BMJ Open...Email – [email protected] Phone - +91 8755 105 391 Postal – Landour Community Hospital Mussoorie, Uttarakhand India 248 179 Author details Kaaren Mathias Department

For peer review only

A cross sectional study of depression and help-seeking in Uttarakhand, North India

Journal: BMJ Open

Manuscript ID: bmjopen-2015-008992

Article Type: Research

Date Submitted by the Author: 06-Jun-2015

Complete List of Authors: Mathias, Kaaren; Emmanuel Hospital Association, Goicolea, Isabel; Umeå University, Department of Public Health and Clinical Medicine, Epidemiology and Global Health Kermode, Michelle; University of Melbourne, Nossal Institute for Global Health Singh, Lawrence; AKS Hope Society, Shidhaye, Rahul; Centre for Mental Health, Public health foundation of India Sebastian, Miguel; Umeå University, Public health and clinical medicine

<b>Primary Subject Heading</b>:

Global health

Secondary Subject Heading: Epidemiology, General practice / Family practice, Mental health, Public health, Sociology

Keywords: MENTAL HEALTH, EPIDEMIOLOGY, Depression & mood disorders < PSYCHIATRY, PUBLIC HEALTH

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

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A cross sectional study of depression and help-seeking in

Uttarakhand, North India

Mathias, K., Goicolea, I, Kermode,M., Singh, L., Shidhaye,R., San Sebastian,M

Corresponding Author

Kaaren Mathias

Email – [email protected]

Phone - +91 8755 105 391

Postal – Landour Community Hospital

Mussoorie, Uttarakhand

India 248 179

Author details

Kaaren Mathias

Department of Community health and Development, Emmanuel Hospital Association, New Delhi,

India and Department of Public Health and Clnical Medicine, Epidemiology and Global Health, Umeå

University, Umeå, Sweden

Isabel Goicolea

Department of Public Health and Clnical Medicine, Epidemiology and Global Health, Umeå


Michelle Kermode

Nossal Institute for Global Health, University of Melbourne, Melbourne, Australia

Lawrence Singh

Agnes Kunze Society, Dehradun, Uttarakhand, India

Rahul Shidhaye

Centre for Mental Health, Public Health Foundation of India, New Delhi, India

Miguel San Sebastian



Keywords

Depression

India

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Help-seeking

Social determinants

Prevalence

Social exclusion

WHAT IS KNOWN ON THIS TOPIC

While the prevalence and risk factors for depression well-described in South India, there

have been very few studies set in North India. Across India there have been almost no

studies describing help-seeking behaviour and treatment gap for depression. This study was

needed to define the treatment gap and set priorities for policy making to increase access to

care for people with depression.

WHAT THIS STUDY ADDS

Depression has a prevalence of 6% and is two to three times more likely among people who

are socially excluded or socioeconomically disadvantaged. This study showed a treatment

gap of 96.7% yet the vast majority of people with depression had visited a health care

provider in the 3 months prior to survey. Measures to address depression must address

social determinants of mental health and focus on building capacity of primary care

providers to identify and treat depression.

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A CROSS SECTIONAL STUDY OF DEPRESSION AND HELP-SEEKING IN

UTTARAKHAND, NORTH INDIA


ABSTRACT

OBJECTIVES

This study sought to use a population-based cross-sectional survey to describe depression

prevalence, health care-seeking and associations with socio-economic determinants in a district in

North India

SETTING

This study was conducted in Sahaspur and Raipur, administrative blocks of Dehradun district,

Uttarakhand in July 2014.

PARTICIPANTS

A population-based sample of 960 people over the age of 18 years was selected in 30 randomised

clusters after being stratified by rural : urban census ratios.

PRIMARY OUTCOME MEASURES

The survey used a validated screening tool, PHQ9, to identify people with depression, and collected

information regarding socio-economic variables and help-seeking behaviours. Depression prevalence

and health seeking behaviours were calculated, and multi-variable logistic regression used to assess

associations between risk factors and depression.

RESULTS

Prevalence of depression was 6% (58/960) with a further 3.9% (37/960) describing a depressive

episode of over 2 weeks in the past 12 months. Statistically significant adjusted odds-ratios for

depression of more than 2 were found for people who were illiterate, classified as Scheduled

Caste/Tribe or Other Backward Castes, living in temporary material housing and who had recently

taken a loan. While over three quarters of people with depression (79%) had attended a private or

government general medical practitioner in the past three months, only two (3.3%) had been

prescribed antidepressants.

CONCLUSIONS

There are clear associations between social, educational and economic disadvantage and depression

in this population. Strategies that address the social determinants of depression, such as education,

social exclusion, financial protection and affordable housing for all are indicated. In addition to

address the large treatment gap in Uttarakhand we must ensure access to primary and secondary

mental health providers who can recognise and appropriately manage depression.

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KEY WORDS

Depression

India

Access to care

Social determinants

Prevalence

Social exclusion

Word count 3002

Abstract 277 words

Tables 5

References 40

STRENGTHS AND LIMITATIONS OF THE STUDY

• Data derived from this community- based cross-sectional randomly selected sample

shows that over 6% of adults in Dehradun district, Uttarakhand are depressed

• Risk of depression is two to three times higher for people who have had very little

schooling, who live in poor housing, who have taken a recent loan and who identify

as belonging to a scheduled caste or tribe (Scheduled Caste are typically the caste

most socially excluded in Indian society and are also referred to as Dalit or Harijan)

• There is a large gap in access to effective care where only 3.3% of people with

depression had been prescribed anti-depressants and 0% had received talking

therapy although the majority had attended a primary care providers in the previous

three months

• A limitation of this study is that the cross-sectional design cannot indicate causation

• This study highlights the urgent need capacity building in mental health for primary

care providers in North India as well as the priority for policy makers to address

mental health determinants such as social inclusion, education and housing.

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INTRODUCTION

Depression, the most common mental disorder and a major contributor to the global burden of

disease (1), accounts for 8.2% years lived with disability in the 2010 Global Burden of Disease study

(2). Depression related disability, compounded by lack of access to care, impacts on social and

physical health. Prevalence estimates of depression in India have varied widely depending on the

assessment tools used and the community’s socio-demographic profile(3, 4). Ganguli reviewed 15

studies of psychiatric morbidity, and found a mean prevalence of 3.4% (4). Reddy and

Chandrasekhar’s meta-analysis of 33572 subjects described prevalence at 8.9%, with urban rates

nearly double rural rates (5). Other studies in South Asia have shown even higher rates e.g. 15.1%

in urban South India (6) and 45.9% in urban Pakistan (7).

Robust evidence from India and other low and middle income countries (LMIC) links socio-economic

deprivation with increased risk of depression (8, 9). Other groups shown to be at higher risk for

depression in India are women, the elderly, urban dwellers and people who are divorced or

widowed (4-6, 8).

India’s highly inequitable distribution of mental health resources means at least 90% of people with

mental disorders (PWMDs) are undiagnosed and untreated (1). There are also huge disparities in

access to mental health services particularly for people in rural areas (10). Barriers to help-seeking

include unavailability of services, poor quality of the majority of existing services, lack of knowledge

about mental illness, and fear of stigma and discrimination (11, 12). People with depression in India

report distress primarily as unexplained somatic symptoms, and usually seek help from primary care

rather than specialist mental health care providers (13). Simultaneously, conceptual understandings

of depression are not well captured by current disease classification systems (14) and ongoing

reflection is needed about when mental distress becomes a disorder.

There is an urgent need to understand the burden of disease in areas where prevalence studies have

never before been conducted (15), and to understand the pathways to care currently utilised (16).

