Depression and Its Associated Factors in Older Indians: A Study Based on Study of Global Aging and Adult Health (SAGE)-2007.

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Depression and Its Associated Factors in Older Indians: A Study Based on Study of Global Aging and Adult Health (SAGE)−2007 Ruhi S. Kulkarni and Ramkrishna L. Shinde J Aging Health published online 3 November 2014 DOI: 10.1177/0898264314556617

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JAHXXX10.1177/0898264314556617Journal of Aging and HealthKulkarni and Shinde

Article

Depression and Its Associated Factors in Older Indians: A Study Based on Study of Global Aging and Adult Health (SAGE)–2007

Journal of Aging and Health 1–28 © The Author(s) 2014 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/0898264314556617 jah.sagepub.com

Ruhi S. Kulkarni, MSc1 and Ramkrishna L. Shinde, PhD1

Abstract Objective: To estimate the prevalence of depression and determine the factors associated with it in older Indians aged 50 years and above. Method: This study was based on a nationally representative sample of 7,150 older Indians from WHO’s Study of Global Aging and Adult Health, SAGE-2007. Mild, moderate, and severe depression was assessed through International Classification of Diseases, 10th revision, Diagnostic Criteria for Research (ICD-10-DCR). Logistic regression analysis was used to assess the impact of socio-demographic, health, and diet-related characteristics on depression. Results: Estimated prevalence of mild, moderate, and severe depression in the past 12 months was 16.3%, 12.4%, and 8.2% respectively, in older Indians. Functional disability, cognitive impairment, low quality of life, low wealth status, and chronic conditions such as angina, asthma, or chronic lung disease were the significant ( p < .05 or .1) risk factors for depression. Discussion: Protective and risk factors identified can be helpful in formulation of different policies for older Indians. 1Departmetn

of Statistics, School of Mathematical Sciences, North Maharashtra University,

Jalgaon, India Corresponding Author: Ruhi S. Kulkarni, Department of Statistics, School of Mathematical Sciences, North Maharashtra University, Jalgaon-425001, Maharashtra, India. Email: [email protected]


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Journal of Aging and Health

Keywords older Indian, SAGE, depression, quality of life, ICD-10-DCR.

Introduction Depression is rising as a major public health problem worldwide. It is a common mental disorder characterized by sadness, loss of interest or pleasure, feelings of guilt or low self-worth, disturbed sleep or appetite, feelings of tiredness, and poor concentration. Currently, depression is estimated to affect approximately 350 million people worldwide (Marcus, Yasamy, Ommeren, Chisholm, & Saxena, 2012). It is projected that, by 2030, depression will be the second leading cause of burden of disease (Mathers & Loncar, 2006). According to a WHO report, patients above the age of 55 years with depression had a death rate 4 times higher than those without depression (Pilania, Bairwa, Kumar, Khanna, & Kurana, 2013). Also, Moussavi et al. (2007) found that depression produces more decrement in health compared with the chronic diseases angina, arthritis, asthma, and diabetes. In India, according to census 2011, 15.9% population is 50 years and above. It is projected that India’s population ages 50 years and older will reach 23% by 2030 (Population Reference Bureau, 2012). Although the major population in India currently aged less than 30, the health status of older population has significant impact on social and economic systems. With the increasing trend of nuclear family set-ups in India and changing social scenario in recent years, older people are likely to be exposed to emotional, physical, and financial insecurities in the years to come. This may make elderly more prone to depressive disorders (Pilania, et al., 2013). In India, according to World Health Survey–2003, past 12-month depression in the age group 60 to 69 and 70 to 79 years was reported as 16.5% and 20.6%, respectively (WHO, 2003). WHO’s Study of Global Aging and Adult Health (SAGE) was conducted in six countries, namely, China, Ghana, India, Mexico, Russia, and South Africa during 2007-2010. A study based on SAGE-2008 in South Africa estimated the prevalence of mild depression as 4.0% and identified the factors associated with mild depression among the older aged 50 years and above (Peltzer & PhaswanaMafuya, 2013). Among very old of age 85 years and above in Northern Sweden, it was observed that depression was more common among women than men (Bergdahl, Allard, Alex, Lundman, & Gustafson, 2007). Among very old of age 85 years and above, 33% women and 18.6% men were diagnosed with depression by using Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV; American Psychiatric Association [APA], 1994) criteria. Barua, Ghosh, Kar, and Basilio (2011) conducted a retrospective study based on analysis of various study reports about depression in elderly of age


Kulkarni and Shinde

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60 years or older satisfying some acceptable definition of depression (either recognized diagnostic criteria or cut-off on a depression rating scale). All such 74 studies published in indexed journals between 1955 and 2005 were included in this retrospective study. Out of 74, 6 studies were from India. They found that the prevalence of depression in India was 18.2% and was significantly higher than the rest of the world (5.4%). Zheng, Schimmele, and Chappell (2012) examined the relationship between age and depression among people aged 65 years and older Canadians. The observed prevalence of major depression in the community sample was 2.6% and in the institution sample was 7.0%. The study found that depression decreases from age 65 to 79 and afterward, it begins to increase. The previous studies in India about late-life depression were area specific. Rajkumar et al. (2009) studied depression among the elderly in a rural South Indian community and found that prevalence of geriatric depression according to International Classification of Diseases, 10th revision (ICD-10) criteria, within the past 1 month, was 12.7%. Maulik and Dasgupta (2012) conducted a cross-sectional study in the rural area of West Bengal among elderly of age more than 60 years, and the prevalence of depression was observed as 53.7%. They assessed depression using a pretested semistructured schedule and geriatric depression scale. In view of the problems and issues of gray population in India, the Ministry of Social Justice and Empowerment, Government of India, adopted “National Policy on Older Persons” in January, 1999. The policy defines older person or senior citizen as a person who is 60 years old or above. However, the life expectancy at birth as of 2011 was 65 years (WHO, 2013), and the median age in India was 26.5 years. Hence, we have considered the older population as aged 50 years and above. In the international population report on health and well-being of the older in six SAGE countries, the population aged 50 years and above was considered for the study of older adults (He, Muenchrath, & Kowal, 2012). In this study, we investigated the prevalence of self-reported symptom-based depression at three levels (mild, moderate, and severe) in the past 12 months and associated factors of moderate and severe depression in the nationally representative sample of older Indians aged 50 years and above, who participated in SAGE-2007.

