾病，慢阻肺和哮喘极易共存，属于一种共病模式。图3C、D表明高血压、脑卒中、糖尿病和血脂异常呈现高度互连特征，属于一种共病模式，因此本研究将高血压、脑卒中、血脂异常、糖尿病命名为心脑血管疾病共病模式，哮喘和慢阻肺命名为慢性呼吸系统疾病共病模式。图3B、C显示高血压可能是多系统共病的关键连接点，可作为以上两种共病模式的桥梁。
2.4　共病模式多因素分析
心脑血管疾病共病模式定义为患有该模式中至少一种疾病，同时不患有恶性肿瘤和慢性呼吸系统疾病共病模式中的疾病。慢性呼吸系统疾病共病模式定义为患有该模式中至少一种疾病，同时不患恶性肿瘤和心脑血管疾病共病模式中的疾病。自变量间相互独立，无共线性。心脑血管疾病共病模式中，年龄增加、以前吸烟现在不吸烟、BMI增加、中心性肥胖可显著增加心脑血管疾病共病的发病风险，饮酒（OR=0.839，95%CI：0.728～0.966）是该模式的保护因素，见表3。慢性呼吸系统疾病共病模式中，以前吸烟现在不吸烟（OR=3.851，95%CI：2.132～6.956）、中心性肥胖（OR=1.696，95%CI：1.064～2.701）是危险因素，女性、BMI下降与风险降低相关，见表4。
3　讨论
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本研究共纳入八种慢性病，≥60岁老年人慢性病患病率81.93%，共病患病率42.62%，未采集肾脏、骨关节、颈腰椎、慢性消化系统和情感精神问题等疾病信息，因此低于2020年中国健康与养老追踪调查（49.46%）[8]、珠三角（60.11%）[9]、四川省（73.16%）[10]等研究结果中≥60岁老年人共病患病率。患病率位于前三的疾病分别为高血压（64.85%）、血脂异常（31.62%）、糖尿病（16.90%），略高于与其他研究数据[11-12]，可能与数据来源、样本量及调查方法不同有关。
层次聚类将八种慢性病聚成三类，其中高血压、糖尿病、血脂异常、脑卒中和心肌梗死为一类，Yao等学者[13]使用层次聚类将高血压、血脂异常、糖尿病和脑卒中归为心血管代谢集群，该结果与本研究类似。Apriori算法关联规则显示，哮喘和慢阻肺（支持度=3.42%、置信度=54.21%、提升度=6.78）关联性极强，与Guo[8]研究结论一致。此外，慢阻肺、高血压与哮喘的共病组合提升度最高为7.05，脑卒中、糖尿病、血脂异常疾病共存的患者中，86.57%的患者同时患有高血压，结合Apriori算法可视化结果，本研究认为高血压是心脑血管疾病共病模式和慢性呼吸系统疾病共病模式的桥梁疾病。
年龄增加是心脑血管疾病共病模式的危险因素，随着年龄增长，机体功能减弱，身体长期暴露在各种致病因素导致多病共存的风险上升。两种共病模式均提示，以前吸烟现在不吸烟相比于现在每天吸烟的患者具有更高的共病风险，研究表明戒烟会增加患高血压、糖尿病等疾病发生风险，尼古丁、焦油、苯并芘等有害物质对心血管系统、肺功能造成的损伤是不可逆的，即使戒烟，早期吸烟史对机体损害仍在继续，甚至可能导致健康风险更大[14]。另外，尼古丁通过上调胆碱能受体刺激交感神经驱动抑制食欲，戒烟会增加食物摄入量，同时戒烟减少机体尼古丁相关代谢所消耗的能量，以上原因导致的体重增加是高血压、糖尿病及心血管疾病的重要危险因素[15]。任何剂量酒精摄入均增加慢性病风险，所谓“安全剂量”仅适用于特定心血管指标[16-18]，本研究发现饮酒与心脑血管疾病共病风险呈现负相关（OR=0.839，95%CI：0.728～0.966），可解释为特定人群的现象描述，本研究完全同意酒精的总体健康风险，结果并非支持饮酒的保护作用。BMI是评估肥胖程度的常用指标，肥胖是高血压、血脂异常、糖尿病等疾病的主要风险因素[19-20]，meta分析指出[21]，过度肥胖导致糖尿病、高血压、脑卒中等14种循环系统疾病发病风险增加，与本研究结果一致。中心性肥胖在两种共病模式中均为风险因素，又称腹部或内脏肥胖。内脏脂肪干扰脂质代谢，导致低密度脂蛋白胆固醇升高和高密度脂蛋白胆固醇降低，此外，通过分泌肿瘤坏死因子-α、白介素-6等多种促炎因子[22]，导致血管收缩和炎症，直接引起血压升高，持续的高血压破坏血管内皮，促进动脉壁损伤和斑块形成，导致动脉粥样硬化，增加心血管疾病风险[23]。中心性肥胖与胰岛素抵抗密切相关[24]，长期的胰岛素抵抗导致2型糖尿病的发生，加快其并发症的进程。
本研究局限性为采集到的慢性病病种较少，导致老年人慢性病患病率相对较低，只纳入淮安地区样本，代表性有限，分类分析时无法判定与其他疾病的潜在关系以及横断面设计在探索共病模式影响因素时难以区分危险因素与共病的时序关系，有学者认为心理健康等因素在心血管相关疾病研究中至关重要[25]，但本研究未采集到心理健康数据。
综上所述，本研究使用层次聚类确定三种共病模式，Apriori算法明确心脑血管疾病、慢性呼吸系统疾病共病模式为两种主要的共病模式，确定高血压为两种共病模式间的桥梁疾病，并得到各病种间的关联情况，在影响因素的探索方面，本研究提出吸烟是两种共病模式的重要危险因素，即使目前戒烟，早期吸烟史对身体造成的不可逆损伤会显著增加共病的发生风险。中心性肥胖是两种共病模式的危险因素，对心脑血管疾病共病模式的发生风险更为显著。
基金
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  • 中华预防医学会科研课题(JKCLPJ202501006)
  • 江苏省预防医学会科研课题(Y12023032)
  • 淮安市卫生健康委员会科研课题(HAWJ2024022)
文献
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2025年第52卷第19期
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doi: 10.20043/j.cnki.MPM.202503186
  • 接收时间：2025-03-14
  • 首发时间：2026-03-17
  • 出版时间：2025-10-10
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  • 收稿日期：2025-03-14
基金
中华预防医学会科研课题(JKCLPJ202501006)
江苏省预防医学会科研课题(Y12023032)
淮安市卫生健康委员会科研课题(HAWJ2024022)
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    淮安市疾病预防控制中心，江苏 淮安 223001

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孙中明，E-mail:；
潘恩春，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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