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ORIGINAL ARTICLE
Year : 2017  |  Volume : 7  |  Issue : 2  |  Page : 34-38

Rate of Depressive Symptoms and Associated Risk Factors Among the Elderly in Haldwani Block of Nainital District, Uttarakhand, India


1 Department of Community Medicine, V.C.S.G.G.M.S&R.I., Srinagar Garhwal, India
2 Government Medical College, Haldwani, Uttarakhand, India

Date of Web Publication27-Apr-2017

Correspondence Address:
Janki Bartwal
V.C.S.G. G.M.S&R.I Srinagar Garhwal 246174, Pauri Garhwal, Uttarakhand
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ijnpnd.ijnpnd_76_16

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   Abstract 

Background: At least 350 million people live with depression, and it is the leading cause of disability worldwide. Symptoms of depression are often overlooked and untreated among the elderly because they coincide with other problems encountered during this age. The aim of this study was to assess the rate of depressive symptoms and associated risk factors among the elderly. Materials and Methods: A community-based, cross-sectional study was conducted among 440 individuals belonging to the elderly population from November 2013 to October 2014. A five-item version of the Geriatric Depression Scale was used to rate the depressive symptoms among the elderly besides asking socio-demographic details. Data were collected, coded and entered into Microsoft Excel spreadsheet, and it was analyzed using the Statistical Package for the Social Sciences version 16 software (SPSS Inc., Chicago, IL, United States). Univariate analysis was performed using chi-square test and odds ratio. Binary logistic regression was used to know the relation of various risk factors with depression. Results: The rate of depressive symptoms was 17.5% ± 6.56. On univariate analysis, depression was observed more in females, the widowed, the illiterate, people with low socio-economic status (SES), those who were financially dependent and those having disturbed sleep. On applying binary logistic regression, only SES and sleep pattern were found to be significant variables influencing the presence of depression in the elderly. Conclusion: Depression is an emerging public health problem of today’s world, especially among the elderly who are less educated and with no source of income, as they are financially dependent on others; in addition, loss of spouse adds to their woes.

Keywords: Depression, elderly, risk factor, rural


How to cite this article:
Bartwal J, Rawat CS, Awasthi S. Rate of Depressive Symptoms and Associated Risk Factors Among the Elderly in Haldwani Block of Nainital District, Uttarakhand, India. Int J Nutr Pharmacol Neurol Dis 2017;7:34-8

How to cite this URL:
Bartwal J, Rawat CS, Awasthi S. Rate of Depressive Symptoms and Associated Risk Factors Among the Elderly in Haldwani Block of Nainital District, Uttarakhand, India. Int J Nutr Pharmacol Neurol Dis [serial online] 2017 [cited 2017 Nov 18];7:34-8. Available from: http://www.ijnpnd.com/text.asp?2017/7/2/34/205288


   Introduction Top


Globally, the population is ageing rapidly. Between 2015 and 2050, the proportion of the world’s population over 60 years will nearly double from 12 to 22%.[1] Over 20% of the elderly suffer from a mental or neurological disorder, and 6.6% of all disabilities among this age group is attributed to neurological and mental disorders. The most common neuro-psychiatric disorders in this age group are dementia and depression.[2]

Neuro-psychiatric disorders are often considered as a part of ageing; in addition, because of lack of access to treatment and stigma associated with it, depression is under-reported and misdiagnosed among the elderly. Depression can lead to loneliness, cognitive impairment, social isolation, lowered self-esteem, suicidal tendency and poor quality of life among the elderly.[3] With this background, because of lack of knowledge about the health status of the elderly in this region, this study was undertaken with the objective to assess the rate of depressive symptoms and associated risk factors among the elderly.


   Materials and Methods Top


Study area

Uttarakhand is a state located in the northern part of India. It has 13 districts and Nainital is one of them; furthermore, this district has eight blocks/tehsils including Haldwani, which lies on its foothills. This study was conducted in the rural areas of Haldwani.

Study period

The study period was for 1 year, that is, November 2013 to October 2014.

Study participants

The elderly, who had completed 60 years of age at the time of investigation, were permanent residents and willing to participate were included in the study after obtaining informed consent and ensuring confidentiality.

Study design

Community-based, cross-sectional study.

Sample size

Considering the prevalence rate of depression among the elderly as 50% of all psychiatric illnesses,[4] the sample size was calculated. The formula used was n = 4pq/d2, where n = required sample size, p = prevalence (50%), q = (100−p = 50%) and d = absolute error taken as 5%. Taking non-response rate of 10%, the final sample size came as 440.

