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ORIGINAL ARTICLE
Year : 2021  |  Volume : 30  |  Issue : 1  |  Page : 90-95  Table of Contents     

The prevalence of depression among the elderly people living in rural Wardha


1 Associate Medical Manager, Covance, Pune, Maharashtra, India
2 Department of Community Medicine, AIIMS, Nagpur, Maharashtra, India

Date of Submission30-May-2017
Date of Acceptance23-Mar-2021
Date of Web Publication11-May-2021

Correspondence Address:
Dr. Sourav Goswami
Covance, Hinjewadi Phase 1, Pune, Maharashtra
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ipj.ipj_43_17

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   Abstract 


Background: Depression is the most common psychiatric disorder among elderly population in India, which generally remains undiagnosed and undertreated. Exact burden of depression among the elderly population in rural India was not known. Aim: This study was conducted to determine the prevalence of depression among the elderly population in rural population of Wardha, Maharashtra. Materials and Methods: This is a cross-sectional study carried out among the elderly (≥60 years) population of both sexes residing in the field practice area of the department of community medicine. Geriatric depression scale was used for screening depression among the study population. Data collection was completed within 2 months using convenience sampling. Ethical approval was taken before beginning the study. Magnitude was expressed in percentage along with its 95% confidence interval (CI). Univariate and multivariate logistic regressions were done. Odds ratio and 95% CI were used to express association. Results: Magnitude of depression among the elderly population was found to be 41.7% (95% CI: 36.1–47.4). In this study, we found the following factors to have positively contributed towards depression among elderly population in rural Wardha: female sex, widowed, separated, divorced, decreased decision-making capability, abused, or being suffering from chronic illnesses. Conclusion: Our study showed the prevalence of mild depression to be 26.72% and that of severe depression to be 15.17% among the elderly study participants.

Keywords: Depression, elderly, geriatric, rural population


How to cite this article:
Goswami S, Deshmukh PR. The prevalence of depression among the elderly people living in rural Wardha. Ind Psychiatry J 2021;30:90-5

How to cite this URL:
Goswami S, Deshmukh PR. The prevalence of depression among the elderly people living in rural Wardha. Ind Psychiatry J [serial online] 2021 [cited 2021 Aug 1];30:90-5. Available from: https://www.industrialpsychiatry.org/text.asp?2021/30/1/90/315776



With the development of improved treatment regimens, better life-saving drugs, and better prevention of infectious diseases, life expectancy in India has increased by 5 years, from 62.3 years for males and 63.9 years for females in 2001–2005 to 67.3 and 69.6 years, respectively, in 2011–2015, and the projected life expectancy during 2012–2025 will be 69.8 and 72.3 years, respectively.[1] However, as people get older, they are vulnerable to different medical and psychological problems and depression in this age group, which needs a special mention. In rural India, physical problems like pain, deafness, deafness etc and health issues like increased blood pressure and increased blood sugar, which can be measured are generally given more importance.[2],[3] Most of their visits to hospital are as a result of these issues. Unfortunately, the mental health issues, specifically depression, are hardly addressed, both by the patients and by the healthcare personnel. This problem is not new. In 1990, the World Health Organization (WHO) described depression as a major cause of disability globally. Mental and behavioral disorders are estimated to account for 12% of the burden of disease worldwide, which affects approximately 450 million people.[4] It has been postulated that depression will become the single most leading cause of disability-adjusted life years in the developing countries.[5] The WHO estimated that the overall rate of prevalence of depressive disorders among the elderly population generally varies between 10% and 20%, depending on the cultural scenarios.[6] The community-based mental health studies in India have revealed that the point prevalence of depressive disorders in the elderly Indian population varies between 13% and 25%.[7],[8]

