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\begin{document}

\title{Advancing methods for the biodemography of aging within social
contexts}



\author[1]{Raisa Hernández-Pacheco}%
\author[1]{Ulrich K Steiner}%
\author[1]{Alexandra Rosati}%
\author[1]{Shripad Tuljapurkar}%
\affil[1]{Affiliation not available}%


\vspace{-1em}



  
  \date{May 30, 2024}


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Raisa Hern\selectlanguage{ngerman}ández-Pacheco\textsuperscript{a*}, Ulrich K.
Steiner\textsuperscript{b}, Alexandra G. Rosati\textsuperscript{c},
Shripad Tuljapurkar\textsuperscript{d}

\textsuperscript{a}California State University-Long Beach, Department of
Biological Sciences, 1250 N Bellflower Blvd, Long Beach, CA, USA
90840-0004

\textsuperscript{b}Freie Universität Berlin, Biological Institute,
Königin-Luise Str. 1-3,14195 Berlin, Germany;

\textsuperscript{c}Departments of Psychology and Anthropology,
University of Michigan, 530 Church St, Ann Arbor, MI, USA 48109

\textsuperscript{d}327 Campus Dr., Rm 233, Stanford University,
Stanford, CA, USA 94305

*Corresponding author:

rai.hernandezpacheco@csulb.edu

\textbf{Competing interests statement} : None.

\par\null\par\null\par\null

\textbf{Abstract} . Several social traits including status, integration,
early-life adversity, and their interactions across the life course can
predict health, reproduction, and mortality in humans. Accordingly,
individual sociality plays a fundamental role in the emergence of
phenotypes driving the evolution of aging. Recent work placing human
social gradients on a biological continuum with other species provides a
useful evolutionary context for aging questions, but there is still a
need for a unified evolutionary framework for sociality, health, and
aging. Here, we first summarize current challenges to disentangle the
effects of the social environment on human life courses. Next, we review
recent advances in comparative biodemography and propose a
biodemographic perspective to address socially driven health phenotype
distributions and their evolutionary consequences using a nonhuman
primate population. This new comparative approach uses evolutionary
demography to address the joint dynamics of populations, sociality,
phenotypes, and life history parameters. The long-term goal is to
advance our understanding of the link between individual sociality,
population-level outcomes, and the evolution of aging.

\par\null

\par\null

\textbf{1. Introduction}

The social environment shapes human life courses, and can generate
strong associations between sociality, health, and longevity. For
example, higher social status predicts longer lifespan (Adler et al.,
1993; Chetty et al., 2016; Hajat et al., 2011; Lantz et al., 2010;
Sapolsky, 2004; Wilkinson and Marmot, 2003), greater social support
predicts healthier aging (Antonucci et al., 2019; Béland et al., 2005;
Holt-Lunstad et al., 2010), and early life social adversity predicts
higher mortality risk (Anda et al., 2006; Ferraro et al., 2016; Hughes
et al., 2017). However, disentangling the effects of the social
environment on human life courses remains challenging due to multiple
uncontrolled (e.g., incongruencies in self-reported health; non-random
attrition) and unknown confounding factors (e.g., biased survey
information). Comparative approaches can both provide evidence
concerning the evolutionary origins of the social determinants of human
health and aging, and provide a model for the intricate interactions and
potential feedback loops between sociality and aging (Carey and Judge,
2001; Lucas and Keller, 2020). This review argues that studies on the
comparative biodemography of aging can advance our understanding of the
link between individual sociality, population-level demographic
outcomes, and the evolution of aging in humans. For this, we define
senescence as a decline in function with age and differentiate it from
health statuses which we define as deviations from such expected mean
decline. We introduce conceptual frameworks for the evolutionary
demography of aging within social contexts, discuss why current
challenges in human health and aging studies call for comparative
approaches, and describe a unifying methodological framework for the
biodemography of aging that links current research in the social
determinants of health to evolutionary demography approaches to the
fundamental rules of life history evolution.

\textbf{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.

\textbf{2. Current challenges in modeling the social determinants of
health and aging call for comparative approaches}

Socially and economically disadvantaged persons experience a higher
accumulation of risk factors (low income: Bor et al., 2017; Hirai et
al., 2012; migrants: Riosmena et al., 2014; racial minorities: Shah et
al., 2020; Wallace, 2015; Williams and Chiquita, 1995) and increased
mortality (Chetty et al., 2016; National Center for Health Statistics,
2016). Yet, common approaches to modeling health trajectories (e.g.,
growth curve models, latent class models) often report similar health
profiles between socially disadvantaged and advantaged groups (Brown et
al., 2012; Gueorguieva et al., 2009; Markides and Coreil, 1986) or
favorable health trajectories in traditionally marginalized groups
(i.e., Migrant Effect and the Hispanic Paradox; Franzini et al., 2001;
Markides and Rote, 2019; Quiñones et al., 2011). This divergence between
evidence of cumulative risk and increased mortality on the one hand, and
methodological approaches that model average health trajectories of
disadvantaged groups on the other, suggests that current forecasting
methods for predicting the progression of individual health may fail to
capture critical aspects of human sociality and thus not accurately
capture human health trajectories (Engelman and Jackson, 2019). Another
issue when modeling the social determinants of human health and aging
concerns limitations in handling missing data when this missing data is
not random. As individuals age and their health deteriorates,
longitudinal studies suffer non-random reductions in the number of
participants due to mortality and other sources of attrition (Vaupel,
2010). Thus, many studies of human health and aging may have biases in
that robust individuals will remain in the study into very old age,
whereas those who die earlier or have unknown fates will not be included
(Jackson et al., 2019). Finally, survey responses -- a common method in
human studies -- are influenced by many other factors such as the
personal perceptions of respondent and interviewers (Courtenay, 2000;
Davis et al., 2010; Dowd and Zajacova, 2010; Gunasekara et al., 2012;
Salazar, 1990; Sorlie et al., 1992; Williams and Chiquita, 1995) that
can limit our understanding of the causal mechanisms of individual
health and consequent aging. We argue that animal models for the social
dimensions of health and aging can therefore provide a new and valuable
opportunity to test novel biodemographic perspectives on analyzing
individual health and improve methods to forecast health over the life
course that can be applied to humans.

