Mental illness, poverty and stigma in India: a case control study

For peer review only

Mental illness, poverty and stigma in India: A case control

study

Journal: BMJ Open

Manuscript ID: bmjopen-2014-006355.R2

Article Type: Research

Date Submitted by the Author: 13-Jan-2015

Complete List of Authors: Trani, Jean-Francois; Washington University, Brown School Bakhshi, Parul; Washington University in St. Louis, school of medicine Kuhlberg, Jill; Washington University in St. Louis, Brown School Venkataraman, Sreelatha; Dr. Ram Manohar Lohia Hospital, Psychiatry & De-addiction Services Venkataraman, Hemalatha; Dr. Ram Manohar Lohia Hospital, Psychiatry & De-addiction Services Mishra, Nagendra; Dr. Ram Manohar Lohia Hospital, Psychiatry & De-

addiction Services Groce, Nora; University College London, Division of Epidemiology and Public Health Jadhav, Sushrut; University College London, Mental health science unit Deshpande, Smita; Dr. Ram Manohar Lohia Hospital, Psychiatry & De-addiction Services

<b>Primary Subject Heading</b>:

Global health

Secondary Subject Heading: Mental health

Keywords: Schizophrenia & psychotic disorders < PSYCHIATRY, PUBLIC HEALTH, MENTAL HEALTH

For peer review only - http://bmjopen.bmj.com/site/about/guidelines.xhtml

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Mental illness, poverty and stigma in India: A case control study

Jean-Francois Trani*; Parul Bakhshi*; Jill Kuhlberg*; Sreelatha S. Narayanan#;

Hemalatha Venkataraman#; Nagendra N. Mishra #; Nora E. Groce; Sushrut Jadhav+ Smita Deshpande#;

Jean-Francois Trani, assistant professor, Brown School, Washington University in St. Louis, Campus Box 1196, Goldfarb Hall, Room 243, One Brookings Drive, St. Louis, MO 63130, United States of America; Parul Bakhshi, assistant professor, program in occupational therapy, school of medicine, Washington University in St. Louis, 4444 Forest Park avenue, 63108 St Louis, MO; Jill Kuhlberg, research assistant, Brown School; Sreelatha S. Narayanan, research assistant, , Dr. Ram Manohar Lohia Hospital, New Delhi 110001, India; Hemalatha Venkataraman, research assistant, Dr. Ram Manohar Lohia Hospital; Nagendra N. Mishra, research associate, Dr. Ram Manohar Lohia Hospital; Nora E. Groce, professor, Leonard Cheshire Chair & Director, Leonard Cheshire Disability & Inclusive Development Centre, Division of Epidemiology and Public Health University College London, Room 308, 1-19 Torrington Place, WC1E 6BT, London UK; Sushrut Jadhav, senior lecturer, Mental health science unit, University College London, Gower Street - London - WC1E 6BT, United Kingdom; Smita Deshpande, Head, Department Of Psychiatry & De-addiction Services & Resource Centre for Tobacco Control, PGIMER- Dr. Ram Manohar Lohia Hospital, New Delhi, India;

Correspondence to: Jean-Francois Trani Brown School Washington University in St. Louis Campus Box 1196, Goldfarb Hall, Room 243 One Brookings Drive St. Louis, MO 63130 [o] 314.935.9277 [c] 314.412.0077 [f] 314. 935.8511 [e] [email protected]

Keywords: mental illness, schizophrenia, bipolar disorders, severe affective

disorders, experienced discrimination, stigma.

Word count: 4687

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Abstract

Objective –To assess the effect of experienced stigma on depth of multidimensional poverty of persons with severe mental illness (PSMI) in Delhi, India, controlling for gender, age and caste. Design – Matching Case (hospital) control (population) study. Setting – University Hospital (cases) and National Capital Region (NCR) (controls), India.

Participants A case-control study was conducted from November 2011 to June 2012. 647 cases diagnosed with schizophrenia or affective disorders were recruited and 647 individuals of same age, sex and location of residence were matched as controls at a ratio of 1:2:1. Individuals who refused consent or provided incomplete interview were excluded.

Main outcome measures – Higher risk of poverty due to stigma among PSMI.

Results - 38.5% of PSMI compared to 22.2% of controls were found poor on 6 dimensions or more. The difference in Multidimensional poverty index (MPI) was 69% between groups with employment and income the main contributors. Multidimensional poverty was strongly associated with stigma (odds ratio [OR] 2.60, 95% CI 1.27-5.31), scheduled castes/scheduled tribes/ other backward castes (SC/ST/OBC) (2.39, 1.39-4.08), mental illness (2.07, 1.25-3.41), and female gender (1.87, 1.36-2.58). A significant interaction between stigma, mental illness and gender or caste indicates female PSMI or PSMI from ‘lower castes’ were more likely to be poor due to stigma than male controls (p<0.001) or controls from other castes (p<0.001). Conclusions – Public stigma and multidimensional poverty linked to SMI are pervasive and intertwined. Particularly for low caste and women, it is a strong predictor of poverty. Exclusion from employment linked to negative attitudes and lack of income are the highest contributors to multidimensional poverty, increasing the burden for the family. Mental health professionals need to be aware of and address these issues.

Article summary

Strengths and limitations

• There is little research on effects of stigma and poverty in developing settings

• Lack of employment and income are major contributors to multidimensional poverty for PSMI

• Intensity of multidimensional poverty is higher for PSMI, particularly women with SMI and those from SC/ST/OBC

• Limitation: Stigma was operationalized through a single item question rather than a multiple-item scale and we could not assess reliability of this item. SMI was diagnosed for persons attending a public psychiatric department; PSMI not receiving medical treatment might be more marginalised and at greater risk of poverty than those receiving healthcare.

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Introduction Mental health problems affect 450 million people worldwide, 80% in middle and low-income countries. In 2010, 2,319,000 persons died of mental and behavioural disorders1. Mental health conditions account for 13% of the total burden of disease, 31% of all years lived with disability and are one of the 4 main contributors to years lived with disability2,

3. Schizophrenia and bipolar disorder represent 7.4 % and 7·0% of DALYs caused by mental and substance use disorders respectively4. Severe mental illness (SMI) is a leading cause of disability and the standard prevalent biomedical care model is neither an exclusive nor a comprehensive solution as it does not address the link between mental illness, stigma and poverty 5. While the literature on poverty, poor mental health6 and disability7-9 is emerging, little has been done to examine the compounding associations between experienced stigma (unfair treatment or discrimination due to having a mental health issue)10, mental illness and poverty, especially in low-income countries. In high-income countries11, income deprivation is identified as a major risk factor for persons with mental health issues, even for common mental disorders12. Poor mental health linked to SMI has been associated with poverty during the recent economic crisis in middle and low-income countries, particularly India and China13-15. People with mental disorders living in these countries are not only more likely to be poorer, but also unemployed and less educated16, 17. Indisputably, a better understanding of the relationship between mental illness and poverty may yield useful knowledge to tailor public health interventions to complement biomedical treatment to improve outcomes. Link and Phelan (2001) defined stigma as a process with five interrelated components: discrimination through a process of separation based on negative attitudes and prejudice resulting from labelling and cultural stereotypes of society towards the stigmatized group leading to social, economic and political power differences18. Thornicroft et al. (2007) identify three elements of stigma: ignorance or misinformation, prejudice and discrimination19. Our paper focuses on the process of experienced discrimination as the manifestation of public stigma20. The congruence of self-stigma and social exclusion may lead persons with SMI (PSMIs) to face unfair treatment or discrimination and develop low self-esteem21-24. Such stigma may prevent mentally ill persons from improving their conditions25 by creating a “barrier to recovery”26 and worsen their situation by pushing them into poverty through discriminatory practices27-29. Stigma towards PSMI resulting in discrimination30, 31 is persistent in India32. Although the factors constituting poverty and discrimination linked to mental illness potentially can deprive persons of many resources33, 34 the dynamics of poverty, discrimination and mental health have not been fully addressed. The clinical literature argues that stigma is caused by mental illness and treating the latter biomedically will weaken the associated stigma35, 36. We argue instead that even treated PSMI are more likely to be multidimensionally poor due to discrimination resulting from stigma.

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Many studies have focused on uni-dimensional effect of poverty on mental health, but have not explained how stigma towards mental illness can be an aggravating contributor to the intensity of poverty. We aimed to estimate the difference in incidence and intensity of poverty between PSMI and a comparable control group using a multidimensional poverty index (MPI) to explore deprivation in various dimensions of life37. Going beyond traditional welfare economics approaches to poverty (i.e. income or per capita expenditure) we explored non-monetary dimensions of poverty such as education, health, quality of shelter, food intake, and political participation. We assessed differences in intensity of poverty between PSMI and controls and explored how these differences vary as a function of discrimination resulting from stigma.

Methods

Study design and setting

The primary objective was to assess differences in exposure to discrimination resulting from stigma and multidimensional poverty among cases compared with non-psychiatrically ill controls. Between November 2011 and June 2012, we carried out a case-control study based at the Department of Psychiatry of the Dr Ram Manohar Lohia (RML) Hospital in New Delhi (cases), and in the neighbourhood of residence of the cases (controls) to assess the impact of stigma associated to mental illness on poverty. The Department of Psychiatry at Dr RML hospital received respectively 10881 and 19528 new outpatients and 52389 and 45319 follow-ups of existing patients in 2012 and 2013. The department has also a 42 bed general psychiatry and de-addiction inpatient facility for men and women. It serves patients from the national Capital Region of Delhi (NCR).

Participants

We defined cases as outpatients diagnosed with schizophrenia or affective disorders by one of the 10 board certified treating psychiatrists following ICD-10 criteria38. Outpatients were informed about the study and if they consented, were referred to researchers for written informed consent and evaluation with no further contact with those who refused. Transportation costs and a meal were provided to maximise recruitment and reduce selection bias. We used a non-psychiatrically ill control group composed of randomly selected individuals matching the patients according to gender, age (plus or minus 5 years) and neighbourhood of residence. Matched controls were selected by spinning a pointer at the door of the case’s home and randomly selecting one household among 30 in the pointed direction. In this household a person of same age and gender with no reported history of a mental health disorder was interviewed. It was not possible to conduct detailed interviews for diagnosis of all controls due to logistics as well as stigma of revealing mental illness. We excluded controls when unable to obtain consent. Only two case patients were not matched. Investigators together with the team manager contributed to sensitisation and awareness raising in the neighbourhoods of interest to maximise controls’ participation rates. Consent for patients and controls adolescent between 13 and 18 was obtained by asking the parent or the legal guardian of the study subjects. We conducted face-to-face interviews with all PSMI or a caregiver during hospital visits,

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and controls at home. We obtained information on demographics, socioeconomic factors, health conditions and accessibility to services, education, employment, income, livelihoods, and social participation. The instrument was translated by experts into Hindi with iterative back-translation and tested in a pilot survey in October 2011. Investigators trained 2 experienced supervisors and 10 Masters-level students over two weeks on survey concepts and goals, mental illness awareness, interview techniques followed by review, test and debriefing.

Sample size

To determine sample size, we used a matched design with a control to case ratio of one, the probability of exposure to poverty among controls of 0.22 and the correlation coefficient for exposure between matched cases and controls of 0.1839. Considering the true odds ratio for one dimension of poverty in exposed subjects relative to unexposed subjects as 2.2, we needed to enroll 205 case patients to be able to reject the null hypothesis that this odds ratio equals 1with probability of 0.9. The type 1 error probability associated with this test of this null hypothesis is 0.05. We enrolled 649 case patients to allow for subgroup analyses including impact on poverty of discrimination stratified by gender, age and caste.

Efforts to minimize bias

New patients were first interviewed by a junior psychiatrist who made a provisional diagnosis and discussed details with a board of certified psychiatrist who then diagnosed and managed the case. To minimise diagnosis bias, we trained all psychiatrists on the ICD 10 criteria. Information bias was minimised by reviewing the questionnaire about exposure to poverty to ensure accuracy, completeness and content validity with experts and by testing it with a sample group of patients and families..Investigators revised the content for relevance to poverty in order to maximize item appropriateness. They first defined the concept of multidimensional poverty and reviewed the empirical and theoretical literature to identify the right deprivation items to include in the instrument they were developing. They then checked if the questions covered all dimensions of the concept of multidimensional poverty, if the phrasing respectively in English and Hindi was accurately reflecting the underlying concept of deprivation we were looking for in each dimension. Two experts familiar with multidimensional poverty reviewed the initial list of items and made suggestions about adding items that were omitted. We then organized a focus group discussion with 7 experts, psychiatrists, psychologists and social workers from Dr Ram Manohar Lohia hospital to establish if the 17 domains of poverty selected were adapted and relevant for the context of New Delhi and were providing a comprehensive overview of the concept. They also ranked these domains by order of importance of deprivation. A similar focus group was organized with 8 hospital outpatients with severe mental illness. We finally tested the poverty questionnaire with a group of 20 outpatients at the department of psychiatry at Dr RML hospital. We prompted them with questions to check for their understanding of poverty, to identify the language they used to explain the notion of poverty as well as ascertain their understanding of the questions in order to make sure the instrument’s purpose made sense to them. Finally, two other experts revised the final version to make sure items illustrate the content of multidimensional poverty.40

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We also carried out test-retest to test for recall bias and social desirability bias. Interviews with 71 respondents (both cases and controls) for test-retest reliability were carried out on two occasions with a gap of 10 to 15 days by the same enumerator to check to what degree a given respondent provided same responses for the poverty items. We compared the scores between the two sets of responses. Results show overall acceptable level of reliability (over 0.7 for Inter class correlation) for the different poverty variables.

Quantitative variables

We selected 17 indicators of deprivation reflecting aspects of wellbeing (Table 1) identified by literature review and validated through focus group discussions (FGDs) with experts and PSMI/caregivers. Both groups identified and agreed on deprivation cut-offs for each indicator through participatory deliberation 41. Some standard dimensions were not included due to lack of relevance in Delhi. For instance, few respondents lacked access to diet staplesi.

