The Paradox of Preparedness - Cold-Related Health Risks in Ukraine's War-Torn Winter

The Paradox of Preparedness - Cold-Related Health Risks in Ukraine's War-Torn Winter | 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 The Paradox of Preparedness - Cold-Related Health Risks in Ukraine's War-Torn Winter Ubydul Haque, Iftikhar Sikder, Julia Pavlova, Emily Barrett This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7370640/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 Older adults in conflict zones face heightened health risks, exacerbated by damaged housing and limited healthcare. In Ukraine, cold injuries and respiratory infections are significant but understudied threats. A survey of 2,311 Ukrainian households (April–May 2023) revealed widespread respiratory infections (86%) and cold injuries (8.6%) among 1,045 older adults. Using chi-squared tests, frequency analysis, and machine learning (Random Forest, XGBoost, AdaBoost, KNN), key predictors were identified with strong model performance (AUC > 0.9). SHAP and Explainable Boosting Machine analyses enhanced interpretability. Not performing home repairs was the strongest predictor of cold injuries (p = 0.004, η² = 0.54). Other key factors included insulation, window installation, and heating methods. For respiratory infections, seven predictors, including internal displacement, marital status, and food/water storage, were significant (q < 0.05). Interaction effects (η² = 0.38–0.58) highlighted compounded socio-economic and housing vulnerabilities. Machine learning consistently prioritized home repairs, insulation, and socioeconomic factors. This hybrid approach balanced statistical rigor with predictive accuracy. Findings underscore the critical role of housing infrastructure in protecting vulnerable populations. Targeted housing interventions and improved resilience are essential to mitigate cold-related health risks and cascading health threats during crises in conflict-affected regions. Health sciences/Risk factors Humanities/Health humanities Figures Figure 1 Figure 2 Figure 3 Figure 4 Full Text Additional Declarations There is NO Competing Interest. Tables are available in the Supplementary Files section. Supplementary Files Supplementtexts.docx Supplement texts SupplementTables.docx Supplement Tables SupplementFigures.pdf Supplement Figures MainTables.docx Main Tables 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-7370640","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":500349139,"identity":"75264304-4a62-4858-a434-82804f5650f3","order_by":0,"name":"Ubydul 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In Ukraine, cold injuries and respiratory infections are significant but understudied threats. A survey of 2,311 Ukrainian households (April–May 2023) revealed widespread respiratory infections (86%) and cold injuries (8.6%) among 1,045 older adults. Using chi-squared tests, frequency analysis, and machine learning (Random Forest, XGBoost, AdaBoost, KNN), key predictors were identified with strong model performance (AUC \u003e 0.9). SHAP and Explainable Boosting Machine analyses enhanced interpretability. Not performing home repairs was the strongest predictor of cold injuries (p = 0.004, η² = 0.54). Other key factors included insulation, window installation, and heating methods. For respiratory infections, seven predictors, including internal displacement, marital status, and food/water storage, were significant (q \u003c 0.05). Interaction effects (η² = 0.38–0.58) highlighted compounded socio-economic and housing vulnerabilities. Machine learning consistently prioritized home repairs, insulation, and socioeconomic factors. This hybrid approach balanced statistical rigor with predictive accuracy. Findings underscore the critical role of housing infrastructure in protecting vulnerable populations. 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