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ORIGINAL ARTICLE
Year : 2015  |  Volume : 59  |  Issue : 1  |  Page : 3-8  

Prevalence of depression and associated risk factors among the elderly in urban and rural field practice areas of a tertiary care institution in Ludhiana


Professor, Department of Community Medicine, Christian Medical College, Ludhiana, Punjab, India

Date of Web Publication9-Mar-2015

Correspondence Address:
Paramita Sengupta
Professor, Department of Community Medicine, Christian Medical College, Ludhiana - 141 008, Punjab
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/0019-557X.152845

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   Abstract 

Background: Depression, the most common psychiatric disorder among the elderly, is not yet perceived as an important health problem in India, where few population-based studies have addressed this problem. Objectives: To estimate the prevalence of depression and identify the associated risk factors in the elderly population. Materials and Methods: 3038 consenting elderly (>60 years old) rural and urban residents of both sexes from the field practice areas were interviewed and examined in a cross-sectional study. Physical impairment in the subjects was assessed with the Everyday Abilities Scale for India (EASI), depression by the 15-item Geriatric Depression Scale (GDS-15), and cognitive impairment by the Mini-Mental State Examination (MMSE). Data were analyzed using Epi Info version-6 software. Statistical analysis included proportions, χ[2] -test, odds ratio, and its 95% confidence interval. Multiple logistic regression was done using SPSS version 21. Results: The prevalence of depression in the study population was 8.9%. It was significantly higher in urban residents, females, older elderly, nuclear families, in those living alone, those not working, illiterates, poor, functionally impaired, and cognitively impaired. In the multivariate analysis, unmarried/widowed status, unemployment, and illiteracy did not emerge as risk factors. Conclusions: Urban residence, female gender, higher age, nuclear family, poverty, and functional and cognitive impairment were found to be associated with depression even after controlling for other factors.

Keywords: Cognitive impairment, Depression, Elderly, Functional impairment, Prevalence


How to cite this article:
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

How to cite this URL:
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 [serial online] 2015 [cited 2018 Dec 18];59:3-8. Available from: http://www.ijph.in/text.asp?2015/59/1/3/152845


   Introduction Top


The Global Burden of Disease (GBD) study projections show that depression will be the single leading cause of Disability Adjusted Life Years by 2020 in the developing world. [1] The GBD 2000 estimates the point prevalence of unipolar depressive episodes to be 1.9% for men and 3.2% for women, and that 5.8% of men and 9.5% of women will experience a depressive episode in a 12-month period. These prevalence figures vary across populations and may be higher in some populations. In a meta-analysis of various study reports of community -based mental health surveys on geriatric depressive disorders in those aged 60 years and above, conducted in the continents of Asia, Europe, Australia, North America, and South America between 1955 and 2005, the median prevalence rate of depressive disorders in the world for the elderly population was determined to be 10.3%, while among the elderly Indian population, it was determined to be 21.9% [interquartile range (IQR) 11.6-31.1)]. Depression is the most common psychiatric disorder among the elderly, [4] and although India is the second-most populous country in the world in terms of elderly population > 60 years of age, depression in the elderly is not yet perceived as an important health problem in the country. Few community-based studies have been conducted in India so far to address this issue. Hence, this study was conducted to estimate the prevalence of depression in the elderly ( > 60 years old) in Ludhiana and to identify the major risk factors for depression in the study population.


   Materials and Methods Top


Study design

Cross-sectional study.

Study period

Six months (from 1 June 2011 to 30 November, 2011).

Study participants

The study participants consisted of consenting elderly ( > 60 years old) residents of the field practice areas of the Department of Community Medicine, covering a population of about 45,000 (30,000 urban and 15,000 rural), identified through house-to-house survey. All the elderly, of both sexes, in the study population were included in the study.

Sample size estimation

In the absence of epidemiological information on depression among the elderly in this area, for the purpose of calculating the minimum sample size required for our study, we considered the lower value of 11.6% of the IQR for the prevalence of depression reported in the Indian elderly in the meta-analysis of six community-based studies. [3] With the allowable error of 10% of the prevalence, within 95% confidence limits, the minimum sample size required was 2842. A total of 3054 elderly were found in the population under study, of which 3038 eligible respondents participated in the investigation.

Ethics approval

Approval of the Institutional Ethics Committee was obtained prior to commencement of the study.