Few studies of depression in India have examined the associations between depression and social

determinants of health and almost none have considered the association between depression and

caste (3-6, 8). There are very few studies of depression prevalence in the Hindi speaking belt with

existing studies predominantly from Southern and Eastern India (3-6, 17). We could find no study on

the prevalence or epidemiology of depression in Uttarakhand, a state with a population of 10

million. This study describes the prevalence of depression, socio-demographic associations, and the

help-seeking behaviours of people with depression.

METHODS

SETTING

This study was conducted in two blocks (administrative unit with up to 200,000 inhabitants) in

Dehradun district Uttarakhand as part of the baseline survey for Burans, a community mental health

partnership project with the Emmanuel Hospital Association (www.eha-health.org) and the

Uttarakhand Community Health Global Network (www.chgnukc.org). Burans is directed by the first

author. The national District Mental Health Plan (DMHP) had not been implemented in Uttarakhand

at the time of this survey. During the survey period there were two government psychiatrists and no

government psychologists with 10-15 private psychiatrists and psychologists across Uttarakhand.

Government primary care services did not generally treat people with mental disorders, nor supply

essential medicines such as anti-depressants.

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SAMPLE SELECTION

We selected 960 people from 30 randomised clusters. Cluster sampling was conducted in three

phases: (1) ward or panchayat (administrative unit, approximately 5000 people) (2) household and

(3) participant. We used STATA (18) to calculate sample size of n = 480 , (estimating depression

prevalence at 10% (based on other Indian population based studies)(4, 6)) and a sampling frame of

235,000 (population of 2 blocks of Dehradun district), 30 clusters and 95% confidence intervals. To

account for the effects of clustering we allowed a design effect of 2, giving a final total of 960

persons.

Clusters were stratified based on rural: urban ratios in the district’s 2011 census(19) to require 21

urban and 9 rural clusters. These were selected by random number generation from the publicly

available list of census panchayats and wards. To select at household level, the surveying team

walked to the centre of the community, and spun a pen to ascertain which direction to start. Every

6th

house on the right was surveyed, and at each junction roads/ alleys on the right were followed. If

no-one was present at a selected household, the team re-visited that household later. If no one was

present on the second visit, the first house to the right was selected. Only one person from each

household was surveyed. Generally male field staff surveyed male respondents, and female staff

surveyed female respondents. Once the requisite 50% female participants was reached, all survey

staff surveyed male respondents. Inclusion criteria were that participants should be occupants of a

household, 18 years or older, and able to comprehend and respond to a survey.

DATA COLLECTION

Project Burans field staff, (equal numbers of males and females), collected data. All were trained in

sampling strategy, use of the survey tool, data recording and management, and ethical research, and

were supervised and supported by KM.

A comprehensive survey tool was piloted, validated, translated to Hindi and back translated to

English by the PRIME team in Sehore, Madhya Pradesh (part of the Project for Improving Mental

health care (PRIME) consortium (20)). The survey was interviewer administered, Components

reported in this paper are:

• Socio-demographic information including indicators of housing quality, indebtedness, caste,

marital status, highest education level attained and employment status.

• General health help seeking behaviour and health service utilisation (including “Have you

visited any health facility/ provider in the last three months?”)

• Type of health service provider utilised (Government primary provider, Government

secondary provider, private medical sector or charity provider, mental health provider,

traditional or religious healer)

• Medication utilisation ( generic or brand name, dose, duration and source)

• Patient Health Questionnaire (PHQ9) – a self-report screening tool assessing clinical

depression (validated internationally and in India) (21). This questionnaire comprises nine

items, each is scored 0 to 3, which thus yields a severity score from 0 and 27. Response

categories, based on frequency of a particular symptom over the last two weeks are scored

0, 1, 2, and 3, for "not at all", "several days", "more than half the days", and "nearly every

day" respectively.

• Mental health service seeking behaviour among those screening positive for depression

using the same codes as for general health seeking behaviour.

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At census enumeration and on birth registration Indians of Jain, Sikh and Hindu religion must identify

themselves as General Caste, Other Backward Classes (OBC) or a member of a Scheduled Tribe/Caste

(SC/ST) based on the identity of their parents(22). In the Dehradun area the vast majority of Muslims

are included under the OBC category. Christians and Buddhists are classified into the General caste

category.

ANALYSIS

Survey data were analysed using STATA version 13.1(18). Open text was translated into English

grouped thematically and coded. Uni-variable logistic regression analysis was performed using all

relevant socio-economic variables and significant variables (p<0.05) were considered in the multi-

variable logistic regression analysis. The dependent variable was dichotomised: no depression = 0,

depression= 1. . The original interpretation defined a total score of 5-9 a mild depression, and above

9 as moderate to severe depression. In this study we designated all respondents with a score of

greater than 9 as being ‘depressed’. Chi squared test was used in analysis of Tables 3 and 4. A p

value <0.05 was considered statistically significant.

All participants gave written consent to participate in the study and respondents who screened

positively for depression were given information on depression and advised on health services and

other sources of help. . Ethics approval was obtained from the Emmanuel Hospital Association

Institutional Review Board of Ethics in April 2014.

RESULTS

Within two contact attempts 958 of 960 selected households were successfully surveyed and the

remaining two on the third visit. Survey participants’ demographic characteristics are summarised in

Table 1. Nearly one-quarter were lowest caste or had tribal status (Scheduled Caste and Scheduled

Tribe).35% had six or fewer years of schooling.

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Table 1 Demographic characteristics of participants

Variables Female

N (%)

Male

N (%)

Total

N (%)

Total 480 480 960

Age (years)

18 -29 159 (33.1) 121 (25.2) 280 (29.2)

30 – 39 145 (30.2) 101 (21.0) 246 (25.6)

40 – 49 100 (20.8) 101 (21.0) 201 (20.9)

50 – 59 44 (9.2) 80 (16.7) 124 (12.9)

60+ 32 (6.7) 77 (16.1) 109 (11.2)

Marital status

Married 366 (50.4) 360 (49.6) 726 (75.6)

Divorced/ separated 48 (10.0) 13 (2.7) 61 (6.4)

Single 66 (13.7) 107 (22.3) 173 (18.0)

Rural/ urban

Rural 145 (30.2) 143 (29.8) 288 (30.0)

Urban 335 (69.8) 337 (70.2) 672 (70.0)

Education

None/ incomplete primary 109 (22.7) 51 (10.6) 160 (16.7)

Primary completion 88 (18.3) 90 (18.7) 178 (18.5)

Secondary completion 199 (41.5) 273 (56.9) 472 (49.2)

Graduate 84 (17.5) 66 (13.7) 150 (15.6)

Religion

Hindu 401 (83.5) 308 (82.9) 799 (83.2)

Muslim 71 (14.8) 70 (14.6) 141 (14.7)

Other 8 (1.7) 11 (2.5) 19 (2.1)

Caste

Scheduled Caste / Tribe 122 (25.4) 116 (24.2) 238 (24.8)

Other Backward Caste 74 (15.4) 73 (15.2) 147 (15.3)

General 284 (59.2) 291 (60.6) 575 (59.9)

The sampled survey population differs significantly from the wider Uttarakhand population (19) by

greater representation of middle-aged, people classified as Scheduled Caste/Tribe and unschooled

people.