Method Data For this study, the data were derived from the WHO’s SAGE conducted in 2007. SAGE interviewed 12,198 respondents of age 18 years and above


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from 6 states selected from 19 states in India. The sample size for the individuals ages 50 years and above was 7,150. Of the 28 states in India, 9 states were excluded because of small population, lack of accessibility, or operational difficulties. Nineteen states included in the design covered 96% Indian population. Multistage, stratified clustered sample design was used for SAGE-2007 in India. Out of the 19 states, 6 states were selected according to level of development and their geographic location. Six levels of development were decided, using the four indicators, namely, infant mortality rates, female literacy rate, percentage of safe deliveries, and per capita income. The six geographic regions considered were North, Central, East, North-east, West, and South. The states were randomly selected such that one state was selected from each region as well as from each level of development category. The sample was stratified by state and locality (urban/rural) resulting in 12 strata and was nationally representative. Two stage and three stage sampling was adopted in rural and urban areas respectively. The individual response rate for older adults aged 50 and above was 68%. The SAGE survey was carried out in India by the Indian Institute of Population Sciences, Mumbai, in collaboration with WHO (He et al., 2012). The SAGE individual questionnaire was used for this study, available at the website of WHO.

Variable Description Depression. Diagnosis of mild, moderate, and severe depression was based on the International Classification of Diseases, 10th revision, namely, “ICD-10 Classification of Mental and Behavioural Disorders, Diagnostic Criteria for Research (DCR)” considering the symptoms of depression during the past 12 months (WHO, 1993). Table 1 represents the criteria for diagnosis of depression as per ICD-10-DCR and the corresponding equivalent questions in SAGE individual questionnaire used in this study. We recoded the responses as 0 for “No” and 1 for “Yes.” In Table 1, RQ1 denotes the response of Question 1. That is, for example, RQ4042 = 1 means the response of Q4042 is “Yes.” The eligible respondents for SAGE were those who were mentally fit, not too ill, and non-institutionalized. So, it is equivalent to assume that they had no hallucinations or delusions or depressive stupor. So, Criterion D was satisfied for all respondents of SAGE-2007. We defined three binary variables as mild, moderate, and severe depression with responses 0 = no and 1 = yes. The case finding method for the mild, moderate, and severe (without psychotic symptoms) depressive episodes is as follows:


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Kulkarni and Shinde

Table 1. Questions From SAGE Individual Questionnaire Corresponding to ICD10-DCR Criteria for Diagnosis of Depression. ICD-10-DCR based criteria Criteria A

B

C

Corresponding questions from SAGE questionnaire

Symptom

Question No.

A1: The general criteria for depressive episode must be met. That is, the depressive episode should last for at least 2 weeks, no hypomanic or manic symptoms, the episode is not attributable to psychoactive substance use or to any organic mental disorder (Satisfied if RQ4045 = 1 where RQ4045 denotes the response of Question Q4045) B1: Depressed mood to a degree that is definitely abnormal for the individual, present for most of the day and almost every day, largely uninfluenced by circumstances, and sustained for at least 2 weeks. (Satisfied if RQ4042 = 1) B2: Loss of interest or pleasure in activities that are normally pleasurable (Satisfied if RQ4043 = 1)

Q4045

Was this period [of sadness/loss of interest/low energy] for more than 2 weeks?

Q4042

During the past 12 months, have you had a period lasting several days when you felt sad, empty, or depressed?

Q4043

During the past 12 months, have you had a period lasting several days when you lost interest in most things you usually enjoy such as personal relationships, work, or hobbies/ recreation? During the past 12 months, have you had a period lasting several days when you have been feeling your energy decreased or that you are tired all the time? Was this period [of sadness/loss of interest/low energy] for more than 2 weeks?

B3: Decreased energy or increased fatigability (Satisfied if RQ4044 = 1)

Q4044

B4: Symptoms from B1 to B3 present for most of the day and almost every day, largely uninfluenced by circumstances, and sustained for at least 2 weeks (Satisfied if max [RQ4045,RQ4046] = 1)

Q4045

C1: Loss of confidence and self-esteem (Satisfied if RQ4055 = 1)

Q4055

C2: Unreasonable feelings of self-reproach or excessive and inappropriate guilt (Satisfied if max [RQ4056,RQ4057] = 1)

Q4056

C3: Recurrent thoughts of death or suicide, or any suicidal behavior

Q4058

Q4046

Q4057

Specific question

Was this period [of sadness/loss of interest/low energy] most of the day, nearly every day? During this period, did you feel negative about yourself or like you had lost confidence? Did you frequently feel hopeless—that there was no way to improve things? During this period, did your interest in sex decrease? Did you think of death, or wish you were dead? (continued)


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Table 1. (continued) ICD-10-DCR based criteria Criteria

D

Corresponding questions from SAGE questionnaire

Symptom

Question No.

Specific question

(Satisfied if max [RQ4058,RQ4059] = 1)

Q4059

C4: Complaints or evidence of diminished ability to think or concentrate, such as indecisiveness or vacillation (Satisfied if max [RQ4048,RQ4051] = 1)

Q4048

During this period, did you ever try to end your life? Did you notice any slowing down in your thinking?

C5: Change in psychomotor activity, with agitation or retardation (either subjective or objective) (Satisfied if max [min (RQ4048, RQ4052), RQ4053, RQ4054] = 1)

Q4048

Q4051

Q4052 Q4053

Q4054

C6: Sleep disturbance of any type

Q4049

(Satisfied if max [RQ4049,RQ4050] = 1)

Q4050

C7: Change in appetite (decrease or increase) (Satisfied if RQ4047 = 1) D1: There must be no hallucinations, delusions, or depressive stupor.

Q4047

During this period, did you have any difficulties concentrating; for example, listening to others, working, watching TV, listening to the radio? Did you notice any slowing down in your thinking? Did you notice any slowing down in your moving around? During this period, did you feel anxious and worried most days? During this period, were you so restless or jittery nearly every day that you paced up and down and could not sit still? Did you notice any problems falling asleep? Did you notice any problems waking up too early? During this period, did you lose your appetite?

Respondents who were mentally unfit or too ill were not eligible for the interview. Hence, data were recorded for eligible respondents only. Hence, Criterion D is satisfied by all respondents of SAGE2007.

Note. SAGE = Study of Global Aging and Adult Health; ICD-10-DCR = International Classification of Diseases, 10th revision, Diagnostic Criteria for Research.

Mild depression. A respondent is classified under mild depression if i. He or she had taken treatment in the past 12 months or 2 weeks (this condition is not included in the ICD-10-DCR criteria for mild depressive episodes but we have added this as it justifies mild depression).


