Dynamics of Mental Health Disorder Prevalence in Himachal Pradesh, India

Dynamics of Mental Health Disorder Prevalence in Himachal Pradesh, India | 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 Dynamics of Mental Health Disorder Prevalence in Himachal Pradesh, India Randhir Singh Ranta, Vikrant Charak, Kiran Chauhan, Tanuj Sharma, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7728454/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 Mental health disorders contribute substantially to the global disease burden. Himachal Pradesh, with its geographic and sociocultural diversity, faces unique challenges in addressing these conditions. Objective To examine temporal trends in the incidence and severity of mental health disorders across 2014, 2022, and 2023. Methods A retrospective analysis was conducted using patient registers from the Department of Psychiatry, Indira Gandhi Medical College and Hospital, Shimla. Patients were classified by diagnostic category and severity (low, moderate, high). Year-wise differences were tested using the Kruskal Wallis test, and chi-square tests were used to assess associations with disorder type and severity (p < .01). Results Significant year-wise differences were observed for schizophrenia and psychotic disorders, substance-related disorders, sleep disorders, mood disorders, and anxiety and stress disorders (p < .01). The overall disorder burden also varied significantly across years ( χ 2 = 57.631, p < . 001). Severity analysis revealed a rising trend, with high-severity cases increasing from 20.2% in 2014 to 28.5% in 2023. Substance-related disorders rose sharply from 14.9% in 2014 to 44.8% in 2023, while miscellaneous disorders increased steadily from 24.0% to 41.0%. Neurodevelopmental, somatic, developmental, behavioural, and cognitive disorders showed relative stability. Conclusion The findings indicate substantial shifts in both the prevalence and severity of mental health disorders in Himachal Pradesh, with sharp increases in substance-related, mood, anxiety, and high-severity cases. These results underscore the need for context-specific interventions, strengthened service delivery, and policies prioritizing early detection and management. Himachal Pradesh mental health disorders prevalence severity temporal trends Introduction Given the significant global burden of mental health disorders, the World Health Organization (WHO) has emphasized that there can be no health without mental health. (Prince et al., 2007 ). In 2019, approximately one in eight individuals worldwide were affected by a mental disorder, with anxiety and depression being the most prevalent conditions (Fan et al., 2025 ; World Health Organization, 2022 ). The COVID-19 pandemic further exacerbates this challenge, leading to a sharp increase in the prevalence of anxiety and depressive disorders (Santomauro et al., 2021; World Health Organization, 2022 ). Acknowledging the critical role of mental health in reducing the overall disease burden, India introduced its first National Mental Health Policy in 2014, followed by the Mental Health Act of 2017, aimed at ensuring equitable, affordable, and universal access to mental health services (Sagar et al., 2020 ). Himachal Pradesh, located in the northern region of India, is a predominantly mountainous state characterized by diverse topography, ranging from the Shivalik hills to the higher Himalayan ranges. The state comprises 12 districts and 21,253 villages, with a population that is largely rural in composition. Its geographical setting, while picturesque, poses challenges for the accessibility and delivery of health care services, making the health infrastructure particularly significant in ensuring equitable access to care. The health facilities in Himachal Pradesh reflect a tiered structure designed to serve both rural and urban populations. At the grassroots level, there are 2,102 rural subcentres and 12 urban subcentres that provide essential health services. These are supported by 549 rural and 25 urban primary health centers (PHCs), which serve as the first points of contact for medical consultation and preventive care. Furthermore, 98 rural and 4 urban community health centers (CHCs) function as referral units for PHCs, and o er specialized care and diagnostic services. Secondary-level health services are provided through 89 sub-divisional and district hospitals, along with 9 dedicated district hospitals across the state. At the tertiary level, Himachal Pradesh hosts 6 medical colleges that contribute not only to advanced treatment and specialty care but also to medical education and research (Ministry of Health and Family Welfare, 2022-23). Himachal Pradesh, with its considerable sociocultural and demographic diversity, presents unique challenges in addressing the burden of mental health disorders. Effective policies and interventions must therefore be sensitive to local contexts rather than adopting a one-size- fits-all approach. Understanding the distribution and temporal trends of mental disorders across districts is essential for designing contextually appropriate and evidence-based responses. While earlier research has highlighted the overall burden of mental disorders in India, including some insights from Himachal Pradesh (Math & Srinivasaraju, 2010 ; Chauhan et al., 2019 ), systematic evidence on the magnitude, patterns, and severity of these disorders at the district level remains scarce. This gap limits the ability of policymakers and healthcare providers to allocate resources effectively and develop targeted interventions. Addressing this need, the present study aimed to investigate temporal trends in the prevalence of mental health disorders in Himachal Pradesh and to assess their association with varying levels of severity, thereby contributing to a more nuanced and actionable understanding of the state’s mental health landscape. Methods This retrospective study was conducted at the Department of Psychiatry, Indira Gandhi Medical College and Hospital (IGMCH), Shimla. Data were extracted from patient registers for the years 2014, 2022, and 2023 following formal institutional approval. Patients residing outside of Himachal Pradesh and those who requested confidentiality of their records were excluded. It is also possible that some cases remained unreported due to reliance on traditional healers or treatment sought at other hospitals. All eligible patients were classified into major diagnostic categories and strati ed by severity level (low, moderate, high). The year of reporting was considered the independent variable, while diagnostic category and severity level were treated as dependent variables. The Kruskal Wallis test was employed to assess year-wise differences, and chi-square tests were applied to examine associations between year, severity, and selected disorder categories. A significance threshold of p < .01 was maintained. Ethical clearance was obtained from the Institutional Ethics Committee of IGMCH. Limitations This study has several limitations. First, data were obtained from a single tertiary