Table 2 summarizes the prevalence of depression, socio-demographic characteristics, and their

association with the PHQ9 depression score with crude and adjusted odds ratios. Depression

prevalence was 6%. The significant association with increased risk of depression in women

disappeared when other confounding factors such as educational status and economic deprivation

were accounted for. People in their middle years had a slightly higher risk of depression than those

under 30 years and over 50 years. People who lived in a house made of temporary materials were

almost twice as likely to be classified as depressed, and those who had taken a loan recently were

three times more likely. Those classified as Scheduled Caste or Scheduled Tribe (SC or ST) had three

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times the odds of being classified as depressed. A dose-response relationship was seen in

educational status with risk of depression increasing with decreasing years of completed schooling.

Table 2 Prevalence and risk factors for depression in Dehradun district, Uttarakhand

Descriptive variable Non-

depressed

N (%)

Depresse

d

N (%)

P value

Crude OR 95% Confidence

intervals

Adjusted

OR

95%

Confidence

intervals

Total 902(94.0) 58 (6.0)

Sex

Male 460 (95.8) 20 (4.2) 1.0

Female 442 (92.1) 38 (7.9) <0.001 2.0 1.1 – 3.5* 0.6 0.3 – 1.2

Age

18 – 29 years 270 (96.4) 10 (3.6) 1.0

30 – 39 years

230 (93.5) 16 (6.5) 1.9 0.8 – 4.2

40 – 49 years 183 (91.0) 18 (7.0) 2.7 1.2 – 5.9*

50 – 59 years 116 (93.5) 8 (6.5) 1.9 0.7 – 4.8

60 years and over 103 (94.5) 6 (5.5) 1.6 0.6 – 4.4

Caste

General 557 (96.9) 18 (3.1) 1.0

OBC 137 (93.2) 10 (6.8) 2.3 1.0 – 5.0* 2.1 0.9 – 4.8

SC / ST 208 (87.4) 30 (12.6) <0.001 4.5 2.4 – 8.2* 3.2 1.7 – 6.2*

Religion

Hindu 755 (94.5) 44 (5.5) 1.0

Non-Hindu 147 (91.3) 14 (8.7) 1.6 0.9 – 3.0

House type

Permanent 760 (96.0 ) 32 (4.0) 1.0

Temporary 142 (84.5) 26 (15.5) <0.001 3.3 1.9 – 5.7 * 1.9 1.0 – 3.5*

Loan in last 6 months

No 845 (95.1) 44(4.9) 1.0

Yes 57(80.3) 14(19.7) <0.001 4.7 2.4 – 9.1* 3.0 1.4 – 6.2*

Education status

Unschooled 135(84.4) 25(15.6) 6.8 2.3 – 19.9 3.7 1.2 – 12.0*

Primary 168(94.4) 10(5.6) 2.2 0.7 – 7.0 2.0 0.6 – 7.1

Secondary 453(96.0) 19(4.0) 1.5 0.5 – 4.6 1.5 0.5 – 4.7

Graduate 146(97.3) 4 (2.7) <0.001 1.0

Employment

Professional / military 102 (92.7) 8 (7.3) 1.0

Self-employed 231 (97.5) 6 (2.5) 0.3 0.1 – 0.98*

Unskilled manual 341 (93.9) 22 (6.1) 0.8 0.4 – 1.9

Unemployed 228 (91.2) 22 (8.8) <0.05 1.2 0.5 – 2.9

NB * signifies statistical significance

Table 3 shows the help seeking behaviours of those screened as depressed (using PHQ9) or who self-

reported a greater than two week episode of depression in the last 12 months. People with

depression were five times more likely to have visited outpatient or inpatient providers than people

without depression. Separate analysis showed that all women, with and without depression, were

significantly more likely than men to have visited a health provider in the prior three months.

Table 3. Health-service-seeking behaviour

Not

depressed

N (%)

Depressed

N (%)

No

depression in

last 12

month

Depression

in last 12

months

Visited outpatient

health provider in last 3

138 (15.3) 45 (77.6)*

120 (13.8) 63 (68.5)*

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months

No visit to outpatient


months

764( 84.7) 13 (22.4)* 748 (86.2)

29 (31.5)*

Hospital admission in

last 12 months

41 (4.5) 15 (25.8)* 35 (4.0) 21 (22.8)*

No hospital admission

in last 12 months

860 (95.5 ) 43(74.2)* 832 (96.0) 71 (77.2)*

nb * signifies statistical significance

Table 4 shows the type of health provider visited by the 183 people who made an outpatient visit in

the previous three month, Depressed people, compared to non-depressed, consult health providers

significantly more, and are far more likely to visit general health providers than mental health

services. Only two people in the entire sample had visited a mental health service provider in the

previous three months and neither was prescribed an anti-depressant. Two people with depression

at the time of the survey had been prescribed antidepressants - both through private practitioners.

One respondent who screened negatively for depression had been prescribed antidepressants.

Table 4. Type of health providers visited in the last three months

Type of outpatient provider

Not

depressed

N (%)

Depressed

N (% )

Government provider (CHC or PHC) 54 (40.3) 22 (47.8)

Community level government provider (PHC or ANM) 7 (5.0) 2 (4.3)

Private health provider 70 (52.2) 18 (41.8)

Mental health provider 1 (0.7) 1 (2.3)

Traditional healer 6 (4.4) 2 (4.3)

Total 138(16.2% of

total non-

depressed)

45 (79% of

total

depressed)

Prescribed anti-depressants 1 (0.7) 2 (4.3)

Supported with talking therapy 0 0

DISCUSSION

This study shows a 6% prevalence of depression in a randomly sampled population in Dehradun

district, Uttarakhand. Some studies suggest prevalence in India may be higher (1, 5, 7). In this study

no one had received talking therapy, one person with depression had accessed a mental health

service provider and two had been prescribed an anti-depressant, a treatment gap of at least 96.3%.

Extrapolating this to Uttarakhand’s adult population of 6.6 million (19) we estimate 400,000 people

may have depression of whom just 4,000 can access anti-depressants and none access talking

therapy: a huge mismatch between disease burden and health service provision.

This study shows much greater risk for three groups – the poorest (those in houses constructed from

temporary materials and who had taken a recent loan), those who self-identify as Other Backward

Caste, Scheduled Caste or Tribe and the unschooled / illiterate. Each of these associations has an

adjusted odds ratio for depression of at least twice their reference group. A systematic literature

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review in low and middle income countries (LMIC) shows depression strongly associated with socio-

economic deprivation (9) supporting the WHO Commission on the Social Determinants of health

concept of mediating pathways that link poverty with lack of access to political recognition and

economic power(23). Other Indian studies also suggest socio-economic factors as the key

determinants of depression (5, 6, 8). Like a ‘canary in a coal mine’ depression may be conceptualised

as an indicator of social inequity and vulnerability.

Although mental health has been linked with socio-economic disadvantage and social exclusion

caste, a key indicator of social identity in India, has not been well investigated as a risk factor. One

Indian study of the prevalence of common mental disorders among rural women found no

association with caste (8). A further study in rural Nepal found that caste-based disparities in mental

health are mediated by poverty, lack of social support, and stressful life events (24). However Sen’s

capabilities approach emphasises rights and command over goods, recognises the relational roots of

deprivation, and underscores the importance of agency and participation for genuine social inclusion

(25). This view is supported by others describing both psychosocial and biological pathways between

social exclusion and health (26, 27). It seems likely therefore that social exclusion, marked by caste,

increases the risk of depression.

For decades Indian national policy has sought to legislatively benefit Scheduled Castes and

Scheduled Tribes, e.g. the Reservation in Admission Act 2006 (28) and the Protection of Civil Rights

Act 1955 (29). Despite such measures, and after controlling for socio-economic status (schooling,

housing, debt etc), members of Scheduled Caste and Scheduled Tribe groups in this study showed a

over two times the risk for depression. It is likely that persisting social structures of exclusion and

discrimination are more penetrating than legislation and continue to create relative deprivation,

reduced agency and exclusion.

