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.