We classified the selected indicators in three major domains of deprivation: individual level capabilities, household level material wellbeing, and individual level psychosocial factors. The first domain was composed of nine indicators. Access to secondary school was the indicator for education; dropping out before reaching secondary school was the cut-off. Unemployment was a major source of vulnerability; deprivation of work was the cut-off. Food security was measured by access to three meals per day and respondents eating less were considered deprived. Following the UNICEF definitions, improved indoor air quality using cooking gas, improved drinking water by pipe into residence and improved sanitation by private flush toilet defined absence of deprivation for indicators six to eight. Finally, individual income constituted a monetary indicator. Material wellbeing of the household was composed of two series of indicators. Three indicators outlined conditions of living: minimum space per person (deprivation threshold of 40 square feet per person); home ownership (renting was the cut-off ); poor quality housing was having either the flooring, walls or roof made of Kutcha (precarious or temporary) material. Material wealth was defined by three complementary indicators: the household average per capita income (threshold at the international poverty line of US$1.25 per day or 68 Indian rupees)42; assets included typical goods owned by the householdii; and monthly household expendituresiii. Finally, two psychosocial indicators were selected: physical safety, measured through an indicator of perception of unsafe environment and political participation in the municipal elections. Studies in India have shown that stigma resulting in discriminatory practices is perceived to be high in the family and the community43, 44. As a result, we measured experienced discrimination as a dimension of stigma through self-evaluation of unfair treatment by the family. We asked all respondents if they were excluded from family decision compared to other household members of the same generation. Unfair treatment within family is a feature of stigma in India44. We tested this through FGDs with PSMI of both genders. We

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found high association between SMI and exclusion from regular family decisions, particularly for women. Other dimensions of participation did not show any discriminatory process. Inclusion in community activities showed similar 30% levels of participation between PSMI and controls. A possible explanation for participation is that where symptoms of mental illness are managed by treatment, family develop coping strategies through symbolic social participation and selective disclosure to avoid rejection, stigma and avoidance by others associated with their relative’s condition45-47. Finally, we enquired about participation in political activities such as “gram sabhas” or local associations. We found generalized low participation in political activities, which is a common feature in New Delhi and therefore not a good indicator of experienced discrimination. Table 1: approximately here

Statistical Analysis Our primary aim was to explore the effect of mental illness and stigma on poverty. We used an unmatched Multidimensional Poverty Index (MPI) measure to identify differences in levels of poverty between PSMI and controls48. Dimensions were independently assessed and the method focused on dimensional shortfalls. This method allowed us to aggregate dimensions of multidimensional poverty measures and consisted of two different forms of cutoffs: one for each dimension and the other relating to cross-cutting dimensions. If an individual fell below the chosen cut-off on a particular dimension he/she was identified as deprived. The second poverty cut-off determined the number of dimensions in which a person must be deprived to be deemed multidimensionally poor. We first performed one-way analyses to assess differences in poverty levels and discrimination between PSMI and controls, by gender and caste adjusting for post-hoc pairwise comparisons using the Scheffe method. We also carried out correlation analysis to assess overlap of dimensions of deprivation. We then calculated 3 indicators of multidimensional poverty: (i) the headcount ratio (H) indicating how many people fall below each deprivation cutoff; (ii) the average poverty gap (A) denoting the average number of deprivations each person experiences; (iii) the adjusted headcount (M0) which is the headcount ratio (H) by the average poverty gap (A) and indicates the breadth of poverty. We established the contribution of each dimension of poverty for cases and controls by dividing each of the two subgroups’ poverty level by the overall poverty level, multiplied by the population portion of each subgroup. To assess potential bias in our estimates of MPI, we carried out sensitivity analysis and compared three measures of poverty with: (i) Equal weight for every indicator in each dimension; (ii) Individual rankings of indicators done by experts at Dr RML hospital during the FGDs transformed into individual weights and then taking the average of the individual weights49; (iii) Group ranking based on the mean of individual rankings of

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indicators during FGDs and taking the weight according to the group ranking 50. We found consistency across measures (data not shown). We finally calculated the crude and adjusted odd ratios (OR) with associated 95% confidence intervals using a logistic regression model to identify association between stigma, SMI and multidimensional poverty. We used ‘no participation’ as the reference category. We defined a binary outcome for poverty (poor/non poor) using the adjusted headcount ratio (M0) for a cutoff k=6 corresponding to the highest gap between PSMI and controls. This cutoff corresponds to a prevalence of poverty of 30.7% above the recent estimates of 13.7% of urban Indians below the poverty line fixed at 28.65 rupees by the Indian Planning Commission51 which has been criticised for being unrealistic. This cutoff is in line with World Bank recent estimate that 33% of India’s population lives below the international poverty line established at $1.25 dollars per capita per day52. We characterised how SMI results in higher intensity of multidimensional poverty due to stigma. Aware that stigma and discrimination may also affect women53 and members of lower castes54, we adjusted the model for potential confounders significantly associated with poverty and family discrimination: caste (in case of difference within the family), gender and age. We carried out sensitivity analysis for different values of the cutoff k and found robustness in our model (data not shown). For all analyses, a P-value of <0.05 was considered significant. Missing values were treated as being missing completely at random. We used Stata (version 12.0) for database processing and all analysis.

Results Participants

We interviewed 649 case patients and 647 controls. Of these, we excluded 110 (17%) cases and 151 (23%) controls respectively who did not complete the interview or for whom the data was incomplete. The final analysis included 537 cases and 496 controls (figure 1). The distribution between cases and controls was similar for gender (305 and 330 males respectively, 61.5% in both cases) and age ( 15-74 and 13-74 and median 35 and 36 respectively). Figure 1 approximately here. Table 2 reports the headcount ratios (H) or incidence of deprivation in each dimension. There were statistically significantly higher numbers of deprived PSMI than controls in nine dimensions. Differences were very high for access to employment (28.1% difference), individual income (20.7%) and relatively high for food security (15.1%) and house ownership (11.7%). In only one dimension -perception of physical safety- was there a reverse non-significant difference as number of controls were higher than the number of PSMI. Table 2 approximately here. Table 2 also show results by gender and caste. Compared to male PSMI, the proportion of deprived female PSMI was significantly higher (10 of 17 dimensions). Similarly, a higher number of PSMI (vs. controls) from ‘scheduled castes’, ‘scheduled tribes’ or ‘other

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backward castes’ (SC/ST/OBC) were poorer on 13 (vs. 16 dimensions) compared to PSMI (vs. controls) from unreserved castes. To investigate possible overlap of dimensions of poverty, we calculated the estimates for the Spearman rank correlation coefficients between each pair of dimensions of deprivation (Table 3). We found no evidence of strong correlation between dimensions, illustrating absence of association except for household income and expenditures. We nevertheless kept both indicators to calculate the MPI to account for information bias (particularly recall bias) often associated with measures of income in household surveys55, 56. Significantly, this result demonstrates that a unique welfare indicator of poverty such as income, cannot represent all aspects of deprivation. Table 3 approximately here.

Multidimensional poverty

Results in Table 4 report the multidimensional headcount ratio (H), the average deprivation shared across the poor (A) and the adjusted headcount ratio (M0) for all possible cutoffs and for the two groups. Depending on the chosen cutoff, the proportion of PSMI and controls who were multidimensionally poor varied greatly. For a cutoff of one, 97.2% of PSMI and 91.7% of controls were deprived: taking a union approach of deprivation in one dimension, this translates into quasi-universal poverty. On average, PSMI were deprived on 5 dimensions and controls on 3.9. If multidimensional poverty requires deprivation in four, five, or six dimensions simultaneously, the proportion of poor PSMI (compared to poor controls) becomes 68.5% (compared to 48.6%), 51.6% (35.9%), or 38.5% (22.2%). Conversely, if we adopt the intersection approach where being poor implies being deprived in all 17 dimensions, nobody in the sample is poor and less than 1% of the sample is deprived in 13. Table 4 approximately here The adjusted headcount ratio (M0) shows that PSMI were worse off than controls for a cutoff (k) value between one and 12 dimensions. This difference is significant (p<0.001) for (k)=1 to (k)=10 dimensions and highest (69% difference) for (k)=6. The average deprivation share (A) is higher among PSMI for a value of (k) between one and five and highest for (k)=1 (22% difference). For a (k) between six and 14, the total number of deprivations faced by poor PSMI is slightly lower on average than for controls. Less than 30% of people were poor in six dimensions or more and the difference between PSMI and controls was the highest for a (k) value of 14 (7%). To further investigate the association between poverty and mental illness, analysis was repeated for all possible cutoffs and for gender and caste (Table 4). Multidimensional poverty was significantly higher for female PSMI compared to female controls for any threshold between one and seven dimensions (p<0.001) but also for male PSMI for any threshold between one and nine dimensions. On average, 62.8% of female PSMI were deprived on five dimensions or more, compared to 35.9% of female controls, 44.5% of male PSMI and 25.6% of male controls. For female PSMI and controls − and male PSMI and controls respectively − the difference is particularly pronounced and significant for

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highest cutoff values, and maximum for six and seven dimensions respectively. The adjusted headcount ratio (M0) shows that SC/ST/OBC PSMI are worse off regardless of the value of (k) 1 through 10, than SC/ST/OBC controls and other caste PSMI or controls. (M0) for SC/ST/OBC controls is higher than for other caste PSMI for all (k) values. Table 5 presents the percentage contribution of each dimension to (M0) for different (k). Deprivations in individual income household expenditures and employment were contributing each more than 10% to the overall (M0) for PSMI, whatever the value (k) between 1 and 8. For controls, employment was a less salient contributor while the contribution from household income was among the highest. Table 5 approximately here

Poverty and stigma

Association between multidimensional poverty and stigma was strong even when controlling for SMI, gender, caste and age (Table 6; all p<0·0001). We included interaction of stigma, SMI with caste and found that this term was strongly and positively associated with a high level of poverty: the odds ratio of being multidimensionally poor for PSMI from SC/ST/OBC compared with controls from unreserved castes was 7.36 (95% confidence interval 3.94 to 13.7). Similarly, we allowed for differential gender effects by including interaction of stigma and SMI with the gender of the respondent and found high effect on poverty: women PSMI were 9.61 (95% CI 5.58 to16.5) more likely to be poor compared to male controls.

Table 6 approximately here

Discussion Our findings establish that intensity of multidimensional poverty is higher for PSMI than the rest of the population. They also indicate that it is higher for women with SMI and for SC/ST/OBC with SMI. Deprivation of employment and income appear to be major contributing factors to MPI. Lack of employment and income appear to aggravate mental illness. Finally, our findings suggest that stigma linked to SMI, compounded with others (particularly SC/ST/OBC and women) negatively impact poverty. The congruence of SMI and poverty, in a context of high prejudice against mental illness compromises improvement. Mental illness in India is linked to lack of knowledge and pervasive negative assumptions, the most common being that PSMI are violent and unable to work18, 31, 44. Not surprisingly, deprivation of employment and income contributes highly to multidimensional poverty of PSMI compared to controls. This finding ties in with a study on employment for Indian men with schizophrenia which found that employment provided not just an essential social role but was also a condition for rehabilitation, enhanced confidence and self-esteem 44. Although there is evidence of differences in mental health outcomes between men and women, analyses of gender disparities are lacking in literature on poverty and mental health in low-income countries44, 57, 58. In our sample, women with SMI were systematically more deprived in higher numbers of dimensions. Similarly, SC/ST/OBC

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SMI-poverty associations were found to be consistent across dimensions of poverty regardless of the threshold for multidimensional poverty. These findings strongly suggest stigma linked to various marginalized groups have the power to accelerate and intensify exclusion and related discrimination. For women, SMI can negatively impact wellbeing in two ways. Firstly, SMI limits women from fulfilling family and social roles, leading to these women being considered a burden for the family. This is true despite studies, such as the Indian study of women with schizophrenia abandoned by their husbands who expressed the desire to work to support themselves 59. Secondly, traditional beliefs (punishment for previous lives, evil eye/curse) as well as negative lay attitudes on causes and behaviours, lead to increased discrimination of and sometimes violence against SMIs, particularly for women 60. Our study finds that SC/ST/OBC and poverty further compound SMI. Discrimination linked to caste in accessing education or employment has been a leitmotif in modern India and only partially addressed through constitutional provisions and reservation policies. Caste discrimination still results in scant employment opportunities, less access to secondary and higher education- key for salaried public and private jobs, perpetuating powerlessness, traditional forms of dominance and oppression, inequalities, lower living standards among SC/ST/OBC as a entrenched social identity in India61, 62. This situation is even more catastrophic for PSMI from SC/ST/OBC. It is clear that a ‘negative feedback loop’ exists: stigma against SMI, particularly for SC/ST/OBC and women, is a strong predictor of persistent poverty. Moreover, stigma strongly bears on intensity of poverty. Stigma leads to difficulty for PSMI in finding and keeping a job, and this also increases the perceived burden of SMI by family members. In turn, this deprivation on various dimensions erodes self-esteem, brings shame and acceptance of discriminatory attitudes 63. These compounding factors may result in a worsening of mental illness. Beyond the PSMI, stigma and discrimination have a negative effect on family members and caregivers who often feel ashamed, embarrassed or unable to cope with the stigma59,

64-68. While there have been campaigns and policies to address discrimination against SC/ST/OBC and women in India, no large-scale awareness campaign has ever addressed the prejudice and discrimination faced by PSMIs. This study has some limitations. First, a potential limitation is that we measured experienced discrimination with a single-item question on exclusion from family decision rather than a multiple-item scale. There was not a specific formalized psychometrically validated measure of experienced stigma available focusing on the scope and content of discrimination before the Discrimination and Stigma Scale (DISC) made available after our study was carried out 10. Other factors may also explain exclusion from family decisions, in particular, symptomatic patients’ disruptive behavior. To account for this issue, we selected a large sample of PSMI at Dr RML hospital representing a wide variety of severity of symptoms. Yet all outpatients were successfully treated and mostly in follow-up, and therefore not symptomatic at the time of the survey. Despite treatment, SMI in cases was significantly associated with our measure of stigma compared to controls, showing that ‘‘pre- existing beliefs’’ or stereotypes linked to past experience

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with the mental illness were critical to the activation of the discrimination process rather than the current mental health status of the person 69. Secondly, it was not possible to establish the direction of the association between poverty, and SMI; poverty can be a cause as well as a consequence of SMI. Thirdly, SMI was diagnosed within a psychiatric department of a free government hospital. Research indicates the poorest members of society may still not access such services, even when free; possibly introducing a selection bias in our sample 70. Additionally, PSMI not receiving medical treatment might be even more marginalised, at greater risk of poverty than those receiving healthcare. Thus the sampling bias might have underestimated association between SMI, stigma and poverty. Finally, due to the large sample size we could not evaluate each control using detailed diagnostic psychiatric questionnaires but only screen them for major mental disorders.

Conclusion Our study provides evidence that mental health professionals must incorporate an understanding of multidimensional poverty stressors as well as address family and community dynamics. Where resources are limited, medical professionals would benefit from working with public health and disability networks to weaken persistent stigma against SMI. Policies promoting employment support for PSMI (notably through reservations or fair employment policies, and access to credit) are critically important. The implications of our findings go beyond medical and public health and link mental health to international development. Promoting employment and fighting social stigma for PSMI not only mitigates the impact of illness for some but appears to be a central concern of global poverty.

Contributorship statement.

Study designed by JFT, SD, PB,SJ. Data collection supervised by SV, NM, SN,SD. Literature review by PB with JFT. Data analysis by JK,JFT. Data interpretation and writing by JFT,PB, and revised by SD and NG. All authors contributed to the final manuscript.

Competing interests

We declare no conflict of interest.

Ethics committee approval

Study approved by University College London Research Ethics Committee and the Dr Ram Manohar Lohia Hospital Institutional Ethics Committee.

Funding

Funded by DFID through the Cross-Cutting Disability Research Programme, Leonard Cheshire Disability and Inclusive Development Centre, University College London (GB-1-200474).

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Study Sponsors had no role in study design, data collection, data analysis, data interpretation or writing, or in submission for publication. The corresponding author had full access to all data and final responsibility for publication submission.

Data sharing

Technical appendix, statistical code, and dataset available from the corresponding author at Dryad repository, which provides a permanent, citable, open access home for the dataset.

