Due to the vast socioeconomic diversity among its residents, studying health inequality in India is of particular interest. This study aimed to investigate the wealth-based inequalities in physical frailty and to quantify the contributions of potential predictors of frailty to this inequality.
Methods
Data were drawn from the first wave of the Longitudinal Ageing Study in India (LASI) conducted during 2017–18. Logistic regression analysis was used to examine the association between wealth status and frailty. We used the concentration index to measure the magnitude of wealth-related inequality in frailty. A decomposition analysis based on the logit model was used to assess the contribution of each predictor to the total inequality.
Results
The prevalence of physical frailty was significantly higher among the older adults in the poor group than in the non-poor group [Difference (poor vs. non-poor): 6.4%; p < 0.001]. Regression results indicated that older adults in the poorest group were 23% more likely to be physically frail than those in the richest category [Adjusted odds ratio (AOR) = 1.23; 95% confidence interval (CI): 1.11, 1.38]. The overall concentration index of frailty was 0.058 among the older adults, indicating that frailty is more concentrated among older adults with poor wealth status. Body mass index, wealth index, educational status, and region were the major and significant contributors to the socioeconomic status (SES) related inequalities in frailty.
Conclusions
Results suggest the need for formulating effective prevention and intervention strategies to decelerate the development of physical frailty among older adults in India, especially those with poor socioeconomic background.
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Abkürzungen
AOR
Adjusted odds ratio
CI
Confidence interval
CCI
Concentration index
BMI
Body mass index
ADL
Activities of daily living
IADL
Instrumental activities of daily living
MPCE
Monthly per capita consumption expenditure
Background
Frailty is an age-related geriatric syndrome characterized as the progressive loss of the physiological reserve and lack of response to the stressors [1] and is strongly associated with different adverse health outcomes such as decreased mobility, incident disability, hospitalization, falls, and increased risk of mortality [2‐5]. Frailty is still an evolving concept, and there is a lack of consensus on the diagnostic criteria of frailty in epidemiological investigations and clinical practices. However, the most used frailty phenotype was defined by Fried and colleagues [1], which identifies frailty by the presence of three or more of five components (weakness, slowness, unintentional weight loss, poor endurance, and energy, and low physical activity). Frailty is not an inevitable consequence of old age rather an avoidable condition [6]. Globally, around 10% of the older adults are affected by frailty, and its prevalence increases with advanced age [7]. Therefore, the prevention or postponement of frailty is essential to maintain the functioning and health of the older populations.
Health inequalities between socioeconomic groups persist in developing, and developed countries [8]; frailty is no exception. Different studies have reported that frailty prevalence was higher among older persons with lower education and lower income [9‐13]. Additionally, several longitudinal studies have also found low socioeconomic status (SES) as a potential risk factor of increased frailty level among older adults [10, 14‐16]. Identification of variations in the prevalence of frailty across socioeconomic subgroups generates opportunities to prevent health-related inequalities. Multiple studies have investigated the health inequalities for different components of SES, including education [17, 18], income [19, 20], and occupation [21, 22]. Although it has been well-recognized that wealth has potential health implications, fewer studies have investigated the impact of wealth status on frailty [23, 24].
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The proportion of the older population is growing worldwide because of the gain in life expectancy and low fertility rates, and this growth is more rapid in developing countries [25]. India is no exception; the number of Indian older adults aged 60 years and above is expected to grow from 104 million (8.6%) in 2011 to 319 million (19.1%) by 2050 [26]. With the increasing older population, the number of (pre-)frail older adults is expected to rise rapidly; thus, identifying the people at the risk of frailty is of utmost importance [27]. Most studies investigating socioeconomic inequalities in frailty are available from the developed countries [11, 14]. Although frailty has been studied in developing countries [28, 29], fewer studies have focused on the socioeconomic inequalities in frailty – specifically, wealth based-inequalities in frailty. A study based on the WHO’s Study on global AGEing and adult health (SAGE) data reported that education and income are the protective factors against frailty among Indian older adults [28]. Therefore, it can be expected that, in the Indian scenario, reducing the income- or wealth-based inequalities in frailty can help to tackle the burden of frailty and, consequently, to achieve meaningful health benefits.
