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ORIGINAL ARTICLE
Year : 2018  |  Volume : 62  |  Issue : 2  |  Page : 89-94  

A study on prevalence of depression and associated risk factors among elderly in a rural block of Tamil Nadu


1 Assistant Professor, Department of Community Medicine, Chettinad Hospital and Research Institute, Chennai, Tamil Nadu, India
2 Professor, Department of Community Medicine, Apollo Institute of Medical Sciences and Research, Chittoor, Andhra Pradesh, India
3 Professor, Department of Community Medicine, SRM Medical College Hospital and Research Centre, Kanchipuram, Tamil Nadu, India

Date of Web Publication14-Jun-2018

Correspondence Address:
M Buvneshkumar
Department of Community Medicine, Chettinad Hospital and Research Institute, Kelambakkam, Chennai, Tamil Nadu
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ijph.IJPH_33_17

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   Abstract 


Background: Depression among elderly is an important public health problem responsible for considerable morbidity and disability. Causes of depression are multifactorial and often preventable. As there was dearth of community studies in Tamil Nadu, the present study was undertaken. Objectives: The objective of this study is to estimate the prevalence of depression and to assess the factors which are associated with depression among elderly. Methods: A cross-sectional study was done from July 2014 to July 2015 among elderly in Kattankulathur block with a sample size of 690 by cluster sampling method. House-to-house interview was conducted using a predesigned, pre-tested questionnaire, and depression was assessed using geriatric depression scale-30. Data were analyzed using SPSS version 20 (Trial). The statistical tests used were proportions, Chi-square test. P<0.05 was considered to be statistically significant. Results: The overall prevalence of depression was 35.5% (95% confidence interval: 31.9%–39.0%). Sociodemographic factors such as female sex, nuclear family, being widowed, unemployed status, low socioeconomic status, financially dependent, medical factors such as cardiac disease, visual impairment, arthritis, anemia, life events such as conflicts in family, death of the family member or close relative, and illness of self/family member were significantly associated with depression (P < 0.05). Conclusions: More strength of association for depression was seen with low socioeconomic status, nuclear family, low-intensity work, conflicts in the family, death of family members using multiple logistic regression. These findings show the need for proper care by the family members and counseling for the elderly which are of much importance in preventing depression.

Keywords: Depression, elderly, prevalence, risk factors


How to cite this article:
Buvneshkumar M, John K R, Logaraj M. A study on prevalence of depression and associated risk factors among elderly in a rural block of Tamil Nadu. Indian J Public Health 2018;62:89-94

How to cite this URL:
Buvneshkumar M, John K R, Logaraj M. A study on prevalence of depression and associated risk factors among elderly in a rural block of Tamil Nadu. Indian J Public Health [serial online] 2018 [cited 2018 Jul 19];62:89-94. Available from: http://www.ijph.in/text.asp?2018/62/2/89/234503




   Introduction Top


Population aging has become a universal phenomenon [1] and is the result of a process known as “demographic transition,” in which there is a shift from high mortality and fertility to low, leading to an increase in the proportion of elderly people in the total population. In India, the elderly population has rose from 12.1 million in 1901 to approximately 77 million in Census 2001 and 104 million in census 2011 which now accounts for 8% of total population.

Approximately 15% of adults aged 60 and over suffer from a mental disorder. The most common neuropsychiatric disorders in this age group are dementia and depression.[2] A meta-analysis by Barua et al. of 74 studies, including 487,275 elderly individuals found that the worldwide prevalence rate of depressive disorders to be between 4.7% and 16%; however, there was higher prevalence of geriatric depression in India ranging from 11.6% to 31.1%.[3]

According to the World Health Organization (WHO) report, patients over 55 years with depression have a four times higher death rate than those without depression, mostly due to heart disease or stroke.[4] Although there are known, effective treatments for depression, fewer than half of those affected in the world (in some countries, <10%) receive such treatments.[5]

The depressive symptoms are likely to be diminished as “normal” by older persons. Given the relative ease with which the depressive illness can be diagnosed and treated, there is enormous potential for alleviating this largely neglected public health burden among the elderly. Community studies from Tamil Nadu at block level were scarce. Taking into consideration the above factors, the present study was conducted in a rural block to estimate the prevalence of depression among the elderly and to assess the factors which are associated with depression among the elderly.