Consenting procedure

The respondents were explained the purpose of the study, and their signed consent was obtained. Consent was obtained from the caregiver in the event of the subject being unable to give informed consent.

Assessment tools

Information was obtained from consenting respondents using a pre-tested questionnaire containing various socio-demographic parameters. The interview was conducted in the language with which the subject was familiar (Hindi/Punjabi), for which purpose the consent form and assessment tools were translated into both the languages and back-translated by an independent coworker proficient in both languages to ensure validity of the translation. Functioning for daily living of the subjects was assessed in the presence of the primary caregiver with the help of Everyday Abilities Scale for India (EASI) and subjects with EASI score of > 3 were classified as functionally impaired. [5] Depression was assessed using the 15-item Geriatric Depression Scale (GDS), [6] which is a 15-item self-report assessment used as a basic screening measure of depression in the elderly. Accuracy of the GDS-15 is not influenced by the severity of medical burden, age, or other socio-demographic characteristics and even the "very old" and ill can be screened appropriately. [7] Moreover, the presence of a major depressive episode among elderly home-bound adults can be reliably detected. In a validation study comparing the long and short forms of the GDS for self-rating of symptoms of depression, both were successful in differentiating depressed from non-depressed adults with a high correlation (r = 0.84, P < 0.001). Hence, this scale is better suited in identifying depression in the elderly. [8] Those with a GDS score > 5 were categorized as depressed. Using this cut-off, a high sensitivity and specificity of the 15-item GDS has been reported. [9] Cognitive impairment was assessed using the Mini-Mental State Examination (MMSE), and respondents with MMSE score < 25 were considered as cognitively impaired. [10] The Hindi translation of MMSE (used for the rural illiterate elderly, validated in a previous study) was suitably modified and used in this study. [11]

Exclusion criteria

The deaf/dumb/blind, those with diagnosed psychiatric illness (schizophrenia, mental retardation) or neurological disorders ( Parkinsonism More Details, severe head injury, or brain neoplasm), and those who were ill at the time of the study were excluded, as there was no way to obtain reliable information from them.

Statistical analysis

Data were analyzed using Epi Info version-6 software. Data analysis included proportions. Odds ratio and its 95% confidence intervals were calculated. Chi-square test was used to determine statistical significance of the observed differences. Multiple logistic regression analysis was carried out using SPSS software version 21 (IBM Corp., Armonk, NY, USA).


   Results Top


[Table 1] shows that the prevalence of depression (GDS score > 5) in the elderly population under study was found to be 8.9%. The prevalence was significantly higher in the urban residents, females, older elderly ( > 80 years old), those living alone without a spouse, i.e., unmarried/widowed, nuclear families, illiterates, those not working, and in the poor with monthly per capita income (MPCI) of < Rs. 1000. The prevalence of functional impairment (EASI score > 3) in the elderly was 7.0% and of cognitive impairment (MMSE score < 25) was 8.8%. The prevalence of depression was found to be significantly higher both in those with functional impairment as well as in those with cognitive impairment.
Table 1: Risk factors for depression in the elderly

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[Table 2] shows that on applying multiple logistic regression model, urban residence, female sex, age > 80 years, nuclear family, MPCI < Rs. 1000, and functional as well as cognitive impairment were found to be strong predictors of depression. Marital status, educational status, or occupational status was not found to have a significant effect on depression in the multivariate analysis.
Table 2: Multivariate analysis of the association of risk factors with depression

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   Discussion Top


The prevalence of depression, based on GDS scores > 5, was found to be 8.9% in the present study. Studies have revealed that the prevalence rates for depression in community samples of elderly in India vary from 6 to 50%. [12] The prevalence has been reported to be 45.9% in the urban slums of Mumbai, [13] 29.36% in the urban slums of Dharwad district, Karnataka, [14] 31.4% in a rural population of Ahmednagar, Maharashtra, [15] and 12.7% in a cross-sectional study of 1000 elderly in Vellore, Tamil Nadu. [16] An increased median prevalence of 21.9% (IQR 11.6-31.1) of the depressed elderly in India in a meta-analysis of 74 studies across the world [3] may be explained by the fact that the meta-analysis consisted of six relevant studies from India, covering only 2499 (0.5%) elderly participants, as compared to 68 studies from the rest of the world covering 487,275 (99.5%) of the participants. A cross-sectional study in a tertiary care hospital in Karachi found the prevalence of depression to be 19.5% in the elderly aged 65 years and above. [17]

