Who Pays More? Gender Gaps in Mental Health Treatment Costs in India

Who Pays More? Gender Gaps in Mental Health Treatment Costs in 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 Who Pays More? Gender Gaps in Mental Health Treatment Costs in India Rohit Yadav, Alok Kumar Yadav, Siddhant Shastri, Vaishnavi Joshi, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6968917/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 Out-of-pocket expenditure (OOPE) on mental health care in India limits progress toward universal health coverage. Detailed analysis of gender-related differences in mental health spending is needed to inform allocation of resources. However, gender-specific patterns in OOPE for mental health have not been adequately studied in existing research. Methods This study used cross-sectional data from the 76th round of the National Sample Survey (NSS) on “Persons with Disabilities in India” to measure gender differences in out-of-pocket expenditure (OOPE) for mental health treatment. The Blinder-Oaxaca decomposition method was applied to estimate the gender gap in OOPE and to quantify the contribution of associated factors, grouped into individual, household, and community-level characteristics. Results On average, males incur 25.4% higher out-of-pocket expenditure (OOPE) for mental health care, while females report higher healthcare burden in specific states and socio-economic groups. Catastrophic health expenditure (CHE) is more frequent among older adults, rural residents, and economically disadvantaged males. Blinder-Oaxaca decomposition attributes 40.2% of the gender gap in OOPE to differences in observed characteristics, with education (76%) and region of residence (29%) being the largest contributors. Individual-level factors, including age, education, and marital status, account for 71.7% of the total explained variation. Conclusion This study shows male-female differences in the financial burden of mental illness treatment in India, with most of the variation explained by individual and community-level factors. The results highlight the need for policy measures that reduce out-of-pocket spending and improve economic support related to mental health care. Improving social protection and economic security may help reduce gender-based differences in access and outcomes. Mental Health Blinder-Oaxaca decomposition out of pocket expenditure Gender Gap National Sample Survey Inequality India Figures Figure 1 Figure 2 Figure 3 What we already know Prevalence of mental illness India and mental illness disorders are a rising public health community concern in India. Considerable proportion of mental illness patients bound to incurred the catastrophic health expenditure (CHE) and push households into below poverty line due higher health acre expenditure cost in India. Gender differentials in health care and health care expenditure among women in India is associated with the socio-cultural and financial reasons What this study adds This study carried out the comprehensive gender-based analysis of treatment cost for mental health care in India using a large-scale survey data based on the nationally representative data (NSS 76th round), and more specially focus on disability survey data. Study informs that males, on average, experienced higher treatment cost for mental health care compared to females. Decomposition analysis shows that almost two fifth (40.2%) of the gender gap in mental health care cost, with education (76%) and region (29%) presence the major contributors. Results indicate that individual-level factors (age, education, marital status) explanation for 71.7% of the described difference in mental health care cost. Highlights the need for gender-specific mental health plans, reducing financial strain Introduction Mental health disorders are a significant and growing public health issue globally, with profound socio-economic impacts. Mental health challenges are linked to substantial disability and disease burden, affecting millions of people[ 1 , 2 ]. In low- and middle-income countries like India, the situation is even more critical due to limited resources and stigma around mental health[ 3 ]. In India, mental disorders affected approximately 14.3% of the population in 2017, a notable rise from previous decades. The disease burden due to mental disorders nearly doubled since 1990, contributing to 4.7% of the total Disability-Adjusted Life Years (DALYs) in the country. Depression and anxiety disorders constitute over half of this mental health burden[ 4 ]. A national survey across 12 Indian states found a lifetime prevalence of mental disorders at 13.7% and a current prevalence of 10.6%, highlighting an urgent need for mental health assessments and interventions[ 5 ]. Beyond disability, factors such as out-of-pocket expenditure leading to catastrophic health expenditure contribute to existing mental illnesses and predispose individuals to premature mortality[ 6 , 7 ]. This economic burden worsens the risk of poverty and financial distress, with studies showing that catastrophic health spending pushes around 63 million Indians into poverty each year[ 8 ]. This compounded strain not only affects health outcomes but also results in significant economic loss, as mental health disorders are estimated to reduce India's GDP by approximately $ 1.03 trillion between 2012 and 2030[ 9 ]. The impact is further intensified by India's crude suicide mortality rate of 16.3 per 100,000 in 2016, the highest in the South Asian region[ 3 ]. Mental health disorders play a significant burden not only on individuals but also on households, where family members are often required to provide long-term support and supervision, especially during acute phases of illness[ 5 , 10 ]. However, stigma surrounding mental health in India often deters people from seeking timely and appropriate treatment, pushing many to turn to local faith healers rather than formal healthcare systems[ 5 ]. This delay in care, coupled with societal stigma, exacerbates the impact of mental illness on both individuals and their families. Recognizing this growing burden, the Indian government enacted the Mental Healthcare Act of 2017 , which guarantees every individual the right to accessible, affordable, and high-quality mental health care[ 11 ]. This legislation aligns with international human rights principles and supports global initiatives such as the World Health Organization’s Comprehensive Mental Health Action Plan 2013–2020 , which was extended to 2030 with the aim of achieving universal coverage for mental health services[ 12 ]. The inclusion of mental health in the Sustainable Development Goals (SDGs) further underscores its global importance. However, significant challenges persist. The Mental Health Atlas 2020 highlights enduring inequalities in the availability and distribution of mental health resources, particularly in low- and lower-middle-income countries like India, where an 83% treatment gap was reported, and most individuals seeking care do so through the private sector[ 7 ]. One of the most pressing concerns in India is the high out-of-pocket expenditures (OOPE) for mental health care. Mental health spending by the Indian government accounts for only 1.30% of its total health expenditure, leaving the majority of patients to cover the costs of services and medications themselves[ 3 ]. This financial burden is especially significant for vulnerable populations, pushing many families into medical poverty. Furthermore, studies indicate stark gender disparities in health care access, treatment-seeking behaviour, and outcomes, with women often bearing a disproportionate share of the mental health-related disability and mortality[ 13 , 14 ]. These disparities are exacerbated by cultural norms and economic inequalities, which limit women’s autonomy in healthcare decision-making and reduce their access to necessary treatment[ 15 , 16 ]. High OOPE also leads to catastrophic health expenditures (CHE), which force households to forgo essential care or fall deeper into poverty. Literature shows that mental health disorders not only magnify existing socio-economic inequalities but also contribute to widening income inequality, particularly along gender lines[ 8 ]. Although India’s National Health Policy 2017 aims to reduce OOPE in healthcare, mental health remains critically underfunded[ 11 ]. These financial barriers, compounded by gender inequalities, undermine India’s efforts to meet the SDG targets for universal health coverage and mental health care access. Previous studies related to Indian mental healthcare, revolve around the direct socio-economic burden of mental health highlighting factors around depression and anxiety, overlooking gender-specific financial challenges in mental healthcare[ 3 , 17 , 18 ]. This study addresses the gap by analysing data from the NSSO 76th round, focusing on out-of-pocket expenditures (OOPE), catastrophic health expenditures (CHE), and poverty gap due to self-reported mental illness, through the lens of gender and socio-economic groups. This study further aims to provide evidence-based recommendations for equitable healthcare access, aligning with the Sustainable Development Goals (SDGs) for universal health coverage (UHC). Methodology Data source This study used cross-sectional data from the 76th round of the National Sample Survey (NSS) titled "Persons with Disabilities in India," conducted between July and December 2018 across all Indian states and union territories[ 19 ]. The NSS defines persons with disabilities as individuals experiencing long-term physical, mental, intellectual, or sensory impairments that limit effective societal participation due to interactions with external barriers. For this study, impairments persisting for 12 months or more were classified as long-term. The survey gathered data on key health indicators, including morbidity, healthcare usage, types of disability, employment status, and socio-economic factors such as household consumption. Sampling strategy and sample size A stratified two-stage sampling design was applied, using the 2011 Census as the sampling frame. Villages and urban blocks were selected in the first stage, followed by the selection of households in the second stage. The 76th round covered a total of 8,992 village and urban blocks (5,378 rural and 3,614 urban) and included 118,152 households (81,004 rural and 37,148 urban), enumerating 576,569 individuals (402,589 in rural areas and 173,980 in urban areas). From this population, 6,677 individuals reported mental illness disabilities, comprising 3813 males and 2864 females. Outcome variables Our study estimated the following outcome variables for those with self-reported mental illness in India: prevalence of overall self-reported mental illness and its symptoms, mean out of pocket expenditure (OOPE), incidence of catastrophic health expenditure, and impoverishment by various sub-groups for males and females who sought health care. Subsequent section details about the methods used for estimating each of the outcome variables. Mental illness In this survey, there were following three defining questions that were asked to respondents regarding mental illness who It identified challenges in social interactions, adaptability, and instances of unusual behavior. If having unnecessary and excessive worry, anxiety, repetitive behavior/ thoughts, changes of mood or mood swings, talking/laughing to self, staring in space. If having unusual experiences of hearing voices, seeing visions, strange smell or sensation or strange taste If having unusual behavior or difficulty in social interactions and adaptability Prevalence of overall self-reported mental illness was calculated used following formula. $$\:\text{P}\text{r}\text{e}\text{v}\text{a}\text{l}\text{e}\text{n}\text{c}\text{e}\:\text{o}\text{f}\:\text{m}\text{e}\text{n}\text{t}\text{a}\text{l}\:\text{i}\text{l}\text{l}\text{n}\text{e}\text{s}\text{s}\:=\frac{Number\:of\:persons\:reporting\:\text{m}\text{e}\text{n}\text{t}\text{a}\text{l}\:\text{i}\text{l}\text{l}\text{n}\text{e}\text{s}\text{s}\:}{Total\:numbers\:of\:survey\:populations}x1000$$ Out-of-pocket expenditure (OOPE) The NSS 76th round survey for disability captured both the medical expenditure (surgery, equipment, hospitalization, etc.) and nonmedical expenditure (transport, lodging, food, etc.) for infrequent expenditure during last 365 days from date of survey along with usual monthly expenditure (excluding those covered infrequent expenditure during last 365 days ) [ 19 ]. Catastrophic health expenditure (CHE) and Poverty impact The burden of Out-of-Pocket Expenditure (OOPE) was calculated as a proportion of total household consumption expenditure, while Catastrophic Health Expenditure (CHE) was determined using WHO-defined thresholds of 10% and 20% of total household expenditure. The poverty impact was assessed using the Poverty Headcount Ratio (PHR), which represents the percentage of the population living below the national poverty line, and the Poverty Gap (PG), which measures the shortfall of average income below the poverty line. The Rangarajan Committee poverty line thresholds were used for these assessments. Using recommendation by Rangarajan committee, poverty line was calculated based on per capita household monthly expenditure at Indian National Rupee (INR) 972 in rural areas and INR 1407 in urban areas [ 20 , 21 ]. Independent Variables The independent variables for assessing Out-of-Pocket Expenditure (OOPE) and Catastrophic Health Expenditure (CHE) related to mental health disorders included socio-demographic and economic factors. Socio-demographic variables comprised age (0–14, 15–35, 36–59, and ≥ 60 years), education level (“Illiterate,” “Up to