Background

Functional and mobility impairments (FMIs) are important health-related challenges in later life [1, 2]. FMIs, which are assessed in many ways, driven by the conceptual models of disablement, indicate one’s ability to function independently [3,4,5,6,7,8]. Given that they compromise independence, reduce social engagement [1, 9], and elevate the risk for falls [1], restricted access to medical services [10, 11], and premature mortality [12,13,14], FMIs constitute a major challenge to the health care systems worldwide [14, 15]. While high-income nations have had the time to acclimate to such challenges, they remain particularly daunting for low and middle-income countries (LMICs) like India that are being pummeled by the simultaneous increases in aging adults and chronic diseases [16,17,18] – both likely to increase the prevalence of FMIs.

A recent study reported that 44% of older adults in India live with functional impairments, as measured by impairments in one or more activities of daily living (ADLs), which include essential competencies for survival, such as personal hygiene, continence, dressing, bathing, ambulating or transferring; and instrumental activities of daily living (IADLs), which are essential for independent living and include managing communication with others, finances, medications, food preparation, and housekeeping [19,20,21,22]. Another study found 57% and 73% of older Indians to have high IADL and bodily impairment scores [23]. ADL and IADL assessments are critical in determining if older adults can “age in place” or require formal care assistance, including skilled nursing. For instance, a reduced ADL capacity to move about may raise the risk of falling [8, 20, 24]. Similarly, impaired ability to carry out IADLs, like managing medications, adversely impacts health in older adults [25, 26]. Given this, identifying factors that render older adults susceptible to FMIs is critical to crafting interventions that protect them against physical decline. One factor, which remains overlooked within the Indian context, is subjective social status (SSS).

SSS is an individual’s perceived social status compared to others in their community [27]. Individuals with higher SSS report better health than their lower-SSS peers [28,29,30]. SSS often predicts health above and beyond the objective indicators of socioeconomic status (SES), including education, occupation, income, wealth, and even caste [29,30,31,32]. This may be because SSS encapsulates the psychosocial aspects associated with SES, such as power, prestige, ties to the mainstream, society, perceived fairness, and status internalization [30, 33,34,35]. Such less obvious aspects related to an individual’s sense of self and identity are not necessarily captured by objective markers of SES that are restricted to static positions in one’s life [36,37,38]. For instance, occupation and income may not accurately reflect later life SES, given that many older adults, especially in LMICs like India, may be retired or financially dependent on their adult children [37, 38]. Likewise, for older Indian women with constrained opportunities for higher education and paid work [39], education and occupational ranking may not accurately represent their SES. As such, it is critical to gauge the association between SSS and FMIs in resource restrained countries.

Low SSS, which often is linked to perceived unfairness, can cause “psychological pain” and set into motion adverse physiological stress reactions (e.g., hypervigilance, increased respiration and heart rate, elevated blood pressure, sleep disturbances, cortisol secretions, and chronic inflammation) [35, 40, 41], and these amplified physiological responses over time can debilitate physical function and mobility. Low SSS may also engender a perceived lack of power to change one’s life circumstances, which could prompt risky health behaviors [42, 43] and prevent health-promoting actions [42], which can shape functional health. The study of stress, no doubt, identifies SSS as a powerful social stressor affecting later life functionality [31, 44, 45].

That said, the stress process framework highlights the centrality of individual characteristics as potential mediators and moderators of the association between stressors and distress [46]. Two such characteristics are life satisfaction and depression. While high life satisfaction, which encompasses feelings of autonomy, openness, optimism, and adaptation [47,48,49], is a resource for maintaining functional health and mobility, low life satisfaction, which is associated with higher levels of physiological stress, psychosocial, and behavioral problems [50], may increase the risk for FMIs. While high levels of depression result in internalizing behaviors of self-blame, denial, rumination, withdrawal, and self-neglect [51], which can increase the risk of FMIs, individuals with lower depression may remain at relatively minimal risk of FMIs. Both high life satisfaction [49, 52] and less depression [53] involve preemptive coping, which increases stress resistance that may be tied to lower SSS. Older adults who are more satisfied with life may adapt better to life circumstances and problem-solve challenges attached to material hardships underlying SSS. Similarly, those less depressed may cognitively reinterpret challenges [54, 55] related to low SES as more manageable, concentrating on those aspects of their SES they believe to be less intractable.

