Beyond the Battlefield: Geospatial Insights of Health Vulnerability in Ukraine During the Russian-Ukrainian War

Beyond the Battlefield: Geospatial Insights of Health Vulnerability in Ukraine During the Russian-Ukrainian War | 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 Beyond the Battlefield: Geospatial Insights of Health Vulnerability in Ukraine During the Russian-Ukrainian War Ubydul Haque This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7376114/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 Although the devastation of war is well recognized, no previous study has systematically integrated the interplay of conflict intensity, access to humanitarian aid, environmental conditions, and infrastructure to understand how these factors collectively shape health vulnerability across war-affected regions. Using spatial and suitability modeling, we assessed multidimensional vulnerabilities in Ukraine during the Russian invasion, including mental health risks, environmental stressors, and infrastructure disruptions. We developed a multi-source conflict-related health impact database (February 2022- December 2023). Sleep, mental health, and casualty data were collected through national online surveys (n > 2,312). Data was cleaned, geolocated across 461 cities, and analyzed using Inverse Distance Weighting interpolation. Logistic and spatial regression were used to assess relationships between conflict exposure, living conditions, mental health, sleep deprivation, and cold injury risks. A composite vulnerability index was created using weighted Principal Component Analysis-based methods. Regions with intense conflict, poor housing, frequent power outages, food shortages, and limited access to healthcare and aid faced the highest vulnerability. Cold and damp conditions, housing damage, and resource scarcity exacerbated household living conditions, especially in eastern and northern Ukraine. Not all high-conflict areas had poor mental health outcomes; cold, damp, crowded housing, food insecurity, and power outages were equally critical drivers. Mental health issues (PTSD, depression, and insomnia) were highest in regions with harsh winters, poor infrastructure, and limited aid. War-related health issues extend beyond direct conflict exposure and involve the interplay of conflict, environmental stressors, and infrastructure damage in shaping casualties, sleep, and mental health outcomes. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Between 1999–2023, an estimated 4.5 to 4.7 million people have died in conflict areas around the world 1 . In addition to the conflict-related deaths, war hampers social and economic development, devastates health systems, and disrupts community life 2 . Since its start in 2022, the conflict in Ukraine has severely damaged infrastructure, disrupting power, water, and fuel supplies 3 . Damage to critical civilian energy infrastructure in the country caused winter blackouts and environmental stressors (i.e., cold, damp housing, and frequent power outages), increasing the risk of cold-related injuries and exacerbating humanitarian needs, particularly among vulnerable sub-populations. Compounding the crisis, the 2023 Kakhovka Dam destruction caused severe flooding, displacing thousands more Ukrainians and worsening vulnerability 4 . It has been reported that war refugees face increased vulnerability to mental health disorders due to compounded trauma from war and environmental hardships 5 , 6 . Understanding how war stress, environment, and social factors affect mental health in conflict zones is vital. Integrating multi-level data on environmental and social factors into risk assessments may help to identify sub-populations at greatest risk of mental health vulnerabilities, and in so doing, it may help shape policies, allocate resources, and create sustainable, impactful interventions 7 . However, vulnerability assessments in conflict zones like Ukraine are hindered by security risks, accessibility issues, and evolving conflicts 8 . Despite those challenges, mapping multidimensional vulnerabilities and underlying community factors is vital for practical risk assessment and planning in conflict-affected, resource-limited settings. This approach supports the development of adaptable strategies and enables more effective, targeted interventions. Moreover, humanitarian planning can become more adaptive and resilient by recognizing these interconnected vulnerabilities 9 . To that end, a composite vulnerability index integrating data on war, environmental, and mental health factors may be valuable. Existing data on mental health, sleep, and conflict severity in Ukraine often lacks regional detail, and the limited use of spatial analyses in Ukraine has hindered support for localized needs. Higher resolution, evidence-based studies are urgently needed to inform effective, targeted interventions in high-risk areas. Here we investigate three primary questions. First, how do environmental factors - such as cold, damp housing and power outages - affect mental health outcomes in conflict-affected populations? We hypothesize that inadequate housing and infrastructure, particularly cold and damp living conditions, significantly worsen mental health outcomes in conflict zones. Second, do mental health vulnerabilities vary spatially across the conflict region, and how do local environmental conditions and conflict intensity shape these vulnerabilities? We hypothesize that areas experiencing high conflict intensity and environmental stressors will show increased mental health vulnerabilities, with these patterns varying spatially and requiring region-specific interventions. Third, how does access to aid, infrastructure, and essential resources influence mental health in high-conflict areas? We hypothesize that limited access to aid, financial support, and basic services exacerbates mental health challenges, especially in regions frequently subjected to attacks and extensive housing damage. This study fills a critical gap by revealing that areas of highest conflict are not always those with the worst health outcomes, highlighting the need for nuanced, location-specific policy strategies to prioritize aid, mental health support, and infrastructure development. Methodology Data Collection Building on a previously described database 10 , we compiled data from online and print news sources in English and Ukrainian, including Al Jazeera, BBC, CNN, Kyiv Independent, Rubryka, Reuters, Institute for the Study of War, and UNICEF through December 2023. Reports covered healthcare facilities, ambulance and pharmacy attacks, civilian deaths and injuries, and damage to civilian infrastructure. Attacks on military targets without civilian casualties were excluded. To avoid duplication, a centralized database tracked hospital destruction, infrastructure damage, shelling, and lethal events, with details on dates, locations, and casualties. Additional data on attacks on hospitals, maternity wards, nuclear plants, homes, schools, and food supplies were included and cross-verified with reports from Ukrainian ministries and local media. Individual-level data on sleep and mental health (i.e., anxiety, depression, post-traumatic stress disorder, PTSD, and sleep quality) were collected through an online survey conducted from April 5 to May 15, 2023. An online quota sampling approach was used to collect data from 2,364 adult participants living in Ukraine (ages 18–79) 11 , 12 . Short sleep duration was defined as ≤ 6 hours, long as ≥ 9 hours, and insomnia was assessed using the Insomnia Symptom Questionnaire 12 . Winter infection data were also collected through another online survey from one adult (ages 18–72) per household in 2311 households across 24 Ukrainian oblasts. The questionnaire gathered information on respondents’ living and health conditions. Data on attacks on health facilities were extracted from the WHO databases 13 . Ukrainian oblasts were categorized by region: North (Chernihiv, Sumy, and Kyiv), South (Odesa, Mykolayiv, Kherson, and Zaporizhzhia), East (Donetsk, Luhansk, and Kharkiv), and West (Zakarpattia, Chernivtsi, Lviv, and Volyn). We integrated survey-based health data, environmental indices, infrastructure accessibility, and casualty records across 461 Ukrainian cities. All data were cleaned, preprocessed, and structured for spatial analysis. The data categories and their corresponding explanatory variables are detailed in Table S1. To estimate city-level prevalence, we applied three spatial interpolation methods: Inverse Distance Weighting (IDW), Empirical Bayesian Kriging (EBK), and Ordinary Least