Abstract
Background:
Depression decreases an individual's quality of life and increases dependence on others. The declining physical potential familiar infrastructure and loss of peer may cause anxiety, stress, depression, etc., in geriatric population.
Objectives:
The objectives of this study were as follows: (A) to assess the extent and pattern of depression in study subjects and (B) to find out the correlates of depression in subjects.
Materials and Methods:
This cross-sectional study was conducted on 616 geriatric subjects selected by multistage sampling procedure from urban Varanasi, India. A predesigned, pretested proforma was used for collecting socioeconomic information. Depression status was assessed using the Geriatric Depression Scale. For analysis of data, SPSS version 22.0 was used. Chi-square and logistic regression were used for inferential purpose.
Results:
The overall prevalence of depression in geriatric subjects was 29.7%; 15.7% of subjects were not satisfied with life and 30% often felt helpless. The adjusted odds ratio (95% confidence interval) for depression was 4.54 (2.99–6.09) in subjects >70 years, 4.00 (2.45–6.54) for widowed, 1.95 (1.13–3.35) for widowers, 4.95 (2.02–12.07) for lower, and 3.17 (1.79–5.64) for upper-lower socioeconomic status.
Conclusion:
Nearly one-third of urban geriatric subjects had depression which was significantly enhanced by advancing age, loss of partner, and socioeconomic adversities.
Keywords: Depression, geriatric subjects, urban area
INTRODUCTION
Rapid industrialization, urbanization, and socioeconomic transitions are leading to weakening of Indian family values, family support system, and spiritual comfort which may lead to increased prevalence of depression in geriatric subjects. Among the various mental disorders, depression accounts for the greatest burden among the elderly. It is a disorder that is characterized by sadness, change in appetite, altered sleep patterns, feeling of rejection or helplessness, and sometimes suicidal tendencies. It represents one of the most profound human problems currently facing the global health-care system.[1,2] The overall prevalence of mental and behavioral disorders including depression tends to increase with age.[3]
In old age, disability arising due to various illnesses, loneliness, lack of family support, restricted personal autonomy, and financial dependency are important contributing factors for higher prevalence of psychosocial abnormality in general and depression in particular. The overall prevalence rate of depressive disorders among the elderly is estimated to range from one out of ten to four out of ten subjects depending on sociocultural context.[4,5,6,7] Although previous studies have suggested that factors such as female gender, lower educational attainment, perceived income inadequacy, and major life events are possible risk factors associated with geriatric depression, the strong and relative importance of some or potential factors associated with geriatric depression varied widely with regions and population.[8,9]
Early recognition, diagnosis, and initiation of treatment for depression in older people present opportunities for improving their quality of life, preventing suffering or premature death, and maintaining optimum levels of functions and independence. A higher number of older people who have spent most of their life with their joint/extended family may face loneliness and marginalization in their old age. Financial security in old age has a significant influence on their physical, mental, and psychosocial well-being. The declining physical potential, familiar infrastructure, and loss of peer may cause anxiety, stress, depression, etc., in geriatric population.
Overall summative status of depression does not provide specific area of concern and input, therefore, segregate analysis of pattern of depression is desired for meaningful actions. Giving due consideration to existing gaps in ascertainment of extent and pattern of depression in urban geriatric population and recognizing the importance of correlates for focused planning and execution of preventing and therapeutic measures, this study was undertaken to assess the extent, pattern and correlates of depression in geriatric subjects.
MATERIALS AND METHODS
Study design and participants
This community based cross-sectional study was conducted in wards of urban Varanasi, India. As per Census 2011, the total population of Varanasi district was 3,682,194 and 43.4% of subjects were from urban Varanasi. The geriatric population constituted 7% of the total population. Urban geriatric subjects having age > 60 years were taken as study subjects.
Sample size and selection of subjects
Taking overall prevalence of depression in urban geriatric subjects as 40%, 5% permissible error (absolute), design effect of 1.5, and nonresponse rate 10% estimated sample size, the final sample size became 616.[6] Selection of subjects was done through multistage sampling procedure. From 90 census enumeration wards, 9 wards were selected by simple random sampling. From the selected census enumeration wards, households were selected according to probability proportion to size adopting systematic random sampling method. This was followed by family and selection of subjects by lottery method. Subjects who gave their consent for the study were included in the study, whereas subjects with terminal illness or having serious mental abnormality and duration of stay in the study area < 6 months were not considered in the sampling frame. Therefore, they were excluded from the study.
Ethical clearance
Ethical clearance was taken from the Ethical Committee of Banaras Hindu University, India, and consent was obtained from participants using bilingual (Hindi and English versions) consent form.