care centre (IGMC Shimla), which may limit generalisability to other regions and care settings in Himachal Pradesh. Second, the retrospective design relied on existing clinical records, which may contain incomplete documentation, variation in diagnostic practices, and potential underreporting. Individuals seeking care from other hospitals or traditional healers were not captured, which may underestimate the true burden of mental health disorders. Third, only three non-consecutive years (2014, 2022, and 2023) were analysed, which may not fully reflect temporal variations across the decade. Fourth, severity classifications were based on clinician documentation and may have been influenced by subjective judgment or changes in service accessibility and referral patterns over time. Finally, the study focused on diagnostic categories and severity only and did not account for socioeconomic, cultural, environmental, or policy-related factors. Future multi-centre, longitudinal studies incorporating contextual variables are recommended to enhance the understanding of mental health trends in the region. Results 1.3.1. Year-wise Differences in Mental Health Disorders (Kruskal Wallis Test) This table evaluates whether the mean ranks of different categories of mental health disorders varied significantly across 2014, 2022, and 2023. The Kruskal Wallis test results highlighted which disorders demonstrated significant temporal shifts and which remained stable. Table 1 presents the results of the Kruskal Wallis test, which was applied to examine year-wise differences in the mean rank of various categories of mental health disorders. Significant differences were detected for schizophrenia and psychotic disorders (χ 2 = 12.72, p < .002), substance-related disorders (χ 2 = 58.643, p < .001), sleep disorders (χ 2 = 26.982, p < .001), mood disorders (χ 2 = 22.053, p < .001), and anxiety and stress disorders (χ 2 = 32.037, p < .001), indicating that the prevalence of these conditions varied considerably across 2014, 2022, and 2023. The analysis of overall disorders also revealed a highly significant difference (χ 2 = 57.631, p < .001), suggesting substantial shifts in the overall burden of mental health disorders over time. However, neurodevelopmental disorders (χ 2 = 1.389, p = 0.499), somatic symptoms and related disorders (χ 2 = 4.659, p = 0.097), miscellaneous and other mental health Table 1 Kruskal-Wallis test for significant differences among year-wise mean ranks with respect to the factors associated with mental health disorders) Mental Health Disorders 2014 2022 2023 Chi-Square Value p-value Schizophrenia and Psychotic Disorders 182.64 208.01 184.44 12.72 < 0.002 ∗∗ Neurodevelopmental Disorders 31.50 33.33 28.12 1.389 0.499 Somatic Symptom and Related Disorders 160.94 192.86 174.54 4.659 0.097 Substance-Related Disorders 870.81 1098.50 1133.12 58.643 < 0.001 ∗∗ Sleep Disorders 51.60 62.47 35.77 26.982 < 0.001 ∗∗ Miscellaneous and Other Mental Disorders 288.63 311.61 316.32 2.288 0.319 Mood Disorders 737.67 879.19 799.16 22.053 < 0.001 ∗∗ Anxiety and Stress Disorders 188.68 210.36 146.82 32.037 < 0.001 ∗∗ Developmental and Behavioural Disorders 26.00 25.73 26.10 0.006 0.997 Cognitive Disorders 11.00 11.92 11.00 0.833 0.659 Overall Disorders 2475.63 2923.88 2811.13 57.631 < 0.001 ∗∗ Note: ** denotes significance at the 1% level . disorders ( χ 2 = 2.288, p = 0.319), developmental and behavioural disorders ( χ 2 = 0.006, p = 0.997), and cognitive disorders ( χ 2 = 0.833, p = 0.659) did not significantly differ, indicating relative stability across the years. 1.2.2. Associations between Year and Mental Disorders (Chi-square Test) This table examines the relationship between years and severity levels of mental health disorders low, moderate, and high. The analysis revealed significant changes over time, reflecting a trend toward greater severity in recent years. Table 2: Chi-square test for the association between year and level of mental disorders Note: **1. The value within ( ) refers to the row percentage 2. The value within [ ] refers to the column percentage 3. ** Denotes significance at the 1% level Table 2 presents the results of the chi-square test for associations between year and level of mental disorders. In 2014, 43.1% of individuals were classified as moderate, 36.6% as low, and 20.2% as high. The column percentages show that 24.1% of all low-level cases, 19.3% of moderate cases, and 14.4% of high-level cases were concentrated in that year, indicating that moderate disorders were relatively more pronounced, while high-level cases were fewer. In 2022, the pattern shifted, with the moderate level rising to 45.2%, the high level increasing to 29.5%, and the low level dropping to 25.3%. Column wise, 38.2% of the high-level cases, 36.9% of the moderate cases, and 30.4% of the low-level cases occurred in that year, reflecting a clear increase in severity, particularly at the high level. In 2023, the distribution remained highest at the moderate level (41.9%), followed by 29.6% at the low level and 28.5% at the high level. The column percentages indicate that 47.4% of the high-level cases, 45.5% of the low-level cases, and 43.8% of the moderate cases were concentrated in that year, showing that 2023 accounted for the largest share across all severity levels. Overall, across the three years, 43.3% of the patients were classified as moderate, 29.4% as low, and 27.2% as high. The chi-square value (57.883, p < .001) is statistically significant at the 1% level, leading to the rejection of the null hypothesis. These findings con rm a significant association between year and the severity of mental disorders, with a clear trend toward increasing severity, particularly in the moderate and high severity categories. 1.3.2. Associations between Year and Specific Disorders (Substance-Related and Miscellaneous) This table focuses on the association between years and two specific categories of disorders: substance-related disorders and miscellaneous mental health disorders. The findings indicate a sharp increase in substance-related cases and a steady upward trend in miscellaneous disorders, both of which reach significance. Table 3 Chi-square test for associations between year and substance-related disorders and between miscellaneous and other mental health disorders Year Substance-Related Disorders Miscellaneous & Other Disorders 2014 112 (100.0) [14.9] 65 (100.0) [24.0] 2022 304 (100.0) [40.4] 95 (100.0) [35.1] 2023 337 (100.0) [44.8] 111 (100.0) [41.0] Total 753 (100.0) [100.0] 271 (100.0) [100.0] Chi-Square 103.363 43.003 P Value .001** .002** Note : **Note: 1. The value within ( ) refers to the row percentage 2. The value within [ ] refers to the column percentage 3. ** Denotes significance at the 1% level Table 3 presents the chi-square test results for the association between year and two categories of mental health disorders: substance-related disorders and miscellaneous and other mental health disorders. Substance-related disorders accounted for 14.9% of the cases in 2014, increasing sharply to 40.4% in 2022 and 