Glossary of terms:

MPI: Multidimensional poverty index NCR: National Capital Region of Delhi PSMI: Patients with Severe Mental Illness SC/ST/OBC: Scheduled Castes/Scheduled Tribes/Other Backward Castes

SMI: Severe mental illness

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Table 1: Dimensions, Indicators and cutoff of deprivation Dimensions Indicators Questions Cutoff

Individual level basic capabilities

Health access Could you receive healthcare when

sick? Deprived of healthcare

Education What is your level of education? Primary education completed

Access to

employment What is your usual primary

activity? Not working

Food Security How many meals are usually

served in your household in a day? 1 or 2 meals

Source of drinking

water What is the primary source of

drinking water?

Pipe outside home/public pump

tanker truck/cart with small tank

water from a covered well unprotected well

spring/river/dam/lake/pond/stream

Indoor air quality What is the primary source of

cooking fuel?

Wood, coal/charcoal, dung, kerosene,

straw/shrubs/grass/crop

Type of sanitation What type of toilet facilities do

you use when at home?

Open field, pit latrine improved ventilated pit

public latrine

Type of lighting What is your primary source of

lighting? Generator, kerosene lamp,

petromax, candle, none

Individual income What is your income? Less than $1.25per day

Household level material wellbeing

Crowded space How many people live in the

dwelling? Less than 50sqfeet per

person

Housing ownership Does the family owns the house Do not own the house

Housing quality Are the material used for walls,

floor and roof in your house kutcha or pucca ?

Any of walls, floor or roof is kutcha

Assets ownership

Do you possess any of the following? Mobile phone,

landline, wooden/steel sleeping cot, mattress, table, clock/watch,

charpoy, refrigerator, radio/transistor, electric fan,

television, bicycle, computer, moped/scooter/motorcycle, car

Lowest two asset quintiles

Household per capita

income What is the family income?

Less than $1.25 per capita per day

Household

expenditures What is the household's monthly

expenditure ? Less than $1.25 per capita

per day

Individual level psychosocial dimensions

Physical safety How safe is the place where you

live? Rather or very unsafe

Political participation Did you vote in the last municipal

election? Did not vote

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Table 2: Characteristics of poverty and discrimination comparing patients and controls and by gender and caste.

Dimension PSMI n=647

control n=649

p value

Male PSMI

(n=411)

Male Controls (n=408)

p value

Other Castes PSMI

Other castes

Controls

p value

Female PSMI

(n=238)

Female Controls (n=238)

p value

ST/SC/ OBC

PSMI

ST/SC/ OBC

Controls

p value

Health access 26 (4.0) 16 (2.9) 0.281 13 (3.2) 4 (1.0) 0.802 17 (4.8) 10 (2.5) 0.630 13 (5.5) 12 (5.0) 1.0 9 (3.3) 6 (2.5) 0.995

Education 155 (23.9) 129 (19.9) 0.086 70 (17.0) 52 (12.8) 0.511 61 (17.3) 59 (14.9) 0.879 85 (35.7) 77 (32.4) 0.843 82 (29.9) 65 (26.8) 0.850

Employment 396 (61.0) 252 (39.0) <0.0001 188 (45.7) 68 (16.7) <0.0001 222 (63.1)151 (38.1)<0.0001 208 (87.4) 184 (77.3) <0.0001 164 (59.9) 96 (39.5) <0.0001

Food Security 343 (52.9) 250 (38.6) 0.103 213 (51.8) 155 (38.0) 0.789 165 (46.9)133 (33.6) 0.413 130 (54.6) 95 (39.9) 0.613 163 (59.5) 113 (46.5) 0.964

Source of water 122 (18.8) 118 (18.2) 0.724 86 (20.9) 74 (18.1) 0.732 62 (17.6) 61 (15.40) 0.881 36 (15.1) 44 (18.5) 0.837 55 (20.1) 56 (23.1) 0.893

Indoor air quality 48 (7.4) 38 (5.9) 0.271 35 (8.5) 24 (5.9) 0.515 17 (4.8) 13 (3.3) 0.861 13 (5.4) 14 (5.9) 0.998 27 (9.9) 24 (9.9) 1.0

Type of sanitation 215 (33.1) 180 (27.8) 0.040 147 (35.8) 60 (25.2) 0.271 93 (26.4) 104 (26.3) 1.0 68 (28.6) 66.7 (29.4) 0.897 112 (40.9) 72 (29.6) 0.050

Type of lighting 7 (1.1) 10 (1.6) 0.458 4 (1.0) 8 (2.0) 0.674 0 (0) 4 (1.0) 0.675 3 (1.3) 2 (0.8) 0.984 6 (2.2) 6 (2.5) 0.994

Individual income 369 (68.7) 238 (47.9) <0.0001 176 (53.3) 74 (24.3) <0.0001 199 (68.9)138 (45.5) 0.932 193 (93.2) 164 (85.9) <0.0001 154 (68.1) 95 (52.8) 0.241

Crowded space 206 (31.7) 164 (25.4) 0.010 130 (32.0) 94 (23.3) 0.059 89 (25.3) 70 (17.7) 0.131 76 (32.3) 70 (29.7) 0.938 104 (38.0) 91 (37.5) 0.999

Housing ownership 223 (41.5) 148 (29.8) <0.0001 160 (39.7) 119 (29.2) 0.028 152 (43.2) 75 (30.9) 0.002 99 (42.1) 78 (32.7) 0.264 99 (36.2) 119 (30.1) 0.667

Housing quality 39 (6.3) 13 (2.2) <0.0001 29 (7.1) 7 (1.67) 0.001 13 (3.7) 6 (1.5) 0.493 10 (4.2) 6 (2.5) 0.830 23 (8.4) 7 (2.9) 0.007

Assets ownership 294 (45.3) 214 (33.1) <0.0001 201 (48.9) 125 (30.6) <0.0001 131 (37.2) 94 (23.7) 0.002 93 (39.1) 89 (37.4) 0.986 148 (54.0) 116 (47.7) 0.531

Household income 287 (44.2) 239 (36.9) 0.002 176 (42.8) 142 (34.8) 0.082 132 (37.5)116 (29.3) 0.096 111 (46.6) 97 (40.8) 0.553 141 (51.5) 119 (49.0) 0.907

Household expenditures 373 (57.5) 393 (60.7) 0.978 238 (58.0) 239 (58.6) 0.799 180 (51.1)209 (52.8) 0.947 135 (56.7) 154 (64.7) 0.571 178 (65.0) 180 (74.0) 0.4291

Physical safety 117 (18.0) 134 (20.7) 0.221 80 (19.6) 80 (19.6) 0.907 51 (14.5) 68 (17.2) 1.0 53 (22.3) 53 (22.3) 0.824 62 (22.6) 65 (26.8) 1.0

Political participation 265 (40.8) 209 (32.3) 0.001 163 (39.7) 122 (29.9) 0.030 152 (43.2)125 (31.6) 0.005 102 (42.9) 86 (36.1) 0.506 102 (37.2) 80 (32.9) 0.760 Discrimination in family decisions

178 (27.4) 116 (17.9) <0.0001 71 (17.3) 12 (2.9) <0.0001

92 (26.1) 71 (17.9) 0.042 107(45.0) 104 (43.7) 0.988

78 (28.5) 43 (17.7)0.020

Note: missing values are missing completely at random and there was no significant statistical difference. Incidence of poverty expressed as a percentage is given in brackets. All P value are corrected for multiple comparisons using Scheffe method.

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Table 3 Spearman correlations between dimensions

Dimensions

Health access

Educ. Access to

work Food

Security Source of

water Air

quality Type of

sanitation Type of lighting

Ind. income

Crowded space

Housing ownership

Housing quality

Assets owner-

ship

HH/cap. income

HH spending

Physical safety

Pol. Partici-pation

Health access 1

Education 0.021 1

Access to work 0.1047* 0.1771* 1

Food Security 0.0016 0.1309* 0.0878* 1

Source of water -0.0277 0.1669* 0.0412 0.1263* 1

Indoor air quality 0.0341 0.1907* 0.0732* 0.1077* 0.1519* 1

Type of sanitation -0.0103 0.1514* 0.0369 0.1045* 0.3026* 0.2440* 1

Type of lighting 0.0193 0.0728* 0.0217 0.0642* 0.1079* 0.3018* 0.1550* 1

Individual income 0.0801* 0.1865* 0.7373* 0.0788* 0.0534 0.0875* 0.0199 -0.0134 1

Crowded space -0.0356 0.2471* 0.0521 0.1031* 0.1807* 0.1743* 0.2709* 0.0786* 0.0800* 1

Housing ownership 0.0145 0.0138 0.029 0.0518 0.0553 -0.0029 0.0207 0.0272 -0.0123 0.1442* 1

Housing quality 0.0087 0.1739* 0.0764* 0.0558 0.2384* 0.2767* 0.3345* 0.0534 0.0824* 0.1969* 0.0182 1

Assets ownership 0.0581 0.2727* 0.0751* 0.2544* 0.2364* 0.2820* 0.2330* 0.1634* 0.0797* 0.3079* 0.2926* 0.2753* 1

HH/capita income 0.0472 0.1949* 0.1623* 0.1513* 0.1989* 0.2070* 0.1597* 0.0805* 0.2066* 0.2712* 0.0443 0.1511* 0.2715* 1

HH spending 0.0428 0.1667* 0.1062* 0.1483* 0.2377* 0.1568* 0.1409* 0.0760* 0.1381* 0.2792* 0.037 0.1533* 0.2331* 0.5360* 1

Physical safety 0.044 0.0406 0.0413 0.0596 0.1026* 0.0602 0.1223* 0.0609 0.0441 0.1723* -0.0252 0.0834* 0.0932* 0.1136* 0.1254* 1

Political participation 0.0188 -0.0167 0.0386 0.0815* 0.1538* 0.031 0.1426* 0.0411 0.0125 0.1077* 0.2296* 0.0365 0.1617* 0.0714* 0.0735* 0.0493 1

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Table 4: Multidimensional poverty measures for PSMI and controls and by gender and caste

Cut All PSMI Controls T-value %

difference

Off k H# A M0 H A M0 H A M0 for M0 in M0*

1 0.946 0.276 0.261 0.972 0.302 0.293 0.917 0.247 0.227 -6.574 29.3

2 0.849 0.301 0.256 0.901 0.321 0.289 0.792 0.277 0.219 -6.583 31.7

3 0.739 0.328 0.243 0.834 0.337 0.281 0.635 0.316 0.201 -7.051 39.9

4 0.590 0.367 0.216 0.685 0.372 0.255 0.486 0.359 0.175 -6.378 46.0

5 0.440 0.411 0.181 0.516 0.417 0.215 0.359 0.403 0.145 -5.210 48.5

6 0.307 0.462 0.142 0.385 0.458 0.177 0.222 0.471 0.104 -5.297 69.2

7 0.224 0.503 0.113 0.277 0.499 0.138 0.165 0.511 0.084 -4.062 64.0

8 0.144 0.553 0.080 0.175 0.550 0.096 0.111 0.559 0.062 -2.791 55.2

9 0.090 0.603 0.054 0.112 0.595 0.066 0.067 0.619 0.041 -2.334 61.6

10 0.055 0.650 0.036 0.069 0.636 0.044 0.040 0.676 0.027 -1.776 60.6

Female Male

Cut PSMI Controls T-value PSMI Controls T-value

Off k H M0 H M0 for M0 H M0 H M0 for M0

1 0.990 0.327 0.917 0.227 -2.237 0.961 0.272 0.879 0.185 -6.797

2 0.981 0.327 0.792 0.219 -2.322 0.852 0.265 0.702 0.175 -6.717

3 0.942 0.322 0.635 0.201 -2.585 0.767 0.255 0.508 0.152 -7.140

4 0.783 0.294 0.486 0.175 -2.157 0.624 0.230 0.364 0.127 -6.652

5 0.628 0.257 0.359 0.145 -1.947 0.445 0.188 0.256 0.101 -5.323

6 0.473 0.212 0.222 0.104 -2.191 0.330 0.154 0.148 0.069 -5.263

7 0.343 0.166 0.165 0.084 -1.415 0.236 0.121 0.105 0.054 -4.302

8 0.184 0.100 0.111 0.062 -0.396 0.170 0.094 0.079 0.043 -3.438

9 0.116 0.068 0.067 0.041 -0.458 0.109 0.065 0.049 0.030 -2.752

10 0.068 0.043 0.040 0.027 -0.157 0.070 0.044 0.030 0.019 -2.266

SC/ST/OBC Other castes



1 0.987 0.320 0.972 0.280 -2.437 0.958 0.264 0.884 0.194 -5.532

2 0.942 0.317 0.900 0.276 -2.458 0.862 0.258 0.723 0.185 -5.510

3 0.863 0.308 0.783 0.262 -2.496 0.799 0.251 0.545 0.164 -6.097

4 0.748 0.288 0.628 0.235 -2.574 0.623 0.220 0.396 0.137 -5.246

5 0.606 0.254 0.494 0.203 -2.262 0.426 0.174 0.274 0.109 -3.927

6 0.460 0.211 0.306 0.148 -2.680 0.304 0.138 0.162 0.076 -3.843

7 0.336 0.168 0.233 0.122 -1.917 0.215 0.106 0.125 0.063 -2.788

8 0.217 0.118 0.161 0.092 -1.160 0.131 0.072 0.086 0.047 -1.809

9 0.133 0.079 0.100 0.064 -0.757 0.090 0.053 0.050 0.030 -1.864

10 0.075 0.048 0.061 0.043 -0.308 0.055 0.034 0.030 0.019 -1.459 Note: Rows 11–17 are omitted very few are deprived in more than 10 dimensions, no-one is deprived in more than 15 dimensions. #H is the percentage of the population that is poor

H=* . SD: Standard deviations. $ Adjusted Wald test for difference in adjusted headcount ratio between patients and controls. The average Poverty Gap (A) is not presented for gender and caste but can be easily calculated dividing the Adjusted Headcount (M0) by the headcount ratio (H)

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Table 5: Percentage contribution of each dimension to poverty for PSMI and controls for k 1 to 8

Cut Health Level of Access to Food Source of

Indoor air

Type of Type of Individual Crowded Housing Housing Assets Household Household Physical

Political

Off k access education employment security drinking water

quality sanitation lighting income space ownership quality ownership

income expenses safety Participation

1 PSMI 0.86 4.63 11.62 10.87 3.74 1.31 4.37 0.22 13.79 4.56 8.33 1.27 4.82 8.86 12.63 3.59 4.52

Controls 0.78 5.33 7.74 10.15 4.86 1.57 3.50 0.47 12.45 4.71 7.74 0.58 3.92 9.62 16.42 5.75 4.39

2 PSMI 0.87 4.70 11.79 10.58 3.75 1.33 4.32 0.23 13.91 4.62 8.04 1.29 4.89 8.95 12.62 3.60 4.51

Controls 0.76 5.41 8.00 9.68 5.03 1.62 3.62 0.49 12.43 4.86 7.19 0.59 4.05 9.95 16.43 5.57 4.32