Sampling procedure

Two-stage sampling technique was applied. In 1st stage, 11 sub-centres (SCs) were selected randomly by lottery method out of 22 SCs attached to block-level Primary Health Centre; 40 elderly individuals were selected from each of these 11 SCs to get the adequate sample size of 440. Considering, 8% of the elderly (as per census 2011) and 5000 population of one SC. 8/100 × 5000 = 400 (each SC would have 400 elderly individuals). 400/40 = 10, that is, every 10th elderly was taken from each of the 11 SCs in the 2nd stage. To achieve the desired target, a list of all the elderly individuals was made for all 11 SCs selected from the SC survey register maintaining the order of the families as per the survey done. If some elderly individual did not give consent for the interview or could not be contacted, then the next name was selected from the list.

Assessment tool and techniques

Data collection

Data collection tools were developed, pre-tested and administered to the participants. Technique used to collect the data was by interview using semi-structured schedules.

Data analysis

The data were coded, entered into Microsoft Excel spreadsheet and were analyzed using the Statistical Package for the Social Sciences version 16 software (SPSS Inc., Chicago, IL, United States). Statistical analysis was done using chi-square test, odds ratio and binary logistic regression.

Interview schedule

It contained questions related to the socio-demographic profile of the study participants. Modified B.G. Prasad classification for the year 2013 was used to assess the socio-economic status (SES) of an individual.[5]

Certain definitions were created for this research including to assess the financial dependency: the elderly individual was considered financially independent if his/her source of personal income or any monetary benefit from a social scheme was sufficient to maintain him/her. The elderly was considered partially dependent if he/she had some personal income or any monetary benefit from a social scheme, but which was not sufficient to maintain him/her. The elderly was considered fully dependent, if there was no personal income or monetary benefit from any social scheme and was totally dependent on other family members.

Similarly, to assess the sleep pattern, the following definitions were created: elderly sleep pattern was classified as disturbed if they experienced difficulty in falling asleep, had reduced sleep duration and had poor quality of sleep.

A five-item version of the Geriatric Depression Scale created by Hoyl et al.[6] was used in this study. The questions were explained by a researcher to the participants in Hindi language, which was understood by everyone, and the response was noted in the dichotomous form; if the answer to more than one question was yes, then the participant was rated as depressive.

Ethical approval

Permission was obtained from the Institutional Ethical Committee of Government Medical College, Haldwani before the commencement of the study. The reference number is 47/IEC/01/12-13.


   Results Top


Out of 440 elderly individuals, majority (59.5%) of the elderly belonged to 60–69 years of age. Females (57.5%) outnumbered males. Most (60%) of the elderly were married. Only 21.4% of them were gainfully employed. Maximum (59.1%) number of elderly individuals belonged to class III as per modified B.G. Prasad classification; about 92.5% of the elderly lived in a joint family, and only 25.5% were financially independent [Table 1].
Table 1: Distribution of the elderly according to the socio-demographic profile

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Depression was present among 17.5% ± 6.56 of the elderly. On univariate analysis, depression was observed more among the elderly females, the widowed, the illiterate, people with low SES, those who were financially dependent and those having disturbed sleep. This difference was statistically significant at P value <0.05 [Table 2].
Table 2: Association between depression and risk factors

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On applying binary logistic regression, only SES and sleep pattern were found to be significant variables influencing the presence of depression in the elderly. No multicollinearity was seen among the different variables, as their standard error was either less or slightly more than their corresponding beta coefficients. The adjusted odds ratio being more than 1 was seen in female sex (1.478), SES-III (13.309), SES-IV (11.722) and SES-V (4.583), as well as those from a nuclear family (1.208), who were partially dependent (1.043), who were fully dependent (1.246) and having disturbed sleep (2.948) with significant odds ratio of SES and sleep pattern. Because there was only one individual having SES-I, the binary logistic regression was performed on N = 439 elderly individuals excluding the one individual with SES-I [Table 3].
Table 3: Binary logistic regression of the association of risk factors with depression

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   Discussion Top


The rate of depressive symptoms in this study was 17.5% ± 6.56. A systematic review performed by Barua et al.[7] among geriatrics of all over the world found the prevalence of depression to be between 10 and 20%. Various other studies conducted in other parts of India and abroad reported the prevalence varying from 8.9%, as per Sengupta et al.,[8] to 61.4%, as per Akhtar et al.[9] Such wide variation may be because of studies being conducted in different settings, sample size and use of varied depression scale.