44.8% in 2023. A chi-square value of 103.363 with a p-value of .001 indicates a highly significant association at the 1% level, reflecting a marked increase over time, with the highest concentration occurring in 2023. However, miscellaneous and other mental health disorders constituted 24.0% of the cases in 2014, increasing moderately to 35.1% in 2022 and 41.0% in 2023. The chi-square value of 43.003 with a p-value of .002 also demonstrated a significant association at the 1% level. While the increase in this category is less pronounced, the results indicate a steady upward trend across years. Overall, substance-related disorders have escalated sharply, whereas miscellaneous disorders have shown a more gradual but consistent increase. Discussion The findings reveal significant year-wise variations in major categories of mental health disorders, with a clear trend toward increasing severity. Schizophrenia and psychotic disorders, mood disorders, anxiety and stress disorders, substance related disorders, and sleep disorders showed notable increases, suggesting that the mental health burden is both intensifying and diversifying. These patterns align with global evidence of rising anxiety, depression, and substance-related problems in the post pandemic era, driven by psychosocial stressors, economic instability, and lifestyle changes (World Health Organization, 2022 ; Vigo et al., 2016 ) Substance-related disorders exhibited a particularly sharp increase, more than doubling between 2014 and 2023, consistent with reports from India and other low- and middle-income countries linking increased substance use to unemployment, stress, and weakened community support (Ambekar et al., 2019 ). Similarly, the rise in sleep, mood, and anxiety disorders aligns with studies highlighting the impact of digital overexposure, sedentary lifestyles, and reduced social cohesion on mental well-being (Patel et al., 2018 ). Conversely, neurodevelopmental, developmental and behavioural, cognitive, and somatic disorders have remained relatively stable, reflecting their chronic and early-onset nature, which has made them less sensitive to short-term sociopolitical or economic changes. This finding is consistent with longitudinal research showing stability in neurodevelopmental conditions, while mood and substance-related disorders are more reactive to contextual stressors (Kessler et al., 2007 ). Chi-square analysis of severity levels indicated that moderate disorders predominated in 2014, whereas high-severity cases increased in 2022 and 2023. This suggests that although more individuals are accessing care, many may present at later stages of illness, reflecting delays in help-seeking and systemic barriers to early intervention in India (Gautham et al., 2020). The concentration of high-severity cases in 2023 may also indicate the cumulative effects of the pandemic, climate-related uncertainties, and economic pressures. Finally, the upward trend in miscellaneous and other disorders may reflect diagnostic overlap or the recognition of conditions outside traditional categories, underscoring the need for broader diagnostic frameworks that account for cultural and contextual expressions of distress (Jacob, 2011 ). While this study provides valuable insights, several limitations should be noted. The analysis was limited to three discrete years (2014, 2022, and 2023), restricting the ability to capture continuous trends and potentially exaggerate changes linked to specific events, such as the COVID-19 pandemic. Reliance on secondary hospital-based records may have excluded undiagnosed patients or individuals seeking care outside formal institutions. Variations in diagnostic practices and recordkeeping across years could have influenced the observed differences, particularly in the miscellaneous category. The study’s regional focus limits generalizability, as demographic, cultural, and healthcare access factors may di er among regions. The classification of severity into low, moderate, and high categories may obscure within-group variation. Finally, unmeasured social, economic, and environmental factors, including unemployment, migration, and climate-related stressors, may have contributed to the observed trends and were not directly assessed. Conclusions The findings demonstrated significant temporal shifts in the burden, type, and severity of mental health disorders in Himachal Pradesh over the study period. Disorders such as schizophrenia, psychotic disorders, substance-related conditions, mood disorders, anxiety, stress, and sleep disturbances showed marked increases, while neurodevelopmental, somatic, developmental, behavioural, and cognitive disorders remained relatively stable. The sharp increase in substance related disorders, which nearly tripled between 2014 and 2023, alongside the increase in mood and anxiety disorders, highlights emerging public health challenges. The severity of the cases also escalated considerably, with high-severity disorders becoming increasingly prominent in recent years. These patterns suggest an urgent need for strengthened mental health services, early detection programs, and targeted interventions addressing substance use and high-severity cases, to mitigate the rising mental health burden in the region. Declarations Ethics Approval and Consent to Participate The study received approval from the Institutional Ethics Committee of Indira Gandhi Medical College (IGMC), Shimla, Himachal Pradesh University of Health Sciences. Initial administrative permission was granted by the Principal, IGMC. Subsequently, departmental clearance was obtained from the Department of Psychiatry, IGMC. After these approvals, our team was granted access to the anonymised data recorded in the hospital’s medical registry. The dataset was then systematically cleaned and organised year-wise, age-wise, disorder-wise, and region-wise before being converted into an Excel database for analysis. Consent to Publish Not applicable. Clinical trial number Not applicable Conflicts of interest There are no conflicts of interest Funding No specific funding was received for this study. Author Contribution Randhir Singh Ranta contributed to the conceptualization, methodology, data collection, data analysis, manuscript writing, statistical analysis and review. Vikranth Charak was involved in the conceptualization, methodology, data collection, data analysis, manuscript writing, and revisions. Kiran Chauhan contributed to the conceptualization and writing of the manuscript, data analysis, and statistical analysis and supported the review and revision of the manuscript. Tanuj Sharma provided supervision and contributed to the review, statistical analysis and revision of the manuscript. Sakshi Sharma participated in the data collection, and data analysis and assisted in the review of the manuscript. Acknowledgement The authors extend their sincere gratitude to Prof. Sita Thakur, Principal, Indira Gandhi Medical College & Hospital (IGMCH), Shimla, and Prof. Dinesh Dutt Sharma, Head, Department of Psychiatry, IGMCH, Shimla, for their invaluable guidance and support in facilitating the data collection process. The authors also wish to acknowledge with deep appreciation the exceptional assistance provided by Dr. Ayush Sharma, Senior Resident, whose contributions were integral to the successful completion of this work. References Prince M, Patel V, Saxena S, Maj M, Maselko J, Phillips MR, Rahman A. No health without mental health. lancet. 