3 PSMI 0.86 4.79 11.77 10.44 3.86 1.36 4.29 0.23 13.64 4.72 8.07 1.33 4.99 9.00 12.51 3.55 4.60

Controls 0.77 5.61 8.15 9.33 5.31 1.77 3.54 0.47 12.16 5.14 6.85 0.65 4.43 10.45 15.58 5.55 4.25

4 PSMI 0.95 4.94 11.05 10.49 3.91 1.46 4.47 0.26 12.77 4.99 7.78 1.46 5.42 9.42 12.55 3.57 4.51

Controls 0.68 5.77 7.95 8.83 5.57 1.90 4.01 0.54 11.14 5.57 6.66 0.75 4.82 10.80 14.95 5.50 4.55

5 PSMI 0.87 5.25 10.30 10.24 4.33 1.63 4.59 0.31 11.67 5.30 7.54 1.68 6.17 9.73 12.18 3.72 4.49

Controls 0.74 6.39 7.79 8.36 5.90 2.05 4.26 0.66 10.49 6.15 6.48 0.90 5.33 10.82 13.77 5.41 4.51

6 PSMI 0.99 5.46 9.86 9.99 4.65 1.86 5.09 0.25 11.10 5.58 6.95 2.05 6.45 9.74 11.85 3.66 4.47

Controls 0.80 7.05 7.27 7.50 6.59 2.73 4.66 0.91 9.32 7.05 6.59 1.25 6.36 9.89 12.05 5.57 4.43

7 PSMI 1.11 5.62 9.65 9.57 4.91 2.14 5.22 0.32 10.76 5.70 6.41 2.37 7.04 9.57 11.16 3.88 4.59

Controls 0.42 7.44 7.02 7.16 6.60 3.37 5.06 1.12 9.13 7.72 6.18 1.40 6.74 9.55 11.38 5.62 4.07

8 PSMI 0.91 5.23 8.65 9.22 5.46 2.62 5.80 0.34 9.90 6.37 7.05 2.73 7.96 8.76 10.35 3.98 4.66

Controls 0.38 7.07 6.12 6.88 6.88 4.21 5.93 1.34 8.22 7.65 6.12 1.91 7.65 9.56 10.52 5.54 4.02

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Table 6: Logistic model for association between multidimensional poverty, stigma and SMI

Unadjusted Model Adjusted Model OR (95%CI) OR (95%CI)

Family participation (no participation) 2.92 2.16-3.93 2.61 1.27-5.31 SMI (Controls) 2.20 1.67-2.89 2.07 1.25-3.41 Female (Male) 2.17 1.65-2.83 1.88 1.36-2.58 SC/ST/OBC (Higher caste) 2.06 1.56-2.70 2.39 1.39-4.08 Age (in year) 0.99 0.97-0.99 0.98 0.96-0.99 Interaction terms No participation*SMI (Participation*controls) 6.38 3.49-11.6 No participation*SC/ST/OBC (Participation*high caste) 4.86 2.19-10.7 No participation*women (Participation*men) 4.63 2.60-8.21 No participation*women*SMI (Participation*male*controls) 9.62 5.58-16.5 No participation*SC/ST/OBC*SMI(Participation*high caste*controls) 7.36 3.94-13.7

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i For vegan individuals, the diet staple included at least dal on a daily basis; for non-vegan individuals,

it included dairy products on a daily basis. Meat for non-vegetarian individuals was not considered as

a diet requirement and therefore deprivation of meat is not an indicator of poor diet. ii Assets include: Landline, mobile phones, wooden/steel sleeping cot, mattress, table, clock/watch,

charpoy, refrigerator, radio/transistor, electric fan, television, bicycle, computer,

moped/scooter/motorcycle, car.

iii Expenditures include: Food, health, school, transportation, savings and personal care products.

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Association between Mental illness, poverty and stigma in India: A case control study

Abstract

Objective –To assess the effect of experienced stigma on depth of multidimensional poverty of persons with severe mental illness (PSMI) in Delhi, India, controlling for gender, age and caste. Design – Matching Case (hospital) control (population) study. Setting – University Hospital (cases) and National Capital Region (NCR) (controls), India.

Participants A case-control study was conducted from November 2011 to June 2012. 647 cases diagnosed with schizophrenia or affective disorders were recruited and 647 individuals of same age, sex and location of residence were matched as controls at a ratio of 1:2:1. Individuals who refused consent or provided incomplete interview were excluded.

Main outcome measures – Higher risk of poverty due to stigma among PSMI.

Results - 38.5% of PSMI compared to 22.2% of controls were found poor on 6 dimensions or more. The difference in Multidimensional poverty index (MPI) was 69% between groups with employment and income the main contributors. Multidimensional poverty was strongly associated with stigma (odds ratio [OR] 2.60, 95% CI 1.27-5.31), scheduled castes/scheduled tribes/ other backward castes (SC/ST/OBC) (2.39, 1.39-4.08), mental illness (2.07, 1.25-3.41), and female gender (1.87, 1.36-2.58). A significant interaction between stigma, mental illness and gender or caste indicates female PSMI or PSMI from ‘lower castes’ were more likely to be poor due to stigma than male controls (p<0.001) or controls from other castes (p<0.001). Conclusions – Public stigma and multidimensional poverty linked to SMI are pervasive and intertwined. Particularly for low caste and women, it is a strong predictor of poverty. Exclusion from employment linked to negative attitudes and lack of income are the highest contributors to multidimensional poverty, increasing the burden for the family. Mental health professionals need to be aware of and address these issues.

Article summary

Strengths and limitations

• There is little research on effects of stigma and poverty in developing settings

• Lack of employment and income are major contributors to multidimensional poverty for PSMI

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• Intensity of multidimensional poverty is higher for PSMI, particularly women with SMI and those from SC/ST/OBC

• Limitation: Stigma was operationalized through a single item question rather than a multiple-item scale and we could not assess reliability of this item. SMI was diagnosed for persons attending a public psychiatric department; PSMI not receiving medical treatment might be more marginalised and at greater risk of poverty than those receiving healthcare.

Introduction Mental health problems affect 450 million people worldwide, 80% in middle and low-income countries. In 2010, 2,319,000 persons died of mental and behavioural disorders1. Mental health conditions account for 13% of the total burden of disease, 31% of all years lived with disability and are one of the 4 main contributors to years lived with disability2,

3. Schizophrenia and bipolar disorder represent 7.4 % and 7·0% of DALYs caused by mental and substance use disorders respectively4. Severe mental illness (SMI) is a leading cause of disability and the standard prevalent biomedical care model is neither an exclusive nor a comprehensive solution as it does not address the link between mental illness, stigma and poverty 5. While the literature on poverty, poor mental health6 and disability7-9 is emerging, little has been done to examine the compounding associations between experienced stigma (unfair treatment or discrimination due to having a mental health issue)10, mental illness and poverty, especially in low-income countries. In high-income countries11, income deprivation is identified as a major risk factor for persons with mental health issues, even for common mental disorders12. Poor mental health linked to SMI has been associated with poverty during the recent economic crisis in middle and low-income countries, particularly India and China13-15. People with mental disorders living in these countries are not only more likely to be poorer, but also unemployed and less educated16, 17. Indisputably, a better understanding of the relationship between mental illness and poverty may yield useful knowledge to tailor public health interventions to complement biomedical treatment to improve outcomes. Link and Phelan (2001) defined stigma as a process with five interrelated components: discrimination through a process of separation based on negative attitudes and prejudice resulting from labelling and cultural stereotypes of society towards the stigmatized group leading to social, economic and political power differences18. Thornicroft et al. (2007) identify three elements of stigma: ignorance or misinformation, prejudice and discrimination19. Our paper focuses on the process of experienced discrimination as the manifestation of public stigma20. The congruence of self-stigma and social exclusion may lead persons with SMI (PSMIs) to face unfair treatment or discrimination and develop low self-esteem21-24. Such stigma may prevent mentally ill persons from improving their conditions25 by creating a “barrier to recovery”26 and worsen their situation by pushing them into poverty through discriminatory practices27-29.

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Stigma towards PSMI resulting in discrimination30, 31 is persistent in India32. Although the factors constituting poverty and discrimination linked to mental illness potentially can deprive persons of many resources33, 34 the dynamics of poverty, discrimination and mental health have not been fully addressed. The clinical literature argues that stigma is caused by mental illness and treating the latter biomedically will weaken the associated stigma35, 36. We argue instead that even treated PSMI are more likely to be multidimensionally poor due to discrimination resulting from stigma. Many studies have focused on uni-dimensional effect of poverty on mental health, but have not explained how stigma towards mental illness can be an aggravating contributor to the intensity of poverty. We aimed to estimate the difference in incidence and intensity of poverty between PSMI and a comparable control group using a multidimensional poverty index (MPI) to explore deprivation in various dimensions of life37. Going beyond traditional welfare economics approaches to poverty (i.e. income or per capita expenditure) we explored non-monetary dimensions of poverty such as education, health, quality of shelter, food intake, and political participation. We assessed differences in intensity of poverty between PSMI and controls and explored how these differences vary as a function of discrimination resulting from stigma.

Methods

Study design and setting

The primary objective was to assess differences in exposure to discrimination resulting from stigma and multidimensional poverty among cases compared with non-psychiatrically ill controls. Between November 2011 and June 2012, we carried out a case-control study based at the Department of Psychiatry of the Dr Ram Manohar Lohia (RML) Hospital in New Delhi (cases), and in the neighbourhood of residence of the cases (controls) to assess the impact of stigma associated to mental illness on poverty. The Department of Psychiatry at Dr RML hospital received respectively 10881 and 19528 new outpatients and 52389 and 45319 follow-ups of existing patients in 2012 and 2013. The department has also a 42 bed general psychiatry and de-addiction inpatient facility for men and women. It serves patients from the national Capital Region of Delhi (NCR).

Participants

We defined cases as outpatients diagnosed with schizophrenia or affective disorders by one of the 10 board certified treating psychiatrists following ICD-10 criteria38. Outpatients were informed about the study and if they consented, were referred to researchers for written informed consent and evaluation with no further contact with those who refused. Transportation costs and a meal were provided to maximise recruitment and reduce selection bias. We used a non-psychiatrically ill control group composed of randomly selected individuals matching the patients according to gender, age (plus or minus 5 years) and neighbourhood of residence. Matched controls were selected by spinning a pointer at the door of the case’s home and randomly selecting one household among 30 in the pointed direction. In this household a person of same age and gender with no reported history of a

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mental health disorder was interviewed. It was not possible to conduct detailed interviews for diagnosis of all controls due to logistics as well as stigma of revealing mental illness. We excluded controls when unable to obtain consent. Only two case patients were not matched. Investigators together with the team manager contributed to sensitisation and awareness raising in the neighbourhoods of interest to maximise controls’ participation rates. Consent for patients and controls adolescent between 13 and 18 was obtained by asking the parent or the legal guardian of the study subjects. We conducted face-to-face interviews with all PSMI or a caregiver during hospital visits, and controls at home. We obtained information on demographics, socioeconomic factors, health conditions and accessibility to services, education, employment, income, livelihoods, and social participation. The instrument was translated by experts into Hindi with iterative back-translation and tested in a pilot survey in October 2011. Investigators trained 2 experienced supervisors and 10 Masters-level students over two weeks on survey concepts and goals, mental illness awareness, interview techniques followed by review, test and debriefing.

Sample size

To determine sample size, we used a matched design with a control to case ratio of one, the probability of exposure to poverty among controls of 0.22 and the correlation coefficient for exposure between matched cases and controls of 0.1839. Considering the true odds ratio for one dimension of poverty in exposed subjects relative to unexposed subjects as 2.2, we needed to enroll 205 case patients to be able to reject the null hypothesis that this odds ratio equals 1with probability of 0.9. The type 1 error probability associated with this test of this null hypothesis is 0.05. We enrolled 649 case patients to allow for subgroup analyses including impact on poverty of discrimination stratified by gender, age and caste.

Efforts to minimize bias

New patients were first interviewed by a junior psychiatrist who made a provisional diagnosis and discussed details with a board of certified psychiatrist who then diagnosed and managed the case. To minimise diagnosis bias, we trained all psychiatrists on the ICD 10 criteria. Information bias was minimised by reviewing the questionnaire about exposure to poverty to ensure accuracy, completeness and content validity with experts and by testing it with a sample group of patients and families..Investigators revised the content for relevance to poverty in order to maximize item appropriateness. They first defined the concept of multidimensional poverty and reviewed the empirical and theoretical literature to identify the right deprivation items to include in the instrument they were developing. They then checked if the questions covered all dimensions of the concept of multidimensional poverty, if the phrasing respectively in English and Hindi was accurately reflecting the underlying concept of deprivation we were looking for in each dimension. Two experts familiar with multidimensional poverty reviewed the initial list of items and made suggestions about adding items that were omitted. We then organized a focus group discussion with 7 experts, psychiatrists, psychologists and social workers from Dr Ram Manohar Lohia hospital to establish if the 17 domains of poverty selected were adapted and relevant for the context of New Delhi and were providing a comprehensive overview of the concept. They also ranked these domains by order of

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importance of deprivation. A similar focus group was organized with 8 hospital outpatients with severe mental illness. We finally tested the poverty questionnaire with a group of 20 outpatients at the department of psychiatry at Dr RML hospital. We prompted them with questions to check for their understanding of poverty, to identify the language they used to explain the notion of poverty as well as ascertain their understanding of the questions in order to make sure the instrument’s purpose made sense to them. Finally, two other experts revised the final version to make sure items illustrate the content of multidimensional poverty.40 We also carried out test-retest to test for recall bias and social desirability bias. Interviews with 71 respondents (both cases and controls) for test-retest reliability were carried out on two occasions with a gap of 10 to 15 days by the same enumerator to check to what degree a given respondent provided same responses for the poverty items. We compared the scores between the two sets of responses. Results show overall acceptable level of reliability (over 0.7 for Inter class correlation) for the different poverty variables.

Quantitative variables

We selected 17 indicators of deprivation reflecting aspects of wellbeing (Table 1) identified by literature review and validated through focus group discussions (FGDs) with experts and PSMI/caregivers. Both groups identified and agreed on deprivation cut-offs for each indicator through participatory deliberation 41. Some standard dimensions were not included due to lack of relevance in Delhi. For instance, few respondents lacked access to diet staplesi.

We classified the selected indicators in three major domains of deprivation: individual level capabilities, household level material wellbeing, and individual level psychosocial factors. The first domain was composed of nine indicators. Access to secondary school was the indicator for education; dropping out before reaching secondary school was the cut-off. Unemployment was a major source of vulnerability; deprivation of work was the cut-off. Food security was measured by access to three meals per day and respondents eating less were considered deprived. Following the UNICEF definitions, improved indoor air quality using cooking gas, improved drinking water by pipe into residence and improved sanitation by private flush toilet defined absence of deprivation for indicators six to eight. Finally, individual income constituted a monetary indicator. Material wellbeing of the household was composed of two series of indicators. Three indicators outlined conditions of living: minimum space per person (deprivation threshold of 40 square feet per person); home ownership (renting was the cut-off ); poor quality housing was having either the flooring, walls or roof made of Kutcha (precarious or temporary) material. Material wealth was defined by three complementary indicators: the household average per capita income (threshold at the international poverty line of US$1.25 per day or 68 Indian rupees)42; assets included typical goods owned by the householdii; and monthly household expendituresiii. Finally, two psychosocial indicators were selected: physical safety, measured through an indicator of perception of unsafe environment and political participation in the municipal elections.

