Linking Retirement Age to Life Expectancy Effects on Healthy Life Expectancy Before and After Retirement

Abstract Objective: An Ecological study using open data collected, compiled, and published in government statistics in Japan was conducted. The study aimed to verify the differences in these three measures of healthy life expectancy (HLE), namely disability-free life expectancy without activity limitation (DFLE-AL), life expectancy with self-perceived health (LE-SH), and disability-free life expectancy without care need (DFLE-CN), in relation to appropriate policymaking.Results: Using data from 47 prefectures in 2010, 2013, and 2016, the three types of HLE were extended over time. There were strong correlations between DFLE-AL and LE-SH (r = 0.69–0.83) as well as LE and DFLE-CN (r = 0.75–0.98) in both sexes. However, the other correlations were either weak or not significant. Regression analysis examining the association between the aging rate, mortality, the proportion of unhealthy people, and three types of HLE showed that the “subjective unhealthy rate” was significant (the standardized partial regression coefficients = -0.56– -0.34) in models with DFLE-AL and LE-SH as dependent variables. Therefore, DFLE-CN was suggested to be a different indicator from the other two HLEs. The “subjective unhealthy rate” had a significant influence on the prefectural DFLE-AL and LE-SH.

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Can We Really (All) Work Longer?Trends in Healthy Life Expectancy According to Social Stratum in Germany

Comparative Population Studies ◽

10.12765/cpos-2013-03 ◽

2013 ◽

Vol 38 (3) ◽

Author(s):

Rainer Unger ◽

Alexander Schulze

Keyword(s):

Life Expectancy ◽

Panel Study ◽

Age Groups ◽

Healthy Life Expectancy ◽

Retirement Age ◽

Social Differences ◽

Healthy Life ◽

Social Stratum ◽

Political Objective ◽

Health Assets

Against the background of raising the retirement age to 67 years and the associated lengthening of working lifetimes in higher age groups, this article examines the question of the extent to which this political objective is covered by the health assets of the population. Here, we will first trace trends in “healthy” life expectancy among the total population for different points in time 1989, 1999 and 2009 on the basis of the data from the Socio-Economic Panel Study (SOEP) and analyse these against the background of social strata indicators such as income and educational levels. Among others, one significant result is the fact that social differences have a far greater effect on healthy life expectancy than on general life expectancy and that these differences increase further over the course of time. This effect can be found particularly in men. One mandatory uniform working lifetime for all persons would however not do justice to these findings of socially highly unequally distributed life opportunities. Instead, the findings support a flexible arrangement of retirement age limits.

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Comparing the differences in three measures of healthy life expectancy, effects of mortality, and proportions of unhealthy people according to prefectures in Japan: An Ecological study

10.21203/rs.3.rs-22909/v1 ◽

2020 ◽

Author(s):

KAZUYA TAIRA ◽

Soshiro Ogata ◽

Kei Kamide

Keyword(s):

Life Expectancy ◽

Ecological Study ◽

Open Data ◽

The Other ◽

Healthy Life Expectancy ◽

Expectancy Effects ◽

Healthy Life ◽

Disability Free Life Expectancy ◽

Significant Regression ◽

Using Data

Abstract Objective Ecological study using open data collected, compiled, and published in government statistics in Japan was conducted. The study aimed to verify the differences in these three measures of healthy life expectancy (HLE), namely disability-free life expectancy without activity limitation (DFLE-AL), life expectancy with self-perceived health (LE-SH), and disability-free life expectancy without care need (DFLE-CN), in relation to appropriate policymaking. Results Using data from 47 prefectures in 2010, 2013, and 2016, the three types of HLE were extended over time. There were strong correlations between DFLE-AL and LE-SH (r = 0.69–0.83) as well as LE and DFLE-CN (r = 0.75–0.98) in both sexes. However, the other correlations were either weak or not significant. Regression analysis examining the association between the aging rate, mortality, the proportion of unhealthy people and three types of HLE showed that the “subjective unhealthy rate” was significant (the standardized partial regression coefficients = -0.56– -0.34) in models with DFLE-AL and LE-SH as dependent variables. Therefore, DFLE-CN was suggested to be a different indicator from the other two HLEs. The “subjective unhealthy rate” had a significant influence on the prefectural DFLE-AL and LE-SH.