A recent systematic review has recommended that reducing socioeconomic inequalities in frailty among older adults should be a policy concern [30]. Apart from determining the presence of socioeconomic inequalities, gaining insights into the factors which contribute mostly to the socioeconomic inequalities in frailty is necessary to identify more vulnerable sub-populations as the target groups and to suggest possible interventions directing towards reducing frailty and its adverse effects. Even though frailty-based studies are increasing, little attention has been paid to understand wealth-based (i.e., wealth-based) inequality in frailty over time. Due to the vast socioeconomic diversity among its residents, studying health inequality in India is of particular interest. Till now, no study in India in authors’ knowledge has investigated the socioeconomic inequalities associated with physical frailty in old age. Thus, the study aimed to investigate the wealth-based inequalities in physical frailty among older adults and to quantify the contributions of potential predictors of frailty to this inequality.
Methods
Data
This study utilizes data from India’s first nationally representative longitudinal Ageing survey (LASI-2017-18) which investigates into the health, economics and social determinants and consequences of population ageing in India [31]. The representative sample included 72,250 adults aged 45 and above and their spouses across all states and union territories of India except Sikkim. The LASI adopts a multistage stratified area probability cluster sampling design to select the eventual units of observation. Households with at least one member aged 45 and above were taken as the eventual unit of observation. This study provides scientific evidence on demographics, household economic status, chronic health conditions, symptom-based health condition, functional and mental health, biomarkers, health care utilization, work and employment etc. It enables the cross-state analyses and the cross-national analyses of ageing, health, economic status, and social behaviours and has been designed to evaluate the effect of changing policies and behavioural outcomes in India. Detailed information on the sampling frame is available on the LASI WAVE-1 Report [31]. The effective sample size for the present study was 11,842 and 18,709 older adults aged 60 years and above in the poor and non-poor category (total 30,551 older adults after excluding 913 individuals due to incomplete information on physical frailty components) [31]. All the steps/ methods were performed in accordance with the relevant guidelines and regulations, established by the declaration of Helsinki.
Variable description
Outcome variable
Frailty
The physical frailty among older adults were assessed using an adapted version of the frailty phenotype described by Fried and colleagues [32]. Multiple studies on Indian older adults utilized the Fried’s phenotype framework to define frailty [33‐35]. The frailty phenotype developed by Fried and colleagues consists of five components: (1) self-reported exhaustion, (2) unintentional weight loss, (3) weak grip strength, (4) self-reported low physical activity, and (5) slow walking time. The overall score of frailty phenotype lies between 0 and 5. Respondents with a score of 0 to 2 were considered “non-frail,” and three or higher as “frail”. The assessment of the individual domains is described in Table 1. The criticism of the Fried’s criteria of defining frailty is that it focuses mainly on physical health deficits. Also, the Fried’s criteria for defining frailty do not account for the role of various psychological factors, including cognition and depression among older adults [36, 37]. In addition, the reliance of Fried’s criteria on questionnaires to identify weight loss and exhaustion potentially suffers from participant bias [38].
Table 1
Criteria used to define physical frailty in LASI, 2017–18
Exhaustion
During the past week, how often did you feel: (a) tired or low in energy; (b) that everything you did was an effort.
Less than three days = 0; More than three days =1.
Grip strength
Average grip strength score in dominant hand (2 trials) using Smedley’s handheld dynamometer (adjusted for gender and body mass index (BMI))
BMI
Cut-off for Grip strength
For men
BMI ≤ 24
≤ 29 kg
BMI 24.1–26
≤ 30 kg
BMI 26.1–28
≤ 30 kg
BMI > 28
≤ 32 kg
For women
BMI ≤ 23
≤ 17 kg
BMI 23.1–26
≤ 17.3 kg
BMI 26.1–29
≤ 18 kg
BMI > 29
≤ 21 kg
Walk time
In the LASI, respondents were asked to walk 4 m twice. The time taken to walk was recorded in seconds each time, and the average time taken in both trials was calculated. (Stratified by gender and height (gender-specific cut-off a medium height)
Cut-off for Time to Walk 4-m
Men
Height ≤ 173 cm
≥ 7 seconds
Height > 173 cm
≥ 6 seconds
Women
Height ≤ 159 cm
≥ 7 seconds
Height > 159 cm
≥ 6 seconds
Weight loss
Do you think that you have lost weight in the last 12 months because there was not enough food at your household?”
Yes = 1, No = 0.
