Climate Stressors, Mental Health Outcomes, and Alcohol-Related Harm Among Rural Adults in Banaskantha District, Gujarat, India: A Community-Based Cross-Sectional Public Health Surveillance Study (2021 - 2025)

Climate Stressors, Mental Health Outcomes, and Alcohol-Related Harm Among Rural Adults in Banaskantha District, Gujarat, India: A Community-Based Cross-Sectional Public Health Surveillance Study (2021 - 2025) | 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 Climate Stressors, Mental Health Outcomes, and Alcohol-Related Harm Among Rural Adults in Banaskantha District, Gujarat, India: A Community-Based Cross-Sectional Public Health Surveillance Study (2021 - 2025) Atul Amarshibhai Devganiya This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8486299/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 Background Climate variability introduces sustained socioeconomic stressors that disproportionately affect rural agrarian populations. Drought, crop loss, and social isolation have been associated with psychological distress and maladaptive coping behaviours, including harmful alcohol use. However, integrated community-level evidence linking climate stressors, mental health symptoms, and alcohol-related harm in rural India remains limited. Methods A community-based cross-sectional public health surveillance study was conducted between 2019 and 2025 in Banaskantha district, Gujarat, India, under the Family Adoption Program. Adults aged 18–70 years with at least five years of residence were recruited from rural talukas experiencing varying levels of climate stress. Depressive and anxiety symptoms were screened using validated PHQ-9 and GAD-7 instruments. Alcohol-related harm was assessed using documented clinical morbidity and reported functional impairment. Climate stress exposure was classified ecologically based on drought frequency, agricultural loss, and social isolation indicators. Analyses were descriptive and exploratory. Results Among 300 participants, depressive symptoms (PHQ-9 ≥ 10) were identified in 26.0%, anxiety symptoms (GAD-7 ≥ 10) in 21.7%, and alcohol-related harm in 32.3%. The highest prevalence of all outcomes was observed in talukas with sustained drought and social isolation. A graded pattern was observed across climate stress exposure categories, with increasing mental health symptoms and alcohol-related harm corresponding to higher climate stress intensity. Conclusions Mental health symptoms and alcohol-related harm were common among rural adults exposed to sustained climate stressors in Banaskantha district. The findings suggest interconnected psychosocial pathways linking environmental stress, psychological distress, and harmful coping behaviours. Integrated mental health surveillance and climate-responsive public health strategies are needed in vulnerable rural settings. Epidemiology climate stressors depression anxiety alcohol-related harm rural public health environmental vulnerability 1. Introduction Mental health disorders constitute a major component of the global burden of disease, yet remain under-detected and under-addressed in rural low-resource settings. In India, rural populations face persistent barriers to mental health care, including limited service availability, stigma, and socioeconomic precarity. Climate variability has emerged as an additional structural determinant of mental health. Recurrent drought, crop failure, extreme weather, and associated livelihood instability impose sustained psychological stress on agrarian households. Such stressors may precipitate depressive and anxiety symptoms and contribute to maladaptive coping strategies, particularly alcohol use, thereby increasing alcohol-related morbidity. Banaskantha district in northern Gujarat is characterized by semi-arid conditions, recurrent drought, agricultural dependence, and seasonal isolation. Despite these vulnerabilities, primary community-level data examining the combined burden of mental health symptoms and alcohol-related harm in relation to climate stressors are scarce. This study aims to address this gap through public health surveillance using validated screening tools and field-based data. 2. Study Context and Conceptual Framework This study is informed by an equity-focused public health framework , which conceptualizes climate stressors as upstream social determinants that interact with economic vulnerability, occupational exposure, and limited healthcare access to shape mental health outcomes. Rather than treating depression, anxiety, or alcohol-related harm as isolated conditions, the framework emphasizes linked pathways : climate stress → livelihood insecurity → psychological distress → maladaptive coping → health and social harm. The present analysis represents a focused mental health and substance-related surveillance component of a larger mixed-methods investigation of climate-sensitive health outcomes in rural Banaskantha. 3. Materials and Methods 3.1 Study Design and Setting A community-based cross-sectional public health study was conducted from January 2019 to August 2025 in Banaskantha district, Gujarat, India. Field activities were carried out through the Family Adoption Program across selected rural talukas. 3.2 Study Population Adults aged 18–70 years with a minimum of five years of residence in the study area were eligible. Participants were purposively recruited from talukas with documented variation in climate stress exposure, including Ratanpur, Tharad, Moriya, and other rural regions. 3.3 Sample Size and Distribution A total of 300 participants were included. Table 1 Study Sample by Taluka (n = 300) Taluka Participants (n) Ratanpur 90 Tharad 80 Moriya 70 Other rural talukas 60 Total 300 No formal sample size calculation was performed, as the study was exploratory and surveillance-oriented. 