BMJ Open...Ram Manohar Lohia Hospital; Nagendra N. Mishra, research associate, Dr. Ram Manohar Lohia...

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

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

study

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

Narayanan#; Hemalatha Venkataraman#; Nagendra N. Mishra #; Nora E. Groce; Sushrut

Jadhav+

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; Smita Deshpande, Head, Department Of

Psychiatry & De-addiction Services & Resource Centre for Tobacco Control, PGIMER-

Dr. Ram Manohar Lohia Hospital, New Delhi, India; 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.

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

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[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: 4900

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Abstract

Objective – To assess the effect of stigma on poverty of persons with severe mental

illness (PSMI).

Design – Matching Case (hospital) control (population) study.

Setting - New Delhi India.

Participants 647 cases diagnosed with schizophrenia or affective disorders and 647 age,

sex and location of residence matched controls completed the survey.

Main outcome measures – A 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. The difference in the MPI was 69% between groups. Employment and

income were the main contributors to the MPI. Multidimensional poverty was strongly

associated with discrimination (odds ratio [OR] 2.60, 95% CI 1.27-5.31), SMI (2.07,

1.25-3.41), female gender (1.87, 1.36-2.58) and scheduled castes/scheduled tribes/ other

backward castes (SC/ST/OBC) (2.39, 1.39-4.08).

Conclusions – Public stigma and multidimensional poverty linked to SMI are pervasive

and intertwined. Public stigma of SMI, particularly for low caste and women, is a strong

predictor of poverty. Mental health professionals need to be aware of and where possible,

address social and economic exclusion by promoting employment and fighting social

stigma in the community.

Article summary

Strengths and limitations of this study

• There has been very little research done on the effects of stigma and poverty in

developing settings

• Our findings support the hypothesis that intensity of multidimensional poverty is

higher for SMI, particularly women with SMI and those from SC/ST/OBC

• It is not possible to establish the direction of the association between poverty, and

SMI.

• SMI is measured within a public psychiatric department and PSMI not receiving

medical treatment might be a more marginalised social group and at greater risk

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of poverty than those receiving healthcare.

Introduction

Mental health affects approximately 450 million people worldwide, 80% 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 5.

While the international development and global health literature on various dimensions of

poverty and poor mental health6 or disability

7-9 is emerging, little has been done to

examine the association between experienced stigma, defined by unfair treatment or

discrimination due to having a mental health issue10

, mental illness and poverty,

especially in low-income countries. In high-income countries 11

, income deprivation is

identified as a major risk factor, even for common mental disorders 12

. Poor mental

health linked to SMI has been associated with poverty in the throes of the recent

economic crisis in middle and low-income countries, particularly India and China 13-15

.

People with common mental disorders living in these countries are not only poorer, but

also unemployed and less educated 16, 17

. Indisputably, a better understanding of the

relationship between mental illness and poverty could tailor public health interventions to

complement biomedical treatment to improve outcomes.

Link and Phelan (2001) defined stigma as a process 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 in a context of social, economic and

political power difference 18

. Thornicroft et al. (2007) identify three elements of stigma:

ignorance or misinformation, prejudice and discrimination19

. Our paper focuses on the

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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

. 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 cultural contexts 30, 31

, is persistent in India 32

. Although

the factors that constitute poverty and discrimination linked to mental illness have the

potential to deprive persons of a multitude of resources 33, 34

the dynamics of poverty,

discrimination and mental health have not been fully addressed. In the clinical literature it

is argued that stigma is caused by mental illness and treating the latter through

biomedical approaches will weaken the stigma associated with it 35, 36

. We argue that

level of multidimensional poverty may be higher for SMI due to experienced

discrimination resulting from stigma.

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 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 assess differences in intensity of poverty between SMI

and controls and how theses 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

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Between November 2 2011 and June 20 2012, we carried out a case-control study at the

Department of Psychiatry of the Dr Ram Manohar Lohia (RML) Hospital in New Delhi,

and in the neighbourhood of residence of the cases 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

existing patients in 2012 and 2013. The department has also a 42 bed general psychiatry

& de-addiction inpatient facility for men and women and caters to patients from Delhi as

well as surrounding Indian states.

Participants

We defined cases as outpatients diagnosed either with schizophrenia or affective

disorders by one of the 10 treating psychiatrists following ICD-10 criteria 38

.They were

informed about the study and if they consented were referred to research personnel for

written informed consent and evaluation. 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 residency. From the front

door of the case’s house, we randomly selected a direction by spinning a pointer, and

interviewed a matching control in the closest household (nearest front door method). We

excluded controls when we were unable to obtain consent and only two case patients

were not matched. Investigators together with the team manager contributed to

sensitisation and awareness rising in the neighbourhoods of interest to maximise controls’

consent to participate.

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,

livelihood 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 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

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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

true odds ratio for 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 viewed by a junior psychiatrist who made a provisional diagnosis,

discussed details with a board of certified psychiatrists who then managed and followed

up the case. To minimise bias associated with diagnosis, we repeatedly trained and

informed all treating psychiatrists of the ICD 10 criteria. Information bias was minimised

by reviewing the questionnaire about exposure to poverty to ensure accuracy,

completeness and face validity. 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 came to a consensus about the

deprivation cutoff for each indicator through participatory deliberation 40

. Some standard

dimensions were not included due to lack of relevance in the context of Delhi. For

instance, a small proportion of respondents did not have access to diet staplesi.