Squares (OLS). After comparing results, the IDW method was selected for final analysis. ArcGIS Pro’s Zonal Statistics tool was used to extract and estimate city-level prevalence values, including cold injury risk, based on IDW outputs. Key predictors in the model included insomnia scores 12 , sleep duration, environmental severity, housing vulnerabilities, and conflict intensity. Statistical Analysis We calculated descriptive statistics, including mean, standard deviation, minimum, and maximum values for continuous variables, while percentages and total counts for categorical variables. Multivariable logistic regression was conducted to determine the association between mental health and sleep outcomes, housing and living conditions, infrastructure and essential services, and economic and social safety. Spatial Analysis We used spatial regression to examine the relationships and spatial variability between mental health and sleep indicators as outcome variables. Explanatory variables included weapon types used in attacks, access to healthcare, medicine, food, humanitarian aid, heating equipment (e.g., logs/wood, coal, fuel briquettes, fuel pellets), ruined housing, and access to heat and water (see Table S1). Ordinary Least Squares (OLS) regression was used for spatial clustering, providing R², adjusted R², and p-values to assess model significance. All variables with a Variance Inflation Factor (VIF > 7.5) were dropped from models due to multicollinearity. OLS regression results are only reliable when the model is correctly specified 14 . We also used a Geographically Weighted Regression (GWR) model. In geography, a strong predictor variable in one city may have little relevance in another city. GWR was used to explore this spatial heterogeneity, as it generates a unique regression equation for each geographic unit (i.e., city here). In this case, a city, weighting nearby observations more heavily in the calibration process. This approach allows model coefficients to vary across space, capturing localized relationships 15 . Vulnerability Index Creation In addition, we combined the risk map with the sleep and mental health index to assess external stressors and internal responses. As described in more detail in the Supplement, this approach used principal component analysis (PCA) to integrate multiple dimensions to create a comprehensive vulnerability index (see supplement), 16 including dependent (e.g., attack frequency) and independent variables (e.g., self-reported mental health) that capture the complex nature of vulnerability in conflict zones. A higher score or index indicates greater vulnerability. Suitability Modeling Here, suitability modeling in GIS helps identify the areas most affected by war by examining the conditions and features of each place. We conducted suitability modeling using the tool in ArcGIS Pro. Composite indices were calculated for each variable class with PCA-derived weights in Python. Rasterized layers were combined using the weighted overlay tool to generate composite suitability maps for four distinct vulnerability categories. Model validation The model was trained randomly on half of the cities selected for cross-validation and then tested on the remaining half. For bootstrapping, 100 random samples were drawn with replacement, and both models were applied to each sample. Across these iterations, over 95% of the explanatory variable coefficients remained statistically significant, demonstrating the models’ robustness and consistency. Ethical approval The study protocol received ethical approval from the Poltava State Medical University Ethics Committee in Ukraine (Approval No. 212) and the Institutional Review Board at Rutgers University in the United States (Protocol #Pro2023000101). Results A total of 1,934 drone attacks occurred from February 2022 to December 2023, resulting in 8,378 casualties (an average of 220.5) in 22 of 28 oblasts (Table S2A). Additionally, 1,162 attacks with other weapons that killed and injured people were also reported. A total of 258 artillery strikes were recorded, averaging 6.8 casualties per attack. PTSD (26.2%), depression (43.6%), anxiety (22.8%), loneliness (39.1%), and insomnia (36.7%) were reported (Table S2B). Regression analysis People living in oblasts that experienced cold, damp, and crowded conditions were more likely to have mental health issues (Table S3). In multivariable models, individuals living in cold (Adjusted odds ratio, AOR 1.55, 95% CI: 1.16-2.06), damp (AOR 1.70, 95% CI: 1.29-2.24), and crowded (AOR 1.44, 95% CI: 1.10-1.87) conditions had significantly higher odds of PTSD. The odds of depression were similarly higher in those living in cold (AOR 1.57, 95% CI: 1.12-2.20), damp (AOR 1.80, 95% CI: 1.30-2.50), crowded (AOR 1.43, 95% CI: 1.05-1.93) conditions, needing house repairs (AOR 1.39, 95% CI: 1.00-1.93), and lacking housing subsidies (AOR 1.54, 95% CI: 1.11-2.12). Higher odds of anxiety, loneliness, and insomnia were found in cold, damp, crowded areas and those lacking housing subsidies. (Table S3). People with less access to food, public transport, and who experienced frequent power outages had significantly higher odds of PTSD and loneliness (Table S4). However, lack of access to food shops and public transport was significantly associated with higher odds of depression, anxiety, and insomnia (Table S4). Those with a lack of access to hospitals and ambulances were significantly associated with higher odds of PTSD, loneliness, insomnia, and depression, with anxiety linked only to a lack of access to ambulances (Table S5). Lastly, individuals who felt unsafe or insecure and lacked income had significantly higher odds of PTSD, while those feeling unsafe, insecure, and lacking food had significantly higher odds of depression, anxiety, loneliness, and insomnia (Table S6). Vulnerability Analysis The analysis revealed significant spatial variability in mental health vulnerability in Ukraine. Severity, environmental conditions, and sleep and mental health indices were combined to create a comprehensive vulnerability map. The Severity Index (Figure 1A) quantified war-related disruptions, including attack frequency and casualties, highlighting regions with severe conflict and mental health impacts. The Environmental Conditions Index (Figure S1) measured stressors like extreme cold, poor housing, and power outages, with high values in areas with severe winter and poor living conditions. The Sleep and Mental Health Index (Figure S2) reflected anxiety, depression, and sleep deprivation, with elevated scores indicating higher psychological distress. The Risk Map (Figure 1B) combined the Severity and Environmental Conditions indices, identifying regions at high risk due to conflict and environmental exposure. The Vulnerability Map (Figure 1C) integrated the Risk Map and Sleep and Mental Health Index, offering a comprehensive assessment of population vulnerability. Spatial analysis The OLS model revealed key relationships between explanatory variables and mental health outcomes, with significant coefficients (Table S7). Given the model’s specifications, spatially adaptive modeling was required, leading to the application of GWR. GWR estimated regression coefficients for each spatial unit (i.e., city), capturing local vulnerability and mental health outcome variations. Model diagnostics indicated that spatially varying coefficients improved the model's performance compared to the global OLS model (Table S8). The GWR analysis showed significant regional variation in vulnerability indices’ influence on mental health, highlighting the importance of location-specific factors during conflict. The Environmental Conditions Index had the most substantial effect in the northern and central regions (Figure 2a), where harsh winters and poor infrastructure were associated with greater vulnerabilities. These areas showed higher rates of anxiety, depression, and sleep disturbances, with positive coefficients linked to poor mental health outcomes. The Severity Index, reflecting conflict intensity (attacks, casualties, and infrastructure damage), showed a substantial impact on mental health in eastern Ukraine (Figure 2b). Areas with prolonged violence and frequent attacks had higher risks of psychological distress, including PTSD and sleep deprivation, as indicated by high coefficient values. The Local R² map (Figure 2c) showed the model’s explanatory power across regions. Central and eastern Ukraine had the highest values, indicating strong model fit, while western areas showed lower values, suggesting unmeasured factors influencing mental health. The variation