Depression among the geriatric population is a neglected problem in India. India is in the phase of demographic transition which is attributed to decreasing fertility and mortality rates as a result of availability of better healthcare services. This has resulted in gradual increase in geriatric population in India.[9] The United Nations Population Fund Report[10] suggests that the number of elderly persons is expected to grow to 2 billion by 2050, accounting for 22% of the total population. Due to modernization, people are now preferring to live in nuclear families, both in rural and urban areas, resulting in loneliness and lack of family, as well as social support to the elderly, which adds on to their deteriorating health conditions, ultimately making them an easy victim of depression. Depression among geriatric population remains an untold truth and is being severely neglected.[4] Although a number of elderly-friendly schemes and programs are being launched in India,[11],[12] it lacks the zeal to deal with this problem of depression. Adding to it, it is unfortunate to say, in India, that very few studies have been conducted in this topic resulting in lack of proper evidence of the burden of the disease. As a result of all these, the current study has been planned to be executed to know the magnitude of depression among the elderly masses in rural Wardha and to find its correlates.


   Materials and Methods Top


Study settings

This is a cross-sectional study carried out in the field practice area of Rural Health and Training Centre (RHTC) Bhidi, under the Department of Community Medicine, Mahatma Gandhi Institute of Medical Sciences (MGIMS), Sewagram. RHTC Bhidi covers a population of 10,739. Apart from having a rural hospital (RH) in Bhidi, the RHTC also runs community-owned clinics, called “Kiran Clinic” in five different villages, namely Kajalsara, Anji, Wabgaon, Kharda, and Babhulgaon, which provide curative, preventive, and promotive services to the rural masses. These clinics were established by the department of community medicine in the period of Community-Led Initiative for Child Survival in the field practice area in 2003 and are still running. The clinic is a collaborative partnership between the department of community medicine and the community of village, wherein the department only provides technical support in the form of doctor and nurse staff.[13] There are also weekly specialist clinics including the department of psychiatry, run by MGIMS, Sewagram, at the RH.

Study population

The study was carried out among the elderly population (age ≥60 years) of both sexes residing in the rural area of Bhidi, which was our study population.

Sampling technique and sample size

Taking the prevalence of depression among geriatric population to be 25%[7] and absolute precision of 5, the sample size required for our study was 287 (≈290) for 95% confidence level. The sample size calculation was calculated using OPEN EPI software.[14] For the ease of the study, convenience sampling technique was used.

Measurements

Two tools were utilized for collecting the data for screening depression and their associated sociodemographic parameters.

Depression

Geriatric Depression Scale,[15] long form of 30 questions, was utilized to screen depression among the elderly population and to classify them into (a) normal (0–9), (b) mild (10–19), and (c) severe (20–30) depression. This scale was developed as a basic screening measure for depression in older adults and is widely used. We have used its Marathi version in this study[16] as the study participants were mostly Marathi speaking and were comfortable in answering to the Marathi questionnaire, though we are not sure if the Marathi version of the questionnaire is psychometrically standardized.

Questionnaire for sociodemographic determinants [Questionnaire 1]

This questionnaire was prepared based on the standard questionnaire for the elderly given in the “The Status of Elderly in Selected states of India, 2011.”[17] This questionnaire was pretested and suitably modified to meet with the study objective. Using this questionnaire, we have captured a number of sociodemographic determinants of the participants, among which the following are worthwhile to be mentioned.

Age ≥60 years is taken as geriatric age group in this study. The age groups are divided into three: 60–69 years, 70–79 years, and ≥80 years. We have included both sexes in our study. Marital status, schooling, and whether suffering from any chronic illness or being abused/neglected since 60 years of age by family members/neighbors were also noted. Annual individual income or whether still dependent on family members financially was also noted.

Data collection

As per the curriculum of postgraduate training program in the Department of Community Medicine, MGIMS, the principal investigator was posted in RHTC Bhidi for 2 months from October to November 2015, where he/she had to attend the community health clinics (Kiran Clinics[13]) at five different villages under the field practice area of RHTC Bhidi. This scope of rural posting was utilized for the collection of data by interviewing the elderly (≥60 years) patients or the elderly relatives who visited the weekly field clinics and RH by the principal investigator. Apart from normal days of working of the clinic, the investigator payed extra visit to the village, to complete the data collection. Questions were asked in the language which the study subjects understood. Data collection was completed within the 2 months of posting at RHTC Bhidi. In an average, 3–4 interviews were conducted per day.