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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 cutoff. Unemployment was a major source of vulnerability; deprivation of work was

the cutoff. Food security was measured by access to three meals per day. Respondents

who had one or two meals a day were considered deprived of food security. Access to

improved indoor air quality 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 household conditions of living: minimum space per person

(deprivation threshold of 40 square feet per person), families who did not own their house

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 average per capita income

based on a monthly household income (threshold at the international poverty line of 1.25

US dollars per day or 68 Indian rupees)41

, assets included a list of typical goods owned by

the householdii, to complement the measure of income, we 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 (table 1).

We measured experienced discrimination as a dimension of stigma through self-

evaluation of unfair treatment by the family. We asked respondents if they were excluded

from family decision in comparison to other members of the same generation in the

household. Unfair treatment within family has been shown to be a feature of stigma in the

context of India42

. We tested this idea through focus group discussions with PSMI of both

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gender. We found a high association between SMI and exclusion from regular family

decisions, particularly for women. We also measured inclusion in community activities

and found a similar 30% level of participation between PSMI and controls. A possible

explanation for participation is that symptoms of mental illness being managed by

treatment, family developed coping stigma strategies through symbolic social

participation and selective disclosure to avoid experiencing rejection, blame and

avoidance by others associated with their relative’s condition 43-45

. 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.

We did not use the 22-items discrimination and stigma scale looking at unfair treatment.

Qualitative interviews with PSMI showed that the scale was not adapted to our

population study. First, some items were not relevant to the cultural context of India, such

as item 3 (have you been treated unfairly in dating or intimate relationships) and 17 (have

you been treated unfairly in your levels of privacy). Second, we faced some difficulty

translating the notion of “fairness” in Hindi; we translated unfairly in Hindi using the

word “anuchit” (not appropriate) which was not well understood. We then used the idea

of being treated “not the same” than other members of the same generation in the

household, and asked for examples to make sure we agreed on the definition. We found

that respondents were often reinterpreting the fact that they were treated differently as

something acceptable considering their condition, therefore showing high internalization

of stigma.

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 controls 46

. Dimensions were

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independently assessed and the method focuses 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 in order to be deemed

multidimensionally poor.

We firstly performed one-way analyses to assess for differences in level of poverty and

discrimination between PSMI and controls, comparing by gender and caste. We adjusted

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)

that indicates how many people fall below each deprivation cutoff; (ii) the average

poverty gap (A) that denotes the average number of deprivations that each person

experiences; (iii) the adjusted headcount (M0) 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 –PSMI 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 weights 47

; (iii) Group ranking based on the mean of individual

rankings of indicators during FGDs and taking the weight according to the group ranking

48. 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

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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 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 Planning Commission50

which has been criticised for being unrealistic. This cutoff is in line with World Bank

recent estimates of 33% of Indian population living below the international poverty line

established at 1.25 dollars per capita per day51

. We characterised how SMI results in

higher intensity of multidimensional poverty due to stigma. Aware that stigma and

discrimination may also affect women52

and members of lower castes53

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-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 interrupted the interview before the end or

for whom we had missing data for variables of interest, and the final analysis included

537 cases and 496 controls (figure 1). The distribution between cases and patients was

similar for gender (305 and 330 males respectively, 61.5% in both cases) and age (range

7-77 and 12-74 and median 36 and 35 respectively).

Figure 1 approximately here.

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Table 2 reports the headcount ratios (H) or incidence of deprivation in each of the

seventeen dimensions. There were statistically significantly higher number of deprived

PSMI than controls in nine dimensions. Differences 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 on 10 out of 17 dimensions. Similarly, a

higher number of PSMI (respectively controls) from ‘scheduled castes’, ‘scheduled

tribes’ or ‘other backward castes’ (SC/ST/OBC) were poorer on 13 (respectively 16

dimensions) compared to PSMI (respectively 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 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

surveys54, 55

. 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

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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 (respectively poor controls) becomes 68.5% (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 5). 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

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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 6 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.


Poverty and stigma

Association between multidimensional poverty and stigma was strong even when

controlling for SMI, gender, caste and age (Table 7; 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.


Discussion

Our findings establish that intensity of multidimensional poverty is higher for SMI. 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

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as major contributors to the MPI. In deciphering multidimensional poverty, deprivation of

employment and income needs to be integrated as a factor that has the potential to curb

mental distress, and lack of which may result in aggravation 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.

Our study demonstrates the dynamic links between stigma, MI and poverty by focusing

on how this congruence of MI and poverty in a context where prejudice against MI is

strong, impacts various aspects that constitute quality of life in a lower-income context.

Moreover, by looking at education, health, employment and social participation, we show

that employment and the related income-generation constitute the first “entry point” that

require policy interventions in order to trigger a step change in the stigmatization process

by simultaneously impacting the two aspects that effect and reinforce the dynamics of

stigma and the associated discrimination/exclusion: self stigma as well as the role within

social groups (family and community).

