For peer review only Mental illness, poverty and stigma in India: A hospital based matching case control study Journal: BMJ Open Manuscript ID: bmjopen-2014-006355 Article Type: Research Date Submitted by the Author: 12-Aug-2014 Complete List of Authors: Trani, Jean-Francois; Washington University, Brown School Deshpande, Smita; Dr. Ram Manohar Lohia Hospital, Psychiatry & De- addiction Services 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 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 <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 BMJ Open on April 24, 2020 by guest. Protected by copyright. http://bmjopen.bmj.com/ BMJ Open: first published as 10.1136/bmjopen-2014-006355 on 23 February 2015. Downloaded from
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For peer review only
Mental illness, poverty and stigma in India: A hospital based
matching case control study
Journal: BMJ Open
Manuscript ID: bmjopen-2014-006355
Article Type: Research
Date Submitted by the Author: 12-Aug-2014
Complete List of Authors: Trani, Jean-Francois; Washington University, Brown School Deshpande, Smita; Dr. Ram Manohar Lohia Hospital, Psychiatry & De-addiction Services 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 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
<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
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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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 for multiple comparisons using Scheffe method.
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on April 24, 2020 by guest. Protected by copyright. http://bmjopen.bmj.com/ BMJ Open: first published as 10.1136/bmjopen-2014-006355 on 23 February 2015. Downloaded from
on April 24, 2020 by guest. Protected by copyright. http://bmjopen.bmj.com/ BMJ Open: first published as 10.1136/bmjopen-2014-006355 on 23 February 2015. Downloaded from
Note: Rows 16–17 are omitted no-one is deprived in more than 15 dimensions. Standard errors in parenthesis.su #H is the percentage of the population
That is poor H=*(�0���� −�0�� ����) (�0����)⁄ . SD: Standard deviations. $ Adjusted Wald test for difference in adjusted headcount ratio between patients and controls.
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on April 24, 2020 by guest. Protected by copyright. http://bmjopen.bmj.com/ BMJ Open: first published as 10.1136/bmjopen-2014-006355 on 23 February 2015. Downloaded from
on April 24, 2020 by guest. Protected by copyright. http://bmjopen.bmj.com/ BMJ Open: first published as 10.1136/bmjopen-2014-006355 on 23 February 2015. Downloaded from
Note: Rows 16–17 are omitted no-one is deprived in more than 15 dimensions. The average Poverty Gap (A) is not presented here but can be easily calculated
dividing the Adjusted Headcount (M0) by the headcount ratio (H). SD: Standard deviations. # Adjusted Wald test for difference in adjusted headcount ratio
between patients and controls.
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on April 24, 2020 by guest. Protected by copyright. http://bmjopen.bmj.com/ BMJ Open: first published as 10.1136/bmjopen-2014-006355 on 23 February 2015. Downloaded from
on April 24, 2020 by guest. Protected by copyright. http://bmjopen.bmj.com/ BMJ Open: first published as 10.1136/bmjopen-2014-006355 on 23 February 2015. Downloaded from
on April 24, 2020 by guest. Protected by copyright. http://bmjopen.bmj.com/ BMJ Open: first published as 10.1136/bmjopen-2014-006355 on 23 February 2015. Downloaded from
on April 24, 2020 by guest. Protected by copyright. http://bmjopen.bmj.com/ BMJ Open: first published as 10.1136/bmjopen-2014-006355 on 23 February 2015. Downloaded from
Mental illness, poverty and stigma in India: A case control
study
Journal: BMJ Open
Manuscript ID: bmjopen-2014-006355.R1
Article Type: Research
Date Submitted by the Author: 30-Dec-2014
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 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
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. 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 from the department and by testing it with a sample group of patients and families. Suggestions from the latter were incorporated40.
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
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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 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
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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 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.
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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 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, supplementary data file). 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.
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
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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 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
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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 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.