3.4 Climate Stress Exposure Assessment Individual-level exposure data were not available; therefore, ecological classification was used as a proxy for long-term climate stress exposure. Classification was informed by district-level drought reports, agricultural loss indicators, and field observations of social isolation. Table 2 Climate Stress Exposure Classification Exposure category Dominant stressors High Recurrent drought, crop loss, social isolation Moderate Mixed agricultural and economic stress Lower Relatively stable agricultural conditions 3.5 Mental Health Outcome Assessment Depressive symptoms were screened using the Patient Health Questionnaire-9 (PHQ-9) and anxiety symptoms using the Generalized Anxiety Disorder-7 (GAD-7) . A score of 10 or higher on each instrument was used to indicate moderate-to-severe symptom burden. These instruments were applied for public health screening and surveillance purposes , not for clinical psychiatric diagnosis. 3.6 Alcohol-Related Harm Assessment Alcohol-related harm was assessed through documented clinical morbidity (e.g., liver disease), reported healthcare utilization, and functional or social impairment attributable to alcohol use. Consumption quantity alone was not used as an outcome. 3.7 Statistical Analysis Descriptive statistics were used to estimate the prevalence of depressive symptoms, anxiety symptoms, and alcohol-related harm. Outcomes were examined across talukas and climate stress exposure categories. Analyses were exploratory and hypothesis-generating. Statistical analyses were conducted using SPSS version 28. 3.8 Ethics Ethical approval was obtained from the Ethics Committee of Banas Medical College and Research Institute. Written informed consent was obtained from all participants. 4. Results 4.1 Prevalence of Mental Health Symptoms and Alcohol-Related Harm Among the 300 participants, depressive symptoms were identified in 26.0%, anxiety symptoms in 21.7%, and alcohol-related harm in 32.3%. Table 3 Mental Health Outcomes by Taluka Taluka Depression (%) Anxiety (%) Alcohol-related harm (%) Ratanpur 31.1 26.7 40.0 Tharad 27.5 22.5 33.8 Moriya 22.9 18.6 27.1 Other 18.3 16.7 23.3 Overall 26.0 21.7 32.3 4.2 Outcomes by Climate Stress Exposure Category A graded pattern was observed across climate stress exposure categories. Table 4 Outcomes by Climate Stress Exposure Exposure category Depression Anxiety Alcohol-related harm High Highest Highest Highest Moderate Intermediate Intermediate Intermediate Lower Lowest Lowest Lowest 5. Discussion This study demonstrates a substantial burden of mental health symptoms and alcohol-related harm among rural adults exposed to sustained climate stressors in Banaskantha district. The observed gradients across exposure categories support a conceptual model in which environmental stress contributes to psychological distress and harmful coping behaviours. The findings extend existing literature by providing primary, community-level surveillance data from a rural Indian setting, highlighting the interconnected nature of climate vulnerability, mental health, and substance-related harm. Importantly, this study frames mental health outcomes within a public health and social determinants context rather than a purely clinical paradigm. 5.1 Limitations The cross-sectional design limits causal inference. Mental health outcomes were assessed using screening instruments rather than diagnostic interviews, and climate exposure was classified ecologically. Nevertheless, the study provides valuable exploratory evidence from an under-represented rural population. 6. Conclusions Mental health symptoms and alcohol-related harm were prevalent among rural adults facing sustained climate stressors in Banaskantha district. These findings underscore the need for integrated, climate-responsive mental health surveillance and public health interventions in vulnerable rural regions. Declarations Conflicts of Interest The author declares no conflicts of interest. Acknowledgments The author thanks Banas Medical College and Research Institute and participating communities under the Family Adoption Program. No external funding was received. Data Availability Statement All data generated in this study are available from the corresponding author upon reasonable request. References Patel V et al (2018) The Lancet Commission on global mental health and sustainable development. Lancet Watts N et al (2021) The Lancet Countdown on health and climate change. Lancet Kroenke K et al (2001) The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med Spitzer RL et al (2006) A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med Additional Declarations The authors declare no competing interests. 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-8486299","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":567637386,"identity":"9a146692-152d-408e-b5b9-599668cfea05","order_by":0,"name":"Atul Amarshibhai Devganiya","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABGElEQVRIiWNgGAWjYHACxgM8ELqBgaHCRg7EPPAAnwY2oAKEljNpxmAtCcRpAelqO5zYAGLg08I/v8fgwJuKw3b9s5vbJH62HU6fH3b4IdAWOzndBuxaJI7xGBycc+Zw8ow7B9ske86l5268nWYA1JJsbHYAhzVALYd529KSGW4ktknwlFnnbpydANJyIHEbDi3yYC3/0pLlgVok/7AxpxvOTv+AV4sBWEuDjZ0BUIs0T5tzgrx0Dn5bDI+lFRycc8wmwfBGYrO1zJk0ww3SOQUHEgxw+0Xu8OGND97USNjL3Uh/ePNNhY28/Oz0zR8+VNjJ4fQ+FICig0UC7FSwSgP8ykHAHoiZP4BY8g2EVY+CUTAKRsHIAgDNlWrSso0waAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0003-1281-4079","institution":"Banas Medical College \u0026 Research Institute","correspondingAuthor":true,"prefix":"","firstName":"Atul","middleName":"Amarshibhai","lastName":"Devganiya","suffix":""}],"badges":[],"createdAt":"2025-12-31 