A Cause-of-Death Decomposition of Young Adult Excess Mortality

Abstract

We propose a method to decompose the young adult mortality hump by cause of death. This method is based on a flexible shape decomposition of mortality rates that separates cause-of-death contributions to the hump from senescent mortality. We apply the method to U.S. males and females from 1959 to 2015. Results show divergence between time trends of hump and observed deaths, both for all-cause and cause-specific mortality. The study of the hump shape reveals age, period, and cohort effects, suggesting that it is formed by a complex combination of different forces of biological and socioeconomic nature. Male and female humps share some traits in all-cause shape and trend, but they also differ by their overall magnitude and cause-specific contributions. Notably, among males, the contributions of traffic and other accidents were progressively replaced by those of suicides, homicides, and poisonings; among females, traffic accidents remained the major contributor to the hump.Electronic supplementary materialThe online version of this article (10.1007/s13524-018-0680-9) contains supplementary material, which is available to authorized users.

Geofaceting: Aligning small-multiples for regions in a spatially meaningful way

“…Gap between observed and best-practice life expectancy for Mexican states: Cause of death contributing the most by age (15-49) and time Cause of death contributing the most by age (15-49) and time Gap between observed and best-practice temporary life expectancy for Mexican males (15-49) To enrich the plot with geofaceted Lexis surfaces (Figure 2), we use ternary colorcoding of the three main groups of causes of death: homicides, road traffic, and suicides, and all other causes combined (Figure 3). These causes of death are known to be the main contributors in midlife and through the young mortality hump (Remund, Camarda, and Riffe 2018). This plot deepens the previous one by representing the relative importance of the two main causes of death compared with all others combined.…”

Lexis fields

“…Other strategies include the composite surfaces of Schöley and Willekens (2017) and the APC curvature plots of Acosta and van Raalte (2019). Small multiples of Lexis surfaces (for example, panel plots of Lexis surfaces), on the other hand, constitute a de-layering (e.g., Remund, Camarda, and Riffe 2018;Kashnitsky and Aburto 2019), as these are spatially disjoint. Comparisons between plots require an extra attentive step from the viewer to cross-reference patterns or values at specific coordinates of age and time.…”

“…Surfaces are often displayed as heat maps, contour maps, perspective plots, or variants of these things (Vaupel, Gambill, and Yashin 1987). Various kinds of quantities, such as raw magnitudes, differences (Minton et al 2017), excesses (Remund, Camarda, and Riffe 2018;Acosta and van Raalte 2019), ratios (Canudas-Romo and Schoen 2005), intensities, proportions, derivatives (Rau et al 2017), and even compositions (Schöley and Willekens 2017) can be displayed on Lexis surfaces to put age, period, cohort, or other patterns in relief.…”

APC curvature plots: Displaying nonlinear age-period-cohort patterns on Lexis plots

“…Several methods are available to estimate a baseline from which it is possible to obtain curvature in vital rates. These approaches include applying interpolation techniques (Camarda 2012), extracting the irregularity using decomposition techniques (Remund, Camarda, and Riffe 2018), using detrended APC models (Chauvel, Leist, and Smith 2017), or simply detrending the smoothed vital rates over the selected perspective of change (i.e., over age, period, or cohort). There is no ideal generic method that can be applied because each demographic phenomenon and research question has specific underlying hypotheses that should be accounted for.…”