in Local R² suggests further investigation into factors affecting mental health in western Ukraine. This highlighted the spatial complexity of vulnerability and the need for tailored interventions. A comparative analysis of the suitability maps showed significant overlap in the most vulnerable regions, particularly in eastern and southeastern Ukraine. These areas faced multidimensional risks from conflict, inadequate aid, environmental challenges, and housing damage, with the combined index offering a comprehensive assessment of mental health vulnerability. Vulnerability related to the availability of humanitarian assistance and financial support Regions with limited access to humanitarian aid and financial support were more vulnerable (Figure 3). Key contributors to this vulnerability included the frequency of attacks and lack of access to essential services, such as food shops and public transport. Ukraine's eastern and southeastern regions were the most affected, aligning with areas of high conflict intensity (Figure 3a-e). Mental Health vulnerabilities linked to environmental factors Environmental stressors like cold, damp housing, and frequent power outages significantly increased vulnerability in northern and eastern regions (Figure 4a-c). Sensitivity indicators, such as insufficient food and medicine, worsened health risks, while adaptive capacity indicators, like access to hospitals and pharmacies, were limited in highly vulnerable areas. Vulnerability related to housing damage The spatial distribution of housing vulnerability reflected the severity of housing damage and access (or lack thereof) to essential recovery resources (Figure 5). Housing vulnerability was highly correlated with conflict intensity, measured by attack frequency and weapon types. Regions with extensive housing damage and limited insulation repairs were particularly at risk during winter (Figure 5a-d). Health and food security vulnerability Conflict exposure and food supply disruptions were key drivers of vulnerability, as shown in Figure 6, highlighting regions impacted by resource scarcity and health effects. Conflict exposure, access to food, and poor housing conditions worsened mental health outcomes, especially in areas with limited access to clean water, heating, and medical services (Figure 6a – e). Discussion Using complementary data from the mainstream media, the Institute for the Study of War, UNICEF, and our cross-sectional data on winter infection, sleep, and mental health, we observed that environmental and conflict-related factors significantly affect sleep and mental health outcomes in Ukraine during the current conflict. Cold, damp, and overcrowded housing, along with limited access to heating, healthcare, food, and transport, were linked to higher rates of PTSD, depression, anxiety, loneliness, and insomnia, particularly in northern and rural regions. Conflict intensity exacerbated mental health issues in eastern Ukraine. Environmental stressors such as cold, damp, and frequent power outages appeared to be most impactful on mental health in northern Ukraine, while conflict severity was most important in the east. As the first analysis in an active war zone, it provides insights into how psychological distress varies across different geographic and contextual factors in Ukraine's ongoing conflict. In 2021, the Uppsala Conflict Data Program recorded 54 state-based conflicts 17 . Research examines armed conflict’s economic, cultural, and humanitarian effects, including impacts on growth and human development. Similar to our findings, their research highlights elevated mortality, disability, and disease rates, as well as a high prevalence of mental disorders in conflict zones, with one in five adults affected living in a conflict zone 18,19 . Consistently, previous studies have shown that socio-demographic and geographic factors shape risks, while displacement, family separation, and service barriers compound psychosocial challenges 20-22 . Building upon that prior work, here we integrated spatial regression and suitability modeling to advance understanding of mental health and vulnerability in war-affected regions. This approach quantifies spatially distributed risk factors like proximity to risks and socioeconomic deprivation, providing deeper insights into localized mental health impacts. Armed conflicts can devastate local environments, further exacerbating mental health issues in affected regions 23 . Eastern Ukraine, closer to the frontlines, is the target of frequent shelling, combat, and civilian-targeted violence 24 . Our results echo those findings, showing that eastern Ukraine endures direct conflict exposure, while also highlighting the spatial variation in vulnerability, such as northern Ukraine’s threats of environmental hazards, such as droughts and floods. This spatial granularity can inform humanitarian aid efforts, helping to prioritize regions with environmental and infrastructural vulnerabilities, not only in active conflict zones. Specifically, our results point to a need for improved heating and housing in northern Ukraine, while eastern regions may benefit most from enhanced PTSD treatment services. More generally, with limited resources in conflict-stricken areas, investment in mental health services may be informed by spatial analyses. Prior conflict research suggests that high-intensity violence correlates with poor mental health outcomes, such as PTSD, depression, and anxiety 25 . However, other studies indicate that the relationship between conflict intensity and mental health is more complex. Some areas, despite high violence exposure, show better mental health outcomes 22 . Key factors include community resilience, community-based mental health interventions and access to mental health services, culturally grounded coping strategies, and humanitarian aid 26,27 . Strong local support systems can buffer the psychological impact of conflict, reducing the long-term mental health burden 28 . Additionally, areas with robust health infrastructure and services experience lower mental disorder prevalence, even in high-conflict regions 29,30 . Adequate physical and adaptive infrastructure resilience helps mitigate societal vulnerability and supports health 31 . Compared with previous studies, our findings reveal that mental health burden is not directly tied to proximity to conflict. Northern oblasts, such as Chernihiv and Sumy, despite moderate attack intensity, experienced high anxiety, depression, and insomnia. Environmental stressors, including cold, housing damage, and poor access to essentials, significantly impacted mental health. Conversely, despite heavy shelling, some eastern areas had lower distress levels due to better humanitarian access. Our findings show that multiple vulnerabilities, such as housing destruction and supply disruptions, jointly predict increased risks of PTSD, depression, and insomnia. Spatial models also showed significant regional variations, underscoring the need for location-specific mental health and humanitarian interventions. Previous studies confirmed that war-related environmental stressors significantly impact mental health and contribute to PTSD 32 . Our study also revealed that environmental conditions, such as extreme cold, poor housing, and power outages, may impact mental health more strongly than direct conflict exposure in some regions. Northern Ukraine, facing harsh winters and infrastructure failures, shows mental health vulnerabilities comparable to or exceeding areas with heavy shelling. In eastern Ukraine, a high-conflict area, PTSD prevalence reaches 26.2%, while northern Ukraine shows higher rates of depression (43.6%) and insomnia (36.7%). Factors like cold, dampness, and overcrowding contribute significantly to these outcomes. Indirect stressors, such as food insecurity and inadequate healthcare, also play a critical role. Compared with the previous studies 11,12,33 the GWR model in this study reveals significant spatial variation in mental health vulnerability across Ukraine. In eastern regions, PTSD and sleep deprivation correlate with conflict exposure, while in northern areas, anxiety and depression are linked to environmental factors. These findings emphasize the need for region-specific interventions. A vulnerability index identifies high-risk communities for targeted aid and preparedness in war and public health crises 34 . Our study introduced a composite vulnerability index that integrates war severity, environmental stressors, and mental health indicators, providing a comprehensive risk assessment in conflict zones. It combines