Periodic Reporting for period 4 - LIFEINEQ (Lifespan Inequalities: Why the age-at-death distribution varies between countries and socioeconomic groups)
Période du rapport: 2022-01-01 au 2023-06-30
Statistical agencies and health researchers require summary metrics of mortality to make sense of the fine-grained information collected at the individual level. These metrics serve four key roles: to set health targets, to compare populations, to uncover emerging threats and to evaluate policy outcomes.
Life expectancy is the most commonly used metric of mortality. It represents the hypothetical average age at death in a population for a given year if death rates were to remain unchanged.
Overlooked in this approach are inequalities in mortality within groups, which are both substantial and changing over time. Lifespan inequality (also known as age-at-death variation) is a metric of mortality difference in age at death between individuals. At the population level, lifespan variation indicates the heterogeneity in population health. This heterogeneity is important to consider when designing health and welfare policies including equitable pension schemes. At the individual level, lifespan variation measures the uncertainty in the timing of death. Economic models have shown that individuals are highly risk averse when it comes to survival, and would prefer to live in a society with a lower life expectancy if they could increase the certainty that they would survive to such an age. While life expectancy captures the magnitude of survival improvement, lifespan inequality is its complement, capturing the equality in survival improvement. A full picture of population health requires us to monitor both.
The LIFEINEQ project is the most comprehensive inquiry to date into the development and causes of lifespan inequality. Specifically LIFEINEQ had four main objectives:
1. To track and forecast the relationship between life expectancy and lifespan inequality in national populations,
2. To determine the ages and causes of death that drive outlying age patterns of mortality,
3. To analyze the development of lifespan inequality by socioeconomic groups, and,
4. To assess the impact of individual differences in behaviour on lifespan inequality.
All projects uncovered novel results with important policy implications. The first objective addresses the degree to which we need to worry about lifespan inequality. The second and third objectives address how populations and socioeconomic groups differ in lifespan inequality--this comparative perspective allows us to identify best practices in reducing inequalities across populations. The fourth objective identifies the reasons why populations differ in lifespan inequality.
Life expectancy is the most commonly used metric of mortality. It represents the hypothetical average age at death in a population for a given year if death rates were to remain unchanged.
Overlooked in this approach are inequalities in mortality within groups, which are both substantial and changing over time. Lifespan inequality (also known as age-at-death variation) is a metric of mortality difference in age at death between individuals. At the population level, lifespan variation indicates the heterogeneity in population health. This heterogeneity is important to consider when designing health and welfare policies including equitable pension schemes. At the individual level, lifespan variation measures the uncertainty in the timing of death. Economic models have shown that individuals are highly risk averse when it comes to survival, and would prefer to live in a society with a lower life expectancy if they could increase the certainty that they would survive to such an age. While life expectancy captures the magnitude of survival improvement, lifespan inequality is its complement, capturing the equality in survival improvement. A full picture of population health requires us to monitor both.
The LIFEINEQ project is the most comprehensive inquiry to date into the development and causes of lifespan inequality. Specifically LIFEINEQ had four main objectives:
1. To track and forecast the relationship between life expectancy and lifespan inequality in national populations,
2. To determine the ages and causes of death that drive outlying age patterns of mortality,
3. To analyze the development of lifespan inequality by socioeconomic groups, and,
4. To assess the impact of individual differences in behaviour on lifespan inequality.
All projects uncovered novel results with important policy implications. The first objective addresses the degree to which we need to worry about lifespan inequality. The second and third objectives address how populations and socioeconomic groups differ in lifespan inequality--this comparative perspective allows us to identify best practices in reducing inequalities across populations. The fourth objective identifies the reasons why populations differ in lifespan inequality.
LIFEINEQ made considerable progress in documenting and understanding the trends and causes of lifespan inequality. The most important findings of the project include the following:
- Lifespan inequalities are stagnant or increasing among individuals with lower socioeconomic status, even when life expectancy itself is rising. This has been uncovered across many parts of Europe and the United States and seems to hold for different measures of socioeconomic position including education, occupation, income, and area-level deprivation.
- A number of populations experienced sustained periods of increasing lifespan inequality in the past few decades, including the United States, Central and Eastern European countries (CEE), and several Latin American countries (LAC). Typically the causes of these increases are related to midlife mortality crises such as the ongoing opioid crisis in the United States, periods of economic crises and hardship in CEE, and periods of increasing homicide mortality in LAC.
- The empirically negative correlation between life expectancy and lifespan inequality is weakening across high-income countries, particularly when absolute rather than relative inequality is measured. It does not appear to hold at all for subgroups with low socioeconomic status. The correlation is also weak or non-existent among populations undergoing midlife mortality crises.