In sum, the present study hypothesizes that older adults with lower SSS are more likely to report more FMIs. We also hypothesize that both life satisfaction and depression mediate this association. Statistically, this implies that the SSS coefficients should attenuate once we adjust for life satisfaction and depression. Lastly, we posit that life satisfaction and depression also moderate the association between SSS and FMIs, such that this association is less pronounced among older adults with higher life satisfaction and lower depression than their peers who report lower life satisfaction and higher levels of depression. Figure 1summarizes the concept of our study.

Fig. 1
figure 1

Conceptual framework of the study

Notes: Dashed arrows indicate the interaction effects (depression and life satisfaction affecting the association between SSS and FMI) whereas, continuous lines indicate the main effects, either direct associations (SSS and FMI) or meditational pathways (i.e., depression and life satisfaction as mediators of the association between SSS and FMI)

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Methods

Study participants

The present study utilizes the individual-level data from the first wave of the Longitudinal Aging Study in India (LASI) conducted during 2017-18. The LASI is a country-representative longitudinal survey of more than 72,000 adults aged 45 years and over across all states and union territories of the country that provides vital information on the social, physical, psychological, and cognitive health of the Indian aging population. The LASI survey was conducted through a partnership of the International Institute for Population Sciences (IIPS), Harvard T. H. Chan School of Public Health (HSPH), and the University of Southern California (USC). In the LASI wave 1, the sample selection is based on a multistage stratified cluster sample design, including a three-stage sampling design in rural areas and a four-stage sampling design in urban areas. The details of the sampling design, survey instruments, and data collection procedures are provided elsewhere [16, 56]. The present study is conducted on the eligible respondents aged 60 years and above. Thus, the total sample size for the present study was 31,464 (15,098 men and 16,366 women) older adults aged 60 years and above.

Measures

The description of the study variables and our rationale for the selected covariates are provided.

below in Table 1.

Table 1 Description of the measures included in the study
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Statistical analysis

We presented the results from descriptive statistics and bivariate analysis (cross-tabulation) and p-values from Chi-Square tests, indicating the statistical significance of the differences in outcome variables across the selected background variables. Multivariable logistic regression analysis was performed to determine the association between the outcome variables (ADL/IADL/mobility impairments) and SSS, depression, and life satisfaction. Further, interaction analyses of SSS, depression and life satisfaction on outcome variables were conducted, and margin plots were presented. As a post-hoc analysis, interaction terms and the Karlson–Holm–Breen (KHB) method were used separately to examine the effect modification and percent mediation by depression and life satisfaction in the association between SSS and outcome variables. The estimates were presented as adjusted beta coefficients with a 95% confidence interval. Statistical models were adjusted for the selected predictor variables, including age, gender, schooling, marital status, living arrangement, work status, multimorbidity, community involvement, household consumption quintiles, religion, caste, place of residence, and region of the country. Individual weights were applied during the analysis to account for the cluster sampling and to provide the population-level estimates. The statistical analysis was performed using Stata 15.1.

Results

Table 2 presents the sample characteristics. A proportion of 39.1% of the sample had a low SSS, whereas only 7.3% had a high SSS in this study. 8.3% of the sample was depressed, and 32.1% reported low life satisfaction. More than 10% of the sample was aged 80 years or older and a significant proportion (56.4%) had no formal education. 36% of the sample was widowed, and 26.5% reported never having worked more than three months during their lifetime.

Table 2 Sample distribution by background variables and SSS
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Table 3 presents the prevalence of ADL/IADL and mobility impairments among the sample population. A total of 8.7%, 10.9%, and 8.5% of the study population reported at least one impairment in ADL, IADL, and mobility, respectively, whereas 8%, 25.5%, and 56.2% of older adults reported more than two impairments in ADL, IADL and mobility, respectively. Those with a high SSS (4.2%, 18.2%, and 49.6%) or high life satisfaction (5.6%, 20.5%, and 52%) had a lower prevalence of ADL, IADL and mobility impairments. Alternatively, those who were depressed had a higher prevalence of ADL, IADL, and mobility impairments (18.7%, 43%, and 74.4%).

