Decomposing rural–urban differences in successful aging among older Indian adults

Muhammad, T.; Srivastava, Shobhit; Hossain, Babul; Paul, Ronak

doi:10.1038/s41598-022-09958-4

Cited by 27 publications

(18 citation statements)

References 77 publications

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“…The oldest-older (80 years and above), lower educated, and unemployed individuals are more likely to experience poor health than their counterparts. With increasing age, the risk of both communicable and non-communicable diseases is high, negatively affecting SRH [ 16 ]. Similarly, the lower educated and unemployed are more vulnerable to socio-economic distress, positively associated with poor SRH [ 45 ].…”

Section: Discussionmentioning

confidence: 99%

“…The studies found significant variation in prevalence and predictors of SRH in accordance with the place of residence. A recent study has examined the rural–urban gap in health status among the older population in India using large-scale sample survey data and found significant variation in predictors of successful aging between rural and urban [ 16 ]. However, it hasn't been investigated how the association between marital status and living arrangements with SRH varies depending on the place of residence in India.…”

Section: Introductionmentioning

confidence: 99%

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Rural urban differences in self-rated health among older adults: examining the role of marital status and living arrangements

et al. 2022

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Background The rural–urban gap in socioeconomic and morbidity status among older adults is prevalent in India. These disparities may impact the levels and factors of self-rated health (SRH). The objective of the study is to compare the levels and determinants of SRH between rural and urban areas by considering the moderating effects of marital status and living arrangements. Subjects and methods The present study used data from the Longitudinal Ageing Study in India (LASI) wave 1 (2017–18). A total sample of 30,633 older adults aged 60 years and above were selected for the study. Descriptive statistics, bivariate chi-square test, the interaction effect of living arrangements and marital status, and logistic estimation were applied to accomplish the study objectives. Results The prevalence of poor SRH was found 7% higher in rural areas compared to urban counterparts. A substantial rural–urban disparity in the patterns of poor SRH was also observed. The interaction effect of marital status and living arrangement on self-rated health suggested that older adults who were currently unmarried and living alone were 38% more likely to report poor SRH than those who were currently married and co-residing in rural India. In addition to marital status and living situation, other factors that significantly influenced SRH include age, socio-cultural background (educational attainment and religion), economic background (employment status), health status (ADLs, IADLs, multi-morbidities), and geographic background (region). Conclusion The present study's findings demonstrated that, notwithstanding local variations, marital status and living circumstances significantly influenced SRH in India. In the present study, unmarried older people living alone were more susceptible to poor SRH in rural areas. The present study supports the importance of reinforcing the concepts of care and support for older individuals. There is a need for special policy attention to older individuals, particularly those unmarried and living alone. Although older individuals had difficulty performing ADLs and IADLs and had multi-morbidities, they reported poorer health. Therefore, offering them social support and top-notch medical assistance is crucial.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Rural urban differences in self-rated health among older adults: examining the role of marital status and living arrangements

et al. 2022

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“…Major depression among older adults with symptoms of dysphoria was calculated using the Composite International Diagnostic Interview - Short Form (CIDI-SF) on a scale of 0–10. This scale estimates a probable psychiatric diagnosis of major depression and has been validated in field settings and widely used in population-based health surveys 44 45. It has 10 questions and respondents with positive answers to 3 or more symptoms are considered ‘depressed’.…”

Section: Methodsmentioning

confidence: 99%

Socioeconomic and health-related inequalities in major depressive symptoms among older adults: a Wagstaff’s decomposition analysis of data from the LASI baseline survey, 2017–2018