psychosocial, infrastructural, environmental, and conflict factors into a geospatial metric, highlighting compounded vulnerability in central and northern rural areas. The findings challenge traditional health frameworks by emphasizing localized conditions, such as infrastructure breakdown and other hardships (e.g., access to food shops and medicine), over direct conflict exposure, revealing spatial variation in mental health outcomes. Wartime aid aims to save lives and uphold dignity, yet existing studies lack robust methods to support the best use and optimal distribution of humanitarian assistance during the conflict 35 . Our study reveals that aid distribution often overlooks areas that suffer from conflict and systemic hardships. These regions face severe shortages of essentials such as food, water, medicine, and heating, worsening psychological distress, PTSD, anxiety, and insomnia. Mental health may deteriorate further under conditions of uncertainty, neglect, and housing damage. Current aid models focus narrowly on combat zones and may miss broader geographic concerns, compounding vulnerabilities. Humanitarian efforts must prioritize regions with both physical insecurity and infrastructure collapse. Tailored interventions such as trauma therapy in conflict zones and housing support elsewhere are essential. Integrating multidimensional vulnerability indices can vastly improve aid allocation and promote health equity. This study introduces a novel approach to modeling mental health vulnerability in conflict zones, combining geographically weighted regression, suitability mapping, and stressor indices. This study has limitations, including recall bias in self-reported data and underrepresentation of isolated populations. The cross-sectional design doesn't capture temporal fluctuations in vulnerability. This study used estimated city-level prevalence data. However, it carefully evaluated three interpolation techniques and chose the most widely used method that ensured precise, strong, and credible results compared with the two other approaches. The approaches here may be generalizable, but the specific pattern of results may be specific to the local context and this conflict in Ukraine. Conclusion The findings highlight the need for adaptive frameworks in conflict-zone health research. A combined approach using spatial and suitability modeling offers a novel, multidimensional method for assessing vulnerability. Future research should explore the relationship between conflict intensity, environmental factors, and mental health to improve interventions. Applying this framework to other war-affected regions may enhance its utility. Mental health should be viewed not only as a trauma outcome but also as a reflection of systemic resilience and infrastructure stability. A shift in intervention strategies from a conflict-centric approach to a multi-dimensional resilience-building strategy that integrates environmental adaptation, mental health support, and infrastructure recovery is needed in Ukraine. 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Epidemiol Psychiatr Sci 33:e78. https://doi.org/10.1017/S2045796024000830 Naeem A et al (2025) Parent-child mental health in Ukraine in relation to war trauma and drone attacks. Compr Psychiatr 139:152590. https://doi.org/https://doi.org/ 10.1016/j.comppsych.2025.152590 Kimhi S, Eshel Y, Marciano H, Adini B (2023) Impact of the war in Ukraine on resilience, protective, and vulnerability factors. Front Public Health 11:1053940. https://doi.org/10.3389/fpubh.2023.1053940 Koppenberg M, Mishra AK, Hirsch S (2023) Food aid and violent conflict: A review and Empiricist’s companion. Food Policy 121:102542. https://doi.org/https://doi.org/10.1016/j.foodpol.2023.102542 Additional Declarations The authors declare no competing interests. Supplementary Files SupplementText.docx SupplementaryTables06.09.2025.docx SupplementFigures.pdf Figure S1. Environmental Conditions Index Figure S2. Sleep and Mental Health Index 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-7376114","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":500680427,"identity":"af99cda3-f2d1-4f72-ad01-b9618c85e6b4","order_by":0,"name":"Ubydul Haque","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYDACCRAqYDBgkGA+AOLKEKnFgMGAR4ItAcTlIUULjwGIT1iLwe0ew9tAxcb20j2fX92oseBhYD98dANeLXfOGFsDtZjxyJzdZp1zDOgwnrS0G3i13MgxkwZqseGRyN1mnMMG1CLBY0aslpxnxjn/SNBiBtTC/Di3jQgtkneOFVvOMZAw5rmRZsac2yfBw0bIL3y3mzfeeFNhY9g+I/nx55xvdXL87IeP4dXCwMABig4JEIsNQuJXDgLsD2As5g+EVY+CUTAKRsFIBACBtT48AWWfsgAAAABJRU5ErkJggg==","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Ubydul","middleName":"","lastName":"Haque","suffix":""}],"badges":[],"createdAt":"2025-08-14 17:48:55","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7376114/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7376114/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89295644,"identity":"5ffb3caf-2ba4-4fc3-9223-40b4bfbe74ac","added_by":"auto","created_at":"2025-08-18 13:17:45","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":177072,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 1A. Severity Index,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 1B. Risk map,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 1C. Vulnerability map\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7376114/v1/c1e62b3e724dba93ac14b73d.jpg"},{"id":89295643,"identity":"560e6a1c-c192-49d6-af79-1c314a1e5fad","added_by":"auto","created_at":"2025-08-18 13:17:45","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":123311,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 2. GWR Coefficients and R-Squared, 2.a. Coefficient Environmental Conditions Index, 2.b. Coefficient Severity Index, 2.c. R-Squared\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7376114/v1/359a116e04c60abc60c4aac6.jpg"},{"id":89296565,"identity":"f7406c13-4e76-4394-a65c-f457b1759fa6","added_by":"auto","created_at":"2025-08-18 13:25:45","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":167822,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 3. Vulnerability related to the availability of humanitarian assistance and financial support. 3.a. Access to Humanitarian Aid, 3.b. Conflict Intensity, 3.c. Psychosocial Condition, 3.d. Environmental and Socioeconomic Condition, 3.e. Access to Infrastructure\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7376114/v1/f8ca84a9a346236764bcbde1.jpg"},{"id":89295306,"identity":"e99a118f-a175-4f3b-b8a1-eb0d14fdfd3d","added_by":"auto","created_at":"2025-08-18 13:09:46","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":156842,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 4. Health vulnerabilities linked to environmental factors. 4.a. Exposure, 4.b. Sensitivity, 4.c. Adaptive Capacity\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7376114/v1/5968d8d8c144c4eadef6f4e2.jpg"},{"id":89296566,"identity":"46d937d7-2e15-40a9-8c22-ea619056f46e","added_by":"auto","created_at":"2025-08-18 13:25:46","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":137324,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 5. Vulnerability related to Housing damage. 5. a. Severity of Damage, 5.b. Living conditions, 5.c. Access to Resources, 5.d. Conflict Intensity\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7376114/v1/8cb4cf8d14ea9235390dfe39.jpg"},{"id":89295302,"identity":"3d05e6d4-7b24-440e-9cd0-97995d1a47e8","added_by":"auto","created_at":"2025-08-18 13:09:46","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":163672,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 6. Health and Food Security Vulnerability. 6.a. Conflict Exposure, 6.b. Access to Infrastructure, 6.c. Psychosocial Condition, 6.d. Resource Scarcity, 6.e. Housing Conditions\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7376114/v1/3eaacaa24ad6c28879b6c739.jpg"},{"id":89296776,"identity":"ec5a1ce0-8d6d-4ffc-bbfa-ba99cebaeec1","added_by":"auto","created_at":"2025-08-18 13:33:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1486306,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7376114/v1/b4fcf1ac-b3ab-4163-abc7-9459b8fda288.pdf"},{"id":89295292,"identity":"f84f6065-a61b-4a07-8b2f-a4ed9679b7cc","added_by":"auto","created_at":"2025-08-18 13:09:45","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":25097,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementText.docx","url":"https://assets-eu.researchsquare.com/files/rs-7376114/v1/79dc146f1bc8a6d47b2672da.docx"},{"id":89295295,"identity":"4a4136e3-0bce-4836-a1d3-4f8b1b29bf48","added_by":"auto","created_at":"2025-08-18 13:09:45","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":52252,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables06.09.2025.docx","url":"https://assets-eu.researchsquare.com/files/rs-7376114/v1/cacde570409bf35922a879e8.docx"},{"id":89295645,"identity":"0bc2f6e7-b529-4780-be0a-9af47583e328","added_by":"auto","created_at":"2025-08-18 13:17:46","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":144359,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S1. Environmental Conditions Index\u003c/p\u003e\n\u003cp\u003eFigure S2. Sleep and Mental Health Index\u003c/p\u003e","description":"","filename":"SupplementFigures.