Mapping Neighborhood-Level Drivers of Type 2 Diabetes: A Predictive-Causal Approach for Precision Public Health

Mapping Neighborhood-Level Drivers of Type 2 Diabetes: A Predictive-Causal Approach for Precision Public Health | 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 Article Mapping Neighborhood-Level Drivers of Type 2 Diabetes: A Predictive-Causal Approach for Precision Public Health Mohammad Noaeen, Amirhosein Rostami, Ibrahim Ghanem, Olli Saarela, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6648511/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract Type 2 diabetes has become an urban epidemic influenced by neighbourhood environments. However, conventional risk models focusing solely on individual factors fail to account for these community influences and often require detailed patient data that may not be available. To address this gap, we developed an integrated approach combining machine learning and causal inference to map type 2 diabetes risk at the community level. Using demographic, health, and socioeconomic data from 1,149 Census Tracts (CTs) in a large metropolitan region, we trained seven machine learning models to identify neighbourhoods with high diabetes prevalence. Although neighbourhood-level diabetes data were available for this study area, our model’s high predictive accuracy on external validation data (area under the curve (AUC) = 0.95), particularly from a distinct geographical region, demonstrates its potential utility in predicting diabetes risk for other regions in Canada or elsewhere where such data are unavailable. The top models achieved high recall (> 90%) and AUC up to 0.96 on test data, indicating accurate identification of high-risk neighbourhoods with few false positives. Survey-derived community health indicators, including obesity rate, physical inactivity, and median age, were strong predictors of diabetes prevalence. We then applied a Causal Forest approach to estimate the impact (Conditional Average Treatment Effect, τ) of modifiable factors. Higher work stress (τ= 0.312) and daily smoking (τ= 0.155) were moderately associated with increased risk, whereas better mental health (τ≈−1.1) was protective, highlighting mental health as a critical intervention priority, especially in neighbourhoods predicted to have high diabetes prevalence. These findings illustrate how community-level factors can guide targeted interventions and advance health equity, particularly for immigrant and visible-minority populations. Our integrated machine-learning and causal framework lays the groundwork for precision public health, demonstrating how modifiable neighbourhood factors can indicate diabetes risk when patient-level data are scarce. Furthermore, our methodology is adaptable to other chronic diseases influenced by social and environmental determinants, potentially guiding targeted prevention efforts beyond type 2 diabetes. Health sciences/Risk factors Biological sciences/Computational biology and bioinformatics/Machine learning Health sciences/Health care/Health services Health sciences/Health care/Public health Full Text Additional Declarations No competing interests reported. Supplementary Files Supplementaryfile.pdf Cite Share Download PDF Status: Published Journal Publication published 05 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 29 Jul, 2025 Reviews received at journal 08 Jul, 2025 Reviewers agreed at journal 01 Jul, 2025 Reviews received at journal 14 Jun, 2025 Reviewers agreed at journal 06 Jun, 2025 Reviewers agreed at journal 06 Jun, 2025 Reviewers agreed at journal 04 Jun, 2025 Reviewers invited by journal 04 Jun, 2025 Editor assigned by journal 02 Jun, 2025 Editor invited by journal 28 May, 2025 Submission checks completed at journal 27 May, 2025 First submitted to journal 12 May, 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. 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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-6648511","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":466788351,"identity":"578af158-51fc-412c-ae4f-27fde79c6dcc","order_by":0,"name":"Mohammad Noaeen","email":"","orcid":"","institution":"University of Toronto","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"","lastName":"Noaeen","suffix":""},{"id":466788352,"identity":"1b741737-83fa-4dd8-9c1d-c2f25a57ce50","order_by":1,"name":"Amirhosein Rostami","email":"","orcid":"","institution":"University of Toronto","correspondingAuthor":false,"prefix":"","firstName":"Amirhosein","middleName":"","lastName":"Rostami","suffix":""},{"id":466788353,"identity":"83399fda-6cda-41e1-aa5d-ad64b1ac2ccf","order_by":2,"name":"Ibrahim Ghanem","email":"","orcid":"","institution":"University of Toronto","correspondingAuthor":false,"prefix":"","firstName":"Ibrahim","middleName":"","lastName":"Ghanem","suffix":""},{"id":466788354,"identity":"144d6e83-9ef0-419c-a176-d10e3527fc0b","order_by":3,"name":"Olli Saarela","email":"","orcid":"","institution":"University of Toronto","correspondingAuthor":false,"prefix":"","firstName":"Olli","middleName":"","lastName":"Saarela","suffix":""},{"id":466788355,"identity":"24633298-1f7d-4f2d-8451-a7b34a7841f9","order_by":4,"name":"Karim Keshavjee","email":"","orcid":"","institution":"University of Toronto","correspondingAuthor":false,"prefix":"","firstName":"Karim","middleName":"","lastName":"Keshavjee","suffix":""},{"id":466788356,"identity":"c2be05de-7a6e-4661-bc55-a335565eb490","order_by":5,"name":"Jeffrey R. 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However, conventional risk models focusing solely on individual factors fail to account for these community influences and often require detailed patient data that may not be available. To address this gap, we developed an integrated approach combining machine learning and causal inference to map type 2 diabetes risk at the community level. Using demographic, health, and socioeconomic data from 1,149 Census Tracts (CTs) in a large metropolitan region, we trained seven machine learning models to identify neighbourhoods with high diabetes prevalence. Although neighbourhood-level diabetes data were available for this study area, our model’s high predictive accuracy on external validation data (area under the curve (AUC) = 0.95), particularly from a distinct geographical region, demonstrates its potential utility in predicting diabetes risk for other regions in Canada or elsewhere where such data are unavailable. 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