|Year : 2016 | Volume
| Issue : 2 | Page : 158-163
Prevalence and risk factors for depression in elderly North Indians
Kamlesh Sharma1, Anmol Gupta1, Ravi C Sharma2, Narinder Mahajan1, Anjali Mahajan1, Deepak Sharma3, Salig Ram Mazta1
1 Department of Community Medicine, Indira Gandhi Medical College, Shimla, Himachal Pradesh, India
2 Department of Psychiatry, Indira Gandhi Medical College, Shimla, Himachal Pradesh, India
3 Department of Community Medicine, Government Medical College and Hospital, Chandigarh, India
|Date of Web Publication||13-Dec-2016|
Department of Community Medicine, Indira Gandhi Medical College, Shimla, Himachal Pradesh
Source of Support: None, Conflict of Interest: None
Background: Depression in elderly is a public health problem.
Aims and Objectives: To determine the prevalence of depression and its correlates among the elderly population.
Materials and Methods: This cross-sectional study was conducted among elderly aged 60 years and above using two-stage cluster sampling technique among elderly residing in the rural and urban areas of Shimla district of Himachal Pradesh, India. A written consent was taken from each participant after explaining the purpose of the study.
Results: Of the 800 subjects, 76 (9.5%) were found to be suffering from depression. The logistic regression analysis revealed that elderly having lower income, consuming tobacco, experiencing stressful life event in the past 1 year, having 3 or more chronic diseases, and lacking positive approach toward life and were found to be more depressed.
Conclusion: The study conducted in Shimla district shows that one-tenth of the elderly residing in the study area suffering from depression. Elderly having lower income, residing in the urban area, experiencing significant life event, suffering from multimorbidity, and lacking positive approach toward life increase should be identified by screening. They should be focused on so as to reduce the probability of occurrence of depression.
Keywords: Depression, elderly, Indian
|How to cite this article:|
Sharma K, Gupta A, Sharma RC, Mahajan N, Mahajan A, Sharma D, Mazta SR. Prevalence and risk factors for depression in elderly North Indians. J Geriatr Ment Health 2016;3:158-63
|How to cite this URL:|
Sharma K, Gupta A, Sharma RC, Mahajan N, Mahajan A, Sharma D, Mazta SR. Prevalence and risk factors for depression in elderly North Indians. J Geriatr Ment Health [serial online] 2016 [cited 2023 Mar 29];3:158-63. Available from: https://www.jgmh.org/text.asp?2016/3/2/158/195673
| Introduction|| |
Globally the number of older persons are projected to be nearly 1 billion by 2030, with the proportion increasing from 7% to 12%.  In India, the percentage of elderly population above 60 years has gone from 5.3-5.7% (census 1991) to 6.0-8.0% (census 2011), respectively, on account of better education, health facilities, and increase in life expectancy. In Himachal Pradesh, the elderly population constitutes 10.4% of the population with a distribution of rural and urban population as 10.5% and 8.7%, respectively. 
Depression is a common problem among elderly. Among them, it is a cause of disability, social deprivation, and loneliness. Often it goes unrecognized because elderly attribute its symptoms to the aging process. Depression in elderly leads to increased use of health services by elderly thereby putting pressure on the already burdened health-care system. Numerous studies have been conducted globally to study depression among elderly which have reported the prevalence in the range of 4-23%. , Studies done in India are relatively sparse. Previous studies conducted in India have reported the prevalence ranging from 21% in South India to 45% in Western India and 52% in Eastern India. ,,
To the best of author's knowledge, there has been no population-based study of depression among elderly residing in Shimla hills of Himachal Pradesh. Hence, this study was conducted to determine the prevalence of depression among elderly and to study the epidemiological factors associated with it.
| Materials and methods|| |
This cross-sectional study was conducted from July 2014 to June 2015 among the elderly population (aged 60 years and above) of Shimla, capital city of Himachal Pradesh. The urban elderly population for the study purpose was chosen from the Municipal Corporation area of Shimla having 25 wards. The rural population was sampled from Mashobra Block having 25 panchayats. For calculating sample size, prevalence as 25% was assumed based on studies done across the world. Taking two-sided significance level (1-alpha, confidence level as 95%, margin of error as 0.05, design effect = 2 and expected response as 80%), the sample size was calculated to be 720. Hence, a total of 800 subjects were taken in which 400 were enrolled from the urban area and 400 from the rural area.