06:06:41","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8486299/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8486299/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":99605206,"identity":"b7cb8658-dbf6-4074-b688-66b33596ded0","added_by":"auto","created_at":"2026-01-06 11:12:35","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":24401,"visible":true,"origin":"","legend":"","description":"","filename":"CrossSectionalPublicHealthSurveillanceStudy.docx","url":"https://assets-eu.researchsquare.com/files/rs-8486299/v1/e5a393d454b9dcbf5607f4bc.docx"},{"id":99793585,"identity":"21136e64-e95e-4f45-9a38-ed681250c4ab","added_by":"auto","created_at":"2026-01-08 13:31:54","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":342,"visible":true,"origin":"","legend":"","description":"","filename":"rs8486299.json","url":"https://assets-eu.researchsquare.com/files/rs-8486299/v1/8868f6d1cbaf78a895d26b7a.json"},{"id":99605209,"identity":"dbde0ce8-8cb6-4868-bde3-f549df6842e0","added_by":"auto","created_at":"2026-01-06 11:12:35","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":30683,"visible":true,"origin":"","legend":"","description":"","filename":"rs84862990enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8486299/v1/1fc50d6331a4c1cc1caceb95.xml"},{"id":99605210,"identity":"7395f2c9-21dd-40a6-81dd-206bbab18b7a","added_by":"auto","created_at":"2026-01-06 11:12:35","extension":"xml","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":28058,"visible":true,"origin":"","legend":"","description":"","filename":"rs84862990structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8486299/v1/ec59eefd7642b353009faa51.xml"},{"id":99605207,"identity":"2f7c0a24-a390-4f15-a39c-6cb6ddb2ce43","added_by":"auto","created_at":"2026-01-06 11:12:35","extension":"html","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":39400,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8486299/v1/5023858211e1562b7ffee2a8.html"},{"id":99804150,"identity":"a1053595-42df-413f-833a-9ab43a155ff2","added_by":"auto","created_at":"2026-01-08 14:11:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":752122,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8486299/v1/bbae7d87-f318-4875-ab5f-a29d18d190be.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eClimate Stressors, Mental Health Outcomes, and Alcohol-Related Harm Among Rural Adults in Banaskantha District, Gujarat, India: A Community-Based Cross-Sectional Public Health Surveillance Study (2021 - 2025)\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMental health disorders constitute a major component of the global burden of disease, yet remain under-detected and under-addressed in rural low-resource settings. In India, rural populations face persistent barriers to mental health care, including limited service availability, stigma, and socioeconomic precarity.\u003c/p\u003e \u003cp\u003eClimate variability has emerged as an additional structural determinant of mental health. Recurrent drought, crop failure, extreme weather, and associated livelihood instability impose sustained psychological stress on agrarian households. Such stressors may precipitate depressive and anxiety symptoms and contribute to maladaptive coping strategies, particularly alcohol use, thereby increasing alcohol-related morbidity.\u003c/p\u003e \u003cp\u003eBanaskantha district in northern Gujarat is characterized by semi-arid conditions, recurrent drought, agricultural dependence, and seasonal isolation. Despite these vulnerabilities, primary community-level data examining the combined burden of mental health symptoms and alcohol-related harm in relation to climate stressors are scarce. This study aims to address this gap through public health surveillance using validated screening tools and field-based data.\u003c/p\u003e"},{"header":"2. Study Context and Conceptual Framework","content":"\u003cp\u003eThis study is informed by an \u003cb\u003eequity-focused public health framework\u003c/b\u003e, which conceptualizes climate stressors as upstream social determinants that interact with economic vulnerability, occupational exposure, and limited healthcare access to shape mental health outcomes.\u003c/p\u003e \u003cp\u003eRather than treating depression, anxiety, or alcohol-related harm as isolated conditions, the framework emphasizes \u003cb\u003elinked pathways\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eclimate stress \u0026rarr; livelihood insecurity \u0026rarr; psychological distress \u0026rarr; maladaptive coping \u0026rarr; health and social harm.\u003c/p\u003e \u003cp\u003eThe present analysis represents a focused mental health and substance-related surveillance component of a larger mixed-methods investigation of climate-sensitive health outcomes in rural Banaskantha.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"3. Materials and Methods","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Study Design and Setting\u003c/h2\u003e \u003cp\u003eA community-based cross-sectional public health study was conducted from January 2019 to August 2025 in Banaskantha district, Gujarat, India. Field activities were carried out through the Family Adoption Program across selected rural talukas.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Study Population\u003c/h2\u003e \u003cp\u003eAdults aged 18\u0026ndash;70 years with a minimum of five years of residence in the study area were eligible. Participants were purposively recruited from talukas with documented variation in climate stress exposure, including Ratanpur, Tharad, Moriya, and other rural regions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Sample Size and Distribution\u003c/h2\u003e \u003cp\u003eA total of \u003cb\u003e300 participants\u003c/b\u003e were included.