\textbf{2.1 Challenge 1: untying individual health forecasting from
gradualist assumptions}

A typical view of senescence is that individual health deteriorates with
increasing age. Such a process can be described by mixed-effects
regression methods through an intercept (i.e., baseline measure of
health) and a slope (i.e., rate of health change over time; DiPrete and
Eirich, 2006). Although these methods allow for individual variation in
baseline levels (i.e., random intercepts) and in the rate of change
(e.g., random slopes, quadratic terms, exponential functions), they
operate under the assumption that health changes smoothly and gradually
as individuals age. In doing so, forecasts from these methods do not
align with empirical evidence demonstrating associations between
cumulative disadvantage and late life outcomes, and this modeling
approach may not fully capture within-person variability in health (Fig
1, survey data). In particular, the smoothing of mean health
trajectories across social groups may not capture the effect of the
social environment on individual health and consequent aging because
this approach may underestimate the accumulation of poor health outcomes
in socially disadvantaged persons (Engelman and Jackson 2019; Fig 1,
mixed-effects).

In contrast to the gradualist assumptions of many models, empirical
evidence suggests that human health can show periods of stability,
slight deterioration, and then recovery from health insults (Bolano et
al., 2019; Gill et al., 2010; Guilley et al., 2008; Keown, 2003). As
life progresses through time, individuals may remain in the same health
status (i.e., stasis) or transition among health states before death.
That is, health does not decline gradually and homogeneously with no
reversals. This highlights a crucial aspect of human health dynamics
that accurate forecasting models must capture. In line with this,
Engelman and Jackson (2019) proposed a new approach to health history
forecasting by describing individual health trajectories as a punctuated
equilibrium pattern where individuals experience periods of long-term
stability interrupted by sudden changes in health status or mortality.
The authors argue that while gradual approaches appeal to an intuitive
reasoning about health change, such a modeling choice produces a mean
health change that is not representative of the changes experienced by
most of the individuals comprising the population.

One way to capture such health dynamics are multi-state models
(Namboodiri and Suchindran, 2013; Schoen, 2006) and sequence analysis
(Abbott and Tsay, 2000). These models describe discrete health states
and transition probabilities among these states, and more accurately
capture the health trajectories observed in real populations (Engelman
and Jackson, 2019). We also argue that multi-state models describing
changes across age as a Markov process may also provide a much-needed
reconciliation between deterministic and stochastic processes when
modeling individual health that would be otherwise obscured by the
smoothing of health trajectories in gradualist approaches (Figure 1,
multi-state).

\textbf{2.2 Challenge 2: Addressing data missing not at random}

Another key issue with human studies is that they likely underestimate
the extent of poor health in aging populations given limited approaches
to handle missing data (Jackson et al., 2019; Jackson and Engelman,
2022). Selective dropout at very old ages, especially among unhealthy
and socially disadvantaged persons, is common (Badawi et al., 1999;
Coste et al., 2013; Duim and Lima Passos, 2020; Mirowsky and Reynolds,
2000; Purdie et al., 2002; Van Beijsterveldt et al., 2002; Young et al.,
2006) and can result in the selection of robust individuals in late
life. Moreover, many approaches to research on health across the life
course do not account for competing risks of mortality, health change,
and attrition across groups. This conceals persistent socially driven
health inequities in late life because the privileged sector often
suffers a higher accumulation of health penalties due to longer lives
(Jackson and Engelman, 2022).

As the limitations of missing data have been increasingly recognized,
methods addressing missing data at random have become more widely used
(e.g., multiple imputation maximum likelihood; Graham, 2009). However,
these methods cannot remove the bias associated with data missing not at
random (Goldberg et al., 2021). In contrast, multi-state models provide
flexibility in the number of meaningful life states describing
individual trajectories, allowing us to incorporate temporary
missingness as a discrete state in the model (Engelman and Jackson,
2019). Here, temporary missingness becomes a life state that individuals
can transition into if they leave the study, or out of if they return to
the study. Multi-state models are thus powerful tools that make it
possible to empirically quantify the likelihood that a particular person
belonging to a health state will leave and return to the sample. Because
mortality (i.e., absorbing state) and other types of attrition can also
be easily distinguished and incorporated, \selectlanguage{english}multi-state models explicitly
account for the contribution of different types of missing data to the
cohort's health experiences (Engelman and Jackson, 2019).

\textbf{2.3 Challenge 3: Recognizing the limitations of survey data}

Survey research and self-reported health are common elements of human
health and aging studies addressing the impact of social inequalities
(Black et al., 2017). Surveys have proven to be a reliable general tool
for quickly producing empirical data from large representative samples,
but these data also have significant disadvantages. For example,
response rates are hard to control, and the data produced can lack many
important details about the topic under investigation (Kelley et al.,
2003). When used longitudinally (e.g., cohort studies), survey research
suffers from other limitations due to the potential for significant
changes in individual's responsiveness over time, as well as in the
consistency of respondents in their replies to public health-relevant
questions when asked again at a later time (ethnic origin: Johnson 1974;
patterns of abuse: Abramsky et al., 2022; Loxton et al., 2019; smoking:
Kaestle, 2015; substance use: Broman et al., 2022; suicidal attempts:
Hart et al., 2013). In general, there may be systematic differences in
the ability of individuals to self-evaluate their own health across time
(Black et al., 2017; Vuolo et al., 2014). Associations between
self-reported health and objective health, as measured by biological
risk factors, may also differ across socially stratified groups (Dowd
and Zajacova, 2010; Layes et al., 2012). Conversely, interviewer effects
also contribute significantly to biases in \selectlanguage{english}survey research, as
respondents may be predisposed to provide socially desirable responses
to the interviewer (Davis et al., 2010; Salazar, 1990). Interviewers
also contribute to misclassification of race and ethnic groups (Massey
1980; Williams and Chiquita, 1995); biased assumptions of health among
old persons (Thorslund and W\selectlanguage{ngerman}ärneryd, 1990); biases in age, race, and
gender perceptions (as reviewed by Davis et al., 2010); and can be
affected by their level of experience interviewing people (Salazar,
1990). Taken together, these data limitations may contribute to the
census miscount of socially stratified groups that ultimately affects
reported rates of health conditions (Williams and Chiquita, 1995). Thus,
both survey and interviewer data can result in biases in our ability to
evaluate the accuracy of models quantifying the effects of the social
environment on human health and consequent life courses.