Two-stage cluster sampling was used to draw representative sample of study population from the study area. In the urban area, probability proportionate sample of 5 wards was selected at the first stage. This was followed by a systematic sample of fixed number of households in the second stage till the desired sample of 400 study subjects in the urban clusters was achieved with at least eighty subjects sampled from each selected ward. A similar strategy was employed to select the rural sample. Similarly, in the rural area, a list of all the panchayats of Mashobra Block was procured from Block Development Office. Then, a probability proportionate sample of five panchayats was selected at the first stage. This was followed by a systematic sample of fixed number of households in the second stage from each of the selected panchayats in the first stage till desired sample size of 400 of study subjects in the rural cluster was achieved with at least eighty subjects sampled from each selected panchayats.
The principal investigator administered Geriatric Depression Scale-30 (GDS-30) was to the study population after taking informed written consent. The participants who screened positive using the GDS-30 Scale were further evaluated using Hamilton Depression Rating Scale (HDRS) to validate the diagnosis. To know the severity of depression which was rated as 8-13 mild depression, 14-18 moderate depression, 19-22 severe depression, and ≥23 very severe depression. , Subjects found to be suffering from depression according to HDRS were referred to psychiatry outpatient department of Indira Gandhi Medical College and Hospital Shimla for further management.
The questionnaire comprised questions to elicit information on sociodemographic profile and behavioral risk factors. Socioeconomic status was assessed using Modified B G Prasad's classification (updated for per capita income according to All India Wholesale Price Index). It is a commonly used scale to measure the socioeconomic status of families and utilizes the per capita monthly income. On the basis of the total score, categorization was done as "Upper," "Upper Middle," "Middle," "Lower Middle," and "Lower."  Behavioral factors assessed were alcohol consumption, tobacco usage, smoking, and activities of daily life. Stressful life events in the past 1 year included conflicts in family, unemployment of self or children, illness of self, illness of family members, death of family members, close relative, and financial problems or loss. Positive life approach was assessed by asking about positive lifestyle changes like doing the exercise daily, doing yoga daily, and going for walk daily. The medical risk factors included diseases, namely diabetes mellitus, hypertension, asthma, arthritis, cardiac diseases, and depression.
The SPSS statistical package (SPSS, Chicago, IL, USA) version 20.0 was used for statistical analysis. Descriptive statistics were reported as mean, standard deviation for continuous variables and as frequency, and percentage for categorical variables. The Chi-square test is used to study the association between sociodemographic variables and behavioral variables with depression. Predictors significant at P < 0.05 in univariate analysis were evaluated by binary logistic regression modeling. Those variables which continued to be significant at P < 0.05 in the multivariate analysis were kept in the final model. The study was approved by the Ethics Committee of the institute.
| Results|| |
The majority of subjects 577 (72.1%) were in the age group of 60-70 years. The mean age of study sample was 67.5 ± 6.8 years (males = 68.8 ± 6.7 years, females = 66.84 ± 6.8 years). Of the 800 subjects, 229 (28.6%) were illiterate, 234 (29.2%) were educated up to primary level, 141 (17.6%) were graduates, and 57 (7.1%) were post graduates. Three hundred and forty-one (42.6%) were homemakers by occupation followed by 250 (31.2%) retired, and 151 (18.9%) were farmers. Most of the participants 596 (74.5%) were married and a majority of them 789 (98.6%) belonged to joint/three generation family. Majority of participants 331 (41.4%) had hypertension followed by 122 (15.2%) with diabetes, 44 (5.5%) with asthma, 74 (9.2%) with arthritis, and 24 (3.0%) had some form of cardiac disease.
Based on GDS (used as a screening tool), the prevalence of depression was 10.3%. Finally based on Hamilton Rating Scale (Diagnostic Scale) 76 (9.5%), of the 800 study subjects were found to be suffering from depression. The prevalence of mild depression was 6.4%, moderate depression was 2.0%, and severe depression was 1.1%.
