Communicating complex statistical models to a public health audience: translating science into action with the FARSI approach

Communicating complex statistical models to a public health audience: translating science into action with the FARSI approach | 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 Communicating complex statistical models to a public health audience: translating science into action with the FARSI approach Mattia Stival, Lorenzo Schiavon, Gaia Bertarelli, Stefano Campostrini This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6403583/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 . Effectively communicating complex statistical model outputs is a major challenge in public health. This study introduces the FARSI approach (Fast, Accessible, Reliable, Secure, Informative) as a framework to enhance the translation of intricate statistical findings into actionable insights for policymakers and stakeholders. We apply this framework in a real-world case study on chronic disease monitoring in Italy. Methods . The FARSI framework outlines key principles for developing user-friendly tools that improve the translation of statistical results. We applied these principles to create an open-access web application using R Shiny, designed to communicate chronic disease prevalence estimates from a Bayesian spatio-temporal logistic model. The case study highlights the importance of an intuitive design for fast accessibility, validated data and expert feedback for reliability, aggregated data for security, and insights into prevalence population subgroups, which were previously unobservable, for informativeness. Results . The web application enables stakeholders to explore disease prevalence across populations and geographical area through dynamic visualizations. It facilitates public health monitoring by, for instance, identifying disparities at the local level and assessing risk factors such as smoking. Its user-friendly interface enhances accessibility, making statistical findings more actionable. Conclusions . The FARSI framework provides a structured approach to improving the communication of complex research findings. By making statistical models more accessible and interpretable, it supports evidence-based decision-making in public health and increases the societal impact of research. Bayesian Analysis Chronic Disease Monitoring Local Health Surveillance Scientific Results Dissemination Web application Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 08 May, 2025 Reviewers agreed at journal 04 May, 2025 Reviewers agreed at journal 28 Apr, 2025 Reviewers invited by journal 24 Apr, 2025 Editor invited by journal 11 Apr, 2025 Editor assigned by journal 10 Apr, 2025 Submission checks completed at journal 10 Apr, 2025 First submitted to journal 08 Apr, 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. 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-6403583","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":441695739,"identity":"4786d370-b736-4cd9-8eb3-80879cba07e8","order_by":0,"name":"Mattia Stival","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIie3QMQuCQBTA8RcHuhzN9yWCIpCgwK9yD8G2lpaGhiCwRWptiPoWzYZgi+DqqPQFbAmn6B1UEOS1NtwfvEPxxz0FMJn+NakWRlcxA7DeTxpjrYV6gSsi0xfRGiJq42rB4OPg73VW2bko5uC6Nu9VuI8nbeFFUNbNxEk9GiwBDBnvCzzGU0v4UjuYEyligeTMkopgwNOunmQXIndwiXg17hTJKj3J6RT66lbIWCJwQcQO9X/MyS+9La4FhjFLBjIZEwm6kfR1g2FxrW8j196clnk1H+JhycqyHjWTZ+LzNvoJTCaTyaTtAeosT6jK/K3UAAAAAElFTkSuQmCC","orcid":"","institution":"Ca' Foscari University of Venice","correspondingAuthor":true,"prefix":"","firstName":"Mattia","middleName":"","lastName":"Stival","suffix":""},{"id":441695740,"identity":"8d40b9df-5e73-42b3-b4a6-dbb5112340d9","order_by":1,"name":"Lorenzo Schiavon","email":"","orcid":"","institution":"Ca' Foscari University of Venice","correspondingAuthor":false,"prefix":"","firstName":"Lorenzo","middleName":"","lastName":"Schiavon","suffix":""},{"id":441695741,"identity":"c79bd181-ec71-4f33-9b8f-03b11eb75d5f","order_by":2,"name":"Gaia Bertarelli","email":"","orcid":"","institution":"Ca' Foscari University of Venice","correspondingAuthor":false,"prefix":"","firstName":"Gaia","middleName":"","lastName":"Bertarelli","suffix":""},{"id":441695742,"identity":"b562f7b1-9b01-441f-ad8f-b53ef4ea1e9e","order_by":3,"name":"Stefano Campostrini","email":"","orcid":"","institution":"Ca' Foscari University of Venice","correspondingAuthor":false,"prefix":"","firstName":"Stefano","middleName":"","lastName":"Campostrini","suffix":""}],"badges":[],"createdAt":"2025-04-08 13:08:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6403583/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6403583/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80588146,"identity":"94713cb7-dadf-41b5-88e7-02e1135d0136","added_by":"auto","created_at":"2025-04-15 01:35:57","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1999563,"visible":true,"origin":"","legend":"","description":"","filename":"manuscripittosubmit.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6403583/v1_covered_1effba8a-12cc-4c6b-b2a8-ab60b8d71273.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Communicating complex statistical models to a public health audience: translating science into action with the FARSI approach","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Bayesian Analysis, Chronic Disease Monitoring, Local Health Surveillance, Scientific Results Dissemination, Web application","lastPublishedDoi":"10.21203/rs.3.rs-6403583/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6403583/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e. Effectively communicating complex statistical model outputs is a major challenge in public health. This study introduces the FARSI approach (Fast, Accessible, Reliable, Secure, Informative) as a framework to enhance the translation of intricate statistical findings into actionable insights for policymakers and stakeholders.\u0026nbsp; We apply this framework in a real-world case study on chronic disease monitoring in Italy.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e. The FARSI framework outlines key principles for developing user-friendly tools that improve the translation of statistical results. We applied these principles to create an open-access web application using R Shiny, designed to communicate chronic disease prevalence estimates from a Bayesian spatio-temporal logistic model. The case study highlights the importance of an intuitive design for fast accessibility, validated data and expert feedback for reliability, aggregated data for security, and insights into prevalence population subgroups, which were previously unobservable, for informativeness.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e. The web application enables stakeholders to explore disease prevalence across populations and geographical area through dynamic visualizations. It facilitates public health monitoring by, for instance, identifying disparities at the local level and assessing risk factors such as smoking. Its user-friendly interface enhances accessibility, making statistical findings more actionable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e. The FARSI framework provides a structured approach to improving the communication of complex research findings. By making statistical models more accessible and interpretable, it supports evidence-based decision-making in public health and increases the societal impact of research.\u003c/p\u003e","manuscriptTitle":"Communicating complex statistical models to a public health audience: translating science into action with the FARSI approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-15 01:27:48","doi":"10.21203/rs.3.rs-6403583/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-05-08T20:38:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"294057802972915948430299671402943694289","date":"2025-05-04T23:54:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"182560329795268102669270684129161038101","date":"2025-04-28T13:35:25+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-24T14:04:07+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-04-11T09:11:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-10T05:17:37+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-10T05:16:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-04-08T12:55:33+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":"02f8042a-0f5f-4f84-9a61-0ef5bdecf507","owner":[],"postedDate":"April 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-04-24T14:08:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-15 01:27:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6403583","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6403583","identity":"rs-6403583","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}