Primary,” “Middle,” and “Secondary and above”), and marital status (“Never married,” “Currently married,” and “Others”). Additional variables included religious affiliation (“Hindu,” “Muslim,” and “Others”), caste categories (SC/ST, OBC, and Others), and economic status, measured using Monthly Per Capita Expenditure (MPCE) quintiles (Poorest, Poorer, Middle, Richer, and Richest). Place of residence (Rural or Urban) and geographic region, grouped into six zones—North, Central, East, Northeast, West, South, and Union Territories—were also considered. Statistical Analysis Descriptive analyses were performed to examine distributional differences in mental health outcomes and economic burden between males and females. The prevalence of self-reported mental illness was assessed across various socio-economic and demographic characteristics, disaggregated by gender. Bivariate analyses were conducted to investigate the financial impact of mental health care on households, with a specific focus on income loss, CHE, and poverty gap levels. These analyses were stratified by selected socio-economic variables to provide gender-specific insights. To build on the analysis of economic impact, gender-based differences in Out-of-Pocket Expenditure (OOPE) and associated financial strain were further examined using the Blinder-Oaxaca decomposition method. The Blinder-Oaxaca approach is widely used to decompose differences between two groups, such as male and female, poor and non-poor, or rural and urban populations[ 21 – 23 ]. In this study, we decomposed the gender-based differences in OOPE into three components: Endowment Effect : This component reflects the gap attributable to differences in the distribution of predictor variables (e.g., socio-economic and demographic factors) between males and females. Coefficient Effect : This part of the gap arises from differences in the effect or influence of predictor variables on OOPE between males and females. Interaction Effect : This component captures the interaction between the endowment and coefficient effects, accounting for the simultaneous influence of both factors. The Blinder-Oaxaca decomposition model is specified as follows: $$\:{Y}^{male}={\beta\:}^{male}{X}^{male}+{e}^{male}$$ $$\:{Y}^{female}={\beta\:}^{female}{X}^{female}+{e}^{female}$$ where YY represents out-of-pocket expenditure, XX is the vector of predictor variables, β\beta denotes the regression coefficients, and ee is the error term. For this analysis, we used a common set of predictors for both males and females ( \(\:{X}^{male}\) = \(\:{X}^{female}\) ). Thus, the overall gender gap in OOPE can be decomposed as: $$\:{Y}^{male}-{Y}^{female}=\varDelta\:x{\beta\:\:}^{male}+\varDelta\:{\beta\:x\:}^{female}+\varDelta\:x\varDelta\:\beta\:=C+E+CE$$ Here: E (Endowment Effect) represents the portion of the gap explained by differences in the predictors' distributions between males and females. C (Coefficient Effect) accounts for the gap caused by differences in the effects of predictors. CE (Interaction Effect) captures the combined effect of differences in predictors and coefficients. By applying the Blinder-Oaxaca decomposition, we quantified the relative contributions of the endowment, coefficient, and interaction effects to the observed gender inequality in Out-of-Pocket Expenditure (OOPE). To account for the complex survey design, including sampling weights, clustering, and stratification, the SVY command in STATA 13.1 was used for estimating bivariate and multivariable statistics. All expenditure values are reported in Indian Rupees (INR). Additionally, R software was used to generate visualizations. Results Gender differentials in prevalence of mental illness Table 1 presents the prevalence of mental illness by gender across various socio-demographic characteristics. The overall nationwide prevalence of any mental illness disability was 1.3 per 1,000 individuals, with a burden of 1.1 per 1,000 among females. Females under 60 years of age had a lower burden of mental illness compared to males, while females aged 60 years and above reported a higher burden across all conditions. Prevalence within caste groups across all conditions was similar for both genders. Among individuals with no formal education, females had a prevalence of 2.1 per 1,000, while males had a slightly higher prevalence at 2.8 per 1,000. North and Central India reported the highest gender disparity, with a difference of 0.7 per 1,000 in the prevalence of any mental illness. Gender differentials in Out-of-pocket spending burden Table 2 explores gender disparities in health care burden (HCB) and out-of-pocket expenditure (OOPE) among individuals with mental illness, stratified by socio-economic and demographic characteristics. Across all age groups, females report lower OOPE compared to males. However, among those with secondary or higher education, females report higher OOPE than males, with a ratio gap of 0.9. Females living alone experience a significantly higher HCB compared to males (46.0% vs. 23.4%; ratio 0.5), despite having lower OOPE (₹3,712 for females vs. ₹3,237 for males). Among Scheduled Castes (SC), females face a higher HCB (27.9%) than males (20.5%). Conversely, males from other caste groups report higher HCB than females. In poorer quintiles, males report a higher HCB compared to females. Rural females experience a lower HCB (17.1%) than rural males (21.0%; ratio 1.2), while the urban gender gap is narrower, with males reporting an HCB of 18.2% compared to 15.0% for females. Urban females incur higher OOPE compared to rural females (₹16,594 vs. ₹9,345). Out-of-pocket expenditure and healthcare burden based on infrequent expenditure during the last 365 days and also usual monthly expenditure (excluding yearly expenses) are available separately for the unnecessary and excessive worry; unusual experiences such as hearing voices, seeing visions, strange smells, sensations, or tastes; and unusual behavior or difficulty in social interactions and adaptability and combined estimates for all three conditions under mental illness are provided in Supplementary Appendix Tables S1 to S8. Figure 1 illustrates state-wise variations in gender disparities in Health Care Burden (HCB) and Out-of-Pocket Expenditure (OOPE) for individuals with mental illness, expressed as gender ratios for each state in India. Maharashtra shows the greatest relative burden on females, with ratios of 0.5 for HCB and 0.4 for OOPE. Similarly, northeastern states such as Arunachal Pradesh (HCB = 0.7, OOPE = 0.5) and Manipur (HCB = 0.5, OOPE = 0.8) indicate significant disparities, with females facing a higher burden than males. A west-to-east belt comprising of Maharashtra, Chhattisgarh, Odisha, and Bihar demonstrates consistent strain on females in terms of both HCB and OOPE. In contrast, Sikkim reports the highest burden on males, with HCB and OOPE ratios of 5.3 and 5.4, respectively. States such as Himachal Pradesh, Telangana, and Gujarat show relatively equal gender disparities in both HCB and OOPE. Uttarakhand has the highest overall burden of mental illness, with a mean OOPE of ₹3,951 and an HCB of 38.6% (Table S1 ). Catastrophic Health Expenditure and Poverty impact Figure 2 , displays the compounded vulnerabilities experienced by socio-economically disadvantaged groups, particularly among males, in terms of catastrophic financial burden due to mental illness. The largest disparities, with males bearing a higher CHE burden, are observed among individuals aged 60 years and above, those who are illiterate, and individuals with widowed, separated, or divorced marital status. Additional groups with significant gender gaps include persons practicing the Muslim religion, individuals belonging to Scheduled Tribes, those in the poorest wealth index strata, and those residing in rural areas. Table 3 shows that household pushed below the poverty line (poverty headcount ratio) and increased their average shortfall from it (poverty gap ratio). Almost one fifth 20.2% of women and 21.1% of men fell below the poverty line as a result of out-of-pocket (OOP) medical expenses related to mental illness. For men and women, the average poverty difference was 8.0% and 7.3%, respectively. The majority of those impacted were elderly rural men (60 + years old) from central India's medium wealth quintile. The greatest impact among females was observed in younger (15–35 years old), married, impoverished, rural people of central India. Decomposition analysis to measure the gender gap in OOPE Table 4 presents the Blinder-Oaxaca decomposition analysis results, examining the gender disparity in OOPE for mental health care. The findings show that on average males incur a higher relative OOPE compared to females, with a statistically significant gap of 25.4% (p < 0.05). Of this difference, 40.2% is attributed to endowment effects (differences in observable characteristics), while 92.2% is explained by coefficient effects (differences in the influence of these characteristics). Key socio-economic factors, such as age, marital status, religion, and wealth, did not significantly contribute to the explained or unexplained components of the disparity. In contrast, education level under individual factors, region of residence under community-level factors, and caste category under household dynamics were significant contributors to the gap. Within the explained component, education contributed the most to the disparity (76%), followed by region of residence (29%). For the unexplained component, the caste category showed a negative contribution to the inequality, accounting for 12%. Figure 3 a illustrates the contribution of individual, community, and household dynamics variables to the disparity in Out-of-Pocket Expenditure (OOPE) for mental health treatment between males and females. Individual characteristics accounted for the largest share, explaining 71.7% of the observed gap. Community-level factors contributed 28.9% to the disparity, while household dynamics had a marginal and negative contribution of -0.6%. (Fig. 3 b). Discussion Mental health remains a critical public health concern in India. This study quantified gender and socio-economic disparities in the financial burden associated with mental health care. On average, males incurred higher out-of-pocket expenditure (OOPE) for mental health treatment, with a gender gap of approximately 25% based on Blinder-Oaxaca decomposition analysis. Considerable state-level variation was observed, with females in Maharashtra and several eastern states experiencing a higher healthcare burden (HCB) and OOPE, while males in Sikkim and rural areas reported greater exposure to catastrophic health expenditure (CHE). Education contributed most to the explained component of the gender gap (76%), followed by region of residence and caste, reflecting the role of structural and social differentials in shaping financial risk. These findings point to persistent and stratified inequalities in the economic burden of mental health care. Although the prevalence of self-reported mental illness in this study was relatively low, its impact is disproportionately concentrated among vulnerable groups. Disabilities related to mental health—such as excessive worry (1.1 per 1,000), auditory hallucinations (0.2 per 1,000), and behavioral disturbances (0.5 per 1,000)—were more commonly reported among older adults (aged 60 years and above), males, and individuals residing in rural areas or belonging to lower wealth quintiles. These patterns are consistent with estimates from the National Mental Health Survey (2015–16), which reported that approximately 15% of Indian adults require active intervention for mental health conditions, with a higher prevalence of depressive and anxiety disorders among women[ 24 ]. Gender differences in OOPE are shaped by differences in health-seeking behaviour. Men are less likely than women to seek help for mental health concerns, often due to norms surrounding masculinity, including self-reliance, emotional restraint, and reluctance to engage with health services[ 25 ]. Existing literature indicates that being male is negatively associated with help-seeking behavior for mental health conditions[ 26 ]. This pattern leads to delayed presentation and more severe illness at the time of treatment, which may require more intensive and costly care. Men often carry the financial responsibility not only for their own care but also for other household members. Novak[ 27 ] suggests that men, as primary financial providers, are often discouraged from taking time away from work to access care, prioritising household responsibilities over personal health. Bass[ 28 ] notes that negative perceptions related to seeking help also act as barriers to men’s timely engagement with mental health services. Together, these behavioural factors contribute to a higher OOPE burden among males. The association between higher educational attainment and increased Out-of-Pocket Expenditure (OOPE) for mental health treatment is a major contributor to the observed gender disparity in OOPE. Socioeconomic factors such as education and employment influence health outcomes differently across genders[ 29 , 30 ]. Limited insurance coverage is more commonly observed among individuals with lower educational levels[ 31 ], and the established relationship between education and income further explains differential financial exposure to health costs. In this study, women with higher education levels were found to incur greater OOPE compared to men. Supporting evidence from a mental health survey conducted across 10 European Union countries indicates that women are more likely to utilize mental health services, contributing to their increased healthcare expenditure[ 32 ]. Caste was also identified