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 42, 56, 57

. In our sample women with SMI were

systematically more deprived and on a higher number 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 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 impedes functioning required for completion of social role and responsibilities and

leads to women being considered a burden for the family unit. Secondly, inherent

traditional representations (punishment for previous lives, evil eye/curse) as well as lay

beliefs resulting from the lack of knowledge on causes and treatment/therapies, lead to

higher discrimination of SMIs compared to sensory and physical forms of disability. A

similar compounding effect of SMI is also evident in the responses of SC/ST/OBC.

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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

. 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 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 India 59, 60

. The new Mental Health Care Bill of India, while

laudable in its ground-breaking recognition of rights to self-determination and decision

making for PSMI will need to more specifically address questions of how to access

gainful employment.

It is clear that public stigma of SMI, particularly for SC/ST/OBC and women, is a strong

predictor of persistence of poverty. Moreover, stigma strongly bears on the intensity of

poverty. Within the family, if stigma leads to beliefs that PSMI have difficulty in finding

and keeping a job, this may result in a continuing cycle of lack of employment

opportunities and, when associated with the perceived burden of the family member with

SMI, subsequently intensify poverty. In turn, this deprivation on various dimensions

erodes self-esteem, brings shame and 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

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reaction attempt to hide the illness and result in mistreating or discriminating the PSMI 62-

64. The label of mental illness in countries like India is linked to lack of knowledge

resulting in pervasive negative expectations, 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 or

embarrassed and unable to cope with the stigma 58, 65, 66

. While there have been

campaigns and policies to address discrimination against SC/ST/OBC and women, no

large-scale awareness campaign has ever addressed the prejudice and discrimination

faced by PSMI. 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. 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. Secondly, SMI is measured within a psychiatric department of a free government

hospital setting. There is some research that indicates that the poorest members of society

may still not access such services, even when free; this may introduce a selection bias in

our sample 67

. Additionally, PSMI not receiving medical treatment might be a more

marginalised social group and 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.

Our study provides evidence for mental health professionals by advocating for the

requirement to incorporate an understanding of stressors from multidimensional poverty

and view wellbeing by including family and community dynamics. 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 SMI in low-income communities. Policies promoting

employment support of all kinds (notably through reservations or fair employment

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policies, and access to credit) are most needed. Finally, the implications of our findings

go beyond the medical and public health fields and may provide some insights into

questions linked to mental health in international development. SMI is a central issue for

global health but also needs to become a central concern of global poverty.

Contributorship statement.

The study was designed by JFT, SD, PB and SJ. The data collection process was

supervised by SV, NM, SN and SD. The literature review was done by PB with JFT. Data

analysis was carried out by JK and JFT. Data interpretation and data writing were

elaborated by JFT and PB. All authors contributed to the final version of the manuscript.

Competing interests

We declare that we have no conflict of interest.

Ethics committee approval

The study was approved by the University College London Research Ethics Committee

and the Dr Ram Manohar Lohia Hospital Institutional Ethics Committee.

Funding

This project was funded by DFID through the Cross-Cutting Disability Research

Programme, Leonard Cheshire Disability and Inclusive Development Centre, University

College London (GB-1-200474).

The sponsors of this study had no role in study design, data collection, data analysis, data

interpretation or writing of the report, or in the decision to submit for publication.

The corresponding author had full access to all the data in the study and had final

responsibility for the decision to submit for publication.

Data sharing

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Technical appendix, statistical code, and dataset available from the corresponding author

at Dryad repository, who will provide a permanent, citable and open access home for the

dataset.

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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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1

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

Item

No

Recommendation

Where this is to be

found in our

submitted paper

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

the title or the abstract

See title and abstract

under ‘Design’ p.1

(b) Provide in the abstract an informative and balanced

summary of what was done and what was found

See abstract under

‘Results’ p.1

Introduction

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

investigation being reported

See ‘introduction’ pp.

1&2

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

Methods

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

setting’ p.3

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

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

See ‘Study design and

setting’ p.3

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

case ascertainment and control selection. Give the rationale for

the choice of cases and controls

See ‘Participants’ p.3

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

of controls per case


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

confounders, and effect modifiers. Give diagnostic criteria, if

applicable

See ‘Variables’ p.3

Data sources/

measurement

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

methods of assessment (measurement). Describe comparability

of assessment methods if there is more than one group

See ‘Data sources’ p.4

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

minimize bias’ p.4

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

Quantitative variables 11 Explain how quantitative variables were handled in the

analyses. If applicable, describe which groupings were chosen

and why

See’ Quantitative

variables’ p.4

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

control for confounding

See ‘Statistical

methods’ p. 5

(b) Describe any methods used to examine subgroups and

interactions

(c) Explain how missing data were addressed

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

was addressed

(e) Describe any sensitivity analyses

Results

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

numbers potentially eligible, examined for eligibility, confirmed

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

See ‘Participants’

p. 6

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2

analysed

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

(c) Consider use of a flow diagram

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

clinical, social) and information on exposures and potential

confounders


and figure 2 p. 6

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

variable of interest


and figure 1 p. 6

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

measures of exposure


and figures 1-3 p. 6

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

adjusted estimates and their precision (eg, 95% confidence

interval). Make clear which confounders were adjusted for and

why they were included

See

‘Multidimensional

poverty’ and

‘Poverty and

stigma’, and tables

2 to 6

pp. 6-7

(b) Report category boundaries when continuous variables were

categorized

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

absolute risk for a meaningful time period

NA

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3

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

Discussion

Key results 18 Summarise key results with reference to study objectives

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

Discuss both direction and magnitude of any potential bias

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

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

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

Other information

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

for the original study on which the present article is based

*Give information separately for cases and controls.