2007;370(9590):859–77. Fan Y, Fan A, Yang Z, Fan D. Global burden of mental disorders in 204 countries and territories, 1990 2021: results from the global burden of disease study 2021. BMC Psychiatry. 2025;25(1):486. World Health Organization. (2022). World mental health report: Transforming mental health for all. Ministry of Health and Family Welfare Statistics Division. Government of India. (2022-23). Health Dynamics of India (Infrastructure & Human Resources). Sagar R, Dandona R, Gururaj G, Dhaliwal RS, Singh A, Ferrari A, Dandona L. The burden of mental disorders across the states of India: the Global Burden of Disease Study 1990 2017. Lancet Psychiatry. 2020;7(2):148–61. Math SB, Srinivasaraju R. Indian Psychiatric epidemiological studies: Learning from the past. Indian J psychiatry. 2010;52(Suppl1):S95–103. Chauhan A, Sahu JK, Jaiswal N, Kumar K, Agarwal A, Kaur J, Singh M. Prevalence of autism spectrum disorder in Indian children: A systematic review and meta-analysis. Neurol India. 2019;67(1):100–4. World Health Organization. World mental health report: Transforming mental health for all. World Health Organization; 2022. Vigo D, Thornicroft G, Atun R. Estimating the true global burden of mental illness. Lancet Psychiatry. 2016;3(2):171–8. Ambekar A, Agrawal A, Rao R, Mishra AK, Khandelwal SK, Chadda RK. Magnitude of substance use in India. New Delhi: Ministry of Social Justice and Empowerment, Government of India,; 2019. p. 23. Patel V, Saxena S, Lund C, Thornicroft G, Baingana F, Bolton P, Unützer. J. The Lancet Commission on global mental health and sustainable development. lancet. 2018;392(10157):1553–98. Kessler RC, Angermeyer M, Anthony JC, De Graaf RON, Demyttenaere K, Gasquet I. stün TB. Lifetime prevalence and age-of-onset distributions of mental disorders in the World Health Organization’s World Mental Health Survey Initiative. World psychiatry. 2007;6(3):168. Gautham MS, Gururaj G, Varghese M, Benegal V, Rao GN, Kokane A, … Shibukumar TM. The National Mental Health Survey of India (2016): Prevalence, socio-demographic correlates and treatment gap of mental morbidity. International Journal of Social Psychiatry,2020; 66(4), 361–372. Jacob KS. Repackaging mental health programs in low-and middle-income countries. Indian J Psychiatry. 2011;53(3):195–8. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7728454","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":547011657,"identity":"058806d9-e96d-4a71-bb91-9ff78c40c4a0","order_by":0,"name":"Randhir Singh 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06:41:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":721334,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7728454/v1/5dea655c-a13c-49c8-a782-24915f9800c6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dynamics of Mental Health Disorder Prevalence in Himachal Pradesh, India","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGiven the significant global burden of mental health disorders, the World Health Organization (WHO) has emphasized that there can be no health without mental health. (Prince et al., \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e). In 2019, approximately one in eight individuals worldwide were affected by a mental disorder, with anxiety and depression being the most prevalent conditions (Fan et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e; World Health Organization, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). The COVID-19 pandemic further exacerbates this challenge, leading to a sharp increase in the prevalence of anxiety and depressive disorders (Santomauro et al., 2021; World Health Organization, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Acknowledging the critical role of mental health in reducing the overall disease burden, India introduced its first National Mental Health Policy in 2014, followed by the Mental Health Act of 2017, aimed at ensuring equitable, affordable, and universal access to mental health services (Sagar et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eHimachal Pradesh, located in the northern region of India, is a predominantly mountainous state characterized by diverse topography, ranging from the Shivalik hills to the higher Himalayan ranges. The state comprises 12 districts and 21,253 villages, with a population that is largely rural in composition. Its geographical setting, while picturesque, poses challenges for the accessibility and delivery of health care services, making the health infrastructure particularly significant in ensuring equitable access to care. The health facilities in Himachal Pradesh reflect a tiered structure designed to serve both rural and urban populations. At the grassroots level, there are 2,102 rural subcentres and 12 urban subcentres that provide essential health services. These are supported by 549 rural and 25 urban primary health centers (PHCs), which serve as the first points of contact for medical consultation and preventive care. Furthermore, 98 rural and 4 urban community health centers (CHCs) function as referral units for PHCs, and o er specialized care and diagnostic services. Secondary-level health services are provided through 89 sub-divisional and district hospitals, along with 9 dedicated district hospitals across the state. At the tertiary level, Himachal Pradesh hosts 6 medical colleges that contribute not only to advanced treatment and specialty care but also to medical education and research (Ministry of Health and Family Welfare, 2022-23).\u003c/p\u003e\n\u003cp\u003eHimachal Pradesh, with its considerable sociocultural and demographic diversity, presents unique challenges in addressing the burden of mental health disorders. Effective policies and interventions must therefore be sensitive to local contexts rather than adopting a one-size- fits-all approach. Understanding the distribution and temporal trends of mental disorders across districts is essential for designing contextually appropriate and evidence-based responses. While earlier research has highlighted the overall burden of mental disorders in India, including some insights from Himachal Pradesh (Math \u0026amp; Srinivasaraju, \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e; Chauhan et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e), systematic evidence on the magnitude, patterns, and severity of these disorders at the district level remains scarce. This gap limits the ability of policymakers and healthcare providers to allocate resources effectively and develop targeted interventions. Addressing this need, the present study aimed to investigate temporal trends in the prevalence of mental health disorders in Himachal Pradesh and to assess their association with varying levels of severity, thereby contributing to a more nuanced and actionable understanding of the state\u0026rsquo;s mental health landscape.