Associations between poverty and common mental disorders in LMIC countries and in India are well-

described (13). This study found that depression prevalence among people who had taken a recent

loan was thrice that of those who had not. Two other studies from India show indebtedness as a risk

factor for primary care attenders diagnosed with a common mental disorder (CMD) (13, 30). Links

between personal debt and mental health are well described (13, 31, 32) although there have been

no prospective longitudinal studies to show the direction of association. Possible mediating

pathways between depression and indebtedness include shame, stress of financial insecurity (31),

and less capacity to earn income due to depression.

Another strong risk factor for depression in this study is educational status. People who had not

completed primary schooling had almost four times greater risk after controlling for caste, housing,

indebtedness and employment status. Our results show a striking dose-response relationship:

increasing years of education provide increasing protection. A meta-analysis examining the

associations between socio-economic inequality and depression also reported this finding (33).

Education status has been described in India and other LMICs as predictive of mental health

outcomes (33-35). Social consequences of low levels of education are multiple and move beyond

schooling as a marker of deprivation, they include reduced opportunity to access resources and

develop protective social and cognitive skills, and increased risk for mental distress. Reverse

causality is unlikely to be a factor as primary education occurs at an age when common mental

disorders are uncommon.

Although people with depression were seeking care, they were not getting the help they needed

This study shows that most people with depression had attended primary health care providers in

the previous three months, in both the Government and private sectors. The somatisation of

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depression among people with depression in Asian cultures, particularly women, is well-described

(36). Many health providers are unable to recognise somatisation, leading to excessive, costly and

often inappropriate investigation and treatment: only two people identified with depression

reported taking an anti-depressant and none received any formal talking therapy. Also noteworthy is

that only eight people in this sample reported consultations with traditional healers. Others have

described up to two thirds of people with severe mental disorders in India seeking help from

traditional healers during their illness (16, 37). The strong association between somatoform

disorders and common mental disorders highlights the need for primary care providers to be

equipped with knowledge and skills to recognise and manage the diverse presentations of common

mental disorders(36)

This study shows depression as one component of the ‘ecology of suffering’ in India where macro-

economic and political decisions lead to mental distress and suffering for both communities and

individuals (38). There are several important implications for policy and practice. Foremost, since

social determinants of health almost certainly contribute to depression, macro-policies that address

determinants such as poor housing, caste, indebtedness and low education will also reduce

depression disease burden. Policies to improve mental health must seek to reduce poverty and

social exclusion (39) and actively include communities (40).

Secondly action is urgently required to increase provision of mental health services and medicines,

as strongly advocated by the Lancet mental health group and series (1, 39). However, perhaps more

importantly primary care doctors in India as the health care providers most commonly consulted by

people with depression, need knowledge, skills and perception to recognise the diverse

presentations of common mental disorders such as depression and anxiety , and treat them

appropriately.

METHODOLOGICAL CONSIDERATIONS

A major strength of this study is that its data is from a randomly selected population covering rural,

semi-urban and urban populations typical for a district in North India in 2014. Multi-variable analysis

ensured potentially confounding factors were considered. However, there are some methodological

limitations: self-reported measures may risk recall bias and cultural factors. The screening tool

(PHQ9) is a screening, not diagnostic tool, constructed using a definition of depression (DSMV and

ICD10) that has been critiqued as being over-simplistic and risks labelling as a disorder components

of normal human experience(14). This cross sectional study cannot attribute causality to apparent

risk factors. The survey tool excluded three key risk factors - stressful life events, chronic illness and

disability. The lower than expected prevalence of depression (6%) may be related to the emphasis in

the PHQ9 on cognitive manifestations of depression possibly missing the somatic features common

in Asian cultures (36). There were practical limitations in data collection e.g. male field workers did

not enter homes of women without male relatives present.

CONCLUSION

Depression in Dehradun district of Uttarakhand with a prevalence of at least 6% is two or three

times more common among people who are economically deprived, part of the most excluded caste

group or who have had little education. Almost no one with depression accesses effective primary or

secondary mental health care. Social policy and health service responses must urgently address this

preventable and treatable disease burden and treatment gap. Common mental disorders are indeed

common, and disproportionately affect the most vulnerable.

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ACKNOWLEDGEMENTS

Appreciation to the hard-working Burans teams of OPEN, LCH, Sneha and HOPE for surveying/ data collection

in a very wet monsoon. Appreciation also goes to Kezia Chand, Gabriella Ailstock and Suraj Chetri for their

support in data entry, to Linda Seefeldt for support with Stata and to Rajan Arora, R. Srivatsan, Jeph Mathias

and Prachin Ghodajkar for suggestions and review of the paper.

a. Contributorship statement

KM conceived of the study and main design, performed data collection and data analysis and wrote the first

draft, IG and MK contributed to design and editing of first and subsequent drafts, LS supported data collection

and analysis, RS contributed to literature review and subsequent drafts and MSS supported study design,

analysis and overview of the whole paper.

b. Competing interests

We declare we have no competing interests

c. Funding

This research was partially funded by the Umeå Centre for Global Health Research, funded by FAS, the

Swedish Council for Working Life and Social Research, Grant number 2006 - 1512

d. Data sharing statement

No data available for sharing

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23. World Health Organisation. Social Commission on the Social Determinants of Health.

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mental and physical health: a systematic review and meta-analysis. Clin Psychol Rev.

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33. Lorant V, Deliege D, Eaton W, Robert A, Philippot P, Ansseau M. Socioeconomic inequalities

in depression: a meta-analysis. Am J Epidemiol. 2003;157(2):98-112.

34. Chatterjee S, Pillai A, Jain S, Cohen A, Patel V. Outcomes of people with psychotic disorders

in a community-based rehabilitation programme in rural India. The British Journal of Psychiatry.

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35. Patel V, Kleinman A. Poverty and common mental disorders in developing countries. Bull

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36. Shidhaye R, Mendenhall E, Sumathipala K, Sumathipala A, Patel V. Association of

Somatoform Disorders with Anxiety and Depression in Women in Low- and Middle-Income

Countries: A Systematic Review. International review of psychiatry (Abingdon, England).

2013;25(1):65-76.

37. Lahariya C, Singhal S, Gupta S, Mishra A. Pathway of care among psychiatric patients

attending a mental health institution in central India. Indian J Psychiatry. 2010;52(4):333-8.

38. Jadhav S, Jain S, Kannuri N, Bayetti C, Barua M. Ecologies of suffering : mental health in India.

Economic and Political Weekly. 2015;50(20):12-5.

39. Lund C, De Silva M, Plagerson S, Cooper S, Chisholm D, Das J, et al. Poverty and mental

disorders: breaking the cycle in low-income and middle-income countries. Lancet.

2011;378(9801):1502-14.

40. Campbell C, Burgess R. The role of communities in advancing the goals of the Movement for

Global Mental Health. Transcultural Psychiatry. 2012;49(3-4):379-95.