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

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

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

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

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

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

without mental illness.

Patients with mental illness (n=647) Controls matching in gender, age and residency (n=647)

Incomplete interview (n=110)

Patients with complete interview (n=537)

Excluded (17%)

Controls with complete interview (n=496)

Excluded (23%)

Incomplete interview (n=151)

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Table 1: Dimensions, Indicators and cutoff of deprivation

Dimensions Indicators Questions Cutoff

Individual level basic capabilities

Health access Could you receive healthcare when

sick? Deprived of healthcare

Education What is your level of education? Primary education completed

Access to

employment What is your usual primary

activity? Not working

Food Security How many meals are usually

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

Source of drinking

water What is the primary source of

drinking water?

Pipe outside home/public pump

tanker truck/cart with small tank

water from a covered well unprotected well

spring/river/dam/lake/pond/stream

Indoor air quality What is the primary source of

cooking fuel?

Wood, coal/charcoal, dung, kerosene,

straw/shrubs/grass/crop

Type of sanitation What type of toilet facilities do

you use when at home?

Open field, pit latrine improved ventilated pit

public latrine

Type of lighting What is your primary source of

lighting? Generator, kerosene lamp, petromax, candle, none

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

Household level material wellbeing

Crowded space How many people live in the

dwelling? Less than 50sqfeet per

person

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

Housing quality Are the material used for walls, floor and roof in your house

kutcha or pucca ?

Any of walls, floor or roof is kutcha

Assets ownership

Do you possess any of the following? Mobile phone,

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

charpoy, refrigerator, radio/transistor, electric fan, television, bicycle, computer, moped/scooter/motorcycle, car

Lowest two asset quintiles

Household per capita

income What is the family income?

Less than $1.25 per capita per day

Household expenditures

What is the household's monthly expenditure ?


Individual level psychosocial dimensions

Physical safety How safe is the place where you

live? Rather or very unsafe

Political participation Did you vote in the last municipal

election? Did not vote

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

Dimension PSMI

n=647

control

n=649

p

value

Male

PSMI

(n=411)

Male

Controls

(n=408)

p

value

Other

Castes

PSMI

Other

castes

Controls

p

value

Female

PSMI

(n=238)

Female

Controls

(n=238)

p

value

ST/SC/

OBC

PSMI

ST/SC/

OBC

Controls

p

value

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Discrimination in family

decisions 178 (27.4) 116 (17.9) <0.0001

71 (17.3) 12 (2.9) <0.0001

92 (26.1) 71 (17.9) 0.042

107(45.0) 104 (43.7) 0.988

78 (28.5) 43 (17.7)

0.020

Note: missing values are missing completely at random and there was no significant statistical difference. Incidence of poverty expressed as a percentage is given in brackets. All P

value for multiple comparisons using Scheffe method.

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Table 3 Spearman correlations between dimensions

Dimensions

Health

access Educ.

Access to

work

Food

Security

Source of

water

Air

quality

Type of

sanitation

Type of

lighting

Ind.

income

Crowded

space

Housing

ownership

Housing

quality

Assets

owner-

ship

HH/cap.

income

HH

spending

Physical

safety

Pol.

Partici-

pation

Health access 1

Education 0.021 1

Access to work 0.1047* 0.1771* 1

Food Security 0.0016 0.1309* 0.0878* 1

Source of water -0.0277 0.1669* 0.0412 0.1263* 1

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

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

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

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

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

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

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

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

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

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

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

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

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Table 4: Multidimensional poverty measures for PSMI and controls

Cut All PSMI Controls T-

value %

difference

off H# SD A SD M0 SD H SD A SD M0 SD H SD A SD M0 SD for M0$

in M0*

1 0.946 0.227 0.276 0.157 0.261 0.165 0.972 0.165 0.302 0.154 0.293 0.160 0.917 0.276 0.247 0.156 0.227 0.164 -6.574 29.3

2 0.849 0.358 0.301 0.147 0.256 0.173 0.901 0.299 0.321 0.144 0.289 0.167 0.792 0.406 0.277 0.148 0.219 0.173 -6.583 31.7

3 0.739 0.440 0.328 0.138 0.243 0.187 0.834 0.372 0.337 0.137 0.281 0.177 0.635 0.482 0.316 0.139 0.201 0.188 -7.051 39.9

4 0.590 0.492 0.367 0.129 0.216 0.206 0.685 0.465 0.372 0.127 0.255 0.202 0.486 0.500 0.359 0.132 0.175 0.202 -6.378 46

5 0.440 0.497 0.411 0.120 0.181 0.219 0.516 0.500 0.417 0.115 0.215 0.224 0.359 0.480 0.403 0.127 0.145 0.208 -5.210 48.5

6 0.307 0.461 0.462 0.109 0.142 0.222 0.385 0.487 0.458 0.104 0.177 0.232 0.222 0.416 0.471 0.119 0.104 0.204 -5.297 69.2

7 0.224 0.417 0.503 0.101 0.113 0.215 0.277 0.448 0.499 0.095 0.138 0.229 0.165 0.372 0.511 0.113 0.084 0.195 -4.062 64