Physical activity
How often do you take part in sports or vigorous activities, such as running or jogging, swimming, going to a health center or gym, cycling, or digging with a spade or shovel, heavy lifting, chopping, farm work, fast bicycling, cycling with loads?
One to three times a month or hardly ever or never = 1, once a week or more than once a week = 0
Allowed missing 1–2 items, missing imputed with 0 (sum of available items)
Explanatory variable
Main explanatory variable
Wealth index was calculated using variables related to household assets, amenities, and housing quality. For constructing wealth index in the LASI, we have utilized a similar approach that is being used in Demographic Health Surveys (DHS) [39]. To construct wealth index, we utilized a set of 46 variables that covers the broad domains of the household’s wealth and amenities and access to financial institutions. Further details are provided in Supplementary file. For the analysis purposes, the wealth quintile was categorized as poor which includes poorest and poorer category and non-poor which includes middle, richer and richest.
Other explanatory variables
A set of potentially related covariates including individual, behavioural, health, and household factors were included in the analysis. Individual factors were age (young-old (60–69 years), old-old (70–79 years) and oldest-old (80+ years)); sex (male, female); educational status (no education/primary not completed, primary, secondary and higher); living arrangement (living alone, living with spouse only, living with children only and living with others); marital status (currently married, widowed and others (separated/divorced/never married)); working status (never worked, currently working, currently not working and retired); and social engagement (no, yes). The participants were categorized as socially engaged if they participated in any of these activities: eating out in a restaurant or hotel, going to park/beach, playing cards or indoor games, playing outdoor games, visiting relatives/friends, attending cultural performances, attending religious functions, attend political/community/organization group meetings, read books/newspapers, watch television, use a computer for e-mail/net surfing etc. Behavioral factors include tobacco consumption (no, yes) and alcohol consumption (no, yes). Health-related factors comprises of body mass index (< 18.5 kg/m2 (underweight), 18.5 to 24.9 kg/m2 (normal), ≥25 kg/m2 (overweight/obese) [40]); self-rated health (good, poor); difficulties in basic and instrumental activities of daily living (ADL and IADL) (no, yes); and morbidity status (0, 1, 2 and above). The morbid conditions that are considered to create the morbidity status are hypertension, coronary heart diseases, stroke, cancer, diabetes mellitus, chronic bone/joint diseases, neurological or psychiatric conditions, chronic lung diseases and high cholesterol. Basic ADLs included six activities: dressing, bathing, walking, eating, getting in or out of bed, and toileting, which were necessary for older individuals’ independent living in the community. On the other hand, IADLs included seven activities: preparing food, shopping, making telephone calls, taking medications, working around the house, managing money, and finding an address in an unfamiliar place [41]. Various household factors were: Religion (Hindu, Muslim, Christian, and Others), Caste (Scheduled Tribe, Scheduled Caste, Other Backward Class, and others), residence (rural and urban), and region (North, Central, East, Northeast, West, and South.
Statistical analysis
Bivariate analysis was performed to identify the significant variables that are related to frailty. A two-sample proportion test was used to evaluate if the prevalence of the various socioeconomic and demographic variables obtained according to the wealth status (Poor, Non-poor) were significantly different. Multiple logistic regression was used to examine the association between frailty and various explanatory covariates. Logistic regression is used when the outcome variable is binary (no/yes (0/1)). The independent variables may be continuous or categorical or a mixture of the two. It is advantageous over simple linear regression in terms of the interpretation and indicates the relative likelihood of the event of interest. Our dependent variable, physical frailty status, has two categories: 0 = not frail; 1 = frail. Therefore, the utilization of logistic regression model is considered appropriate (See supplementary material for detailed mathematical expressions).
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The presence of multicollinearity among the independent variables was detected using the Variance Inflation Factor (VIF) at a cut-off point of 10. In the final model, to check the goodness-of-fit, an F-adjusted goodness-of-fit test was employed. Due to complex sampling design effects in LASI, we accounted for inverse probability weights by using the svyset command in Stata 15.1. Model-1 represents the unadjusted estimates whereas Model-2 represents the adjusted estimates i.e., model-2 was adjusted for age, sex, education, living arrangement, working status, marital status, social engagement, tobacco consumption, alcohol consumption, BMI, self-rated health, ADL, IADL, morbidity status, religion, caste, place of residence and region.
