Spatially Varying Associations between Community Level Sociodemographic Predictors and Cumulative COVID-19 Outcomes in New York City

Spatially Varying Associations between Community Level Sociodemographic Predictors and Cumulative COVID-19 Outcomes in New York City | 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 Spatially Varying Associations between Community Level Sociodemographic Predictors and Cumulative COVID-19 Outcomes in New York City Glen D. Johnson, Rebecca Gordon, Rachel L. Thompson, Tomoki Nakaya This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7545744/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 Nearly three years after onset of the COVID-19 pandemic in New York City (NYC), cumulative hospitalization and mortality rates were not uniformly distributed across the city. To help understand potential drivers of this geospatial disparity, we applied geographically weighted Poisson regression (GWPR), which allows associations between these outcomes and community-level predictors to be location-dependent. Cumulative COVID-19 hospitalization and mortality rates in n = 177 NYC modified ZIP code tabulation areas as of December 31, 2022 were obtained from the NYC Department of Health and Mental Hygiene, while socioeconomic and demographic predictors were queried from the 2018 American Community Survey. The GWPR model, and its multiscale generalization, yielded better diagnostics than the more conventional global models that assume spatially stationary associations, with the non-multiscale GWPR model performing the best. Several predictors acted as both risk and protective factors for both outcomes, depending on location, which means one common model for the whole city would be misleading. These include the percentage of non-Hispanic whites, foreign born citizens, male, mean commute time and having had at least one vaccination. Other predictors showed more geographically consistent effects. For mortality, the percentage of residents without health insurance acted solely as a risk factor. Similarly, for hospitalizations, the percentage of residents with a disability acted solely as a risk factor. The percentage of residents > 24y with a bachelor’s degree or higher acted solely as a protective factor against both outcomes. These community factors could therefore help guide local place-based interventions to reduce disparities and the overall burden of future epidemics. Covid New York City Geographically weighted Poisson regression Community-level predictors 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. 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-7545744","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":538768049,"identity":"e523c9b2-3495-46b8-8e0c-4b221e4c853d","order_by":0,"name":"Glen D. 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City","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Covid, New York City, Geographically weighted Poisson regression, Community-level predictors","lastPublishedDoi":"10.21203/rs.3.rs-7545744/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7545744/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNearly three years after onset of the COVID-19 pandemic in New York City (NYC), cumulative hospitalization and mortality rates were not uniformly distributed across the city. To help understand potential drivers of this geospatial disparity, we applied geographically weighted Poisson regression (GWPR), which allows associations between these outcomes and community-level predictors to be location-dependent.\u003c/p\u003e\u003cp\u003eCumulative COVID-19 hospitalization and mortality rates in \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;177 NYC modified ZIP code tabulation areas as of December 31, 2022 were obtained from the NYC Department of Health and Mental Hygiene, while socioeconomic and demographic predictors were queried from the 2018 American Community Survey. The GWPR model, and its multiscale generalization, yielded better diagnostics than the more conventional global models that assume spatially stationary associations, with the non-multiscale GWPR model performing the best.\u003c/p\u003e\u003cp\u003eSeveral predictors acted as both risk and protective factors for both outcomes, depending on location, which means one common model for the whole city would be misleading. These include the percentage of non-Hispanic whites, foreign born citizens, male, mean commute time and having had at least one vaccination.\u003c/p\u003e\u003cp\u003eOther predictors showed more geographically consistent effects. For mortality, the percentage of residents without health insurance acted solely as a risk factor. Similarly, for hospitalizations, the percentage of residents with a disability acted solely as a risk factor. The percentage of residents\u0026thinsp;\u0026gt;\u0026thinsp;24y with a bachelor\u0026rsquo;s degree or higher acted solely as a protective factor against both outcomes. These community factors could therefore help guide local place-based interventions to reduce disparities and the overall burden of future epidemics.\u003c/p\u003e","manuscriptTitle":"Spatially Varying Associations between Community Level Sociodemographic Predictors and Cumulative COVID-19 Outcomes in New York City","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-05 19:18:11","doi":"10.21203/rs.3.rs-7545744/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":"e8ddefb7-c3af-4a2d-a8bc-d4033eed71fa","owner":[],"postedDate":"November 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-17T07:24:37+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-05 19:18:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7545744","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7545744","identity":"rs-7545744","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}