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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
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
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ovember 2015. D
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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
University, Umeå, Sweden
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
Department of Public Health and Clnical Medicine, Epidemiology and Global Health, Umeå
University, Umeå, Sweden
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
Mathias, K., Goicolea, I, Kermode,M., Singh, L., Shidhaye,R., San Sebastian,M
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
health provider in last 3
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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item inventory (PHQ12) as a screening instrument for assesing depression in Asian Indians (CURES -
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22. Mukherjee S. Conceptualisation and classification of caste and tribe by the Census of India.
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23. World Health Organisation. Social Commission on the Social Determinants of Health.
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24. Kohrt BA, Speckman RA, Kunz RD, Baldwin JL, Upadhaya N, Acharya NR, et al. Culture in
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relationship of caste with depression and anxiety in Nepal. Ann Hum Biol. 2009;36(3):261-80.
25. Sen AK. Social exclusion: Concept, application, and scrutiny: Office of Environment and Social
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26. Slavich GM, O’Donovan A, Epel ES, Kemeny ME. Black sheep get the blues: A
psychobiological model of social rejection and depression. Neurosci Biobehav Rev. 2010;35(1):39-45.
27. Ahmed AT, Mohammed SA, Williams DR. Racial discrimination & health: pathways &
evidence. Indian J Med Res. 2007;126(4):318-27.
28. Development MoHR. Central Educational Institutions (Reservation in Admission) Act. India:
Ministry of Human Resource Development; 2006.
29. Protection of Civil Right Act, (1955).
30. Patel V, Pereira J, Coutinho L, Fernandes R, Fernandes J, Mann A. Poverty, psychological
disorder and disability in primary care attenders in Goa, India. Br J Psychiatry. 1998;172:533-6.
31. Reading R, Reynolds S. Debt, social disadvantage and maternal depression. Soc Sci Med.
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mental and physical health: a systematic review and meta-analysis. Clin Psychol Rev.
2013;33(8):1148-62.
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
World Health Organ. 2003;81(8):609-15.
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
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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
University, Umeå, Sweden
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
Department of Public Health and Clnical Medicine, Epidemiology and Global Health, Umeå
University, Umeå, Sweden
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
Mathias, K., Goicolea, I, Kermode,M., Singh, L., Shidhaye,R., San Sebastian,M
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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health provider in last 3
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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