\textbf{2.4 Social animal models for the biodemography of aging}

\selectlanguage{english}\textbf{} The biological pathways from social adversity to health and
longevity, together with the proximate physiological and molecular
mechanisms that shape these changes, are now being revealed (Cavigelli
and Caruso, 2015; Snyder-Mackler et al., 2020; Shively and Wilson,
2016). Yet, the need for a unified evolutionary framework for the social
determinants of health and aging across species remains. Animal models
provide several advantages relative to human studies, as they allow us
to record the specific nature of social relationships through direct
observations without complex processes of human cultural evolution
(Mesoudi and Thornton, 2018). Animal models allow us to measure the
natural course of health deterioration and recovery with no
interventions or significant confounding factors that may impact human
outcomes (Challenge 1; Blumstein et al., 2018). Animal models also allow
us to quantify nonrandom mortality risks given that each individual's
endpoint is known (Challenge 2). Finally, animal models allow us to
evaluate the accuracy of health and aging forecasting models using
systematic data collection methods impossible to implement in most human
studies, such as survey-based research (Challenge 3; Colman, 2018).
Thus, animal models provide comparative approaches that could become our
gateway to explore the evolutionary origins of the social determinants
of human aging and how this relates to health: whether and how our
closest relatives are similarly shaped by social gradients, and why
certain aging trajectories across the tree of life are shared by some
but not others (Jones et al., 2014). We recognize that our human concept
of aging cannot be directly transferred to numerous species, especially
within social contexts, but we also emphasize that this represents an
opportunity and not an occasion for disengagement (Cohen, 2018).

A call to advance studies on cross-species comparisons of social
environments and their effects on health, longevity, and life histories
was enthusiastically made almost a decade ago when the National Research
Council of the National Academies prompted a discussion about sociality,
hierarchy, and health within a comparative biodemographic perspective
(Committee on Population National Research Council, 2014). Since then,
several advances in our understanding of the social mechanisms of aging
have highlighted the complex dynamics between social relationships and
life outcomes, as well as the need to study animals with long lifespans
if we intend to understand the extraordinary longevity of humans
(Colchero et al., 2016; Korb and Heinze, 2021). In this section, we
review recent comparative reports on the evolution of aging within
social contexts that followed such call.

 \selectlanguage{english}While many mechanistic questions on the evolution of increased
longevity remain unanswered, both physiological and social mechanisms
appear to shape mortality schedules across species (Lucas and Keller,
2020; Noren Hooten et al., 2022; Snyder-Mackler et al., 2020). Evidence
that sociality is associated with long lives across the tree of life has
been accumulating, partly due to the recent focus of aging researchers
\selectlanguage{english}on eusocial insects (Johnson and Carey 2014) and the counterintuitive
observation that those who reproduce more also have exceptionally long
lives (i.e., absence of the fecundity/longevity trade-off;~Dixon et al.,
2014; Heinze and Giehr, 2021; Korb et al., 2021; Kramer et al., 2021;
Negroni et al., 2021; Rau and Korb, 2021; Tasaki et al., 2021). Here,
the evolution of a reproductive division of labor confers strong
advantage to reproductive individuals through increased survival.
Transcriptome analyses revealed that experimental reproductive
activation in worker honeybees increased survival through a reduction in
risk of disease and increased oxidative stress resistance (Kennedy et
al., 2021).~ Similar patterns of resilience to oxidative stress were
observed in leaf-cutting ant workers (Majoe et al., 2021) and the
ant\emph{Temnothorax rugatulus} (Korb et al., 2021) after experimental
loss of the nest's queen. This is especially intriguing because
leaf-cutting ant workers, for example, do not produce fertile offspring.
Thus, such findings raise important questions regarding the evolution of
improved health trajectories in queenless workers (Majoe et al., 2021).

By expanding comparative studies beyond eusocial insects, we gain
further insights into whether and how multiple social dimensions
including status, integration, and early life environments shape health
and aging trajectories across a physiological and cognitive complexity
gradient (Marmot and Sapolsky 2014). For example, social status in a
cooperative breeder population of Seychelles warblers is associated to
the pace of aging through a reduction in telomere attrition (a marker of
cellular senescence) among dominant females, likely due to reduced costs
of parental care trading-off against increased senescence (Hammers et
al., 2019). The observation that breeders receiving help in raising the
young age more slowly than the helpers has been observed across several
taxa (Berger et al., 2018; Downing et al., 2021), although causality or
associations to health remain unknown. In primates, evidence from
genome-wide and multi-region transcriptomic studies show that social
status affects immune regulation and aging producing evidence of
antiviral phenotypes (Snyder-Mackler et al., 2016; 2018) and younger
relative transcriptional ages (Chiou et al., 2022) in high-status
females. However, associations among social status, health, and aging
are often sex-specific and context-dependent. High-status male baboons
exhibit up-regulation in inflammation and immune defense-related genes,
but such traits may have been present in these males before moving up in
the hierarchy (Lea et al., 2018). This complex causal relationship
between socioenvironmental factors and aging trajectories was further
highlighted by Anderson et al. (2021), who found that high-status males
were predicted to be older than their chronological ages with respect to
a DNA methylation-based age predictor (`epigenetic clock'). High-status
meerkats similarly show higher rates of both telomere attrition and
survival (Cram et al., 2018). While such accelerated aging may be
indicative of costs associated to higher reproductive effort in high
social status individuals, this raises questions regarding the role, if
any, of other social dimensions on epigenetic age across populations.