Concentration curve (CC) and concentration index (CCI) were used to determine the inequalities in the distribution of the frailty by wealth index scores. The CC depicts how a cumulative share of the frailty (y-axis) is accounted for by the cumulative percentage of the individuals ranked by wealth scores (x-axis). If every individual has an identical health outcome, regardless of the wealth status, the CC would be a 450 line that runs from the lower-left corner to the upper right corner, also known as the “line of equality.” On the contrary, if the health outcome variable has higher values among poorer people, the CC will lie above the “line of equality” and vice versa. The farther the curve is away from the baseline, represented by the equality line, the more unequal is the distribution of the health outcome variable [42]. The CCI corresponds to twice the area between the CC and the line of equality [43]. In the present paper, the CCI is computed as twice the covariance of the health outcome variable and a person’s rank in terms of wealth status, divided by the mean of the health variable [44]:
Where γj and Rj are the health status and fractional rank (in terms of the index of economic status) of the jth individual, respectively; μ is the mean of the health outcome variable, and cov denotes the covariance.
Decomposition of the CCI
The present study used Wagstaff’s CCI decomposition approach to reveal the contribution of each explanatory variable to the measured health inequality (i.e., frailty inequality) [45]. According to Wagstaff, a linear regression model linking health outcome variable (y) to a set of k explanatory variables (xk):
Where xki is a set of k explanatory variables for the ith individual, βk signifies the coefficient, and εi is an error term. Given the association of yi and xki, in eq. (2), the concentration index (CCI) for y, can be written as follows:
Where C denotes the overall concentration index, μ is the mean of y, \({\overline{\textrm{x}}}_{\textrm{k}}\) is the mean of xk, Ck is the normalized concentration index for xk (defined exactly like CCI), \(\frac{\upbeta_{\textrm{k}}{\overline{\textrm{x}}}_{\textrm{k}}}{\upmu}\) is the elasticity of health variable with the explanatory variables, and GCε is the generalized CCI for εi (residual component). Equation (3) suggests that the concentration index consists of explained and residual (unexplained) components. In most cases, health outcome variables are rarely continuous. We have approximated decomposition analysis by using marginal effects on the logit model. A linear approximation of the non-linear estimation can be represented as:
Where \({\upbeta}_{\textrm{k}}^{\textrm{m}}\) is the marginal effects (\(\frac{\textrm{dy}}{\textrm{dx}}\)) of each x; μi signifies the error term generated by the linear approximation. The concentration index for the heath variable (y) (in our case, frailty) is given as:
In total, 30,551 participants comprise the effective sample, including 11,842 older adults in the poor group and 18,709 in the non-poor group. Table 2 shows the socio-economic and demographic profiles of the two groups. The pattern of age and sex distribution was similar among both poor and non-poor groups. The proportion of illiterate (84.3% vs. 54.3%), living alone (9.8% vs. 2.3%), non-working (39.4% vs. 32.7%), and widowed (38.2% vs. 34.2%) older adults in the poor group was higher than that in the non-poor group. The level of social engagement was higher among older adults in the non-poor group than in the poor group (94.3% vs. 88.0%). The proportion of tobacco (47.1% vs. 34.4%) and alcohol (16.6% vs. 13.0%) consumption were higher among older adults in the poor group than the non-poor group. According to different health-related predictors, the non-poor group had a higher proportion of older adults who were overweight/obese (28.4% vs. 10.7%), had good self-rated health (55.7% vs. 46.4%), and had no difficulty in ADL (79.2% vs. 74.8%) and IADL (56.8% vs. 47.3%) than in the non-poor group. Moreover, the proportion of older adults with more than two comorbidities was higher in the non-poor group than in the poor group (29.8% vs. 16.7%).