Significant predictors for depression found among the elderly in our study were urban residence, female sex, increasing age, nuclear family, illiteracy, and poverty. A meta-analysis of the risk factors of depression in the elderly [18] found bereavement, sleep disturbance, disability, prior depression, and female gender to be significant risk factors for depression. A study in Pakistan also reported female gender, elderly without a spouse, low level of education, and unemployment to be independent predictors of depression. [17] Similar findings have been reported among the geriatric population in the urban slums of Mumbai [13] and Dharwad, [14] as well as in the rural elderly of district Ahmednagar, Maharashtra. [15] However, a study on community-dwelling elderly in Tamil Nadu [16] found that age, female gender, cognitive impairment, and disability status were not significantly associated with geriatric depression. In the present study, marital status, educational status, and occupational status were not found to be predictors of depression in the multivariate analysis.

Nuclear family was found to be an independent risk factor for depression in the present study. The elderly living in a nuclear family were more than 3 times likely to suffer from depression than those living in a joint family. Similar findings have been reported by other researchers. [17],[19] Urbanization leads to households becoming less extended and more nuclear. Data from household surveys conducted in 43 developing countries in the 1990s suggest a trend toward convergence to predominantly nuclear households. [20] Nucleation of the family leads to a decrease in coresidence of the elderly with adult children and, therefore, a decrease in their care and support. [17],[20],[21]

Those not working/retired subjects in our study were 3 times more likely to suffer from depression than those who were employed, although it was not found to be an independent risk factor for depression. Other studies [14],[17] have also reported depression to be more in those not working.

Functional impairment was found to be a strong predictor for depression, with those functionally impaired having more than 11 times a risk of depression. Lenze et al. [22] observed that though depressive symptoms in late life may predict functional disability in older persons, persistent depression is thought to be associated with a greater linear increase in functional disability than remitted depression. Payne et al. [23] observed that patients with Alzheimer's disease who are less severely demented but more functionally impaired are more likely to be depressed. One explanation for this finding is that since patients with less severe cognitive impairment are usually less functionally impaired, greater functional impairment in a mildly demented patient might indicate that a process other than dementia leads to the functional impairment. This might be a physical disability such as blindness or difficulty in walking, but it could also be the coexisting depressive features. Alternatively, difficulties with activities of daily living might be more likely to cause early dementia patients to feel depressed. Treatment of depression has been associated with improved functional status.

Those cognitively impaired were also found to be more than 3 times at risk of depression in the present study. Depression is associated with cognitive impairment in older adults, [24] and a meta-analysis of case-control and prospective studies of the association between depression and dementia conducted in 2001 concluded that history of depression approximately doubled the risk for dementia. [25] The prevalence of depression in dementias has been reported to be between 9% and 68%, and it has been proposed to be both a risk factor for as well as a prodrome of dementia. [26] People with mild cognitive impairment and dementia exhibit greater rates of depression than age-matched cognitively intact individuals. [27]

Among the Ballabgarh elderly also, higher depressive symptom scores were independently and significantly associated both with lower cognitive function scores and with greater levels of functional impairment. [6] A consensus opinion from the National Institute of Mental Health is that late life depression may represent an independent risk factor predisposing to dementing disorders, even when depressive symptoms occur more than 10 years before the onset of dementia. [28] The correct diagnosis of depression in the elderly suffering from Alzheimer's disease is important because, in most cases, it can be treated successfully. [29]


   Conclusions Top


The prevalence of symptoms of depression in the elderly population under study was 8.9%. Urban residence, female gender, higher age, nuclear family, poverty, functional and cognitive impairment were found to be associated with depression, even after controlling for other factors. With increasing longevity and proportion of the elderly population in India, and the trend toward urbanization and nucleation of the families, depression among the elderly is likely to become a disease demanding "public health problem" status in the near future. The care of the elderly, including their mental health care, requires to be brought on the national health care agenda.


   Acknowledgment Top


The authors wish to acknowledge the contribution of Dr. Paramdeep Kaur for the multivariate analysis.

 
   References Top

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    Tables

  [Table 1], [Table 2]


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