Behavioral and Sociodemographic determinants of poor self-rated health among U.S. adults: an interpretable machine learning analysis

Behavioral and Sociodemographic determinants of poor self-rated health among U.S. adults: an interpretable machine learning analysis | 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 Behavioral and Sociodemographic determinants of poor self-rated health among U.S. adults: an interpretable machine learning analysis Rezwan Ahmed, Arnob Zahid This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8295005/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background: Self-rated health (SRH) is a validated, single-item measure that captures morbidity, functional status, and social vulnerability in population health. Understanding the determinants of poor SRH can support targeted public health interventions and policy planning. Methods: Using the 2023 Behavioral Risk Factor Surveillance System (BRFSS), this study examined behavioral, sociodemographic, and clinical determinants of poor SRH among 302,125 U.S. adults. We trained Light Gradient-Boosting Machine (LGBM), Extreme Gradient Boosting, Random Forest, and Logistic Regression models. Class imbalance was addressed using SMOTE-NC (oversampling) versus algorithm-level class-weighting, and models were calibrated via isotonic regression. Variable importance was interpreted using Shapley Additive Explanations (SHAP) and validated via weighted multivariable logistic regression. Subgroup analyses examined performance variations across demographic and socioeconomic groups. Results: Class-weighted LGBM provided the best balance of performance, achieving a ROC-AUC of 0.83, sensitivity of 0.75, and specificity of 0.76, outperforming data-level oversampling strategies. Multivariable regression identified frequent poor mental-health days (≥15 days/month) as the strongest predictor (adjusted odds ratio [aOR] = 4.23), followed by diabetes (aOR = 2.43), annual household income <$25,000 (aOR = 2.02), physical inactivity (aOR = 1.99), and obesity (aOR = 1.70). Subgroup analyses revealed significant variation in model sensitivity across age and socioeconomic strata. Conclusions: Findings underscore the intertwined effects of mental health challenges, socioeconomic disadvantage, and chronic conditions on perceived health. This study demonstrates a transparent, equity-oriented machine learning approach to guide data-driven public health strategies. Self-Rated Health Healthcare Analytics LightGBM Model Health Inequities BRFSS Data Mental Health Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 22 Apr, 2026 Reviewers agreed at journal 21 Apr, 2026 Reviewers agreed at journal 10 Feb, 2026 Reviewers invited by journal 10 Feb, 2026 Editor invited by journal 09 Dec, 2025 Editor assigned by journal 08 Dec, 2025 Submission checks completed at journal 08 Dec, 2025 First submitted to journal 06 Dec, 2025 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-8295005","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":591732186,"identity":"da99ba56-37e6-463e-875a-06fb1c75eedd","order_by":0,"name":"Rezwan Ahmed","email":"data:image/png;base64,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","orcid":"","institution":"BRAC University","correspondingAuthor":true,"prefix":"","firstName":"Rezwan","middleName":"","lastName":"Ahmed","suffix":""},{"id":591732189,"identity":"30675811-9a56-4ddc-b04f-f912cce8e267","order_by":1,"name":"Arnob Zahid","email":"","orcid":"","institution":"University of Waikato","correspondingAuthor":false,"prefix":"","firstName":"Arnob","middleName":"","lastName":"Zahid","suffix":""}],"badges":[],"createdAt":"2025-12-06 13:38:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8295005/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8295005/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102962384,"identity":"a4bf1c23-1155-4c63-aef7-6ccc95064c59","added_by":"auto","created_at":"2026-02-19 04:07:58","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":815217,"visible":true,"origin":"","legend":"","description":"","filename":"RevisedManuscript1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8295005/v1_covered_d2d20f5c-22a2-473a-99ff-75cf63e11b68.pdf"},{"id":102762427,"identity":"1385e3d0-9a02-4de8-b6ce-8cddf07847a4","added_by":"auto","created_at":"2026-02-16 10:51:31","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":365979,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-8295005/v1/f35adb4b07bee6c4a137bd6d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Behavioral and Sociodemographic determinants of poor self-rated health among U.S. adults: an interpretable machine learning analysis","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":"Self-Rated Health, Healthcare Analytics, LightGBM Model, Health Inequities, BRFSS Data, Mental Health","lastPublishedDoi":"10.21203/rs.3.rs-8295005/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8295005/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Self-rated health (SRH) is a validated, single-item measure that captures morbidity, functional status, and social vulnerability in population health. Understanding the determinants of poor SRH can support targeted public health interventions and policy planning.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Using the 2023 Behavioral Risk Factor Surveillance System (BRFSS), this study examined behavioral, sociodemographic, and clinical determinants of poor SRH among 302,125 U.S. adults. We trained Light Gradient-Boosting Machine (LGBM), Extreme Gradient Boosting, Random Forest, and Logistic Regression models. Class imbalance was addressed using SMOTE-NC (oversampling) versus algorithm-level class-weighting, and models were calibrated via isotonic regression. Variable importance was interpreted using Shapley Additive Explanations (SHAP) and validated via weighted multivariable logistic regression. Subgroup analyses examined performance variations across demographic and socioeconomic groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eClass-weighted LGBM provided the best balance of performance, achieving a ROC-AUC of 0.83, sensitivity of 0.75, and specificity of 0.76, outperforming data-level oversampling strategies. Multivariable regression identified frequent poor mental-health days (≥15 days/month) as the strongest predictor (adjusted odds ratio [aOR] = 4.23), followed by diabetes (aOR = 2.43), annual household income \u0026lt;$25,000 (aOR = 2.02), physical inactivity (aOR = 1.99), and obesity (aOR = 1.70). Subgroup analyses revealed significant variation in model sensitivity across age and socioeconomic strata.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eFindings underscore the intertwined effects of mental health challenges, socioeconomic disadvantage, and chronic conditions on perceived health. This study demonstrates a transparent, equity-oriented machine learning approach to guide data-driven public health strategies.\u003c/p\u003e","manuscriptTitle":"Behavioral and Sociodemographic determinants of poor self-rated health among U.S. adults: an interpretable machine learning analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-16 10:51:26","doi":"10.21203/rs.3.rs-8295005/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-22T18:11:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"18057583882560965124721030139421705410","date":"2026-04-21T16:46:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"327433993537553950199087383264816648731","date":"2026-02-10T22:36:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-10T15:05:40+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-09T10:14:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-09T01:23:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-09T01:21:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-12-06T13:27:38+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":"fd2144b8-e42e-4ee4-91da-4eadcc668bd8","owner":[],"postedDate":"February 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-16T10:51:26+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-16 10:51:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8295005","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8295005","identity":"rs-8295005","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}