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis retrospective study was conducted at the Department of Psychiatry, Indira Gandhi Medical College and Hospital (IGMCH), Shimla. Data were extracted from patient registers for the years 2014, 2022, and 2023 following formal institutional approval. Patients residing outside of Himachal Pradesh and those who requested confidentiality of their records were excluded. It is also possible that some cases remained unreported due to reliance on traditional healers or treatment sought at other hospitals. All eligible patients were classified into major diagnostic categories and strati ed by severity level (low, moderate, high). The year of reporting was considered the independent variable, while diagnostic category and severity level were treated as dependent variables. The Kruskal Wallis test was employed to assess year-wise differences, and chi-square tests were applied to examine associations between year, severity, and selected disorder categories. A significance threshold of p\u0026thinsp;\u0026lt;\u0026thinsp;.01 was maintained. Ethical clearance was obtained from the Institutional Ethics Committee of IGMCH.\u003c/p\u003e"},{"header":"Limitations","content":"\u003cp\u003eThis study has several limitations. First, data were obtained from a single tertiary care centre (IGMC Shimla), which may limit generalisability to other regions and care settings in Himachal Pradesh. Second, the retrospective design relied on existing clinical records, which may contain incomplete documentation, variation in diagnostic practices, and potential underreporting. Individuals seeking care from other hospitals or traditional healers were not captured, which may underestimate the true burden of mental health disorders.\u003c/p\u003e\n\u003cp\u003eThird, only three non-consecutive years (2014, 2022, and 2023) were analysed, which may not fully reflect temporal variations across the decade. Fourth, severity classifications were based on clinician documentation and may have been influenced by subjective judgment or changes in service accessibility and referral patterns over time.\u003c/p\u003e\n\u003cp\u003eFinally, the study focused on diagnostic categories and severity only and did not account for socioeconomic, cultural, environmental, or policy-related factors. Future multi-centre, longitudinal studies incorporating contextual variables are recommended to enhance the understanding of mental health trends in the region.\u003c/p\u003e\n"},{"header":"Results","content":"\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\n \u003ch2\u003e1.3.1. Year-wise Differences in Mental Health Disorders (Kruskal Wallis Test)\u003c/h2\u003e\n \u003cp\u003eThis table evaluates whether the mean ranks of different categories of mental health disorders varied significantly across 2014, 2022, and 2023. The Kruskal Wallis test results highlighted which disorders demonstrated significant temporal shifts and which remained stable.\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presents the results of the Kruskal Wallis test, which was applied to examine year-wise differences in the mean rank of various categories of mental health disorders. Significant differences were detected for schizophrenia and psychotic disorders (\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;12.72, p\u0026thinsp;\u0026lt;\u0026thinsp;.002), substance-related disorders\u003c/p\u003e\n \u003cp\u003e(\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;58.643, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), sleep disorders (\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;26.982, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), mood disorders (\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;22.053, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), and anxiety and stress disorders (\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;32.037, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), indicating that the prevalence of these conditions varied considerably across 2014, 2022, and 2023. The analysis of overall disorders also revealed a highly significant difference (\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;57.631, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), suggesting substantial shifts in the overall burden of mental health disorders over time. However, neurodevelopmental disorders (\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;1.389, p\u0026thinsp;=\u0026thinsp;0.499), somatic symptoms and related disorders (\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;4.659, p\u0026thinsp;=\u0026thinsp;0.097), miscellaneous and other mental health\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eKruskal-Wallis test for significant differences among year-wise mean ranks with respect to the factors associated with mental health disorders)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMental Health Disorders\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eChi-Square Value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSchizophrenia and Psychotic Disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e182.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e208.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e184.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;\u003c/em\u003e\u0026thinsp;0.002\u003csup\u003e\u0026lowast;\u0026lowast;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeurodevelopmental Disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.499\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSomatic Symptom and Related Disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e160.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e192.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e174.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSubstance-Related Disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e870.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1098.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1133.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58.643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;\u003c/em\u003e\u0026thinsp;0.001\u003csup\u003e\u0026lowast;\u0026lowast;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSleep Disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;\u003c/em\u003e\u0026thinsp;0.001\u003csup\u003e\u0026lowast;\u0026lowast;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMiscellaneous and Other Mental Disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e288.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e311.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e316.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.319\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMood Disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e737.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e879.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e799.