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Studies in India have shown that stigma resulting in discriminatory practices is perceived to be high in the family and the community43, 44. As a result, we measured experienced discrimination as a dimension of stigma through self-evaluation of unfair treatment by the family. We asked all respondents if they were excluded from family decision compared to other household members of the same generation. Unfair treatment within family is a feature of stigma in India44. We tested this through FGDs with PSMI of both genders. We found high association between SMI and exclusion from regular family decisions, particularly for women. Other dimensions of participation did not show any discriminatory process. Inclusion in community activities showed similar 30% levels of participation between PSMI and controls. A possible explanation for participation is that where symptoms of mental illness are managed by treatment, family develop coping strategies through symbolic social participation and selective disclosure to avoid rejection, stigma and avoidance by others associated with their relative’s condition45-47. Finally, we enquired about participation in political activities such as “gram sabhas” or local associations. We found generalized low participation in political activities, which is a common feature in New Delhi and therefore not a good indicator of experienced discrimination. Table 1: approximately here

Statistical Analysis Our primary aim was to explore the effect of mental illness and stigma on poverty. We used an unmatched Multidimensional Poverty Index (MPI) measure to identify differences in levels of poverty between PSMI and controls48. Dimensions were independently assessed and the method focused on dimensional shortfalls. This method allowed us to aggregate dimensions of multidimensional poverty measures and consisted of two different forms of cutoffs: one for each dimension and the other relating to cross-cutting dimensions. If an individual fell below the chosen cut-off on a particular dimension he/she was identified as deprived. The second poverty cut-off determined the number of dimensions in which a person must be deprived to be deemed multidimensionally poor. We first performed one-way analyses to assess differences in poverty levels and discrimination between PSMI and controls, by gender and caste adjusting for post-hoc pairwise comparisons using the Scheffe method. We also carried out correlation analysis to assess overlap of dimensions of deprivation. We then calculated 3 indicators of multidimensional poverty: (i) the headcount ratio (H) indicating how many people fall below each deprivation cutoff; (ii) the average poverty gap (A) denoting the average number of deprivations each person experiences; (iii) the adjusted headcount (M0) which is the headcount ratio (H) by the average poverty gap (A) and indicates the breadth of poverty. We established the contribution of each dimension of poverty for cases and controls by dividing each of the two subgroups’ poverty level by the overall poverty level, multiplied by the population portion of each subgroup.

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To assess potential bias in our estimates of MPI, we carried out sensitivity analysis and compared three measures of poverty with: (i) Equal weight for every indicator in each dimension; (ii) Individual rankings of indicators done by experts at Dr RML hospital during the FGDs transformed into individual weights and then taking the average of the individual weights49; (iii) Group ranking based on the mean of individual rankings of indicators during FGDs and taking the weight according to the group ranking 50. We found consistency across measures (data not shown). We finally calculated the crude and adjusted odd ratios (OR) with associated 95% confidence intervals using a logistic regression model to identify association between stigma, SMI and multidimensional poverty. We used ‘no participation’ as the reference category. We defined a binary outcome for poverty (poor/non poor) using the adjusted headcount ratio (M0) for a cutoff k=6 corresponding to the highest gap between PSMI and controls. This cutoff corresponds to a prevalence of poverty of 30.7% above the recent estimates of 13.7% of urban Indians below the poverty line fixed at 28.65 rupees by the Indian Planning Commission51 which has been criticised for being unrealistic. This cutoff is in line with World Bank recent estimate that 33% of India’s population lives below the international poverty line established at $1.25 dollars per capita per day52. We characterised how SMI results in higher intensity of multidimensional poverty due to stigma. Aware that stigma and discrimination may also affect women53 and members of lower castes54, we adjusted the model for potential confounders significantly associated with poverty and family discrimination: caste (in case of difference within the family), gender and age. We carried out sensitivity analysis for different values of the cutoff k and found robustness in our model (data not shown). For all analyses, a P-value of <0.05 was considered significant. Missing values were treated as being missing completely at random. We used Stata (version 12.0) for database processing and all analysis.

Results Participants

We interviewed 649 case patients and 647 controls. Of these, we excluded 110 (17%) cases and 151 (23%) controls respectively who did not complete the interview or for whom the data was incomplete. The final analysis included 537 cases and 496 controls (figure 1). The distribution between cases and controls was similar for gender (305 and 330 males respectively, 61.5% in both cases) and age ( 15-74 and 13-74 and median 35 and 36 respectively). Figure 1 approximately here. Table 2 reports the headcount ratios (H) or incidence of deprivation in each dimension. There were statistically significantly higher numbers of deprived PSMI than controls in nine dimensions. Differences were very high for access to employment (28.1% difference), individual income (20.7%) and relatively high for food security (15.1%) and house ownership (11.7%). In only one dimension -perception of physical safety- was there a reverse non-significant difference as number of controls were higher than the number of PSMI.

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Table 2 approximately here. Table 2 also show results by gender and caste. Compared to male PSMI, the proportion of deprived female PSMI was significantly higher (10 of 17 dimensions). Similarly, a higher number of PSMI (vs. controls) from ‘scheduled castes’, ‘scheduled tribes’ or ‘other backward castes’ (SC/ST/OBC) were poorer on 13 (vs. 16 dimensions) compared to PSMI (vs. controls) from unreserved castes. To investigate possible overlap of dimensions of poverty, we calculated the estimates for the Spearman rank correlation coefficients between each pair of dimensions of deprivation (Table 3). We found no evidence of strong correlation between dimensions, illustrating absence of association except for household income and expenditures. We nevertheless kept both indicators to calculate the MPI to account for information bias (particularly recall bias) often associated with measures of income in household surveys55, 56. Significantly, this result demonstrates that a unique welfare indicator of poverty such as income, cannot represent all aspects of deprivation. Table 3 approximately here.

Multidimensional poverty

Results in Table 4 report the multidimensional headcount ratio (H), the average deprivation shared across the poor (A) and the adjusted headcount ratio (M0) for all possible cutoffs and for the two groups. Depending on the chosen cutoff, the proportion of PSMI and controls who were multidimensionally poor varied greatly. For a cutoff of one, 97.2% of PSMI and 91.7% of controls were deprived: taking a union approach of deprivation in one dimension, this translates into quasi-universal poverty. On average, PSMI were deprived on 5 dimensions and controls on 3.9. If multidimensional poverty requires deprivation in four, five, or six dimensions simultaneously, the proportion of poor PSMI (compared to poor controls) becomes 68.5% (compared to 48.6%), 51.6% (35.9%), or 38.5% (22.2%). Conversely, if we adopt the intersection approach where being poor implies being deprived in all 17 dimensions, nobody in the sample is poor and less than 1% of the sample is deprived in 13. Table 4 approximately here The adjusted headcount ratio (M0) shows that PSMI were worse off than controls for a cutoff (k) value between one and 12 dimensions. This difference is significant (p<0.001) for (k)=1 to (k)=10 dimensions and highest (69% difference) for (k)=6. The average deprivation share (A) is higher among PSMI for a value of (k) between one and five and highest for (k)=1 (22% difference). For a (k) between six and 14, the total number of deprivations faced by poor PSMI is slightly lower on average than for controls. Less than 30% of people were poor in six dimensions or more and the difference between PSMI and controls was the highest for a (k) value of 14 (7%). To further investigate the association between poverty and mental illness, analysis was repeated for all possible cutoffs and for gender and caste (Table 4). Multidimensional

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poverty was significantly higher for female PSMI compared to female controls for any threshold between one and seven dimensions (p<0.001) but also for male PSMI for any threshold between one and nine dimensions. On average, 62.8% of female PSMI were deprived on five dimensions or more, compared to 35.9% of female controls, 44.5% of male PSMI and 25.6% of male controls. For female PSMI and controls − and male PSMI and controls respectively − the difference is particularly pronounced and significant for highest cutoff values, and maximum for six and seven dimensions respectively. The adjusted headcount ratio (M0) shows that SC/ST/OBC PSMI are worse off regardless of the value of (k) 1 through 10, than SC/ST/OBC controls and other caste PSMI or controls. (M0) for SC/ST/OBC controls is higher than for other caste PSMI for all (k) values. Table 5 presents the percentage contribution of each dimension to (M0) for different (k). Deprivations in individual income household expenditures and employment were contributing each more than 10% to the overall (M0) for PSMI, whatever the value (k) between 1 and 8. For controls, employment was a less salient contributor while the contribution from household income was among the highest. Table 5 approximately here

Poverty and stigma

Association between multidimensional poverty and stigma was strong even when controlling for SMI, gender, caste and age (Table 6; all p<0·0001). We included interaction of stigma, SMI with caste and found that this term was strongly and positively associated with a high level of poverty: the odds ratio of being multidimensionally poor for PSMI from SC/ST/OBC compared with controls from unreserved castes was 7.36 (95% confidence interval 3.94 to 13.7). Similarly, we allowed for differential gender effects by including interaction of stigma and SMI with the gender of the respondent and found high effect on poverty: women PSMI were 9.61 (95% CI 5.58 to16.5) more likely to be poor compared to male controls.

Table 6 approximately here

Discussion Our findings establish that intensity of multidimensional poverty is higher for PSMI than the rest of the population. They also indicate that it is higher for women with SMI and for SC/ST/OBC with SMI. Deprivation of employment and income appear to be major contributing factors to MPI. Lack of employment and income appear to aggravate mental illness. Finally, our findings suggest that stigma linked to SMI, compounded with others (particularly SC/ST/OBC and women) negatively impact poverty. The congruence of SMI and poverty, in a context of high prejudice against mental illness compromises improvement. Mental illness in India is linked to lack of knowledge and pervasive negative assumptions, the most common being that PSMI are violent and unable to work18, 31, 44. Not surprisingly, deprivation of employment and income contributes highly to multidimensional poverty of PSMI compared to controls. This finding ties in with a study on employment for Indian men with schizophrenia which

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found that employment provided not just an essential social role but was also a condition for rehabilitation, enhanced confidence and self-esteem 44. Although there is evidence of differences in mental health outcomes between men and women, analyses of gender disparities are lacking in literature on poverty and mental health in low-income countries44, 57, 58. In our sample, women with SMI were systematically more deprived in higher numbers of dimensions. Similarly, SC/ST/OBC SMI-poverty associations were found to be consistent across dimensions of poverty regardless of the threshold for multidimensional poverty. These findings strongly suggest stigma linked to various marginalized groups have the power to accelerate and intensify exclusion and related discrimination. For women, SMI can negatively impact wellbeing in two ways. Firstly, SMI limits women from fulfilling family and social roles, leading to these women being considered a burden for the family. This is true despite studies, such as the Indian study of women with schizophrenia abandoned by their husbands who expressed the desire to work to support themselves 59. Secondly, traditional beliefs (punishment for previous lives, evil eye/curse) as well as negative lay attitudes on causes and behaviours, lead to increased discrimination of and sometimes violence against SMIs, particularly for women 60. Our study finds that SC/ST/OBC and poverty further compound SMI. Discrimination linked to caste in accessing education or employment has been a leitmotif in modern India and only partially addressed through constitutional provisions and reservation policies. Caste discrimination still results in scant employment opportunities, less access to secondary and higher education- key for salaried public and private jobs, perpetuating powerlessness, traditional forms of dominance and oppression, inequalities, lower living standards among SC/ST/OBC as a entrenched social identity in India61, 62. This situation is even more catastrophic for PSMI from SC/ST/OBC. It is clear that a ‘negative feedback loop’ exists: stigma against SMI, particularly for SC/ST/OBC and women, is a strong predictor of persistent poverty. Moreover, stigma strongly bears on intensity of poverty. Stigma leads to difficulty for PSMI in finding and keeping a job, and this also increases the perceived burden of SMI by family members. In turn, this deprivation on various dimensions erodes self-esteem, brings shame and acceptance of discriminatory attitudes 63. These compounding factors may result in a worsening of mental illness. Beyond the PSMI, stigma and discrimination have a negative effect on family members and caregivers who often feel ashamed, embarrassed or unable to cope with the stigma59,

64-68. While there have been campaigns and policies to address discrimination against SC/ST/OBC and women in India, no large-scale awareness campaign has ever addressed the prejudice and discrimination faced by PSMIs. This study has some limitations. First, a potential limitation is that we measured experienced discrimination with a single-item question on exclusion from family decision rather than a multiple-item scale. There was not a specific formalized psychometrically validated measure of experienced stigma available focusing on the scope and content of discrimination before the Discrimination and Stigma Scale (DISC) made available after

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our study was carried out 10. Other factors may also explain exclusion from family decisions, in particular, symptomatic patients’ disruptive behavior. To account for this issue, we selected a large sample of PSMI at Dr RML hospital representing a wide variety of severity of symptoms. Yet all outpatients were successfully treated and mostly in follow-up, and therefore not symptomatic at the time of the survey. Despite treatment, SMI in cases was significantly associated with our measure of stigma compared to controls, showing that ‘‘pre- existing beliefs’’ or stereotypes linked to past experience with the mental illness were critical to the activation of the discrimination process rather than the current mental health status of the person 69. Secondly, it was not possible to establish the direction of the association between poverty, and SMI; poverty can be a cause as well as a consequence of SMI. Thirdly, SMI was diagnosed within a psychiatric department of a free government hospital. Research indicates the poorest members of society may still not access such services, even when free; possibly introducing a selection bias in our sample 70. Additionally, PSMI not receiving medical treatment might be even more marginalised, at greater risk of poverty than those receiving healthcare. Thus the sampling bias might have underestimated association between SMI, stigma and poverty. Finally, due to the large sample size we could not evaluate each control using detailed diagnostic psychiatric questionnaires but only screen them for major mental disorders.

Conclusion Our study provides evidence that mental health professionals must incorporate an understanding of multidimensional poverty stressors as well as address family and community dynamics. Where resources are limited, medical professionals would benefit from working with public health and disability networks to weaken persistent stigma against SMI. Policies promoting employment support for PSMI (notably through reservations or fair employment policies, and access to credit) are critically important. The implications of our findings go beyond medical and public health and link mental health to international development. Promoting employment and fighting social stigma for PSMI not only mitigates the impact of illness for some but appears to be a central concern of global poverty.

Contributorship statement.

Study designed by JFT, SD, PB,SJ. Data collection supervised by SV, NM, SN,SD. Literature review by PB with JFT. Data analysis by JK,JFT. Data interpretation and writing by JFT,PB, and revised by SD and NG. All authors contributed to the final manuscript.

Competing interests

We declare no conflict of interest.

Ethics committee approval

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Study approved by University College London Research Ethics Committee and the Dr Ram Manohar Lohia Hospital Institutional Ethics Committee.

Funding

Funded by DFID through the Cross-Cutting Disability Research Programme, Leonard Cheshire Disability and Inclusive Development Centre, University College London (GB-1-200474). Study Sponsors had no role in study design, data collection, data analysis, data interpretation or writing, or in submission for publication. The corresponding author had full access to all data and final responsibility for publication submission.