Tools and techniques
Each study subject was administrated a predesigned and pretested proforma to obtain information about sociodemographic characteristics (viz., age, gender, material status, religion, caste, educational status, occupation; type of family, and total number of family members). Subjects were categorized into upper, upper-middle, lower-middle, upper-lower, and lower classes as per Kuppuswamy classification.[10] Pattern and status of depression in geriatric subjects were assessed by interviewing them using the Geriatric Depression Scale.[11] This scale comprises 15 items (yes/no format). Of the 15 items, 10 indicate the presence of depression when answered positively while the other 5 are indicative of depression when answered negatively. On the basis of scores, subjects were categorized as normal (0–4), mild (5–8), moderate (9–11), and severe (12–15) depressed. The data were analyzed using SPSS software (SPPS version 22.0 IBM Corp., Armonk, NY, USA). Descriptive statistics were used to present categorical data. Univariate analysis was performed for associates of depression. In order to pinpoint the correlates of depression, logistic regression analysis was done.
RESULTS
Of 616 subjects, 70.3% were normal whereas 19.6%, 8.1%, and 1.9% had mild, moderate, and severe depression, respectively. In all, 183 (29.7%) subjects were with depression, of which 66.1%, 27.3%, and 6.6% had mild, moderate, and severe depression, respectively. Pattern of depression was as shown in Table 1.
Table 1.
Pattern of depression of study subjects (n=616)
| Depression | Response | Frequency, n (%) |
|---|---|---|
| Satisfied with life | No | 97 (15.7) |
| Dropped activities | Yes | 200 (32.5) |
| Feel life is empty | Yes | 169 (27.4) |
| Often get bored | Yes | 148 (24.0) |
| Good spirit most of the time | No | 105 (17.0) |
| Afraid bad going happen | Yes | 53 (8.6) |
| Feel happy | No | 106 (17.2) |
| Often feel helpless | Yes | 185 (30.0) |
| Prefer to stay home | Yes | 193 (31.3) |
| Problems with memory | Yes | 93 (15.1) |
| Wonderful alive now | No | 96 (15.6) |
| Pretty worthless now | No | 136 (22.1) |
| Full of energy | No | 218 (35.4) |
| Situation hopeless | Yes | 74 (12.0) |
| People better than the subjects | Yes | 156 (25.3) |
Distribution of select demographic and socioeconomic variables is as shown in Table 2. Age, marital status, education and socioeconomic class were significantly associated with depression. There existed no significant (P > 0.05) association of depression with gender, religion, caste, occupation, type, and size of family of subjects.
Table 2.
Association of demographic and socioeconomic variables with depression
| Particulars (616) | Depression status | ||||
|---|---|---|---|---|---|
|
| |||||
| Normal, n (%) | Mild, n (%) | Moderate/severe, n (%) | Total, n (%) | Test of significance | |
| Age (years) | |||||
| 60-69 | 321 (81.9) | 62 (15.8) | 9 (2.3) | 392 (100) | χ2: 17.52 df: 4 P<0.01 |
| 70-79 | 107 (61.8) | 41 (23.7) | 25 (14.5) | 173 (100) | |
| >80 | 5 (9.8) | 18 (35.3) | 28 (54.9) | 51 (100) | |
| Marital status | |||||
| Married | 320 (80.4) | 56 (14.1) | 22 (5.5) | 398 (100) | χ2: 71.27 df: 4 P<0.01 |
| Widower | 57 (62.6) | 25 (27.5) | 9 (9.9) | 91 (100) | |
| Widowed | 56 (44.1) | 40 (31.5) | 31 (24.4) | 127 (100) | |
| Education | |||||
| Illiterate + just literate | 207 (66.6) | 62 (19.9) | 42 (13.5) | 311 (100) | χ2: 23.60 df: 6 P<0.01 |
| Primary + middle | 106 (66.2) | 43 (26.9) | 11 (6.9) | 160 (100) | |
| High school + intermediate | 63 (77.8) | 12 (14.8) | 6 (7.4) | 81 (100) | |
| Graduate and above | 57 (89.1) | 4 (6.2) | 3 (4.7) | 64 (100) | |
| Socioeconomic class | |||||
| Upper class | 35 (83.3) | 2 (4.8) | 5 (11.9) | 42 (100) | χ2: 42.50 df: 8 P<0.01 |
| Upper middle | 124 (79.5) | 23 (14.7) | 9 (5.8) | 156 (100) | |
| Lower middle | 123 (76.9) | 31 (19.4) | 6 (3.8) | 160 (100) | |
| Upper lower | 129 (59.4) | 56 (25.8) | 32 (14.7) | 217 (100) | |
| Lower class | 22 (53.7) | 9 (22.0) | 10 (24.4) | 41 (100) | |
Logistic regression analysis identified age, marital status, and SES as significant correlates of depression in geriatric subjects. When the age group of 60–69 years was taken as reference, the adjusted odds ratio (AOR) for depression for subjects > 70 years was 4.54 (95% confidence interval [CI]: 2.99–6.09). Considering married subjects as reference, AORs for depression for widower and widowed subjects were 1.94 (95% CI: 1.13–3.35) and 4.00 (95% CI: 2.45–6.54), respectively. In comparison to subjects having SES status as upper and upper middle, significantly higher (P < 0.01) AORs for depression were observed for subjects belonging to lower (AOR: 4.94; 95% CI: 2.02–12.07) and upper-lower classes (AOR: 3.17; 95% CI: 1.79–5.64). The significant influence of literacy status on depression in univariate analysis got eliminated in the logistic model. In case of literate + just literate and primary + middle educated subjects, AOR was 1.03 (95% CI: 0.56–1.92) and 1.81 (95% CI: 0.96–3.42), respectively [Table 3].
