Cohort Characteristics and Factors Associated With Cannabis Use Among Adolescents in Canada Using Pattern Discovery and Disentanglement Method

Cohort Characteristics and Factors Associated With Cannabis Use Among Adolescents in Canada Using Pattern Discovery and Disentanglement Method | 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 Cohort Characteristics and Factors Associated With Cannabis Use Among Adolescents in Canada Using Pattern Discovery and Disentanglement Method Peiyuan Zhou, Andrew K.C. Wong, Yang Yang, Scott T. Leatherdale, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-928545/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: COMPASS is a longitudinal, prospective cohort study collecting data annually from students attending high school in jurisdictions across Canada. We aimed to discover significant frequent/rare associations of behavioral factors among Canadian adolescents related to cannabis use. Methods: We use a subset of the COMPASS dataset which contains 18,761 records of students in grades 9 to 12 with 31 selected features (attributes) involving various characteristics, from living habits to academic performance. We then used the Pattern Discovery and Disentanglement (PDD) algorithm to detect strong and rare (yet statistically significant) associations from the dataset. Results: Cohort characteristics and factors associated with cannabis use and other associations detected by PDD show consistent results with common sense and literature surveys. In addition, PDD outperformed methods using other criteria (i.e. support and confidence ) popular as reported in the literature. Association results showed that PDD could discover: i) a smaller set of succinct significant associations in clusters; ii) frequent and rare, yet significant, patterns supported by population health relevant study; iii) patterns from a dataset with extremely imbalanced groups (majority class (None-user): minority class (Regular) = 88.3%: 11.7%). Conclusions: Results on the COMPASS dataset have validated PDD’s efficacy in discovering succinct interpretable frequent associations with comprehensive coverage and rare yet significant associations from datasets with extremely imbalanced class distribution without relying on any balancing process. The frequent associations show consistent results with common sense and literature surveys, while the rare patterns show very special cases. The success of PDD on this project indicates that PDD has great potential for population health data analysis. Health Economics & Outcomes Research Other Public Policy Cannabis Use Population Health Machine Learning Adolescent in Canada Pattern Discovery Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. 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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-928545","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":55186662,"identity":"d4ed93f9-fc91-4273-ba75-74a3d69a21ef","order_by":0,"name":"Peiyuan Zhou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYFCCAwnMQFIOyrMgQgfjgQcgLcYgZgMDgwQRWpgPgrUkNhCtxZztcPLngorD6RtuJD9/8HGPhBw/A/PDD/i0WPYcSzCeceZw7oYbaYaNM55JGEs2sBnjtcvgxpmEZN42oJbbCYbNPAckEjcc4MHvPIP77z8cBmpJN7id/rH5D0QL8w+8Wg4cSGwGakkwuJ1j2MwA0cKG1xbLhgPJzDxn0g1n3n9TOLPnANAvzWxmeGPHnOFA8meeCmt5vjPHN3z4ccBGjp+9+fENvA7DFGLGpx67llEwCkbBKBgFaAAA1clRo+9Ajd4AAAAASUVORK5CYII=","orcid":"","institution":"University of Waterloo","correspondingAuthor":true,"prefix":"","firstName":"Peiyuan","middleName":"","lastName":"Zhou","suffix":""},{"id":55186663,"identity":"08f90201-9217-4c6a-89aa-5c9e8f13cfd6","order_by":1,"name":"Andrew K.C. 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We aimed to discover significant frequent/rare associations of behavioral factors among Canadian adolescents related to cannabis use.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe use a subset of the COMPASS dataset which contains 18,761 records of students in grades 9 to 12 with 31 selected features (attributes) involving various characteristics, from living habits to academic performance. We then used the Pattern Discovery and Disentanglement (PDD) algorithm to detect strong and rare (yet statistically significant) associations from the dataset.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eCohort characteristics and factors associated with cannabis use and other associations detected by PDD show consistent results with common sense and literature surveys. In addition, PDD outperformed methods using other criteria (i.e. \u003cem\u003esupport\u003c/em\u003e and \u003cem\u003econfidence\u003c/em\u003e) popular as reported in the literature. Association results showed that PDD could discover: i) a smaller set of succinct significant associations in clusters; ii) frequent and rare, yet significant, patterns supported by population health relevant study; iii) patterns from a dataset with extremely imbalanced groups (majority class (None-user): minority class (Regular) = 88.3%: 11.7%). \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eResults on the COMPASS dataset have validated PDD’s efficacy in discovering succinct interpretable frequent associations with comprehensive coverage and rare yet significant associations from datasets with extremely imbalanced class distribution without relying on any balancing process. The frequent associations show consistent results with common sense and literature surveys, while the rare patterns show very special cases. The success of PDD on this project indicates that PDD has great potential for population health data analysis.\u003c/p\u003e","manuscriptTitle":"Cohort Characteristics and Factors Associated With Cannabis Use Among Adolescents in Canada Using Pattern Discovery and Disentanglement Method","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-10-05 14:38:12","doi":"10.21203/rs.3.rs-928545/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e7ec4398-af52-4396-803d-288a36e07192","owner":[],"postedDate":"October 5th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":7652692,"name":"Health Economics \u0026 Outcomes Research"},{"id":7652693,"name":"Other Public Policy"}],"tags":[],"updatedAt":"2022-04-18T13:29:17+00:00","versionOfRecord":[],"versionCreatedAt":"2021-10-05 14:38:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-928545","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-928545","identity":"rs-928545","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}