Social networks metrics, such as how integrated and connected an
individual is to others in the network, have recently emerged as an
important domain for understanding aging and mortality processes (Silk
2014). Social network statistics have open the opportunity to
deconstruct sociality into the types of social connections that predict
longevity (Ellis et al., 2019). Individuals with strong connections and
central roles in the network, or those that are highly integrated,
exhibit lower risks of mortality. This is potentially mediated through
social security (Montero et al., 2020), mutualistic behaviors (Archie et
al., 2014; Cheney et al., 2016; Ellis et al., 2019; Lehmann et al.,
2015), stronger social support (Nu\selectlanguage{ngerman}ñez et al., 2015), and better access
to social information (Ellis et al., 2017). Whether these associations
between an individual's social integration and connectedness and their
life trajectory are equally conserved at old ages requires more
attention. Using physiological and anatomical markers of immunity in an
adult population of rhesus macaques which included aging individuals,
Pavez-Fox et al. (2021) found associations between social integration
and low white blood cell counts suggesting links between social
integration and inflammation markers. On the other hand, increased
social support through higher pack size in cooperative grey wolves was
found to offset individual costs of disease (Almberg et al., 2015). The
absence of an association between group size and increased senescence
was also described for a socially foraging bat (Gager et al., 2016).
These findings contradict long-standing hypothesized costs of group
living (i.e., disease transmission, increased infection rates) and
further highlights the need to revisit classical hypotheses on life
history trade-offs in social animals.

Finally, several comparative studies echoing the potential role that the
early life social environment has on compromising health and shaping the
fate of individuals have emerged. An accumulation of adverse events
early in life predicted longevity in baboons (Tung et al., 2016) and
such adverse environment had intergenerational effects (Zipple et al.,
2019). Early adversity was also found to elevate glucocorticoid levels
in adult female baboons, a measure of stress response associated to
health (Patterson et al., 2021; Rosenbaum et al., 2020). The mechanisms
behind the relationship between early life adversity and health across
the lifespan may involve physiological changes such as inflammation and
disease risk (Kinnally et al. 2019)

These relationships between sociality, health and aging also involve
complex interactions among them. Multiple species show shifts in
patterns of social behavior and underlying psychological processes as
individuals age (Kroeger et al., 2021; Machanda and Rosati, 2020;
Siracusa et al., 2022) indicating that sociality trajectories are as
varied as health and aging trajectories and likely modulated by social
status, social organization, and sex. For example, while many primates
show reductions in sociality during aging, in very long-lived
chimpanzees older males have higher-quality relationships and are more
gregarious by many metrics than are younger males, despite their lower
social status (Rosati et al 2020). Thus, there are likely reciprocal
causalities whereby longevity changes an individual's social patterns,
which in turn impacts senescence (Carey and Judge, 2001; Lucas and
Keller, 2020). Other, contrasting patterns have also been reported.
Several mammal species have shown increased mortality risk in highly
connected individuals (Blumstein et al., 2018; Thompson and Cords,
2018), in cooperatively breeding species versus non-cooperative ones
(Vágási et al., 2021) and in individuals lacking social support (Begall
et al., 2021), suggesting that benefits from social relations may not be
universal across species (Blumstein et al., 2018). Together, these
patterns highlight further the need for a foundational eco-evolutionary
methodological framework to study health and aging within social
contexts (Lange et al., 2022).

\textbf{3. A comparative biodemographic perspective of health and aging
withing social contexts}

To advance our understanding of the biodemography of aging within social
contexts, we need a unifying method linking current research in the
social determinants of individual variability to evolutionary demography
approaches to the fundamental rules of life history evolution. Here, we
provide a general roadmap for accomplishing this using rhesus macaques
living at the Cayo Santiago Biological Field Station as an example.
Although we do not provide empirical data, our model formulation is
based on realistic demographic and health phenotype metrics currently
being collected in this population. Our methods can be applied across
the entire spectrum of animal models, yet nonhuman primates provide
unique advantages to gain insights into aging due to their complex
social behavior with analogs of human health-relevant status disparities
(Phillips et al., 2014).

\textbf{3.1 Conceptual framework of evolutionary demography}

Classical work on life history theory traces back to the foundations of
evolutionary theory (Charlesworth, 1994; Hamilton, 1966; Stearns, 1992).
In any given age-structured population, individuals have a survival rate
and a fertility rate that both depend on age. The famous Lotka equation
demonstrates that in this scenario the annual population growth rate, \selectlanguage{greek}λ,
\selectlanguage{english}is a function of age, survival and fertility. Because these three
parameters make up a life history, the Lotka equation provided means to
quantify associations between life history components and the
corresponding values of \selectlanguage{greek}λ \selectlanguage{english}that these associations yield. The realization
that the population growth rate, \selectlanguage{greek}λ, \selectlanguage{english}was a quantitative definition of
population fitness -- it is defined by fitness components -- allowed a
direct connection between life history theory and evolutionary theory
(Hamilton 1966). Now, estimating changes in fitness \selectlanguage{greek}λ \selectlanguage{english}produced by
changes in the life history was made possible. For example, if changes
are assumed to arise from random mutations, consequent changes in
fitness can be interpreted as selection acting on the mutations
(Charlesworth 1994). Crucially, such fitness changes can also be
computed from \selectlanguage{greek}λ \selectlanguage{english}and its partial derivatives (i.e., sensitivity; Caswell
2001). The definition of these quantitative associations between fitness
and the life history directly linked individual phenotypes, to
population and evolutionary dynamics setting the stage for the field of
evolutionary demography.