The univariate analysis shows those elderly residing in urban areas (7.3%) and those having lower income (17.9%) were significantly more depressed [Table 1]. The behavioral risk factors revealed that elderly tobacco users had significantly more depression (19.2%). Subjects who did not have a positive approach toward life (25.7%) and those who were suffering from three or more chronic diseases had significantly more depression (6.94%) as compared to their counterparts [Table 2].
|Table 1: Association between sociodemographic variables with depression in elderly|
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|Table 2: Association between behavioral risk factors, chronic diseases, and depression in elderly|
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To examine the predictors of depression, the significant sociodemographic factors (residence and socioeconomic status) and behavioral factors (tobacco use, positive approach toward life, and multimorbidity) were included in the logistic regression model. Using entry method, the logistic regression model revealed that lower income (adjusted odds ratio [AOR] =1.4), tobacco consumption (AOR = 2.8), experiencing stressful life event in the past 1 year (AOR = 4.8), presence of 3 or more chronic diseases (AOR = 5.9), and lacking of positive approach toward life (AOR = 4.8) were significantly associated with elderly depression [Table 3].
|Table 3: Multiple logistic regression analysis of risk factors associated with depression in elderly|
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| Discussion|| |
The overall prevalence of depression in the present study was found to be 9.5% which is quite consistent with the results of a study done by Sengupta and Benjamin in Punjab, India (8.9%).  Similar findings were observed in the study done by Goel et al. in Uttar Pradesh, wherein the prevalence of depression in elderly was 9.5%.  Internationally, studies conducted by Schoevers et al. in Amsterdam, Newman et al. in Canada, and Liu et al. in China have reported nearly similar prevalence rate of 10.5%, 11.2%, and 12.9%, respectively. ,, A study conducted in China by Wang et al. among elderly reported almost similar results (prevalence 9.9%).  The prevalence rate of depression is also comparable to that reported in the WHO report on estimates of depression in geriatric population (10-20%).  Contrary to our finding, comparatively higher prevalence of depression among elderly population has been reported by Nandi et al. in West Bengal (22%), Ramachandran et al. in Chennai (24%), and Barua and Kar in Karnataka (21.7%). ,, This difference in prevalence could be attributed to the different geographical area, cultural differences, different study tools used for measuring depression, and also the difference in sample size.
In our study, those elderly who had lower income were more depressed. Similar to our findings, studies done by Jain et al. in Mumbai and Poongothai et al. in Chennai have identified a significant correlation between geriatric depression and socioeconomic status. , This could probably be due to the fact that those who are poor face more financial stress, which might have precipitated depression. The present study noted that prevalence of depression was more in urban elderly. The finding is quite similar to that reported in studies done by Atram in Saudi Arabia and Sengupta and Benjamin in India. , We observed that illiterate elderly were more depressed. The results are consistent with the community-based study done by Sengupta and Benjamin which observed that illiteracy is a risk factor for depression.  Barua and Kar noted that the prevalence of depression among illiterates was higher. 
The present study found that tobacco use was a risk factor for depression. This might be attributed to the fact that nicotine present in tobacco damages certain pathways in the brain that regulate mood. Due to the addictive nature of nicotine, they become dependent on tobacco. Similar to our finding a study conducted by Jain and Aras reported substance use as a predictor for depression.  Other studies by Niti et al., and Verma et al., have also reported a higher prevalence of depressive symptoms in smokers. ,, Contrary to our finding, a community-based study done by Sharma and Sharma reported that depression was more among those who were not using tobacco. 
In the current study, there was a significantly higher prevalence of depression among those experiencing any significant life events, which is in accordance with a study done by Nakulan et al. and Murphy which observed a significant relation of depression with significant life events in the past 1 year. , Contrary to our finding, Barnes Nacoste and Wise concluded that the occurrence of a significant life event has no association with depression.  In our study, the prevalence of depression was significantly higher among individuals having more than three chronic diseases when compared to those with <3 chronic diseases. A similar finding has been observed by Niti et al. and Ma et al. in their study. ,
The study has two major limitations. We did not attempt a clinical interview of cases diagnosed through GDS and HDRS scale. All such patients were referred to the psychiatric department for further evaluation and treatment. Other limitations include the fact that we did not use the Endicotts criteria to obviate the overdiagnosis of depression in the presence of physical disorder or attribution of the items score due to physical symptoms. Despite these limitations, this study adds to literature on elderly depression and generates evidence for activities and factors which pose a risk for them in the study area. It is suggested that in future large scale epidemiological studies should be done across India addressing these limitations.
| Conclusion|| |
The study conducted in Shimla district shows that one-tenth of the elderly residing in Shimla district of Himachal Pradesh are suffering from depression. Elderly having lower income, residing in urban area, experiencing significant life event, suffering from multimorbidity, and lacking positive approach toward life increase should be identified by screening. They should be focused on so as to reduce the probability of occurrence of depression. It is suggested that trained geriatric care counselors should be appointed who can regularly screen for depression using standard study tools and offer counseling for instilling positive life approach in elderly. A support system can be setup in the community wherein they can interact with each other on a regular basis.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Ma X, Xiang YT, Li SR, Xiang YQ, Guo HL, Hou YZ, et al.