Ethical consideration

Ethical approval was taken from the institutional ethical committee before beginning the study. Written informed consent was taken from each participant before starting the interview. All cases of depression who were diagnosed during the study were referred to the weekly psychiatry clinic at the RH for further management and were followed up subsequently.

Analysis

Data entry and analysis were done using EPI Info Software.[18] Prevalence was expressed using percentage and 95% confidence interval (CI). Association with various determinants was studied using odds ratio with 95% CIs derived using univariate and multivariate logistic regressions.


   Results Top


[Table 1] shows the age- and sex-wise distribution of the study subjects. Of the 290 study population, 129 were male and 161 were female. It is seen that the maximum number of the population was in the age group between 60 and 69 years.
Table 1: Age and sex distribution among the study population (n=290)

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In our study, 41.7% (95% CI: 36.1–47.4) were suffering from depression, among which 63.7% were suffering from mild depression and 36.37% were suffering from severe depression, which is depicted in [Figure 1]. In our study, the females (60.9%) were found to be more depressed than the males (33.3%) and it was found to be statistically significant.
Figure 1: Prevalence of depression among the study population as per Geriatric Depression Scale-30

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[Table 2] shows the results of both univariate and multivariate logistic regressions. In univariate logistic regression, higher odds were observed among the females (1.9 [95% CI: 1.2–3.0]) as compared to the males; those elderly who were widowed or separated or divorced were also found to have higher odds (2.5 [95% CI: 1.5–4.2]) when compared to the elderly population who were having spouse. Similar findings of having higher odds of 4.8 (95% CI: 2.5–9.8) were found among those elderly who were suffering from any of the chronic illnesses such as hypertension, diabetes, multiple joint pains, myalgia, respiratory problems, and cancer than those who were not suffering from those diseases. Odds were also found to be higher (4.4 [95% CI: 1.3–20.3]) among the study population whose role as a decision-maker in the family has decreased after becoming aged compared to those who still took important decisions of the family. Finally, the study population who reported to have been victim of abuse or violence or neglect, mostly by family members and neighbors, was also found to have higher odds of 2.7 (95% CI: 1.5–4.9) when compared to those who never suffered from abuse, violence, or neglect. The reference category has been selected after literature review and brain storming. They represented the group of population, who were considered more immune to depression.
Table 2: Association of sociodemographic characteristics and depression (n=290)

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Odds of older age groups (≥70 years), illiterate, inability to work at present, decreased individual annual income, and financial and physical dependency were not found to be significantly higher than their counterparts. After adjusting for other factors in multivariate logistic regression, none of the factors were found to be significant.


   Discussion Top


In our study, the prevalence of depression among the elderly population was found to be 41.7%. Various studies have revealed that the prevalence rates for depression in the community samples of the elderly in India varied from 6% to 58%.[24],[25],[26],[27] We also tried to look for association of depression with a number of factors such as age group, sex, marital status, schooling, working status, annual income, dependency, disease status, role as decision-maker, and whether being a victim of abuse or not. As we considered that more than one factor of independent variables influenced the variability of dependent variables, so, to draw a more accurate conclusion, we have done the multivariate analysis after the bivariate analysis. Although many of those factors showed positive association in univariate analysis, none of them showed a significant positive association in multivariate logistic regression.

Magnitude of depression (41.7%) among geriatric population, in our study, was found to be at per with other similar studies conducted at Salem, Kanchipuram, and Hoogly,[21] India. However, in a study conducted in Ludhiana,[19] the prevalence of depression among the elderly was found to be only 8.9%; one reason for this could be the inclusion of urban population in their study. Urbanization has its own set of advantages and disadvantages. In spite of overcrowding, pollution, high levels of violence, and reduced social support, people have better facilities and are at ease for early diagnosis of their mental health.[28] Movement of people to urban areas has led to a large number of elderly men and women left to look after themselves in the villages, while the young generation lives in urban areas for their livelihood. Hence, the elderly population in the rural areas are staying all alone most of the time, which gives rise to depression. Poor health infrastructure in the rural setup forbids early diagnosis of their depressed mood, and their condition gradually starts to aggravate. Further, the depressive symptoms are generally dismissed as “normal” by the elderly person, by their family members, and even by the healthcare providers.[29]