as a significant social determinant influencing gendered economic burden in mental health care. Data from the World Health Organization’s Study on Global Ageing and Adult Health (WHO-SAGE) documented persistent disparities in self-reported mental health between higher caste Hindus and marginalized groups, including Scheduled Castes and Scheduled Tribes[ 33 ]. These disparities are not attributable to inherent biological or cultural differences but are instead linked to long-standing socio-economic disadvantages and systemic exclusion[ 33 ]. Women from lower caste backgrounds are particularly vulnerable, facing higher risks of domestic violence, greater disease burden, and limited access to healthcare beyond their immediate localities. Some evidence suggests that self-help groups have provided modest financial relief by reducing dependency on informal credit sources and offering limited support to meet private healthcare costs[ 34 ]. In the Indian context, gender disparities in healthcare expenditure are further shaped by patriarchal norms. Structural barriers—such as limited financial autonomy, lower income generation, lack of community support, and restrictive gender roles—limit women’s access to timely and adequate mental health care[ 35 , 36 ]. Despite these challenges, women who do seek care are more likely to utilize services consistently. In contrast, men are more likely to delay seeking care due to societal expectations, leading to more advanced illness at the time of intervention and, consequently, higher OOPE. Strengths of the study The strength of this study derived from its use of nationally representative data from the 76th round of the National Sample Survey on Persons with Disabilities, covering all Indian states. The use of a large, population-based dataset allows for generalisable estimates. Standardised definitions were applied to measure health care burden, catastrophic expenditure, and impoverishment, strengthening the comparability of findings. Limitations of the study However, the study has certain limitations. The cross-sectional design limits causal interpretation, and recall bias in expenditure reporting is a known concern. The survey did not include information on indirect costs such as income loss due to disability or caregiver burden, nor did it capture data on household-level hardship financing, which may lead to an underestimation of the total economic impact. Conclusion All in all, this study quantifies gender and socio-economic differences in out-of-pocket expenditure (OOPE) for mental health care in India. Males face higher OOPE due to delayed care-seeking and financial responsibilities, while females encounter structural barriers that limit access. These differences are shaped by behavioural norms and systemic inequities, indicating a need for gender-responsive health financing and service delivery. Stigma, limited engagement with general practice, and normative expectations around masculinity reduce men’s use of mental health services, increasing treatment costs. Service models designed to respond to gender-specific needs, including dedicated mental health clinics, may improve access and reduce financial strain. Policy and research must address the interaction between gender, health system use, and economic burden to improve equity in mental health care. Policy Recommendations A comprehensive policy approach is essential to alleviate the financial burden associated with mental health care in India. Government initiatives such as PMJAY–Ayushman Bharat should explicitly incorporate mental health services, covering both hospitalization and outpatient expenses. Special attention must be given to vulnerable groups—including the elderly, rural residents, married individuals, and youth—through subsidized medical support and community-based care models to help mitigate poverty-related impacts. Integrating mental health services into primary healthcare can facilitate early diagnosis and reduce overall treatment costs. Additionally, nationwide awareness campaigns aimed at reducing stigma and promoting early intervention are crucial for improving mental health literacy and easing the financial strain of treatment. Future Research A longitudinal study is essential to understand the long-term healthcare costs and coping mechanisms associated with mental health conditions. Developing gender-specific strategies for mental health care and treatment expenses is also critical for designing targeted interventions that address the unique needs of men and women. Furthermore, conducting a cost-benefit analysis of expanding public mental health services can provide valuable insights to support increased investment and guide strategic planning for scalable and equitable mental health care delivery across India. Declarations Ethics declaration — This study used anonymized, publicly available secondary data from the NSS 76th round and did not involve direct interaction with human subjects; hence, no separate ethical approval was required. The study adheres to the ethical principles of the Declaration of Helsinki (2013) and relevant national guidelines. Approval Committee: The NSS survey was conducted by MoSPI, Government of India, with protocols reviewed by internal expert committees. Consent to Participate declaration : Secondary data from the 76th round of the National Sample Survey (NSS) was used in this study and it is publically available. Prior to conducting the survey, the NSS received ethical clearance from its internal review committee, and all respondents gave their informed consent at the time of data collection. Therefore, the current study does not require separate ethical approval. Funding : No funding was issued for this research Availability of data and materials -- The secondary data used in this study are publicly available from the National Sample Survey Office (NSSO), under the Ministry of Statistics and Programme Implementation (MoSPI), Government of India. Competing Interests -- The authors declare that they have no competing interests. Author’s Contribution — RY and AY conceptualised the study, extracted and analysed the data. JY contributed to study design, data analysis, and project management. SS was responsible for data interpretation, manuscript drafting, and final revisions. VJ contributed to the initial drafting of the manuscript. All authors reviewed and approved the final version. Acknowledgements —NA References Mental disorders [Internet]. [cited 2025 Apr 18]. Available from: https://www.who.int/news-room/fact-sheets/detail/mental-disorders Mental health [Internet]. [cited 2025 Apr 18]. Available from: https://www.who.int/health-topics/mental-health Patel V, Ramasundarahettige C, Vijayakumar L, Thakur JS, Gajalakshmi V, Gururaj G, Suraweera W, Jha P. Million Death Study Collaborators. Suicide mortality in India: a nationally representative survey. Lancet. 2012;379(9834):2343–51. Available from: https://doi.org/10.1016/S0140-6736(12)60606-0 Sagar R, Dandona R, Gururaj G et al. (2017). The National Mental Health Survey of India (2016): Prevalence, patterns and outcomes. Indian Journal of Psychiatry , 59(1), 21–26. Available from: https://doi.org/10.4103/0019-5545.204438 National Mental Health Survey of India. 2015-16 [Internet]. [cited 2025 Apr 18]. Available from: https://indianmhs.nimhans.ac.in/phase1/Docs/Report2.pdf Xu K, Evans DB, Carrin G, Aguilar-Rivera AM, Musgrove P, Evans T. Protecting households from catastrophic health spending. Health Aff (Millwood). 2007;26(4):972–83. Available from: https://doi.org/10.1377/hlthaff.26.4.972 Mental Health Atlas. 2017 Country Profile: India [Internet]. [cited 2025 Apr 21]. Available from: https://www.who.int/publications/m/item/mental-health-atlas-2017-country-profile-india Deepening Health Insecurity in India. Evidence from National Sample Surveys since 1980s | Economic and Political Weekly [Internet]. 2009 [cited 2025 Apr 21]. Available from: https://www.epw.in/journal/2009/40/special-articles/deepening-health-insecurity-india-evidence-national-sample-surveys Investing in treatment for depression. and anxiety leads to fourfold return [Internet]. [cited 2025 Apr 21]. Available from: https://www.who.int/news/item/13-04-2016-investing-in-treatment-for-depression-and-anxiety-leads-to-fourfold-return Jaisoorya TS, Janardhan Reddy YC, Nair BS, Rani A, Menon PG, Revamma M, Jeevan CR, Radhakrishnan KS, Jose V, Thennarasu K. Prevalence and correlates of obsessive-compulsive disorder and subthreshold obsessive-compulsive disorder among college students in Kerala, India. Indian Journal of Psychiatry [Internet]. 2017 Mar [cited 2025 Apr 21];59(1):56. Available from: https://journals.lww.com/indianjpsychiatry/fulltext/2017/59010/prevalence_and_correlates_of_obsessive_compulsive.11.aspx National Health Policy. 2017.pdf [Internet]. [cited 2025 Apr 21]. Available from: https://mohfw.gov.in/sites/default/files/9147562941489753121.pdf Mental health action. plan 2013–2020 [Internet]. [cited 2025 Apr 21]. Available from: https://www.who.int/publications/i/item/9789241506021 Nanda P. Gender Dimensions of User Fees: Implications for Women’s Utilization of Health Care. Reproductive Health Matters [Internet]. 2002 Jan 1 [cited 2025 Apr 18];10(20):127–34. Available from: https://doi.org/10.1016/S0968-8080(02)00083-6 Inequities in Access to Health Services in India. Caste, Class and Region | Economic and Political Weekly [Internet]. 2010 [cited 2025 Apr 18]. Available from: https://www.epw.in/journal/2010/38/special-articles/inequities-access-health-services-india-caste-class-and-region.html Pratley P. Associations between quantitative measures of women’s empowerment and access to care and health status for mothers and their children: A systematic review of evidence from the developing world. Social Science & Medicine [Internet]. 2016 Nov 1 [cited 2025 Apr 21];169:119–31. Available from: https://www.sciencedirect.com/science/article/pii/S0277953616304087 Women Empowerment and Maternal Healthcare Service Utilization. Evidence from Bangladesh [Internet]. ResearchGate. [cited 2025 Apr 21]. Available from: https://www.researchgate.net/publication/338526796_Women_Empowerment_and_Maternal_Healthcare_Service_Utilization_Evidence_from_Bangladesh Gupta M, Sarangi R, Sharma S, Patnaik L. Mental health issues and lifestyle changes associated with the COVID-19 pandemic in adolescents: A cross-sectional study in selected schools of Eastern India. J Educ Health Promot [Internet]. 2024 Feb 26 [cited 2025 Apr 21];13:80. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10979783/ Patel V, Saxena S et al. The Lancet Commission on global mental health and sustainable development. The Lancet [Internet]. 2018 Oct [cited 2025 Apr 21];392(10157):1553–98. Available from: https://linkinghub.elsevier.com/retrieve/pii/S014067361831612X Persons with Disabilities in India [Internet]. [cited 2025 Apr 21]. Available from: https://www.mospi.gov.in/sites/default/files/publication_reports/Report_583_Final_0.pdf Key Indicators of Social Consumption in India. Health of NSS 75th Report [Internet]. [cited 2025 Apr 21]. Available from: https://www.mospi.gov.in/sites/default/files/publication_reports/KI_Health_75th_Final.pdf Rangarajan Report on Poverty [Internet]. [cited 2025 Apr 21]. Available from: https://pib.gov.in/newsite/printrelease.aspx?relid=108291 Oaxaca R. Male-Female Wage Differentials in Urban Labor Markets. Int Econ Rev . 1973;14(3):693–709. Available from: https://inequality.stanford.edu/sites/default/files/media/_media/pdf/Classic_Media/Oaxaca_1973_Discrimination%20and%20Prejudice.pdf Blinder AS. Wage Discrimination: Reduced Form and Structural Estimates. The Journal of Human Resources [Internet]. 1973 [cited 2025 Apr 21];8(4):436–55. Available from: https://www.jstor.org/stable/144855 Adam,Lindelow,Magnus OO, Van Andrew KA. Wagstaff. Analyzing health equity using household survey data : a guide to techniques and their implementation [Internet]. World Bank. [cited 2025 Apr 21]. Available from: https://documents.worldbank.org/en/publication/documents-reports/documentdetail/en/633931468139502235 Murthy RS. National Mental Health Survey of India 2015–2016. Indian J Psychiatry [Internet]. 2017 [cited 2025 Apr 21];59(1):21–6. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5419008/ Gough B, Novikova I. Mental health, men and culture: how do sociocultural constructions of masculinities relate to men’s mental health help-seeking behaviour in the WHO European Region? [Internet]. Copenhagen: WHO Regional Office for Europe; 2020 [cited 2025 Apr 21]. (WHO Health Evidence Network Synthesis Reports). Available from: http://www.ncbi.nlm.nih.gov/books/NBK559706/ Gonzalez JM, Alegría M, Prihoda TJ, Copeland LA, Zeber JE. How the relationship of attitudes toward mental health treatment and service use differs by age, gender, ethnicity/race and education. Soc Psychiat Epidemiol [Internet]. 2011 Jan 1 [cited 2025 Apr 21];46(1):45–57. Available from: https://doi.org/10.1007/s00127-009-0168-4 Novak JR, Peak T, Gast J, Arnell M. Associations Between Masculine Norms and Health-Care Utilization in Highly Religious, Heterosexual Men. Am J Mens Health [Internet]. 2019 May 1 [cited 2025 Apr 21];13(3):1557988319856739. Available from: https://doi.org/10.1177/1557988319856739 Bass SB, Muñiz J, Gordon TF, Maurer L, Patterson F. Understanding help-seeking intentions in male military cadets: An application of perceptual mapping. BMC Public Health [Internet]. 2016 May 17 [cited 2025 Apr 21];16(1):413. Available from: https://doi.org/10.1186/s12889-016-3092-z Gender. and Health in the Western Pacific Region [Internet]. [cited 2025 Apr 21]. Available from: https://iris.who.int/bitstream/handle/10665/208162/Gender_health_fact_sheet_2014_eng.pdf?sequence=1&isAllowed=y Maharlouei N, Cobb S, Bazargan M, Assari S. Subjective Health and Happiness in the United States: Gender Differences in the Effects of Socioeconomic Status Indicators. Journal of Mental Health & Clinical Psychology [Internet]. 