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Do Americans Live Longer and Healthier? Forecasting Healthy Life Expectancy by Including Dynamic Evolutions of Mortality, Health, and Macroeconomic Variables

SSRN Electronic Journal ◽

10.2139/ssrn.2662978 ◽

2014 ◽

Author(s):

Ying Yang ◽

Anja De Waegenaere ◽

Bertrand Melenberg

Keyword(s):

Life Expectancy ◽

Healthy Life Expectancy ◽

Healthy Life ◽

Macroeconomic Variables

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DIAGNOSTICS OF THE CURRENT STATE OF HEALTHCARE IN RUSSIA

Central Russian Journal of Social Sciences ◽

10.22394/2071-2367-2020-15-5-35-55 ◽

2020 ◽

Vol 15 (5) ◽

pp. 35-55

Author(s):

N.P. STARYKH ◽

◽

A.V. EGOROVA ◽

Keyword(s):

Health Care ◽

Life Expectancy ◽

Statistical Data ◽

Secondary Analysis ◽

World Health ◽

Healthy Life Expectancy ◽

Federal State ◽

Sociological Research ◽

Healthy Life ◽

Current State

The purpose of the article is to analyze the current state of healthcare in Russia. Scientific novelty of the study: the authors suggest that the efficiency of the health care system depends on the state of such indicators of public health as life expectancy and healthy life expectancy. Life expectancy is an integrated demographic indicator that characterizes the number of years that a person would live on average, provided that the age-specific mortality rate of a generation would be at the level for which the indicator was calculated throughout life. The indicator ‘healthy life expectancy’ is formed by subtracting the number of years of unhealthy life (due to chronic diseases, disabilities, mental and behavioral disorders, etc.) from the life expectancy indicator. Results: the article presents an analysis of the current state of Russian healthcare based on statistical data provided by the Federal State Statistics Service, the World Health Organization, and world rankings. Attention is focused on the perceptions of Russians about the quality of medical services and Russian healthcare. Conclusions about the current state of health care in Russia are formulated by the authors, based on a secondary analysis of statistical data, as well as data from sociological research presented by leading Russian sociological centers.

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Changing Educational Gradients in Life Expectancy With and Without Disease Among U.S. Older Adults From 2000 to 2010

Innovation in Aging ◽

10.1093/geroni/igaa057.1630 ◽

2020 ◽

Vol 4 (Supplement_1) ◽

pp. 505-505

Author(s):

Matthew Farina ◽

Phillip Cantu ◽

Mark Hayward

Keyword(s):

Older Adults ◽

Life Expectancy ◽

Chronic Diseases ◽

Population Aging ◽

Good Health ◽

Vital Statistics ◽

Healthy Life Expectancy ◽

Healthy Life ◽

Education Group ◽

Education Groups

Abstract Recent research has documented increasing education inequality in life expectancy among U.S. adults; however, much is unknown about other health status changes. The objective of study is to assess how healthy and unhealthy life expectancies, as classified by common chronic diseases, has changed for older adults across education groups. Data come from the Health and Retirement Study and National Vital Statistics. We created prevalence-based life tables using the Sullivan method to assess sex-specific life expectancies for stroke, heart disease, cancer, and arthritis by education group. In general, unhealthy life expectancy increased with each condition across education groups. However, the increases in unhealthy life expectancy varied greatly. While stroke increased by half a year across education groups, life expectancy with diabetes increased by 3 to 4 years. In contrast, the evidence for healthy life expectancy provides mixed results. Across chronic diseases, healthy life expectancy decreased by 1 to 3 years for respondents without a 4-year degree. Conversely, healthy life expectancy increased for the college educated by .5 to 3 years. While previous research shows increases in life expectancy for the most educated, trends in life expectancy with chronic conditions is less positive: not all additional years are in lived in good health. In addition to documenting life expectancy changes across education groups, research assessing health of older adults should consider the changing inequality across a variety of health conditions, which will have broad implications for population aging and policy intervention.

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