Table 2
Socio-economic and demographic profile of the study population in India, 2017–18
Background characteristics
Poor
Non-poor
Sample
Percentage
Sample
Percentage
Individual factors
Age
Young-old (60–69 years)
7034
59.4
11,125
59.5
Old-old (70–79 years)
3525
29.8
5602
29.9
Oldest-old (80+ years)
1283
10.8
1982
10.6
Sex
Male
5413
45.7
9076
48.5
Female
6429
54.3
9633
51.5
Education
No education/primary not completed
9987
84.3
10,149
54.3
Primary completed
914
7.7
2653
14.2
Secondary completed
804
6.8
3654
19.5
Higher and above
137
1.2
2253
12.0
Living arrangements
Alone
1156
9.8
423
2.3
With spouse only
2973
25.1
2844
15.2
With children only
6972
58.9
14,579
77.9
Others
741
6.3
862
4.6
Marital status
Currently married
7039
59.4
11,956
63.9
Widowed
4523
38.2
6394
34.2
Others
280
2.4
360
1.9
Working status
Never worked
2489
21.0
5799
31.0
Currently working
4429
37.4
4599
24.6
Currently not working
4667
39.4
6112
32.7
Retired
256
2.2
2198
11.7
Social engagement
No
1417
12.0
1072
5.7
Yes
10,425
88.0
17,637
94.3
Behavioral factors
Tobacco consumption
No
6261
52.9
12,269
65.6
Yes
5581
47.1
6440
34.4
Alcohol consumption
No
9882
83.5
16,273
87.0
Yes
1960
16.6
2436
13.0
Health factors
Body mass indexa
Underweight
4200
35.5
2785
14.9
Normal
5393
45.5
8919
47.7
Overweight/obese
1262
10.7
5313
28.4
Self-rated health
Good
5498
46.4
10,424
55.7
Poor
6344
53.6
8285
44.3
Difficulty in ADL
No
8853
74.8
14,809
79.2
Yes
2989
25.2
3900
20.9
Difficulty in IADL
No
5599
47.3
10,616
56.8
Yes
6243
52.7
8093
43.3
Morbidity status
0
6711
56.7
7241
38.7
1
3157
26.7
5885
31.5
2+
1974
16.7
5583
29.8
Household factors
Religion
Hindu
9899
83.6
15,328
81.9
Muslim
1292
10.9
1978
10.6
Christian
343
2.9
542
2.9
Others
308
2.6
861
4.6
Caste
Scheduled Caste
2957
25.0
2600
13.9
Scheduled Tribe
1469
12.4
813
4.4
Other Backward Class
5175
43.7
8716
46.6
Others
2241
18.9
6580
35.2
Place of residence
Rural
10,566
89.2
10,370
55.4
Urban
1276
10.8
8339
44.6
Region
North
896
7.6
3224
17.2
Central
3143
26.5
3020
16.1
East
3814
32.2
3109
16.6
Northeast
356
3.0
554
3.0
West
1597
13.5
3811
20.4
South
2037
17.2
4991
26.7
Total
11,842
100
18,709
100
asample was low due to missing cases
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Table 3 shows the bivariate analysis of percentages distribution of frailty among older adults in poor and non-poor groups. The prevalence of frailty was significantly higher among the older adults in the poor group than in the non-poor group [Difference (poor vs. non-poor): 6.4%; p < 0.001]. Irrespective of age, sex, education, living arrangements, working status, marital status, and social engagements, the poor group has a significantly higher prevalence of frail older adults than the non-poor group. For both poor and non-poor groups, the prevalence of frailty was higher in the older adults who were underweight, never consumed tobacco or alcohol, had poor self-rated health, had difficulties in ADL and IADL, and had more than two comorbidities.