In the current study, the magnitude of depression was found to increase with increasing age. Although age effect was not statistically significant, similar findings were found in the prevalent studies conducted by Sengupta and Benjamin[19] and Radhakrishnan and Nayeem.[20] Some of the reasons for the sudden increase in the prevalence from the age of 70 years may be an increased economical and physical dependency, loss of the spouse, negligence by the family members, and loss of self-esteem.[25],[26]

We found the female elderly population to be suffering more from depression as compared to the male population. Increased rate of widowhood, living alone, negligence by family members, poor social status in the family, increased physical dependency, lack of income, and poor health among the elderly females may contribute to the increased prevalence of depression among them. Studies[19],[20],[30] conducted in different parts of India came up with the conclusion that females were more depressed than the males.

Elderly people who were living without their spouse, that is either being widowed, or separated, or being divorced, and those who were suffering from chronic illnesses were also found to be suffering from depression in the studies conducted by Radhakrishnan and Nayeem[20] and Maulik and Dasgupta.[21] Study conducted in Hoogly, West Bengal, found that those elderly who were not involved in taking important decisions in the family had a higher prevalence of depression. The studies conducted in Ludhiana, Salem, and Hoogly,[19],[21],[27] found a higher prevalence of depression among uneducated and non-working elderly people. Similarly, there are studies[26],[22],[31],[32],[33] that found a significant association of depression with no personal income that we did not get in our study. By abuse, we included abuse in the form of psychological abuse, exploitation, physical abuse, and neglect by the family members, mostly their sons or daughter-in-law, and also by the neighbors. It was found that abuse was positively correlated to depression among the elderly study population. In an Indian study,[23] it was found that around 54% of the elderly population with severe depression had experienced abuse.

A reason for all these differences between our study and the studies that have been discussed above[19],[21],[27],[32],[33] might be because of different study settings and sociocultural factors which differ in different settings.

Further, the difference of findings between our current study and the other studies could be explained by Rothman's model of causal pie.[33] In the causal pie model, the outcomes result from sufficient causes. Each sufficient cause is made up of a “causal pie” of “component causes.” Several different causal pies may exist for the same outcome. Now, the outcome here, for example, depression among the elderly population, will occur, if and only if, all component causes of a sufficient cause were present, that is, the causal pie was completed. Hence, the effect of a component cause depended on the presence of the other component causes that constituted some of the causal pie. This explains why we did not get any positive association of depression with any of the factors that we have studied.

The higher prevalence of depression observed among the elderly population was a matter to think about. No clear guidelines were available which mentioned about a routine screening of depression for the geriatric population and for their counseling. At the same time, it is very important to work for community and social support of the elderly people. As a result of strict guidelines, the different governmental schemes for financial support of elderly people (9) are not utilized in the way, it should have been. This type of study worked as an eye-opener for measuring the burden of depression among the elderly. While treating the elderly patients, the health personnel should be aware enough to rule out depression among the elderlies, as many of them come with somatic symptoms such as headache, myalgia, and tension, for which patients visit the general outdoor services, instead of visiting the psychiatrists.

Following limitations were identified in our study: (i) we have used convenience sampling technique and (ii) the cross-sectional nature of study design. Hence, the causal relationships could not be inferred and results could not be generalized. As a result of this, a bigger study might be required to know the actual picture of depression among the geriatric population


   Conclusion Top


To deal with this huge social problem of depression among the elderly population, provision of screening programs and timely counseling facilities should be available in the community itself. Social security policies had to be revised and initiatives had to be taken for community participation in dealing with this problem so that the younger members of the family, in spite of moving out of the family, leaving the old parents alone, should be involved in increasing the family support for them. Already existing mental health program should give more stress on the problems of geriatric depressions. Further, there had been a great scope for the nongovernmental organizations and other voluntary workers to participate in this process with a more active and enthusiastic approach.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.