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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 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
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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, 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). 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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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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on April 24, 2020 by guest. Protected by copyright. http://bmjopen.bmj.com/ BMJ Open: first published as 10.1136/bmjopen-2014-006355 on 23 February 2015. Downloaded from
on April 24, 2020 by guest. Protected by copyright. http://bmjopen.bmj.com/ BMJ Open: first published as 10.1136/bmjopen-2014-006355 on 23 February 2015. Downloaded from
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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on April 24, 2020 by guest. Protected by copyright. http://bmjopen.bmj.com/ BMJ Open: first published as 10.1136/bmjopen-2014-006355 on 23 February 2015. Downloaded from
on April 24, 2020 by guest. Protected by copyright. http://bmjopen.bmj.com/ BMJ Open: first published as 10.1136/bmjopen-2014-006355 on 23 February 2015. Downloaded from
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 Delhi (NCR) (controls)New Delhi, 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. completed the survey
Main outcome measures – A hHigher risk of poverty measured on multiple dimensions 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 the an author designed Multidimensional poverty index (MPI) was 69% between groups with . Ememployment and income were the main contributors to the MPI. Multidimensional poverty was strongly associated with discrimination 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), SMI mental illness (2.07, 1.25-3.41), and female gender (gender (1.87, 1.36-2.58) and scheduled castes/scheduled tribes/ other backward castes (SC/ST/OBC) (2.39, 1.39-4.08). 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 sStigma and multidimensional poverty linked to SMI are strong predictor of poverty ppervasive and intertwined. . Public stigma of SMI, and Pparticularly 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 sense of burden for the family. Mental health professionals need to be aware of and address social and economic exclusion by promoting employment and fighting social stigma in the communitythese issues as well.
Article summary
Strengths and limitations of this study
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• There has beenis very little research done on the on the effects of stigma and poverty in developing settings
• Lack of employment and income are the major contributors to multidimensional poverty for PSMI
• Our findings support the hypothesis thatI intensity of multidimensional poverty is higher for PSMI, particularly women with SMI and those from SC/ST/OBC
• It is not possible toWe could not establish the direction of the association between poverty, and SMI
• Limitation: Stigma iswas operationalized through a single item question rather than a multiple-item scale and we could not assess reliability of this item. SMI is was measured diagnosed within afor persons attending a public psychiatric department;department; and PSMI not receiving medical treatment might be a more marginalised socially group and at greater risk of poverty than those receiving healthcare.
Introduction Mental health problems affects approximately 450 million people worldwide, 80% of whom live 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 disability 2, 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 international development and global health literature on various dimensions of poverty, and poor mental health6 and or disability7-9 is emerging, little has been done to examine the compounding associations between experienced stigma, (defined by unfair treatment or discrimination due to having a mental health issue)10, mental illness and poverty, especially in low-income countries. In high-income countries 11, income deprivation is identified as a major risk factor for persons with mental health issues, even for common mental disorders 12. Poor mental health linked to SMI has been associated with poverty during in the throes of 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 educated 16,
17. Indisputably, a better understanding of the relationship between mental illness and poverty could tailormay 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 resulting from five interrelated components: stigma is characterised by discrimination that occurs 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 in a context of social,
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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-esteem 21-24. S such Furthermore, stigma may prevent mentally ill persons from improving their conditions 25 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 experienced discrimination, prevalent across culturals contexts, 30, 31, is persistent in India 32. Although the factors that constitutinge poverty and discrimination linked to mental illness have the potentially can to deprive persons of a many multitude of resources 33, 34 the dynamics of poverty, discrimination and mental health have not been fully addressed. In The the clinical literature argues it is argued that stigma is caused by mental illness and treating the latter through biomedically approaches will weaken the associated stigma associated with it 35, 36. We argue instead that level of even treated PSMI are more likely to be multidimensionally poverty poor may be higher for SMI due to experienced discrimination resulting from stigma. Many studies have focused on uni-dimensional effect of poverty on mental health, but have not explained explicated how stigma towardsof 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 Many studies have focused on uni-dimensional effect of poverty on mental health, but have not explicated how stigma of mental illness can be an aggravating contributor to the intensity of poverty. and a comparable control group using a multidimensional poverty index (MPI) to explore deprivation in various dimensions of life 37. 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 then assessed differences in intensity of poverty between PSMI and controls and explored how thesesthese differences vary as a function of discrimination resulting from stigma. Many studies have focused on uni-dimensional effect of poverty on mental health, but have not explicated how stigma of mental illness can be an aggravating contributor to the intensity of poverty.