Table 3 Prevalence estimates of ADL/IADL/mobility impairments among older adults by background variables
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Table 4 presents the estimates from the multivariable analysis. Older adults in the higher SSS group were less likely to have ADL impairment (beta: -0.017, CI: -0.030, -0.0032) and mobility impairment (beta: -0.044, CI: -0.076, -0.013). Older adults who were depressed were more likely to have ADL (beta: 0.071, CI: 0.056, 0.086), IADL (beta: 0.12, CI: 0.096, 0.14), and mobility impairments (beta: 0.14, CI: 0.11, 0.16). Alternatively, those who report a higher level of life satisfaction were less likely to have any ADL (beta: -0.012, CI: -0.015, -0.0079), IADL (beta: -0.014, CI: -0.022, -0.0056) and mobility impairments (beta: -0.019, CI: -0.026, -0.011) in this study.

Table 4 Multivariable OLS regression estimates of ADL/IADL/mobility impairments among older adults by background variables
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Figures 2, 3, 4, 5, 6 and 7 present the margin plots of the interaction effects of depression and life satisfaction on the associations of SSS with ADL, IADL, and mobility impairments. As can be seen from the plots, depression moderated the association between SSS and mobility impairment (p-value: 0.025). However, it did not moderate the association between SSS and ADL impairment (p-value: 0.137) and SSS and IADL impairment (p-value:0.690). Alternatively, life satisfaction moderated the association between SSS and ADL impairment (p-value: 0.041) and SSS and IADL impairment (p-value: 0.037). However, it did not moderate the association between SSS and mobility impairment (p-value: 0.446).

Fig. 2
figure 2

Effect of depression on the relationship between SSS and ADL impairment

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Fig. 3
figure 3

Effect of life satisfaction on the relationship between SSS and ADL impairment

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Fig. 4
figure 4

Effect of depression on the relationship between SSS and IADL impairment

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Fig. 5
figure 5

Effect of life satisfaction on the relationship between SSS and IADL impairment

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Fig. 6
figure 6

Effect of depression on the relationship between SSS and mobility impairment

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Fig. 7
figure 7

Effect of life satisfaction on the relationship between SSS -mobility impairment

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Table 5 presents the estimates from the mediation analysis. Depression mediated 20.3%, 31.9%, and 18.4% of the associations of SSS with ADL, IADL, and mobility impairments, respectively. Similarly, life satisfaction mediated 23.2%, 52.7%, and 27.2% of the associations of SSS with ADL, IADL, and mobility impairments, respectively, in this study.

Table 5 Direct and indirect effects of the SSS-FMIs associations via depression and life satisfaction
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Discussion

The present study focused on the association between SSS and FMIs and examined the extent to which this association is mediated and moderated by life satisfaction and depression. Our data support our hypothesis that older Indians who report low SSS also report higher ADL and mobility impairments, and their peers with higher SSS are less likely to report ADL and mobility deficits. SSS remains consequential for measures of functional and mobility outcomes even after accounting for objective SES. This underscores that objective and subjective measures of SES are not interchangeable. Moreover, the association between SSS and FMIs is mediated and moderated by life satisfaction and depression.

That SSS is relevant for FMIs matches findings in past literature on SSS and physical health [27, 35, 41, 42, 44, 45]. Our study contributes to this literature by exploring the linkage between SSS and FMIs among aging adults in India, an otherwise underexamined LMIC, in this body of work. Examining the relevance of SSS for health in different aging populations across different countries is important, given that research points out group-level differences in self-appraised social standing. For example, studies on SSS in the US have suggested that the positive association between low SSS and poor health is less pronounced among Black Americans relative to their non-Hispanic White counterparts [45, 78, 79], and this probably is because individuals from diverse racial, ethnic, and cultural backgrounds may draw on diverse sources for their social status [45, 80, 81]. For example, compared to their non-Hispanic White peers, Black Americans may base their social status on racial identity, group solidarity, and intergenerational harmony [81]. An additional advantage to a study of this type is that examining the health significance of SSS for older adults in a country with different physical, social, family, and financial contexts helps clarify elements of the aging experience that are likely universal and others which may manifest out of broader macrosocial conditions specific to one country or culture, rather than aging in and of itself.

