Prevalence of Depression Among Indian Population.

Meitei KT

ISSN 0972-5997

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Mangalore, India

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OJHAS Vol. 18, Issue 4: (October-December 2019)

Review
Prevalence of Depression Among Indian Population

Authors:
Khangembam Taibanganba Meitei, Ph.D. Scholar,
Huidrom Suraj Singh, Assistant Professor,
Department of Anthropology, Manipur University, Canchipur, Imphal West, Manipur, 795003, India.

Address for Correspondence
Khangembam Taibanganba Meitei,
Department of Anthropology,
Manipur University,
Canchipur, Imphal West,
Manipur, 795003, India.
E-mail: nganbakhangembam@manipuruniv.ac.in.

Citation
Meitei KT, Singh HS. Prevalence of Depression Among Indian Population. Online J Health Allied Scs. 2019;18(4):12. Available at URL: https://www.ojhas.org/issue72/2019-4-12.html

Submitted: Nov 13, 2019; Accepted: Jan 15, 2020; Published: Feb 28, 2020

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This work is licensed under a Creative Commons Attribution-No Derivative Works 2.5 India License

Abstract: Depression becomes one of the most serious global health problems affecting at every stage of an individual life, thereby imparting the ability to function one's daily activities. India was accounting for about 13.98 % of the total population with depression in the world. The present paper attempts to understand the prevalence of depression among the Indian population. Relevant studies were identified using an electronic database such as PubMed, Medline, PsycINFO, and Google Scholar and included those studies which show the prevalence of depression among the Indian population for the last fifteen years. The overall prevalence of depression was pooled using random-effect models. The prevalence rate of depression varied from adolescents (6.7 to 79.21 %), college students (16.5 to 71.3 per cent), elderly population (8.9 to 77 %) and general population (6 to 52.7 per cent) with an overall pooled prevalence of depression 36.2 per cent (95% CI 31.6 to 41.8 %). Poor academic performance, family problems, and female gender are highly associated with depression among adolescents and college students. Among Elderly and General population groups increasing age, illiterate, unemployment, female gender, and medical comorbidities are highly associated with depression. Depression can be controlled by giving public awareness about its health consequences and educate people that mental illness is the same as other types of illnesses like diabetes, heart attack, liver problems, and one's need to take counseling or medications with the advice of mental health professionals.
Key Words: Depression, Mental illness, Adolescents, College students, Elderly, General population, Mental health professionals

Introduction

Depression is a common mental disorder, characterized by persistent sadness and a loss of interest in activities that one usually enjoys, accompanied by an inability to carry out daily activities, for at least two weeks (1). As the slogan for World Health Day 7th April 2017 – "Depression: Let's Talk" indicates depression as one of the leading causes of disability globally. Globally, depression is ranked as the single most significant contributor to non-fatal health loss, accounting for 7.5 % of global years lived with disability (YLDs) and 2.0 % of global disability-adjusted life years (DALYs) (1). Depression has ranked as the 4th leading cause of disability worldwide, and it is believed to be the second leading cause of disability by 2020 (2). Depression can be long-lasting or recurrent, substantially impairing an individual's ability to function at work or school or cope with daily life. In its most severe condition, it can lead to suicide (1).

Depression can affect people from all backgrounds across the life-course, from early childhood to the end stages of life. As per National Mental Health Survey, 2015-16 (3) in India, 1 in 20 (5.25 %) people over 18 years of age have ever suffered (at least once in their lifetime) from depression amounting to a total of over 45 million (13.98 %) persons with depression in 2015. It also reported a prevalence rate of 0.8% for depression among adolescents (13- 17 years old). So, the present study tries to understand the prevalence of depression among India population-based on previously reported studies (2004-2018) among Indian populations.

FIGURE 1: Flow chart of the articles included in the study

Methods

A search of articles that described the prevalence of depression among the Indian population was identified using electronic search engines such as PubMed, Medline, PsycINFO, and Google Scholar. The search words included "depression," "depressive disorders," and "India" with the combination of "prevalence," "outcomes," and "associated risk factors."