Methods
Study design and setting
The primary objective of the study was to assess differences in exposure to discrimination resulting from stigma and multidimensional poverty among cases compared with non non-psychiatrically ill controls. Between November 2 2011 and June 20 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 Ddepartment of Psychiatry at Dr RML hospital received respectively 10881 and
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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. and caters to It mainly serves patients from Delhi as well as and surrounding Indian statesthe national Capital Region of Delhi (NCR).
Participants
We defined cases as outpatients diagnosed either with schizophrenia or affective disorders by one of the 10 Board certified treating psychiatrists following ICD-10 criteria 38.They Outpatients were informed about the study and if they consented, were referred to researchers personnel for written informed consent and evaluation with no further contact with those who refused. We excluded cases when we could not obtain consent to participate. Transportation costs and a meal were provided to patients to maximise recruitment and reduce selection bias. We used a non-psychiatrically ill control group also composed of randomly selected individuals matching the patients according to gender, age (plus or minus 5 years) and by neighbourhood of residencey. It was not possible (nor would they have consented for the time) to individually interview each control. Each control family was asked for any contact with psychiatric services, which in Delhi are well distributed and well known. Using ‘the front door method,’ mMatched controls subjects were randomly locatedselected by spinning a pointer at the door of the subjectscase’s home, From the front door of the case’s house, we randomly selected a direction by spinning a pointer, and interviewed and randomly selecting one household among 30 in the pointed direction. In this household a person of same age and gender the a matching control in the closest household with no reported history of a mental health disorder was interviewed interviewed(nearest front door method).. We excluded controls when we were unable to obtain consent. and Oonly two case patients were not matched. Investigators together with the team manager contributed to sensitisation and awareness raising rising in the neighbourhoods of interest to maximise controls’ consent to participation ratese.
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 conditions, and social participation. The instrument was translated into Hindi with iterative back-translation methods and tested in a pilot survey in October 2011. Investigators trained 2 experienced supervisors and as well as 10 Masters-level students over two weeks on survey concepts and goals, mental illness awareness, interview techniques followed by review, test and debriefing. The primary objective of the study was to assess differences in exposure to discrimination resulting from stigma and multidimensional poverty among cases compared with non non-psychiatrically ill controls.
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
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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 a total of 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 psychiatrists who then diagnosed and managed and followed up the case. To minimise diagnosis bias associated with diagnosis, we repeatedly trained and informed all treating psychiatrists onf the ICD 10 criteria. Information bias was minimised by reviewing the questionnaire about exposure to poverty to ensure accuracy, completeness and face content validity with experts from the department and by testing it with a sample group of patients and families. Suggestions from the latter were incorporated40. Why have the reviewers asked repeatedly about this then? Please see my insertions above- SNDThe questioner 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).