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Or the following two conditions are satisfied: ii. Criterion A should be satisfied. iii. The respondent should report at least two of the three symptoms from Criterion B lasting for at least 2 weeks and at least one or two symptoms from Criterion C to give a total of at least four symptoms. Moderate depression. To be classified under moderate depression, Criterion A should be satisfied, and the respondent should report at least two of the three symptoms from Criterion B lasting for at least 2 weeks and at least three or four symptoms from Criterion C to give a total of at least six symptoms. Severe depression. To be classified under severe depression, (i) Criterion A should be satisfied, (ii) the respondent should report all the three symptoms from Criterion B lasting for at least 2 weeks and at least five symptoms from Criterion C to give a total of at least eight symptoms, and (iii) Criterion D should be satisfied. SAGE individual questionnaire included the question about the diagnosis of depression as Q4040: “Have you ever been diagnosed with depression?” with response as “Yes” or “No.” Followed by “Yes” answer of Q4040, respondents were asked about any treatment or medication for depression in the past 2 weeks or 12 months in Questions Q4041a and Q4041b. Those who responded affirmatively to Questions Q4041a and Q4041b regarding any medication or treatment in the past 2 weeks or 12 months for depression were directly considered as having depression in the past 12 months in this study. Respondents with “No” answer of Q4040 were asked directly Question Q4042 and onward. Physical activity. Physical activities of the older were measured using the Global Physical Activity Questionnaire (GPAQ) Version 2. GPAQ was used to measure the intensity, duration, and frequency of physical activity in three domains: occupational, transport-related, and discretionary or leisure time. Questions based on total time spent for a physical activity during a typical week, number of days per week, and intensity of the physical activity were asked to the respondents. The volume of physical activity was computed by weighing each type of activity by its energy requirements in metabolic equivalents (METs). One MET was defined as the energy cost of sitting quietly and was equivalent to a calorie consumption of 1 Kcal/Kg/hr. A MET minute was calculated by multiplying the time spent (in minutes) on each activity during a week by the MET values of each level of activity. MET values for different


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Table 2. Descriptive Statistics for the Total Scores of Different Variables Defined. Variables

Statistic Sample size Valid Missing M Mode SD Minimum Maximum Quartiles Q1 Q2 Q3

Maximum of (out Total of 10) words physical activity score recalled in 3 trials

Servings for fruits or vegetables

Quality of life total score

Social cohesion total score

Functional disability total score

6,449 701 2.88 2.00 1.67 0.00 21.00

6,554 596 27.44 29.00 4.83 8.00 40.00

6,555 595 17.77 15.00 4.98 8.0 44.0

6,559 591 23.53 21.00 8.64 7.0 60.0

5,773 1,377 6,347.0 1,680.00 7,265.77 16.0 61,320.0

6,473 677 6.40 6.00 1.59 0.00 10.0

2.0 3.0 4.0

24.0 28.0 31.0

14.0 17.0 21.0

17.0 22.0 28.0

1,560.0 3,780.0 8,400.0

5.0 6.0 7.0

levels of activities were set as (a) 4 MET for moderate intensity physical activity, (b) 8 MET for vigorous intensity physical activity, and (c) 4 MET for transport-related (walking or cycling) intensity physical activity. The total physical activity was calculated as the sum of total moderate, vigorous, and transport-related activities (walk or bicycle use) per week (WHO, n.d.1). Responses from Questions Q3016 to Q3030 were used to calculate the total physical activity score for a respondent. The descriptive statistics for this score is given in Table 2. Using the quartiles of distribution of total physical activity score, respondents were classified into three categories of low (score ≤ Q1), moderate (Q1 < score ≤ Q3 ), and high ( score > Q3 ) levels of physical activity. Functional disability status. Functional Disability was measured by the 12-item WHO Disability Assessment Schedule, Version 2 (WHODAS-II), which is an instrument to measure health, functionality, and disability (Üstün, Kostanjsek, Chatterji, & Rehm, 2010). The 12-item instrument included the questions about difficulties in the past 30 days with performing activities of daily living such as standing, taking care of household responsibilities, learning a new task, concentrating on a work, getting dressed, and participation in community activity. In SAGE individual questionnaire, Q2011, Q2014, Q2015, Q2028, Q2032, Q2033, Q2035 to Q2039, and Q2047 represent the items in WHODAS-II. The responses to these questions were scored using a 5-point Likerttype scale as none = 1, mild = 2, moderate = 3, severe = 4, and extreme = 5.


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The simple scoring method was used to find a composite score by adding the scores of each item for each respondent. A higher score represents the greater perceived difficulty in performing activities of daily living. The descriptive statistics for this functional disability score is represented in Table 2. Using the quartiles of this score, the functional disability status was categorized as low (score ≤ Q1), medium (Q1 < score ≤ Q3 ), and high ( score > Q3 ). Cronbach’s alpha for this 12-question measure in this sample was 0.89. Social cohesion. It was measured by using nine variables Q6001 to Q6009 in the Social Cohesion section of SAGE questionnaire about the involvement of the individual in the community. Each question has responses with code as never (1), once or twice per year (2), once or twice per month (3), once or twice per week (4), and daily (5). The scores assigned to each of the variables were summed to get a social cohesion score (SCS) whose descriptive statistics are as given in Table 2. Based on this score, social cohesion of respondent was categorized as low (score ≤ Q1), medium (Q1 < SCS ≤ Q3 ), and high ( SCS > Q3 ). Cronbach’s alpha for this social cohesion score in this sample was 0.75. Quality of life. Quality of Life was assessed by using the World Health Organization Quality of Life (WHOQOL)-8, which contains eight items derived from WHOQOL-Bref (Schmidt, Muhlan, & Power, 2005). In the SAGE questionnaire, eight questions in the Section “Subjective Well-Being and Quality of Life,” that is, Q7001 to Q7007 and Q7009, represent these eight items. Scores from 1 to 5 assigned to responses of these questions were coded such that 1 represents worse condition and 5 represents best condition. These responses were then summed to get a composite score. This composite score was such that high score represents high quality of life. The descriptive statistics for this score are given in Table 2. Using the quartiles of this score, quality of life was categorized as low(score ≤ Q1), medium (Q1 < score ≤ Q3 ), and high ( score > Q3 ). Cronbach’s alpha for these eight items was 0.85. Chronic conditions. The chronic conditions such as stroke, angina, diabetes, arthritis, asthma, and chronic lung disease were reported by the respondent. Hypertension was determined by using the measurements of blood pressure taken 3 times. The average of the last two readings was considered to diagnose hypertension. Individuals with systolic blood pressure ≥140 mmHg and/ or diastolic blood pressure ≥90 mmHg and/or who reported the current use of antihypertensive medication were considered to be suffering from high blood pressure (National High Blood Pressure Education Program, 2004). It was observed that among the respondents having angina, chronic lung disease,