Because of its flexible applicability grounded in general theory, many
developments to this approach have been made for both ecological and
evolutionary theory (Charlesworth 1994; Caswell 2001). More recent
studies addressing the evolutionary demography of animal populations
have focused on integral projection models (IPMs) as a flexible method
to quantify population and evolutionary dynamics while accounting for
continuous phenotypes (Levin et al., 2021). Since the development of
IPMs (Easterling et al., 2000), studies addressing size-based
demographics by structuring populations into a body size continuum have
become common mostly in mammals and birds (Coulson, 2012; Levin et al.,
2021; Merow et al., 2014; Rees et al., 2014), however few studies have
attempted to incorporate social structures into the model (Kappeler et
al., 2019; Paniw et al., 2021), while studies explicitly incorporating
health structures have just started to emerge (Vincze et al., 2022).

\textbf{3.2 Integral projection model for socially driven health
dynamics in a nonhuman primate}

Following the conceptual framework of evolutionary demography, if the
evidence indicates that changes in the life history are being driven by
sociality, then we argue that changes in fitness can be interpreted as
selection acting on the socially driven life courses. Integral
projection models allow us to quantitatively test this. IPMs are
flexible population models that describe how a population structured by
a continuous individual-level state variable (i.e., individual
phenotype) changes in discrete time (Easterling et al., 2000). Their
construction depends on four fundamental relationships that describe the
associations between the phenotype and survival, the phenotype and
fertility, the phenotype dynamics (e.g., ontogeny, growth) among
survivors and the probability density distribution of offspring
phenotypes given parental phenotypes (i.e., heritability; Coulson et
al., 2010; Table 1). Importantly, IPMs are realistic as these
relationships can vary with age, time and environments (Ellner and Rees,
2006). IPMs are also flexible as they can be extended to incorporate
covariation between demographic rates, as well as their uncertainty, by
including demographic parameters estimated in Bayesian frameworks
(Elderd \& Miller, 2016; Hern\selectlanguage{ngerman}ández-Pacheco et al. 2020; Plard et al.,
2019).

For our purpose, individual sociality can be defined by any relevant
metric such as social status or social integration as either categorical
or continues variable, while individual phenotypes can be defined by any
relevant continuous metric of individual health in the population of
interest (e.g., biomarkers, body-mass index, cortisol levels). The
rhesus macaques of the Cayo Santiago Biological Field Station live in a
naturalistic environment with well-known demographics (Hernández-Pacheco
et al., 2013) and exhibit a complex dominance hierarchy (i.e., social
status or rank) involving relationships between both kin and unrelated
individuals (i.e., social integration and connectedness; Ellis et al.,
2019; Pfefferle et al., 2014). In particular, these monkeys are good
comparative models for addressing questions about psychological health,
physical health, and aging. Macaques share human-like social preferences
for attending to socially relevant information (Burrows et al., 2009;
Hoffman et al., 2007), can further reason about complex social
information such as other's perceptual and goal states (Flombaum and
Santos, 2005; Santos et al., 2006), and show important similarities with
human reward-based decision-making (Santos and Platt, 2014). Lifespan
trajectories of these cognitive traits show that older macaques exhibit
declines in social attention (co-orienting with another individual), and
adult females are more attentive to social information than males
(Rosati et al., 2016). On the other hand, increased body condition
defined by mass influences their overall health status (Bauer et al.,
2011). Thus, to gain insights into the social determinants of health and
the evolution of aging, we propose to structure our rhesus macaque
population into proxies of both psychological (cognitive) health based
on looking time metrics (Rosati et al., 2018) and physical health based
on body mass. With this information, we can quantify the individual- and
population-level demographic effects of changes in individual health
across a sociality gradient using IPMs.

To describe the annual dynamics of the distribution of psychological and
physical phenotypes across the socially stratified lifespan, we propose
to build an IPM based on phenotype-demography associations evaluated
with generalized linear models (Fig 2). Here, social stratification is
defined by social status as a categorical variable of two levels,
\(s\)and \(s^{\prime}\) (e.g., high status, low status)
and we assume such level remains constant throughout the life of
individuals. We consider individuals tracked annually (Fig 2, individual
life course from time\emph{t} to \emph{t} +1). Thus, for a given
individual, ontogenetic changes are defined by cognitive changes and
gains or losses in body mass from one year to the next. In a given year,
a monkey of sociality\(s\), age \(a\), and
health \(z\) has a 1-year probability of survival given by
a function \(s_{s,a}(z)\), i.e., the fraction of \(s\),
\(a\),\(z\) monkeys that survive from age
\(a\) to age \(a\) + 1 (Fig 2,
health-survival function). Ontogenetic changes of survivors are
described by a distribution \(G_{s,a}(y|z)\), i.e., the fraction
of\(s\), \(a\), and \(z\)
monkeys that change health from \(z\) to
\(y\)when transitioning from age \(a\) to
\(a\) + 1 (Fig 2, health-change function), where
\(y\) represents the health phenotype \(z\)
of surviving individuals one year later. Reproduction by monkeys of
sociality \(s\), age \(a\), and health
\(z\) will be described by\(M_{s,a}(z)\), i.e., the
fraction of \(s\), \(a\),
\(z\) monkeys that produce an offspring (Fig 2,
health-fertility function). Finally, inheritance is described by a
function \(D_{s,a}(y|z)\) that links the health of offspring
\(y\) and parental health \(z\) according to
parental age and sociality (Fig 2, health heritability function). For
our population, the health-dependent demographic performance and
population dynamics across social stratification will be the outcome of
two demographic processes: survival and ontogenetic
development,\(P_{s,a}\left(y\middle|z\right)=G_{s,a}\left(y\middle|z\right)s_{s,a}(z)\), and reproduction,\(F_{s,a}\left(y\middle|z\right)=M_{a,s}(z)D_{s,a}(y|z)\).
Thus, we propose the following general IPM formulation:

\begin{equation}
\mathbf{n}(s,a+1,\ y)=\int{\left[P_{s,a}\left(y\middle|z\right)F_{s,a}\left(y\middle|z\right)\right]\mathbf{n}\left(s,a\right)\text{dz}}\nonumber \\
 \end{equation}