Prevalence and sociodemographic correlates of depression in an elderly population living with family members in Beijing, China. Psychol Med 2008;38:1723-30.
Ganatra HA, Zafar SN, Qidwai W, Rozi S. Prevalence and predictors of depression among an elderly population of Pakistan. Aging Ment Health 2008;12:349-56.
Barua A, Acharya D, Nagaraj K, Bhat HV, Nair NS. Depression in elderly: A cross sectional study in rural South India. JIMSA 2007;20:259-61.
Jain RK, Aras RY. Depression in geriatric population in urban slums of Mumbai. Indian J Public Health 2007;51:112-3.
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.
The Geriatric Depression Scale (GDS). Available from: https://www.consultgeri.org/try-this/general-assessment/issue-4.pdf. [Last cited on 2016 Jun 26].
Shankar RD, Arlappa N. An updated Prasad′s socio economic status classification for 2013. Int J Res Dev Health 2013;1:26-8.
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.
Goel PK, Muzammil K, Kumar S, Singh JV, Raghav SK. Sociodemographic correlates of depression among elderly slum dwellers of North India. Nepal J Epidemiol 2014;4:317-22.
Schoevers RA, Geerlings MI, Beekman AT, Penninx BW, Deeg DJ, Jonker C, et al.
Association of depression and gender with mortality in old age. Results from the Amsterdam Study of the Elderly (AMSTEL). Br J Psychiatry 2000;177:336-42.
Newman SC, Bland RC, Orn HT. The prevalence of mental disorders in the elderly in Edmonton: A community survey using GMS-AGECAT. Geriatric mental state-automated geriatric examination for computer assisted taxonomy. Can J Psychiatry 1998;43:910-4.
Liu CY, Wang SJ, Teng EL, Fuh JL, Lin CC, Lin KN, et al.
Depressive disorders among older residents in a Chinese rural community. Psychol Med 1997;27:943-9.
Wang JK, Su TP, Chou P. Sex differences in prevalence and risk indicators of geriatric depression: The Shih-Pai community-based survey. J Formos Med Assoc 2010;109:345-53.
The World Health Organization. World Health Report 2001: Mental Health: New Understanding and New Hope. Available from: http://www.who.int/whr/2001/en/
. [Last cited on 2016 Jun 26].
Ramachandran V, Menon MS, Arunagiri S. Socio-cultural factors in late onset depression. Indian J Psychiatry 1982;24:268-73.
Barua A, Kar N. Screening for depression in elderly Indian population. Indian J Psychiatry 2010;52:150-3.
Poongothai S, Pradeepa R, Ganesan A, Mohan V. Prevalence of depression in a large urban South Indian population - The Chennai Urban Rural Epidemiology Study (CURES-70). PLoS One 2009;4:e7185.
Atram AR. Prevalence of Psychiatric disorders in a sample of elderly residents in rural and urban population of Zulfia Region-Saudia Arabia. J Psychol Psychother 2015;5:170.
Niti M, Ng TP, Kua EH, Ho RC, Tan CH. Depression and chronic medical illnesses in Asian older adults: The role of subjective health and functional status. Int J Geriatr Psychiatry 2007;22:1087-94.
Verma RK, Lin RSG, Chakravarthy S, Barua A, Kar N. Sociodemographic correlates of unipolar major depression among the Malay elderly in Klang valley, Malaysia: An intensive study. Int J Pharm Pharmaecutical Sci 2014;6:158-64.
Nakulan A, Sumesh TP, Kumar S, Rejani PP, Shaji KS. Prevalence and risk factors for depression among community resident older people in Kerala. Indian J Psychiatry 2015;57:262-6.
Sharma R, Sharma R. Depression among the elderly population in a rural community: A study of its prevalence and correlates. Indian Med Gaz 2012;145:467-72.
Murphy E. Social origins of depression in old age. Br J Psychiatry 1982;141:135-42.
Barnes Nacoste DR, Wise EH. The relationship among negative life events, cognitions, and depression within three generations. Gerontologist 1991;31:397-403.
[Table 1], [Table 2], [Table 3]
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