In this study, increase in age was not found to be associated with depression. Similar findings were observed by various other researchers, [10, 11, 12, 13, 14, 15] whereas in the study conducted by Sengupta et al.,[8] Akhtar et al.[9] and Barua et al.,[16] increase in age was identified as an independent predictor for depression.

The sex of a person was not found to be influencing depression in our study. Similar observations were made in other studies.[10] Contrary observations were made by Sengupta et al.,[8] Ferreria et al.[11] and Nakulan et al.,[17] wherein female sex was observed to be significantly associated with depression.

The marital status of the elderly was not found as a significant predictor for depression. Coherent findings were seen by Sengupta et al.,[8] Akhtar et al.,[9] Ferreria et al.,[11] Haseen et al.,[12] Barua et al.,[16] Nakulan et al.[17] and Rashid et al.[18] The marital status was found significantly associated with depression in other studies.[14],[15]

In this study, the literacy status of the elderly was not significantly associated with depression. Similar observations were made by Sengupta et al.,[8] Ferreria et al.,[11] Bodhare et al.[13] and Dasgupta et al.[15] Other researchers[9],[14] too found illiteracy to be a significant independent predictor of depression.

The current employment status was found to have no significant association with depression. Similar findings were seen by Sengupta et al.,[8] Akhtar et al.[9] and Haseen et al.,[12] whereas Rashid et al.[18] observed significant association between unemployment and depression.

Low SES was found to be an independent predictor for depression in this study. Other researchers[8],[10],[13],[15],[18] also made similar observations that depression is caused in the elderly belonging to low SES or having low income. Akhtar et al.[9] could not find a significant effect of low income on depression.

In this study, the type of family had no significant effect on the depression status of the elderly. Akhtar et al.[9] also observed similar findings in his study, whereas Sengupta et al.[8] and Dasgupta et al.[15] found that the elderly belonging to a nuclear family were significantly depressed in comparison to those of a joint or extended family.

Financial dependency was not a significant predictor of depression in this study. A similar observation was made by Nakulan et al.,[17] whereas Akhtar et al.[9] and Dasgupta et al.[15] found that it as a significant independent predictor of depression in their study.

Disturbed sleep pattern was another significant independent predictor of depression in our study. A similar finding was reported by Pracheth et al.[19] in a study conducted in an urban slum of Karnataka.


   Conclusion Top


In this study, the rate of depressive symptoms was 17.5% ± 6.56. Binary logistic regression revealed that factors such as low SES and disturbed sleep were independent predictors for depressive symptoms in the elderly. It indicated the need for social benefit schemes especially for those elderly belonging to the poor strata of society, because many a times their needs are neglected to provide for other members of the family, which leads to various psychosomatic changes in them.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
   References Top

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Sharma R. Revision of Prasad’s social classification and provision of an online tool for real-time updating. South Asian J Cancer 2013;2:157.  Back to cited text no. 5
    
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Hoyl MT, Alessi CA, Harker JO, Josephson KR, Pietruszka FM, Koelfgen M et al. Development and testing of a five-item version of the Geriatric Depression Scale. J Am Geriatr Soc 1999;47:873-8. Available from: http://onlinelibrary.wiley.com/doi/10.1111/j.1532-5415.1999.tb03848.x/pdf. [Last accessed on 2013 Jun 3].  Back to cited text no. 6
    
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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. 8
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Ferreira PC, Tavares DM. Prevalence and factors associated with the rates of depression among elderly residents in rural areas. Rev Esc Enferm USP 2013;47:396-401. Available from: http://www.scielo.br/pdf/reeusp/v47n2/en_18.pdf. [Last accessed on 2016 Sep 4].  Back to cited text no. 11
    
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Bodhare TN, Kaushal V, Venkatesh K, Kumar MA. Prevalence and risk factors of depression among elderly population in a rural area. Perspect Med Res 2013;1:11-5. Available from: http://www.pimr.org.in/BODHARE.PDF. [Last accessed on 2015 Sep 13].  Back to cited text no. 13
    
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Radhakrishnan S, Nayeem A. Prevalence of depression among geriatric population in a rural area in Tamilnadu. Int J Nutr Pharmacol Neurol Dis 2013;3:309-12.  Back to cited text no. 14
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Dasgupta A, Ray D, Roy S, Sarkar T, Ghosal A, Das A et al. Depression among the geriatric population is a matter of concern: A community based study in a rural area of West Bengal. Nepal J Epidemiol 2013;3:282-7.  Back to cited text no. 15
    
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