where n is the population vector describing the total number of
individuals of a given sociality, a given age, and a given health state
at a given time. IPM analyses require the integral above to be
discretized and ultimately be analyzed as traditional multi-state models
based on Markov chains (Levin et al., 2021). Multi-state models (i.e.,
matrix population models; Caswell 2001) yield a demographic equilibrium,
and we can use them to compute the fitness \(\lambda\), as well
as the expected population distribution across health for individuals of
a particular sociality and age (i.e., stable distribution; Fig 3).
Similarly, for a given sociality and age class, we can estimate the
expected contribution to births of an individual to the next generation
given its current health state (i.e., reproductive value, Fig 3). Many
of our model assumptions can be relaxed and adapted to the population of
interest. Sociality metrics can be used in categorical (e.g., status) or
continues (e.g., group size) forms and transitions among social
categories can be included for populations showing social aging.
Multiple heritability functions concerning sociality and health
phenotypes can also be added. The variances around the
health-demographic functions can also be integrated, and thus we can
include estimates of variability around health change. Finally, this
approach can be performed using single or two-sex models.

With this information on fitness \(\lambda\), the stable
population structure, and the reproductive value, we can evaluate
phenotype-specific selection gradients on demographic and life history
parameters through sensitivity analysis (Caswell 2001; Coulson et al.
2010). For example, we can evaluate whether the strength and direction
of selection on health-demography function parameters (i.e., GLMs
coefficients) and their variances, vary with sociality and aging. If
there is high sensitivity of \(\lambda\) to the survival
function parameters of high social status or highly integrated monkeys,
then an appropriate conclusion is that there is a strong selection
acting on the health-survival function parameters of these monkeys as
these population metrics contribute more to fitness overall. On the
other hand, low social status or poorly integrated individuals may
experience a stronger selection on their health-fertility function
parameters, given their expected shorter lifespan and potential
selection for a faster reproduction. IPMs versatility also provides the
novel opportunity of exploring the complex relationships between
sociality, health and aging. For example, we can directly quantify
shifts in the cognitive response and body mass distributions as
individuals age and determine whether such shifts correspond to
sociality and whether they have an influence on fitness (Fig 2; Fig 3).
If individuals are likely to remain within a health state class
throughout life (i.e., stasis), then we can use the IPM to ask whether
the social environment is driving such state persistence. Given that
IPMs can be used to calculate generation time and net reproductive rate,
sensitivity analysis can also evaluate how changes in selection
gradients affect these life history descriptors (Coulson et al., 2010).
These important features of IPMs, within the multi-state framework,
ultimately allows us to quantify the variability in individual health
that underlies the observed prevalence of stability, deterioration, and
recovery from disabilities (i.e., health states) among socially
advantaged and disadvantaged subgroups (Fig 1, multi-state). 

\selectlanguage{english}\textbf{4. Conclusions}

The social environment predicts health risks and mortality in humans and
many other animals. Thus, new advances in the biodemography of aging
require the consideration of social factors shaping the life course of
people. Because most human health and aging studies depend on
self-reports of health, several challenges to disentangle the role of
sociality on human life courses remain. Animal models for the social
dimensions of health and aging provide new perspectives on the evolution
of aging that can be exploited within the methodological framework of
evolutionary demography.

\textbf{Acknowledgements}

This work was supported by the Animal Models for the Social Dimensions
of Health and Aging Research Network (NIH/NIA R24 AG065172), the
National Institute of Health (R37AG049395 and R21AG072285), and the
National Science Foundation (2217812). We acknowledge that figures 2 and
3 were created with BioRender.com.

\textbf{References}

Abbott, A., Tsay, A., 2000. Sequence analysis and optimal matching
methods in sociology. Annu. Rev. Sociol. 29, 3--33.

Abramsky, T., Harvey, S., Mosha, N., Mtolela, G., Gibbs, A., Mshana, G.,
Lees, S., Kapiga, S., St\selectlanguage{ngerman}öckl, H., 2022. Longitudinal inconsistencies in
women's self-reports of lifetime experience of physical and sexual IPV:
evidence from the MAISHA trial and follow-on study in North-western
Tanzania. BMC Womens Health 22, 1--13.
https://doi.org/10.1186/s12905-022-01697-y

Achey, R.L., 2016. Life Expectancy. J. Palliat. Med. 19, 468.
https://doi.org/10.1089/jpm.2015.0452

Adler, N.E., Boyce, W.T., Chesney, M.A., Folkman, S., Syme, S.L., 1993.
Socioeconomic Inequalities in Health: No Easy Solution. JAMA 269,
3140--3145. https://doi.org/10.1001/jama.1993.03500240084031

Almberg, E.S., Cross, P.C., Dobson, A.P., Smith, D.W., Metz, M.C.,
Stahler, D.R., Hudson, P.J., 2015. Social living mitigates the costs of
a chronic illness in a cooperative carnivore. Ecol. Lett. 18, 660--667.
https://doi.org/10.1111/ele.12444

Anda, R.F., Felitti, V.J., Bremner, J.D., Walker, J.D., Whitfield, C.,
Perry, B.D., Dube, S.R., Giles, W.H., 2006. The enduring effects of
abuse and related adverse experiences in childhood: A convergence of
evidence from neurobiology and epidemiology. Eur. Arch. Psychiatry Clin.
Neurosci. 256, 174--186. https://doi.org/10.1007/s00406-005-0624-4

Anderson, J.A., Johnston, R.A., Lea, A.J., Campos, F.A., Voyles, T.N.,
Akinyi, M.Y., Alberts, S.C., Archie, E.A., Tung, J., 2021. High social
status males experience accelerated epigenetic aging in wild baboons.
Elife 10, 1--22. https://doi.org/10.7554/ELIFE.66128

Antonucci, T.C., Ajrouch, K.J., Webster, N.J., Zahodne, L.B., 2019.
Social relations across the life span: scientific advances, emerging
issues, and future challenges. Annu. Rev. Dev. Psychol. 1, 313--336.
https://doi.org/10.1146/annurev-devpsych-121318-085212