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;\u003c/em\u003e\u0026thinsp;0.001\u003csup\u003e\u0026lowast;\u0026lowast;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnxiety and Stress Disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e188.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e210.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e146.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;\u003c/em\u003e\u0026thinsp;0.001\u003csup\u003e\u0026lowast;\u0026lowast;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDevelopmental and Behavioural Disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.997\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCognitive Disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.659\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOverall Disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2475.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2923.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2811.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57.631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;\u003c/em\u003e\u0026thinsp;0.001\u003csup\u003e\u0026lowast;\u0026lowast;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e\u003cstrong\u003eNote: ** denotes significance at the 1% level\u003c/strong\u003e.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003edisorders (\u003cem\u003e\u0026chi;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;2.288, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.319), developmental and behavioural disorders (\u003cem\u003e\u0026chi;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.006, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.997), and cognitive disorders (\u003cem\u003e\u0026chi;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.833, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.659) did not significantly differ, indicating relative stability across the years.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\n \u003ch2\u003e1.2.2. Associations between Year and Mental Disorders (Chi-square Test)\u003c/h2\u003e\n \u003cp\u003eThis table examines the relationship between years and severity levels of mental health disorders low, moderate, and high. The analysis revealed significant changes over time, reflecting a trend toward greater severity in recent years.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2: Chi-square test for the association between year and level of mental disorders\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cimg 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\"\u003e\u003c/strong\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e **1. The value within ( ) refers to the row percentage\u003c/p\u003e\n \u003cp\u003e2. The value within [ ] refers to the column percentage\u003c/p\u003e\n \u003cp\u003e3. ** Denotes significance at the 1% level\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e presents the results of the chi-square test for associations between year and level of mental disorders. In 2014, 43.1% of individuals were classified as moderate, 36.6% as low, and 20.2% as high. The column percentages show that 24.1% of all low-level cases, 19.3% of moderate cases, and 14.4% of high-level cases were concentrated in that year, indicating that moderate disorders were relatively more pronounced, while high-level cases were fewer. In 2022, the pattern shifted, with the moderate level rising to 45.2%, the high level increasing to 29.5%, and the low level dropping to 25.3%. Column wise, 38.2% of the high-level cases, 36.9% of the moderate cases, and 30.4% of the low-level cases occurred in that year, reflecting a clear increase in severity, particularly at the high level. In 2023, the distribution remained highest at the moderate level (41.9%), followed by 29.6% at the low level and 28.5% at the high level. The column percentages indicate that 47.4% of the high-level cases, 45.5% of the low-level cases, and 43.8% of the moderate cases were concentrated in that year, showing that 2023 accounted for the largest share across all severity levels. Overall, across the three years, 43.3% of the patients were classified as moderate, 29.4% as low, and 27.2% as high. The chi-square value (57.883, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) is statistically significant at the 1% level, leading to the rejection of the null hypothesis. These findings con rm a significant association between year and the severity of mental disorders, with a clear trend toward increasing severity, particularly in the moderate and high severity categories.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\n \u003ch2\u003e1.3.2. Associations between Year and Specific Disorders (Substance-Related and Miscellaneous)\u003c/h2\u003e\n \u003cp\u003eThis table focuses on the association between years and two specific categories of disorders: substance-related disorders and miscellaneous mental health disorders. The findings indicate a sharp increase in substance-related cases and a steady upward trend in miscellaneous disorders, both of which reach significance.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eChi-square test for associations between year and substance-related disorders and between miscellaneous and other mental health disorders\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSubstance-Related Disorders\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMiscellaneous \u0026amp; Other Disorders\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e112 (100.0) [14.9]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65 (100.0) [24.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e304 (100.0) [40.4]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e95 (100.0) [35.1]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e337 (100.0) [44.8]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e111 (100.0) [41.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e753 (100.0) [100.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e271 (100.0) [100.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChi-Square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e103.363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.002**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003e\u003cstrong\u003eNote\u003c/strong\u003e: **Note: 1. The value within ( ) refers to the row percentage\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e2. The value within [ ] refers to the column percentage\u003c/p\u003e\n \u003cp\u003e3. ** Denotes significance at the 1% level\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e presents the chi-square test results for the association between year and two categories of mental health disorders: substance-related disorders and miscellaneous and other mental health disorders. Substance-related disorders accounted for 14.9% of the cases in 2014, increasing sharply to 40.4% in 2022 and 44.8% in 2023. A chi-square value of 103.363 with a p-value of .001 indicates a highly significant association at the 1% level, reflecting a marked increase over time, with the highest concentration occurring in 2023. However, miscellaneous and other mental health disorders constituted 24.0% of the cases in 2014, increasing moderately to 35.1% in 2022 and 41.0% in 2023. The chi-square value of 43.003 with a p-value of .002 also demonstrated a significant association at the 1% level. While the increase in this category is less pronounced, the results indicate a steady upward trend across years. Overall, substance-related disorders have escalated sharply, whereas miscellaneous disorders have shown a more gradual but consistent increase.