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and asthma, prevalence of depression was higher (see Table 3). So, we categorized the chronic condition into three categories for further analysis as (a) no disease; (b) having any of angina, asthma, or lung disease; and (c) having any of diabetes, arthritis, hypertension, or stroke. Fruit and vegetable consumption. To assess the adequacy of the consumption of fruits and vegetables, two questions were asked as Q3012: “How many servings of fruits do you eat on a typical day?” and Q3013: “How many servings of vegetables do you eat on a typical day?” The servings of fruits and vegetables were summed to get total number of servings of fruits and/or vegetables. The distribution of the total number of servings is given in Table 2. Based on this distribution, we defined adequate consumption of fruits and/or vegetables as total servings greater than five servings, servings between 3 and 5 as low consumption, and 2 or less than 2 servings as very low consumption of fruits and/or vegetables a day. Cognitive impairment. The important domains of cognition are orientation, memory, executive function (planning, sequencing), and language. High cognitive impairment was defined as those who endorsed the question of having severe or extreme difficulty with concentrating or remembering things in the past 30 days. Those who had mild or moderate difficulty were categorized having moderate impairment, and those who responded none were categorized into no impairment. Question Q2010 was used to assess the cognitive impairment. Verbal recall impairment. Respondents were tested for their memory functioning. For this, a list of 10 words was told to them at the time of the interview, and they were asked to remember as many words as possible. The numbers of words recalled in three trials were recorded. We defined verbal recall impairment in three levels as high for those who failed to recall maximum of more than three words in three trials, moderate for four to seven maximum of words recalled in three trials, and low for greater than seven maximum of words recalled in these three trials. Socio-demographic variables. Age, sex, education status, marital status, type of place of residence, and household economic status provide socio-demographic information of respondents. Age was categorized into three categories: 50 to 59, 60 to 69, and 70 and above. Marital status was categorized as married and single. Married included currently married or living with partner and single included separated/divorced/widowed. Education status was classified into four subgroups as no formal education, primary, secondary, and higher education.


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Kulkarni and Shinde Table 3. Distribution of Sample and Prevalence of Three Types of Depression According to Background Characteristics. No. of depressed (weighted % in valid cases) Characteristic

Sample size (weighted % in valid cases)

Sex Male Female Type of place of residence Urban Rural Age groups 50-59 60-69 70 or above Current marital status Married Single Highest educational level No formal education Primary Secondary Higher Wealth status Poor Middle Rich Missing cases Quality of life Low Medium High Missing cases Fruit and/or vegetable consumption Very low Low Adequate Missing cases Chronic diseases Angina Asthma Lung disease Diabetes Arthritis Hypertension Stroke

Mild

Moderate

Severe

3,303 (51.0) 3,255 (49.0)

350(11.8) 435 (15.5)

313 (10.6) 404 (14.4)

184 (5.6) 314 (11.0)

1,676 (28.9) 4,882 (71.1)

150 (13.0) 635 (13.9)

140 (12.1) 577 (12.6)

98 (8.2) 400 (8.2)

2,942 (48.6) 2,226 (30.8) 1,390 (20.6)

310 (12.9) 263 (13.1) 212 (16.2)

275 (11.2) 243 (12.5) 199 (15.5)

194 (7.4) 168 (8.4) 136 (10.1)

4,861 (76.9) 1,697 (23.1)

545 (12.7) 240 (16.8)

495 (11.4) 222 (16.0)

339 (7.6) 159 (10.3)

3,340 (50.8) 1.685 (15.0) 1,210 (19.1) 322 (5.0)

501 (16.4) 174 (12.6) 95 (10.0) 15 (4.5)

465 (15.2) 159 (11.6) 79 (8.4) 14 (4.3)

341 (11.0) 98 (6.4) 50 (4.5) 9 (3.4)

2,623 (43.5) 2,623 (37.4) 1,312 (19.1) 592

433 (17.6) 269 (12.0) 83 (7.9)

398 (16.4) 245 (10.5) 74 (7.4)

282 (11.1) 175 (6.9) 41 (4.4)

1,673 (24.6) 3,702 (54.5) 1,179 (20.9) 596

364 (23.9) 362 (12.3) 59 (5.2)

335 (22.0) 330 (11.1) 52 (4.7)

258 (17.1) 208 (6.4) 32 (2.7)

3,030 (47.9) 3,063 (47.2) 355 (4.8) 702

402 (15.7) 327 (12.3) 41(9.6)

368 (14.3) 298 (11.2) 38 (9.0)

253 (9.2) 211 (7.8) 25 (5.5)

322 (5.5) 455 (7.2) 267 (4.5) 478 (6.9) 1,174 (18.2) 2,363 (34.4) 147 (2.0)

75 (23.4) 95 (25.5) 66 (28.2) 54 (14.6) 191 (17.9) 288 (13.5) 26 (18.1)

70 (22.4) 87 (24.3) 61 (27.1) 49 (13.3) 172 (15.6) 259 (11.9) 25 (18.1)

54 (18.2) 69 (18.5) 39 (16.1) 40 (11.3) 135 (11.8) 190 (8.4) 21 (16.4) (continued)


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Table 3. (continued) No. of depressed (weighted % in valid cases) Characteristic Chronic diseases (grouped) No disease Any of angina, lung disease, or asthma Any of diabetes, hypertension, arthritis, or stroke Missing cases Cognitive impairment No Moderate High Missing cases Physical activity level Low Medium High Missing cases Verbal recall impairment Low Moderate High Missing cases Functional disability Low Medium High Missing cases Social cohesion Low Medium High Missing cases States (national region) Assam (North-east) Karnataka (South) Maharashtra (West) Rajasthan (North) Uttar Pradesh (Central) West Bengal (East) All India Missing cases

Sample size (weighted % in valid cases)

Mild

Moderate

Severe

3,145 (49.4) 867 (14.5)

323 (12.0) 181 (24.2)

299 (10.9) 168 (23.3)

189 (6.1) 127 (17.3)

2,483 (36.1)

275 (12.0)

244 (10.5)

177 (7.6)

655

2,144 (33.2) 3,589 (53.4) 824 (13.4) 593

122 (6.9) 459 (14.7) 204 (26.4)

111 (6.0) 418 (13.3) 188 (25.1)

65 (3.3) 295 (8.9) 138 (17.8)

1,460 (25.7) 2,923 (50.6) 1,395 (23.7) 1,372

171 (12.8) 358 (13.9) 129 (10.9)

156 (11.2) 331 (12.9) 112 (9.4)

104 (8.1) 232 (8.2) 76 (5.8)

1,604 (24.9) 4,669 (72.0) 199 (3.1) 678

163 (10.5) 582 (14.6) 31 (19.0)

149 (9.8) 531 (13.2) 29 (17.8)

96 (6.4) 374 (8.7) 20 (10.3)

1,824 (27.3) 3,104 (48.7) 1,630 (23.9) 592

55 (3.6) 345 (13.8) 385 (24.8)

49 (3.1) 310 (12.4) 358 (23.4)

33 (1.5) 214 (8.1) 251 (16.1)