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7376114/v1/a557ecf601bb12a78dc20aa3.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eBeyond the Battlefield: Geospatial Insights of Health Vulnerability in Ukraine During the Russian-Ukrainian War\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBetween 1999\u0026ndash;2023, an estimated 4.5 to 4.7\u0026nbsp;million people have died in conflict areas around the world\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. In addition to the conflict-related deaths, war hampers social and economic development, devastates health systems, and disrupts community life\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Since its start in 2022, the conflict in Ukraine has severely damaged infrastructure, disrupting power, water, and fuel supplies\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Damage to critical civilian energy infrastructure in the country caused winter blackouts and environmental stressors (i.e., cold, damp housing, and frequent power outages), increasing the risk of cold-related injuries and exacerbating humanitarian needs, particularly among vulnerable sub-populations. Compounding the crisis, the 2023 Kakhovka Dam destruction caused severe flooding, displacing thousands more Ukrainians and worsening vulnerability\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIt has been reported that war refugees face increased vulnerability to mental health disorders due to compounded trauma from war and environmental hardships\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Understanding how war stress, environment, and social factors affect mental health in conflict zones is vital. Integrating multi-level data on environmental and social factors into risk assessments may help to identify sub-populations at greatest risk of mental health vulnerabilities, and in so doing, it may help shape policies, allocate resources, and create sustainable, impactful interventions\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. However, vulnerability assessments in conflict zones like Ukraine are hindered by security risks, accessibility issues, and evolving conflicts\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Despite those challenges, mapping multidimensional vulnerabilities and underlying community factors is vital for practical risk assessment and planning in conflict-affected, resource-limited settings. This approach supports the development of adaptable strategies and enables more effective, targeted interventions. Moreover, humanitarian planning can become more adaptive and resilient by recognizing these interconnected vulnerabilities\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. To that end, a composite vulnerability index integrating data on war, environmental, and mental health factors may be valuable. Existing data on mental health, sleep, and conflict severity in Ukraine often lacks regional detail, and the limited use of spatial analyses in Ukraine has hindered support for localized needs. Higher resolution, evidence-based studies are urgently needed to inform effective, targeted interventions in high-risk areas.\u003c/p\u003e\u003cp\u003eHere we investigate three primary questions. First, how do environmental factors - such as cold, damp housing and power outages - affect mental health outcomes in conflict-affected populations? We hypothesize that inadequate housing and infrastructure, particularly cold and damp living conditions, significantly worsen mental health outcomes in conflict zones. Second, do mental health vulnerabilities vary spatially across the conflict region, and how do local environmental conditions and conflict intensity shape these vulnerabilities? We hypothesize that areas experiencing high conflict intensity and environmental stressors will show increased mental health vulnerabilities, with these patterns varying spatially and requiring region-specific interventions. Third, how does access to aid, infrastructure, and essential resources influence mental health in high-conflict areas? We hypothesize that limited access to aid, financial support, and basic services exacerbates mental health challenges, especially in regions frequently subjected to attacks and extensive housing damage. This study fills a critical gap by revealing that areas of highest conflict are not always those with the worst health outcomes, highlighting the need for nuanced, location-specific policy strategies to prioritize aid, mental health support, and infrastructure development.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eData Collection\u003c/h2\u003e\u003cp\u003eBuilding on a previously described database\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, we compiled data from online and print news sources in English and Ukrainian, including Al Jazeera, BBC, CNN, Kyiv Independent, Rubryka, Reuters, Institute for the Study of War, and UNICEF through December 2023. Reports covered healthcare facilities, ambulance and pharmacy attacks, civilian deaths and injuries, and damage to civilian infrastructure. Attacks on military targets without civilian casualties were excluded. To avoid duplication, a centralized database tracked hospital destruction, infrastructure damage, shelling, and lethal events, with details on dates, locations, and casualties. Additional data on attacks on hospitals, maternity wards, nuclear plants, homes, schools, and food supplies were included and cross-verified with reports from Ukrainian ministries and local media.\u003c/p\u003e\u003cp\u003eIndividual-level data on sleep and mental health (i.e., anxiety, depression, post-traumatic stress disorder, PTSD, and sleep quality) were collected through an online survey conducted from April 5 to May 15, 2023. An online quota sampling approach was used to collect data from 2,364 adult participants living in Ukraine (ages 18\u0026ndash;79)\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Short sleep duration was defined as \u0026le;\u0026thinsp;6 hours, long as \u0026ge;\u0026thinsp;9 hours, and insomnia was assessed using the Insomnia Symptom Questionnaire\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Winter infection data were also collected through another online survey from one adult (ages 18\u0026ndash;72) per household in 2311 households across 24 Ukrainian oblasts. The questionnaire gathered information on respondents\u0026rsquo; living and health conditions. Data on attacks on health facilities were extracted from the WHO databases\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eUkrainian oblasts were categorized by region: North (Chernihiv, Sumy, and Kyiv), South (Odesa, Mykolayiv, Kherson, and Zaporizhzhia), East (Donetsk, Luhansk, and Kharkiv), and West (Zakarpattia, Chernivtsi, Lviv, and Volyn). We integrated survey-based health data, environmental indices, infrastructure accessibility, and casualty records across 461 Ukrainian cities. All data were cleaned, preprocessed, and structured for spatial analysis. The data categories and their corresponding explanatory variables are detailed in Table S1.\u003c/p\u003e\u003cp\u003eTo estimate city-level prevalence, we applied three spatial interpolation methods: Inverse Distance Weighting (IDW), Empirical Bayesian Kriging (EBK), and Ordinary Least Squares (OLS). After comparing results, the IDW method was selected for final analysis. ArcGIS Pro\u0026rsquo;s Zonal Statistics tool was used to extract and estimate city-level prevalence values, including cold injury risk, based on IDW outputs. Key predictors in the model included insomnia scores\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, sleep duration, environmental severity, housing vulnerabilities, and conflict intensity.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eWe calculated descriptive statistics, including mean, standard deviation, minimum, and maximum values for continuous variables, while percentages and total counts for categorical variables. Multivariable logistic regression was conducted to determine the association between mental health and sleep outcomes, housing and living conditions, infrastructure and essential services, and economic and social safety.