A modeling study of the opioid epidemic for vulnerable communities in Knoxville, Tennessee

A modeling study of the opioid epidemic for vulnerable communities in Knoxville, Tennessee | 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 A modeling study of the opioid epidemic for vulnerable communities in Knoxville, Tennessee Kimberlyn Eversman, Adam Spannaus, Ryan Campbell, Leigh B. Pearcy, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8148367/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Background: Over the past three decades, the opioid epidemic has emerged as one of the most pressing public health crises in the United States, disproportionately affecting vulnerable communities and minority populations. Mathematical modeling, widely applied in the study of infectious diseases, offers a rigorous framework to investigate the dynamics of opioid use disorder. Methods: We present a compartmental model to examine two primary risk factors for opioid misuse: opioid availability and access to preventive resources. The framework consists of two interacting systems of equations, one representing the general population and the other a subset community of interest. The general population model was calibrated using 2016-2019 data from the Knoxville, Tennessee Metropolitan Statistical Area, a region heavily impacted by the epidemic. Sensitivity analyses were conducted to quantify the influence of model inputs on outcomes. Results: Embedded within a data-calibrated general population, simulations of hypothetical communities were conducted under varying levels of treatment accessibility, recovery retention, and fatal overdose rates. We found that variations in recovery retention and fatal overdose risk exerted the strongest influence on total overdoses. Notably, increasing treatment access alone---without simultaneously addressing other factors such as relapse reduction---was associated with higher overdose rates, as individuals almost exclusively relapsed using fentanyl or heroin, rather than prescription opioids, all of which carry elevated fatal overdose risk. Conclusions: For Knoxville during 2016–2019, increasing resources aimed at preventing the initial development of opioid use disorder would likely have had minimal effect on the overall prevalence of opioid use disorder. Our analysis highlights the importance of model scope, as a related study, which was fitted to a broader geographic area and a longer time span, produced findings that differed from ours. While our conclusions are specific to this location and period, the modeling framework we created is readily adaptable to other regions and populations, provided appropriate data is available, and offers a valuable tool for studying vulnerable groups. Opioid use disorder Substance use disorder Opioid epidemic Public health Compartment model Epidemiological model Full Text Additional Declarations No competing interests reported. Supplementary Files EversmanOpioidSuppMaterial.pdf Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 20 Apr, 2026 Reviews received at journal 23 Mar, 2026 Reviewers agreed at journal 19 Mar, 2026 Reviewers agreed at journal 19 Feb, 2026 Reviewers agreed at journal 18 Feb, 2026 Reviews received at journal 22 Jan, 2026 Reviewers agreed at journal 08 Dec, 2025 Reviewers agreed at journal 05 Dec, 2025 Reviewers invited by journal 02 Dec, 2025 Editor invited by journal 22 Nov, 2025 Editor assigned by journal 21 Nov, 2025 Submission checks completed at journal 21 Nov, 2025 First submitted to journal 18 Nov, 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-8148367","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":554275641,"identity":"418f8cd8-5b0e-43ab-8eb3-8479d55bc928","order_by":0,"name":"Kimberlyn Eversman","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYBACCRiDn4GB8QBMRAKXchQtkg0MDCAtEsRrMThArBbJ9h7DxxV/tskb30g+cPAHw+E6yQbmg7d58GiR5jljbHi27bbhthtpCYd5GA5LSDOwJVvj0yInkZYm2dhwm3HbmTMGhxmAWuQYeMyk8WqRf5b+s+HPbfvNPWcMQA4DauH/hleLtATzMcYGttuJG9h7DA5AHMbDhleLZE/yYcnGttvJM463Af1ikC45s5nN2HIOHi0Sxw82fgQ6zLa/mfngwx8V1vwSx5sf3niDRwsaMABiZuKVj4JRMApGwSjAAQC9/Eq4ChnLiQAAAABJRU5ErkJggg==","orcid":"","institution":"University of Tennessee at Knoxville","correspondingAuthor":true,"prefix":"","firstName":"Kimberlyn","middleName":"","lastName":"Eversman","suffix":""},{"id":554275643,"identity":"a7fd8b5f-de0b-43d6-b1eb-15b354386cb2","order_by":1,"name":"Adam Spannaus","email":"","orcid":"","institution":"Oak Ridge National Laboratory","correspondingAuthor":false,"prefix":"","firstName":"Adam","middleName":"","lastName":"Spannaus","suffix":""},{"id":554275648,"identity":"b7ee8dd2-e373-46c0-b2c9-8c3f7608b70f","order_by":2,"name":"Ryan Campbell","email":"","orcid":"","institution":"University of Tennessee at Knoxville","correspondingAuthor":false,"prefix":"","firstName":"Ryan","middleName":"","lastName":"Campbell","suffix":""},{"id":554275650,"identity":"9820b8cd-43f6-4b86-ba51-edef04fe89b8","order_by":3,"name":"Leigh B. 