1,840 (29.2) 3,306 (49.6) 1,409 (21.3) 595

247 (15.8) 364 (13.3) 174 (11.5)

224 (13.9) 336 (12.5) 157 (10.3)

173 (11.0) 228 (7.6) 97 (6.1)

677 (5.0) 923 (12.2) 1,097 (21.5) 1,377 (11.1) 1,311 (32.9)

22 (3.6) 162 (17.3) 57 (6.3) 165 (11.7) 279 (22.3)

18 (3.0) 150 (15.9) 52 (5.8) 154 (11.0) 253 (20.3)

14 (2.3) 136 (13.7) 43 (5.1) 94 (6.8) 136 (11.0)

1,173 (17.4) 6,558(90.6) 592

100 (7.9) 785 (13.6)

90 (7.0) 717 (12.4)

75 (5.6) 498 (8.2)


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Class of primary education included preprimary education as well as primary education completed. Class of secondary education included secondary school completed or high school (or equivalent) completed. Higher education included those who completed college/university education or post-graduate degree. Class of no formal education included those who had no formal education such as primary, secondary, or higher education. Illiterates as well as those who can read and write but had not taken any formal education were also included in this group. However, from SAGE data, we cannot separately count illiterate respondents. Residence was categorized into urban and rural. The wealth quintiles were used to represent the economic status of respondent. Wealth quintiles were based on possessions and housing characteristics. The wealth status was defined based on these quintiles. The wealth status was categorized as poor (include individuals in poor and poorest wealth quintile), middle (include individuals in middle and high wealth quintile), and rich (include individuals in the highest wealth quintile). To account for the geographical variation and level of development, states under study were considered one of the explanatory variables.

Data Analysis The analysis of the data was carried out in SPSS 21. We estimated the prevalence of mild, moderate, and severe depression among Indian older adults. Post-stratified weights were used to calculate the prevalence of depression among the older Indians. Chi-square test was used to check the dependency of depression with the above discussed different explanatory variables (p values for χ2 test are not tabulated for each variable). Determinants of moderate and severe depression were studied further. Logistic regression analysis was used to measure the association of different socio-demographic and health variables with moderate and severe depression prevalence and to assess the impact of different factors on prevalence of moderate and severe depression through odds ratios. We first carried out the univariate logistic regression analysis considering separately each of the variables, and then a multivariate model was constructed using the results of univariate analysis for moderate and severe types of depression. Forward selection likelihood ratio method was used for multivariable logistic regression model.

Results Characteristics of Study Participants Total sample size included in the analysis was 7,150 Indians aged 50 years and above, in which 51% were men and 49% were women. Of the total


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sample, 28.9% was from urban area, while 71.1% from rural area. A total of 48.6% respondents were of age group 50 to 59 years. Indian states were represented in the sample as 5% respondents from Assam, 12.2% from Karnataka, 21.5% from Maharashtra, 11.1% from Rajasthan, 32.9% from Uttar Pradesh, and 17.4% from West Bengal. Five percent of the respondents were highly educated while about 50.8% had no formal education. In all, 51.6% had at least one of the chronic diseases (stroke, angina, diabetes, arthritis, asthma, and hypertension); 47.9% older had very low intake of fruits and vegetables; 24.6% and 54.5% had low and medium quality of life, respectively; and 43.5% had poor wealth status.

Prevalence of Depression and Factors Associated With Depression We found prevalence rates of symptom-based depression of the past 12 months before survey among older Indians as 13.6% for mild, 12.4% for moderate, and 8.2% for severe depression. The prevalence of mild, moderate, and severe depression according to some background characteristics is presented in Table 3. Except diabetes and hypertension, all other explanatory variables discussed above were significantly ( p < .05) associated with mild, moderate, and severe types of depression from the results of the χ2 test. Surprisingly, it is observed that among the respondents identified under mild, moderate, and severe depression, only 7.3%, 4.4%, and 5.4% were taking treatment/medication for depression, respectively. This shows that the rate of taking medication was very low among older Indians suffering from depression. The results of the logistic regression analysis are presented in Table 4. According to clinical descriptions and diagnostic guidelines by WHO for “The ICD-10 Classification of Mental and Behavioural Disorders,” individuals with mild depression have some difficulty in continuing with ordinary work and social activities, but their functioning do not cease completely. Individuals with moderate depression have considerable difficulty and with severe depression will be unable to continue with social, domestic activities (WHO, n.d.2). So, we focused on factors associated with moderate and severe depression. The variables that were statistically significant in the univariate models were included in the multivariate models. Forward selection likelihood ratio method was used for fitting multivariable logistic regression model. According to results of univariate logistic regression analysis, sex, type of place of residence, highest education level, states, current marital status, wealth status, age group, chronic condition, cognitive impairment, functional disability status, quality of life, physical activity level, and verbal


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Sex Male Female Type of place of residence Urban Rural Age groups 50-59 60-69 70 or above Current marital status Married Single Highest educational level No formal education Primary Secondary Higher

Characteristic

—

—

—

—

—

1 1.471 [1.212, 1.785]*

1 1.188 [0.991, 1.426]** 1.620 [1.334, 1.969]*

1 1.328 [1.121, 1.572]*

3.558 [2.064, 6.133]* 2.292 [1.309, 4.013]* 1.537 [0.859, 2.750] 1

OR [95% CI]

Multivariate

1 1.354 [1.158, 1.583]*

OR [95% CI]

Univariate

Models for moderate depression

3.954 [2.019, 7.744]* 2.148 [1.074, 4.296]* 1.499 [0.729, 3.081] 1

1 1.379 [1.132, 1.679]*

1 1.156 [0.933, 1.433] 1.536 [1.222, 1.932]*

1 1.437 [1.144, 1.805]*

1 1.810 [1.498, 2.186]*

OR [95% CI]

Univariate

—

—

—

—

(continued)

1 1.678 [1.337, 2.107]*

OR [95% CI]

Multivariate

Models for severe depression

Table 4. Results of Logistic Regression Analysis for Factors Associated With Moderate and Severe Depression Among Older Indians.