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSpatial Analysis\u003c/h3\u003e\n\u003cp\u003eWe used spatial regression to examine the relationships and spatial variability between mental health and sleep indicators as outcome variables. Explanatory variables included weapon types used in attacks, access to healthcare, medicine, food, humanitarian aid, heating equipment (e.g., logs/wood, coal, fuel briquettes, fuel pellets), ruined housing, and access to heat and water (see Table S1). Ordinary Least Squares (OLS) regression was used for spatial clustering, providing R\u0026sup2;, adjusted R\u0026sup2;, and p-values to assess model significance. All variables with a Variance Inflation Factor (VIF\u0026thinsp;\u0026gt;\u0026thinsp;7.5) were dropped from models due to multicollinearity. OLS regression results are only reliable when the model is correctly specified\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWe also used a Geographically Weighted Regression (GWR) model. In geography, a strong predictor variable in one city may have little relevance in another city. GWR was used to explore this spatial heterogeneity, as it generates a unique regression equation for each geographic unit (i.e., city here). In this case, a city, weighting nearby observations more heavily in the calibration process. This approach allows model coefficients to vary across space, capturing localized relationships\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eVulnerability Index Creation\u003c/h3\u003e\n\u003cp\u003eIn addition, we combined the risk map with the sleep and mental health index to assess external stressors and internal responses. As described in more detail in the Supplement, this approach used principal component analysis (PCA) to integrate multiple dimensions to create a comprehensive vulnerability index (see supplement),\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e including dependent (e.g., attack frequency) and independent variables (e.g., self-reported mental health) that capture the complex nature of vulnerability in conflict zones. A higher score or index indicates greater vulnerability.\u003c/p\u003e\n\u003ch3\u003eSuitability Modeling\u003c/h3\u003e\n\u003cp\u003eHere, suitability modeling in GIS helps identify the areas most affected by war by examining the conditions and features of each place. We conducted suitability modeling using the tool in ArcGIS Pro. Composite indices were calculated for each variable class with PCA-derived weights in Python. Rasterized layers were combined using the weighted overlay tool to generate composite suitability maps for four distinct vulnerability categories.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eModel validation\u003c/h2\u003e\u003cp\u003eThe model was trained randomly on half of the cities selected for cross-validation and then tested on the remaining half. For bootstrapping, 100 random samples were drawn with replacement, and both models were applied to each sample. Across these iterations, over 95% of the explanatory variable coefficients remained statistically significant, demonstrating the models\u0026rsquo; robustness and consistency.\u003c/p\u003e\u003c/div\u003e\n\u003ch2\u003eEthical approval\u003c/h2\u003e\n\u003cp\u003eThe study protocol received ethical approval from the Poltava State Medical University Ethics Committee in Ukraine (Approval No. 212) and the Institutional Review Board at Rutgers University in the United States (Protocol #Pro2023000101).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 1,934 drone attacks occurred from February 2022 to December 2023, resulting in 8,378 casualties (an average of 220.5) in 22 of 28\u0026nbsp;oblasts (Table S2A). Additionally, 1,162 attacks with other weapons that killed and injured people were also reported. A total of 258 artillery strikes were recorded, averaging 6.8 casualties per attack. PTSD (26.2%), depression (43.6%), anxiety (22.8%), loneliness (39.1%), and insomnia (36.7%) were reported (Table S2B). \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eRegression analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePeople living in oblasts that experienced cold, damp, and crowded conditions were more likely to have mental health issues (Table S3). In multivariable models, individuals living in cold (Adjusted odds ratio, AOR 1.55, 95% CI: 1.16-2.06), damp (AOR 1.70, 95% CI: 1.29-2.24), and crowded (AOR 1.44, 95% CI: 1.10-1.87) conditions had significantly higher odds of PTSD. The odds of depression were similarly higher in those living in cold (AOR 1.57, 95% CI: 1.12-2.20), damp (AOR 1.80, 95% CI: 1.30-2.50), crowded (AOR 1.43, 95% CI: 1.05-1.93) conditions, needing house repairs (AOR 1.39, 95% CI: 1.00-1.93), and lacking housing subsidies (AOR 1.54, 95% CI: 1.11-2.12). Higher odds of anxiety, loneliness, and insomnia were found in cold, damp, crowded areas and those lacking housing subsidies. (Table S3).\u003c/p\u003e\n\u003cp\u003ePeople with less access to food, public transport, and who experienced frequent power outages had significantly higher odds of PTSD and loneliness (Table S4). However, lack of access to food shops and public transport was significantly associated with higher odds of depression, anxiety, and insomnia (Table S4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThose with a lack of access to hospitals and ambulances were significantly associated with higher odds of PTSD, loneliness, insomnia, and depression, with anxiety linked only to a lack of access to ambulances (Table S5). Lastly, individuals who felt unsafe or insecure and lacked income had significantly higher odds of PTSD, while those feeling unsafe, insecure, and lacking food had significantly higher odds of depression, anxiety, loneliness, and insomnia (Table S6).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eVulnerability Analysis\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analysis revealed significant spatial variability in mental health vulnerability in Ukraine. Severity, environmental conditions, and sleep and mental health indices were combined to create a comprehensive vulnerability map.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Severity Index (Figure 1A) quantified war-related disruptions, including attack frequency and casualties, highlighting regions with severe conflict and mental health impacts. The Environmental Conditions Index (Figure S1) measured stressors like extreme cold, poor housing, and power outages, with high values in areas with severe winter and poor living conditions. The Sleep and Mental Health Index (Figure S2) reflected anxiety, depression, and sleep deprivation, with elevated scores indicating higher psychological distress. The Risk Map (Figure 1B) combined the Severity and Environmental Conditions indices, identifying regions at high risk due to conflict and environmental exposure. The Vulnerability Map (Figure 1C) integrated the Risk Map and Sleep and Mental Health Index, offering a comprehensive assessment of population vulnerability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSpatial analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe OLS model revealed key relationships between explanatory variables and mental health outcomes, with significant coefficients (Table S7). Given the model\u0026rsquo;s specifications, spatially adaptive modeling was required, leading to the application of GWR. GWR estimated regression coefficients for each spatial unit (i.e., city), capturing local vulnerability and mental health outcome variations. Model diagnostics indicated that spatially varying coefficients improved the model\u0026apos;s performance compared to the global OLS model (Table S8). The GWR analysis showed significant regional variation in vulnerability indices\u0026rsquo; influence on mental health, highlighting the importance of location-specific factors during conflict.\u003c/p\u003e\n\u003cp\u003eThe Environmental Conditions Index had the most substantial effect in the northern and central regions (Figure 2a), where harsh winters and poor infrastructure were associated with greater vulnerabilities. These areas showed higher rates of anxiety, depression, and sleep disturbances, with positive coefficients linked to poor mental health outcomes. The Severity Index, reflecting conflict intensity (attacks, casualties, and infrastructure damage), showed a substantial impact on mental health in eastern Ukraine (Figure 2b). Areas with prolonged violence and frequent attacks had higher risks of psychological distress, including PTSD and sleep deprivation, as indicated by high coefficient values. The Local R\u0026sup2; map (Figure 2c) showed the model\u0026rsquo;s explanatory power across regions. Central and eastern Ukraine had the highest values, indicating strong model fit, while western areas showed lower values, suggesting unmeasured factors influencing mental health. The variation in Local R\u0026sup2; suggests further investigation into factors affecting mental health in western Ukraine. This highlighted the spatial complexity of vulnerability and the need for tailored interventions.