Alyson A. van Raalte

Perturbation Analysis of Indices of Lifespan Variability

A number of indices exist to calculate lifespan variation, each with different underlying properties. Here, we present new formulae for the response of seven of these indices to changes in the underlying mortality schedule (life disparity, Gini coefficient, standard deviation, variance, Theil’s index, mean logarithmic deviation, and interquartile range). We derive each of these indices from an absorbing Markov chain formulation of the life table, and use matrix calculus to obtain the sensitivity and the elasticity (i.e., the proportional sensitivity) to changes in age-specific mortality. Using empirical French and Russian male data, we compare the underlying sensitivities to mortality change under different mortality regimes to determine the conditions under which the indices might differ in their conclusions about the magnitude of lifespan variation. Finally, we demonstrate how the sensitivities can be used to decompose temporal changes in the indices into contributions of age-specific mortality changes. The result is an easily computable method for calculating the properties of this important class of longevity indices.

The contribution of educational inequalities to lifespan variation

BackgroundStudies of socioeconomic inequalities in mortality consistently point to higher death rates in lower socioeconomic groups. Yet how these between-group differences relate to the total variation in mortality risk between individuals is unknown.MethodsWe used data assembled and harmonized as part of the Eurothine project, which includes census-based mortality data from 11 European countries. We matched this to national data from the Human Mortality Database and constructed life tables by gender and educational level. We measured variation in age at death using Theils entropy index, and decomposed this measure into its between- and within-group components.ResultsThe least-educated groups lived between three and 15 years fewer than the highest-educated groups, the latter having a more similar age at death in all countries. Differences between educational groups contributed between 0.6% and 2.7% to total variation in age at death between individuals in Western European countries and between 1.2% and 10.9% in Central and Eastern European countries. Variation in age at death is larger and differs more between countries among the least-educated groups.ConclusionsAt the individual level, many known and unknown factors are causing enormous variation in age at death, socioeconomic position being only one of them. Reducing variations in age at death among less-educated people by providing protection to the vulnerable may help to reduce inequalities in mortality between socioeconomic groups.

Lifespan variation by occupational class: compression or stagnation over time?

Cross-sectional analyses of adult lifespan variation have found an inverse association between socioeconomic position and lifespan variation, but the trends by social class are unknown. We investigated trends in lifespan variation over four decades (1971–2010) by occupational social class (manual, lower nonmanual, upper nonmanual, other) using Finnish register data. We performed age and cause-of-death decompositions of lifespan variation for each sex (a) by occupational class over time and (b) between occupational classes at a shared level of life expectancy. Although life expectancy increased in all classes, lifespan variation was stable among manual workers and decreased only among nonmanual classes. These differences were caused by early-adult mortality: older-age lifespan variation declined for all the classes, but variation in early-adult mortality increased for all classes except the highest. The manual class’s high and stagnant lifespan variation was driven by declines in circulatory diseases that were equally spread over early mortality-compressing and older mortality-expanding ages, as well as by high early-adult mortality from external causes. Results were similar for men and women. The results of this study, which is the first to document trends in lifespan variation by social class, suggest that mortality compression is compatible with increasing life expectancy but currently achieved only by higher occupational classes.

The German East-West Mortality Difference: Two Crossovers Driven by Smoking

Before the fall of the Berlin Wall, mortality was considerably higher in the former East Germany than in West Germany. The gap narrowed rapidly after German reunification. The convergence was particularly strong for women, to the point that Eastern women aged 50–69 now have lower mortality despite lower incomes and worse overall living conditions. Prior research has shown that lower smoking rates among East German female cohorts born in the 1940s and 1950s were a major contributor to this crossover. However, after 1990, smoking behavior changed dramatically, with higher smoking intensity observed among women in the eastern part of Germany. We forecast the impact of this changing smoking behavior on East-West mortality differences and find that the higher smoking rates among younger East German cohorts will reverse their contemporary mortality advantage. Mortality forecasting methods that do not account for smoking would, perhaps misleadingly, forecast a growing mortality advantage for East German women. Experience from other countries shows that smoking can be effectively reduced by strict anti-smoking policies. Instead, East Germany is becoming an example warning of the consequences of weakening anti-smoking policies and changing behavioral norms.

Correction to: Perturbation Analysis of Indices of Lifespan Variability

We discovered an error in Eq. (12) (p. 1621).

Decomposing Current Mortality Differences Into Initial Differences and Differences in Trends: The Contour Decomposition Method

This study proposes a new decomposition method that permits a difference in an aggregate measure at a final time point to be split into additive components corresponding to the initial differences in the event rates of the measure and differences in trends in these underlying event rates. For instance, when studying divergence in life expectancy, this method allows researchers to more easily contrast age-specific mortality trends between populations by controlling for initial age-specific mortality differences. Two approaches are assessed: (1) an additive change method that uses logic similar to cause-of-death decomposition, and (2) a contour decomposition method that extends the stepwise replacement algorithm along an age-period demographic contour. The two approaches produce similar results, but the contour method is more widely applicable. We provide a full description of the contour replacement method and examples of its application to life expectancy and lifetime disparity differences between the United States and England and Wales in the period 1980–2010.