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 came to a consensus about the deprivation cut-offs for each indicator through participatory deliberation 41. Some standard dimensions were not included due to lack of relevance in the context of Delhi. For instance, few a small proportion of respondents lacked did not have 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; and 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 . Respondents who had one or two meals a day were considered deprived of food security. Following the UNICEF definitions, iAccess to improved indoor air quality using by use of cooking gas, rather than wood or charcoal for cooking, improved source of drinking water by use of pipe into residence and improved sanitation by use of private flush toilet defined absence of deprivation for indicators six to eight . We used the UNICEF definitions in all three indicators to delineate deprivation cutoff.. Finally, individual income constituted a monetary indicator. Material wellbeing of the household was composed of two series of indicators. Three indicators reflected householdoutlined conditions of living: minimum space per person
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(deprivation threshold of 40 square feet per person);, home ownership (families who did not own their houserenting was the cut-off ) were considered deprived; poor quality of housing was defined as 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 based on a monthly household income (threshold at the international poverty line of US$1.25 US dollars per day or 68 Indian rupees)42;, assets included a list of typical goods owned by the householdii;, to complement the measure of income, weand assessed monthly household expendituresiii. Finally, two psychosocial indicators were selected: physical safety, was measured through an indicator of perception of unsafe environment and political participation in the municipal elections (Ttable 1). We measured experienced discrimination as a dimension of stigma through self-evaluation of unfair treatment by the family. 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 (?PSMI) if they were excluded from family decision in comparedison to other household members of the same generation in the household. Unfair treatment within family is has been shown to be a feature of stigma in the context of India44. We tested this idea through focus group discussionsFGDs with PSMI of both genders. We found a high association between SMI and exclusion from regular family decisions, particularly for women. We also measured Other dimensions of participation did not show any discriminatoryion’s process. Iinclusion in community activities and foundshowed a similar 30% levels of difference of participation between PSMI and controls. A possible explanation for participation is that where symptoms of mental illness are being managed by treatment, family developedevelopd coping stigma strategies through symbolic social participation and selective disclosure to avoid experiencing rejection, stigma blame and avoidance by others associated with their relative’s condition 45-47. Finally, we enquired about participation in political activities such as taking part in “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 focuseds 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-
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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 in order to be deemed multidimensionally poor. We firstly performed one-way analyses to assess for differences in level of poverty levels and discrimination between PSMI and controls, comparing by gender and caste adjusting . We adjustedfor 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) that indicatinges how many people fall below each deprivation cutoff; (ii) the average poverty gap (A) that denotinges the average number of deprivations that each person experiences; (iii) the adjusted headcount (M0) which is the headcount ratio (H) by the average poverty gap (A) and indicates the breadth or intensity of poverty. We established the contribution of each dimension of poverty for both subgroups –PSMIcases 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 the potential bias in our estimates of the 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 (see online appendix). We finally calculated the crude and adjusted odd ratios (OR) with associated 95% confidence intervals using a logistic regression model to identify the association between experienced discrimination as a component of stigma, SMI and multidimensional poverty. Studies in India have shown that stigma resulting in discriminatory practices is perceived to be high in the family and the community 42, 49. As a result, experienced discrimination was estimated in our study using participation in family decisions as a proxy.. and Wwe 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 thats of 33% of India’sn population livesing 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 in India, 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 we found robustness in our model (data not shown). For all analyses, a P-
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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 interrupted the interview before the end or for whom we had the missing data for variables of interest was incomplete., and Tthe final analysis included 537 cases and 496 controls (figure 1). The distribution between cases and patients controls was similar for gender (305 and 330 males respectively, 61.5% in both cases) and age (range 157-747 and 132-74 74 and median 36 35 and 36 35 respectively). Figure 1 approximately here. Table 2 reports the headcount ratios (H) or incidence of deprivation in each of the seventeen dimensions. There were statistically significantly higher numbers of deprived PSMI than controls in nine dimensions. Differences were appeared to be 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 ( ion 10 out of 17 dimensions). Similarly, a higher number of PSMI (respectively vs. controls) from ‘scheduled castes’, ‘scheduled tribes’ or ‘other backward castes’ (SC/ST/OBC) were poorer on 13 (respectively vs. 16 dimensions) compared to PSMI (respectively 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, supplementary data file). We found no evidence of strong correlation between dimensions, illustrating the 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, tThis result demonstrates that a unique welfare indicator of poverty such as income, cannot represent all aspects of deprivation. Table 3 approximately here.