2020 May 14 [cited 2025 Apr 21];4(2). Available from: https://doi.org/10.29245/2578-2959/2020/2.1196 Mohanty SK, Upadhyay AK, Maiti S, Mishra RS, Kämpfen F, Maurer J, O’Donnell O. Public health insurance coverage in India before and after PM-JAY: repeated cross-sectional analysis of nationally representative survey data. BMJ Glob Health [Internet]. 2023 Aug 28 [cited 2025 Apr 21];8(8):e012725. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10462969/ Are there gender differences in service use for mental disorders across countries in the European. Union? Results from the EU-World Mental Health survey | Journal of Epidemiology & Community Health [Internet]. [cited 2025 Apr 21]. Available from: https://link.springer.com/article/ 10.1007/s11113-020-09585-9 Gupta A, Coffey D. Caste, Religion, and Mental Health in India. Popul Res Policy Rev [Internet]. 2020 Dec 1 [cited 2025 Apr 21];39(6):1119–41. Available from: https://doi.org/10.1007/s11113-020-09585-9 Thapa R, van Teijlingen E, Regmi PR, Heaslip V. Caste Exclusion and Health Discrimination in South Asia: A Systematic Review. Asia Pac J Public Health [Internet]. 2021 Nov [cited 2025 Apr 21];33(8):828–38. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8592103/ Agarwal B. ‘Bargaining’ and Gender Relations: Within and Beyond the Household. Feminist Economics [Internet]. 1997 Jan [cited 2025 Apr 21];3(1):1–51. Available from: http://www.tandfonline.com/doi/abs/10.1080/135457097338799 Saikia N, Moradhvaj, Bora JK. Gender Difference in Health-Care Expenditure: Evidence from India Human Development Survey. PLoS One [Internet]. 2016 Jul 8 [cited 2025 Apr 21];11(7):e0158332. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4938214/ Tables Tables 1 to 4 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table.docx Suplammentarytables.docx 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-6968917","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":534012762,"identity":"79fcfc8b-3795-4f6b-855b-045e7ac86edc","order_by":0,"name":"Rohit Yadav","email":"","orcid":"","institution":"International Institute for Population Sciences","correspondingAuthor":false,"prefix":"","firstName":"Rohit","middleName":"","lastName":"Yadav","suffix":""},{"id":534012773,"identity":"bea21f32-72ca-427e-b046-d2623a85df17","order_by":1,"name":"Alok Kumar Yadav","email":"","orcid":"","institution":"Babasaheb Bhimrao Ambedkar University (A Central University)","correspondingAuthor":false,"prefix":"","firstName":"Alok","middleName":"Kumar","lastName":"Yadav","suffix":""},{"id":534012774,"identity":"6566aee1-6879-4839-826c-8b38530cc53c","order_by":2,"name":"Siddhant Shastri","email":"","orcid":"","institution":"ICMR-National Institute for Research in Digital Health and Data Science","correspondingAuthor":false,"prefix":"","firstName":"Siddhant","middleName":"","lastName":"Shastri","suffix":""},{"id":534012779,"identity":"941cc626-75a4-4da6-898c-a91c6332e907","order_by":3,"name":"Vaishnavi Joshi","email":"","orcid":"","institution":"London School of Economics and Political Science","correspondingAuthor":false,"prefix":"","firstName":"Vaishnavi","middleName":"","lastName":"Joshi","suffix":""},{"id":534012781,"identity":"0992159d-d957-41bb-b469-fbb397a87f75","order_by":4,"name":"Jeetendra Yadav","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYBACxgYIzcPAwHwASEvIEKMFrAuohS0BpIWHJIsMoNYRAMztzccf8zDckdHtP/P51Y0aCx4G9sNHN+C1oudYYjMPwzMesxu526xzjgEdxpOWdgOvlhk5hkAth4FaeLcZ57ABtUgA2Xi1zH//EaLl/Jlnxjn/iNEyg4cRouVADvPj3DZitPSkGc6cYwByWJoZc26fBA8bIb8Yth9+8OFNxWF7s/OHH3/O+VYnx89++Bh+LQ0gEhwjDGwSYBKfchCQR2IzfyCkehSMglEwCkYmAAD7zEW2WfdcVwAAAABJRU5ErkJggg==","orcid":"","institution":"ICMR-National Institute for Research in Digital Health and Data Science","correspondingAuthor":true,"prefix":"","firstName":"Jeetendra","middleName":"","lastName":"Yadav","suffix":""}],"badges":[],"createdAt":"2025-06-24 21:23:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6968917/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6968917/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":94881073,"identity":"cd77859d-a21e-4ba1-80eb-eece58f86743","added_by":"auto","created_at":"2025-10-31 16:54:24","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":68022,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscriptwithtitle.docx","url":"https://assets-eu.researchsquare.com/files/rs-6968917/v1/12f54f076a713a46188dd9d4.docx"},{"id":94881080,"identity":"5260e836-3de3-4d96-97e7-7b7790e88da7","added_by":"auto","created_at":"2025-10-31 16:54:24","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":568289,"visible":true,"origin":"","legend":"","description":"","filename":"Figures.docx","url":"https://assets-eu.researchsquare.com/files/rs-6968917/v1/60525fea407f013ec95d2497.docx"},{"id":94986692,"identity":"3ec05115-4365-4aee-80a8-8f20b54fa908","added_by":"auto","created_at":"2025-11-03 07:00:37","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":45349,"visible":true,"origin":"","legend":"","description":"","filename":"Table.docx","url":"https://assets-eu.researchsquare.com/files/rs-6968917/v1/613dc8e0315c0594b25f2faf.docx"},{"id":94987082,"identity":"89cea7c4-e273-400a-b9e0-f2556c5cdc05","added_by":"auto","created_at":"2025-11-03 07:01:12","extension":"json","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7187,"visible":true,"origin":"","legend":"","description":"","filename":"7542df9b1c4a44d1b4aa188cc6b2e87c.json","url":"https://assets-eu.researchsquare.com/files/rs-6968917/v1/8c891bda63dcf60377f44aa4.json"},{"id":94986401,"identity":"a70ede75-2bb4-4716-a05f-bfc07a933893","added_by":"auto","created_at":"2025-11-03 07:00:16","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":158692,"visible":true,"origin":"","legend":"","description":"","filename":"Suplammentarytables.docx","url":"https://assets-eu.researchsquare.com/files/rs-6968917/v1/b1cf19f9e95224b3984b6654.docx"},{"id":94986861,"identity":"785aa123-638c-4be8-a402-efa5cef71ec5","added_by":"auto","created_at":"2025-11-03 07:00:54","extension":"xml","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":210689,"visible":true,"origin":"","legend":"","description":"","filename":"7542df9b1c4a44d1b4aa188cc6b2e87c1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-6968917/v1/49fe1bc0e92e1ae73c41566c.xml"},{"id":94881081,"identity":"8ac43a5a-a181-494a-a11d-9d2a53328bfa","added_by":"auto","created_at":"2025-10-31 16:54:24","extension":"jpeg","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":113032,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6968917/v1/b701342a0a1b1252386528b2.jpeg"},{"id":94881083,"identity":"2e0af30e-ac06-43f1-a7a3-fcaee573967a","added_by":"auto","created_at":"2025-10-31 16:54:24","extension":"jpeg","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":549828,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6968917/v1/b9eb08b873ac335b2f746f45.jpeg"},{"id":94881089,"identity":"2eb478ef-5dde-43cb-b23e-82bb7af9dbfd","added_by":"auto","created_at":"2025-10-31 16:54:24","extension":"jpeg","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":181563,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6968917/v1/bcd528a2a602e8b865dd373e.jpeg"},{"id":94987013,"identity":"2d115fe5-cf86-4967-b041-fe384a657e0f","added_by":"auto","created_at":"2025-11-03 07:01:06","extension":"jpeg","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":150974,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6968917/v1/b1f9b2fd9f5dd2bca9be23f6.jpeg"},{"id":94881088,"identity":"9366adb4-f5ed-476e-b1ad-56d9bdc58708","added_by":"auto","created_at":"2025-10-31 16:54:24","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":100492,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6968917/v1/e7c2c05fe4a208aad3480261.png"},{"id":94881090,"identity":"7d43e35b-b773-4c8b-b5e6-c5a2d68eec8d","added_by":"auto","created_at":"2025-10-31 16:54:24","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":136205,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6968917/v1/b60a3532383243c2623e149c.png"},{"id":94987184,"identity":"790534ad-beba-4c1d-be0f-25d24fa195fd","added_by":"auto","created_at":"2025-11-03 07:01:26","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":22930,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6968917/v1/087b89a6fac07bbe0e1b1cb1.png"},{"id":94881085,"identity":"48039050-e94d-4336-a3fc-a83455cda151","added_by":"auto","created_at":"2025-10-31 16:54:24","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":36830,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6968917/v1/7904b261ef57c2bc2b7dffc9.png"},{"id":94881092,"identity":"09ace3c9-66fc-4614-aed0-bfd4954d8a85","added_by":"auto","created_at":"2025-10-31 16:54:24","extension":"xml","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":209512,"visible":true,"origin":"","legend":"","description":"","filename":"7542df9b1c4a44d1b4aa188cc6b2e87c1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-6968917/v1/1d79c578f4095114b938d086.xml"},{"id":94881093,"identity":"888fca25-d2d5-49f2-8058-88df38a678e5","added_by":"auto","created_at":"2025-10-31 16:54:24","extension":"html","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":219570,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-6968917/v1/6b3706a0496c558004b54e4d.html"},{"id":94986690,"identity":"66e8740f-1ff4-436e-8bb1-be1dc9ecb519","added_by":"auto","created_at":"2025-11-03 07:00:37","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":113032,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eState-wise Male-to-Female Ratios in Gender Gap for Health Care Burden (Left) and Out-of-Pocket Expenditure (Right) for Mental Health Treatment in India\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6968917/v1/05939aea76375f84c2cc90e1.jpg"},{"id":94881078,"identity":"762608b6-0c34-491e-95b8-045432431d03","added_by":"auto","created_at":"2025-10-31 16:54:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":363992,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAbsolute Percentage Gap in Catastrophic Health Expenditure (CHE) Between Males and Females Across Socioeconomic and Demographic Groups (10% Threshold of Household Consumption Expenditure)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6968917/v1/cf488622f61d96ac7af8ac19.png"},{"id":94881075,"identity":"8c857ebd-0c13-4ddc-a710-0a51f60c685f","added_by":"auto","created_at":"2025-10-31 16:54:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":85837,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDecomposition of Gender Differences in Out-of-Pocket Expenditure for Mental Health Care in India (a. Domain-Specific Covariate Contributions, b. Aggregate Effects by Individual, Community, and Household Characteristics)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6968917/v1/9b756702b3c3e316b7dc96e8.png"},{"id":106916280,"identity":"3866db6b-782a-4e3d-9e8e-6ba21be5be54","added_by":"auto","created_at":"2026-04-14 18:10:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1440120,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6968917/v1/93cd74d4-6bfe-4f25-b0dd-d9db5c867b6d.pdf"},{"id":94987355,"identity":"f5127422-90b7-4d7d-8b74-73afedbaa347","added_by":"auto","created_at":"2025-11-03 07:01:45","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":45349,"visible":true,"origin":"","legend":"","description":"","filename":"Table.docx","url":"https://assets-eu.researchsquare.com/files/rs-6968917/v1/8407a12bdf37322223c2520a.docx"},{"id":94881082,"identity":"8af8e4de-c996-4d40-b711-439dd6b73af6","added_by":"auto","created_at":"2025-10-31 16:54:24","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":158692,"visible":true,"origin":"","legend":"","description":"","filename":"Suplammentarytables.docx","url":"https://assets-eu.researchsquare.com/files/rs-6968917/v1/39058090daf2db503882b911.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Who Pays More? Gender Gaps in Mental Health Treatment Costs in India","fulltext":[{"header":"What we already know","content":"\u003cul\u003e\n \u003cli\u003ePrevalence of mental illness India and mental illness disorders are a rising public health community concern in India.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eConsiderable proportion of mental illness patients bound to incurred the catastrophic health expenditure (CHE) and push households into below poverty line due higher health acre expenditure cost in India.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eGender differentials in health care and health care expenditure among women in India is associated with the socio-cultural and financial reasons\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eWhat this study adds\u003c/strong\u003e\u003c/p\u003e\n\u003cul class=\"decimal_type\"\u003e\n \u003cli\u003eThis study carried out the comprehensive gender-based analysis of treatment cost for mental health care in India using a large-scale survey data based on the nationally representative data (NSS 76th round), and more specially focus on disability survey data.\u003c/li\u003e\n \u003cli\u003eStudy informs that males, on average, experienced higher treatment cost for mental health care compared to females.\u003c/li\u003e\n \u003cli\u003eDecomposition analysis shows that almost two fifth (40.2%) of the gender gap in mental health care cost, with education (76%) and region (29%) presence the major contributors.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eResults indicate that individual-level factors (age, education, marital status) explanation for 71.7% of the described difference in mental health care cost.