   Materials and Methods Top


This cross-sectional study was done from July 2014 to July 2015 in a rural block in Kancheepuram district in Tamil Nadu. After getting clearance from the Institutional Ethical Committee, the study population of elderly aged 60 years and above residing at that area for at least 1 year was included in this study. The exclusion criterion was elderly person with poor cognition screened through Mini-cog [6] and those not willing and consenting to participate in the study for their own reasons. The sampling method used was cluster sampling method. The sample size was calculated with prevalence 21.9%[3] and with precision of 20%. Using the formula, sample size n = Z2 PQ/L 2 (Z = constant (1.96), P = prevalence, Q = 1-p, L = Precision) the sample size obtained was 343.8. For cluster sampling, with design effect of 2, the sample size was calculated to be 687.6. Kattankulathur block has a total of 39 village panchayats and 133 habitations/villages. The population for individual habitations were computed (Population and Housing Census enumerated population 2012–2013) with total population being 2, 13, 850 distributed in 133 habitations. For the study to be representative of the block population, 30 habitations (out of 133) were selected. To attain the sample interval, the total population (213850) was divided with 30 and the sampling interval attained was 7129. First village/habitation was selected using a random number from table of random numbers and subsequent clusters were selected by adding 7129 for each cluster and 30 villages/habitations were selected. Twenty-three samples were collected from each cluster. Data collection was done by house-to-house survey using an interview schedule. The extended program for immunization recommendation for the selection of the first household was done.[7] Informed written consent was obtained from the participants. A predesigned questionnaire pro forma in their local language was administered to each elderly to collect data on sociodemographic profile such as age, sex, religion, marital status, family size and type, education, income, socioeconomic status (Modified B. G Prasad Scale for socioeconomic status (SES)[8],[9]), occupation, and financial dependency. Screening for dementia using Mini-cog assessment was done individually. Those who were positive for dementia using Mini-cog (score <3) were excluded from the study. Geriatric depression scale (GDS)-30 created by Yesavage et al.[10] which has 30 questions was administered individually through face-to-face interview in their local language, and depression levels were thus assessed for the elderly who screened negative for dementia. GDS-30 is not a substitute for a diagnostic interview but is a useful screening tool which facilitates the assessment of depression in older adults. GDS-30 has been extensively used in the community, acute and long-term care settings among healthy, medically ill, and mild to moderately cognitively impaired older adults. The GDS was found to have 92% sensitivity and 89% specificity when evaluated against diagnostic criteria. The validity and reliability of the tool have been supported through both clinical practice and research.[10] A score of one or zero is given for each question depending on the answer for 30 questions and the cutoff for normal is score of 0–9; for mild depression-10–19; and for severe depression-20–30. The behavioral factors such as hours of sleep in daytime and night time, smoking, alcohol habits, physical activity and exercise, and history of medical illness was taken based on the respondents' self-reports of illnesses that were diagnosed, under follow-up and treatment by doctors at medical and health facilities, life events (in the past 1 year), and history of any psychiatric illness was asked. Data were entered in MS-Excel spreadsheet and analyzed using SPSS software (IBM SPSS statistics for windows, Version 20.0 (Trial), Armonk, New York, USA). The statistical tests used were proportions with 95% confidence interval (CI) and Chi-square test. P < 0.05 was considered to be statistically significant.


   Results Top


Of the total 690 subjects, 363 (52.6%) were female, and 327 (47.4%) were male. The mean age was 68.14 years with standard deviation – 6.65. On grouping the elderly based on age, Young old (60–69 years) were 432 (62.6%), Old old (70–79 years) were 201 (29.1%), Oldest old (80 years and above) were 57 (8.3%). Majority of the population 391 (56.7%) were married, followed by 291 (42.2%) were widowed, followed by 4 (0.6%) never married, and 4 (0.6%) separated. Based on the religion, majority were Hindu 601 (87.1%), followed by Christian's 71 (10.3%), and Muslim's 18 (2.6%). Based on the educational qualification, majority 324 (47%) being illiterates, primary schooling 140 (20.3%), secondary schooling 162 (23.5%), high schooling 42 (6.1%), college 18 (2.6%), and postgraduate degree 4 (0.6%). Based on the occupational status, unemployed were 500 (72.4%) of which 110 were retired personnel, followed by coolie 132 (19.2%), self-employed 37 (5.4%), farmer 11 (1.6%), and petti shop owners 10 (1.4%).