Firstly, titles and abstracts of the articles identified by the electronic search were listed out, and duplicate articles were excluded. Secondly, abstracts of the retrieved articles were screened to sort out relevant articles. For finding relevant articles, reference lists of the selected articles were also screened. All studies included in the present study were performed on representatives and probabilistic samples to enhance the value of the review.

Study inclusion criteria are as follows:

Among the Indian population (adolescents, college students, elderly population and the general population)
Only studies indicating the prevalence of depression
Articles published in the English language for the past 15 years (2004-2018).

Exclusion criteria are as follows:

Hospital-based study
Studies associated with other psychiatric disorder
Studies conducted in association with any form of illness

For extraction of data from the included articles, a standardized data collection form was used in Microsoft word and the following information were extracted from the articles: first author of the study, publication date, population studied, study design, sample size, associated risk factors, assessment tools used and prevalence rate by both the researcher independently. All inconsistencies were resolved through discussion and consensus.

Since the included articles vary in terms of the population studied and assessment tools used to understand the prevalence of depression, considerable heterogeneity was expected, and a random effect model was used to estimate the pooled prevalence is necessary (4). The random-effect model attempts to generalized findings of the included studies by assuming that the selected studies are random samples from a larger population (5). A random effect-models was used to estimate the pooled prevalence of depression for meta-analysis using excel (6).

FIGURE 2: Indian Map showing the Prevalence of Depression

Results

A total of 150 articles were obtained through electronic search engines, 50 were duplicate articles, and were excluded. Titles and abstracts of 100 articles were screened, and 60 full-text articles were eligible for undergoing full assessment. 15 full-text articles were excluded as they failed to meet the eligible population criteria (Figure 1). Finally, a total of 45 articles were included in the review, among them 22, 9, 9, and 5 studies reported prevalence of depression among adolescents, college students, elderly population, and the general population of India, respectively (Table 1). The majority of the studies were conducted among a few states (Punjab, Uttar Pradesh, Maharashtra, Karnataka Tamil Nadu) of India. Little studies were conducted among the states (Haryana, Gujarat, Uttarakhand, Madhya Pradesh, Kerala, Tamil Nadu, West Bengal, Bihar and Manipur and Union Territories (Delhi, Puducherry) of India. Studies, among other states, were not found (Figure 2).

The most common depression assessment tools (Table 1) for all studies which shows prevalence of depression in India were Becks Depression Inventory (BDI) followed by Patient health questionnaire-9 (PHQ-9), Geriatric depression scale (GDS), DASS (Depression Anxiety Stress Scale), Kutcher Adolescent Depression Scale (KADS), Zung Self-Rating Depression Scale (ZDS), Children’s Depression Inventory (CDI), Depressive Experiences Questionnaire (DEQ), WHO–five well-being index and Centre for Epidemiologic Studies Depression Scale (CESD) rating scales. All tools are internationally accepted for assessing depression, and a researcher has the liberty to use any tool in their study. The present review proposes PHQ-9 will be the most suitable tool that a researcher can employ for assessing depression among the Indian population as it can be used among every population (adolescents, college students, elderly population, and the general population) included in the study which is summarized at Table 1.