Table 3.
Correlates of depression in geriatric subjects
| Particulars | Estimate of β | SE of β | P | AOR | 95% CI (lower-upper) |
|---|---|---|---|---|---|
| Age (years) | |||||
| ≥70 | 1.51 | 0.21 | 0.000 | 4.54 | 2.99-6.09 |
| 60-69 (reference) | - | - | - | - | - |
| Marital status | |||||
| Widower | 0.67 | 0.28 | 0.017 | 1.95 | 1.13-3.35 |
| Widowed | 1.39 | 0.25 | 0.000 | 4.00 | 2.45-6.54 |
| Married (reference) | - | - | - | - | - |
| Educational status | |||||
| Illiterate + just literate | 0.03 | 0.32 | 0.918 | 1.03 | 0.56-1.92 |
| Primary + middle | 0.59 | 0.32 | 0.066 | 1.81 | 0.96-3.42 |
| High school and above (reference) | - | - | - | - | - |
| Kuppuswamy SES | |||||
| Lower | 1.60 | 0.46 | 0.000 | 4.94 | 2.02-12.07 |
| Upper lower | 1.16 | 0.29 | 0.000 | 3.17 | 1.79-5.64 |
| Lower middle | 0.15 | 0.30 | 0.617 | 1.16 | 0.64-2.16 |
| Upper + upper middle (reference) | - | - | - | - | - |
AOR: Adjusted odds ratio, CI: Confidence interval, SE: Standard error, SES: Socioeconomic status
DISCUSSION
Depression itself refers to a heterogeneous set of phenomena ranging from simple mood swings to severe affective state. According to this study, one out of three subjects had depression. Similar findings have been observed in meta-analysis of 81 published articles on depressive symptoms in older Chinese adults[12] as well as in studies from South,[13] North,[14] and West[15] India. Pattern of depression revealed through this study reflects that depression in geriatric subjects is cause of serious concern. It provides action points for their psychosocial well-being. In order to find out the correlates of depression, factors significantly associated with depression were identified and significant factors from association analysis were put in the logistic model. Nearly 8 out of 10 subjects in the age group of 60–69 years were without depression whereas depression prevailed in approximately 4 out of 10 subjects from 70 to 79 years and 9 out of 10 subjects >80 years of age. In conformity with the findings of this study, several workers have reported a significant association between age and depression.[13,16] In this study, age emerged as a significant correlate of depression in the logistic model as well. Influence of gender, religion and type as well as size of family of study subjects with their depression level was not significant.
Two out of ten married subjects were without depression. Higher prevalence of depression prevailed in widower (7 out of 20) and widowed subjects (11 out of 20). This may be attributed to the absence of close interpersonal relationship due to death of the spouse which renders them vulnerable to stress and causes major depression disorder. This finding has been supported by other studies as well.[13,15,17] This study pinpoints that subjects without spouse had higher adjusted odds for depression. In conformity with the present study, lower level of education has been found to be associated with depression in several other studies as well.[13,17,18] A study has identified literacy status as a predictor of depression in logistic model.[17] However, in the present study, a significant association between depression and literacy status could not be demonstrated in logistic model.
Most of the people who are entering in geriatric age group have no old-age financial planning. Higher prevalence of depression in poor economic status has been reported by a study from Visakhapatnam[13] and among the elderly living in the urban poor locality in Bangalore city.[19] As observed in this study, higher AORs in subjects belonging to lower and upper-lower SES categories have been reported in another study as well.[20] This study provides significant inputs for prioritization of action for psychosocial well-being of geriatric subjects.