The study subjects based on the SES (Modified BG Prasad Classification), majority 187 (27.1%) were in Class III, followed by Class II 164 (23.8%), Class IV 144 (20.9%), Class I 103 (14.9%), and Class V 92 (13.3%), respectively. According to type of family, 330 (47.8%) participants were from nuclear family and 360 (52.2%) from joint family. According to financial dependency, 317 (45.9%) were totally dependent, 122 (17.7%) are partially dependent, and 251 (36.4%) are independent.

Based on hours of sleep in the night among the participants, majority 343 (49.7%) sleeping for 6–8 h followed by 260 (37.7%) sleeping for <6 h and 87 (12.60%) for greater than 8 h.

The prevalence of depression among the subjects was 35.5%. The distribution of depression among the participants with 195 (28.3%) was in mild depression 50 (7.2%) in severe depression and 445 (64.5%) with no depression (Normal). Females were more depressed when compared to males.

[Table 1] shows the association between sociodemographic factors and depression in which female sex, illiteracy, no income, low SES, nuclear type of family, nonpucca housing, and financially dependent was significantly associated with depression.
Table 1: Association between sociodemographic variables of the study participants and depression

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[Figure 1] shows the distribution of behavioral factors and depression among participants. There was statistically significant association between behavioral factors such as sleep less than 8 h and depression (χ2 value-25.06; P < 0.0001*), low intensity work and depression (χ2 value-81.45; P < 0.0001*), no exercise and depression (χ2 value-20.58; P < 0.0001*). There was no statistically significant association between smoking and depression, alcohol consumption, and depression among the study participants.
Figure 1: Behavioral factors and depression among the study participant.

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[Table 2] shows the association between history of medical illness and depression of which cardiac disease, visual impairment, arthritis, constipation, and anemia was statistically significantly associated with depression. History of diabetes, hypertension, chronic kidney disease, asthma, and hearing impairment were not significantly associated with depression (P > 0.05).
Table 2: Association between history of medical illness and depression among study subjects

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[Table 3] shows the association between life events (in the past 1 year) and depression among the study participants in which death of family member, death of close relative, no birth in the family, unemployment, illness of self, illness of family members, debt/loss, and self-fall/accidents were statistically significant (P < 0.05).
Table 3: Association between life events (in past one year) and depression among study participants

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On running the multiple logistic regression (MLR) analysis (after dichotomizing the factors), sociodemographic factors such as marital status (widowed/separated/un married), nuclear family, low SES, behavioral factors such as nighttime sleep <8 h, low-intensity/sedentary work; medical illness such as cardiac disease; life events such as conflicts in the family, unemployment of self/children, illness of self, family member death, debt/loss, and fall were associated with depression.


   Discussion Top


The age and gender distribution of the elderly as obtained in the study results were almost similar to the findings of reports pertinent to elderly in rural India [11] and similar findings were found in a study done by Hakmaosa et al.[12] In this study, 56.5% participants were not employed and the rest 43.5% were working which corresponds to the report on situation analysis of elderly where 40% of persons aged 60 years and above (60% of men and 19% of women) were working.[13]

The present study showed the prevalence of depression among the elderly persons as 35.5%, similar findings were found in study done by Sundru and Goru in Visakhapatnam done in 2013[14] and Sanjay et al. in Parvithapura, Bengaluru locality 2013–2014,[15] showed prevalence of depression to be 36% using GDS-15 as our study prevalence. A study by Sinha et al.[16] in Sembakkam village in Kancheepuram district in 2012 showed the prevalence of depression to be 42.7% which was higher than our study prevalence. A study done by the WHO on global aging and adult health wave 1 (2007–2010) was used by Anand [17] to assess the prevalence of depression of older adults 50 and above in China, Ghana, India, Mexico, Russian Federation, South Africa, showed prevalence of depression highest in India (27.4), Mexico (23.7%), Russia (15.6%), Ghana (11.0%), South Africa (6.4%), and China (2.6%). Of the overall 35.5% prevalence of depression in our study, 28.3% had mild depression which corresponds to similar finding in a study done by Dumbray et al.[18] as 29%, with mild depression.