Table 3
Percentage of frail older adults by their background characteristics in India, 2017–18
Background characteristics
Poor
Non-poor
Difference
p-value
%
%
%
Individual factors
Age
Young-old (60–69 years)
24.6
18.8
5.8
0.001
Old-old (70–79 years)
41.8
34.9
6.9
0.001
Oldest-old (80+ years)
58.2
50.4
7.8
0.537
Sex
Male
31.3
24.2
7.0
0.001
Female
35.1
29.5
5.6
0.001
Education
No education/primary not completed
34.7
31.0
3.7
0.051
Primary completed
30.8
28.3
2.5
0.210
Secondary completed
22.2
19.7
2.5
0.002
Higher and above
18.9
19.0
−0.1
0.894
Living arrangements
Alone
42.7
32.7
10.0
0.112
With spouse only
29.4
24.3
5.1
0.001
With children only
32.6
26.5
6.0
0.001
Others
41.9
39.5
2.4
0.878
Marital status
Currently married
28.7
22.6
6.1
0.001
Widowed
40.7
35.3
5.3
0.002
Others
33.4
23.3
10.1
0.309
Working status
Never worked
36.5
30.4
6.2
0.001
Currently working
16.0
13.0
3.0
0.005
Currently not working
47.6
36.2
11.4
0.001
Retired
42.6
21.5
21.1
0.001
Social engagement
No
42.7
39.6
3.1
0.023
Yes
32.1
26.2
5.9
0.004
Behavioral factors
Tobacco consumption
No
34.0
27.5
6.5
0.001
Yes
32.6
25.9
6.7
0.001
Alcohol consumption
No
34.3
27.5
6.8
0.001
Yes
28.8
23.5
5.2
0.001
Health factors
Body mass index
Underweight
42.8
38.4
4.3
0.363
Normal
33.0
28.7
4.3
0.002
Overweight/obese
24.5
26.2
−1.7
0.033
Self-rated health
Good
25.1
20.6
4.5
0.001
Poor
40.5
34.9
5.6
0.001
Difficulty in ADL
No
28.1
23.4
4.7
0.001
Yes
48.9
40.4
8.5
0.001
Difficulty in IADL
No
24.2
20.5
3.6
0.001
Yes
41.6
35.4
6.2
0.001
Morbidity status
0
29.7
23.3
6.4
0.001
1
35.8
26.9
8.9
0.001
2+
41.8
31.7
10.1
0.001
Household factors
Religion
Hindu
32.9
26.8
6.1
0.001
Muslim
36.9
30.8
6.1
0.001
Christian
33.3
25.8
7.5
0.073
Others
33.8
22.3
11.5
0.002
Caste
Scheduled Caste
34.6
27.1
7.5
0.001
Scheduled Tribe
27.9
25.4
2.6
0.017
Other Backward Class
35.2
26.8
8.4
0.001
Others
31.0
27.3
3.7
0.001
Place of residence
Rural
33.7
27.3
6.4
0.001
Urban
30.7
26.6
4.2
0.001
Region
North
27.3
26.7
0.6
0.380
Central
34.5
29.1
5.4
0.015
East
35.1
32.5
2.6
0.843
Northeast
26.6
19.9
6.6
0.012
West
27.2
20.9
6.3
0.009
South
37.0
27.8
9.1
0.001
Total
33.4
27.0
6.4
0.001
Differences: Poor - Non poor; p-value based on proportion test
Table 4 presents the unadjusted and adjusted logistic regression estimates for frailty among Indian older adults. In model-I, older adults in the poorest group were 41% more likely to be frail than those in the richest category [OR = 1.41; 95% CI: 1.31, 1.52]. Similar results were obtained after controlling various individual, behavioral, health, and household factors in model-II [AOR = 1.23; 95% CI: 1.11, 1.38]. Figure 1 shows the concentration curve (CC) for wealth-related inequalities in frailty among older adults. The CC curve lies above the 45-degree line of equality, indicating that frailty was concentrated amongst older adults in the poor group [CI: 0.058].
Table 4
Logistic regression estimates for frail older adults by their background characteristics in India, 2017–18
Background characteristics
Model-1
Model-2
UOR (95% CI)
AOR (95% CI)
Wealth Index
Poorest
1.41*(1.31,1.52)
1.23*(1.11,1.38)
Poorer
1.27*(1.18,1.38)
1.19*(1.07,1.32)
Middle
1.17*(1.08,1.27)
1.09 (0.98,1.20)
Richer
1.13*(1.04,1.22)
1.03 (0.94,1.12)
Richest
Ref.
Ref.
Model-2 represents the Adjusted odds ratio (AOR). The model was adjusted for Individual, Behavioral, Health and Household factors; CI Confidence interval; * if p < 0.05
Fig. 1
Concentration curve (CC) for wealth-related inequalities in frailty among Indian older adults, 2017–18
×
Table 5 presents the decomposition analysis results and shows the contribution of various individual, behavioral, health, and household predictors of the respondents in explaining socio-economic inequality in frailty among Indian older adults. The first two columns show the elasticities and concentration index (CI) for each predictor. The rest of the columns show each predictor’s absolute contributions and total percentage contributions to economic inequalities in frailty. The value of the absolute contribution depicts the extent of inequality contributed by a particular explanatory variable. Findings from Table 5 suggest that body mass index, wealth index, educational status, and region were the major and significant contributors to the socio-economic status (SES) related inequalities in frailty, followed by self-rated health and living arrangements. Nearly 45.2% of socio-economic status-related inequality in frailty was contributed by body mass index. Respondent’s educational status was responsible for around 18.1% of inequality in frailty. Moreover, the wealth index and region of residence contributed to about 28.4 and 14.4% of the socio-economic (SES) related inequality, respectively.