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1

STROBE Statement—checklist of items that should be included in reports of observational studies

Mathias et al – Cross- sectional study of depression in Dehradun, Uttarakhand

Item

No Recommendation

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

Y

(b) Provide in the abstract an informative and balanced summary of what was done

and what was found Y

Introduction

Background/rationale 2 Explain the scientific background and rationale for the investigation being reported

Y

Objectives 3 State specific objectives, including any prespecified hypotheses Y

Methods

Study design 4 Present key elements of study design early in the paper Y

Setting 5 Describe the setting, locations, and relevant dates, including periods of recruitment,

exposure, follow-up, and data collection Y

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

selection of participants. Describe methods of follow-up

Case-control study—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

Cross-sectional study—Give the eligibility criteria, and the sources and methods of

selection of participants Y

(b) Cohort study—For matched studies, give matching criteria and number of

exposed and unexposed

Case-control study—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 Y

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 Y

Bias 9 Describe any efforts to address potential sources of bias Y

Study size 10 Explain how the study size was arrived at Y

Quantitative variables 11 Explain how quantitative variables were handled in the analyses. If applicable,

describe which groupings were chosen and why Y

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

Y

(b) Describe any methods used to examine subgroups and interactions NA

(c) Explain how missing data were addressed NA

(d) Cohort study—If applicable, explain how loss to follow-up was addressed

Case-control study—If applicable, explain how matching of cases and controls was

addressed

Cross-sectional study—If applicable, describe analytical methods taking account of

sampling strategy NA

(e) Describe any sensitivity analyses

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2

Continued on next page

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3

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

analysed Y

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

(c) Consider use of a flow diagram NA

Descriptive

data

14* (a) Give characteristics of study participants (eg demographic, clinical, social) and information

on exposures and potential confounders Y

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

(c) Cohort study—Summarise follow-up time (eg, average and total amount)

Outcome data 15* Cohort study—Report numbers of outcome events or summary measures over time

Case-control study—Report numbers in each exposure category, or summary measures of

exposure

Cross-sectional study—Report numbers of outcome events or summary measures Y

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 Y

(b) Report category boundaries when continuous variables were categorized NA

(c) If relevant, consider translating estimates of relative risk into absolute risk for a meaningful

time period

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

analyses Y

Discussion

Key results 18 Summarise key results with reference to study objectives Y

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 Y

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

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

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

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 Y

*Give information separately for cases and controls in case-control studies and, if applicable, for exposed and

unexposed groups in cohort and cross-sectional studies.

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 www.strobe-statement.org.

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A cross sectional study of depression and help-seeking in Uttarakhand, North India

Journal: BMJ Open

Manuscript ID bmjopen-2015-008992.R1

Article Type: Research

Date Submitted by the Author: 22-Oct-2015

Complete List of Authors: Mathias, Kaaren; Emmanuel Hospital Association, Goicolea, Isabel; Umeå University, Department of Public Health and Clinical Medicine, Epidemiology and Global Health Kermode, Michelle; University of Melbourne, Nossal Institute for Global Health Singh, Lawrence; AKS Hope Society, Shidhaye, Rahul; Centre for Mental Health, Public health foundation of India Sebastian, Miguel; Umeå University, Public health and clinical medicine

<b>Primary Subject Heading</b>:

Global health

Secondary Subject Heading: Epidemiology, General practice / Family practice, Mental health, Public health, Sociology

Keywords: MENTAL HEALTH, EPIDEMIOLOGY, Depression & mood disorders < PSYCHIATRY, PUBLIC HEALTH


BMJ Open on June 6, 2020 by guest. P

rotected by copyright.http://bm

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A cross sectional study of depression and help-seeking in

Uttarakhand, North India


Corresponding Author

Kaaren Mathias

Email – [email protected]

Phone - +91 8755 105 391

Postal – Landour Community Hospital

Mussoorie, Uttarakhand

India 248 179

Author details

Kaaren Mathias

Department of Community health and Development, Emmanuel Hospital Association, New Delhi,

India and Department of Public Health and Clnical Medicine, Epidemiology and Global Health, Umeå


Isabel Goicolea



Michelle Kermode

Nossal Institute for Global Health, University of Melbourne, Melbourne, Australia

Lawrence Singh

Agnes Kunze Society, Dehradun, Uttarakhand, India

Rahul Shidhaye

Centre for Mental Health, Public Health Foundation of India, New Delhi, India

Miguel San Sebastian



Keywords

Depression

India

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Help-seeking

Social determinants

Prevalence

Social exclusion

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A CROSS- SECTIONAL STUDY OF DEPRESSION AND HELP-SEEKING IN

UTTARAKHAND, NORTH INDIA


ABSTRACT

OBJECTIVES

This study sought to use a population-based cross-sectional survey to describe depression

prevalence, health care-seeking and associations with socio-economic determinants in a district in

North India

SETTING

This study was conducted in Sahaspur and Raipur, administrative blocks of Dehradun district,

Uttarakhand in July 2014.

PARTICIPANTS

A population-based sample of 960 people over the age of 18 years was selected in 30 randomised

clusters after being stratified by rural: urban census ratios.

PRIMARY OUTCOME MEASURES

The survey used a validated screening tool, PHQ9, to identify people with depression, and collected

information regarding socio-economic variables and help-seeking behaviours. Depression prevalence

and health seeking behaviours were calculated, and multi-variable logistic regression used to assess

associations between risk factors and depression.

RESULTS

Prevalence of depression was 6% (58/960) with a further 3.9% (37/960) describing a depressive

episode of over 2 weeks in the past 12 months. Statistically significant adjusted odds-ratios for

depression of more than 2 were found for people who were illiterate, classified as Scheduled

Caste/Tribe or Other Backward Castes, living in temporary material housing and who had recently

taken a loan. While over three quarters of people with depression (79%) had attended a private or

government general medical practitioner in the past three months, none had received talking

therapy (100% treatment gap) and two people (3.3%) had been prescribed antidepressants.

CONCLUSIONS

There are clear associations between social, educational and economic disadvantage and depression

in this population. Strategies that address the social determinants of depression, such as education,

social exclusion, financial protection and affordable housing for all are indicated. To address the

large treatment gap in Uttarakhand we must ensure access to primary and secondary mental health

providers who can recognise and appropriately manage depression.

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KEY WORDS

Depression

India

Access to care

Social determinants

Prevalence

Social exclusion

STRENGTHS AND LIMITATIONS OF THE STUDY

• Data derived from this community- based cross-sectional randomly selected sample

shows that over 6% of adults in Dehradun district, Uttarakhand are depressed

• Risk of depression is two to three times higher for people who have had very little

schooling, who live in poor housing, who have taken a recent loan and who identify

as belonging to a scheduled caste or tribe (Scheduled Caste are typically the caste

most socially excluded in Indian society, and are also referred to as Dalit or Harijan)

• There is a large gap in access to effective care where 0% had received talking therapy

and only 3.3% of people with depression had been prescribed anti-depressants

although the majority had attended a primary care providers in the previous three

months

• A limitation of this study is that the estimate of prevalence is through a screening

tool rather than definitive diagnosis by a psychiatrist. The cross-sectional design

cannot indicate causation and the survey covered only one district of Uttarakhand

state, which may limit generalisability.

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INTRODUCTION

Depression, the most common mental disorder, accounts for 9.7% years lived with disability in the

2010 Global Burden of Disease study1. Depression related disability, compounded by lack of access

to care, impacts on social and physical health. Prevalence estimates of depression in India have

varied widely depending on the assessment tools used and the community’s socio-demographic

profile2 3

. Ganguli reviewed 15 studies of psychiatric morbidity, and found a mean prevalence of

3.4% 3. Reddy and Chandrasekhar’s meta-analysis of 33572 subjects described prevalence at 8.9%,

with urban rates nearly double rural rates 4. Other studies in South Asia have shown even higher

rates e.g. 15.1% in urban South India 5 and 45.9% in urban Pakistan

6.

