Time Sequence Rules of Chronic Disease Multimorbidity: An Analysis of China Health and Retirement Longitudinal Study Using Sequential Pattern Mining

Time Sequence Rules of Chronic Disease Multimorbidity: An Analysis of China Health and Retirement Longitudinal Study Using Sequential Pattern Mining | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Time Sequence Rules of Chronic Disease Multimorbidity: An Analysis of China Health and Retirement Longitudinal Study Using Sequential Pattern Mining MeiJiao Wang, Yingqi Shen, Beiying Chen, Xiaotong Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8695765/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Chronic disease multimorbidity has become a significant global public health issue, imposing a heavy burden on patients' quality of life and the healthcare system. In China, a country with a large population, it is of vital importance to investigate the sequence relationship between chronic diseases in the middle-aged and elderly population. This study was based on the analysis of data from 10,827 participants in the China Health and Retirement Longitudinal Study(CHARLS 2011–2020). Hierarchical clustering analysis and sequence pattern mining were adopted to identify the temporal correlations in the development of 14 chronic diseases and the differences among different populations, and a chronic disease multimorbidity network was constructed to visualize these relationships. The research results showed that arthritis was at the core of the association network; chronic lung disease, digestive diseases, dyslipidemia, heart attack and hypertension were in the middle layer of the network; while asthma, cancer, diabetes, kidney disease, liver disease, memory related problems and stroke were at the periphery of the network. Meanwhile, significant differences in the multimorbidity pattern of chronic diseases were observed by gender, age, residential area and educational level. Arthritis was identified as a key disease that was prone to occur and could easily trigger other diseases. Patients with chronic lung diseases, digestive diseases, dyslipidemia, heart attack and hypertension had a higher risk of developing other diseases. By exploring the multimorbidity patterns of chronic diseases, this study provides empirical evidence for early prevention and multimorbidity management of diseases, which may help reduce health and social burdens. Multimorbidity Time Sequence Rules Sequential Pattern Mining Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 08 Feb, 2026 Editor invited by journal 30 Jan, 2026 Editor assigned by journal 28 Jan, 2026 Submission checks completed at journal 28 Jan, 2026 First submitted to journal 25 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8695765","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":588282240,"identity":"8a449bf1-90e7-4271-bcd3-9bd8c9f9d584","order_by":0,"name":"MeiJiao Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIiWNgGAWjYPACCWY2BgbGBzwFYJ4B0VqYDXgMiNcCBmwSRGkxOH728KsbFRbsfOyHj1W8MdiW2MDevE2CoeYObi1n8tKsc84AHcaTlnZzjsHtxAaeY2USDMee4dRidiDHzDi3DeSXHLPbPCAtEjlmEowNh3FrOf8GqOUfUAv/G7NisBb5NwS03MgxfpzbANQCNJwZYgsPfi32N96YMeccA2l5liwJ9ItxG09asUXCMdxaJPtzjD/n1NQly/cnH/zwpuK2bD/74Y03PtTg1sIAig4gkYzggogEfBqAkf4BSNjhVzMKRsEoGAUjGgAAWIhPZpszrn8AAAAASUVORK5CYII=","orcid":"","institution":"Dalian Maritime University","correspondingAuthor":true,"prefix":"","firstName":"MeiJiao","middleName":"","lastName":"Wang","suffix":""},{"id":588282243,"identity":"d7569cee-2f54-403c-be8c-1c99616dc4b3","order_by":1,"name":"Yingqi Shen","email":"","orcid":"","institution":"Dalian Maritime University","correspondingAuthor":false,"prefix":"","firstName":"Yingqi","middleName":"","lastName":"Shen","suffix":""},{"id":588282245,"identity":"fc9a55a7-423a-43e0-aca4-8c9fb765bdfc","order_by":2,"name":"Beiying Chen","email":"","orcid":"","institution":"Dalian Maritime University","correspondingAuthor":false,"prefix":"","firstName":"Beiying","middleName":"","lastName":"Chen","suffix":""},{"id":588282246,"identity":"f7c1791b-efff-4d79-b98b-56d2f2633593","order_by":3,"name":"Xiaotong Chen","email":"","orcid":"","institution":"BronxCare Health System Department of Ophthalmology","correspondingAuthor":false,"prefix":"","firstName":"Xiaotong","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2026-01-26 02:38:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8695765/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8695765/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102541481,"identity":"da912dc6-1eb8-480d-bd55-9a679f89f7a9","added_by":"auto","created_at":"2026-02-12 19:12:08","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":919971,"visible":true,"origin":"","legend":"","description":"","filename":"TimeSequenceRulesofChronicDiseaseMultimorbid.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8695765/v1_covered_a12c2892-7097-4ecb-896c-722ebe93cc45.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Time Sequence Rules of Chronic Disease Multimorbidity: An Analysis of China Health and Retirement Longitudinal Study Using Sequential Pattern Mining","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Multimorbidity, Time Sequence Rules, Sequential Pattern Mining","lastPublishedDoi":"10.21203/rs.3.rs-8695765/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8695765/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eChronic disease multimorbidity has become a significant global public health issue, imposing a heavy burden on patients' quality of life and the healthcare system. In China, a country with a large population, it is of vital importance to investigate the sequence relationship between chronic diseases in the middle-aged and elderly population. This study was based on the analysis of data from 10,827 participants in the China Health and Retirement Longitudinal Study(CHARLS 2011\u0026ndash;2020). Hierarchical clustering analysis and sequence pattern mining were adopted to identify the temporal correlations in the development of 14 chronic diseases and the differences among different populations, and a chronic disease multimorbidity network was constructed to visualize these relationships. The research results showed that arthritis was at the core of the association network; chronic lung disease, digestive diseases, dyslipidemia, heart attack and hypertension were in the middle layer of the network; while asthma, cancer, diabetes, kidney disease, liver disease, memory related problems and stroke were at the periphery of the network. Meanwhile, significant differences in the multimorbidity pattern of chronic diseases were observed by gender, age, residential area and educational level. Arthritis was identified as a key disease that was prone to occur and could easily trigger other diseases. Patients with chronic lung diseases, digestive diseases, dyslipidemia, heart attack and hypertension had a higher risk of developing other diseases. By exploring the multimorbidity patterns of chronic diseases, this study provides empirical evidence for early prevention and multimorbidity management of diseases, which may help reduce health and social burdens.\u003c/p\u003e","manuscriptTitle":"Time Sequence Rules of Chronic Disease Multimorbidity: An Analysis of China Health and Retirement Longitudinal Study Using Sequential Pattern Mining","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-12 19:11:41","doi":"10.21203/rs.3.rs-8695765/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-02-08T13:16:02+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-30T11:43:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-29T03:45:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-29T03:44:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2026-01-26T02:29:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"66933d5e-2d51-47f6-a171-7ea50d3f2e81","owner":[],"postedDate":"February 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-12T19:11:41+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-12 19:11:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8695765","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8695765","identity":"rs-8695765","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}