   Questionnaire Top





   Section 1: Sociodemographic Profile Top





   Section 2: Work History and Benefits Top





   Section 3: Income and Assets Top





   Section 4: Living Arrangements Top





   Section 5: Health Status Top






 
   References Top

1.
Ministry of Health and Family Welfare. Family Welfare Statistics in India; 2011. Available from: http://Mohfw.nic.in/WrtiteReadData/l892/35. [Last accessed on 2015 Nov 15; Last updated on 2011 Jul 20].  Back to cited text no. 1
    
2.
Parkar SR. Elderly mental health: Needs. Mens Sana Monogr 2015;13:91-9.  Back to cited text no. 2
[PUBMED]  [Full text]  
3.
Fiske A, Wetherell JL, Gatz M. Depression in older adults. Annu Rev Clin Psychol 2009;5:363-89.  Back to cited text no. 3
    
4.
Pilania M, Bairwa M, Kumar N, Khanna P, Kurana H. Elderly depression in India: An emerging public health challenge. Australas Med J 2013;6:107-11.  Back to cited text no. 4
    
5.
Qadir F, Haqqani S, Khalid A, Huma Z, Medhin G. A pilot study of depression among older people in Rawalpindi, Pakistan. BMC Res Notes 2014;7:409.  Back to cited text no. 5
    
6.
World Health Organization. World Report on Ageing and Health. Available from: http://apps.who.int/iris/bitstream/10665/186463/1/9789240694811_eng.pdf?ua=1. [Last accessed on 2015 Nov 02; Last updated on 2014 Oct 03].  Back to cited text no. 6
    
7.
Barua A, Ghosh M, Kar N, Basilio M. Distribution of depressive disorders in the elderly. J Neurosci Rural Pract 2010;1:67-73.  Back to cited text no. 7
[PUBMED]  [Full text]  
8.
Abhishekh HA, Raghuram K, Shivakumar S, Balaji AL. Prevalence of depression in community dwelling elderly: Study from rural population of India. J Neurosci Rural Pract 2013;4:S138.  Back to cited text no. 8
[PUBMED]  [Full text]  
9.
Ingle GK, Nath A. Geriatric health in India: Concerns and solutions. Indian J Commun Med 2008;33:214-8.  Back to cited text no. 9
    
10.
United Nations Population Fund. Caring for Our Elders: Early Responses India Aging Report – 2017. New Delhi; 2017. Available from: https://india.unfpa.org/sites/default/files/pub-pdf/India%20Ageing%20Report%20-%202017%20%28Final%20Version%29.pdf. [Last accessed on 2020 Aug 15].  Back to cited text no. 10
    
11.
Raja V. 5 Government Schemes for Senior Citizens that can Help Create a Steady Income. The Better India; 2019. Available from: https://www.thebetterindia.com/192312/government-scheme-senior-citizen-pension-investment- income-india/. [Last accessed on 2020 Aug 20].  Back to cited text no. 11
    
12.
Verma R, Khanna P. National program of health-care for the elderly in India: A hope for healthy ageing. Int J Prev Med 2013;4:1103-7.  Back to cited text no. 12
    
13.
Bhagat VA, Raut AV, Gupta SS, Mehandale AM, Garg BS. Kiran Clinic: Community owned primary health care – A case study from rural Wardha. Indian J Commun Fam Med 2017;3:61-4.  Back to cited text no. 13
    
14.
Dean AG, Sullivan KM, Soe MM. OpenEpi: Open Source Epidemiologic Statistics for Public Health, Version. Available from: http://www.OpenEpi.com. [Last accessed on 2016 Dec 20; Last updated on 2013 Apr 06].  Back to cited text no. 14
    
15.
Yesavage JA, Brink TL, Rose TL, Lum O, Huang V, Adey M, et al. Development and validation of a geriatric depression screening scale: A preliminary report. J Psychiatr Res 1982;17:37-49.  Back to cited text no. 15
    
