Which Factors do Older Adults Consider When Estimating the Time Left for Them to Live?

Palgi, Yuval; Shrira, Amit; Kavé, Gitit; Rubinstein, Tom; Shmotkin, Dov

doi:10.1093/geronb/gby048

Cited by 14 publications

(11 citation statements)

References 22 publications

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“…But how are subjective survival assessments constructed? Aside from one's own health status, they are shaped and constantly updated by the experience of death in one's immediate social circle, particularly the loss of a parent or a spouse (19,35). Because kinship and other social ties run along ethnic and class divisions (36), members of different social groups are effectively exposed to different everyday experiences of mortality among family and friends.…”

Section: Subjective Survival Expectations and Aggregate Mortalitymentioning

confidence: 99%

The case for monitoring life-span inequality

Raalte

Sasson

Martikainen

2018

Science

150

163

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Inequality in length of life is the most fundamental of all inequalities; every other type of inequality is conditional upon being alive. As has long been recognized in studies of economic inequality, we can compare populations based on per capita gross national income, but there is a pressing need to further examine how income varies within populations via Gini coefficients and percentile-based metrics. Mortality inequalities should be approached in the same way. Human population health is generally monitored by average mortality levels, typically in terms of life expectancies, which belie substantial variation in length of life. Variation in ages at death, captured by a metric of lifespan variation, should be used to supplement measures of average longevity when comparing or monitoring societies and population subgroups (1). Although lifespan variation has historically been strongly inversely correlated with life expectancy (2, 3), we are beginning to see this relationship reversed, resulting in positive correlation in some countries or subnational populations. Often these changes reflect midlife mortality crises with roots in stratified education and wealth. We discuss these measures and trends and how they can have profound implications for how individuals might plan and live their lives, and for how societies might organize and manage health care, insurance, pensions, and other social policies and programs. Life expectancy at birth (or simply life expectancy, as we refer to it in the rest of the text) is the most common metric of survival. It is the hypothetical average age at death given age-specific death rates in a given year. Lifespan variation, the variability in ages at death around that average, can be measured by using an index of variation or inequality-for instance, the standard deviation, Gini coefficient, or interquartile range. To illustrate, consider age-at-death distributions of non-Hispanic black and non-Hispanic white men in the United States based on 2012-2016 death rates. The life expectancy from this distribution is 72 years for blacks and 77 years for whites [see supplementary materials (SM)]. But the timing of death was variable, skewed below the average in both groups, meaning that deaths were more spread out below the life expectancy than above it. Among blacks, the spread in survival was noticeably wider. Men in the 25th to 75th percentile (the interquartile range) died between 63 and 85 years in the black distribution, whereas those in the white distribution died between 69 and 88 years. Although life expectancy for blacks was only 6% lower than for whites, the age window over which these deaths occurred was 17% larger for blacks. Some early efforts by the Organization for Economic Cooperation and Development (OECD) to monitor within-group variability included a one-off report that measured lifespan variation conditional upon survival to age 10 (4). However, although we currently monitor life expectancy at birth in all countries of the world, which captures between-country differenc...

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Section: Subjective Survival Expectations and Aggregate Mortalitymentioning

confidence: 99%

The case for monitoring life-span inequality

Raalte

Sasson

Martikainen

2018

Science

150

163

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“…However, we could not control for characteristics of the care recipients who are cared for by the informal caregivers in our sample (e.g., age and type and degree of health impairment of care recipient), which can play a relevant role for the outcomes in caregivers [60]. Also, the importance that is ascribed to different aspects that are used to evaluate subjective life expectancy can differ in individuals [8]. Thus, adult child caregivers may ascribe more importance to parental longevity for their own life expectancy than spousal caregivers.…”

Section: Discussionmentioning

confidence: 99%

Do Informal Caregivers Expect to Die Earlier? A Longitudinal Study with a Population-Based Sample on Subjective Life Expectancy of Informal Caregivers

