1.1 The biodemography of human aging within social contexts
Mortality in human populations has been delayed substantially in recent
years (Vaupel, 2010). Senescence, the process of aging, has been slowed
down or postponed largely due to progress in public-health efforts,
access to education, socioeconomic mobility, and cultural changes in
lifestyles (Achey, 2016; Cundiff et al., 2017; Oeppen and Vaupel, 2002;
Riley, 2001; Vable et al., 2019), rather than strong selection on
genetic factors (Christensen et al., 2006; Hjelmborg et al., 2006; McGue
et al., 1993). Yet, accumulating evidence shows that there are
persistent health inequalities within today’s aging populations as well,
highlighting the importance of mechanistic questions regarding how and
why variability in the aging process across individuals emerges and is
maintained, and how individual sociality and health influences such
process (Crimmins and Vasunilashorn, 2016; Gutin and Hummer, 2021).
The emerging field of biodemography integrates such sociality factors in
moving towards a full understanding of the evolutionary roots of health
and aging (Hooper et al. 2014). In particular, the biodemography of
aging incorporates biological theory and methods on ecological and
evolutionary processes with traditional demographic approaches to better
understand the dynamics of health and mortality within populations
(Baudisch, 2015; Carnes, 2007; Christensen, 2008; Gavrilov and
Gavrilova, 2015; Vaupel, 2004; Wachter, 2008; Yashin et al., 2016).
Biodemography also incorporates theories of life history trade-offs,
allowing us to quantify dynamics between survival and reproduction
(Tuljapurkar et al., 2020). This interdisciplinarity encourages an
alliance between the social and the biological sciences that expands
beyond traditional demographic structures (e.g., age, race/ethnicity,
socioeconomic status) as it provides novel opportunities to address how
these structures are linked to the underlying pathways that modulate
health (Arbeev et al., 2019; Crimmins and Vasunilashorn, 2016; Giuliani
et al., 2018; Palma-Gudiel et al., 2020). These advances occurred in
concert with work by social scientists who incorporated a life course
perspective into studies of health inequality (Dannefer, 2003, 1987;
Ferraro et al., 2009; Ferraro and Shippee, 2009; Morton and Ferraro,
2020). In just the past few years, evidence has continued to accumulate
concerning the role that structural inequality (e.g., discrimination,
racism) and associated factors shaping the social environment (e.g.,
poverty, stress) have on major disparities in health (e.g., Jackson and
Engelman, 2022; Morton and Ferraro, 2020; Noren Hooten et al., 2022;
Sauerteig et al., 2022; Williams et al., 2019). Such evidence shapes
paradigms in biodemography and the social sciences (e.g., “the
gerontological imagination”, Ferraro 2018). It is now time to parallel
these efforts with methods for the quantification of the effect of
individual sociality on health phenotypes, and how these associations
translate into the evolutionary dynamics of human aging.