8 0.144 0.352 0.553 0.094 0.080 0.198 0.175 0.380 0.550 0.084 0.096 0.212 0.111 0.314 0.559 0.109 0.062 0.179 -2.791 55.2

9 0.090 0.286 0.603 0.086 0.054 0.175 0.112 0.315 0.595 0.074 0.066 0.189 0.067 0.249 0.619 0.104 0.041 0.157 -2.334 61.6

10 0.055 0.228 0.650 0.080 0.036 0.150 0.069 0.254 0.636 0.068 0.044 0.162 0.040 0.197 0.676 0.096 0.027 0.135 -1.776 60.6

11 0.026 0.160 0.719 0.068 0.019 0.115 0.028 0.165 0.706 0.054 0.020 0.117 0.024 0.154 0.735 0.081 0.018 0.114 -0.268 10.8

12 0.018 0.134 0.749 0.058 0.014 0.101 0.019 0.135 0.735 0.042 0.014 0.100 0.018 0.134 0.765 0.072 0.014 0.103 0.029 -1.3

13 0.009 0.093 0.797 0.052 0.007 0.074 0.007 0.086 0.779 0.029 0.006 0.067 0.010 0.100 0.812 0.064 0.008 0.081 0.514 -29.1

14 0.003 0.054 0.863 0.034 0.003 0.046 0.002 0.043 0.824 . 0.002 0.036 0.004 0.063 0.882 0.000 0.004 0.056 0.699 -56.9

15 0.002 0.044 0.882 0.000 0.002 0.039 0 0 . . 0 0 0.004 0.063 0.882 0.000 0.004 0.056 1.473 29.3

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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Table 5: Multidimensional poverty measures for SMI and controls by gender and by caste

Female Male

Patients Controls

Patients Controls

Cut off H SD A SD M0 SD H SD A SD M0 SD T value

for M0# H SD A SD M0 SD H SD A SD M0 SD

T value

for M0

1 0.9900.0980.330 0.138 0.3270.141 0.917 0.276 0.247 0.156 0.227 0.164 -2.237 0.9610.195 0.283 0.162 0.272 0.168 0.879 0.327 0.211 0.145 0.185 0.152 -6.797

2 0.9810.1380.333 0.136 0.3270.142 0.792 0.406 0.277 0.148 0.219 0.173 -2.322 0.8520.356 0.312 0.149 0.265 0.177 0.702 0.458 0.249 0.138 0.175 0.162 -6.717

3 0.9420.2340.342 0.131 0.3220.150 0.635 0.482 0.316 0.139 0.201 0.188 -2.585 0.7670.424 0.333 0.141 0.255 0.188 0.508 0.501 0.299 0.131 0.152 0.177 -7.140

4 0.7830.4130.375 0.118 0.2940.187 0.486 0.500 0.359 0.132 0.175 0.202 -2.157 0.6240.485 0.369 0.133 0.230 0.207 0.364 0.482 0.348 0.126 0.127 0.184 -6.652

5 0.6280.4850.410 0.107 0.2570.216 0.359 0.480 0.403 0.127 0.145 0.208 -1.947 0.4450.498 0.423 0.121 0.188 0.225 0.256 0.437 0.395 0.122 0.101 0.183 -5.323

6 0.4730.5010.448 0.096 0.2120.234 0.222 0.416 0.471 0.119 0.104 0.204 -2.191 0.3300.471 0.467 0.110 0.154 0.229 0.148 0.355 0.469 0.113 0.069 0.172 -5.263

7 0.3430.4760.484 0.090 0.1660.236 0.165 0.372 0.511 0.113 0.084 0.195 -1.415 0.2360.425 0.513 0.098 0.121 0.223 0.105 0.307 0.517 0.100 0.054 0.162 -4.302

8 0.1840.3880.546 0.081 0.1000.215 0.111 0.314 0.559 0.109 0.062 0.179 -0.396 0.1700.376 0.553 0.087 0.094 0.211 0.079 0.270 0.551 0.091 0.043 0.151 -3.438

9 0.1160.3210.591 0.070 0.0680.191 0.067 0.249 0.619 0.104 0.041 0.157 -0.458 0.1090.312 0.598 0.078 0.065 0.188 0.049 0.217 0.600 0.084 0.030 0.131 -2.752

10 0.0680.2520.634 0.062 0.0430.160 0.040 0.197 0.676 0.096 0.027 0.135 -0.157 0.0700.255 0.637 0.072 0.044 0.163 0.030 0.170 0.647 0.078 0.019 0.110 -2.266

11 0.0290.1680.696 0.044 0.0200.117 0.024 0.154 0.735 0.081 0.018 0.114 0.812 0.0270.163 0.712 0.062 0.019 0.117 0.013 0.114 0.721 0.056 0.009 0.082 -1.237

12 0.0190.1380.721 0.029 0.014 0.099 0.018 0.134 0.765 0.072 0.014 0.103 0.875 0.018 0.134 0.745 0.048 0.014 0.100 0.010 0.099 0.745 0.034 0.007 0.074 -0.887

13 0.0050.0700.765. 0.0040.053 0.010 0.100 0.812 0.064 0.008 0.081 1.155 0.0090.095 0.784 0.034 0.007 0.075 0.007 0.081 0.765 0.000 0.005 0.062 -0.387