Data sharing

Technical appendix, statistical code, and dataset available from the corresponding author at Dryad repository, which provides a permanent, citable, open access home for the dataset.

Glossary of terms:

MPI: Multidimensional poverty index NCR: National Capital Region of Delhi PSMI: Patients with Severe Mental Illness SC/ST/OBC: Scheduled Castes/Scheduled Tribes/Other Backward Castes

SMI: Severe mental illness

Figure 1: Flow chart depicting enrollment of patients with mental illness and

controls without mental illness.

Patients with mental illness (n=649) Controls matching in gender, age and residency (n=649)

Incomplete interviews (n=110)

Patients with complete interview (n=537)

Excluded (17%)

Controls with complete interview (n=496)

Excluded (23%)

Refused interviews (n=2)

Incomplete interviews (n=151)

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Table 1: Dimensions, Indicators and cutoff of deprivation Dimensions Indicators Questions Cutoff

Individual level basic capabilities

Health access Could you receive healthcare when

sick? Deprived of healthcare

Education What is your level of education? Primary education completed

Access to

employment What is your usual primary

activity? Not working

Food Security How many meals are usually

served in your household in a day? 1 or 2 meals

Source of drinking

water What is the primary source of

drinking water?

Pipe outside home/public pump

tanker truck/cart with small tank

water from a covered well unprotected well

spring/river/dam/lake/pond/stream

Indoor air quality What is the primary source of

cooking fuel?

Wood, coal/charcoal, dung, kerosene,

straw/shrubs/grass/crop

Type of sanitation What type of toilet facilities do

you use when at home?

Open field, pit latrine improved ventilated pit

public latrine

Type of lighting What is your primary source of

lighting? Generator, kerosene lamp,

petromax, candle, none

Individual income What is your income? Less than $1.25per day

Household level material wellbeing

Crowded space How many people live in the

dwelling? Less than 50sqfeet per

person

Housing ownership Does the family owns the house Do not own the house

Housing quality Are the material used for walls,

floor and roof in your house kutcha or pucca ?

Any of walls, floor or roof is kutcha

Assets ownership

Do you possess any of the following? Mobile phone,

landline, wooden/steel sleeping cot, mattress, table, clock/watch,

charpoy, refrigerator, radio/transistor, electric fan,

television, bicycle, computer, moped/scooter/motorcycle, car

Lowest two asset quintiles

Household per capita

income What is the family income?

Less than $1.25 per capita per day

Household

expenditures What is the household's monthly

expenditure ? Less than $1.25 per capita

per day

Individual level psychosocial dimensions

Physical safety How safe is the place where you

live? Rather or very unsafe

Political participation Did you vote in the last municipal

election? Did not vote

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Table 2: Characteristics of poverty and discrimination comparing patients and controls and by gender and caste.

Dimension PSMI n=647

control n=649

p value

Male PSMI

(n=411)

Male Controls (n=408)

p value

Other Castes PSMI

Other castes

Controls

p value

Female PSMI

(n=238)

Female Controls (n=238)

p value

ST/SC/ OBC

PSMI

ST/SC/ OBC

Controls

p value

Health access 26 (4.0) 16 (2.9) 0.281 13 (3.2) 4 (1.0) 0.802 17 (4.8) 10 (2.5) 0.630 13 (5.5) 12 (5.0) 1.0 9 (3.3) 6 (2.5) 0.995

Education 155 (23.9) 129 (19.9) 0.086 70 (17.0) 52 (12.8) 0.511 61 (17.3) 59 (14.9) 0.879 85 (35.7) 77 (32.4) 0.843 82 (29.9) 65 (26.8) 0.850

Employment 396 (61.0) 252 (39.0) <0.0001 188 (45.7) 68 (16.7) <0.0001 222 (63.1)151 (38.1)<0.0001 208 (87.4) 184 (77.3) <0.0001 164 (59.9) 96 (39.5) <0.0001

Food Security 343 (52.9) 250 (38.6) 0.103 213 (51.8) 155 (38.0) 0.789 165 (46.9)133 (33.6) 0.413 130 (54.6) 95 (39.9) 0.613 163 (59.5) 113 (46.5) 0.964

Source of water 122 (18.8) 118 (18.2) 0.724 86 (20.9) 74 (18.1) 0.732 62 (17.6) 61 (15.40) 0.881 36 (15.1) 44 (18.5) 0.837 55 (20.1) 56 (23.1) 0.893

Indoor air quality 48 (7.4) 38 (5.9) 0.271 35 (8.5) 24 (5.9) 0.515 17 (4.8) 13 (3.3) 0.861 13 (5.4) 14 (5.9) 0.998 27 (9.9) 24 (9.9) 1.0

Type of sanitation 215 (33.1) 180 (27.8) 0.040 147 (35.8) 60 (25.2) 0.271 93 (26.4) 104 (26.3) 1.0 68 (28.6) 66.7 (29.4) 0.897 112 (40.9) 72 (29.6) 0.050

Type of lighting 7 (1.1) 10 (1.6) 0.458 4 (1.0) 8 (2.0) 0.674 0 (0) 4 (1.0) 0.675 3 (1.3) 2 (0.8) 0.984 6 (2.2) 6 (2.5) 0.994

Individual income 369 (68.7) 238 (47.9) <0.0001 176 (53.3) 74 (24.3) <0.0001 199 (68.9)138 (45.5) 0.932 193 (93.2) 164 (85.9) <0.0001 154 (68.1) 95 (52.8) 0.241

Crowded space 206 (31.7) 164 (25.4) 0.010 130 (32.0) 94 (23.3) 0.059 89 (25.3) 70 (17.7) 0.131 76 (32.3) 70 (29.7) 0.938 104 (38.0) 91 (37.5) 0.999

Housing ownership 223 (41.5) 148 (29.8) <0.0001 160 (39.7) 119 (29.2) 0.028 152 (43.2) 75 (30.9) 0.002 99 (42.1) 78 (32.7) 0.264 99 (36.2) 119 (30.1) 0.667

Housing quality 39 (6.3) 13 (2.2) <0.0001 29 (7.1) 7 (1.67) 0.001 13 (3.7) 6 (1.5) 0.493 10 (4.2) 6 (2.5) 0.830 23 (8.4) 7 (2.9) 0.007

Assets ownership 294 (45.3) 214 (33.1) <0.0001 201 (48.9) 125 (30.6) <0.0001 131 (37.2) 94 (23.7) 0.002 93 (39.1) 89 (37.4) 0.986 148 (54.0) 116 (47.7) 0.531

Household income 287 (44.2) 239 (36.9) 0.002 176 (42.8) 142 (34.8) 0.082 132 (37.5)116 (29.3) 0.096 111 (46.6) 97 (40.8) 0.553 141 (51.5) 119 (49.0) 0.907

Household expenditures 373 (57.5) 393 (60.7) 0.978 238 (58.0) 239 (58.6) 0.799 180 (51.1)209 (52.8) 0.947 135 (56.7) 154 (64.7) 0.571 178 (65.0) 180 (74.0) 0.4291

Physical safety 117 (18.0) 134 (20.7) 0.221 80 (19.6) 80 (19.6) 0.907 51 (14.5) 68 (17.2) 1.0 53 (22.3) 53 (22.3) 0.824 62 (22.6) 65 (26.8) 1.0

Political participation 265 (40.8) 209 (32.3) 0.001 163 (39.7) 122 (29.9) 0.030 152 (43.2)125 (31.6) 0.005 102 (42.9) 86 (36.1) 0.506 102 (37.2) 80 (32.9) 0.760 Discrimination in family decisions

178 (27.4) 116 (17.9) <0.0001 71 (17.3) 12 (2.9) <0.0001

92 (26.1) 71 (17.9) 0.042 107(45.0) 104 (43.7) 0.988

78 (28.5) 43 (17.7)0.020

Note: missing values are missing completely at random and there was no significant statistical difference. Incidence of poverty expressed as a percentage is given in brackets. All P value are corrected for multiple comparisons using Scheffe method.

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Table 3 Spearman correlations between dimensions

Dimensions

Health access

Educ. Access to

work Food

Security Source of

water Air

quality Type of

sanitation Type of lighting

Ind. income

Crowded space

Housing ownership

Housing quality

Assets owner-

ship

HH/cap. income

HH spending

Physical safety

Pol. Partici-pation

Health access 1

Education 0.021 1

Access to work 0.1047* 0.1771* 1

Food Security 0.0016 0.1309* 0.0878* 1

Source of water -0.0277 0.1669* 0.0412 0.1263* 1

Indoor air quality 0.0341 0.1907* 0.0732* 0.1077* 0.1519* 1

Type of sanitation -0.0103 0.1514* 0.0369 0.1045* 0.3026* 0.2440* 1

Type of lighting 0.0193 0.0728* 0.0217 0.0642* 0.1079* 0.3018* 0.1550* 1

Individual income 0.0801* 0.1865* 0.7373* 0.0788* 0.0534 0.0875* 0.0199 -0.0134 1

Crowded space -0.0356 0.2471* 0.0521 0.1031* 0.1807* 0.1743* 0.2709* 0.0786* 0.0800* 1

Housing ownership 0.0145 0.0138 0.029 0.0518 0.0553 -0.0029 0.0207 0.0272 -0.0123 0.1442* 1

Housing quality 0.0087 0.1739* 0.0764* 0.0558 0.2384* 0.2767* 0.3345* 0.0534 0.0824* 0.1969* 0.0182 1

Assets ownership 0.0581 0.2727* 0.0751* 0.2544* 0.2364* 0.2820* 0.2330* 0.1634* 0.0797* 0.3079* 0.2926* 0.2753* 1

HH/capita income 0.0472 0.1949* 0.1623* 0.1513* 0.1989* 0.2070* 0.1597* 0.0805* 0.2066* 0.2712* 0.0443 0.1511* 0.2715* 1

HH spending 0.0428 0.1667* 0.1062* 0.1483* 0.2377* 0.1568* 0.1409* 0.0760* 0.1381* 0.2792* 0.037 0.1533* 0.2331* 0.5360* 1

Physical safety 0.044 0.0406 0.0413 0.0596 0.1026* 0.0602 0.1223* 0.0609 0.0441 0.1723* -0.0252 0.0834* 0.0932* 0.1136* 0.1254* 1

Political participation 0.0188 -0.0167 0.0386 0.0815* 0.1538* 0.031 0.1426* 0.0411 0.0125 0.1077* 0.2296* 0.0365 0.1617* 0.0714* 0.0735* 0.0493 1

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Table 4: Multidimensional poverty measures for PSMI and controls and by gender and caste

Cut All PSMI Controls T-value %

difference

Off k H# A M0 H A M0 H A M0 for M0 in M0*

1 0.946 0.276 0.261 0.972 0.302 0.293 0.917 0.247 0.227 -6.574 29.3

2 0.849 0.301 0.256 0.901 0.321 0.289 0.792 0.277 0.219 -6.583 31.7

3 0.739 0.328 0.243 0.834 0.337 0.281 0.635 0.316 0.201 -7.051 39.9

4 0.590 0.367 0.216 0.685 0.372 0.255 0.486 0.359 0.175 -6.378 46.0

5 0.440 0.411 0.181 0.516 0.417 0.215 0.359 0.403 0.145 -5.210 48.5

6 0.307 0.462 0.142 0.385 0.458 0.177 0.222 0.471 0.104 -5.297 69.2

7 0.224 0.503 0.113 0.277 0.499 0.138 0.165 0.511 0.084 -4.062 64.0

8 0.144 0.553 0.080 0.175 0.550 0.096 0.111 0.559 0.062 -2.791 55.2

9 0.090 0.603 0.054 0.112 0.595 0.066 0.067 0.619 0.041 -2.334 61.6

10 0.055 0.650 0.036 0.069 0.636 0.044 0.040 0.676 0.027 -1.776 60.6

Female Male



1 0.990 0.327 0.917 0.227 -2.237 0.961 0.272 0.879 0.185 -6.797

2 0.981 0.327 0.792 0.219 -2.322 0.852 0.265 0.702 0.175 -6.717

3 0.942 0.322 0.635 0.201 -2.585 0.767 0.255 0.508 0.152 -7.140

4 0.783 0.294 0.486 0.175 -2.157 0.624 0.230 0.364 0.127 -6.652

5 0.628 0.257 0.359 0.145 -1.947 0.445 0.188 0.256 0.101 -5.323

6 0.473 0.212 0.222 0.104 -2.191 0.330 0.154 0.148 0.069 -5.263

7 0.343 0.166 0.165 0.084 -1.415 0.236 0.121 0.105 0.054 -4.302

8 0.184 0.100 0.111 0.062 -0.396 0.170 0.094 0.079 0.043 -3.438

9 0.116 0.068 0.067 0.041 -0.458 0.109 0.065 0.049 0.030 -2.752

10 0.068 0.043 0.040 0.027 -0.157 0.070 0.044 0.030 0.019 -2.266

SC/ST/OBC Other castes



1 0.987 0.320 0.972 0.280 -2.437 0.958 0.264 0.884 0.194 -5.532

2 0.942 0.317 0.900 0.276 -2.458 0.862 0.258 0.723 0.185 -5.510

3 0.863 0.308 0.783 0.262 -2.496 0.799 0.251 0.545 0.164 -6.097

4 0.748 0.288 0.628 0.235 -2.574 0.623 0.220 0.396 0.137 -5.246

5 0.606 0.254 0.494 0.203 -2.262 0.426 0.174 0.274 0.109 -3.927

6 0.460 0.211 0.306 0.148 -2.680 0.304 0.138 0.162 0.076 -3.843

7 0.336 0.168 0.233 0.122 -1.917 0.215 0.106 0.125 0.063 -2.788

8 0.217 0.118 0.161 0.092 -1.160 0.131 0.072 0.086 0.047 -1.809

9 0.133 0.079 0.100 0.064 -0.757 0.090 0.053 0.050 0.030 -1.864

10 0.075 0.048 0.061 0.043 -0.308 0.055 0.034 0.030 0.019 -1.459 Note: Rows 11–17 are omitted very few are deprived in more than 10 dimensions, no-one is deprived in more than 15 dimensions. #H is the percentage of the population that is poor

H=* . SD: Standard deviations. $ Adjusted Wald test for difference in adjusted headcount ratio between patients and controls. The average Poverty Gap (A) is not presented for gender and caste but can be easily calculated dividing the Adjusted Headcount (M0) by the headcount ratio (H)

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Table 5: Percentage contribution of each dimension to poverty for PSMI and controls for k 1 to 8

Cut Health Level of Access to Food Source of

Indoor air

Type of Type of Individual Crowded Housing Housing Assets Household Household Physical

Political

Off k access education employment security drinking water

quality sanitation lighting income space ownership quality ownership

income expenses safety Participation

1 PSMI 0.86 4.63 11.62 10.87 3.74 1.31 4.37 0.22 13.79 4.56 8.33 1.27 4.82 8.86 12.63 3.59 4.52

Controls 0.78 5.33 7.74 10.15 4.86 1.57 3.50 0.47 12.45 4.71 7.74 0.58 3.92 9.62 16.42 5.75 4.39

