Understanding the Rise in Life Expectancy Inequality

Dahl, Gordon B.; Kreiner, Claus Thustrup; Nielsen, Torben Heien; Serena, Benjamin Ly

doi:10.1162/rest_a_01148

Cited by 4 publications

(2 citation statements)

References 36 publications

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“…It is well established that individuals with higher income are more likely to have better living conditions, healthier behaviors, and access to better healthcare, which together lead to better health and longer life. Generally speaking, healthcare is widely accessible in Denmark, leaving behavioral factors as the main explanation for inequalities in mortality across SES groups (Dahl et al, 2021). At the same time, individuals in better health, including mental health, are also more likely to work longer, accumulate more wealth, and consequently have higher incomes at an age when those who are less healthy are more likely to have already retired (Dowd & Hamoudi, 2014;Tetzlaff et al, 2020).…”

Section: Resultsmentioning

confidence: 99%

Socioeconomic inequalities in survival to retirement age: a register-based analysis

Strozza¹,

Vigezzi²,

Callaway³

et al. 2022

Preprint

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Larger numbers of people are living to retirement and old age, posing a threat to the financing of the welfare state. In Denmark, statutory retirement age is increasing gradually to account for changes in life expectancy. However, the chances of reaching retirement age are not equal across the population, and raising the retirement age could disproportionally affect those from lower socioeconomic backgrounds. In this paper, we investigate socioeconomic inequalities in mortality before retirement age or shortly thereafter in Denmark. We use Danish registry data over a 30-year period, focusing on individuals aged 50 to 70. We perform sex-specific survival analyses across socioeconomic groups using three measures of socioeconomic status: education, income, and occupation. We observe an increase in survival inequalities over time between the lowest and highest socioeconomic groups for each measure. The reason for the widening gap lies in minimal mortality improvements in the lowest socioeconomic group. These results are complemented by lifespan inequality measures, which have the same mortality trends. We show that individual level variability in socioeconomic characteristics play a crucial role in defining the survival chances just before and shortly after retirement and thus should be accounted for in designing retirement policies.

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Section: Resultsmentioning

confidence: 99%

Socioeconomic inequalities in survival to retirement age: a register-based analysis

Strozza¹,

Vigezzi²,

Callaway³

et al. 2022

Preprint

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“…Alterations that are exhibited throughout life, including lifestyle, mobility, nutritional changes, chronic consumption of medications, and change in residential status, may impact the aging process, which in turn impacts life expectancy [ 6 ]. This concept is illustrated by the variation in life expectancy across countries [ 7 , 8 , 9 , 10 , 11 , 12 ]. For example, due to lifestyle-related factors, the average lifespan in the United States is amongst the lowest in the developed world [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Utilization of Host and Microbiome Features in Determination of Biological Aging

et al. 2022

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The term ‘old age’ generally refers to a period characterized by profound changes in human physiological functions and susceptibility to disease that accompanies the final years of a person’s life. Despite the conventional definition of old age as exceeding the age of 65 years old, quantifying aging as a function of life years does not necessarily reflect how the human body ages. In contrast, characterizing biological (or physiological) aging based on functional parameters may better reflect a person’s temporal physiological status and associated disease susceptibility state. As such, differentiating ‘chronological aging’ from ‘biological aging’ holds the key to identifying individuals featuring accelerated aging processes despite having a young chronological age and stratifying them to tailored surveillance, diagnosis, prevention, and treatment. Emerging evidence suggests that the gut microbiome changes along with physiological aging and may play a pivotal role in a variety of age-related diseases, in a manner that does not necessarily correlate with chronological age. Harnessing of individualized gut microbiome data and integration of host and microbiome parameters using artificial intelligence and machine learning pipelines may enable us to more accurately define aging clocks. Such holobiont-based estimates of a person’s physiological age may facilitate prediction of age-related physiological status and risk of development of age-associated diseases.

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Regional Heterogeneity in the Link between Lifetime Earnings and Life Expectancy

Glaubitz

2023

SSRN Journal

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Understanding the Rise in Life Expectancy Inequality

Cited by 4 publications

References 36 publications

Socioeconomic inequalities in survival to retirement age: a register-based analysis

Socioeconomic inequalities in survival to retirement age: a register-based analysis

Utilization of Host and Microbiome Features in Determination of Biological Aging

Regional Heterogeneity in the Link between Lifetime Earnings and Life Expectancy

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