2021

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Introduction: Subjective life expectancy is a good predictor of health and could therefore be a relevant factor in the informal caregiving context. However, no research has been conducted on the perception of life expectancy by informal caregivers. This is the first study that examines the association between transitioning into, and out of, informal caregiving, and subjective life expectancy, and the relevance of employment status and gender for these associations. Methods: A longitudinal study was conducted with data from the German Ageing Survey (waves 2008, 2011, 2014, and 2017). Up to 20,774 observations pooled over all waves were included in the main models. In total, 1,219 transitions into and 1,198 transitions out of informal caregiving were observed. Fixed effects (FE) regression analysis was used. Moderator and stratified analyses were conducted with gender and employment status used as moderator variables and to stratify the sample. Sociodemographic information, health, and lifestyle factors were controlled for. Results: Results of adjusted FE regression analyses indicated a significant reduction of subjective life expectancy when transitioning into informal caregiving. No significant change was found when transitioning out of informal caregiving. Subjective life expectancy was significantly decreased when employed individuals transitioned into informal caregiving and significantly increased when they transitioned out of caregiving. Findings for women transitioning into informal caregiving indicated a significant decrease in subjective life expectancy, while no significant change was found among men. Conclusion: The study’s findings indicate that informal caregivers, female and employed caregivers in particular, perceive informal care provision as dangerous for their longevity and expect to die earlier when transitioning into informal caregiving. Thus, supportive interventions for informal caregivers, particularly employed and female informal caregivers, are recommended.

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“…Although our study confirms that older adults’ subjective lifetime perspectives are associated with health, all of the associations were rather weak. This suggests that other factors are more important in shaping each perspective, such as optimism, living arrangements, and memory functioning ( Palgi et al, 2019 ). Furthermore, based on our results, we cannot conclude that health is more closely linked to one perspective over another.…”

Section: Discussionmentioning

confidence: 99%

“…Studies providing evidence of the association of at least two health measures with SLE took place among older people in England, 18 European countries jointly, and the United States ( Benítez-Silva & Ni, 2008 ; Kobayashi et al, 2017 ; Palgi et al, 2019 ; Papachristos et al, 2020 ). They again show the strongest associations for self-rated health, followed by disability.…”

Section: Introductionmentioning

confidence: 99%

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Capturing Subjective Age, Subjective Life Expectancy, and Their Links With Older Adults’ Health: The Dutch Longitudinal Aging Study Amsterdam

2021

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Objectives: This study compares the associations of two subjective lifetime perspectives, subjective age (SA) and subjective life expectancy (SLE), with physical performance, self-rated health, and depressive symptoms. Methods: 64 91-year-old participants were selected from three waves of the Longitudinal Aging Study Amsterdam (2008/09, 2011/12, 2015/16; n = 1822 participants, n = 3500 observations) that included graphical and numerical measures of SA and SLE. We used generalized estimating equations to examine their associations with health. Results: Associations of SA/SLE with health were weaker for physical performance than for self-rated health and depressive symptoms. The associations of SA and SLE with physical performance were of similar magnitude but with self-rated health depended on the type of measure. Depressive symptoms, instead, showed a stronger association with SA than with SLE. Graphical measures showed weaker associations than numerical measures. Discussion: The way in which subjective lifetime perspectives and health are conceptualized and measured influences the strength of their associations.

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Which Factors do Older Adults Consider When Estimating the Time Left for Them to Live?

Abstract: Older adults appear to have implicit knowledge of the factors that affect their SNtD. Yet, this knowledge is sometimes biased and does not necessarily represent variables that have been identified as related to actual longevity.

Cited by 14 publications

References 22 publications

The case for monitoring life-span inequality

The case for monitoring life-span inequality

Do Informal Caregivers Expect to Die Earlier? A Longitudinal Study with a Population-Based Sample on Subjective Life Expectancy of Informal Caregivers

Capturing Subjective Age, Subjective Life Expectancy, and Their Links With Older Adults’ Health: The Dutch Longitudinal Aging Study Amsterdam

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