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eHighlights the need for gender-specific mental health plans, reducing financial strain\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Introduction","content":"\u003cp\u003eMental health disorders are a significant and growing public health issue globally, with profound socio-economic impacts. Mental health challenges are linked to substantial disability and disease burden, affecting millions of people[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In low- and middle-income countries like India, the situation is even more critical due to limited resources and stigma around mental health[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn India, mental disorders affected approximately 14.3% of the population in 2017, a notable rise from previous decades. The disease burden due to mental disorders nearly doubled since 1990, contributing to 4.7% of the total Disability-Adjusted Life Years (DALYs) in the country. Depression and anxiety disorders constitute over half of this mental health burden[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. A national survey across 12 Indian states found a lifetime prevalence of mental disorders at 13.7% and a current prevalence of 10.6%, highlighting an urgent need for mental health assessments and interventions[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBeyond disability, factors such as out-of-pocket expenditure leading to catastrophic health expenditure contribute to existing mental illnesses and predispose individuals to premature mortality[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This economic burden worsens the risk of poverty and financial distress, with studies showing that catastrophic health spending pushes around 63\u0026nbsp;million Indians into poverty each year[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This compounded strain not only affects health outcomes but also results in significant economic loss, as mental health disorders are estimated to reduce India's GDP by approximately \u003cspan\u003e$\u003c/span\u003e1.03 trillion between 2012 and 2030[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The impact is further intensified by India's crude suicide mortality rate of 16.3 per 100,000 in 2016, the highest in the South Asian region[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMental health disorders play a significant burden not only on individuals but also on households, where family members are often required to provide long-term support and supervision, especially during acute phases of illness[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, stigma surrounding mental health in India often deters people from seeking timely and appropriate treatment, pushing many to turn to local faith healers rather than formal healthcare systems[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This delay in care, coupled with societal stigma, exacerbates the impact of mental illness on both individuals and their families.\u003c/p\u003e\u003cp\u003eRecognizing this growing burden, the Indian government enacted the \u003cem\u003eMental Healthcare Act of 2017\u003c/em\u003e, which guarantees every individual the right to accessible, affordable, and high-quality mental health care[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This legislation aligns with international human rights principles and supports global initiatives such as the \u003cem\u003eWorld Health Organization\u0026rsquo;s Comprehensive Mental Health Action Plan 2013\u0026ndash;2020\u003c/em\u003e, which was extended to 2030 with the aim of achieving universal coverage for mental health services[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The inclusion of mental health in the \u003cem\u003eSustainable Development Goals (SDGs)\u003c/em\u003e further underscores its global importance. However, significant challenges persist. The \u003cem\u003eMental Health Atlas 2020\u003c/em\u003e highlights enduring inequalities in the availability and distribution of mental health resources, particularly in low- and lower-middle-income countries like India, where an 83% treatment gap was reported, and most individuals seeking care do so through the private sector[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOne of the most pressing concerns in India is the high out-of-pocket expenditures (OOPE) for mental health care. Mental health spending by the Indian government accounts for only 1.30% of its total health expenditure, leaving the majority of patients to cover the costs of services and medications themselves[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This financial burden is especially significant for vulnerable populations, pushing many families into medical poverty. Furthermore, studies indicate stark gender disparities in health care access, treatment-seeking behaviour, and outcomes, with women often bearing a disproportionate share of the mental health-related disability and mortality[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. These disparities are exacerbated by cultural norms and economic inequalities, which limit women\u0026rsquo;s autonomy in healthcare decision-making and reduce their access to necessary treatment[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eHigh OOPE also leads to catastrophic health expenditures (CHE), which force households to forgo essential care or fall deeper into poverty. Literature shows that mental health disorders not only magnify existing socio-economic inequalities but also contribute to widening income inequality, particularly along gender lines[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Although India\u0026rsquo;s \u003cem\u003eNational Health Policy 2017\u003c/em\u003e aims to reduce OOPE in healthcare, mental health remains critically underfunded[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. These financial barriers, compounded by gender inequalities, undermine India\u0026rsquo;s efforts to meet the SDG targets for universal health coverage and mental health care access.\u003c/p\u003e\u003cp\u003ePrevious studies related to Indian mental healthcare, revolve around the direct socio-economic burden of mental health highlighting factors around depression and anxiety, overlooking gender-specific financial challenges in mental healthcare[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This study addresses the gap by analysing data from the NSSO 76th round, focusing on out-of-pocket expenditures (OOPE), catastrophic health expenditures (CHE), and poverty gap due to self-reported mental illness, through the lens of gender and socio-economic groups. This study further aims to provide evidence-based recommendations for equitable healthcare access, aligning with the Sustainable Development Goals (SDGs) for universal health coverage (UHC).\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eData source\u003c/h2\u003e\u003cp\u003eThis study used cross-sectional data from the 76th round of the National Sample Survey (NSS) titled \u003cem\u003e\"Persons with Disabilities in India,\"\u003c/em\u003e conducted between July and December 2018 across all Indian states and union territories[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The NSS defines persons with disabilities as individuals experiencing long-term physical, mental, intellectual, or sensory impairments that limit effective societal participation due to interactions with external barriers. For this study, impairments persisting for 12 months or more were classified as long-term. The survey gathered data on key health indicators, including morbidity, healthcare usage, types of disability, employment status, and socio-economic factors such as household consumption.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSampling strategy and sample size\u003c/h3\u003e\n\u003cp\u003eA stratified two-stage sampling design was applied, using the 2011 Census as the sampling frame. Villages and urban blocks were selected in the first stage, followed by the selection of households in the second stage. The 76th round covered a total of 8,992 village and urban blocks (5,378 rural and 3,614 urban) and included 118,152 households (81,004 rural and 37,148 urban), enumerating 576,569 individuals (402,589 in rural areas and 173,980 in urban areas). From this population, 6,677 individuals reported mental illness disabilities, comprising 3813 males and 2864 females.\u003c/p\u003e\n\u003ch3\u003eOutcome variables\u003c/h3\u003e\n\u003cp\u003eOur study estimated the following outcome variables for those with self-reported mental illness in India: prevalence of overall self-reported mental illness and its symptoms, mean out of pocket expenditure (OOPE), incidence of catastrophic health expenditure, and impoverishment by various sub-groups for males and females who sought health care.\u003c/p\u003e\u003cp\u003eSubsequent section details about the methods used for estimating each of the outcome variables.\u003c/p\u003e\n\u003ch3\u003eMental illness\u003c/h3\u003e\n\u003cp\u003eIn this survey, there were following three defining questions that were asked to respondents regarding mental illness who It identified challenges in social interactions, adaptability, and instances of unusual behavior.\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eIf having unnecessary and excessive worry, anxiety, repetitive behavior/ thoughts, changes of mood or mood swings, talking/laughing to self, staring in space.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eIf having unusual experiences of hearing voices, seeing visions, strange smell or sensation or strange taste\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eIf having unusual behavior or difficulty in social interactions and adaptability\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003ePrevalence of overall self-reported mental illness was calculated used following formula.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{P}\\text{r}\\text{e}\\text{v}\\text{a}\\text{l}\\text{e}\\text{n}\\text{c}\\text{e}\\:\\text{o}\\text{f}\\:\\text{m}\\text{e}\\text{n}\\text{t}\\text{a}\\text{l}\\:\\text{i}\\text{l}\\text{l}\\text{n}\\text{e}\\text{s}\\text{s}\\:=\\frac{Number\\:of\\:persons\\:reporting\\:\\text{m}\\text{e}\\text{n}\\text{t}\\text{a}\\text{l}\\:\\text{i}\\text{l}\\text{l}\\text{n}\\text{e}\\text{s}\\text{s}\\:}{Total\\:numbers\\:of\\:survey\\:populations}x1000$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eOut-of-pocket expenditure (OOPE)\u003c/h3\u003e\n\u003cp\u003eThe NSS 76th round survey for disability captured both the medical expenditure (surgery, equipment, hospitalization, etc.) and nonmedical expenditure (transport, lodging, food, etc.) for infrequent expenditure during last 365 days from date of survey along with usual monthly expenditure (excluding those covered infrequent expenditure during last 365 days\u003cb\u003e)\u003c/b\u003e [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eCatastrophic health expenditure (CHE) and Poverty impact\u003c/h2\u003e\u003cp\u003eThe burden of Out-of-Pocket Expenditure (OOPE) was calculated as a proportion of total household consumption expenditure, while Catastrophic Health Expenditure (CHE) was determined using WHO-defined thresholds of 10% and 20% of total household expenditure. The poverty impact was assessed using the Poverty Headcount Ratio (PHR), which represents the percentage of the population living below the national poverty line, and the Poverty Gap (PG), which measures the shortfall of average income below the poverty line. The Rangarajan Committee poverty line thresholds were used for these assessments. Using recommendation by Rangarajan committee, poverty line was calculated based on per capita household monthly expenditure at Indian National Rupee (INR) 972 in rural areas and INR 1407 in urban areas [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eIndependent Variables\u003c/h3\u003e\n\u003cp\u003eThe independent variables for assessing Out-of-Pocket Expenditure (OOPE) and Catastrophic Health Expenditure (CHE) related to mental health disorders included socio-demographic and economic factors. Socio-demographic variables comprised age (0\u0026ndash;14, 15\u0026ndash;35, 36\u0026ndash;59, and \u0026ge;\u0026thinsp;60 years), education level (\u0026ldquo;Illiterate,\u0026rdquo; \u0026ldquo;Up to Primary,\u0026rdquo; \u0026ldquo;Middle,\u0026rdquo; and \u0026ldquo;Secondary and above\u0026rdquo;), and marital status (\u0026ldquo;Never married,\u0026rdquo; \u0026ldquo;Currently married,\u0026rdquo; and \u0026ldquo;Others\u0026rdquo;). Additional variables included religious affiliation (\u0026ldquo;Hindu,\u0026rdquo; \u0026ldquo;Muslim,\u0026rdquo; and \u0026ldquo;Others\u0026rdquo;), caste categories (SC/ST, OBC, and Others), and economic status, measured using Monthly Per Capita Expenditure (MPCE) quintiles (Poorest, Poorer, Middle, Richer, and Richest). Place of residence (Rural or Urban) and geographic region, grouped into six zones\u0026mdash;North, Central, East, Northeast, West, South, and Union Territories\u0026mdash;were also considered.\u003c/p\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eDescriptive analyses were performed to examine distributional differences in mental health outcomes and economic burden between males and females. The prevalence of self-reported mental illness was assessed across various socio-economic and demographic characteristics, disaggregated by gender. Bivariate analyses were conducted to investigate the financial impact of mental health care on households, with a specific focus on income loss, CHE, and poverty gap levels. These analyses were stratified by selected socio-economic variables to provide gender-specific insights.