14 0 0. . 0 0 0.004 0.063 0.882 0.000 0.004 0.056 1.476 0.0030.055 0.824. 0.002 0.045 0 0. . 0 0 -0.961

15 0 0. . 0 0 0.004 0.063 0.882 0.000 0.004 0.056 1.476 0 0. . 0 0 0 0. . 0 0

SC/ST/OBC Other castes

Patients Controls Patients Controls

Cut

off H SD A SD M0 SD H SD A SD M0 SD

T

value

for M0

H SD A SD M0 SD H SD A SD M0 SD

T

value

For

M0

1 0.987 0.115 0.324 0.155 0.320 0.158 0.972 0.165 0.288 0.166 0.280 0.170 -2.437 0.958 0.200 0.276 0.147 0.264 0.154 0.884 0.320 0.219 0.145 0.194 0.154 -5.532

2 0.942 0.233 0.337 0.147 0.317 0.163 0.900 0.301 0.306 0.158 0.276 0.176 -2.458 0.862 0.346 0.300 0.135 0.258 0.163 0.723 0.448 0.255 0.137 0.185 0.163 -5.510

3 0.863 0.345 0.357 0.137 0.308 0.177 0.783 0.413 0.335 0.151 0.262 0.192 -2.496 0.799 0.401 0.314 0.130 0.251 0.171 0.545 0.499 0.301 0.129 0.164 0.178 -6.097

4 0.748 0.435 0.385 0.126 0.288 0.200 0.628 0.485 0.374 0.143 0.235 0.214 -2.574 0.623 0.486 0.353 0.121 0.220 0.196 0.396 0.490 0.347 0.123 0.137 0.187 -5.246

5 0.606 0.490 0.419 0.114 0.254 0.224 0.494 0.501 0.411 0.140 0.203 0.228 -2.262 0.426 0.495 0.408 0.110 0.174 0.214 0.274 0.447 0.397 0.117 0.109 0.187 -3.927

6 0.460 0.500 0.459 0.103 0.211 0.240 0.306 0.462 0.483 0.134 0.148 0.235 -2.680 0.304 0.461 0.453 0.098 0.138 0.216 0.162 0.369 0.468 0.103 0.076 0.178 -3.843

7 0.336 0.473 0.498 0.094 0.168 0.242 0.233 0.424 0.524 0.128 0.122 0.231 -1.917 0.215 0.411 0.495 0.088 0.106 0.208 0.125 0.332 0.502 0.093 0.063 0.170 -2.788

8 0.217 0.413 0.546 0.085 0.118 0.229 0.161 0.369 0.574 0.125 0.092 0.217 -1.160 0.131 0.339 0.548 0.073 0.072 0.187 0.086 0.281 0.543 0.085 0.047 0.154 -1.809

9 0.133 0.340 0.594 0.076 0.079 0.204 0.100 0.301 0.637 0.121 0.064 0.195 -0.757 0.090 0.287 0.584 0.060 0.053 0.168 0.050 0.217 0.596 0.077 0.030 0.131 -1.864

10 0.075 0.264 0.644 0.067 0.048 0.171 0.061 0.240 0.706 0.108 0.043 0.171 -0.308 0.055 0.229 0.618 0.053 0.034 0.142 0.030 0.170 0.641 0.069 0.019 0.109 -1.459

11 0.035 0.185 0.706 0.044 0.025 0.131 0.044 0.207 0.750 0.093 0.033 0.156 0.586 0.017 0.131 0.682 0.053 0.012 0.089 0.013 0.114 0.706 0.048 0.009 0.081 -0.355

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12 0.027 0.161 0.725 0.030 0.019 0.117 0.033 0.180 0.784 0.080 0.026 0.142 0.536 0.007 0.083 0.735 0.042 0.005 0.061 0.010 0.099 0.725 0.034 0.007 0.072 0.381

13 0.009 0.094 0.765 0.000 0.007 0.072 0.022 0.148 0.824 0.068 0.018 0.122 1.186 0.003 0.059 0.765 . 0.003 0.045 0.003 0.057 0.765 . 0.003 0.044 -0.033

14 0 0 . . 0 0 0.011 0.105 0.882 0.000 0.010 0.093 1.590 0 0 . . 0 0 0 0 . . 0 0 .

15 0 0 . . 0 0 0.011 0.105 0.882 0.000 0.010 0.093 1.590 0 0 . . 0 0 0 0 . . 0 0 .

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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Table 6: Percentage contribution of each dimension to poverty for PSMI and controls for k=6

Cut Health Level of Access to Food Source

of

Indoor

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

Off

k access education employment security

drinking

water quality sanitation lighting income space ownership quality

ownersh

ip income expenses safety

Participat

ion

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Table 7: Logistic model for association between multidimensional poverty, stigma and SMI Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

crude OR (95%CI)

Adjusted

OR (95%CI)

Adjusted

OR (95%CI)

Adjusted

OR (95%CI)

Adjusted

OR (95%CI)

Adjusted

OR (95%CI)

Adjusted

OR (95%CI)

Indicator of stigma

Family participation 1 1 1 1 1 1 1

No family participation 2.92 2.16-3.93 2.20 1.55-3.10 2.34 1.35-4.04 2.88 1.83-4.52 2.12 1.18-3.79 3.64 0.80-16.5 2.61 1.27-5.31