In our study, females had 1.81 times more risk than males of being depressed which was found to be statistically significant (P < 0.0001). This might be because women, face more stressful events throughout their lifetime and have a greater sensitivity toward these events which tends to get depressed in response. Similar findings were seen with depression to be higher in elderly females in studies done by Arumugam et al[19] (P = 0.01), Radhakrishnan and Nayeem [20] (P<0.0001) and also by Seby et al[21] (P = 0.02). The prevalence of depression was found to be inversely proportional to the literacy status (i.e., as the literacy status increases risk for depression decreases). Association between illiteracy and depression was found in studies done in India by Kamble et al.; Swarnalatha, Singh et al., Reddy et al.[22],[23],[24],[25]

The finding that the association of elderly depression and being widowed was significant (P < 0.0001) and had risk 1.85 (95% CI-1.35–2.55) times higher when compared to married participants. It could be explained by the fact that late life emotional support by the partner is of importance to their psychological health. A study done by Sengupta and Benjamin [26] showed risk 1.94 (95% CI-1.49–2.55) as our study. Elderly with occupation, income and not dependent on others lead a normal life (take care of themselves, able to access health services effectively). There was statistically significant association between depression and unemployment (P < 0.0001). Similar finding (P < 0.001) was seen in study by Assil and Zeidan [27] and Abe et al.[28] In our study, lower the SES higher the risk for depression which was found in studies done by Beekman et al.,[29] Woo et al.[30] too.

In behavioral factors, there was statistically significant association between depression and participants whose night sleep is less than 8 h (P < 0.0001), Hoffmann et al.,[31] Mubeen et al.[32] showed significant association between sleep and depression. When sleep is inadequate/disturbed, it can lead to increased tension, irritability, and vigilance. Hence, lack of sleep also plays a role in causing depression. The risk for depression in nonexercising participants was 6.64 (95% CI-2.62–16.85) when compared to exercising participants. A study by Palinkas et al.[33] showed that depressive symptoms were associated with amount of exercise.

In comparison to medical illness, there was statistically significant association (P = 0.04) between history of cardiac disease and depression in which risk was 1.89 (95% CI-1.01–3.54) times higher when compared to no history of cardiac disease particpantssimilar to study done by Rajkumar et al.[34] where the odds ratio (OR) was 4.75 with 95% CI-1.96–11.52. There was statistically significant association (P < 0.02) between a history of arthritis and depression with risk 1.65 (95% CI-1.07–2.55) times higher than those who are not having history of arthritis. In arthritis, pain is the most common feature which is chronic and everlasting and participants has to be in suffering which may lead to depression. ADAMS study by Steffans et al.[35] showed high prevalence of depression associated with pain severity.

It was observed that there was statistically significant association (P < 0.0001) between conflicts in the family and depression with risk 61.31 (95% CI-19.06–197.21) times higher than those without conflicts in the family. A study done by Assil and Zeidan [27] showed two times greater risk with elderly living problems. Death of the family member/close relative was associated with depression which was found to be statistically significant (P < 0.05) similar to study by Hoffmann et al[31] (P = 0.017). The risk of depression is 7.52 (95% CI-4.31–13.10) times higher in participants with debts/loss when compared to no debts/loss. A cohort study by Lorant et al.[36] showed association between depression and financial strain with a longitudinal variance of 43%.

Using the MLR, the relatively more strength of association for depression was found for sociodemographic factors such as marital status (widowed/separated/unmarried), nuclear family, and low SES with OR: 2.41, 3.86, and1.98, respectively. Similarly, a study done by Sengupta and Benjamin [26] showed in multivariate analysis that nuclear family had OR-3.64 as compared to our study (OR-3.86). Meta-analysis by Cole and Dendukuri [37] showed that risk factors for depression identified by multivariate techniques were medical illness, poor health status, bereavement similar to our study. Based on the MLR findings, the risk for depression is more closely associated with the family. Any changes in the family equilibrium (conflicts, bereavement, illness etc.,) have an impact on the individual's mental health.

Limitations

As GDS – 30 being a screening tool, generalizability of prevalence of depression has its limitations over diagnostic criteria. Comorbidities were assessed based on history and medical records.


   Conclusions Top


The overall prevalence of depression was 35.5%, with 28.5% in males and 41.8% in females. The prevalence of depression was found to be positively associated with sociodemographic factors, behavioral factors, and life events. The MLR analysis showed marital status (widowed/separated/unmarried), nuclear family, low SES, nighttime sleep <8 h, low intensity/sedentary work; cardiac disease; conflicts in the family, unemployment of self/children, illness of self, family member death, debt/loss. These findings show the need for proper care by the family members and counseling for the elderly which are of much importance in preventing depression. The key factors for meeting the mental health needs of the elderly subjects are by collaborating with governmental or nongovernmental organization to the community through the health workers and creating awareness, knowledge about the common problems of the elderly, screen and diagnose, and start treatment at the earliest.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
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