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OR [95% CI]

Univariate

3.734 (2.672, 5.218)* 2.216 [1.567, 3.135]* 1 6.535 [4.490, 9.514]* 2.134 [1.462, 3.114]* 1 1.203 [0.785, 1.842] 0.977 [0.635, 1.501] 1 1 2.684 [2.114, 3.408]* 1.20 [0.971, 1.484]**

2.980 [2.028, 4.379]* 1.611 [1.141, 2.275]* 1 Not included in this model as insignificant in univariate model 1 1.711 [1.330, 2.200]* 0.978 [0.791, 1.210]

OR [95% CI]

Univariate

(continued)

0.944 [0.734, 1.213]

1 2.006 [1.501, 2.681]*

Not included in this model as insignificant in univariate model

3.093 (1.930, 4.957)* 1.472 [0.952, 2.276]** 1

2.569 (1.715, 3.849)* 2.045 [1.361, 3.074]* 1

OR [95% CI]

Multivariate


2.087 (1.531, 2.844)* 1.591 [1.163, 2.176]* 1

OR [95% CI]

Multivariate


Wealth status Poor 2.993 [2.313, 3.872]* Middle 1.724 [1.317, 2.255]* Rich 1 Quality of life Low 5.426 [4.009, 7.345]* Medium 2.121 [1.571, 2.863]* High 1 Fruit and/or vegetable consumption Very low 1.153 [0.810, 1.643] Low 0.899 [0.629, 1.285] Adequate 1 Chronic diseases No disease 1 Any of angina, asthma, 2.288 [1.861, 2.812]* or lung disease Any of diabetes, 1.037 [0.868, 1.239] arthritis, hypertension, or stroke

Characteristic

Table 4. (continued)

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Cognitive impairment No Moderate High Physical activity level Low Medium High Verbal recall impairment Low Moderate High Functional disability Low Medium High

Characteristic


1 1.235 [0.946, 1.610] 1.617 [1.145, 2.283]* 0.780 [0.586, 1.037]** 1.040 [0.815, 1.328]

—

1 2.531 [1.779, 3.600]* 5.205 [3.516, 7.706]*

1.370 [1.062, 1.768]* 1.463 [1.169, 1.831]* 1

1 1.253 [1.035, 1.517]* 1.666 [1.085, 2.557]*

1 4.019 [2.956, 5.464]* 10.195 [7.499,13.860]*

OR [95% CI]

Multivariate

1 2.414 [1.944, 2.998]* 5.414 [4.212, 6.958]*

OR [95% CI]

Univariate


1 4.019 [2.772, 5.825]* 9.879 [6.826, 14.296]*

1 1.368 [1.085, 1.725]* 1.755 [1.058, 2.911]*

1.331 [0.981, 1.806]** 1.496 (1.145, 1.955)* 1

1 2.864 [2.178, 3.768]* 6.434 [4.732, 8.748]*

OR [95% CI]

Univariate

(continued)

1 2.331 [1.515, 3.586]* 4.307 [2.681, 6.918]*

—

0.724 [0.516, 1.016]** 1.073 [0.804, 1.434] 1

1 1.329 [0.958, 1.846]** 1.698 [1.123, 2.567]*

OR [95% CI]

Multivariate


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Not included in this model as insignificant in univariate model 1 0.564 [0.299, 1.061]** 5.193 [3.528, 7.644]* 2.792 [1.914, 4.074]* 5.849 [4.052, 8.442]* 1.163 [0.764, 1.769]

1 0.549 [0.318, 0.946]* 3.900 [2.807, 5.418]* 2.531 [1.828, 3.502]* 4.806 [3.523, 6.555]* 1.670 [1.175, 2.374]*

OR [95% CI]

Multivariate

1.105 [0.890, 1.373] 0.902 [0.738, 1.103] 1

OR [95% CI]

Univariate


Note. OR = odds ratio; CI = confidence interval. —Variable not retained by the multivariate model in forward selection procedure. *Significant at 5% level of significance. **Significant at 10% level of significance.

Social cohesion Low Medium High States Maharashtra Assam Karnataka Rajasthan Uttar Pradesh West Bengal

Characteristic


1 0.518 [0.281, 0.953]* 4.236 [2.970, 6.042]* 1.796 [1.241, 2.600]* 2.837 [1.994, 4.037]* 1.674 [1.140, 2.459]*

1.404 [1.083, 1.819]* 1.002 [0.783, 1.282] 1

OR [95% CI]

Univariate

1 0.470 [0.233, 0.945]* 4.957 [3.307, 7.428]* 1.684 [1.111, 2.551]* 2.790 [1.866, 4.173]* 0.974 [0.622, 1.526]

—

OR [95% CI]

Multivariate


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recall impairment were significantly associated with both moderate and severe types of depression. In multivariate analysis, consumption of fruits and/or vegetables and social cohesion were not included in the logistic regression model as they were not significant in univariate analysis for moderate depression. In multivariate logistic regression model, fitted using forward selection likelihood ratio method, wealth status, quality of life, physical activity level, cognitive impairment, functional disability status, state, and chronic condition remained associated with both moderate and severe depression. Variable sex was also associated with severe depression in multivariate logistic regression model. The noticeable results of the analysis for each variable are discussed below using the complete outputs reported in Tables 3 and 4. The 95% confidence interval (CI) for the odds ratios were also reported in parenthesis. Sex. The estimated prevalence of all three types of depression was more among women than men. The prevalence of severe depression among women was 11.0% while that for men was 5.6%. In the univariate model, women were 1.810 times (CI = [1.498, 2.186]) significantly more likely to have severe depression than men. However, the effect was attenuated in multivariate model. Similar result was observed for moderate depression. Type of place of residence. Prevalence of severe depression in both urban and rural areas was 8.2%. In the univariate model, respondents residing in rural areas were 1.471 times (CI = [1.212, 1.785]) and 1.437 times (CI = [ 1.144, 1.805]) more likely to have moderate and severe depression as compared with urban residents, respectively. Age-group. The highest prevalence of depression of any type was observed among the respondents of age group 70 years or above whereas the lowest prevalence was observed in age group 50 to 59 years. Age gradient was also observed in univariate logistic regression model. Individuals in age group 70 years and above were 1.620 times (CI = [1.334, 1.969]) and 1.536 times (CI = [1.222, 1.932]) significantly more likely to have moderate and severe depression as compared with respondents of age group 50 to 59 years, respectively. Age-group variable was not significant in univariate analysis and thus not included in the multivariate model fitted by using forward selection method. Current marital status. The estimated prevalence of mild, moderate, and severe depression was higher in respondents who were living single than married. Respondents who were living without partner, that is, single were