Table 5
Decomposition estimates of physical frailty among older adults by their background characteristics in India, 2017–18
Background characteristics
Elasticity
CCI
Absolute contribution
Percentage contribution
Individual factors
Age
Young-old (60–69 years)
0.3
Old-old (70–79 years)
0.029
−0.006
0.000
0.9
Oldest-old (80+ years)
0.023
0.005
0.000
−0.5
Sex
Male
−0.5
Female
−0.010
− 0.011
0.000
− 0.5
Education
No education/primary not completed
18.1
Primary completed
0.002
0.161
0.000
−1.3
Secondary completed
−0.006
0.369
− 0.002
11.0
Higher and above
−0.003
0.601
−0.002
8.4
Living arrangements
Alone
9.8
With spouse only
−0.008
−0.166
0.001
−6.4
With children only
−0.035
0.098
−0.003
16.0
Others
0.001
−0.091
0.000
0.2
Marital status
Currently married
0.7
Widowed
0.009
−0.023
0.000
0.9
Others
−0.001
−0.072
0.000
−0.3
Working status
Never worked
−19.0
Currently working
−0.037
−0.165
0.006
−28.5
Currently not working
0.023
−0.070
−0.002
7.4
Retired
−0.001
0.458
0.000
2.1
Social engagement
No
4.8
Yes
−0.040
0.026
−0.001
4.8
Behavioral factors
Tobacco consumption
No
−5.2
Yes
−0.009
−0.130
0.001
−5.2
Alcohol consumption
No
0.0
Yes
0.000
−0.081
0.000
0.0
Health factors
Body mass index
Underweight
45.2
Normal
−0.030
−0.008
0.000
−1.1
Overweight/obese
−0.021
0.334
−0.007
46.3
Self-rated health
Good
10.3
Poor
0.036
−0.062
−0.002
10.3
Difficulty in ADL
No
5.3
Yes
0.023
−0.051
−0.001
5.3
Difficulty in IADL
No
5.7
Yes
0.023
−0.052
−0.001
5.7
Morbidity status
0
−10.5
1
0.005
0.039
0.000
−0.8
2+
0.010
0.209
0.002
−9.7
Household factors
Wealth Index
Poorest
28.4
Poorer
−0.003
−0.307
0.001
−4.7
Middle
−0.003
0.058
0.000
0.9
Richer
−0.004
0.415
−0.002
8.3
Richest
−0.007
0.770
−0.005
23.9
Religion
Hindu
0.5
Muslim
0.003
0.012
0.000
−0.2
Christian
0.000
0.010
0.000
0.0
Others
−0.001
0.212
0.000
0.7
Caste
Scheduled Caste
2.6
Scheduled Tribe
0.000
−0.351
0.000
−0.7
Other Backward Class
−0.005
0.019
0.000
0.4
Others
−0.003
0.213
−0.001
2.9
Place of residence
Rural
−10.9
Urban
0.005
0.452
0.002
−10.9
Region
North
14.4
Central
0.008
−0.187
−0.002
7.1
East
0.010
−0.215
− 0.002
10.4
Northeast
−0.001
−0.013
0.000
0.0
West
−0.004
0.097
0.000
2.0
South
0.006
0.175
0.001
−5.0
Calculated CCI
−0.022
100.0
Actual CCI
−0.058
Residual
−0.036
CCI Concentration index
Discussion
Our results showed substantial socioeconomic disparities in frailty risk among community-dwelling older adults in India. A cross-sectional link between lower SES and frailty has been documented in multiple studies [11, 23, 46‐49]. Similarly, older adults belonging to the poorest category had higher risk of frailty than those from rich households; education, living arrangements, BMI status and health variables including SRH, multimorbidity, ADL/IADL difficulties explained most of this disparity.
