The case for monitoring life-span inequality

Abstract

Inequality in length of life is the most fundamental of all inequalities; every other type of inequality is conditional upon being alive. As has long been recognized in studies of economic inequality, we can compare populations based on per capita gross national income, but there is a pressing need to further examine how income varies within populations via Gini coefficients and percentile-based metrics. Mortality inequalities should be approached in the same way. Human population health is generally monitored by average mortality levels, typically in terms of life expectancies, which belie substantial variation in length of life. Variation in ages at death, captured by a metric of lifespan variation, should be used to supplement measures of average longevity when comparing or monitoring societies and population subgroups (1). Although lifespan variation has historically been strongly inversely correlated with life expectancy (2, 3), we are beginning to see this relationship reversed, resulting in positive correlation in some countries or subnational populations. Often these changes reflect midlife mortality crises with roots in stratified education and wealth. We discuss these measures and trends and how they can have profound implications for how individuals might plan and live their lives, and for how societies might organize and manage health care, insurance, pensions, and other social policies and programs. Life expectancy at birth (or simply life expectancy, as we refer to it in the rest of the text) is the most common metric of survival. It is the hypothetical average age at death given age-specific death rates in a given year. Lifespan variation, the variability in ages at death around that average, can be measured by using an index of variation or inequality-for instance, the standard deviation, Gini coefficient, or interquartile range. To illustrate, consider age-at-death distributions of non-Hispanic black and non-Hispanic white men in the United States based on 2012-2016 death rates. The life expectancy from this distribution is 72 years for blacks and 77 years for whites [see supplementary materials (SM)]. But the timing of death was variable, skewed below the average in both groups, meaning that deaths were more spread out below the life expectancy than above it. Among blacks, the spread in survival was noticeably wider. Men in the 25th to 75th percentile (the interquartile range) died between 63 and 85 years in the black distribution, whereas those in the white distribution died between 69 and 88 years. Although life expectancy for blacks was only 6% lower than for whites, the age window over which these deaths occurred was 17% larger for blacks. Some early efforts by the Organization for Economic Cooperation and Development (OECD) to monitor within-group variability included a one-off report that measured lifespan variation conditional upon survival to age 10 (4). However, although we currently monitor life expectancy at birth in all countries of the world, which captures between-country differenc...

A Decomposition of Life Expectancy and Life Disparity: Comparison Between Hong Kong and Japan

“…This study has again confirmed that different levels of life disparity can be found in societies with similar levels of life expectancy. 1,25,26 Based on life tables for 212 countries, Smits and Monden argued that countries that achieved a certain level of life expectancy earlier in time were more likely to experience higher levels of inequality. 1 Nevertheless, the study conducted by Seaman and colleagues has shown some opposite findings: Scotland, which caught up with England and Wales in life expectancy, had higher lifespan variations, because of its lower older age mortality yet higher premature adult age mortality.…”

“…Such reversal is possible if deaths at old ages are further reduced or delayed, while mortality rates at younger age and midlife stall or start to rise. 26 Previous studies have found the stagnation or increase in mortality at younger ages might be related with various socioeconomic factors, contributing to the differences in lifespan variation, particularly among some subpopulations. 27,28 Therefore, policies related to the justification of social arrangements should be in place.…”

Dynamics of life expectancy and life span equality

“…Life span equality matters because it captures a fundamental type of inequality: variation in length of life. This variation is not revealed by life expectancy and other measures of average mortality levels (12). Two populations that share the same level of life expectancy could experience substantial differences in the timing of death, e.g., deaths could be more evenly spread over age in one population than another.…”

Pursuing the limits of child survival in the most and least developed countries

“…Compared to other forms of inequality, age-inequality has been characterized as the most fundamental because it is not conditional upon survivorship and mortality is the ultimate indicator of health (Tuljapurkar 2010;Raalte, Sasson, and Martikainen 2018). The association of socioeconomic status (SES)-or income-and health/mortality has been of great interest for quite a time, and some hypotheses-absolute-/relative-income and income-inequality hypotheses (Wilkinson 1996(Wilkinson , 1997)-have emerged that provided supporting evidence on the positive relationship between the distribution of income and health-although mixed findings have also been reported in different studies (Wilkinson and Pickett 2006), the shape of that association, and the extent to which higher differentials in the income distribution affect the health of the whole population rather than just disadvantaged groups (Preston 1975 Our study builds on previous research that analyzed inequality in the length of life and convergence of mortality across the whole life span, but that emphasized mortality patterns at adult/old ages (Shkolnikov, Andreev, and Begun 2003;Edwards and Tuljapurkar 2005; Engelman, Canudas-Romo, and Agree 2010).…”

“…The importance of the age distribution of under-5 deaths has been underscored by methodological work (Guillot et al 2012) demonstrating that traditional life table models do not adequately approximate child mortality patterns in SSA, and by the use of age profiles of under-5 mortality to forecast mortality rates (Mejía-Guevara et al 2019) for the assessment of Sustainable Development Goals (SDGs) in SSA. In addition, between-and within-variation in the length of life is an important indicator of health inequality (Edwards and Tuljapurkar 2005;Raalte, Sasson, and Martikainen 2018) and then analyses of the differences in the age pattern of under-5 mortality across and within countries should reflect health disparities at young ages.…”