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significantly ( p < .05) more likely to have severe or moderate depression than married, in univariate models. Similar result was reported by Rajkumar et al. (2009) in the study of depression among the elderly in the rural South Indian community. Education status. The estimated prevalence of depression was highest in respondents having no formal education. The prevalence of severe depression in respondents having no formal education was 11%, while that for respondents with higher education was 3.4%. It was observed that for higher levels of education, the estimated prevalence of all three types of depression was decreased. Education gradient among older adults was observed. Adults with no education were 3.558 times (CI = [2.064, 6.133]) and 3.954 times (CI = [2.019, 7.744]) more likely to have moderate and severe depression, respectively, as compared with those having higher education level. Wealth status. The estimated prevalence rates of mild, moderate, and severe depression were highest for the individuals with poor wealth status. For the improved wealth status, it was observed that the prevalence of depression was decreased. Wealth status was significantly associated with prevalence of mild, moderate, and severe depression in both univariate and multivariate logistic models. The effect got attenuated in the multivariate model as compared with univariate models. Poor and middle-class respondents were 2.569 times (CI = [1.715, 3.849]) and 2.045 times (CI = [1.361, 3.074]) more likely to have severe depression as compared with rich respondents, respectively, from multivariate model. Quality of life. It was observed that the prevalence of depression decreased as the quality of life improved. From multivariate models, respondents with low quality of life were almost 3 times more likely to have moderate and severe depression as compared with those with high quality of life. In the class of medium quality of life, respondents were 1.611 times (CI = [1.141, 2.275]) and 1.472 times (CI = [0.952, 2.276]) more likely to have moderate and severe depression as compared with those with high quality of life. Fruits and vegetable consumption. Respondents with very low consumption of fruits and/or vegetables had higher prevalence of depression. Older adults with very low consumption of fruits and/or vegetables were more likely to have depression than those having adequate consumption of fruits and/or vegetables. As this variable was not significant in univariate logistic models, it was not included in multivariate logistic models.


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Chronic condition. High level of prevalence of mild, moderate, and severe depression was observed in the class of respondents having any chronic condition of angina, asthma, or lung disease as 24.2%, 23.3%, and 17.3%, respectively. Respondents having any chronic condition of angina, asthma, or lung disease were significantly 1.711 times (CI = [1.330, 2.200]) and 2.006 times (CI = [1.501, 2.681]) more likely to have moderate and severe depression than those without any chronic disease, respectively, in multivariate models. Cognitive impairment. The prevalence of mild and severe depression among individuals with high cognitive impairment was 26.2% and 17.8%, respectively. The prevalence of severe depression was 8.9% in individuals with moderate cognitive impairment, while it was 3.3% in those without cognitive impairment. High cognitive impairment had significant association with the onset of depression in both univariate and multivariate models for moderate and severe depression. Individuals with high cognitive impairment were 1.617 times (CI = [1.145, 2.283]) and 1.698 times (CI = [1.123, 2.567]) more likely to have moderate and severe depression as compared with those without cognitive impairment, respectively, in multivariate model. Physical activity level. The prevalence of mild, moderate, and severe depression was observed as lowest among those with high levels of physical activities. In univariate logistic models, individuals with low and medium levels of physical activities were significantly more likely to have moderate or severe depression than those with high level of physical activities. However, after adjusting for other factors, the reverse effect was observed in multivariate model. In multivariate model, individuals with low level of physical activities were significantly ( p < .1) less likely to have moderate or severe depression than those with high level of physical activities. Verbal recall impairment. It was observed that the prevalence of depression was increased, as the level of impairment was increased. The prevalence of severe depression was 8.7% and 10.3% among individuals with moderate and high levels of verbal recall impairment, respectively. Individuals with high level of verbal recall impairment were 1.666 times (CI = [1.085, 2.557]) and 1.755 times (CI = [1.058, 2.911]) more likely to have moderate and severe depression than those with low level of verbal recall impairment, respectively, in univariate models. Functional disability status. As the functional disability level was increased, the estimated prevalence of depression was found to be increased. Among the


22


respondents with high functional disability, 16.1% were suffering from severe depression, while only 1.5% with low functional disability were having severe depression. Functional disability status was significant in both univariate and multivariate models. The effect of functional disability was attenuated in multivariate model as compared with univariate models for moderate and severe depression. From multivariate model, respondents with high functional disability were 5.205 times (CI = [3.516, 7.706]) and 4.307 times (CI = [2.681, 6.918]) more likely to have moderate and severe depression as compared with those having low level of functional disability, respectively. Social cohesion. Respondents with high social cohesion had low while those with low social cohesion had high prevalence of mild, moderate, and severe depression. Social cohesion factor was not significant in univariate model, hence not included in multivariate model. States. In Karnataka and Uttar Pradesh, the estimated prevalence of mild, moderate, and severe depression was high. The prevalence of severe depression was 5.1% in Maharashtra and, lowest, 2.3% in Assam. Respondents from Assam were significantly less likely to have depression (mild, moderate, and severe) than those from Maharashtra. Older adults from Karnataka had almost 5 times higher likelihood of moderate or severe depression than those from Maharashtra. Older adults from Uttar Pradesh were 5.849 times (CI = [4.052, 8.442]) more likely to have moderate depression than those from Maharashtra. Hence, south region and central region of India were more prone to depression while depression was less prevalent in the north-east region.

Conclusion and Discussion The major conclusions of our study are as follows: i. The estimated prevalence of mild, moderate, and severe depression in the past 12 months in a national sample of older adults in India was 13.6%. 12.4%, and 8.2%, respectively. ii. Factors significantly associated with the onset of both moderate and severe depression were wealth status, functional disability, cognitive impairment, chronic condition, and quality of life. iii. The prevalence of severe depression was high (higher than national estimate 8.2%) in individuals with no formal education (11.0%); poor wealth status (11.1%); in the class of low quality of life (17.1%); having any of angina, lung disease, or asthma (17.3%); high cognitive


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impairment (17.8%); high functional disability (16.1%); and residing in central region (11.0%) of India. iv. Risk factors for severe depression found under multivariate model: lower wealth condition; low quality of life; having any of the chronic diseases angina, lung disease, or asthma; moderate/high cognitive impairment; medium/high functional disability; and residing in south, central, or north region of the country. In our study sample, the mean age of the respondents was 61.5 years with standard deviation 9.01. The median and mode age of the respondents were 60 and 55 years, respectively. In the international population report by He et al. (2012), among all six SAGE countries, prevalence of depression in older population aged 50 and above years was highest in India (13.6%), while it was the lowest in China (1.1%). Prevalence rates of depression in South Africa, Russia, Ghana, and Mexico were 3%, 3.6%, 7.2%, and 10.7%, respectively. It is not clear from the report whether they have reported rates for mild, moderate, or severe depression. However, from their results, the prevalence rate of depression for older Indians was 13.6% based on SAGE-2007; it seems that prevalence rate for mild depression was reported as it also matched with our result for mild depression. Study in South Africa (Peltzer & Phaswana-Mafuya, 2013) found 4.0% prevalence of depression among older adults, and they also concluded that functional disability, lack of quality of life, and chronic conditions such as angina, asthma, arthritis, and nocturnal sleep problem were associated with the onset of depression. From the study for depression among the elderly in a rural South Indian community, risk factors for depression included poverty and physical ill health (Rajkumar et al., 2009). In our study, wealth status was one of the significant factors associated with depression. Older adults with low wealth status were more prone to depressive disorders. The national sample surveys on the condition of aged (2004) revealed that about 65% of those aged 60 years and above had to economically depend on others for their day-to-day maintenance. The situation was worse for older women. About 86% and 83% women were economically dependent on others either partially or fully in rural and urban areas, respectively (Central Statistics Office, 2011). The economic dependency may lead to sense of worthlessness, lack of self-confidence, and insecurity among older adults. Poor wealth status also leads to inadequate nutritional status, inability to assess medical care, and poor quality of life. All these conditions lead to depressive disorders. These can be overcome by providing financial security to older adults. In India, the age group of 60 years and above constitutes an almost non-working group. A study reported that less than 11% of