2 PSMI 0.87 4.70 11.79 10.58 3.75 1.33 4.32 0.23 13.91 4.62 8.04 1.29 4.89 8.95 12.62 3.60 4.51

Controls 0.76 5.41 8.00 9.68 5.03 1.62 3.62 0.49 12.43 4.86 7.19 0.59 4.05 9.95 16.43 5.57 4.32

3 PSMI 0.86 4.79 11.77 10.44 3.86 1.36 4.29 0.23 13.64 4.72 8.07 1.33 4.99 9.00 12.51 3.55 4.60

Controls 0.77 5.61 8.15 9.33 5.31 1.77 3.54 0.47 12.16 5.14 6.85 0.65 4.43 10.45 15.58 5.55 4.25

4 PSMI 0.95 4.94 11.05 10.49 3.91 1.46 4.47 0.26 12.77 4.99 7.78 1.46 5.42 9.42 12.55 3.57 4.51

Controls 0.68 5.77 7.95 8.83 5.57 1.90 4.01 0.54 11.14 5.57 6.66 0.75 4.82 10.80 14.95 5.50 4.55

5 PSMI 0.87 5.25 10.30 10.24 4.33 1.63 4.59 0.31 11.67 5.30 7.54 1.68 6.17 9.73 12.18 3.72 4.49

Controls 0.74 6.39 7.79 8.36 5.90 2.05 4.26 0.66 10.49 6.15 6.48 0.90 5.33 10.82 13.77 5.41 4.51

6 PSMI 0.99 5.46 9.86 9.99 4.65 1.86 5.09 0.25 11.10 5.58 6.95 2.05 6.45 9.74 11.85 3.66 4.47

Controls 0.80 7.05 7.27 7.50 6.59 2.73 4.66 0.91 9.32 7.05 6.59 1.25 6.36 9.89 12.05 5.57 4.43

7 PSMI 1.11 5.62 9.65 9.57 4.91 2.14 5.22 0.32 10.76 5.70 6.41 2.37 7.04 9.57 11.16 3.88 4.59

Controls 0.42 7.44 7.02 7.16 6.60 3.37 5.06 1.12 9.13 7.72 6.18 1.40 6.74 9.55 11.38 5.62 4.07

8 PSMI 0.91 5.23 8.65 9.22 5.46 2.62 5.80 0.34 9.90 6.37 7.05 2.73 7.96 8.76 10.35 3.98 4.66

Controls 0.38 7.07 6.12 6.88 6.88 4.21 5.93 1.34 8.22 7.65 6.12 1.91 7.65 9.56 10.52 5.54 4.02

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Table 6: Logistic model for association between multidimensional poverty, stigma and SMI

Unadjusted Model Adjusted Model OR (95%CI) OR (95%CI)

Family participation (no participation) 2.92 2.16-3.93 2.61 1.27-5.31 SMI (Controls) 2.20 1.67-2.89 2.07 1.25-3.41 Female (Male) 2.17 1.65-2.83 1.88 1.36-2.58 SC/ST/OBC (Higher caste) 2.06 1.56-2.70 2.39 1.39-4.08 Age (in year) 0.99 0.97-0.99 0.98 0.96-0.99 Interaction terms No participation*SMI (Participation*controls) 6.38 3.49-11.6 No participation*SC/ST/OBC (Participation*high caste) 4.86 2.19-10.7 No participation*women (Participation*men) 4.63 2.60-8.21 No participation*women*SMI (Participation*male*controls) 9.62 5.58-16.5 No participation*SC/ST/OBC*SMI(Participation*high caste*controls) 7.36 3.94-13.7

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i For vegan individuals, the diet staple included at least dal on a daily basis; for non-vegan individuals,

it included dairy products on a daily basis. Meat for non-vegetarian individuals was not considered as

a diet requirement and therefore deprivation of meat is not an indicator of poor diet. ii Assets include: Landline, mobile phones, wooden/steel sleeping cot, mattress, table, clock/watch,

charpoy, refrigerator, radio/transistor, electric fan, television, bicycle, computer,

moped/scooter/motorcycle, car.

iii Expenditures include: Food, health, school, transportation, savings and personal care products.

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Flow chart depicting enrollment of patients with mental illness and controls without mental illness. 69x35mm (300 x 300 DPI)

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1

STROBE Statement—Checklist of items that should be included in reports of case-control studies

Item

No

Recommendation

Where this is to be

found in our

submitted paper

Title and abstract 1 (a) Indicate the study’s design with a commonly used term in

the title or the abstract

See title and abstract

under ‘Design’ p.1

(b) Provide in the abstract an informative and balanced

summary of what was done and what was found

See abstract under

‘Results’ p.1

Introduction

Background/rationale 2 Explain the scientific background and rationale for the

investigation being reported

See ‘introduction’ pp.

1&2

Objectives 3 State specific objectives, including any prespecified hypotheses See ‘introduction’ p 2

Methods

Study design 4 Present key elements of study design early in the paper See ‘Study design and

setting’ p.3

Setting 5 Describe the setting, locations, and relevant dates, including

periods of recruitment, exposure, follow-up, and data collection

See ‘Study design and

setting’ p.3

Participants 6 (a) Give the eligibility criteria, and the sources and methods of

case ascertainment and control selection. Give the rationale for

the choice of cases and controls

See ‘Participants’ p.3

(b) For matched studies, give matching criteria and the number

of controls per case

See ‘Participants’ p.3

Variables 7 Clearly define all outcomes, exposures, predictors, potential

confounders, and effect modifiers. Give diagnostic criteria, if

applicable

See ‘Variables’ p.3

Data sources/

measurement

8* For each variable of interest, give sources of data and details of

methods of assessment (measurement). Describe comparability

of assessment methods if there is more than one group

See ‘Data sources’ p.4

Bias 9 Describe any efforts to address potential sources of bias See ‘Efforts to

minimize bias’ p.4

Study size 10 Explain how the study size was arrived at See ‘Sample size’ p.4

Quantitative variables 11 Explain how quantitative variables were handled in the

analyses. If applicable, describe which groupings were chosen

and why

See’ Quantitative

variables’ p.4

Statistical methods 12 (a) Describe all statistical methods, including those used to

control for confounding

See ‘Statistical

methods’ p. 5

(b) Describe any methods used to examine subgroups and

interactions

(c) Explain how missing data were addressed

(d) If applicable, explain how matching of cases and controls

was addressed

(e) Describe any sensitivity analyses

Results

Participants 13* (a) Report numbers of individuals at each stage of study—eg

numbers potentially eligible, examined for eligibility, confirmed

eligible, included in the study, completing follow-up, and

See ‘Participants’

p. 6

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analysed

(b) Give reasons for non-participation at each stage

(c) Consider use of a flow diagram

Descriptive data 14* (a) Give characteristics of study participants (eg demographic,

clinical, social) and information on exposures and potential

confounders


and figure 2 p. 6

(b) Indicate number of participants with missing data for each

variable of interest


and figure 1 p. 6

Outcome data 15* Report numbers in each exposure category, or summary

measures of exposure


and figures 1-3 p. 6

Main results 16 (a) Give unadjusted estimates and, if applicable, confounder-

adjusted estimates and their precision (eg, 95% confidence

interval). Make clear which confounders were adjusted for and

why they were included

See

‘Multidimensional

poverty’ and

‘Poverty and

stigma’, and tables

2 to 6

pp. 6-7

(b) Report category boundaries when continuous variables were

categorized

(c) If relevant, consider translating estimates of relative risk into

absolute risk for a meaningful time period

NA

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Other analyses 17 Report other analyses done—eg analyses of subgroups and interactions, and sensitivity analyses

Discussion

Key results 18 Summarise key results with reference to study objectives

Limitations 19 Discuss limitations of the study, taking into account sources of potential bias or imprecision.

Discuss both direction and magnitude of any potential bias

Interpretation 20 Give a cautious overall interpretation of results considering objectives, limitations, multiplicity

of analyses, results from similar studies, and other relevant evidence

Generalisability 21 Discuss the generalisability (external validity) of the study results

Other information

Funding 22 Give the source of funding and the role of the funders for the present study and, if applicable,

for the original study on which the present article is based

*Give information separately for cases and controls.

Note: An Explanation and Elaboration article discusses each checklist item and gives methodological background and

published examples of transparent reporting. The STROBE checklist is best used in conjunction with this article (freely

available on the Web sites of PLoS Medicine at http://www.plosmedicine.org/, Annals of Internal Medicine at

http://www.annals.org/, and Epidemiology at http://www.epidem.com/). Information on the STROBE Initiative is

available at http://www.strobe-statement.org.

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To The Editor

Ref: Our manuscript “Association between Mental illness, poverty and

stigma in India: A case control study ”

Response to reviewer’s comments.

Dear Editor,

Please find enclosed the revised version of our paper, entitled “Association

between Mental illness, poverty and stigma in India: A case control study ”

Thank you for giving us the opportunity to resubmit a revised manuscript to

British Medical Journal Open.

We are grateful for the positive comments of the reviewers. Indeed we

agree with them that this is an area, which needs a lot more research.

We addressed extensively all comments for improving the manuscript. Yet,

changes in the paper itself were necessarily brief due to word limits. If the

panel deems it necessary we can provide more details about methodology

in an online appendix.

In particular, we simplified tables, revised the methodology section for the

selection process of controls, and shortened the discussion.

Please find below after the reviewer’s comment our answers and then the

part of the manuscript with modified text in yellow highlights. We

extensively changed the discussion section as requested but it was too

large to include it in response to comments.

Thank you for considering our paper for possible publication in BMJO.

Yours Sincerely,

Jean-Francois Trani

Corresponding author

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Reviewer(s)' Comments to Author:

Reviewer Name R PADMAVATI

Institution and Country SCHIZOPHRENIA RESEARCH FOUNDATION

R/7A NORTH MAIN ROAD

ANNA NAGAR WEST EXTENSION

CHENNAI 600101

TAMILNADU, INDIA

Please state any competing interests or state ‘None declared’: NONE

DECLARED

General comment

The selection of the control group need more elaboration

The results could be better presented, especially the Tables

Discussions need to be made more succinct

Response to general comments

We are thankful to reviewer for these pointers. In the revised version of

the manuscript, we have addressed these general comments. We have

further elaborated on the selection of the control group as explained in

detail in response to comment 2 (para 2 of ‘participants’ in methods,

copied below). We have simplified the Tables and shortened the discussion.

‘We used a non-psychiatrically ill control group composed of randomly selected

individuals matching the patients according to gender, age (plus or minus 5 years) and

neighbourhood of residence. Matched controls were selected by spinning a pointer at the

door of the case’s home and randomly selecting one household among 30 in the pointed

direction. In this household a person of same age and gender with no reported history of a

mental health disorder was interviewed. It was not possible to conduct detailed

interviews (for diagnosis) of all controls due to logistics as well as stigma of

revealing mental illness. We excluded controls when unable to obtain consent. Only

two case patients were not matched. Investigators together with the team manager

contributed to sensitisation and awareness raising in the neighbourhoods of interest to

maximise controls’ participation rates. Due to logistics as well as stigma towards mental

illness, it was not possible to psychiatrically examine all controls.’

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Comment 1. Abstract : Acronyms which may not be understood by readers

outside India Eg : MPI ( Multidimensional poverty index), must be stated as

such .

Response to comment 1. We are thankful to the reviewer for identifying

this mistake: we checked and made sure that we specified the full form for

all acronyms. We have also included an appendix with terms and

abbreviations at the end.

Comment 2. Methods: How was the non-psychiatrically ill status of the

control group determined.

Response to comment 2. We could not establish the non-psychiatrically ill

status of the control group through detailed psychiatric interview, due to

logistics reasons of time, number of participants and stigma linked to

mental illness in New Delhi. This has been clarified in the method section as

well as in the discussion section under limitations. We inserted a revised

text as follows ( Methods- participants- para 2):

We used a non-psychiatrically ill control group composed of randomly selected

individuals matching the patients according to gender, age (plus or minus 5 years) and

neighbourhood of residence. Matched controls were selected by spinning a pointer at the

door of the case’s home and randomly selecting one household among 30 in the pointed

direction. In this household a person of same age and gender with no reported history of a

mental health disorder was interviewed. It was not possible to conduct detailed

interviews (for diagnosis) of all controls due to logistics as well as stigma of

revealing mental illness. We excluded controls when unable to obtain consent. Only

two case patients were not matched. Investigators together with the team manager

contributed to sensitisation and awareness raising in the neighbourhoods of interest to

maximise controls’ participation rates.

Comment 3. Efforts to minimize bias: Who reviewed the questionnaire

about “exposure to poverty to ensure accuracy, completeness”? Were any

potential subjects/controls involved in this process? This is important to

understand the sources of response errors.

Response to comment 3. We are grateful to the reviewer for bringing up

this issue. It would have been more accurate to mention “accuracy,

completeness and content validity” instead of “face validity”, and we

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modified the manuscript accordingly ( see “Efforts to minimize bias page 4

in methods section):

‘New patients were first interviewed by a junior psychiatrist who made a provisional

diagnosis and discussed details with a board of certified psychiatrist who then diagnosed

and managed the case. To minimise diagnosis bias, we trained all psychiatrists on the

ICD 10 criteria. Information bias was minimised by reviewing the questionnaire about

exposure to poverty to ensure accuracy, completeness and content validity with experts

from the department and by testing it with a sample group of patients and families.

Suggestions from the latter were incorporated’.

.’

We address the reviewer’s concerns in greater detail below. This

explanation was not included in the manuscript for obvious reasons of

word limit. To test the poverty questionnaire for accuracy, completeness

and content validity, investigators in New Delhi and at University College

London revised the content for relevance to poverty in order to maximize

item appropriateness. They first defined the concept of multidimensional

poverty and reviewed the empirical and theoretical literature to identify

the right deprivation items to include in the instrument they were

developing. They then checked if the questions covered all dimensions of

the concept of multidimensional poverty, if the phrasing respectively in

English and Hindi was accurately reflecting the underlying concept of

deprivation we were looking for in each dimension (see Table 1 for the

questions asked for various dimensions of poverty). Two experts familiar

with multidimensional poverty reviewed the initial list of items and made

suggestions about adding items that were omitted. We then organized a

focus group discussion with 7 experts, psychiatrists, psychologists and

social workers from Dr Ram Manohar Lohia hospital to establish if the 17

domains of poverty selected were adapted and relevant for the context of

New Delhi and were providing a comprehensive overview of the concept.

They also ranked these domains by order of importance of deprivation. A

similar focus group was organized with 8 hospital outpatients with severe

mental illness. We finally tested the poverty questionnaire with a group of

20 outpatients at the department of psychiatry at Dr RML hospital. We

prompted them with questions to check for their understanding of poverty,

to identify the language they used to explain the notion of poverty as well

as ascertain their understanding of the questions in order to make sure the

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instrument’s purpose made sense to them. Finally, two other experts

revised the final version to make sure items illustrate the content of

multidimensional poverty.

Comment 4. Did the researchers employ any other methods to “validate”

the tool ?