Table 1: Studies showing the prevalence of depression among adolescents, college students, elderly population and general Population of India (2004-2018)
(a) Adolescents
Sl. No.	Author year	Study design, Study population	Individuals with depression/ Total sample	Class/ Age groups (yrs.)	Findings of the study associated with depression	Tool used	Prevalence of depression (%)
1	Nair et al., 2004	Adolescents of school/college/ school dropouts Thiruvananthapuram, Kerala	220/ 1014 [Both Sex]	(13-19)	Female gender, school dropout girls	BDI	21.7
2	Bansal et al., 2009	Cross-sectional one-time observational study, school going students, Pune	23/125 [Both Sex]	Class 9 students	Economic difficulty, physical punishment at school, parental fights	BDI	18.4
3	Rani and Karunanidhi (2010)	Cross sectional study, 21 private schools, Chennai, Tamil Nadu	586/964 [Both Sex]	Class 10-12 students (14- 18)	Increasing age (Older adolescents),	BDI	60.8
4	Nagendra et al., 2012	Cross sectional study, adolescent students, Karnataka	1805/3126 [Both Sex]	(5-19)	Male gender, increased age, residential school students, nuclear family	BDI	57.7
5	Manna and Pandit (2014)	Cross sectional study, school-going adolescent girls, west Bengal	86/435 [only girls]	Class 8-10 Students (13-18)	Nuclear families, absence of siblings, high expectations of academics performance from parents	DASS	19.8
6	Mehdi (2014)	Adolescents going schools from Delhi, Mumbai, and Chandigarh	125/800 [Both Sex]	(14-19)	Female gender	ZSRS	15.6
7	Naushad et al., 2014	Cross sectional study, college-going adolescents, Karnataka	244/308 [Both Sex]	(16-19)	Increasing age, male gender, students of government college	BDI	79.2
8	Surabhi et al., 2014	Cross sectional study, public school, Noida, Uttar Pradesh	136/360 [Both Sex]	Class 9-12 students (15-17)	Female gender, increasing Body Mass Index (BMI)	PHQ-9	37.8
9	Umesh et al., 2014	Cross sectional study, school-going adolescents of rural Maharashtra	20/300 [Both Sex]	Class 8-12 (12-18)	Student residing at hostels, nuclear family	KADS	6.7
10	Jayanthi and Thirunavukarasu (2015)	Cross sectional study Higher secondary students, Tamil Nadu	612/2432 [Both Sex]	Class 9- 12 students (14-17)	Higher grades, increasing age	BDI	25
11	Malik et al., 2015	Cross sectional study, school going students, urban areas of city Rohtak, Haryana	198/374 [Both Sex]	Class 9-10 students (13-17)	-	BDI	52.9
12	Patil (2015)	Cross sectional survey, college-going adolescents, Karnataka	340/500 [Both Sex]	-	-	BDI	68
13	Meghna et al., 2016	English-medium schools of Bangalore, Karnataka	144/800 [Both Sex]	Class 8, 9 and 11 students (13-18)	Female gender, lower academic performance	CDI	18
14	Trivedi et al., 2016	Adolescent students, Karnataka	88/392 [Both Sex]	Class 7,8,9	Older adolescent age (14-15 yrs.), female gender, lack of moral support from parents	BDI	22.45
15	Kunal et al., 2017	Cross sectional, observational study, school-going Adolescents, Patna district, Bihar	695/1412 [Both Sex]	Class 9- 12 students (14-18)	Female gender, religion (minority)	BDI	49.2
16	Mohan et al., 2017	Cross sectional study, multi-stage sampling technique, School going adolescents, Chandigarh	217/542 [Both Sex]	Class 9-12 (13-18)	Government schools, lower socio-economic status, students from rural areas, poor parenting, physical punishments in schools, lack of self-satisfaction in exams	PHQ-9	41.33
17	Raman et al., 2017	Cross sectional survey of government schools of Chandigarh	307/470 [Both Sex]	Class 9-12 (13-18)	Female gender	DASS	65.32
18	Satish and Brogen (2017)	Cross- sectional study, higher secondary students, Imphal Manipur	75/405 [Both Sex]	(16-19)	Female gender, Higher among 12^th standard students	DASS	18.5
19	Shukla et al., 2017	Cross sectional study, school-going adolescent girls in Barabanki district, Uttar Pradesh	56/336 [only girls]	(10-19)	Lower socio-economic status	KADS	16.7
20	Archana and Ravneet (2018)	Cross sectional study, institutional homes, Vishakhapatnam, Andhra Pradesh	19/150 [Both Sex]	(12-15)	Female gender, poor academic performance	PHQ-9	12.5
21	Mishra et al., 2018	Community-based, cross-sectional study, Varanasi district, Uttar Pradesh	29/200 [Both Sex]	(11-18)	Living in a joint family, lower-middle socio-economic groups	CDI	14.5
22	Subhashini et al.,2018	Undergraduate College going adolescents, Karnataka	86/300 [Both Sex]	-	Engineering students, male gender, smoking, Alcoholism	BDI	28.7
		Total	6111/15,745
(b) College students:
1	Ganesh et al., 2012	Cross sectional study, Medical students, Mangalore, Karnataka	285/400 [Both Sex]	1^st, 2^nd, 3^rd, and 4^th years	Family problems, higher grades, family history of depression	BDI	71.3
2	Jagdish et al., 2014	Cross-sectional survey, private medical students, Gujarat	212/331 [Both Sex]	1^st, 2^nd, 3^rd, and 4^th years (17-23)	Lower grades, Female gender, emotional distress, exam stress	PHQ-9	64.1