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStudy Sample by Taluka (n\u0026thinsp;=\u0026thinsp;300)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTaluka\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParticipants (n)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRatanpur\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTharad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoriya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther rural talukas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e300\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNo formal sample size calculation was performed, as the study was exploratory and surveillance-oriented.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Climate Stress Exposure Assessment\u003c/h2\u003e \u003cp\u003eIndividual-level exposure data were not available; therefore, \u003cb\u003eecological classification\u003c/b\u003e was used as a proxy for long-term climate stress exposure. Classification was informed by district-level drought reports, agricultural loss indicators, and field observations of social isolation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClimate Stress Exposure Classification\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDominant stressors\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRecurrent drought, crop loss, social isolation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMixed agricultural and economic stress\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRelatively stable agricultural conditions\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Mental Health Outcome Assessment\u003c/h2\u003e \u003cp\u003eDepressive symptoms were screened using the \u003cb\u003ePatient Health Questionnaire-9 (PHQ-9)\u003c/b\u003e and anxiety symptoms using the \u003cb\u003eGeneralized Anxiety Disorder-7 (GAD-7)\u003c/b\u003e. A score of \u003cb\u003e10 or higher\u003c/b\u003e on each instrument was used to indicate moderate-to-severe symptom burden.\u003c/p\u003e \u003cp\u003eThese instruments were applied for \u003cb\u003epublic health screening and surveillance purposes\u003c/b\u003e, not for clinical psychiatric diagnosis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Alcohol-Related Harm Assessment\u003c/h2\u003e \u003cp\u003eAlcohol-related harm was assessed through documented clinical morbidity (e.g., liver disease), reported healthcare utilization, and functional or social impairment attributable to alcohol use. Consumption quantity alone was not used as an outcome.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Statistical Analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics were used to estimate the prevalence of depressive symptoms, anxiety symptoms, and alcohol-related harm. Outcomes were examined across talukas and climate stress exposure categories. Analyses were exploratory and hypothesis-generating. Statistical analyses were conducted using SPSS version 28.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Ethics\u003c/h2\u003e \u003cp\u003eEthical approval was obtained from the Ethics Committee of Banas Medical College and Research Institute. Written informed consent was obtained from all participants.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Prevalence of Mental Health Symptoms and Alcohol-Related Harm\u003c/h2\u003e \u003cp\u003eAmong the 300 participants, depressive symptoms were identified in 26.0%, anxiety symptoms in 21.7%, and alcohol-related harm in 32.3%.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMental Health Outcomes by Taluka\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTaluka\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDepression (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnxiety (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAlcohol-related harm (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRatanpur\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTharad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoriya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOverall\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e26.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e21.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e32.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Outcomes by Climate Stress Exposure Category\u003c/h2\u003e \u003cp\u003eA graded pattern was observed across climate stress exposure categories.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOutcomes by Climate Stress Exposure\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAlcohol-related harm\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHighest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHighest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHighest\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntermediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntermediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIntermediate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLowest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLowest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLowest\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThis study demonstrates a substantial burden of mental health symptoms and alcohol-related harm among rural adults exposed to sustained climate stressors in Banaskantha district. The observed gradients across exposure categories support a conceptual model in which environmental stress contributes to psychological distress and harmful coping behaviours.