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe findings reveal significant year-wise variations in major categories of mental health disorders, with a clear trend toward increasing severity. Schizophrenia and psychotic disorders, mood disorders, anxiety and stress disorders, substance related disorders, and sleep disorders showed notable increases, suggesting that the mental health burden is both intensifying and diversifying. These patterns align with global evidence of rising anxiety, depression, and substance-related problems in the post pandemic era, driven by psychosocial stressors, economic instability, and lifestyle changes (World Health Organization, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Vigo et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003c/p\u003e\n\u003cp\u003eSubstance-related disorders exhibited a particularly sharp increase, more than doubling between 2014 and 2023, consistent with reports from India and other low- and middle-income countries linking increased substance use to unemployment, stress, and weakened community support (Ambekar et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). Similarly, the rise in sleep, mood, and anxiety disorders aligns with studies highlighting the impact of digital overexposure, sedentary lifestyles, and reduced social cohesion on mental well-being (Patel et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). Conversely, neurodevelopmental, developmental and behavioural, cognitive, and somatic disorders have remained relatively stable, reflecting their chronic and early-onset nature, which has made them less sensitive to short-term sociopolitical or economic changes. This finding is consistent with longitudinal research showing stability in neurodevelopmental conditions, while mood and substance-related disorders are more reactive to contextual stressors (Kessler et al., \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eChi-square analysis of severity levels indicated that moderate disorders predominated in 2014, whereas high-severity cases increased in 2022 and 2023. This suggests that although more individuals are accessing care, many may present at later stages of illness, reflecting delays in help-seeking and systemic barriers to early intervention in India (Gautham et al., 2020). The concentration of high-severity cases in 2023 may also indicate the cumulative effects of the pandemic, climate-related uncertainties, and economic pressures.\u003c/p\u003e\n\u003cp\u003eFinally, the upward trend in miscellaneous and other disorders may reflect diagnostic overlap or the recognition of conditions outside traditional categories, underscoring the need for broader diagnostic frameworks that account for cultural and contextual expressions of distress (Jacob, \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e). While this study provides valuable insights, several limitations should be noted. The analysis was limited to three discrete years (2014, 2022, and 2023), restricting the ability to capture continuous trends and potentially exaggerate changes linked to specific events, such as the COVID-19 pandemic. Reliance on secondary hospital-based records may have excluded undiagnosed patients or individuals seeking care outside formal institutions. Variations in diagnostic practices and recordkeeping across years could have influenced the observed differences, particularly in the miscellaneous category. The study\u0026rsquo;s regional focus limits generalizability, as demographic, cultural, and healthcare access factors may di er among regions. The classification of severity into low, moderate, and high categories may obscure within-group variation. Finally, unmeasured social, economic, and environmental factors, including unemployment, migration, and climate-related stressors, may have contributed to the observed trends and were not directly assessed.\u003c/p\u003e\n"},{"header":"Conclusions","content":"\u003cp\u003eThe findings demonstrated significant temporal shifts in the burden, type, and severity of mental health disorders in Himachal Pradesh over the study period. Disorders such as schizophrenia, psychotic disorders, substance-related conditions, mood disorders, anxiety, stress, and sleep disturbances showed marked increases, while neurodevelopmental, somatic, developmental, behavioural, and cognitive disorders remained relatively stable. The sharp increase in substance related disorders, which nearly tripled between 2014 and 2023, alongside the increase in mood and anxiety disorders, highlights emerging public health challenges. The severity of the cases also escalated considerably, with high-severity disorders becoming increasingly prominent in recent years. These patterns suggest an urgent need for strengthened mental health services, early detection programs, and targeted interventions addressing substance use and high-severity cases, to mitigate the rising mental health burden in the region.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e\u003cp\u003e The study received approval from the Institutional Ethics Committee of Indira Gandhi Medical College (IGMC), Shimla, Himachal Pradesh University of Health Sciences. Initial administrative permission was granted by the Principal, IGMC. Subsequently, departmental clearance was obtained from the Department of Psychiatry, IGMC. After these approvals, our team was granted access to the anonymised data recorded in the hospital\u0026rsquo;s medical registry. The dataset was then systematically cleaned and organised year-wise, age-wise, disorder-wise, and region-wise before being converted into an Excel database for analysis.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent to Publish\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eClinical trial number\u003c/h2\u003e\u003cp\u003eNot applicable\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eConflicts of interest\u003c/h2\u003e\u003cp\u003eThere are no conflicts of interest\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eNo specific funding was received for this study.