Multidimensional poverty
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Results in Ttable 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. On average, PSMI were deprived on 5 dimensions and controls on 3.9; taking a union approach of deprivation in one dimension, this translates into quasi-universal poverty. 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, 16 or 15 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, the analysis was repeated for all possible cutoffs and for gender and caste (table 45). Multidimensional poverty was found to be 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 respectively 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. Tables 5 approximately here Table 56 presents the percentage contribution of each dimension to (M0) for different (k). Deprivations in terms of 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, access to employment was a less salient contributor while the contribution from household income was among the highest. Table 56 approximately here
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Association between multidimensional poverty and stigma was strong even when controlling for SMI, gender, caste and age (Table 67; 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 multidimensional 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 to13.7). 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 67 approximately here
Discussion Jean I think you would be the best person, who has an overview of the whole project to rewrite the discussion part. I have pasted the comments here for reference. I think we need to be less descriptive of what gender and caste/class mean and as usual focus only on what our results say. I have tried to address the comment about sigma above. 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. Furthermore, deprivation on dimensions of employment and income has been singled out as major contributors to the MPI. In deciphering multidimensional poverty, dDeprivation of employment and income needs to be integrated asappear to be major contributing factors to MPI. a factor that haves the potential to mitigate curb mental distress. , and Llack of employment and income appear to which may result in aggravateion or relapse of 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 improvementof the illness. 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. TheNot surprisingly, deprivation of employment and income highest contributioncontributes highly to multidimensional poverty of PSMI compared to controls. is for dimensions of employment and individual income Our study demonstrates the dynamic links between stigma, MI and poverty by focusing on how theis congruence of MI and poverty. in a context where Where pprejudice against MI is strong, it impacts various aspects a series of factors that constitute quality of life in a lower-income context. By Moreover, by looking at education, health, employment and social participation, we show that employment and the related income-generation constitute an important the first “entry point” that could respond to require pPolicy interventions in order tocould trigger a step change in the stigmatization process by simultaneously impacting these two key aspects that aeffect and reinforce the dynamics of stigma: and the associated discrimination/exclusion: self- stigma and as well as the role within social groupsdiscrimination within (family and community). This findings ties in with a study on Studies have established the importance
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of employment for Indian men with schizophrenia which found in Indian society that employment provided not just as an essential social role but was also as a condition for rehabilitation,, and enhancement of enhanced confidence and self- esteem 42. 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 countries 44, 57, 58. In our sample, women with SMI were systematically more deprived and on in a higher numbers of dimensions. Similarly, SC/ST/OBC SMI-poverty associations were found to be consistent across dimensions of poverty and regardless of the threshold for multidimensional poverty. These findings strongly suggest that when compounded, stigma linked to various marginalized social groups have the power to accelerate and intensify the dynamics of exclusion and related discrimination. For women, SMI can negatively impact wellbeing in two ways: simultaneously. Firstly, SMI limits women from fulfuilingfulfilling family and social roles, leading to these impedes functioning required for completion of social role and responsibilities and leads to women being considered a burden for the family unit. This is true despite studies, such as the A study the in Indian study ofon women with schizophrenia abandoned by their husbands showed that despite accusations of being useless by family members, many who expressed the desire to work to support themselves 59. Secondly, inherent traditional beliefs representations (punishment for previous lives, evil eye/curse) as well as negative lay attitudes lay beliefs resulting from the lack of knowledge on causes and behaviorsbehaviours treatment/therapies, lead to increased discrimination of and sometimes violence against SMIs, particularly for women 60. lead to higher discrimination of SMIs even compared to people with other types of sensory and physical forms of disability. A similar compounding effect of SMI is also reflected our findings on evident in the responses of SC/ST/OBC in this study. However, the modalities of social exclusion for these groups, unlike for women, also reside outside of the family within the wider community. The highest contribution to multidimensional poverty of PSMI compared to controls is for dimensions of employment and individual income. Studies have established the importance of employment for men in Indian society not just as an essential social role but also as a condition for rehabilitation and enhancement of confidence and self esteem 42. A study in India on women with schizophrenia abandoned by their husbands showed that despite accusations of being useless by family members, many express the desire to work to support themselves 58. 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 implementing quotas in public employment and educational institutions. Pervasive cCaste 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 India 61, 62. This situation is even more catastrophic for PSMI from SC/ST/OBC. Our study finds that caste and poverty further compounds SMI. The new Mental Health Care Bill of India, while laudable in its ground-breaking recognition of rights to self-