Robust evidence from India and other low and middle income countries (LMIC) links socio-economic

deprivation with increased risk of depression 7-9

. Other groups shown to be at higher risk for

depression in India are women, the elderly, urban dwellers and people who are divorced or

widowed 3-5 7 9

.

India’s highly inequitable distribution of mental health resources means at least 90% of people with

mental disorders (PWMDs) are undiagnosed and untreated10

. There are also huge disparities in

access to mental health services particularly for people in rural areas4. Barriers to help-seeking

include unavailability of services, poor quality of the majority of existing services, lack of knowledge

about mental illness, and fear of stigma and discrimination 11 12

. People with depression in India

report distress primarily as unexplained somatic symptoms, and usually seek help from primary care

rather than specialist mental health care providers 13 14

. Simultaneously, conceptual understandings

of depression are not well captured by current disease classification systems 15

and ongoing

reflection is needed about when mental distress becomes a disorder.

There is an urgent need to understand the burden of disease in areas where prevalence studies have

never before been conducted 16

, and to understand the pathways to care currently utilised17

. Few

studies of depression in India have examined the associations between depression and social

determinants of health and almost none have considered the association between depression and

caste 2-5 7

. There are very few studies of depression prevalence in the Hindi speaking belt with

existing studies predominantly from Southern and Eastern India 2-5 9 18

. We could find no study on the

prevalence or epidemiology of depression in Uttarakhand, a state with a population of 10 million.

This study describes the prevalence of depression, socio-demographic associations, and the help-

seeking behaviours of people with depression.

METHODS

SETTING

This study was conducted in two blocks (administrative unit with up to 200,000 inhabitants) in

Dehradun district Uttarakhand as part of the baseline survey for Burans, a community mental health

partnership project with the Emmanuel Hospital Association (www.eha-health.org) and the

Uttarakhand Community Health Global Network (www.chgnukc.org). Burans is directed by the first

author. Housing is an important indicator of socio-economic status in this setting. Permanent

materials housing refers to housing that is predominantly made with a sealed floor, solid materials

walls (e.g. brick) and a corrugated iron roof. Temporary materials housing refers to housing with a

dirt floor and/or walls and roofing constructed from straw/tarpaulin or plastic sheets. The national

District Mental Health Plan (DMHP) had not been implemented in Uttarakhand at the time of this

survey. During the survey period there were two government psychiatrists and no government

psychologists with 10-15 private psychiatrists and psychologists across Uttarakhand. Government

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primary care services did not generally treat people with mental disorders, nor supply essential

medicines such as anti-depressants.

SAMPLE SELECTION

We selected 960 people from 30 randomised clusters. Cluster sampling was conducted in three

phases: (1) ward or panchayat (administrative unit, approximately 5000 people) (2) household and

(3) participant. We used STATA 19

to calculate sample size of n = 480 , (estimating depression

prevalence at 10% (based on other Indian population based studies)3 5

) and a sampling frame of

235,000 (population of 2 blocks of Dehradun district), 30 clusters and 95% confidence intervals. To

account for the effects of clustering we allowed a design effect of 2, giving a final total of 960

persons.

Clusters were stratified based on rural: urban ratios in the district’s 2011 census20

to require 21

urban and 9 rural clusters. These were selected by random number generation from the publicly

available list of census panchayats and wards. To select at household level, the surveying team

walked to the centre of the community, and spun a pen to ascertain which direction to start. Every

6th

house on the right was surveyed, and at each junction roads/ alleys on the right were followed. If

no-one was present at a selected household, the team re-visited that household later. If no-one was

present on the second visit, the first house to the right was selected. Only one person from each

household was surveyed. Generally male field staff surveyed male respondents, and female staff

surveyed female respondents. Once the requisite 50% female participants was reached, all survey

staff surveyed male respondents. Inclusion criteria were that participants should be occupants of a

household, 18 years or older, and able to comprehend and respond to a survey.

DATA COLLECTION

Project Burans field staff, all from the district of Dehradun, (equal numbers of males and females),

collected data in July and August 2014. All were trained in sampling strategy, use of the survey tool,

data recording and management, and ethical research, and were supervised and supported by KM.

A comprehensive survey tool was translated to Hindi, back translated to English and piloted

extensively by the PRIME team in Madhya Pradesh 21

. The survey was interviewer administered in

Hindi. Components reported in this paper are:

• Socio-demographic information including indicators of housing quality, indebtedness, caste,

marital status, highest education level attained and employment status adapted from the

Indian version of the Demographic and Health surveys22

. Proxy measures of socio-economic

status included housing quality, educational status and employment status. We used norms

of the Government of India to assess housing quality where Permanent material housing

referred to classifications of “Pukka” and “Semi-kaccha” and Temporary material housing

referred to the “Kaccha” classification23

.

• General health help seeking behaviour and health service utilisation (including “Have you

visited any health facility/ provider in the last three months?”)23

• We used adapted questions from the Client Service Receipt Inventory 24

to ask participants

about recent inpatient and outpatient services used inclusing type of provider (Government

primary provider, Government secondary provider, private medical sector or charity

provider, mental health provider, traditional or religious healer)

• Talking therapy or medication prescription received ( generic or brand name, dose, duration

and source)

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• Patient Health Questionnaire (PHQ9) – a self-report screening tool assessing clinical

depression (validated internationally and in India)25-27

. This questionnaire comprises nine

items, each is scored 0 to 3, which thus yields a severity score from 0 and 27. Response

categories, based on frequency of a particular symptom over the last two weeks are scored

0, 1, 2, and 3, for "not at all", "several days", "more than half the days", and "nearly every

day" respectively. In our study a person with a PHQ9 score of 10 or higher was assessed as

having at least moderate depression, in line with international norms for PHQ927

• Mental health service seeking behaviour among those screening positive for depression

using the same codes as for general health seeking behaviour.

At census enumeration and on birth registration Indians of Jain, Sikh and Hindu religion must identify

themselves as General Caste, Other Backward Classes (OBC) or a member of a Scheduled Tribe/Caste

(SC/ST) based on the identity of their parents28

. In the Dehradun area the vast majority of Muslims

are included under the OBC category. Christians and Buddhists are classified into the General caste

category.

ANALYSIS

Survey data were analysed using STATA version 13.119

. Open text was translated into English

grouped thematically and coded. Uni-variable logistic regression analysis was performed using all

relevant socio-economic variables and significant variables (p<0.05) were considered in the multi-

variable logistic regression analysis. The dependent variable was dichotomised: no depression = 0,

depression= 1. . The original interpretation defined a total score of 5-9 a mild depression, and above

9 as moderate to severe depression. In this study we designated all respondents with a score of

greater than 9 as being ‘depressed’. Chi squared test was used in analysis of Tables 3 and 4. A p

value <0.05 was considered statistically significant.

All participants gave written consent to participate in the study and respondents who screened

positively for depression were given information on depression and advised on health services and

other sources of help. . Ethics approval was obtained from the Emmanuel Hospital Association

Institutional Review Board of Ethics in New Delhi in April 2014.

RESULTS

Within two contact attempts 958 of 960 selected households were successfully surveyed and the

remaining two on the third visit. Survey participants’ demographic characteristics are summarised in

Table 1. Nearly one-quarter were lowest caste or had tribal status (Scheduled Caste and Scheduled

Tribe). Thirty-five percent had six or fewer years of schooling.