Participants

Controls 1 1 1 1 1 1 1

SMI 2.20 1.67-2.89 2.08 1.54-2.79 2.14 1.50-3.03 2.09 1.55-2.81 2.09 1.54-2.81 2.51 1.62-3.89 2.07 1.25-3.41

Gender

Male 1 1 1 1 1 1 1

Female 2.17 1.65-2.83 1.87 1.36-2.56 1.86 1.35-2.55 1.88 1.37-2.58 1.85 1.27-2.66 2.41 1.38-4.19 1.88 1.36-2.58

Caste

Higher caste 1 1 1 1 1 1

SC/ST/OBC 2.06 1.56-2.70 2.08 1.55-2.77 2.08 1.55-2.77 2.48 1.74-3.51 2.08 1.55-2.77 2.07 1.54-2.76

Age (in year) 0.99 0.97-0.99 0.98 0.96-0.99 0.98 0.96-0.99 0.98 0.96-0.99 0.98 0.96-0.99 0.98 0.96-0.99 0.98 0.96-0.99

Interaction terms

Participation*controls 1 1 1

No participation*SMI 4.54 2.91-7.06 4.66 2.39-9.05 6.38 3.49-11.6

Participation*high caste 1 1

No participation*SC/ST/OBC 3.94 2.43-6.38 4.86 2.19-10.7

Participation*men 1 1

No participation*women 4.14 2.83-6.04 4.63 2.60-8.21

Participation*male*controls 1

No participation*women*SMI 9.62 5.58-16.5

Participation*high caste*controls 1

No participation*SC/ST/OBC*SMI 7.36 3.94-13.7

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

study

Journal: BMJ Open

Manuscript ID: bmjopen-2014-006355.R1


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


Global health




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

Abstract

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

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

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

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

Article summary

Strengths and limitations

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

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

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

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

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

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

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

Methods


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.


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.


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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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 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.


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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to be poor compared to male controls.


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.


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.


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

controls without mental illness.


Incomplete interviews (n=110)


Excluded (17%)


Excluded (23%)

Refused interviews (n=2)


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





Access to





Source of drinking


drinking water?






cooking fuel?






public latrine


lighting? Generator, kerosene lamp,

petromax, candle, none





person


Housing quality Are the material used for walls,

floor and roof in your house kutcha or pucca ?


Assets ownership



charpoy, refrigerator, radio/transistor, electric fan,

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





Household

expenditures What is the household's monthly

expenditure ? Less than $1.25 per capita

per day






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Dimension PSMI n=647

control n=649

p value

Male PSMI

(n=411)

Male Controls (n=408)

p value

Other Castes PSMI

Other castes

Controls

p value

Female PSMI

(n=238)

Female Controls (n=238)

p value

ST/SC/ OBC

PSMI

ST/SC/ OBC

Controls

p value

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

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

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

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

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



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


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


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




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

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

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

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

78 (28.5) 43 (17.7)0.020

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

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Dimensions

Health access

Educ. Access to

work Food

Security Source of

water Air

quality Type of

sanitation Type of lighting

Ind. income

Crowded space

Housing ownership

Housing quality

Assets owner-

ship

HH/cap. income

HH spending

Physical safety

Pol. Partici-pation

Health access 1

Education 0.021 1

Access to work 0.1047* 0.1771* 1

Food Security 0.0016 0.1309* 0.0878* 1

Source of water -0.0277 0.1669* 0.0412 0.1263* 1



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


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


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


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

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

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


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

Cut All PSMI Controls T-value %

difference

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

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

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

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

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

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

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

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

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

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

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

Female Male

Cut PSMI Controls T-value PSMI Controls T-value

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

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

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

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

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

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

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

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

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

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

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




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

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

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

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

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

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

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

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

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

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

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

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

Cut Health Level of Access to Food Source of

Indoor air

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

Political

Off k access education employment security drinking water

quality sanitation lighting income space ownership quality ownership

income expenses safety Participation

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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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


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.


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).


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.


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.


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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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Poverty and stigma

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.


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.


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




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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


Glossary of terms:



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

controls without mental illness.




Excluded (17%)


Excluded (23%)

Refused interviews (n=2)


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Access to





Source of drinking


drinking water?






cooking fuel?






public latrine








person





Assets ownership









Household



per day






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control n=649

p value

Male PSMI

(n=411)


p value

Other Castes PSMI

Other castes

Controls

p value

Female PSMI

(n=238)


p value

ST/SC/ OBC

PSMI

ST/SC/ OBC

Controls

p value

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

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

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

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

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



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


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


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




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


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

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

78 (28.5) 43 (17.7)0.020


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Dimensions

Health access

Educ. Access to

work Food

Security Source of

water Air

quality Type of


Ind. income

Crowded space

Housing ownership

Housing quality

Assets owner-

ship

HH/cap. income

HH spending

Physical safety

Pol. Partici-pation

Health access 1

Education 0.021 1

Access to work 0.1047* 0.1771* 1

Food Security 0.0016 0.1309* 0.0878* 1

Source of water -0.0277 0.1669* 0.0412 0.1263* 1



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


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


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


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

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

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


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difference


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

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

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

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

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

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

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

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

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

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

Female Male



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

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

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

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

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

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

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

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

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

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




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

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

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

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

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

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

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

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

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



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Indoor air


Political




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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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References




2012;380(9859):2095-2128.