3	Kaur et al., 2014	Cross sectional study, medical and engineering college students, Amritsar, Punjab	33/200 [Both Sex]	(18-24)	Break up from friends/ lovers, Engineering students, parental conflicts, family history of depression	PHQ-9	16.5
4	Anshuman et al., 2015	Cross sectional study, Medical students of private university, Bhopal	123/390 [Both Sex]	(19-24)	Regularity in attendance, internship students,	DEQ	31.5
5	Rashmi et al., 2016	Cross sectional study. Medical students, Jhansi, Uttar Pradesh	188/330 [Both Sex]	1^st, 2^nd, 3^rd, and 4^th years	Family problems, staying at the hostel. Lower grades, family history of depression, substance abuse	DASS	56.9
6	Ranu et al., 2016	Cross sectional study, private medical students, Kerala	174/300 [Both Sex]	1^st, 2^nd, 3^rd, and 4^th years	Lower grades, family problems, family history of depression, financial problems, substance abuse	PHQ-9	58
7	Ganesh et al., 2017	Cross sectional study, medical students, Puducherry	215/444 [Both Sex]	(17-24)	Poor school performance (less than 35 %), Interpersonal Problems	BDI	48.4
8	Manjot et al., 2017	Cross sectional study, students of Punjab University, Chandigarh	237/400 [Both Sex]	(17-31)	Female gender	DASS	59.3
9	Neha et al., 2018	Medical college students, Delhi	60/187 [Both Sex]	1^st, 2^nd, 3^rd, and 4^th years	Family history of mental illness, poor relationship with family members, dissatisfaction with body image, lower grades	DASS	32.1
		Total:	1527/2982
(c) Elderly population:
1	Barua and Kar (2010)	Cross sectional study, the elderly population of rural area, Udupi district, Karnataka	132/609 [Both Sex]	(≥ 60)	Illiterate, female gender, older age (≥75yrs.)	WHO-5	21.7
2	Shankar and Abdul (2013)	Cross sectional study, the elderly population in a rural area, Tamil Nadu	235/400 [Both Sex]	(≥ 60)	Older age (≥70 yrs.), low socio-economic status, loss of spouse, smoking (more than 20 years), lower education (illiterate), medical co-morbidities (hypertension, diabetes, orthopaedic problems)	GDS	58.8
3	Anita and Kajal (2014)	Cross sectional study, elderly population, southern part of Punjab	77/100 [Both Sex]	(≥ 60)	Illiterate, unemployed	GDS	77
4	Sanjay et al., 2014	Cross sectional study, the elderly population of poor urban locality, Bengaluru	36/100 [Both Sex]	(≥ 60)	No financial assistance, no assets, medical co-morbidities ( hypertension, diabetes)	GDS	36
5	Sengupta and Benjamin (2015)	Cross sectional study, elderly population of urban and rural areas, Ludhiana Punjab	271/3038 [Both Sex]	(> 60)	Urban residents, female gender, older age (80 and above), widow/ widower, lower education (illiterate), nuclear families	GDS	8.9
6	Nageswara (2016)	Non-experimental survey method, purposive sampling technique, the elderly population of rural and urban areas, Pune, Maharashtra	127/200 [Both Sex]	(≥ 60)	Unemployed, urban residents	GDS	63.5
7	Pooja et al., 2016	Cross sectional study, elderly population of rural, Nellore Andhra Pradesh	27/290 [Both Sex]	(≥ 60)	Increase age (≥ 70 years), widow/ widower, female gender, chronic medical illness (heart disease, diabetes, cancer, and hypertension)	GDS	9.3
8	Soni et al., 2016	Cross sectional, elderly population, Bihar	179/450 [Both Sex]	(≥ 60)	Female gender , single/widowed/widower/separated/ divorce	GDS	39.8
9	Kanimozhi et al., 2017	Cross sectional study, elderly population, Puducherry	369/640 [Both Sex]	(≥ 60)	Female gender, single/widowed, illiterate, lesser physical activity	GDS	57.7
		Total	1453/5827
(d) General population:
1	Poongothai et al., 2009	Urban population. Chennai, Tamil Nadu	3847/25,455 [Both Sex]	(≥ 20)	Increase age (≥60 yrs.), female gender, lower economic status (<5000/month), lower education (Illiterate), medical co-morbidities (high blood pressure and diabetes)	PHQ-12 (modified PHQ-9)	15.1
2	Jos et al., 2014	Cross sectional study, rural region, Maharashtra	2457/4698 [Both Sex]	(36-62)	Female gender	CESD	52.7
3	Kaaren et al., 2015	Population-based study, Dehradun, Uttarkhand	58/960 [Both Sex]	(≥ 18)	Urban residents, female gender, older age (≥ 80 yrs.), widow/ widower, lower education, nuclear families	PHQ-9	6
4	Rahul et al., 2016	Cross sectional study, Amravati district in Vidarbha, Maharashtra	212/1456 [Both Sex]	(≥ 18)	Increasing age, female gender, lower education, lower socio-economic status	PHQ-9	14.6
5	Sadia et al., 2016	Community based, cross sectional study, Uttar Pradesh	35/360 [Both Sex]	(≥ 19)	Female gender, increase age (≥ 60 yrs.), widowed/divorced/separated, unemployed, illiterate, family history of depression, lower economic status (less than Rs. 5000/month)	PHQ-9	9.7
		Total	6609/32,929
	Overall population	Grand total	15,700/57,483
BMI- Body Mass Index; BDI- Becks Depression Inventory; PHQ-9- Patient Health Questionnaire-9; GDS- Geriatric Depression Scale; DASS- Depression Anxiety Stress Scale; KADS- Kutcher Adolescent Depression Scale; ZSRS-Zung Self-Rating Depression Scale; CDI- Children’s Depression Inventory; DEQ- Depressive Experiences Questionnaire; Who-5-WHO–five well-being index; CESD- Centre for Epidemiologic Studies Depression Scale