\u003c/p\u003e \u003cp\u003eThe findings extend existing literature by providing \u003cb\u003eprimary, community-level surveillance data\u003c/b\u003e from a rural Indian setting, highlighting the interconnected nature of climate vulnerability, mental health, and substance-related harm. Importantly, this study frames mental health outcomes within a public health and social determinants context rather than a purely clinical paradigm.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Limitations\u003c/h2\u003e \u003cp\u003eThe cross-sectional design limits causal inference. Mental health outcomes were assessed using screening instruments rather than diagnostic interviews, and climate exposure was classified ecologically. Nevertheless, the study provides valuable exploratory evidence from an under-represented rural population.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"6. Conclusions","content":"\u003cp\u003eMental health symptoms and alcohol-related harm were prevalent among rural adults facing sustained climate stressors in Banaskantha district. These findings underscore the need for integrated, climate-responsive mental health surveillance and public health interventions in vulnerable rural regions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflicts of Interest\u003c/h2\u003e \u003cp\u003eThe author declares no conflicts of interest.\u003c/p\u003e \u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eThe author thanks Banas Medical College and Research Institute and participating communities under the Family Adoption Program. No external funding was received.\u003c/p\u003e\u003ch2\u003eData Availability Statement\u003c/h2\u003e \u003cp\u003eAll data generated in this study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePatel V et al (2018) The Lancet Commission on global mental health and sustainable development. Lancet\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWatts N et al (2021) The Lancet Countdown on health and climate change. Lancet\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKroenke K et al (2001) The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpitzer RL et al (2006) A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med\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":"Banas Medical College \u0026 Research Institute ","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":"climate stressors, depression, anxiety, alcohol-related harm, rural public health, environmental vulnerability","lastPublishedDoi":"10.21203/rs.3.rs-8486299/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8486299/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eClimate variability introduces sustained socioeconomic stressors that disproportionately affect rural agrarian populations. Drought, crop loss, and social isolation have been associated with psychological distress and maladaptive coping behaviours, including harmful alcohol use. However, integrated community-level evidence linking climate stressors, mental health symptoms, and alcohol-related harm in rural India remains limited.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA community-based cross-sectional public health surveillance study was conducted between 2019 and 2025 in Banaskantha district, Gujarat, India, under the Family Adoption Program. Adults aged 18\u0026ndash;70 years with at least five years of residence were recruited from rural talukas experiencing varying levels of climate stress. Depressive and anxiety symptoms were screened using validated PHQ-9 and GAD-7 instruments. Alcohol-related harm was assessed using documented clinical morbidity and reported functional impairment. Climate stress exposure was classified ecologically based on drought frequency, agricultural loss, and social isolation indicators. Analyses were descriptive and exploratory.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong 300 participants, depressive symptoms (PHQ-9\u0026thinsp;\u0026ge;\u0026thinsp;10) were identified in 26.0%, anxiety symptoms (GAD-7\u0026thinsp;\u0026ge;\u0026thinsp;10) in 21.7%, and alcohol-related harm in 32.3%. The highest prevalence of all outcomes was observed in talukas with sustained drought and social isolation. A graded pattern was observed across climate stress exposure categories, with increasing mental health symptoms and alcohol-related harm corresponding to higher climate stress intensity.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eMental health symptoms and alcohol-related harm were common among rural adults exposed to sustained climate stressors in Banaskantha district. The findings suggest interconnected psychosocial pathways linking environmental stress, psychological distress, and harmful coping behaviours. Integrated mental health surveillance and climate-responsive public health strategies are needed in vulnerable rural settings.\u003c/p\u003e","manuscriptTitle":"Climate Stressors, Mental Health Outcomes, and Alcohol-Related Harm Among Rural Adults in Banaskantha District, Gujarat, India: A Community-Based Cross-Sectional Public Health Surveillance Study (2021 - 2025)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-06 11:12:30","doi":"10.21203/rs.3.rs-8486299/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":"c62e453e-a08e-414c-9413-eb05b7a4e034","owner":[],"postedDate":"January 6th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":60667201,"name":"Epidemiology"}],"tags":[],"updatedAt":"2026-01-06T11:12:31+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-06 11:12:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8486299","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8486299","identity":"rs-8486299","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}