Arbeev, K.G., Ukraintseva, S. V., Bagley, O., Zhbannikov, I.Y., Cohen,
A.A., Kulminski, A.M., Yashin, A.I., 2019. ``Physiological
Dysregulation'' as a Promising measure of robustness and resilience in
studies of aging and a new indicator of preclinical disease. J.
Gerontol. A. Biol. Sci. 74, 462--468.
https://doi.org/10.1093/gerona/gly136

Archie, E.A., Tung, J., Clark, M., Altmann, J., Alberts, S.C., 2014.
Social affiliation matters: both same-sex and opposite-sex relationships
predict survival in wild female baboons. Proc. Royal Soc. B 281,
20141261. https://doi.org/10.1098/rspb.2014.1261

Badawi, M.A., Eaton, W.W., Myllyluoma, J., Weimer, L.G., Gallo, J.,
1999. Psychopathology and attrition in the Baltimore ECA 15-year
follow-up 1981-1996. Soc. Psychiatry Psychiatr. Epidemiol. 34, 91--98.
https://doi.org/10.1007/s001270050117

Baudisch, A., 2015. Perspectives on the Biodemography of Longevity and
Aging. KZfSS Kölner Zeitschrift für Soziologie und Sozialpsychologie 67,
425--439. https://doi.org/10.1007/s11577-015-0321-x

Bauer, S.A., Arndt, T.P., Leslie, K.E., Pearl, D.L., Turner, P. V.,
2011. Obesity in rhesus and cynomolgus macaques: A comparative review of
the condition and its implications for research. Comp. Med. 61,
514--526.

Begall, S., Nappe, R., Hohrenk, L., Schmidt, T.C., Burda, H., Sahm, A.,
Szafranski, K., Dammann, P., Henning, Y., 2021. Life expectancy, family
constellation and stress in giant mole-rats (\emph{Fukomys mechowii} ).
P. Philos. Trans. R. Soc 376. https://doi.org/10.1098/rstb.2020.0207

Béland, F., Zunzunegui, M.V., Alvarado, B., Otero, A., Del Ser, T.,
2005. Trajectories of cognitive decline and social relations. J.
Gerontol. B Psychol. Sci. 60, 320--330.
https://doi.org/10.1093/geronb/60.6.P320

Berger, V., Lemaître, J.F., Allainé, D., Gaillard, J.M., Cohas, A.,
2018. Early and adult social environments shape sex-specific actuarial
senescence patterns in a cooperative breeder. Am. Nat. 192, 525--536.
https://doi.org/10.1086/699513

Black, N., Johnston, D.W., Shields, M.A., Suziedelyte, A., 2017. Who
provides inconsistent reports of their health status? The importance of
age, cognitive ability and socioeconomic status. Soc. Sci. Med. 191,
9--18. https://doi.org/10.1016/j.socscimed.2017.08.032

Blumstein, D.T., Williams, D.M., Lim, A.N., Kroeger, S., Martin, J.G.A.,
2018. Strong social relationships are associated with decreased
longevity in a facultatively social mammal. Proc. Royal Soc. B 285.
https://doi.org/10.1098/rspb.2017.1934

Bolano, D., Berchtold, A., Bürge, E., 2019. The Heterogeneity of
Disability Trajectories in Later Life: Dynamics of Activities of Daily
Living Performance Among Nursing Home Residents. J. Aging Health 31,
1315--1336. https://doi.org/10.1177/0898264318776071

Bor, J., Cohen, G.H., Galea, S., 2017. Population health in an era of
rising income inequality: USA, 1980--2015. The Lancet 389, 1475--1490.
https://doi.org/10.1016/S0140-6736(17)30571-8

Broman, M.J., Bista, S., Broman, C.L., 2022. Inconsistency in
Self-Reporting the Use of Substances over Time. Subst. Use Misuse 57,
1356--1364. https://doi.org/10.1080/10826084.2022.2083168

Brown, T.H., O'Rand, A.M., Adkins, D.E., 2012. Race-Ethnicity and Health
Trajectories: Tests of Three Hypotheses across Multiple Groups and
Health Outcomes. J.. Health Soc Behav. 53, 359--377.
https://doi.org/10.1177/0022146512455333

Burrows, A.M., Waller, B.M., Parr, L.A., 2009. Facial musculature in the
rhesus macaque (Macaca mulatta): Evolutionary and functional contexts
with comparisons to chimpanzees and humans. J. Anat. 215, 320--334.
https://doi.org/10.1111/j.1469-7580.2009.01113.x

Carey, J.R., Judge, D.S., 2001. Principles of biodemography with special
reference to human longevity. Population; An English selection 13:9--24.

Carnes, B.A., 2007. Senescence viewed through the lens of comparative
biology. Ann N Y Acad Sci. 1114:14--22.

Caswell, H., 2001. Matrix population models: construction, analysis and
interpretation. 2nd edition. Sunderland, MA: Sinauer Associates, Inc.

Cavigelli, S.A., Caruso, M.J., 2015. Sex, social status and
physiological stress in primates: The importance of social and
glucocorticoid dynamics. Philos. Trans. R. Soc. 370.
https://doi.org/10.1098/rstb.2014.0103

Charlesworth, B., 1994. Evolution in Age-Structured Populations.
Cambridge, Cambridge University Press.