16.
Marathi Version of GDS Scale. Available from: https://web.stanford.edu/~yesavage/MARATHI.pdf. [Last accessed on 2015 Dec 30].  Back to cited text no. 16
    
17.
United Nations Population Fund. The Status of Elderly in Select States of India; 2011. Available from: https://india.unfpa.org/en/publications/report-status-elderly-select-states-india-2011. [Last accessed on 2021 Apr 02; Last updated on 2013 Oct 15].  Back to cited text no. 17
    
18.
Centers for Disease Control and Prevention. Epi Info 2016;6:1-11. Available from: https://www.cdc.gov/epiinfo/index.html. [Last accessed on 2016 Feb 27].  Back to cited text no. 18
    
19.
Sengupta P, Benjamin AI. Prevalence of depression and associated risk factors among the elderly in urban and rural field practice areas of a tertiary care institution in Ludhiana. Indian J Public Health 2015;59:3-8.  Back to cited text no. 19
[PUBMED]  [Full text]  
20.
Radhakrishnan S, Nayeem A. Prevalence of depression among geriatric population in a rural area in Tamil Nadu. Int J Nutr Pharmacol Neurol Dis 2013;3:309-12.  Back to cited text no. 20
  [Full text]  
21.
Maulik S, Dasgupta A. Depression and its determinants in the rural elderly of West Bengal: A cross sectional study. Int J Biol Med Res 2012;3:1299-302.  Back to cited text no. 21
    
22.
Tiwari SC. Geriatric psychiatric morbidity in rural northern India: Implications for the future. Int Psychogeriatr 2000;12:35-48.  Back to cited text no. 22
    
23.
Patel VK, Tiwari DS, Shah VR, Patel MG, Raja HH, Patel DS. Prevalence and predictors of abuse in elderly patients with depression at a tertiary care centre in Saurashtra, India. Indian J Psychol Med 2018;40:528-33.  Back to cited text no. 23
[PUBMED]  [Full text]  
24.
Nandi PS, Banerjee G, Mukherjee SP, Nandi S, Nandi DN. A study of psychiatric morbidity of the elderly population of a rural community in West Bengal. Indian J Psychiatry 1997;39:122-9.  Back to cited text no. 24
[PUBMED]  [Full text]  
25.
Vishal J, Bansal RK, Swati P, Bimal T. A study of depression among aged in Surat city. Natl J Commun Med 2010;1:47-9.  Back to cited text no. 25
    
26.
Venkoba RA. Psychiatry of old age in India. Int Rev Psychiatry 1993;5:165-70.  Back to cited text no. 26
    
27.
Rajkumar AP, Thangadurai P, Senthilkumar P, Gayathri K, Prince M, Jacob KS. Nature, prevalence and factors associated with depression among the elderly in a rural south Indian community. Int Psychogeriatr 2009;21:372-8.  Back to cited text no. 27
    
28.
Srivastava K. Urbanization and mental health. Ind Psychiatry J 2009;18:75-6.  Back to cited text no. 28
[PUBMED]  [Full text]  
29.
Swarnalatha N. The prevalence of depression among the rural elderly in Chittoor district, Andhra Pradesh. J Clin Diagn Res 2013;7:1356-60.  Back to cited text no. 29
    
30.
Seby K, Chaudhury S, Chakraborty R. Prevalence of psychiatric and physical morbidity in an urban geriatric population. Indian J Psychiatry 2011;53:121-7.  Back to cited text no. 30
[PUBMED]  [Full text]  
31.
Sinha SP, Shrivastava SR, Ramasamy J. Depression in an older adult rural population in India. MEDICC Rev 2013;15:41-4.  Back to cited text no. 31
    
32.
Prina AM, Ferri CP, Guerra M, Brayne C, Prince M. Prevalence of anxiety and its correlates among older adults in Latin America, India and China: Cross-cultural study. Br J Psychiatry 2011;199:485-91.  Back to cited text no. 32
    
33.
Wensink M, Westendorp RG, Baudisch A. The causal pie model: An epidemiological method applied to evolutionary biology and ecology. Ecol Evol 2014;4:1924-30.  Back to cited text no. 33
    


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