Part (a), (b) (c), and (d) of Table 1 show the studies indicating the prevalence of depression among adolescents, college students, the elderly population, and the general population.

FIGURE 3: Forest Plot of 45 studies which shows the prevalence of depression in India

Adolescents

It belongs to the adolescents of school students, college students, and drop out students belonging to the age groups of 10 to 19 years, which includes 22 studies published from the year 2004 to 2018 (7-28). The sample size of the studies ranges from 125 to 3126, giving a total of 15745 samples. The prevalence rate of depression varied from 6.7 to 79.2 %. The pooled prevalence rate of depression for adolescents (n=6111) was 33.9 % (95% CI 26.3 to 41.4 %) (Figure 3). The most common associated risk factors of depression among adolescents were physical punishment at schools, parental conflicts, poor academic performance, belonging to nuclear families, older adolescents, and the female gender. Few studies also reported that male gender, poor academic performance, Decrease Body Mass Index (BMI), smoking, and alcoholism are associated with adolescent depression.

College Students

Students of Medical, Engineering, and General Course Colleges / University were included, with a maximum number of studies among Medical students with age ranges from 17 to 31 years, which includes 9 studies from the year 2012 to 2018 (29-37). The sample size of the studies ranges from 187 to 444, with a total of 2989 samples. The prevalence rate of depression varied from 16.5 to 71.3 %. The pooled prevalence rate of depression among college students (n=1527) was 48.5 % (95% CI 35.9 to 61.1 %) (Figure 3). The most common risk factors associated with depression among college students were family problems, female gender, poor performance in exams, Breakups from friends or loved ones, first-year students, financial problems, and family history of depression. Few studies also find that dissatisfaction with self-body image, substance abuse, higher year, and internship students are associated with depression during this stage of life.