\u003c/p\u003e\n\u003cp\u003eA comparative analysis of the suitability maps showed significant overlap in the most vulnerable regions, particularly in eastern and southeastern Ukraine. These areas faced multidimensional risks from conflict, inadequate aid, environmental challenges, and housing damage, with the combined index offering a comprehensive assessment of mental health vulnerability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eVulnerability related to the availability of humanitarian assistance and financial support\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRegions with limited access to humanitarian aid and financial support were more vulnerable (Figure 3). Key contributors to this vulnerability included the frequency of attacks and lack of access to essential services, such as food shops and public transport. Ukraine\u0026apos;s eastern and southeastern regions were the most affected, aligning with areas of high conflict intensity (Figure 3a-e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMental Health vulnerabilities linked to environmental factors\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEnvironmental stressors like cold, damp housing, and frequent power outages significantly increased vulnerability in northern and eastern regions (Figure 4a-c). Sensitivity indicators, such as insufficient food and medicine, worsened health risks, while adaptive capacity indicators, like access to hospitals and pharmacies, were limited in highly vulnerable areas.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eVulnerability related to housing damage\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe spatial distribution of housing vulnerability reflected the severity of housing damage and access (or lack thereof) to essential recovery resources (Figure 5). Housing vulnerability was highly correlated with conflict intensity, measured by attack frequency and weapon types. Regions with extensive housing damage and limited insulation repairs were particularly at risk during winter (Figure 5a-d).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eHealth and food security vulnerability\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConflict exposure and food supply disruptions were key drivers of vulnerability, as shown in Figure 6, highlighting regions impacted by resource scarcity and health effects. Conflict exposure, access to food, and poor housing conditions worsened mental health outcomes, especially in areas with limited access to clean water, heating, and medical services (Figure 6a \u0026ndash; e).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eUsing complementary data from the mainstream media, the Institute for the Study of War, UNICEF, and our cross-sectional data on winter infection, sleep, and mental health, we observed that environmental and conflict-related factors significantly affect sleep and mental health outcomes in Ukraine during the current conflict. Cold, damp, and overcrowded housing, along with limited access to heating, healthcare, food, and transport, were linked to higher rates of PTSD, depression, anxiety, loneliness, and insomnia, particularly in northern and rural regions. Conflict intensity exacerbated mental health issues in eastern Ukraine. Environmental stressors such as cold, damp, and frequent power outages appeared to be most impactful on mental health in northern Ukraine, while conflict severity was most important in the east.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs the first analysis in an active war zone, it provides insights into how psychological distress varies across different geographic and contextual factors in Ukraine\u0026apos;s ongoing conflict.\u003c/p\u003e\n\u003cp\u003eIn 2021, the Uppsala Conflict Data Program recorded 54 state-based conflicts\u003csup\u003e17\u003c/sup\u003e. Research examines armed conflict\u0026rsquo;s economic, cultural, and humanitarian effects, including impacts on growth and human development. Similar to our findings, their research highlights elevated mortality, disability, and disease rates, as well as a high prevalence of mental disorders in conflict zones, with one in five adults affected living in a conflict zone\u003csup\u003e18,19\u003c/sup\u003e. Consistently, previous studies have shown that socio-demographic and geographic factors shape risks, while displacement, family separation, and service barriers compound psychosocial challenges\u003csup\u003e20-22\u003c/sup\u003e. Building upon that prior work, here we integrated spatial regression and suitability modeling to advance understanding of mental health and vulnerability in war-affected regions. This approach quantifies spatially distributed risk factors like proximity to risks and socioeconomic deprivation, providing deeper insights into localized mental health impacts.\u003c/p\u003e\n\u003cp\u003eArmed conflicts can devastate local environments, further exacerbating mental health issues in affected regions\u003csup\u003e23\u003c/sup\u003e. Eastern Ukraine, closer to the frontlines, is the target of frequent shelling, combat, and civilian-targeted violence\u003csup\u003e24\u003c/sup\u003e. Our results echo those findings, showing that eastern Ukraine endures direct conflict exposure, while also highlighting the spatial variation in vulnerability, such as northern Ukraine\u0026rsquo;s threats of environmental hazards, such as droughts and floods. This spatial granularity can inform humanitarian aid efforts, helping to prioritize regions with environmental and infrastructural vulnerabilities, not only in active conflict zones. Specifically, our results point to a need for improved heating and housing in northern Ukraine, while eastern regions may benefit most from enhanced PTSD treatment services. More generally, with limited resources in conflict-stricken areas, investment in mental health services may be informed by spatial analyses.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePrior conflict research suggests that high-intensity violence correlates with poor mental health outcomes, such as PTSD, depression, and anxiety\u003csup\u003e25\u003c/sup\u003e. However, other studies indicate that the relationship between conflict intensity and mental health is more complex. Some areas, despite high violence exposure, show better mental health outcomes\u003csup\u003e22\u003c/sup\u003e. \u0026nbsp;Key factors include community resilience, community-based mental health interventions and access to mental health services, culturally grounded coping strategies, \u0026nbsp;and humanitarian aid\u003csup\u003e26,27\u003c/sup\u003e. Strong local support systems can buffer the psychological impact of conflict, reducing the long-term mental health burden\u003csup\u003e28\u003c/sup\u003e. Additionally, areas with robust health infrastructure and services experience lower mental disorder prevalence, even in high-conflict regions\u003csup\u003e29,30\u003c/sup\u003e. Adequate physical and adaptive infrastructure resilience helps mitigate societal vulnerability and supports health\u003csup\u003e31\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCompared with previous studies, our findings reveal that mental health burden is not directly tied to proximity to conflict. Northern oblasts, such as Chernihiv and Sumy, despite moderate attack intensity, experienced high anxiety, depression, and insomnia. Environmental stressors, including cold, housing damage, and poor access to essentials, significantly impacted mental health. Conversely, despite heavy shelling, some eastern areas had lower distress levels due to better humanitarian access. Our findings show that multiple vulnerabilities, such as housing destruction and supply disruptions, jointly predict increased risks of PTSD, depression, and insomnia. Spatial models also showed significant regional variations, underscoring the need for location-specific mental health and humanitarian interventions.