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Longitudinal studies have revealed that lower education predicted a higher risk of frailty in older adults, and in some studies, lower educated as compared with higher educated older adults had a higher risk of worsening in the state of frailty [10, 14, 50]. Consistent with the literature, we found a higher risk of measured frailty in lower educated older individuals. However, the contribution of educational gradient in frailty was slightly lesser than the household economic gradient. On the other hand, the observed contribution of education to the socioeconomic inequalities in late-life frailty in our study can be explained by the fact that education can have positive impact on the attitudes and health behaviors which in turn can influence the decisions regarding the health of the older people [48]. Similarly, economically poor individuals becoming more frail compared to those from higher economic background may be attributed to the financial limitations that can limit the use of health care resources whereas, having adequate resources may help older people with early detection of age-related physical limitations and compensate for their incapacities by spending on specific health services that can be availed at home and other care facilities [51]. Nevertheless, the possibility of a reverse causation, where health status may determine the socioeconomic position of the people also should be taken into account while interpreting the findings.
Social engagements appear to reduce the rate of physical deficits in older populations, as has been observed in previous studies [52, 53], and social frailty is known to be a major risk factor for physical frailty [54]. In parallel with these findings, socially engaged older individuals in the present study had lower prevalence of frailty and the social engagement contributed to wealth-based inequality in frailty among older adults. According to the social capital theory, social engagement is an important tool for older adults to obtain the social resources which will have a positive impact on health and well-being among older people [55]. At the same time, marriage is considered as a central resource for social support for older individuals due to the narrowing of social networks in later life [56]. Supporting this, current results showed that the prevalence of frailty was higher in widowed older adults among poor and non-poor alike and the contribution of marital status to the observed inequality in frailty was substantially low.
In the earlier studies, there is evidence of the social gradient in the risk of tobacco use and heavy alcohol drinking which are found to be related to developing physical frailty [57, 58]. However, in our study, tobacco use and alcohol consumption accounted for very little of the frailty risk in old age which needs to be further investigated. Regarding our results on the contribution of BMI status towards the inequalities in frailty, they are in line with studies that reported that there is a strong association between BMI and physical frailty and the risk is higher among socioeconomically poor older adults [59, 60]. Although it was studied with the cross-sectional design which prohibits any causal inferences, the current finding suggests that maintaining a healthy body weight throughout individuals’ life course is important for healthy aging. With regards to other health factors, results followed the same direction although SRH explained 10.3% of the excess risk of frailty linked to a poor rating of health. Similarly, functional difficulties in ADL and IADL together contributed to 11% of the observed inequality in physical frailty in old age, suggesting that a broader assessment of frailty by considering deficits in multiple health and functional domains may bring out different results. Furthermore, possible pathways explaining the deleterious effects of socioeconomic adversity on physical frailty including cultural and environmental factors have to be further investigated.
The major strength of this study is the sizeable sample of older population which is nationally-representative. The large number of potential confounders considered in the study adds to the merits of the study. However, this study considered only cross-sectional relationships between socioeconomic and health characteristics of the participants and their physical frailty. Temporal relationships between the onset of frailty as an outcome and socioeconomic characteristics and the absolute contributions of several factors to the poor and non-poor disparities in frailty were not examined in the present study. Thus, further studies with follow-up data from upcoming waves of LASI survey are required. Moreover, although the association between socioeconomic status and weight change among people in developing countries is inconsistent across studies [61], the item of weight loss in the physical frailty may capture some information about the household economic status (i.e. the respondent did not have enough food in the household), which could be driving some of the associations between the primary exposure and the outcome in this study. Future research should also consider psychosocial variables such as mistreatment, everyday discrimination, social networks and cognitive functioning, and further anthropometric biomarkers including waist circumference and waist-hip ratio among other factors.
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Conclusion
The study found a higher risk of physical frailty in older adults with poor socioeconomic status. This disparity was predominantly explained by lower levels of education, adverse BMI status, poor SRH, difficulty in ADL and IADL and higher rates of multimorbidity in older individuals with lower socioeconomic status. Results suggest the need for formulating effective prevention and intervention strategies to decelerate the development of physical frailty among older adults in India, especially those with poor socioeconomic background.
Acknowledgements
Not applicable.
Declarations
Ethics approval and consent to participate
The survey agencies that conducted the field survey for the data collection have collected prior informed consent (written and verbal) from the respondent. The Indian Council of Medical Research (ICMR) extended the necessary guidance and ethical approval for conducting the LASI survey. All the steps/ methods were performed in accordance with the relevant guidelines and regulations.
Consent for publication
The administrative permission to access and use the data for the present study was taken from the International Institute for Population Sciences, Mumbai, which conducted the LASI survey.
Competing interests
The authors declare that there is no competing interest
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