\u003c/p\u003e\u003cp\u003eTo build on the analysis of economic impact, gender-based differences in Out-of-Pocket Expenditure (OOPE) and associated financial strain were further examined using the Blinder-Oaxaca decomposition method. The Blinder-Oaxaca approach is widely used to decompose differences between two groups, such as male and female, poor and non-poor, or rural and urban populations[\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In this study, we decomposed the gender-based differences in OOPE into three components:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eEndowment Effect\u003c/b\u003e: This component reflects the gap attributable to differences in the distribution of predictor variables (e.g., socio-economic and demographic factors) between males and females.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eCoefficient Effect\u003c/b\u003e: This part of the gap arises from differences in the effect or influence of predictor variables on OOPE between males and females.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eInteraction Effect\u003c/b\u003e: This component captures the interaction between the endowment and coefficient effects, accounting for the simultaneous influence of both factors.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eThe Blinder-Oaxaca decomposition model is specified as follows:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{Y}^{male}={\\beta\\:}^{male}{X}^{male}+{e}^{male}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:{Y}^{female}={\\beta\\:}^{female}{X}^{female}+{e}^{female}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere YY represents out-of-pocket expenditure, XX is the vector of predictor variables, β\\beta denotes the regression coefficients, and ee is the error term. For this analysis, we used a common set of predictors for both males and females (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}^{male}\\)\u003c/span\u003e\u003c/span\u003e=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}^{female}\\)\u003c/span\u003e\u003c/span\u003e). Thus, the overall gender gap in OOPE can be decomposed as:\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:{Y}^{male}-{Y}^{female}=\\varDelta\\:x{\\beta\\:\\:}^{male}+\\varDelta\\:{\\beta\\:x\\:}^{female}+\\varDelta\\:x\\varDelta\\:\\beta\\:=C+E+CE$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eHere:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eE (Endowment Effect) represents the portion of the gap explained by differences in the predictors' distributions between males and females.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eC (Coefficient Effect) accounts for the gap caused by differences in the effects of predictors.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eCE (Interaction Effect) captures the combined effect of differences in predictors and coefficients.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eBy applying the Blinder-Oaxaca decomposition, we quantified the relative contributions of the endowment, coefficient, and interaction effects to the observed gender inequality in Out-of-Pocket Expenditure (OOPE). To account for the complex survey design, including sampling weights, clustering, and stratification, the \u003cem\u003eSVY\u003c/em\u003e command in STATA 13.1 was used for estimating bivariate and multivariable statistics. All expenditure values are reported in Indian Rupees (INR). Additionally, R software was used to generate visualizations.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eGender differentials in prevalence of mental illness\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presents the prevalence of mental illness by gender across various socio-demographic characteristics. The overall nationwide prevalence of any mental illness disability was 1.3 per 1,000 individuals, with a burden of 1.1 per 1,000 among females. Females under 60 years of age had a lower burden of mental illness compared to males, while females aged 60 years and above reported a higher burden across all conditions. Prevalence within caste groups across all conditions was similar for both genders. Among individuals with no formal education, females had a prevalence of 2.1 per 1,000, while males had a slightly higher prevalence at 2.8 per 1,000. North and Central India reported the highest gender disparity, with a difference of 0.7 per 1,000 in the prevalence of any mental illness.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eGender differentials in Out-of-pocket spending burden\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e explores gender disparities in health care burden (HCB) and out-of-pocket expenditure (OOPE) among individuals with mental illness, stratified by socio-economic and demographic characteristics. Across all age groups, females report lower OOPE compared to males. However, among those with secondary or higher education, females report higher OOPE than males, with a ratio gap of 0.9. Females living alone experience a significantly higher HCB compared to males (46.0% vs. 23.4%; ratio 0.5), despite having lower OOPE (₹3,712 for females vs. ₹3,237 for males). Among Scheduled Castes (SC), females face a higher HCB (27.9%) than males (20.5%). Conversely, males from other caste groups report higher HCB than females. In poorer quintiles, males report a higher HCB compared to females. Rural females experience a lower HCB (17.1%) than rural males (21.0%; ratio 1.2), while the urban gender gap is narrower, with males reporting an HCB of 18.2% compared to 15.0% for females. Urban females incur higher OOPE compared to rural females (₹16,594 vs. ₹9,345).\u003c/p\u003e\n \u003cp\u003eOut-of-pocket expenditure and healthcare burden based on infrequent expenditure during the last 365 days and also usual monthly expenditure (excluding yearly expenses) are available separately for the unnecessary and excessive worry; unusual experiences such as hearing voices, seeing visions, strange smells, sensations, or tastes; and unusual behavior or difficulty in social interactions and adaptability and combined estimates for all three conditions under mental illness are provided in Supplementary Appendix Tables S1 to S8.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates state-wise variations in gender disparities in Health Care Burden (HCB) and Out-of-Pocket Expenditure (OOPE) for individuals with mental illness, expressed as gender ratios for each state in India. Maharashtra shows the greatest relative burden on females, with ratios of 0.5 for HCB and 0.4 for OOPE. Similarly, northeastern states such as Arunachal Pradesh (HCB\u0026thinsp;=\u0026thinsp;0.7, OOPE\u0026thinsp;=\u0026thinsp;0.5) and Manipur (HCB\u0026thinsp;=\u0026thinsp;0.5, OOPE\u0026thinsp;=\u0026thinsp;0.8) indicate significant disparities, with females facing a higher burden than males. A west-to-east belt comprising of Maharashtra, Chhattisgarh, Odisha, and Bihar demonstrates consistent strain on females in terms of both HCB and OOPE. In contrast, Sikkim reports the highest burden on males, with HCB and OOPE ratios of 5.3 and 5.4, respectively. States such as Himachal Pradesh, Telangana, and Gujarat show relatively equal gender disparities in both HCB and OOPE. Uttarakhand has the highest overall burden of mental illness, with a mean OOPE of ₹3,951 and an HCB of 38.6% (Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eCatastrophic Health Expenditure and Poverty impact\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, displays the compounded vulnerabilities experienced by socio-economically disadvantaged groups, particularly among males, in terms of catastrophic financial burden due to mental illness. The largest disparities, with males bearing a higher CHE burden, are observed among individuals aged 60 years and above, those who are illiterate, and individuals with widowed, separated, or divorced marital status. Additional groups with significant gender gaps include persons practicing the Muslim religion, individuals belonging to Scheduled Tribes, those in the poorest wealth index strata, and those residing in rural areas.\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows that household pushed below the poverty line (poverty headcount ratio) and increased their average shortfall from it (poverty gap ratio). Almost one fifth 20.2% of women and 21.1% of men fell below the poverty line as a result of out-of-pocket (OOP) medical expenses related to mental illness. For men and women, the average poverty difference was 8.0% and 7.3%, respectively. The majority of those impacted were elderly rural men (60\u0026thinsp;+\u0026thinsp;years old) from central India\u0026apos;s medium wealth quintile. The greatest impact among females was observed in younger (15\u0026ndash;35 years old), married, impoverished, rural people of central India.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eDecomposition analysis to measure the gender gap in OOPE\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e presents the Blinder-Oaxaca decomposition analysis results, examining the gender disparity in OOPE for mental health care. The findings show that on average males incur a higher relative OOPE compared to females, with a statistically significant gap of 25.4% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Of this difference, 40.2% is attributed to endowment effects (differences in observable characteristics), while 92.2% is explained by coefficient effects (differences in the influence of these characteristics). Key socio-economic factors, such as age, marital status, religion, and wealth, did not significantly contribute to the explained or unexplained components of the disparity. In contrast, education level under individual factors, region of residence under community-level factors, and caste category under household dynamics were significant contributors to the gap. Within the explained component, education contributed the most to the disparity (76%), followed by region of residence (29%). For the unexplained component, the caste category showed a negative contribution to the inequality, accounting for 12%. Figure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea illustrates the contribution of individual, community, and household dynamics variables to the disparity in Out-of-Pocket Expenditure (OOPE) for mental health treatment between males and females. Individual characteristics accounted for the largest share, explaining 71.7% of the observed gap. Community-level factors contributed 28.9% to the disparity, while household dynamics had a marginal and negative contribution of -0.6%. (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eMental health remains a critical public health concern in India. This study quantified gender and socio-economic disparities in the financial burden associated with mental health care. On average, males incurred higher out-of-pocket expenditure (OOPE) for mental health treatment, with a gender gap of approximately 25% based on Blinder-Oaxaca decomposition analysis. Considerable state-level variation was observed, with females in Maharashtra and several eastern states experiencing a higher healthcare burden (HCB) and OOPE, while males in Sikkim and rural areas reported greater exposure to catastrophic health expenditure (CHE). Education contributed most to the explained component of the gender gap (76%), followed by region of residence and caste, reflecting the role of structural and social differentials in shaping financial risk. These findings point to persistent and stratified inequalities in the economic burden of mental health care.\u003c/p\u003e\u003cp\u003eAlthough the prevalence of self-reported mental illness in this study was relatively low, its impact is disproportionately concentrated among vulnerable groups. Disabilities related to mental health\u0026mdash;such as excessive worry (1.1 per 1,000), auditory hallucinations (0.2 per 1,000), and behavioral disturbances (0.5 per 1,000)\u0026mdash;were more commonly reported among older adults (aged 60 years and above), males, and individuals residing in rural areas or belonging to lower wealth quintiles. These patterns are consistent with estimates from the National Mental Health Survey (2015\u0026ndash;16), which reported that approximately 15% of Indian adults require active intervention for mental health conditions, with a higher prevalence of depressive and anxiety disorders among women[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGender differences in OOPE are shaped by differences in health-seeking behaviour. Men are less likely than women to seek help for mental health concerns, often due to norms surrounding masculinity, including self-reliance, emotional restraint, and reluctance to engage with health services[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Existing literature indicates that being male is negatively associated with help-seeking behavior for mental health conditions[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. This pattern leads to delayed presentation and more severe illness at the time of treatment, which may require more intensive and costly care. Men often carry the financial responsibility not only for their own care but also for other household members. Novak[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] suggests that men, as primary financial providers, are often discouraged from taking time away from work to access care, prioritising household responsibilities over personal health. Bass[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] notes that negative perceptions related to seeking help also act as barriers to men\u0026rsquo;s timely engagement with mental health services. Together, these behavioural factors contribute to a higher OOPE burden among males.