Response to comment 4. Besides the methods described above in response

to the comment 3, we also carried out test-retest to test for recall bias and

social desirability bias. Interviews with 71 respondents (both cases and

controls) for test-retest reliability were carried out on two occasions with a

gap of 10 to 15 days by the same enumerator to check to what degree a

given respondent provided same responses for the poverty items. We

compared the scores between the two sets of responses. Results show

overall acceptable level of reliability (over 0.7 for Inter class correlation) for

the different poverty variables. Due to constraints of space we did not

mention this in the manuscript, but can do so if the editor permits, or place

these results online.

Comment 5. What is the “22-items discrimination and stigma scale looking

at unfair treatment” This makes a sudden appearance. It need not be

mentioned if it is not used.

Response to comment 5. Thank you for this useful comment. We removed

the reference as requested. This scale is a measure of stigma that was

developed after our case control study was carried out. Nevertheless, we

tested it for content validity with patients and controls and found that it

was not adapted to the north Indian context.

Comment 6. Results: Sample – The age range is 7-77 (patients) and 12-

74(controls) – Why were children chosen to participate? Who signed the

consent form for the children below legal age? What was the nature of the

responses obtained in this group?

Response to comment 6. This is a typo and we are grateful for the reviewer

for identifying it. The younger case was 15 at the time. We did not

interview children below 15 among cases. 6 controls were between 13 and

15 because of our rule of age selection with a 5 years gap. Consent for

adolescent between 15 and 18 were asked from the parent or the legal

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guardian of the adolescent. We included adolescents because symptoms of

severe mental disorders often occur at this point in life. We explored

multidimensional poverty by age groups (i.e. below 25, 26-45, more than

45) and did not find any major differences. Considering that our paper is

already long and complex, we decided to not include additional analysis by

age group but can provide details on request.

Comment 7. Tables appear very complicated and crowded. Needs to be

represented.

Response to comment 7. We understand the concern of the reviewer. We

simplified the Tables as much as possible while keeping the statistical

information required.

Comment 8. Discussion would read better if made crisper. The implications

are not very clear and needs to be rewritten

Response to comment 8. We agree with the reviewer and we extensively

rewrote the discussion section. We tried more particularly to emphasise

the implications of our findings.

Reviewer Name Aliya Kassam

Institution and Country University of Calgary, Canada

Please state any competing interests or state ‘None declared’: None

declared

Please leave your comments for the authors below

General comment. This is an interesting paper. The authors have worked

hard to conduct the study given the conditions in India and the findings are

important. The abstract needs to be re-written and the word count

decreased to 4000 words. I also suggest having a glossary of terms for

abbreviations.

Response to comment. We thank the reviewer for considering that this

study is an important research. We rewrote the abstract and we reduced

the overall length of the paper which is now only 4369. We added a

glossary of terms for abbreviations.

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Reviewer Name Alexandre Andrade Loch

Institution and Country Institute of Psychiatry, University of Sao Paulo,

Brasil

Please state any competing interests or state ‘None declared’: None

declared.

Please leave your comments for the authors below

This is an interesting study assessing the relationship between mental

illness stigma and poverty in India. Results support this association, and

cover an important issue in an under researched area.

However, there seems to be several methodological questions regarding

the study.

Comment 1. First, the stigma outcome used by authors here is problematic.

Authors argue that this would be a proxy for experienced discrimination;

however, there might be several factors why someone with a mental illness

would not take part in family decisions. It could be, for example, that these

patients might be symptomatic (paranoid, unwilling, etc.), or that they

would have cognitive deficits, or too severe negative symptoms.

Nevertheless, authors in the discussion do not explore these possibilities.

Response to comment 1. We are aware of this limitation but under the

circumstances we believe that the variable of exclusion from family

decision is a good proxy for experienced discrimination. We explored how

this notion was perceived through focus group discussions with experts and

with patients. Results show that not taking part in important decisions is a

common way to express social distance within the family.

We did explore the possibility of using a stigma questionnaire, but could

not find one suited to North India and especially for the approach we took

to compare treated outpatients with SMI to controls from the community.

However we have removed the extra paragraph we put in about using a

stigma questionnaire, in view of the reviewer comment above as well as in

interest of reducing manuscript length. We address the esteemed

reviewer’s concerns in greater detail below.

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First, at that time there was no available scale to measure personally

experienced stigma in India for the type of approach we favoured of

comparing patients with mental illness to a control group in the general

population. We carefully explored the literature to identify an adequate

scale measuring experienced discrimination that could be adapted to the

context of Delhi. Among the various measures reviewed in (Link, et al.

2004), only those related to the fourth component of the Link and Phelan

(2001) conceptualization of stigma, i.e. the “person experience of status

loss and discrimination” ((Link and Phelan 2001) p.370) could have been

used for the purpose of the present paper. We explored all of them (22)

and chose to build our own questionnaire to measure experienced stigma

while we used the Stigma Questionnaire (SQ) for exploration of labelling

and stereotyping which has been validated in Hindi (Littlewood, et al.

2007). This decision was taken after we tested some of the questions from

existing scales that we initially thought culturally adapted to the specific

hospital context, to our specific population –outpatients with severe

mental illness from low social class background and to our aim of showing

the link between poverty, mental illness and experienced discrimination. In

fact, there were few suitable measures of “experienced discrimination”. For

instance, we looked at the Consumer Experiences of Stigma Questionnaire

(CESQ) (Wahl 1999). We found that the notion of being a consumer was not

adapted. Furthermore, several items within the questions of the

discrimination section Consumer Experiences of Stigma Questionnaire

(CESQ) were not deemed relevant to the context by the research team. For

instance, very few patients ever tried to rent an apartment; patients at Dr

RML hospital do not benefit from the health insurance system; very few

have a licence or a passport.

Following Link et al. (2004) we considered that “very few aim to assess

discriminatory behaviour directly” (p. 527) and measuring experienced

stigma is not really a stigma measure, but a measurement of whether and

to what extent persons with mental illnesses “are denied access to the good

things our society affords and are differentially exposed to the bad things it

confers”” (p. 527). For instance, Link et al. (2004) explain, the measures

used can assess discrimination in employment, access to medical

treatment, or being put down or ignored among other discriminatory

processes. The stigma researcher is then tasked with the work of linking the

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“bad outcome assessed” with “processes of labelling, stereotyping, and/or

being set apart”. S/he must “select outcome measures from the vast array

of possible good or bad outcome measures” and link this outcome “to the

theory being tested”. Link et al. (2004) review both “laboratory

experiments and behavioural measures indicating discrimination” and

“observational studies and behavioural measures indicating

discrimination”. There was not a specific formalized psychometrically

validated measure of experienced stigma available focusing “on the scope

and content of these experiences” before the Discrimination and Stigma

Scale (DISC) made available only recently and in any case after our study

was carried out (Brohan, et al. 2013), p. 33).

Second, we also tested the importance of participation in family decisions

through focus group discussions and in depth interviews with patients. We

proceeded as suggested by Link et al. (2004) identifying questions through

focus group discussions and in depth interviews with patients and experts,

then building and testing for reliability a non-experimental self reported

survey, looking at unfair treatment in various areas of everyday life as

reported by patients and controls. We then compared responses for these

questions: participation in family decisions, inclusion in community

activities, participation in political activities, participation on the labour

market, access to education. These questions were defined at hoc but were

influenced by existing scales particularly the Chinese Psychiatric Stigma

Experience Questionnaire (PSEQ) (Lee, et al. 2005). Two focus group

discussions and ten in depth qualitative interviews with adult out-patients

with severe mental illness demonstrated that participation in family

decisions made on a daily basis (decision for meals, visits to neighbours or

relatives, etc.), as well as for important matters (such as marriage of a child,

choice of a school or organisation of a ceremony) was a central indicator of

inclusion/exclusion for people with mental illness. Adult respondents

reported that they were excluded from these decisions: this was even more

the case for women and those persons with SMI with little education. Only

some of the respondents who were heads of family reported being the

ones taking the decisions. But in general, FGD participants and respondents

often internalised the stigma as they considered this absence of being

asked what they think as something if not totally acceptable, at least very

common and anticipated. To assess reliability, we asked follow up

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questions for each household member regarding whether they were

included in family decisions. We then found significant differences between

the two groups for participation in family decision.

Third, we selected a large sample of 649 hospital outpatients with severe

mental illness (diagnosis of schizophrenia, affective disorders and recurrent

depression from low social class background) in order to represent diverse

degrees of disorders. Most patients were in follow-up. All patients

benefited from medical assistance from the team at the hospital. The

patients included in our study were successfully treated: a rather large

group were working (29%). Even the schizophrenia group were, for the

most part either working or helping out at home. Some were not

symptomatic or were presenting limited cognitive deficits or moderate

negative symptoms associated to schizophrenia, few would present

relatively more severe symptoms. The fact that we interviewed a large

sample of people was a way to control for the specific role of the individual

diagnostic condition in explaining the discrimination faced within the

family. Despite the treatment of the mental illness, we found that

discrimination was significantly linked to poverty for cases compared to

controls.

Comment 2. Furthermore, the issue of diagnosis here is another problem.

There is a study conducted in China and in Japan (Haragucki et al, 2009)

where higher stigma levels in China were attributed to the fact that Chinese

individuals with mental illness might not be treated adequately due to

lacking outpatient mental healthcare services. This would lead to a negative

impression towards them. So, what was the severity of the participants’

mental illnesses in the study? Could this have influenced families’ opinion

towards them? We know that there are several degrees of severity of

bipolar disorder and schizophrenia. On the one hand we might see fully

functional individuals with one or the other disorder, while on the other

hand we also see patients whose social functioning are greatly

compromised. We also know that, for instance, violence of individuals with

schizophrenia is greater towards their closest relatives, particularly when

they are undertreated. These greatly influences stigma, but is not explored

nor discussed in the work.

Response to comment 2. We agree with the reviewer that, as shown in

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several cross-cultural studies in Asia, stigmatising attitudes vary depending

on the severity of the disorder (Griffiths, et al. 2006; Haraguchi, et al. 2009;

Kurihara, et al. 2000; Phillips, et al. 2000). As stated in response to

comment 1, we selected a large sample of 649 patients who were

successfully treated for various degrees of severity of mental illness. Thus

accessibility of treatment was not an issue in our sample. We do agree that

untreated patient would be more violent, symptomatic and therefore even

more exposed to discrimination and exclusion.

All patients were being treated for their condition at Dr Ram Manohar

Lohia hospital and were displaying limited degrees of disorders. The

hospital also dispenses essential medicines free of cost therefore

availability of medicines was not an issue in this sample. Due to the large

sample size we could not conduct more detailed assessment of symptoms,

through using a scale for example. We argue that the family’s attitude

reflects past experience with the patients’ disorders more than current

treated condition that allows the patient to function within the family.

Comment 3. Authors should state which were the Kappa values for the

other items of the scale. Also, they should explain why they maintained the

two items that displayed a rather low Kappa value.

Response to comment 3. We are sorry for the confusion we might have

introduced: The poverty questionnaire is not a scale but rather a

survey/series of questions examining the various domains of deprivation.

As a result, the responses that were captured in the scale did not vary since

the conditions did not change during the course of this study. Further,

calculating Kappa did not yield any significant information or change

regarding these conditions because this was not a RCT or introduced a

treatment that would alter their lifestyles/demographic/economic

information. To avoid confusion, we removed the sentence about the

Kappa measure (i.e. “It was pilot tested in the field and we validated the

measure of poverty using test-retest and inter-rater reliability measures.

The Kappa coefficient for both measures was between 0.5 and 1 for all

dimensions of poverty with two exceptions: food security (0.265) and

physical security (0.372)”.

The two questions represent important dimensions of deprivation and

were self-reported perception of food security and physical security. The

variation observed between the two interviews is probably due to a short-

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term variation (within the 10-15 days of the second interview) in the

severity of food or physical security deprivation. Deprivation of food

reflects a basic need as well as a basic capability that is central to survival.

Similarly, deprivation of physical security is very important in the context of

New Delhi, which is a major metropolis with a rather high crime rate. Often

families are afraid of potential violence against their members with mental

illness, as they are perceived more susceptible to violence, particularly

women.

Comment 4. Analyzes displayed in tables 4 and 5 are very interesting,

however they are way too extensive and leaves the reader tired and

disinterested. Authors should summarize their findings, or should find a

way to simplify their statistical analysis.

Response to comment 4. We understand the concern of the reviewer but it

is uneasy to remove such important information. In any case, we did try to

summarize the information as follow. Tables 4 and 5 were replaced by a

unique Table 4 displaying multidimensional poverty measures for PSMI and

controls and by gender and caste. We removed the information for a cut off

k superior to 10 indicators of deprivation considering that very few

respondents were deprived on more than 10 dimensions (2.6% only of total

respondents, both cases and controls were deprived on 11 dimensions or

more).

Comment 5. Table 3 should be excluded from the manuscript, authors

should provide it as supplemental material.

Response to comment 5. Thank you, we agree to this very practical

suggestion. Table 3 is provided as a supplemental material available online.

Comment 6. Table 7 is also too extensive. Authors should exclude the lines

with “1” values, perhaps placing the reference category in parenthesis, just

after the comparison variable.

Response to comment 6. We agree with the reviewer and we modified

Table 7, now Table 6, to take into account this valuable suggestion. We put

the reference category into parenthesis. We also reported just two models

(unadjusted versus adjusted) for the association between the indicator of

stigma and the multidimensional poverty.

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Comment 7. Discussion: authors fail to provide more in-depth discussion

regarding their results, and specially their limitations. Discussing the

possibilities of what not participating in family decisions might represent

and the issues point out above might produce a more balanced manuscript.

Response to comment 7. We rewrote the discussion to take into account

this comment. Limitations have now been described in greater detail.

Brohan, E., et al.

2013 Development and psychometric evaluation of the Discrimination and

Stigma Scale (DISC). Psychiatry Research 208(1):33-40.

Griffiths, K. M., et al.

2006 Stigma in response to mental disorders: A comparison of Australia

and Japan. BMC Psychiatry 6.

Haraguchi, K., et al.

2009 Stigma associated with schizophrenia: Cultural comparison of social

distance in Japan and China. Psychiatry and Clinical Neurosciences

63(2):153-160.

Kurihara, T., et al.

2000 Outcome of schizophrenia in a non-industrialized society:

Comparative study between Bali and Tokyo. Acta Psychiatrica Scandinavica

101(2):148-152.

Lee, S., et al.

2005 Experience of social stigma by people with schizophrenia in Hong

Kong. British Journal of Psychiatry 186(FEB.):153-157.

Link, B. G., and J. C. Phelan

2001 Conceptualizing stigma. Annual Review of Sociology 27:363-385.

Link, B. G., et al.

2004 Measuring mental illness stigma. Schizophrenia Bulletin 30(3):511-

541.

Littlewood, R., S. Jadhav, and A. G. Ryder

2007 A cross-national study of the stigmatization of severe psychiatric

illness: Historical review, methodological considerations and development of

the questionnaire. Transcultural Psychiatry 44(2):171-202.

Phillips, M. R., et al.

2000 Causes of Schizophrenia reported by patients' family members in

China. British Journal of Psychiatry 177(JUL.):20-25.

Wahl, O. F.

1999 Mental health consumers' experience of stigma. Schizophrenia

Bulletin 25(3):467-478.

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Mental illness, poverty and stigma in India: a case control study

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Transcript of Mental illness, poverty and stigma in India: a case control study