- In the United States, many of the determinants of mortality from extrinsic causes of death, for instance, influenza, drug overdose, alcohol abuse, HIV/AIDS, Hepatitis C, and suicide show strong patterning by birth cohort, with trailing-edge baby boomers fairing poorly compared to those born before and afterwards.
- There is a disconnect between population-level ranking of lifespan inequality depending on the temporal frame examined, i.e. period (year), cohort (birth year), or cross-sectional average (average exposure to ages at death over the lifetime of those present).
- Ages at death are becoming increasingly distinct across income groups in Finland. We used a statistical overlap measure of the age-at-death distributions from life tables to measure this. This finding is related to both gaps in mean ages at death as well as differences in lifespan variation.
Altogether, LIFEINEQ members published 34 peer-reviewed articles, 4 book or encyclopedia chapters, and 3 PhD dissertations (which were carried out fully or in part while working on the project). The findings above appeared in leading journals of general science, demography, and public health/epidemiology such as Science, Demography, International Journal of Epidemiology and Social Science and Medicine.
In addition to our scientific output, LIFEINEQ members organized numerous training courses at the PhD and early-career researcher stages on demographic methods, published an R-package "LifeIneq" for calculating measures of lifespan variability from life tables, and co-organized an externally-funded workshop on ageing and health in Sub-Saharan Africa, held in Entebbe, Uganda.
Our researchers and results received considerable international media attention, including die Welt, FAZ, el Pais, etc., as well as over social media channels including Twitter.
- Lifespan inequalities are stagnant or increasing among individuals with lower socioeconomic status, even when life expectancy itself is rising. This has been uncovered across many parts of Europe and the United States and seems to hold for different measures of socioeconomic position including education, occupation, income, and area-level deprivation.
- A number of populations experienced sustained periods of increasing lifespan inequality in the past few decades, including the United States, Central and Eastern European countries (CEE), and several Latin American countries (LAC). Typically the causes of these increases are related to midlife mortality crises such as the ongoing opioid crisis in the United States, periods of economic crises and hardship in CEE, and periods of increasing homicide mortality in LAC.
- The empirically negative correlation between life expectancy and lifespan inequality is weakening across high-income countries, particularly when absolute rather than relative inequality is measured. It does not appear to hold at all for subgroups with low socioeconomic status. The correlation is also weak or non-existent among populations undergoing midlife mortality crises.
- In the United States, many of the determinants of mortality from extrinsic causes of death, for instance, influenza, drug overdose, alcohol abuse, HIV/AIDS, Hepatitis C, and suicide show strong patterning by birth cohort, with trailing-edge baby boomers fairing poorly compared to those born before and afterwards.
- There is a disconnect between population-level ranking of lifespan inequality depending on the temporal frame examined, i.e. period (year), cohort (birth year), or cross-sectional average (average exposure to ages at death over the lifetime of those present).
- Ages at death are becoming increasingly distinct across income groups in Finland. We used a statistical overlap measure of the age-at-death distributions from life tables to measure this. This finding is related to both gaps in mean ages at death as well as differences in lifespan variation.
Altogether, LIFEINEQ members published 34 peer-reviewed articles, 4 book or encyclopedia chapters, and 3 PhD dissertations (which were carried out fully or in part while working on the project). The findings above appeared in leading journals of general science, demography, and public health/epidemiology such as Science, Demography, International Journal of Epidemiology and Social Science and Medicine.
In addition to our scientific output, LIFEINEQ members organized numerous training courses at the PhD and early-career researcher stages on demographic methods, published an R-package "LifeIneq" for calculating measures of lifespan variability from life tables, and co-organized an externally-funded workshop on ageing and health in Sub-Saharan Africa, held in Entebbe, Uganda.
Our researchers and results received considerable international media attention, including die Welt, FAZ, el Pais, etc., as well as over social media channels including Twitter.
Does it matter if everyone dies at similar ages, or if ages at death are highly spread out in society? We argue yes. Typically, demographers look at average outcomes, such as life expectancy. This overlooks the variability in age at death, which is considerable and differs across countries and socioeconomic groups.
The LIFEINEQ project produced robust scientific findings that underscore the policy relevance of monitoring lifespan inequality, which is currently not undertaken by any major statistical agency in the world. In addition, the LIFEINEQ project made substantial methodological contributions that help us to understand the changing mortality patterning across the different temporal dimensions of age, period and cohort (generation). We also derived the mathematical properties of several indices of lifespan inequality.
The LIFEINEQ project produced robust scientific findings that underscore the policy relevance of monitoring lifespan inequality, which is currently not undertaken by any major statistical agency in the world. In addition, the LIFEINEQ project made substantial methodological contributions that help us to understand the changing mortality patterning across the different temporal dimensions of age, period and cohort (generation). We also derived the mathematical properties of several indices of lifespan inequality.