2012;380(9859):2163-2196.



























Research 2012;200(2-3):917-920.


2009;373(9670):1160-1161.





Aug 2007;65(3):467-480.

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2012;46(2):95-103.


2001;27:363-385.


















2005;20(8):529-539.




2006;367(9509):528-529.





2013;103(5):813-821.










194.



Psychiatry 2010;22(3):235-244.

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Neurosciences 2002;25(8):430-431.












Oaks: Sage; 2012.




Review 2009;23(2):163-184.




2011;1(1):67-72.





2004;13(6):537-548.





Psychiatry 2008;32(2):87-91.









2011-12. New Delhi; 2013.

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Sons; 1991.


Sons; 2003.





and Medicine 2007;65:467-480.









2005;33(12):2085-2101.





81.








2005;186(FEB.):153-157.






Therapy 2002;24(3):233-257.

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1987;92(6):1461–1500.



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

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Cut Health Level of Access to Food Source

of

Indoor

air Type of Type of Individual Crowded Housing Housing Assets Household Household

Physi

cal

Politica

l

Off k access education employment security drinking

water quality sanitation lighting income space ownership quality

ownersh

ip income expenses safety

Partici

pation

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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1


Item

No

Recommendation

Where this is to be

found in our

submitted paper







See abstract under

‘Results’ p.1

Introduction




1&2


Methods


setting’ p.3




setting’ p.3










applicable


Data sources/

measurement










and why

See’ Quantitative

variables’ p.4



See ‘Statistical

methods’ p. 5


interactions



was addressed


Results





p. 6

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analysed





confounders


and figure 2 p. 6




and figure 1 p. 6









See

‘Multidimensional

poverty’ and

‘Poverty and


2 to 6

pp. 6-7


categorized



NA

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Discussion







Other information









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

study

Journal: BMJ Open

Manuscript ID: bmjopen-2014-006355.R2


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


Global health




BMJ Open on A


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j.com/

BM



ownloaded from



1

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

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

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

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

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

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

disorders, experienced discrimination, stigma.

Word count: 4687

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Abstract

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

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

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

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

Article summary

Strengths and limitations

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

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

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

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

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

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

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

Methods


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.


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.


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.


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.


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.


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.


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




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


Glossary of terms:



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Access to





Source of drinking


drinking water?






cooking fuel?






public latrine








person





Assets ownership









Household



per day






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control n=649

p value

Male PSMI

(n=411)


p value

Other Castes PSMI

Other castes

Controls

p value

Female PSMI

(n=238)


p value

ST/SC/ OBC

PSMI

ST/SC/ OBC

Controls

p value

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

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

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

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

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



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


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


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




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


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

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

78 (28.5) 43 (17.7)0.020


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Dimensions

Health access

Educ. Access to

work Food

Security Source of

water Air

quality Type of


Ind. income

Crowded space

Housing ownership

Housing quality

Assets owner-

ship

HH/cap. income

HH spending

Physical safety

Pol. Partici-pation

Health access 1

Education 0.021 1

Access to work 0.1047* 0.1771* 1

Food Security 0.0016 0.1309* 0.0878* 1

Source of water -0.0277 0.1669* 0.0412 0.1263* 1



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


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


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


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

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

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


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difference


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

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

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

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

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

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

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

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

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

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

Female Male



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

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

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

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

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

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

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

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

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

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




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

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

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

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

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

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

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

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

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



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Indoor air


Political




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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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References




2012;380(9859):2095-2128.






2012;380(9859):2163-2196.



























Research 2012;200(2-3):917-920.


2009;373(9670):1160-1161.





Aug 2007;65(3):467-480.

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2012;46(2):95-103.


2001;27:363-385.


















2005;20(8):529-539.




2006;367(9509):528-529.





2013;103(5):813-821.










194.



Psychiatry 2010;22(3):235-244.

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Neurosciences 2002;25(8):430-431.












Oaks: Sage; 2012.




Review 2009;23(2):163-184.




2011;1(1):67-72.





2004;13(6):537-548.





Psychiatry 2008;32(2):87-91.









2011-12. New Delhi; 2013.

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Sons; 1991.


Sons; 2003.





and Medicine 2007;65:467-480.









2005;33(12):2085-2101.





81.








2005;186(FEB.):153-157.






Therapy 2002;24(3):233-257.

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1987;92(6):1461–1500.



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

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Item

No

Recommendation

Where this is to be

found in our

submitted paper







See abstract under

‘Results’ p.1

Introduction




1&2


Methods


setting’ p.3




setting’ p.3










applicable


Data sources/

measurement










and why

See’ Quantitative

variables’ p.4



See ‘Statistical

methods’ p. 5


interactions



was addressed


Results





p. 6

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2

analysed





confounders


and figure 2 p. 6




and figure 1 p. 6









See

‘Multidimensional

poverty’ and

‘Poverty and


2 to 6

pp. 6-7


categorized



NA

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Discussion







Other information









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BMJ Open...Ram Manohar Lohia Hospital; Nagendra N. Mishra, research associate, Dr. Ram Manohar Lohia...

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