Elderly Population

60 years and above the elderly population were included from 9 studies from the year 2010 to 2017 (38-46). The sample size of the studies ranges from 100 to 3038, with a total of 5827 samples. The prevalence rate of depression varied from 8.9 to 77 %. The pooled prevalence rate of depression among Elderly Population (n=1453) was 40.5 % (95% CI 27.1 to 53.9 %) (Figure 3). The most common risk factors associated with depression among the Elderly were female gender, illiterate, lack of financial assistance, increasing age, medical comorbidities like hypertension and diabetes, lower socio-economic status, single/widowed/widower/separated/ divorce and lesser physical activity.

General population

It includes 5 studies form the year 2009 to 2016, which shows the prevalence of depression among the general population age ranges from 18 years and above (47-51). The sample size of the studies ranges from 360 to 25,455, with a total of 32,929 samples. The prevalence rate of depression varied from 6 to 52.7 %. The pooled prevalence rate of depression among the General Population (n=6609) was 19.6 % (95% CI 7.1 to 32 %) (Figure 3). The most common risk factors associated with depression among the General population were female gender, illiterate, increasing age, lower socio-economic status, medical comorbidities (hypertension, diabetes, orthopaedic problems), and family history of depression.

The overall pooled prevalence rate of depression of all the studies included in the present review is 36.2 % (95% CI 31.6 to 41.8 %) (Figure 3).

Discussion and Conclusion

We reviewed the included articles to understand the overall prevalence of depression in the studies conducted in India. The study places were confined to a few states of India, and very fewer literature was found in North-east India (Figure 2). Different studies adopt different assessment tools, and the most widely used tools were BDI, PHQ-9, and GDS. BDI and PHQ-9 were used to screen depression in all population groups, whereas GDS was specially designed to screen depression among elderly groups. The present review proposed BDI and PHQ-9 to be the most suitable assessment tools that a researcher can employ to screen depression among the Indian population, and a researcher has the liberty to choose any tool in their study. The review also indicates that depression affects profoundly among college students (48.5 %), followed by the Elderly population (40.5 %) Adolescents (33.9 %), and the General population (19.6 %) with an overall prevalence rate of 36.2 % (Figure 3). Indicating College students and Elderly groups are more prone to developing depression, which may hamper both mental and physical health. The prevalence of depression varies from one study to another, depending on the population studied and the employment of assessment tools (Table 1). Most of the studies were conducted among adolescents (22 studies) and 9 studies among college students and elderly groups and least among the General population (5 studies). Prevalence of depression is low among the general population group as it covers a wide range of age understudy while studies on other studies were confined to a limited age range. Most of the studies show that depression was higher among females as compared to males. While a few studies by Nagendra et al., 2012; Naushad et al., 2014; Subashini et al., 2018(10,13,28) among adolescents show, depression was higher among males than females. All the studies favor depression increases with increasing age. Among adolescent's punishment at schools, family conflicts, poor parenting, poor performance in exams, and belonging lower socio-economic status were the most common risks associated with depression. Among college students' emotional distress, break ups, family problems, more pressure in their study, and substance abuse were the most common risks associated with depression. Illiterate, unemployment, widow/widower/ separated, and medical comorbidities were the risks associated with depression among the elderly population and the general population.

Pros: It summarises research studies that show the prevalence of depression for the last fifteen years (2004-2018) in India. It is the first study that attempts to show the combine prevalence of depression in all age groups of the Indian population.

Cons: Meta-regression was not performed in order to identify substantial heterogeneity between the studies included in the present review. Assessment tools were also different among the studies included in the review.

Suggestions

The researcher suggests that depression is an illness which is like other physical illness such as diabetes, cardiovascular, cancer, etc. It also affects both the body and mind of an individual, thereby imparting daily activities and increase mortality. Proper awareness of the ill effects of depression should be given. Necessary programs should be taken up to educate people to free from social stigma about depression. Health agencies should promote different ways to overcome depression and develop many health programs to reach the public and helps them to understand the disorder. There should be a proper assessment tool to used specific only among the Indian population.

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