Dataset Information

Life expectancy and disparity: an international comparison of life table data.

ABSTRACT:

Objectives

To determine the contribution of progress in averting premature deaths to the increase in life expectancy and the decline in lifespan variation.

Design

International comparison of national life table data from the Human Mortality Database.

Setting

40 developed countries and regions, 1840-2009.

Population

Men and women of all ages.

Main outcome measure

We use two summary measures of mortality: life expectancy and life disparity. Life disparity is a measure of how much lifespans differ among individuals. We define a death as premature if postponing it to a later age would decrease life disparity.

Results

In 89 of the 170 years from 1840 to 2009, the country with the highest male life expectancy also had the lowest male life disparity. This was true in 86 years for female life expectancy and disparity. In all years, the top several life expectancy leaders were also the top life disparity leaders. Although only 38% of deaths were premature, fully 84% of the increase in life expectancy resulted from averting premature deaths. The reduction in life disparity resulted from reductions in early-life disparity, that is, disparity caused by premature deaths; late-life disparity levels remained roughly constant.

Conclusions

The countries that have been the most successful in averting premature deaths have consistently been the life expectancy leaders. Greater longevity and greater equality of individuals' lifespans are not incompatible goals. Countries can achieve both by reducing premature deaths.

SUBMITTER: Vaupel JW

PROVIDER: S-EPMC3191439 | biostudies-literature | 2011 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Life expectancy and disparity: an international comparison of life table data.

Vaupel James W JW Zhang Zhen Z van Raalte Alyson A AA

BMJ open 20110729 1

<h4>Objectives</h4>To determine the contribution of progress in averting premature deaths to the increase in life expectancy and the decline in lifespan variation.<h4>Design</h4>International comparison of national life table data from the Human Mortality Database.<h4>Setting</h4>40 developed countries and regions, 1840-2009.<h4>Population</h4>Men and women of all ages.<h4>Main outcome measure</h4>We use two summary measures of mortality: life expectancy and life disparity. Life disparity is a m ...[more]

PMID: 22021770

Similar Datasets

Project description:Aims/hypothesisTables reporting life expectancies by common risk factors are available for individuals with type 2 diabetes; however, there is currently no published equivalent for individuals with type 1 diabetes. We aimed to develop a life expectancy table using a recently published simulation model for individuals with type 1 diabetes.MethodsThe simulation model was developed using data from a real-world population of patients with type 1 diabetes selected from the Swedish National Diabetes Register. The following six important risk factors were included in the life table: sex; age; current smoking status; BMI; eGFR; and HbA1c. For each of 1024 cells in the life expectancy table, a synthetic cohort containing 1000 individuals was created, with other risk factors assigned values representative of the real-world population. The simulations were executed for all synthetic cohorts and life expectancy for each cell was calculated as mean survival time of the individuals in the respective cohort.ResultsThere was a substantial variation in life expectancy across patients with different risk factor levels. Life expectancy of 20-year-old men varied from 29.3 years to 50.6 years, constituting a gap of 21.3 years between those with worst and best risk factor levels. In 20-year-old women, this gap was 18.9 years (life expectancy range 35.0-53.9 years). The variation in life expectancy was a function of the combination of risk factor values, with HbA1c and eGFR consistently showing a negative and positive correlation, respectively, with life expectancy at any level combination of other risk factors. Individuals with the lowest level (20 kg/m2) and highest level of BMI (35 kg/m2) had a lower life expectancy compared with those with a BMI of 25 kg/m2. Non-smokers and women had a higher life expectancy than smokers and men, respectively, with the difference in life expectancy ranging from 0.4 years to 2.7 years between non-smokers and smokers, and from 1.9 years to 5.9 years between women and men, depending on levels of other risk factors.Conclusions/interpretationThe life expectancy table generated in this study shows a substantial variation in life expectancy across individuals with different modifiable risk factors. The table allows for rapid communications of risk in an easily understood format between healthcare professionals, health economists, researchers, policy makers and patients. Particularly, it supports clinicians in their discussion with patients about the benefits of improving risk factors.

Project description:BackgroundMeasuring life expectancy and life disparity can assist in comprehending how the COVID-19 pandemic has affected the mortality estimates in the Indian population. The present study aims to study the life expectancy and life disparity at birth at the national and subnational levels before and during the COVID-19 pandemic using the NFHS and SRS data.MethodsThe measures Life expectancy at birth ([Formula: see text]) and Life disparity at birth ([Formula: see text]) were computed for the non-pandemic and pandemic years from NFHS (2015-16), SRS (2015) and NFHS (2019-21), SRS (2020) respectively at the national and Subnational level in India. Using NFHS data for the 36 states and SRS data for the 22 states, the study calculates [Formula: see text] and [Formula: see text] by total, male and female population.ResultsThe [Formula: see text] for male and female decline from 64.3 years and 69.2 years in 2015-16 to 62.9 years and 68.9 years in 2019-21. The [Formula: see text] shows a drop of approximately 1.4 years for males and 0.3 years for females in the pandemic year 2019-21 when compared to the non-pandemic year 2015-16. At the subnational level [Formula: see text] shows a decline for 22 states in person, 23 states in males and 21 states in females in the pandemic year 2019-21 as compared to the non-pandemic years 2015-16. The [Formula: see text] shows a increase for 21 states in person, 24 states in females and 17 states in males in the pandemic year than non-pandemic year. The findings shows a significant losses in [Formula: see text] and gains in [Formula: see text] for males than females in the pandemic year as compared to the non-pandemic year at the subnational level in India.ConclusionsCOVID-19 pandemic has decreased [Formula: see text] and increased [Formula: see text] in the pandemic year 2019-21 at the national and subnational level in India. COVID-19 had a significant impact on the age pattern of mortality for many states and male, female population and delayed the mortality transition in India.

Dataset Information

Life expectancy and disparity: an international comparison of life table data.

Objectives

Design

Setting

Population

Main outcome measure

Results

Conclusions

Publications

Life expectancy and disparity: an international comparison of life table data.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets