Development of a Cancer Prevention Vulnerability Score to Support Physician Assistant–Led Risk Stratification in Primary Care

Development of a Cancer Prevention Vulnerability Score to Support Physician Assistant–Led Risk Stratification in Primary Care | 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 Development of a Cancer Prevention Vulnerability Score to Support Physician Assistant–Led Risk Stratification in Primary Care Isha Shah, Naman Dhariwal This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8952618/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: Physician Assistants (PAs) are central to cancer prevention in primary care, yet scalable tools to identify patients with clustered, modifiable prevention vulnerabilities remain limited. Objective: To develop and temporally evaluate a nationally representative, survey-weighted Cancer Prevention Vulnerability Score (CPVS) and examine its discrimination, calibration, and transport performance across survey cycles. Methods: Using HINTS 6 data (n=6,252), we defined high prevention vulnerability as the presence of at least two of three components: current smoking, insufficient physical activity, and lack of knowledge that alcohol increases cancer risk. CPVS was developed using survey-weighted gradient boosting and internally validated with cross-validation. Model performance was evaluated using area under the receiver operating characteristic curve (AUC), Brier score, and calibration. Temporal validation was conducted in HINTS 7 (n=7,278) using frozen transport and structured updating strategies. Results: Internal validation demonstrated moderate discrimination (surveyweighted AUC = 0.678) with acceptable overall accuracy (weighted Brier 0.20) and appropriate calibration. Under temporal transport, performance attenuated with the frozen model but improved after updating, with reclassification analyses indicating greater upward risk movement among vulnerable individuals. Conclusions: The CPVS offers a representative, weighted framework for identifying cancer risk clusters in primary care. Its strong validity and performance support its integration into PA-led preventive workflows. Cancer prevention Risk stratification Primary care Survey-weighted modeling Calibration Transportability Decision curve analysis 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. 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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-8952618","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":598787838,"identity":"4a510075-3666-47b2-a9fa-39c3148b296b","order_by":0,"name":"Isha Shah","email":"","orcid":"","institution":"Wayne State University","correspondingAuthor":false,"prefix":"","firstName":"Isha","middleName":"","lastName":"Shah","suffix":""},{"id":598787840,"identity":"43fa3426-f798-486a-bc44-a40e2bf5aab0","order_by":1,"name":"Naman Dhariwal","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArUlEQVRIiWNgGAWjYNACNhsGBmYGNgYQSayWNNK1HAaTxGkxZz+d+Lmg7Hzi2nbmZw8YKqwTGwhpsezJ3Sw949ztxG2H2cwNGM6kE9ZicCB3gzRvG0gLD5sEY9thIrScf7v5N2/bOaiWf8RouZG7DWjLAaiWBqK0vN1mzXMu2RjoFzOJhGPpxkQ4LHfzbZ4yO9lt5w8/k/hQYy1LUAsqSCBN+SgYBaNgFIwCXAAAtTY+htoRLcYAAAAASUVORK5CYII=","orcid":"","institution":"University of Michigan–Ann Arbor","correspondingAuthor":true,"prefix":"","firstName":"Naman","middleName":"","lastName":"Dhariwal","suffix":""}],"badges":[],"createdAt":"2026-02-24 04:39:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8952618/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8952618/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108599408,"identity":"b1662c4e-91e0-4c96-bd9c-613fe9a00691","added_by":"auto","created_at":"2026-05-06 11:13:36","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":546623,"visible":true,"origin":"","legend":"","description":"","filename":"HINTSResearch.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8952618/v1_covered_096f17f0-d67e-4c67-b32c-5b36c962496e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development of a Cancer Prevention Vulnerability Score to Support Physician Assistant–Led Risk Stratification in Primary Care","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"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},"keywords":"Cancer prevention, Risk stratification, Primary care, Survey-weighted modeling, Calibration, Transportability, Decision curve analysis","lastPublishedDoi":"10.21203/rs.3.rs-8952618/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8952618/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: Physician Assistants (PAs) are central to cancer prevention in primary care, yet scalable tools to identify patients with clustered, modifiable prevention vulnerabilities remain limited. Objective: To develop and temporally evaluate a nationally representative, survey-weighted Cancer Prevention Vulnerability Score (CPVS) and examine its discrimination, calibration, and transport performance across survey cycles.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethods: Using HINTS 6 data (n=6,252), we defined high prevention vulnerability as the presence of at least two of three components: current smoking, insufficient physical activity, and lack of knowledge that alcohol increases cancer risk. CPVS was developed using survey-weighted gradient boosting and internally validated with cross-validation. Model performance was evaluated using area under the receiver operating characteristic curve (AUC), Brier score, and calibration. 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