Another way in which our study contributes to the existing literature is that our findings underscore the dual role that life satisfaction and depression play in both mediating and moderating the association between SSS and FMIs. Specifically, the mediating influence of these two factors is evident in our study because low SSS is associated with lower levels of life satisfaction and higher levels of depression —and this, in turn, is found linked to both functional and mobility impairments. Adverse health outcomes like FMIs do not always directly result from low SSS. Rather, the psychosocial stress -- as manifested in reduced life satisfaction and higher levels of depression -- of being socioeconomically disadvantaged is what produces adverse health outcomes [78]. This finding is important because higher life satisfaction and lower depression are associated with greater social engagement, health-promoting behaviors (e.g., regular exercise, healthier nutrition, use of preventive health care), reduced risky behaviors (e.g., smoking, drinking, substance use), and enhanced biologic function (e.g., less hypertension, low inflammation, low levels of cortisol) [50, 52, 82]. Social engagement and better health behaviors, in turn, are critical to sustaining functional health and mobility [82,83,84].

In contrast to mediation, the moderation analyses reveal that the association between SSS and FMIs differs among older adults based on life satisfaction and depression. Older adults who are more satisfied with their lives experience fewer negative health consequences of SSS. Moreover, those with lower levels of depression may be psychologically better equipped to withstand the otherwise negative repercussions of SSS. Older adults with higher life satisfaction and lower depression may be more likely to appraise their lower social status as challenging rather than threatening, to be optimistic and hopeful about their financial situation, and to make favorable social comparisons. It also is possible that those who are relatively more satisfied and less depressed may increase their engagement in domains where SES may be less important, such as family relationships, friendships, and volunteering.

These findings provide evidence that perceived social standing is a means through which SES is linked to physical health. This finding implies that researchers, providers, and practitioners should redouble efforts to understand how people “feel” relative to others in their social circle instead of merely focusing on the objective markers of SES. Moreover, findings here compel us to consider that if higher SSS is indeed associated with higher life satisfaction and lower depression, then applying specific cognitive-behavioral therapies to minimize a person’s sense of social inferiority may be crucial for sustaining physical health. Most interventions directly tackle health deficits (e.g., increasing access to health care and the use of pharmacologics), and some aim to reduce the stigma attached to lower SES. While such interventions are invaluable, they need to be supplemented by initiatives and efforts to reduce the stress that may accrue due to lower SSS.

Limitations and future directions

The findings of this study must be considered within the context of important limitations. First, as the project is cross-sectional, we cannot make any predictive claims. A more definitive statement about the relationships among SSS, life satisfaction, depression, and FMIs may be reached by using the forthcoming waves of LASI. Second, although life satisfaction and depression are consequential in explaining the link between SSS and FMIs, neither fully explains it. Therefore, it would be worthwhile to identify other mediators of this association. For example, SSS may either undermine social relationships or motivate individuals to invest more time and effort into them, and social relationships, and in turn, social support, are powerful predictors of physical health [85]. Stable social relationships and social support may mediate the link between SSS and FMIs; the implications of SSS for FMIs may also be differentially distributed based on the availability, quality, stability, and type of social resources. Third, the possibility of reverse causality lingers, given that FMIs, depression, and life satisfaction, each separately and interactively, could negatively shape perceptions of social status. Fourth, primary variables of interest, including depression, in our study are self-reported, which may overstate the relationship between SSS and FMIs due to their shared variance. Notwithstanding these limitations, our study is among the first in India to evaluate the impact of both objective SES and SSS on later life FMIs. Moreover, we do so by engaging a sizeable sample of a nationally representative aging population.

Conclusion

In conclusion, low SSS is associated with FMIs among older Indians. This association persisted even after accounting for objective SES and other conceptually relevant sociodemographic factors. However, the relationship between SSS and FMIs is mediated and moderated by individual characteristics of life satisfaction and depression. This knowledge may give providers and practitioners additional information to identify older adults most susceptible to FMIs.