\u003c/p\u003e\n\u003cp\u003ePrevious studies confirmed that war-related environmental stressors significantly impact mental health and contribute to PTSD\u003csup\u003e32\u003c/sup\u003e. Our study also revealed that environmental conditions, such as extreme cold, poor housing, and power outages, may impact mental health more strongly than direct conflict exposure in some regions. Northern Ukraine, facing harsh winters and infrastructure failures, shows mental health vulnerabilities comparable to or exceeding areas with heavy shelling. In eastern Ukraine, a high-conflict area, PTSD prevalence reaches 26.2%, while northern Ukraine shows higher rates of depression (43.6%) and insomnia (36.7%). Factors like cold, dampness, and overcrowding contribute significantly to these outcomes. Indirect stressors, such as food insecurity and inadequate healthcare, also play a critical role.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCompared with the previous studies\u003csup\u003e11,12,33\u003c/sup\u003e the GWR model in this study reveals significant spatial variation in mental health vulnerability across Ukraine. In eastern regions, PTSD and sleep deprivation correlate with conflict exposure, while in northern areas, anxiety and depression are linked to environmental factors. These findings emphasize the need for region-specific interventions.\u003c/p\u003e\n\u003cp\u003eA vulnerability index identifies high-risk communities for targeted aid and preparedness in war and public health crises\u003csup\u003e34\u003c/sup\u003e. Our study introduced a composite vulnerability index that integrates war severity, environmental stressors, and mental health indicators, providing a comprehensive risk assessment in conflict zones. It combines psychosocial, infrastructural, environmental, and conflict factors into a geospatial metric, highlighting compounded vulnerability in central and northern rural areas. The findings challenge traditional health frameworks by emphasizing localized conditions, such as infrastructure breakdown and other hardships (e.g., access to food shops and medicine), over direct conflict exposure, revealing spatial variation in mental health outcomes.\u003c/p\u003e\n\u003cp\u003eWartime aid aims to save lives and uphold dignity, yet existing studies lack robust methods to support the best use and optimal distribution of humanitarian assistance during the conflict\u003csup\u003e35\u003c/sup\u003e. Our study reveals that aid distribution often overlooks areas that suffer from conflict and systemic hardships. These regions face severe shortages of essentials such as food, water, medicine, and heating, worsening psychological distress, PTSD, anxiety, and insomnia. Mental health may deteriorate further under conditions of uncertainty, neglect, and housing damage. Current aid models focus narrowly on combat zones and may miss broader geographic concerns, compounding vulnerabilities. Humanitarian efforts must prioritize regions with both physical insecurity and infrastructure collapse. Tailored interventions such as trauma therapy in conflict zones and housing support elsewhere are essential. Integrating multidimensional vulnerability indices can vastly improve aid allocation and promote health equity.\u003c/p\u003e\n\u003cp\u003eThis study introduces a novel approach to modeling mental health vulnerability in conflict zones, combining geographically weighted regression, suitability mapping, and stressor indices.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study has limitations, including recall bias in self-reported data and underrepresentation of isolated populations. The cross-sectional design doesn\u0026apos;t capture temporal fluctuations in vulnerability. This study used estimated city-level prevalence data. However, it carefully evaluated three interpolation techniques and chose the most widely used method that ensured precise, strong, and credible results compared with the two other approaches. The approaches here may be generalizable, but the specific pattern of results may be specific to the local context and this conflict in Ukraine.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe findings highlight the need for adaptive frameworks in conflict-zone health research. A combined approach using spatial and suitability modeling offers a novel, multidimensional method for assessing vulnerability. Future research should explore the relationship between conflict intensity, environmental factors, and mental health to improve interventions. Applying this framework to other war-affected regions may enhance its utility. Mental health should be viewed not only as a trauma outcome but also as a reflection of systemic resilience and infrastructure stability. A shift in intervention strategies from a conflict-centric approach to a multi-dimensional resilience-building strategy that integrates environmental adaptation, mental health support, and infrastructure recovery is needed in Ukraine.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDeclaration of interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone declared\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData sharing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting this study\u0026apos;s findings will be available on request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank all participants in Ukraine.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Institute of Environmental Health Sciences Center (grant P30ES005022).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHerre B, Rod\u0026eacute;s-Guirao L, Roser M (2024) - War and Peace. 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Food Policy 121:102542. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.1016/j.foodpol.2023.102542\u003c/span\u003e\u003cspan address=\"10.1016/j.foodpol.2023.102542\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"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":"","lastPublishedDoi":"10.21203/rs.3.rs-7376114/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7376114/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAlthough the devastation of war is well recognized, no previous study has systematically integrated the interplay of conflict intensity, access to humanitarian aid, environmental conditions, and infrastructure to understand how these factors collectively shape health vulnerability across war-affected regions. Using spatial and suitability modeling, we assessed multidimensional vulnerabilities in Ukraine during the Russian invasion, including mental health risks, environmental stressors, and infrastructure disruptions. We developed a multi-source conflict-related health impact database (February 2022- December 2023). Sleep, mental health, and casualty data were collected through national online surveys (n\u0026thinsp;\u0026gt;\u0026thinsp;2,312). Data was cleaned, geolocated across 461 cities, and analyzed using Inverse Distance Weighting interpolation. Logistic and spatial regression were used to assess relationships between conflict exposure, living conditions, mental health, sleep deprivation, and cold injury risks. A composite vulnerability index was created using weighted Principal Component Analysis-based methods. Regions with intense conflict, poor housing, frequent power outages, food shortages, and limited access to healthcare and aid faced the highest vulnerability. Cold and damp conditions, housing damage, and resource scarcity exacerbated household living conditions, especially in eastern and northern Ukraine. Not all high-conflict areas had poor mental health outcomes; cold, damp, crowded housing, food insecurity, and power outages were equally critical drivers. Mental health issues (PTSD, depression, and insomnia) were highest in regions with harsh winters, poor infrastructure, and limited aid. War-related health issues extend beyond direct conflict exposure and involve the interplay of conflict, environmental stressors, and infrastructure damage in shaping casualties, sleep, and mental health outcomes.\u003c/p\u003e","manuscriptTitle":"Beyond the Battlefield: Geospatial Insights of Health Vulnerability in Ukraine During the Russian-Ukrainian War","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-18 13:09:41","doi":"10.21203/rs.3.rs-7376114/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":"6590c0df-1a06-46be-bc1f-8abc5ee5a4a5","owner":[],"postedDate":"August 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-18T13:09:41+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-18 13:09:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7376114","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7376114","identity":"rs-7376114","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}