CONCLUSION
Advancing age, loss of partner, and socioeconomic adversities predisposed urban geriatric subjects to depression. These findings call for targeted attention for curbing depression in urban geriatric subjects. Familial support and economic security are critical inputs for psychosocial well-being of geriatric subjects.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
REFERENCES
- 1.Ebmeier KP, Donaghey C, Steele JD. Recent developments and current controversies in depression. Lancet. 2006;367:153–67. doi: 10.1016/S0140-6736(06)67964-6. [DOI] [PubMed] [Google Scholar]
- 2.Kessler RC, Berglund P, Demler O, Jin R, Koretz D, Merikangas KR, et al. The epidemiology of major depressive disorder: Results from the National Comorbidity Survey Replication (NCS-R) JAMA. 2003;289:3095–105. doi: 10.1001/jama.289.23.3095. [DOI] [PubMed] [Google Scholar]
- 3.Ingle GK, Nath A. Geriatric health in India: Concerns and solutions. Indian J Community Med. 2008;33:214–8. doi: 10.4103/0970-0218.43225. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Patel V, Kleinman A. Poverty and common mental disorders in developing countries. Bull World Health Organ. 2003;81:609–15. [PMC free article] [PubMed] [Google Scholar]
- 5.Malhotra R, Chan A, Ostbye T. Prevalence and correlates of clinically significant depressive symptoms among elderly people in Sri Lanka: Findings from a national survey. Int Psychogeriatr. 2010;22:227–36. doi: 10.1017/S1041610209990871. [DOI] [PubMed] [Google Scholar]
- 6.Jariwal V, Bansal RK, Patel S, Tamakuwala B. A study of depression among aged in Surat city. Natl J Community Med. 2010;1:47–9. [Google Scholar]
- 7.Sherina M, Rampal LS, Aini M. The prevalence of depression among elderly in Urban Area of Selangore, Malaysia. Int Med J. 2005;4:57–63. [Google Scholar]
- 8.United Nations Department of Economic and Social Affairs/Population Division World Population Prospects: The 2004 Revision, Volume I: Comprehensive Tables. 2005;1:1–763. [Google Scholar]
- 9.Wilson RS, Mendes De Leon CF, Bennett DA, Bienias JL, Evans DA. Depressive symptoms and cognitive decline in a community population of older persons. J Neurol Neurosurg Psychiatry. 2004;75:126–9. [PMC free article] [PubMed] [Google Scholar]
- 10.Kohli C, Kishore J, Kumar N. Kuppuswamy's socioeconomic scale-update for July 2015. Int J Preven Curat Comm Med. 2015;1:26–8. [Google Scholar]
- 11.Greg SA. Geriatric Depression Scale (GDS) 2012. [Last accessed on 2014 Jul 18]. p. 4. Available from: http://consultgerirn.org/uploads/File/trythis/try_this_4.pdf .
- 12.Li D, Zhang DJ, Shao JJ, Qi XD, Tian L. A meta-analysis of the prevalence of depressive symptoms in Chinese older adults. Arch Gerontol Geriatr. 2014;58:1–9. doi: 10.1016/j.archger.2013.07.016. [DOI] [PubMed] [Google Scholar]
- 13.Sundrumb MB, Goru KB, Krishnaveni A. Epidemiological study of depression among population above 60 years in Visakhapatnam, India. Int J Med Sci Public Health. 2013;2:695–702. [Google Scholar]
- 14.Nautiyal A, Madhav NV, Ojha A, Sharma RK, Bharjana S, Kothiyal P, et al. Prevalence of depression among geriatric people in Dehradun city of Uttarakhand, India. J Depress Anxiety. 2015;4:1–3. [Google Scholar]
- 15.Seby K, Chaudhury S, Chakraborty R. Prevalence of psychiatric and physical morbidity in an urban geriatric population. Indian J Psychiatry. 2011;53:121–7. doi: 10.4103/0019-5545.82535. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Subba R, Subba HK. Level of depression among elderly in selected old age homes at Mangalore. India J Chitwan Med Coll. 2015;5:28–32. [Google Scholar]
- 17.Grima M, Hailu M, Wakwoya A, Yohannis Z, Ebrahim J. Geriatric depression in Ethopia: Prevalence and associated factors. J Psychiatry. 2016;20:1–5. [Google Scholar]
- 18.Anand A. Understanding depression among older adults in six low middle income countries using WHO-SAGE survey. Behav Health. 2014;1:1–11. [Google Scholar]
- 19.Sanjay TV, Jahnavi R, Gangaboraiah B, Lakshmi P, Jayanthi S. Prevalence and factors influencing depression among elderly living in the urban poor locality of Bengaluru city. Int J Health Allied Sci. 2014;3:105–9. [Google Scholar]
- 20.Thirthahalli C, Suryanarayana SP, Sukumar GM, Bharath S, Rao GN, Murthy NS. Proportion and factors associated with depressive symptoms among elderly in an urban slum in Bangalore. J Urban Health. 2014;91:1065–75. doi: 10.1007/s11524-014-9903-6. [DOI] [PMC free article] [PubMed] [Google Scholar]