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Table 1 Demographic characteristics of participants

Variables Female

N (%)

Male

N (%)

Total

N (%)

Total 480 480 960

Age (years)

18 -29 159 (33.1) 121 (25.2) 280 (29.2)

30 – 39 145 (30.2) 101 (21.0) 246 (25.6)

40 – 49 100 (20.8) 101 (21.0) 201 (20.9)

50 – 59 44 (9.2) 80 (16.7) 124 (12.9)

60+ 32 (6.7) 77 (16.1) 109 (11.2)

Marital status

Married 366 (50.4) 360 (49.6) 726 (75.6)

Divorced/ separated 48 (10.0) 13 (2.7) 61 (6.4)

Single 66 (13.7) 107 (22.3) 173 (18.0)

Rural/ urban

Rural 145 (30.2) 143 (29.8) 288 (30.0)

Urban 335 (69.8) 337 (70.2) 672 (70.0)

Education

None/ incomplete primary 109 (22.7) 51 (10.6) 160 (16.7)

Primary completion 88 (18.3) 90 (18.7) 178 (18.5)

Secondary completion 199 (41.5) 273 (56.9) 472 (49.2)

Graduate 84 (17.5) 66 (13.7) 150 (15.6)

Religion

Hindu 401 (83.5) 308 (82.9) 799 (83.2)

Muslim 71 (14.8) 70 (14.6) 141 (14.7)

Other 8 (1.7) 11 (2.5) 19 (2.1)

Caste

Scheduled Caste / Tribe 122 (25.4) 116 (24.2) 238 (24.8)

Other Backward Caste 74 (15.4) 73 (15.2) 147 (15.3)

General 284 (59.2) 291 (60.6) 575 (59.9)

The sampled survey population had a mean age of 39.4 and a median age of 37.5. The mean years of

education completed was 8.0 years and the median was 9 years. The sample differs significantly

from the wider Uttarakhand population 20

by greater representation of middle-aged, people

classified as Scheduled Caste/Tribe and unschooled people.

Table 2 summarizes the prevalence of depression, socio-demographic characteristics, and their

association with the PHQ9 depression score with crude and adjusted odds ratios. Depression

prevalence was 6%. The significant association with increased risk of depression in women

disappeared when other confounding factors such as educational status and economic deprivation

were accounted for. People in their middle years had a slightly higher risk of depression than those

under 30 years and over 50 years. People who lived in a house made of temporary materials were

almost twice as likely to be classified as depressed, and those who had taken a loan recently were

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three times more likely. Those classified as Scheduled Caste or Scheduled Tribe (SC or ST) had three

times the odds of being classified as depressed. A dose-response relationship was seen in

educational status with risk of depression increasing with decreasing years of completed schooling.

Table 2 Prevalence and risk factors for depression in Dehradun district, Uttarakhand

Descriptive variable Non-

depressed

N (%)

Depresse

d

N (%)

P value

Crude OR 95% Confidence

intervals

Adjusted

OR

95%

Confidence

intervals

Total 902(94.0) 58 (6.0)

Sex

Male 460 (95.8) 20 (4.2) 1.0

Female 442 (92.1) 38 (7.9) <0.001 2.0 1.1 – 3.5* 0.6 0.3 – 1.2

Age

18 – 29 years 270 (96.4) 10 (3.6) 1.0

30 – 39 years

230 (93.5) 16 (6.5) 1.9 0.8 – 4.2

40 – 49 years 183 (91.0) 18 (7.0) 2.7 1.2 – 5.9*

50 – 59 years 116 (93.5) 8 (6.5) 1.9 0.7 – 4.8

60 years and over 103 (94.5) 6 (5.5) 1.6 0.6 – 4.4

Caste

General 557 (96.9) 18 (3.1) 1.0

OBC 137 (93.2) 10 (6.8) 2.3 1.0 – 5.0* 2.1 0.9 – 4.8

SC / ST 208 (87.4) 30 (12.6) <0.001 4.5 2.4 – 8.2* 3.2 1.7 – 6.2*

Religion

Hindu 755 (94.5) 44 (5.5) 1.0

Non-Hindu 147 (91.3) 14 (8.7) 1.6 0.9 – 3.0

House type

Permanent 760 (96.0 ) 32 (4.0) 1.0

Temporary 142 (84.5) 26 (15.5) <0.001 3.3 1.9 – 5.7 * 1.9 1.0 – 3.5*

Loan in last 6 months

No 845 (95.1) 44(4.9) 1.0

Yes 57(80.3) 14(19.7) <0.001 4.7 2.4 – 9.1* 3.0 1.4 – 6.2*

Education status

Unschooled 135(84.4) 25(15.6) 6.8 2.3 – 19.9 3.7 1.2 – 12.0*

Primary 168(94.4) 10(5.6) 2.2 0.7 – 7.0 2.0 0.6 – 7.1

Secondary 453(96.0) 19(4.0) 1.5 0.5 – 4.6 1.5 0.5 – 4.7

Graduate 146(97.3) 4 (2.7) <0.001 1.0

Employment

Professional / military 102 (92.7) 8 (7.3) 1.0

Self-employed 231 (97.5) 6 (2.5) 0.3 0.1 – 0.98*

Unskilled manual 341 (93.9) 22 (6.1) 0.8 0.4 – 1.9

Unemployed 228 (91.2) 22 (8.8) <0.05 1.2 0.5 – 2.9

NB * signifies statistical significance

Table 3 shows the help seeking behaviours of those screened as depressed (using PHQ9) or who self-

reported a greater than two week episode of depression in the last 12 months. People with

depression were five times more likely to have visited outpatient or inpatient providers than people

without depression. Separate analysis showed that all women, with and without depression, were

significantly more likely than men to have visited a health provider in the prior three months.

Table 3. Health-service-seeking behaviour

Not depressed

N (%)

Depressed

N (%)

No depression

in last 12

month

Depression in

last 12

months

Visited outpatient health

provider in last 3 months

138 (15.3) 45 (77.6)*

120 (13.8) 63 (68.5)*

No visit to outpatient 764( 84.7) 13 (22.4)* 748 (86.2) 29 (31.5)*

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months

Hospital admission in last

12 months

41 (4.5) 15 (25.8)* 35 (4.0) 21 (22.8)*

No hospital admission in

last 12 months

860 (95.5 ) 43(74.2)* 832 (96.0) 71 (77.2)*

nb * signifies statistical significance

Table 4 shows the type of health provider visited by the 183 people who made an outpatient visit in

the previous three month, Depressed people, compared to non-depressed, consult health providers

significantly more, and are far more likely to visit general health providers than mental health

services. Only two people had visited a mental health service provider in the previous three months.

No-one with depression had received talking therapy. Two people with depression at the time of the

survey had been prescribed antidepressants through private practitioners, while one respondent

who screened negatively for depression had been prescribed antidepressants.

Table 4. Type of health providers visited in the last three months

Type of outpatient provider

Not

depressed

N (%)

Depressed

N (% )

Government provider (CHC or PHC) 54 (40.3) 22 (47.8)

Community level government provider (PHC or ANM) 7 (5.0) 2 (4.3)

Private health provider 70 (52.2) 18 (41.8)

Mental health provider 1 (0.7) 1 (2.3)

Traditional healer 6 (4.4) 2 (4.3)

Total 138(16.2%

of total

non-

depressed)

45 (79% of

total

depressed)

Prescribed anti-depressants 1 (0.7) 2 (4.3)

Supported with talking therapy 0 0

DISCUSSION

This study shows a 6% prevalence of depression using a depression screening tool in a randomly

sampled population in Dehradun district, Uttarakhand. Some studies suggest prevalence in India

may be higher 4 6 29

. In this study no one had received talking therapy indicating a treatment gap of

100% for the recommended first-line treatment of mild or moderate depression 30

Anti-depressants

are the recommended second-line treatment for depression30

and although we cannot ascertain

how many of the 96.3% people would have benefitted from these medicine, it is likely this also

represents a large treatment gap.. Extrapolating these findings to Uttarakhand’s adult population of

6.6 million 20

we estimate 400,000 people may have depression of whom just 4,000 may have access

to anti-depressants and almost no-one is likely to have access talking therapy: a huge mismatch

between disease burden and health service provision.

