A distributional approach to measuring lifespan stratification

Shi, Jiaxin; Aburto, José Manuel; Martikainen, Pekka; Tarkiainen, Lasse; Raalte, Alyson A. van

doi:10.1080/00324728.2022.2057576

Cited by 10 publications

(15 citation statements)

References 86 publications

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“…Other measures of overlap and distance between distributions could have been used. In the online supplemental materials, we compare the outsurvival statistic with a stratification index used by Shi and colleagues 20 and the KL divergence. We found that all three indicators are Open access strongly correlated and using any one of these would not have changed the general conclusions from this article.…”

Section: Discussionmentioning

confidence: 99%

“…A disadvantage of this indicator is that it is not symmetrical, meaning that the effort needed to transform the male’s distribution into the female’s is not the same as the effort needed to transform the female’s distribution into the male’s. Stratification indexes, based on how much two lifespan distributions overlap or do not overlap, have also been used to study mortality differences between socioeconomic groups 20. The larger the overlap, the more likely the individuals in two populations are to survive to the same age.…”

Section: Introductionmentioning

confidence: 99%

“…Stratification indexes, based on how much two lifespan distributions overlap or do not overlap, have also been used to study mortality differences between socioeconomic groups. 20 The larger the overlap, the more likely the individuals in two populations are to survive to the same age. This index is meant to reflect unequal distribution at the societal level, with values varying between 0 (no overlap) and 1 (perfect overlap).…”

Section: Introductionmentioning

confidence: 99%

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Probability of males to outlive females: an international comparison from 1751 to 2020

et al. 2022

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ObjectiveTo measure sex differences in lifespan based on the probability of males to outlive females.DesignInternational comparison of national and regional sex-specific life tables from the Human Mortality Database and the World Population Prospects.Setting199 populations spanning all continents, between 1751 and 2020.Primary outcome measureWe used the outsurvival statistic ( φ ) to measure inequality in lifespan between sexes, which is interpreted here as the probability of males to outlive females.ResultsIn random pairs of one male and one female at age 0, the probability of the male outliving the female varies between 25% and 50% for life tables in almost all years since 1751 and across almost all populations. We show that φ is negatively correlated with sex differences in life expectancy and positively correlated with the level of lifespan variation. The important reduction of lifespan inequality observed in recent years has made it less likely for a male to outlive a female.ConclusionsAlthough male life expectancy is generally lower than female life expectancy, and male death rates are usually higher at all ages, males have a substantial chance of outliving females. These findings challenge the general impression that ‘men do not live as long as women’ and reveal a more nuanced inequality in lifespans between females and males.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Probability of males to outlive females: an international comparison from 1751 to 2020

et al. 2022

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“…However, this approach can hide important differences in the underlying mortality patterns, and therefore provide an incomplete or biased assessment of socioeconomic inequalities in mortality. For example, one recent study showed that comparing distributional differences in mortality of groups defined by income quintiles in Finland revealed different patterns of mortality inequality than those derived from commonly used measures (Shi et al, 2023). Assessing socioeconomic inequalities in mortality using the full age-at-death distribution seems theoretically preferable, as it allows to consider all dimensions of inequality simultaneously rather than a single dimension only (e.g.…”

Section: Introductionmentioning

confidence: 99%

Reassessing socioeconomic inequalities in mortality via distributional similarities

Gómez Ugarte Valerio,

Basellini,

Camarda

et al. 2024

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“…Moreover, in line with the studies on short-term trends, the studies on long-term trends in socioeconomic inequalities in mortality primarily have examined the overall trends from the first to the last observations, without paying particular attention to trend breaks or changes in between 1 2 10–13. Furthermore, most of these studies, including those that analysed trends over a longer period,1 3 10 11 14–18 have used data aggregated over periods of 5 or 10 years, instead of data by a single calendar year. Overall, these studies have shown increases in relative educational inequalities in all-cause mortality,3 declines in absolute educational inequalities in all-cause mortality3 12 and increases in educational inequalities in life expectancy2 4 7 for the European countries they have studied.…”

Section: Introductionmentioning

confidence: 99%

Reversals in past long-term trends in educational inequalities in life expectancy for selected European countries

Borboa

Martikainen

Aburto

et al. 2023

J Epidemiol Community Health

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BackgroundAcross Europe, socioeconomic inequalities in mortality are large and persistent. To better understand the drivers of past trends in socioeconomic mortality inequalities, we identified phases and potential reversals in long-term trends in educational inequalities in remaining life expectancy at age 30 (e30), and assessed the contributions of mortality changes among the low-educated and the high-educated at different ages.MethodsWe used individually linked annual mortality data by educational level (low, middle and high), sex and single age (30+) from 1971/1972 onwards for England and Wales, Finland and Italy (Turin). We applied segmented regression to trends in educational inequalities in e30 (e30 high-educated minus e30 low-educated) and employed a novel demographic decomposition technique.ResultsWe identified several phases and breakpoints in the trends in educational inequalities in e30. The long-term increases (Finnish men, 1982–2008; Finnish women, 1985–2017; and Italian men, 1976–1999) were driven by faster mortality declines among the high-educated aged 65–84, and by mortality increases among the low-educated aged 30–59. The long-term decreases (British men, 1976–2008, and Italian women, 1972–2003) were driven by faster mortality improvements among the low-educated than among the high-educated at age 65+. The recent stagnation of increasing inequality (Italian men, 1999) and reversals from increasing to decreasing inequality (Finnish men, 2008) and from decreasing to increasing inequality (British men, 2008) were driven by mortality trend changes among the low-educated aged 30–54.ConclusionEducational inequalities are plastic. Mortality improvements among the low-educated at young ages are imperative for achieving long-term decreases in educational inequalities in e30.

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A distributional approach to measuring lifespan stratification

Cited by 10 publications

References 86 publications

Probability of males to outlive females: an international comparison from 1751 to 2020

Probability of males to outlive females: an international comparison from 1751 to 2020

Reassessing socioeconomic inequalities in mortality via distributional similarities

Reversals in past long-term trends in educational inequalities in life expectancy for selected European countries

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