\u003c/p\u003e\u003cp\u003eThe association between higher educational attainment and increased Out-of-Pocket Expenditure (OOPE) for mental health treatment is a major contributor to the observed gender disparity in OOPE. Socioeconomic factors such as education and employment influence health outcomes differently across genders[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Limited insurance coverage is more commonly observed among individuals with lower educational levels[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], and the established relationship between education and income further explains differential financial exposure to health costs. In this study, women with higher education levels were found to incur greater OOPE compared to men. Supporting evidence from a mental health survey conducted across 10 European Union countries indicates that women are more likely to utilize mental health services, contributing to their increased healthcare expenditure[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eCaste was also identified as a significant social determinant influencing gendered economic burden in mental health care. Data from the World Health Organization\u0026rsquo;s Study on Global Ageing and Adult Health (WHO-SAGE) documented persistent disparities in self-reported mental health between higher caste Hindus and marginalized groups, including Scheduled Castes and Scheduled Tribes[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. These disparities are not attributable to inherent biological or cultural differences but are instead linked to long-standing socio-economic disadvantages and systemic exclusion[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Women from lower caste backgrounds are particularly vulnerable, facing higher risks of domestic violence, greater disease burden, and limited access to healthcare beyond their immediate localities. Some evidence suggests that self-help groups have provided modest financial relief by reducing dependency on informal credit sources and offering limited support to meet private healthcare costs[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn the Indian context, gender disparities in healthcare expenditure are further shaped by patriarchal norms. Structural barriers\u0026mdash;such as limited financial autonomy, lower income generation, lack of community support, and restrictive gender roles\u0026mdash;limit women\u0026rsquo;s access to timely and adequate mental health care[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Despite these challenges, women who do seek care are more likely to utilize services consistently. In contrast, men are more likely to delay seeking care due to societal expectations, leading to more advanced illness at the time of intervention and, consequently, higher OOPE.\u003c/p\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eStrengths of the study\u003c/h2\u003e\u003cp\u003eThe strength of this study derived from its use of nationally representative data from the 76th round of the National Sample Survey on Persons with Disabilities, covering all Indian states. The use of a large, population-based dataset allows for generalisable estimates. Standardised definitions were applied to measure health care burden, catastrophic expenditure, and impoverishment, strengthening the comparability of findings.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eLimitations of the study\u003c/h2\u003e\u003cp\u003eHowever, the study has certain limitations. The cross-sectional design limits causal interpretation, and recall bias in expenditure reporting is a known concern. The survey did not include information on indirect costs such as income loss due to disability or caregiver burden, nor did it capture data on household-level hardship financing, which may lead to an underestimation of the total economic impact.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAll in all, this study quantifies gender and socio-economic differences in out-of-pocket expenditure (OOPE) for mental health care in India. Males face higher OOPE due to delayed care-seeking and financial responsibilities, while females encounter structural barriers that limit access. These differences are shaped by behavioural norms and systemic inequities, indicating a need for gender-responsive health financing and service delivery. Stigma, limited engagement with general practice, and normative expectations around masculinity reduce men\u0026rsquo;s use of mental health services, increasing treatment costs. Service models designed to respond to gender-specific needs, including dedicated mental health clinics, may improve access and reduce financial strain. Policy and research must address the interaction between gender, health system use, and economic burden to improve equity in mental health care.\u003c/p\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003ePolicy Recommendations\u003c/h2\u003e\u003cp\u003eA comprehensive policy approach is essential to alleviate the financial burden associated with mental health care in India. Government initiatives such as PMJAY\u0026ndash;Ayushman Bharat should explicitly incorporate mental health services, covering both hospitalization and outpatient expenses. Special attention must be given to vulnerable groups\u0026mdash;including the elderly, rural residents, married individuals, and youth\u0026mdash;through subsidized medical support and community-based care models to help mitigate poverty-related impacts. Integrating mental health services into primary healthcare can facilitate early diagnosis and reduce overall treatment costs. Additionally, nationwide awareness campaigns aimed at reducing stigma and promoting early intervention are crucial for improving mental health literacy and easing the financial strain of treatment.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eFuture Research\u003c/h2\u003e\u003cp\u003eA longitudinal study is essential to understand the long-term healthcare costs and coping mechanisms associated with mental health conditions. Developing gender-specific strategies for mental health care and treatment expenses is also critical for designing targeted interventions that address the unique needs of men and women. Furthermore, conducting a cost-benefit analysis of expanding public mental health services can provide valuable insights to support increased investment and guide strategic planning for scalable and equitable mental health care delivery across India.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics declaration\u003c/em\u003e\u003c/strong\u003e\u0026mdash; This study used anonymized, publicly available secondary data from the NSS 76th round and did not involve direct interaction with human subjects; hence, no separate ethical approval was required. The study adheres to the ethical principles of the Declaration of Helsinki (2013) and relevant national guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eApproval Committee:\u003c/em\u003e\u003c/strong\u003e The NSS survey was conducted by MoSPI, Government of India, with protocols reviewed by internal expert committees.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent to Participate declaration\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e:\u0026nbsp;\u003c/em\u003eSecondary data from the 76th round of the National Sample Survey (NSS) was used in this study and it is publically available. Prior to conducting the survey, the NSS received ethical clearance from its internal review committee, and all respondents gave their informed consent at the time of data collection. Therefore, the current study does not require separate ethical approval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e No funding was issued for this research\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/strong\u003e-- The secondary data used in this study are publicly available from the National Sample Survey Office (NSSO), under the Ministry of Statistics and Programme Implementation (MoSPI), Government of India.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting Interests\u003c/em\u003e-- The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthor\u0026rsquo;s Contribution\u003c/em\u003e\u0026mdash; RY and AY conceptualised the study, extracted and analysed the data. JY contributed to study design, data analysis, and project management. SS was responsible for data interpretation, manuscript drafting, and final revisions. VJ contributed to the initial drafting of the manuscript. All authors reviewed and approved the final version.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u0026mdash;NA\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMental disorders [Internet]. [cited 2025 Apr 18]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/news-room/fact-sheets/detail/mental-disorders\u003c/span\u003e\u003cspan address=\"https://www.who.int/news-room/fact-sheets/detail/mental-disorders\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMental health [Internet]. [cited 2025 Apr 18]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/health-topics/mental-health\u003c/span\u003e\u003cspan address=\"https://www.who.int/health-topics/mental-health\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePatel V, Ramasundarahettige C, Vijayakumar L, Thakur JS, Gajalakshmi V, Gururaj G, Suraweera W, Jha P. Million Death Study Collaborators. Suicide mortality in India: a nationally representative survey. Lancet. 2012;379(9834):2343\u0026ndash;51. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0140-6736(12)60606-0\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(12)60606-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSagar R, Dandona R, Gururaj G et al. (2017). The National Mental Health Survey of India (2016): Prevalence, patterns and outcomes. \u003cem\u003eIndian Journal of Psychiatry\u003c/em\u003e, 59(1), 21\u0026ndash;26. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4103/0019-5545.204438\u003c/span\u003e\u003cspan address=\"10.4103/0019-5545.204438\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNational Mental Health Survey of India. 2015-16 [Internet]. [cited 2025 Apr 18]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://indianmhs.nimhans.ac.in/phase1/Docs/Report2.pdf\u003c/span\u003e\u003cspan address=\"https://indianmhs.nimhans.ac.in/phase1/Docs/Report2.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXu K, Evans DB, Carrin G, Aguilar-Rivera AM, Musgrove P, Evans T. Protecting households from catastrophic health spending. Health Aff (Millwood). 2007;26(4):972\u0026ndash;83. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1377/hlthaff.26.4.972\u003c/span\u003e\u003cspan address=\"10.1377/hlthaff.26.4.972\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMental Health Atlas. 2017 Country Profile: India [Internet]. [cited 2025 Apr 21]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/publications/m/item/mental-health-atlas-2017-country-profile-india\u003c/span\u003e\u003cspan address=\"https://www.who.int/publications/m/item/mental-health-atlas-2017-country-profile-india\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDeepening Health Insecurity in India. Evidence from National Sample Surveys since 1980s | Economic and Political Weekly [Internet]. 2009 [cited 2025 Apr 21]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.epw.in/journal/2009/40/special-articles/deepening-health-insecurity-india-evidence-national-sample-surveys\u003c/span\u003e\u003cspan address=\"https://www.epw.in/journal/2009/40/special-articles/deepening-health-insecurity-india-evidence-national-sample-surveys\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eInvesting in treatment for depression. and anxiety leads to fourfold return [Internet]. [cited 2025 Apr 21]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/news/item/13-04-2016-investing-in-treatment-for-depression-and-anxiety-leads-to-fourfold-return\u003c/span\u003e\u003cspan address=\"https://www.who.int/news/item/13-04-2016-investing-in-treatment-for-depression-and-anxiety-leads-to-fourfold-return\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJaisoorya TS, Janardhan Reddy YC, Nair BS, Rani A, Menon PG, Revamma M, Jeevan CR, Radhakrishnan KS, Jose V, Thennarasu K. Prevalence and correlates of obsessive-compulsive disorder and subthreshold obsessive-compulsive disorder among college students in Kerala, India. Indian Journal of Psychiatry [Internet]. 2017 Mar [cited 2025 Apr 21];59(1):56. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://journals.lww.com/indianjpsychiatry/fulltext/2017/59010/prevalence_and_correlates_of_obsessive_compulsive.11.aspx\u003c/span\u003e\u003cspan address=\"https://journals.lww.com/indianjpsychiatry/fulltext/2017/59010/prevalence_and_correlates_of_obsessive_compulsive.11.aspx\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNational Health Policy. 2017.pdf [Internet]. [cited 2025 Apr 21]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mohfw.gov.in/sites/default/files/9147562941489753121.pdf\u003c/span\u003e\u003cspan address=\"https://mohfw.gov.in/sites/default/files/9147562941489753121.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMental health action. plan 2013\u0026ndash;2020 [Internet]. [cited 2025 Apr 21]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/publications/i/item/9789241506021\u003c/span\u003e\u003cspan address=\"https://www.who.int/publications/i/item/9789241506021\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNanda P. Gender Dimensions of User Fees: Implications for Women\u0026rsquo;s Utilization of Health Care. Reproductive Health Matters [Internet]. 2002 Jan 1 [cited 2025 Apr 18];10(20):127\u0026ndash;34. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0968-8080(02)00083-6\u003c/span\u003e\u003cspan address=\"10.1016/S0968-8080(02)00083-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eInequities in Access to Health Services in India. Caste, Class and Region | Economic and Political Weekly [Internet]. 2010 [cited 2025 Apr 18]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.epw.in/journal/2010/38/special-articles/inequities-access-health-services-india-caste-class-and-region.html\u003c/span\u003e\u003cspan address=\"https://www.epw.in/journal/2010/38/special-articles/inequities-access-health-services-india-caste-class-and-region.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePratley P. Associations between quantitative measures of women\u0026rsquo;s empowerment and access to care and health status for mothers and their children: A systematic review of evidence from the developing world. Social Science \u0026amp; Medicine [Internet]. 2016 Nov 1 [cited 2025 Apr 21];169:119\u0026ndash;31. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.sciencedirect.com/science/article/pii/S0277953616304087\u003c/span\u003e\u003cspan address=\"https://www.sciencedirect.com/science/article/pii/S0277953616304087\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWomen Empowerment and Maternal Healthcare Service Utilization. Evidence from Bangladesh [Internet]. ResearchGate. [cited 2025 Apr 21]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.researchgate.net/publication/338526796_Women_Empowerment_and_Maternal_Healthcare_Service_Utilization_Evidence_from_Bangladesh\u003c/span\u003e\u003cspan address=\"https://www.researchgate.net/publication/338526796_Women_Empowerment_and_Maternal_Healthcare_Service_Utilization_Evidence_from_Bangladesh\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGupta M, Sarangi R, Sharma S, Patnaik L. Mental health issues and lifestyle changes associated with the COVID-19 pandemic in adolescents: A cross-sectional study in selected schools of Eastern India. J Educ Health Promot [Internet]. 2024 Feb 26 [cited 2025 Apr 21];13:80. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10979783/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10979783/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePatel V, Saxena S et al. The Lancet Commission on global mental health and sustainable development. The Lancet [Internet]. 2018 Oct [cited 2025 Apr 21];392(10157):1553\u0026ndash;98. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://linkinghub.elsevier.com/retrieve/pii/S014067361831612X\u003c/span\u003e\u003cspan address=\"https://linkinghub.elsevier.com/retrieve/pii/S014067361831612X\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePersons with Disabilities in India [Internet]. [cited 2025 Apr 21]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mospi.gov.in/sites/default/files/publication_reports/Report_583_Final_0.pdf\u003c/span\u003e\u003cspan address=\"https://www.mospi.gov.in/sites/default/files/publication_reports/Report_583_Final_0.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKey Indicators of Social Consumption in India. Health of NSS 75th Report [Internet]. [cited 2025 Apr 21]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mospi.gov.in/sites/default/files/publication_reports/KI_Health_75th_Final.pdf\u003c/span\u003e\u003cspan address=\"https://www.mospi.gov.in/sites/default/files/publication_reports/KI_Health_75th_Final.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRangarajan Report on Poverty [Internet]. [cited 2025 Apr 21]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pib.gov.in/newsite/printrelease.aspx?relid=108291\u003c/span\u003e\u003cspan address=\"https://pib.gov.in/newsite/printrelease.aspx?relid=108291\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOaxaca R. Male-Female Wage Differentials in Urban Labor Markets. \u003cem\u003eInt Econ Rev\u003c/em\u003e. 1973;14(3):693\u0026ndash;709. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://inequality.stanford.edu/sites/default/files/media/_media/pdf/Classic_Media/Oaxaca_1973_Discrimination%20and%20Prejudice.pdf\u003c/span\u003e\u003cspan address=\"https://inequality.stanford.edu/sites/default/files/media/_media/pdf/Classic_Media/Oaxaca_1973_Discrimination%20and%20Prejudice.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBlinder AS. Wage Discrimination: Reduced Form and Structural Estimates. The Journal of Human Resources [Internet]. 1973 [cited 2025 Apr 21];8(4):436\u0026ndash;55. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.jstor.org/stable/144855\u003c/span\u003e\u003cspan address=\"https://www.jstor.org/stable/144855\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAdam,Lindelow,Magnus OO, Van Andrew KA. Wagstaff. Analyzing health equity using household survey data : a guide to techniques and their implementation [Internet]. World Bank. [cited 2025 Apr 21]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://documents.worldbank.org/en/publication/documents-reports/documentdetail/en/633931468139502235\u003c/span\u003e\u003cspan address=\"https://documents.worldbank.org/en/publication/documents-reports/documentdetail/en/633931468139502235\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMurthy RS. National Mental Health Survey of India 2015\u0026ndash;2016. Indian J Psychiatry [Internet]. 2017 [cited 2025 Apr 21];59(1):21\u0026ndash;6. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5419008/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5419008/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGough B, Novikova I. Mental health, men and culture: how do sociocultural constructions of masculinities relate to men\u0026rsquo;s mental health help-seeking behaviour in the WHO European Region? [Internet]. Copenhagen: WHO Regional Office for Europe; 2020 [cited 2025 Apr 21]. (WHO Health Evidence Network Synthesis Reports). Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/books/NBK559706/\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/books/NBK559706/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGonzalez JM, Alegr\u0026iacute;a M, Prihoda TJ, Copeland LA, Zeber JE. How the relationship of attitudes toward mental health treatment and service use differs by age, gender, ethnicity/race and education. Soc Psychiat Epidemiol [Internet]. 2011 Jan 1 [cited 2025 Apr 21];46(1):45\u0026ndash;57. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00127-009-0168-4\u003c/span\u003e\u003cspan address=\"10.1007/s00127-009-0168-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNovak JR, Peak T, Gast J, Arnell M. Associations Between Masculine Norms and Health-Care Utilization in Highly Religious, Heterosexual Men. Am J Mens Health [Internet]. 2019 May 1 [cited 2025 Apr 21];13(3):1557988319856739. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/1557988319856739\u003c/span\u003e\u003cspan address=\"10.1177/1557988319856739\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBass SB, Mu\u0026ntilde;iz J, Gordon TF, Maurer L, Patterson F. Understanding help-seeking intentions in male military cadets: An application of perceptual mapping. BMC Public Health [Internet]. 2016 May 17 [cited 2025 Apr 21];16(1):413. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12889-016-3092-z\u003c/span\u003e\u003cspan address=\"10.1186/s12889-016-3092-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGender. and Health in the Western Pacific Region [Internet]. [cited 2025 Apr 21]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://iris.who.int/bitstream/handle/10665/208162/Gender_health_fact_sheet_2014_eng.pdf?sequence=1\u0026amp;isAllowed=y\u003c/span\u003e\u003cspan address=\"https://iris.who.int/bitstream/handle/10665/208162/Gender_health_fact_sheet_2014_eng.pdf?sequence=1\u0026amp;isAllowed=y\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMaharlouei N, Cobb S, Bazargan M, Assari S. Subjective Health and Happiness in the United States: Gender Differences in the Effects of Socioeconomic Status Indicators. Journal of Mental Health \u0026amp; Clinical Psychology [Internet]. 2020 May 14 [cited 2025 Apr 21];4(2). Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.29245/2578-2959/2020/2.1196\u003c/span\u003e\u003cspan address=\"10.29245/2578-2959/2020/2.1196\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMohanty SK, Upadhyay AK, Maiti S, Mishra RS, K\u0026auml;mpfen F, Maurer J, O\u0026rsquo;Donnell O. Public health insurance coverage in India before and after PM-JAY: repeated cross-sectional analysis of nationally representative survey data. BMJ Glob Health [Internet]. 2023 Aug 28 [cited 2025 Apr 21];8(8):e012725. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10462969/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10462969/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAre there gender differences in service use for mental disorders across countries in the European. Union? Results from the EU-World Mental Health survey | Journal of Epidemiology \u0026amp; Community Health [Internet]. [cited 2025 Apr 21]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://link.springer.com/article/\u003c/span\u003e\u003cspan address=\"https://link.springer.com/article/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11113-020-09585-9\u003c/span\u003e\u003cspan address=\"10.1007/s11113-020-09585-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGupta A, Coffey D. Caste, Religion, and Mental Health in India. Popul Res Policy Rev [Internet]. 2020 Dec 1 [cited 2025 Apr 21];39(6):1119\u0026ndash;41. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11113-020-09585-9\u003c/span\u003e\u003cspan address=\"10.1007/s11113-020-09585-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eThapa R, van Teijlingen E, Regmi PR, Heaslip V. Caste Exclusion and Health Discrimination in South Asia: A Systematic Review. Asia Pac J Public Health [Internet]. 2021 Nov [cited 2025 Apr 21];33(8):828\u0026ndash;38. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8592103/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8592103/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAgarwal B. \u0026lsquo;Bargaining\u0026rsquo; and Gender Relations: Within and Beyond the Household. Feminist Economics [Internet]. 1997 Jan [cited 2025 Apr 21];3(1):1\u0026ndash;51. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.tandfonline.com/doi/abs/10.1080/135457097338799\u003c/span\u003e\u003cspan address=\"http://www.tandfonline.com/doi/abs/10.1080/135457097338799\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSaikia N, Moradhvaj, Bora JK. Gender Difference in Health-Care Expenditure: Evidence from India Human Development Survey. PLoS One [Internet]. 2016 Jul 8 [cited 2025 Apr 21];11(7):e0158332. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4938214/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4938214/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 4 are available in the Supplementary Files section.\u003c/p\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":"Mental Health, Blinder-Oaxaca decomposition, out of pocket expenditure, Gender Gap, National Sample Survey, Inequality, India","lastPublishedDoi":"10.21203/rs.3.rs-6968917/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6968917/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eOut-of-pocket expenditure (OOPE) on mental health care in India limits progress toward universal health coverage. Detailed analysis of gender-related differences in mental health spending is needed to inform allocation of resources. However, gender-specific patterns in OOPE for mental health have not been adequately studied in existing research.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis study used cross-sectional data from the 76th round of the National Sample Survey (NSS) on \u0026ldquo;Persons with Disabilities in India\u0026rdquo; to measure gender differences in out-of-pocket expenditure (OOPE) for mental health treatment. The Blinder-Oaxaca decomposition method was applied to estimate the gender gap in OOPE and to quantify the contribution of associated factors, grouped into individual, household, and community-level characteristics.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eOn average, males incur 25.4% higher out-of-pocket expenditure (OOPE) for mental health care, while females report higher healthcare burden in specific states and socio-economic groups. Catastrophic health expenditure (CHE) is more frequent among older adults, rural residents, and economically disadvantaged males. Blinder-Oaxaca decomposition attributes 40.2% of the gender gap in OOPE to differences in observed characteristics, with education (76%) and region of residence (29%) being the largest contributors. Individual-level factors, including age, education, and marital status, account for 71.7% of the total explained variation.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis study shows male-female differences in the financial burden of mental illness treatment in India, with most of the variation explained by individual and community-level factors. The results highlight the need for policy measures that reduce out-of-pocket spending and improve economic support related to mental health care. Improving social protection and economic security may help reduce gender-based differences in access and outcomes.\u003c/p\u003e","manuscriptTitle":"Who Pays More? Gender Gaps in Mental Health Treatment Costs in India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-31 16:54:19","doi":"10.21203/rs.3.rs-6968917/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":"8e4d86cf-9d08-43c3-ae92-0a5e6fe52a38","owner":[],"postedDate":"October 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-14T18:09:34+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-31 16:54:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6968917","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6968917","identity":"rs-6968917","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}