24


older Indians have a pension of any sort, according to national surveys (Population Reference Bureau, 2012). Thus, older adults are more vulnerable for financial insecurity. The Government of India has Indira Gandhi National Old Age Pension Scheme (IGNOAPS) implemented by the Ministry of Rural Development. Older adults of ages 60 years or above and belonging to below poverty line (BPL) category according to the criteria prescribed by the Government of India time to time are the beneficiaries of the IGNOAPS (Ministry of Rural Development, 2011). The pension amount under IGNOAPS as of union budget 2012-2013 is 200 (US$ 3.38) per month per person from 60 to 79 years age group and 500 (US$ 8.44) per month per person for those 80 years or above (Key Features of Budget 2012-13, 2014). The Ministry of Rural Development is paying 300 (US$ 5.07) per month to widows and disabled under Indira Gandhi National Pension Scheme. In all these pension schemes, the pension amount is quite less to satisfy day-to-day expenditure as well as health care expenditure of older adults. The Ministry has also implemented the “Annapurna Scheme” for older adults. The Annapurna Scheme is intended to provide 10 kg of food grains per month free of cost to all such persons who are otherwise eligible for old age pension under NOAPS, but they are presently not receiving it (Ministry of Rural Development, 2011). The National Food Security Act, 2013, will also be beneficial for the elderly to satisfy the need of food, as it offers food grains at subsidized prices to all eligible households. As this act will make access for sufficient and nutritious food for 75% of rural and 50% of urban population of India including all poor, this will help to improve the nutritional status of Indians and, consequently, older adults also (National Food Security Act, 2013). Functional disability was another important factor associated with depression. In this study, among the highly functionally disabled respondents, we found 62.1% women and 37.9% men. Among those having high functional disability, 40.4% had high cognitive impairment also, 19.2% respondents were severely depressed in the class of high quality of life, and 47.4% respondents having higher education were severely depressed. Functional disability can lead to low self-confidence, frustration, and helplessness. Functional disability cannot be recovered completely but can be reduced. Constant care and help to carry out daily activities can be beneficial to reduce the mental burden on the elderly. Cognition is related with the memory and other mental abilities. As individuals age, they gradually lose nerve cells that they had from birth and process information more slowly. As a consequence, learning new concepts and patterns becomes more difficult. Memory also begins to fail (Population Reference Bureau, 2007). High cognitive impairment was observed more in


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women (67.3%) than men (32.7%). Also, among men, 8.6% had high cognitive impairment while, among women, 18.4% had high cognitive impairment. Among the respondents having high cognitive impairment, 25.7% respondents having any of angina, lung disease, or asthma were severely depressed. Cognitive impairment may lead to self-doubts, irritation, dependency on others, and failure in learning new tasks. Zheng et al. (2012) observed that the age-related increase of depression symptoms was a consequence of medical illness such as dementia, chronic conditions, and functional limitations. They have reported that if these factors are controlled, the relationship between age and depression symptoms reduces to non-significance. In our study, almost one half of the older Indians had at least one chronic disease such as angina, diabetes, stroke, asthma, arthritis, hypertension, or chronic lung disease. Chronic conditions such as angina, asthma, or lung disease were risk factors for depression. In age group 70 years and above, 42.8% had no chronic disease; 19.7% had any of angina, asthma, or lung disease; and 37.6% had any of diabetes, hypertension, arthritis, or stroke. Among the respondents with any of angina, lung disease, or asthma, 23.3% respondents with poor wealth status, 32.7% respondents having high level of verbal recall impairment, and 27% in the class of low quality of life were having severe depression. The higher prevalence of chronic diseases demands higher access to health care services. The health insurance schemes will be beneficial for older adults to provide the medical care. However, awareness about health insurance schemes is very low among Indians. From SAGE-2007, it was observed that only 2% older adults had mandatory health insurance, 2% had voluntary, and 0.2% had both mandatory and voluntary health insurance. The Ministry of Labor and Employment (2014), Government of India, implemented “Rashtriya Swasthya Bima Yojana” (RSBY) to provide health insurance coverage for BPL families. Under RSBY, beneficiaries need to pay 30 (US$0.51) as the registration fee to get a biometric enabled smart card containing their fingerprints and photographs. This enables them to receive inpatient medical care of up to 30,000 (US$506.64) for most of the diseases that require hospitalization. Quality of life was determined based on the availability of enough money to meet individual’s needs, having enough energy for everyday life, satisfaction about health state, ability of performing daily living activities, and social life; 41.7% women and 38.8% men had low quality of life; 25.5% men and 16.1% women had high quality of life. Members in the class of low and medium quality of life had significantly higher risk of depression. The overall quality of life of older adults can be improved by providing them financial security, adequate nutrition, basic health care services, and mental support, developing independent living skills and making social contacts.


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The other policies implemented by the Government of India include national policy on older persons (1999), central sector scheme of Integrated Program for older persons, and other schemes of different ministries to provide necessary health facilities, travel facilities, and pension schemes to senior citizens (Central Statistics Office, 2011). The National Program of Health Care for the elderly is also being implemented to provide an active and healthy aging to the elderly. In India, although various schemes and policies were being implemented by the government since 1992 for senior citizens, the awareness about these schemes is limited. The utilization levels among BPL families of special government facilities/schemes were low. However, awareness of national social security programs is higher. Among the elderly belonging to the BPL category, the awareness of IGNOAPS is 75.2%, Indira Gandhi National Widow Pension Scheme (IGNWPS) is 67.7%, and Annapurna scheme is 32.9%. Only 11.1% elderly belonging to BPL category were aware of RSBY, and 6.9% were registered under RSBY (United Nations Population Fund, 2012). Thus, although there are many schemes or policies to improve the overall quality of life of the elderly, their awareness and utilization should increase. Publicity of such programs and procedures to assess them should be undertaken. Acknowledgments The authors are thankful to the editor of the journal and the reviewers for their valuable comments and suggestions. The first author is thankful to the Department of Science and Technology, New Delhi, for awarding Innovation in Science Pursuit for Inspired Research (INSPIRE) Junior Research Fellowship.

Declaration of Conflicting Interests The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding The authors received no financial support for the research, authorship, and/or publication of this article.

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