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determination and decision making for PSMI will need to more specifically address questions of how to access gainful employment for PSMI from low caste. It is clear that a ‘negative feedback loop’ exists: public stigma against of SMI, particularly for SC/ST/OBC and women, is a strong predictor of persistant persistent ence of poverty. Moreover, stigma strongly bears on the intensity of poverty. Within the family, if This Sstigma leads to beliefs that PSMI have difficulty for PSMI in finding and keeping a job, and this may also increases result in a continuing cycle of lack of employment opportunities and, when associated with the perceived burden of SMI by the family members. , with SMI, subsequently intensify poverty. In 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. In addition, studies have demonstrated that public stigma operating in wider spheres is also conducive to self-stigma and the resulting low self-esteem and self-efficacy, causing a weakening of ability as well as acceptance of discriminatory attitudes 61. Examples from the Chinese cultural context have shown that the whole family can be stigmatized and in reaction attempt to hide the illness and result in mistreating or discriminating the PSMI The label of Mmental illness in countries like India is also linked to lack of knowledge resulting in and pervasive negative expectations assumptions, the most common being that PSMI are violent and unable to work 18, 31, 42. 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. Furthermore, recent research has shown that efficient anti-stigma interventions must target local communities where PSMI live and experience public stigma and discrimination. This lack of understanding of the condition and treatment has led to validation and perpetuation of social exclusion. 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,. Iin particular, symptomatic patients’ disruptive behavior can make difficult any family interaction. 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 most of these outpatients were treated, and therefore not symptomatic at the time of the survey, as well as presenting limited cognitive deficits or moderate negative symptoms associated to schizophrenia. 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. Each and every individual described in the study was not interviewed individually. Due
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to stigma, denial or ignorance, controls may have concealed knowledge of family members psychiatric illness. ItSecondly, it was not possible to establish the direction of the association between poverty, and SMI; as poverty can be a cause as well as a consequence of SMI. SecondlyThirdly, SMI is was measured diagnosed within a psychiatric department of a free government hospital setting. There is some Rresearch that indicates that the poorest members of society may still not access such services, even when free; this may possibly introducing e aa selection bias in our sample 70. Additionally, PSMI not receiving medical treatment might be a even more marginalised social group, and atgroup, at greater risk of poverty than those receiving healthcare.;, Tthus 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 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 for mental health professionals by advocating for the requirement to must incorporate an understanding of stressors from multidimensional poverty stressors as well as and view wellbeing by including address family and community dynamics. W In a low/middle income country like India, where resources are limited, medical professionals would benefit from working with public health and disability networks to weaken persistent stigma and create visibility for againtagainst SMI in low-income communities. Policies promoting employment support of all kinds for PSMI (notably through reservations or fair employment policies, and access to credit) are critically important.most needed. Finally, Tthe implications of our findings go beyond the medical and public health fields and may provide some insights into questions linked to mental health into international development. SMI is a central issue Promoting employment and fighting social stigma forstigma for PSMI not only mitigates the impact of illness for some but appears to be for global health but also needs to become 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, 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.
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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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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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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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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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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
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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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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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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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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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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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