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eRandhir Singh Ranta contributed to the conceptualization, methodology, data collection, data analysis, manuscript writing, statistical analysis and review. Vikranth Charak was involved in the conceptualization, methodology, data collection, data analysis, manuscript writing, and revisions. Kiran Chauhan contributed to the conceptualization and writing of the manuscript, data analysis, and statistical analysis and supported the review and revision of the manuscript. Tanuj Sharma provided supervision and contributed to the review, statistical analysis and revision of the manuscript. Sakshi Sharma participated in the data collection, and data analysis and assisted in the review of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors extend their sincere gratitude to Prof. Sita Thakur, Principal, Indira Gandhi Medical College \u0026amp; Hospital (IGMCH), Shimla, and Prof. Dinesh Dutt Sharma, Head, Department of Psychiatry, IGMCH, Shimla, for their invaluable guidance and support in facilitating the data collection process. The authors also wish to acknowledge with deep appreciation the exceptional assistance provided by Dr. Ayush Sharma, Senior Resident, whose contributions were integral to the successful completion of this work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePrince M, Patel V, Saxena S, Maj M, Maselko J, Phillips MR, Rahman A. No health without mental health. lancet. 2007;370(9590):859\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFan Y, Fan A, Yang Z, Fan D. Global burden of mental disorders in 204 countries and territories, 1990 2021: results from the global burden of disease study 2021. BMC Psychiatry. 2025;25(1):486.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. (2022). World mental health report: Transforming mental health for all.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMinistry of Health and Family Welfare Statistics Division. Government of India. (2022-23). Health Dynamics of India (Infrastructure \u0026amp; Human Resources).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSagar R, Dandona R, Gururaj G, Dhaliwal RS, Singh A, Ferrari A, Dandona L. The burden of mental disorders across the states of India: the Global Burden of Disease Study 1990 2017. Lancet Psychiatry. 2020;7(2):148\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMath SB, Srinivasaraju R. Indian Psychiatric epidemiological studies: Learning from the past. Indian J psychiatry. 2010;52(Suppl1):S95\u0026ndash;103.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChauhan A, Sahu JK, Jaiswal N, Kumar K, Agarwal A, Kaur J, Singh M. Prevalence of autism spectrum disorder in Indian children: A systematic review and meta-analysis. Neurol India. 2019;67(1):100\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. World mental health report: Transforming mental health for all. World Health Organization; 2022.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVigo D, Thornicroft G, Atun R. Estimating the true global burden of mental illness. Lancet Psychiatry. 2016;3(2):171\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAmbekar A, Agrawal A, Rao R, Mishra AK, Khandelwal SK, Chadda RK. Magnitude of substance use in India. New Delhi: Ministry of Social Justice and Empowerment, Government of India,; 2019. p. 23.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePatel V, Saxena S, Lund C, Thornicroft G, Baingana F, Bolton P, Un\u0026uuml;tzer. J. The Lancet Commission on global mental health and sustainable development. lancet. 2018;392(10157):1553\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKessler RC, Angermeyer M, Anthony JC, De Graaf RON, Demyttenaere K, Gasquet I. st\u0026uuml;n TB. Lifetime prevalence and age-of-onset distributions of mental disorders in the World Health Organization\u0026rsquo;s World Mental Health Survey Initiative. World psychiatry. 2007;6(3):168.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGautham MS, Gururaj G, Varghese M, Benegal V, Rao GN, Kokane A, \u0026hellip; Shibukumar TM. The National Mental Health Survey of India (2016): Prevalence, socio-demographic correlates and treatment gap of mental morbidity. International Journal of Social Psychiatry,2020; 66(4), 361\u0026ndash;372.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJacob KS. Repackaging mental health programs in low-and middle-income countries. Indian J Psychiatry. 2011;53(3):195\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Himachal Pradesh, mental health disorders, prevalence, severity, temporal trends","lastPublishedDoi":"10.21203/rs.3.rs-7728454/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7728454/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eMental health disorders contribute substantially to the global disease burden. Himachal Pradesh, with its geographic and sociocultural diversity, faces unique challenges in addressing these conditions.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eTo examine temporal trends in the incidence and severity of mental health disorders across 2014, 2022, and 2023.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA retrospective analysis was conducted using patient registers from the Department of Psychiatry, Indira Gandhi Medical College and Hospital, Shimla. Patients were classified by diagnostic category and severity (low, moderate, high). Year-wise differences were tested using the Kruskal Wallis test, and chi-square tests were used to assess associations with disorder type and severity (p\u0026thinsp;\u0026lt;\u0026thinsp;.01).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eSignificant year-wise differences were observed for schizophrenia and psychotic disorders, substance-related disorders, sleep disorders, mood disorders, and anxiety and stress disorders (p\u0026thinsp;\u0026lt;\u0026thinsp;.01). The overall disorder burden also varied significantly across years (\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;57.631, \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;.\u003c/em\u003e001). Severity analysis revealed a rising trend, with high-severity cases increasing from 20.2% in 2014 to 28.5% in 2023. Substance-related disorders rose sharply from 14.9% in 2014 to 44.8% in 2023, while miscellaneous disorders increased steadily from 24.0% to 41.0%. Neurodevelopmental, somatic, developmental, behavioural, and cognitive disorders showed relative stability.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThe findings indicate substantial shifts in both the prevalence and severity of mental health disorders in Himachal Pradesh, with sharp increases in substance-related, mood, anxiety, and high-severity cases. These results underscore the need for context-specific interventions, strengthened service delivery, and policies prioritizing early detection and management.\u003c/p\u003e","manuscriptTitle":"Dynamics of Mental Health Disorder Prevalence in Himachal Pradesh, India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-19 09:08:03","doi":"10.21203/rs.3.rs-7728454/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":"90341fd1-627f-4934-af69-1a16a73733ca","owner":[],"postedDate":"November 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-04T06:40:34+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-19 09:08:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7728454","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7728454","identity":"rs-7728454","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}