Ruchira Chakraborty

Rising life expectancy and population ageing necessitate a better understanding of quality of later life in rapidly ageing societies like India. As this country undergoes a rapid demographic transition, it is anticipated to have a significantly higher proportion of older adults in the future. This study tries to shed light on the well-being of the older-adult population in India and its major states by employing the novel ‘Years of Good Life’ (YoGL) indicator. According to the YoGL approach, a person-year is counted as ‘good’ if spent simultaneously out of poverty, free from severe activity limitations and cognitive impairments, along with positive life satisfaction. Thus, rather than focusing merely on the fulfillment of material wants and needs, this index is based on ‘capabilities’. Our YoGL estimates for Indian states reveal substantial sub-national variation in well-being by gender and place of residence. Despite enjoying higher life expectancy, females can expect to enjoy considerably fewer good years than males. At age 60, YoGL for rural women is 4.6 years (less than one fourth of their remaining life expectancy), while YoGL for urban males is 10.2 years (around half of their remaining life expectancy). Decomposition analyses reveal that males are losing good life to poor functional health while females are being penalized in YoGL-terms due to cognitive impairment. We conclude that YoGL can be an effective socio-demographic tool for measuring and monitoring human well-being at the sub-national level to provide potential inputs for policy framing in India.

India is moving fast to an advanced stage of demographic transition with rapid ageing of its population, yet it lacks the required support systems to meet the needs for care of its older population. Providing care to the elderly is a burning issue in all ageing societies. In the Indian scenario, caregiving heavily relies on informal sources, i.e., family. And thus, the role of gender becomes relevant in both receiving and providing care. This study explored the nature and extent of gender differentials regarding in-house instrumental support received by the elderly by analysing the nationally representative data from the Longitudinal Ageing Study in India (LASI), wave I. Significant gender differentials are observed, and the likelihood of receiving care is higher for elderly females than for males. Among the demographic factors, the number of children the respondent has, the place of residence, and the living arrangement come out to be significantly related to receiving informal care. From a caregiving perspective, the female spouse is identified as the primary caregiver while, if we consider intergenerational aspects, the daughters-in-law come out to be the major caregivers, establishing the feminization of the role of caregiving. Our study further concludes that longer life expectancy with higher prevalence of limitations in activities of daily living and economic dependency increases females’ dependency on informal care in Indian households.

Developing countries like India grapple with significant challenges due to the double burden of communicable and non-communicable disease in older adults. Examining the distribution of the burden of different communicable and non-communicable diseases among older adults can present proper evidence to policymakers to deal with health inequality. The present study aimed to determine socioeconomic inequality in the burden of communicable and noncommunicable diseases among older adults in India. This study used Longitudinal Ageing study in India (LASI), Wave 1, conducted during 2017–2018. Descriptive statistics along with bivariate analysis was used in the present study to reveal the initial results. Binary logistic regression analysis was used to estimate the association between the outcome variables (communicable and non-communicable disease) and the chosen set of separate explanatory variables. For measurement of socioeconomic inequality, concentration curve and concentration index along with state wise poor-rich ratio was calculated. Additionally, Wagstaff’s decomposition of the concentration index approach was used to reveal the contribution of each explanatory variable to the measured health inequality (Communicable and non- communicable disease). The study finds the prevalence of communicable and non-communicable disease among older adults were 24.9% and 45.5% respectively. The prevalence of communicable disease was concentrated among the poor whereas the prevalence of NCDs was concentrated among the rich older adults, but the degree of inequality is greater in case of NCD. The CI for NCD is 0.094 whereas the CI for communicable disease is -0.043. Economic status, rural residence are common factors contributing inequality in both diseases; whereas BMI and living environment (house type, drinking water source and toilet facilities) have unique contribution in explaining inequality in NCD and communicable diseases respectively. This study significantly contributes in identifying the dichotomous concentration of disease prevalence and contributing socio- economic factors in the inequalities.

: Background: In an ageing society, the provision of long-term care is the prime need. In Indian cultural setting, family members are the informal, albeit primary caregivers to the elderly. Caregiving demands intense emotional and financial involvement. While taking care of elderly persons’ health and wellbeing, these family members, acting as informal caregivers, may themselves become vulnerable to poor health due to additional stress and burden. Using a nationally representative survey, the study tried to identify how health condition varies within caregivers and a comparative analysis of how in similar socio-economic background health condition varies between caregivers and non-caregivers. Method: The data, used for the analysis, is taken from Longitudinal Ageing Study in India (LASI), Wave I. Both descriptive and multivariable regression analysis are done in different models along with interaction effect of caregiving to understand the difference in health status between caregiver and non-caregivers. Results: Nearly 29% and 11% of the informal caregivers, reported to have depressive symptoms and poor self-rated health (SRH), respectively. Almost half of the caregivers, who provide care for more than 40 h a week, are diagnosed to have depressive symptoms. They are also at higher risk of having depressive symptoms (AOR 1.59 CI 1.16–2.18) and poor SRH (AOR 1.73 CI 1.11–2.69) than those who invest less than 40 h in a week. In almost every socio-economic condition, caregivers are at a higher risk of having depression and poor health than non-caregivers. Caregivers, who are widowed, live in rural areas or are not satisfied with current living arrangement are more vulnerable to have depressive symptoms. On the other hand, caregivers of age 45–59 years, widowed, male and who live only with their children with spouse absent, have almost 2 times higher odds of poor SRH than non-caregivers. Conclusion: Caregivers are more susceptible to depression and poor self-rated health compared to non-caregivers irrespective of their socio-economic characteristics, only the magnitude of vulnerability varies.