et al. 2022

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ObjectivesTo find out the association between socioeconomic and health status and depression among older adults and explore the contributing factors in the socioeconomic and health-related inequalities in late-life depression.DesignA cross-sectional study was conducted using large representative survey data.Setting and participantsData for this study were derived from the baseline wave of the Longitudinal Ageing Study in India conducted during 2017–2018. The effective sample size was 30 888 older adults aged 60 years and above.Primary and secondary outcome measuresThe outcome variable in this study was depression among older adults. Descriptive statistics along with bivariate analysis was conducted to report the preliminary results. Multivariable binary logistic regression analysis and Wagstaff’s decomposition were used to fulfil the objectives of the study.ResultsThere was a significant difference for the prevalence of depression (4.3%; p<0.05) among older adults from poor (11.2%) and non-poor categories (6.8%). The value of the Concentration Index was −0.179 which also confirms that the major depression was more concentrated among poor older adults. About 38.4% of the socioeconomic and health-related inequality was explained by the wealth quintile for major depression among older adults. Moreover, about 26.6% of the inequality in major depression was explained by psychological distress. Self-rated health (SRH), difficulty in activities of daily living (ADL) and instrumental ADL (IADL) contributed 8.7%, 3.3% and 4.8% to the inequality, respectively. Additionally, region explained about 23.1% of inequality followed by life satisfaction (11.2) and working status (9.8%) for major depression among older adults.ConclusionsFindings revealed large socioeconomic and health-related inequalities in depression in older adults which were especially pronounced by poor household economy, widowhood, poor SRH, ADL and IADL difficulty, and psychological distress. In designing prevention programmes, detection and management of older adults with depression should be a high priority, especially for those who are more vulnerable.

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“…Aging population across world countries and in low-and middle-income countries in particular poses wide-ranging health, social, and economic challenges, both current and future. India is projected to surpass China as the world's most populous country during 2023 [1] with a Open Access *Correspondence: shobhitsrivastava889@gmail.com International Institute for Population Sciences, Mumbai, Maharashtra, India rapidly growing proportion of older persons that requires improvement of social security and long-term care systems in terms of ensuring a successful aging process [2].…”

Section: Introductionmentioning

confidence: 99%

How socioeconomic status, social capital and functional independence are associated with subjective wellbeing among older Indian adults? A structural equation modeling analysis

2022

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Background Subjective well-being (SWB) is of particular interest among gerontologists and health researchers with important implications for interventions especially in poor-resource settings. This study aimed to explore the possible pathways from socioeconomic status (SES), functional independence and social capital towards SWB among older adults in India. Methods Cross-sectional data from the “Building a Knowledge Base on Population Aging in India” (BKPAI) survey with a total sample of 9231 older adults aged 60 years and above were used. The outcome variable was low SWB (LSWB). The study used univariate and bivariate analysis for reporting the initial results. Further, the study employed the structural equation modeling (SEM) technique using maximum likelihood estimation (MLE) procedure to estimate the covariance matrix. Results Overall, about 27% of older adults reported LSWB. Reporting LSWB was more prevalent among older adults who had no income (30.8%) and those who had income but not sufficient to fulfil their basic needs (39.4%, p < 0.001). The prevalence of reporting LSWB was significantly higher among older adults who had no asset ownership (36.5%, p < 0.001) than those who had asset ownership. The path from the SEM shows that LSWB and SES are negatively related to each other. Moreover, LSWB had significant negative relationship with independence (β = -0.032, p < 0.001) and social capital (β = -0.020; p < 0.001). In addition, results found a positive relationship between SES and independence (β = 0.019; p < 0.001), SES and social capital (β = 0.016; p < 0.001), and independence and social capital (β = 0.033; p < 0.001). Conclusions The findings highlight that higher SES, good physical functioning as well as favorable social capital are interdependent factors of late-life wellbeing and a multidimensional approach in policymaking can ensure a successful and active ageing among older Indian adults.

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Decomposing rural–urban differences in successful aging among older Indian adults

Cited by 27 publications

References 77 publications

Rural urban differences in self-rated health among older adults: examining the role of marital status and living arrangements

Rural urban differences in self-rated health among older adults: examining the role of marital status and living arrangements

Socioeconomic and health-related inequalities in major depressive symptoms among older adults: a Wagstaff’s decomposition analysis of data from the LASI baseline survey, 2017–2018

How socioeconomic status, social capital and functional independence are associated with subjective wellbeing among older Indian adults? A structural equation modeling analysis

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