This study shows much greater risk of depression for three groups – the poorest (those in houses

constructed from temporary materials and who had taken a recent loan), those who self-identify as

Other Backward Caste, Scheduled Caste or Tribe and the unschooled / illiterate. Each of these

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associations has an adjusted odds ratio for depression of at least twice their reference group. A

systematic literature review in low and middle income countries (LMIC) shows depression strongly

associated with socio-economic deprivation 8 supporting the WHO Commission on the Social

Determinants of health concept of mediating pathways that link poverty with lack of access to

political recognition and economic power31

. Other Indian studies also suggest socio-economic factors

as the key determinants of depression 4 5 7

. Like a ‘canary in a coal mine’ depression may be

conceptualised as an indicator of social inequity and vulnerability.

Although mental health has been linked with socio-economic disadvantage and social exclusion,

caste, a key indicator of social identity in India, has not been well investigated as a risk factor. One

Indian study of the prevalence of common mental disorders among rural women found no

association with caste 7. A further study in rural Nepal found that caste-based disparities in mental

health are mediated by poverty, lack of social support, and stressful life events 32

. However Sen’s

capabilities approach emphasises rights and command over goods, recognises the relational roots of

deprivation, and underscores the importance of agency and participation for genuine social

inclusion33

. This view is supported by others describing both psychosocial and biological pathways

between social exclusion and health 34 35

. It seems likely therefore that social exclusion, marked by

caste, increases the risk of depression.

For decades Indian national policy has sought to legislatively benefit Scheduled Castes and

Scheduled Tribes, e.g. the Reservation in Admission Act 2006 36

and the Protection of Civil Rights Act

1955 37

. Despite such measures, and even after controlling for socio-economic status (years of

schooling, housing quality, indebtedness), members of Scheduled Caste and Scheduled Tribe groups

in this study had more than twice the risk for depression compared to General Caste. It is likely that

persisting social structures of exclusion and discrimination are more penetrating than legislation and

continue to create relative deprivation, reduce agency and exclude.

Associations between poverty and common mental disorders in LMIC countries and in India are well-

described 13

. This study found that depression prevalence among people who had taken a recent

loan was thrice that of those who had not. Two other studies from India show indebtedness as a risk

factor for primary care attenders diagnosed with a common mental disorder (CMD) 13 38

. Links

between personal debt and mental health are well described 13 39 40

although there have been no

prospective longitudinal studies to show the direction of association. Possible mediating pathways

between depression and indebtedness include shame, stress of financial insecurity 39

, and less

capacity to earn income due to depression.

Another strong risk factor for depression in this study is educational status. People who had not

completed primary schooling had almost four times greater risk after controlling for caste, housing,

indebtedness and employment status. Our results show a striking dose-response relationship:

increasing years of education provide increasing protection. A meta-analysis examining the

associations between socio-economic inequality and depression also reported this finding 41

.

Education status has been described in India and other LMICs as predictive of mental health

outcomes 41-43

. Social consequences of low levels of education are multiple and move beyond

schooling as a marker of deprivation, including reduced opportunity to access resources and develop

protective social and cognitive skills, and increased risk for mental distress. Reverse causality is

unlikely to be a factor as primary education occurs at an age when common mental disorders are

uncommon.

Although people with depression were seeking care, they were not getting the help they needed

This study shows that most people with depression had attended primary health care providers in

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the previous three months, in both the Government and private sectors. The somatisation of

depression among people with depression in Asian cultures, particularly women, is well-described 44

.

Many health providers are unable to recognise somatisation, leading to excessive, costly and often

inappropriate investigation and treatment:. Also noteworthy is that only eight people in this sample

reported consultations with traditional healers. Others have described up to two thirds of people

with severe mental disorders in India seeking help from traditional healers during their illness17

. The

strong association between somatoform disorders and common mental disorders highlights the

need for primary care providers to be equipped with knowledge and skills to recognise and manage

the diverse presentations of common mental disorders 14 44

.

This study shows depression as a community outcome of macro-economic and political decisions

which can lead to mental distress and suffering for both communities and individuals. There are

several important implications for policy and practice. Foremost, since social determinants of health

almost certainly contribute to depression, macro-policies that address determinants such as poor

housing, caste, indebtedness and low education will also reduce depression disease burden. Policies

to improve mental health must seek to reduce poverty and social exclusion 45

and actively include

communities.

Secondly action is urgently required to increase provision of mental health services and medicines,

as strongly advocated by the Lancet mental health group and series 29 45

. However, perhaps more

importantly primary care doctors in India as the health care providers most commonly consulted by

people with depression 9 14

, need knowledge, skills and perception to recognise the diverse

presentations of common mental disorders such as depression and anxiety, and treat them

appropriately with both talking and pharmaceutical therapies.

METHODOLOGICAL CONSIDERATIONS

A major strength of this study is that its data is from a randomly selected population covering rural,

semi-urban and urban populations typical for a district in North India in 2014. Multi-variable analysis

ensured potentially confounding factors were considered. However, there are some methodological

limitations: self-reported measures may risk recall bias and cultural factors. The screening tool

(PHQ9) is a screening, not diagnostic tool, constructed using a definition of depression (DSMV and

ICD10) that has been critiqued as being over-simplistic and risks labelling components of normal

human experience as a disorder 15

. This cross sectional study cannot attribute causality to apparent

risk factors. The survey tool excluded three key risk factors - stressful life events, chronic illness and

disability. The lower than expected prevalence of depression (6%) may be related to the emphasis in

the PHQ9 on cognitive manifestations of depression possibly missing the somatic features common

in Asian cultures 44

.

CONCLUSION

Depression in Dehradun district of Uttarakhand with a prevalence of at least 6% is two or three

times more common among people who are economically deprived, part of the most excluded caste

group or who have had little education. Almost no one with depression accesses effective primary or

secondary mental health care. Social policy and health service responses must urgently address this

preventable and treatable disease burden and treatment gap. Common mental disorders are indeed

common, and disproportionately affect the most vulnerable.

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ACKNOWLEDGEMENTS

We would like to thank the hard-working Burans teams of OPEN, LCH, Sneha and HOPE for surveying/ data

collection in a very wet monsoon. Appreciation also goes to Kezia Chand, Gabriella Ailstock and Suraj Chetri for

their support in data quality, to Linda Seefeldt for support with Stata and to Rajan Arora, R. Srivatsan, Jeph

Mathias and Prachin Ghodajkar for suggestions on drafts of the paper.

a. Contributorship statement

KM conceived of the study and main design, performed data collection and data analysis and wrote the first

draft, IG and MK contributed to design and editing of first and subsequent drafts, LS supported data collection

and analysis, RS contributed to literature review and subsequent drafts and MSS supported study design,

analysis and overview of the whole paper.

b. Competing interests

We declare we have no competing interests

c. Funding

This research was partially funded by the Umeå Centre for Global Health Research, funded by FAS, the

Swedish Council for Working Life and Social Research, Grant number 2006 - 1512

d. Data sharing statement

No additional data available.

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BMJ Open...Email – [email protected] Phone - +91 8755 105 391 Postal – Landour Community Hospital Mussoorie, Uttarakhand India 248 179 Author details Kaaren Mathias Department