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Tennessee\u003c/p\u003e","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":"Opioid use disorder, Substance use disorder, Opioid epidemic, Public health, Compartment model, Epidemiological model","lastPublishedDoi":"10.21203/rs.3.rs-8148367/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8148367/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e\u003cp\u003eOver the past three decades, the opioid epidemic has emerged as one of the most pressing public health crises in the United States, disproportionately affecting vulnerable communities and minority populations. Mathematical modeling, widely applied in the study of infectious diseases, offers a rigorous framework to investigate the dynamics of opioid use disorder.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e\u003cp\u003eWe present a compartmental model to examine two primary risk factors for opioid misuse: opioid availability and access to preventive resources. The framework consists of two interacting systems of equations, one representing the general population and the other a subset community of interest. The general population model was calibrated using 2016-2019 data from the Knoxville, Tennessee Metropolitan Statistical Area, a region heavily impacted by the epidemic. Sensitivity analyses were conducted to quantify the influence of model inputs on outcomes.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e\u003cp\u003eEmbedded within a data-calibrated general population, simulations of hypothetical communities were conducted under varying levels of treatment accessibility, recovery retention, and fatal overdose rates. We found that variations in recovery retention and fatal overdose risk exerted the strongest influence on total overdoses. Notably, increasing treatment access alone---without simultaneously addressing other factors such as relapse reduction---was associated with higher overdose rates, as individuals almost exclusively relapsed using fentanyl or heroin, rather than prescription opioids, all of which carry elevated fatal overdose risk.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e\u003cp\u003eFor Knoxville during 2016\u0026ndash;2019, increasing resources aimed at preventing the initial development of opioid use disorder would likely have had minimal effect on the overall prevalence of opioid use disorder. Our analysis highlights the importance of model scope, as a related study, which was fitted to a broader geographic area and a longer time span, produced findings that differed from ours. While our conclusions are specific to this location and period, the modeling framework we created is readily adaptable to other regions and populations, provided appropriate data is available, and offers a valuable tool for studying vulnerable groups.\u003c/p\u003e","manuscriptTitle":"A modeling study of the opioid epidemic for vulnerable communities in Knoxville, Tennessee","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-04 23:04:54","doi":"10.21203/rs.3.rs-8148367/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-20T18:50:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-23T23:43:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"338926139102914646626521457772595525786","date":"2026-03-19T18:46:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"31139625262011309775860388899696349522","date":"2026-02-19T15:53:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"193117966731098999107786883078631990608","date":"2026-02-18T16:04:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-22T16:57:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"248054609992772964249739299840415142736","date":"2025-12-08T15:48:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"102466696572572250538726619061296671784","date":"2025-12-05T08:55:44+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-02T20:13:03+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-22T14:56:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-21T09:28:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-21T09:27:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-11-18T18:26:50+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":"89e47011-175c-44b0-848b-96f84c62a498","owner":[],"postedDate":"December 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-04T23:04:54+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-04 23:04:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8148367","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8148367","identity":"rs-8148367","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}