Cheney, D.L., Silk, J.B., Seyfarth, R.M., 2016. Network connections,
dyadic bonds and fitness in wild female baboons. R. Soc. Open Sci. 3.
https://doi.org/10.1098/rsos.160255

Chetty, R., Stepner, M., Abraham, S., Lin, S., Scuderi, B., Turner, N.,
Bergeron, A., Cutler, D., 2016. The association between income and life
expectancy in the United States, 2001-2014. JAMA 315, 1750--1766.
https://doi.org/10.1001/jama.2016.4226

Chiou, K.L., DeCasien, A.R., Rees, K.P. et al., 2022. Multiregion
transcriptomic profiling of the primate brain reveals signatures of
aging and the social environment. Nat Neurosci.
https://doi.org/10.1038/s41593-022-01197-0

Christensen, K., 2008. Human biodemography: Some challenges and
possibilities for aging research. Demographic Res. 19, 1575--1586.
https://doi.org/10.4054/DemRes.2008.19.43

Christensen, K., Johnson, T.E., Vaupel, J.W., 2006. The quest for
genetic determinants of human longevity: Challenges and insights. Nat.
Rev. Genet. 7, 436--448. https://doi.org/10.1038/nrg1871

Cohen, A.A., 2018. Aging across the tree of life: The importance of a
comparative perspective for the use of animal models in aging. Biochim.
Biophys. Acta Mol. Basis. Dis. 1864, 2680--2689.
https://doi.org/10.1016/j.bbadis.2017.05.028

Colchero, F., Rau, R., Jones, O.R., Barthold, J.A., Conde, D.A., Lenart,
A., Nemeth, L., Scheuerlein, A., Schoeley, J., Torres, C., Zarulli, V.,
Altmann, J., Brockman, D.K., Bronikowski, A.M., Fedigan, L.M., Pusey,
A.E., Stoinski, T.S., Strier, K.B., Baudisch, A., Alberts, S.C., Vaupel,
J.W., 2016. The emergence of longevous populations. Proc. Natl. Acad.
Sci. USA 113, E7681--E7690. https://doi.org/10.1073/pnas.1612191113

Colman, R.J., 2018. Non-human primates as a model for aging. Biochim.
Biophys. Acta Mol. Basis Dis. 1864, 2733--2741.
https://doi.org/10.1016/j.bbadis.2017.07.008

Committee on Population; Division of Behavioral and Social Sciences and
Education; National Research Council. Sociality, Hierarchy, Health:
Comparative Biodemography: A Collection of Papers. Weinstein M, Lane MA,
editors. Washington (DC): National Academies Press (US); 2014 Sep 22.
PMID: 25254285.

Coste, J., Quinquis, L., Audureau, E., Pouchot, J., 2013. Non response,
incomplete and inconsistent responses to self-administered
health-related quality of life measures in the general population:
Patterns, determinants and impact on the validity of estimates - a
population-based study in France using the MOS S. Health Qual Life
Outcomes 11. https://doi.org/10.1186/1477-7525-11-44

Coulson, T., 2012. Integral projections models, their construction and
use in posing hypotheses in ecology. Oikos 121, 1337--1350.
https://doi.org/10.1111/j.1600-0706.2012.00035.x

Coulson, T., Tuljapurkar, S., Childs, D.Z., 2010. Using evolutionary
demography to link life history theory, quantitative genetics and
population ecology. J. Anim. Ecol. 79, 1226--1240.
https://doi.org/10.1111/j.1365-2656.2010.01734.x

Courtenay, W.H., 2000. Constructions of masculinity and their influence
on men's well-being: a theory of gender and health. Soc. Sci. Med. 50,
1385--401. https://doi.org/10.1016/s0277-9536(99)00390-1

Cram, D.L., Monaghan, P., Gillespie, R., Dantzer, B., Duncan, C.,
Spence-Jones, H., Clutton-Brock, T., 2018. Rank-Related Contrasts in
Longevity Arise from Extra-Group Excursions Not Delayed Senescence in a
Cooperative Mammal. Curr. Biol. 28, 2934-2939.e4.
https://doi.org/10.1016/j.cub.2018.07.021

Crimmins, E.M., Vasunilashorn, S.M., 2016. Chapter 3 - Biodemography:
Adding Biological Insight into Social, Economic, and Psychological
Models of Population and Individual Health Change with Age, in: George,
L.K., Ferraro, K.F. (Eds.), Handbook of Aging and the Social Sciences
(Eighth Edition). Academic Press, San Diego, pp. 55--75.
https://doi.org/https://doi.org/10.1016/B978-0-12-417235-7.00003-2

Cundiff, J.M., Boylan, J.M., Pardini, D.A., Matthews, K.A., 2017. Moving
up matters: Socioeconomic mobility prospectively predicts better
physical health. Health Psychology. https://doi.org/10.1037/hea0000473

Dannefer, D., 2003. Cumulative Advantage/Disadvantage and the Life
Course: Cross-Fertilizing Age and Social Science Theory. J Gerontol B
Psychol Sci Soc Sci. 58, 327--337.
https://doi.org/10.1093/geronb/58.6.S327

Dannefer, D., 1987. Aging as intracohort differentiation: Accentuation,
the Matthew effect, and the life course. Sociol. Forum 2, 211--236.
https://doi.org/10.1007/BF01124164

Davis, R.E., Couper, M.P., Janz, N.K., Caldwell, C.H., Resnicow, K.,
2010. Interviewer effects in public health surveys. Health Educ. Res.
25, 14--26. https://doi.org/10.1093/her/cyp046

DiPrete, T.A., Eirich, G.M., 2006. Cumulative advantage as a mechanism
for inequality: A review of theoretical and empirical developments.
Annu. Rev. Sociol. 32, 271--297.
https://doi.org/10.1146/annurev.soc.32.061604.123127

Dixon, L., Kuster, R., Rueppell, O., 2014. Reproduction, social
behavior, and aging trajectories in honeybee workers. Age (Omaha) 36,
89--101. https://doi.org/10.1007/s11357-013-9546-7

Dowd, J.B., Zajacova, A., 2010. Does self-rated health mean the same
thing across socioeconomic groups? Evidence from biomarker data. Ann.
Epidemiol. 20, 743--749. https://doi.org/10.1016/j.annepidem.2010.06.007

Downing, P.A., Griffin, A.S., Cornwallis, C.K., 2021. Hard-working
helpers contribute to long breeder lifespans in cooperative birds.
Philos. Trans. R. Soc. 376. https://doi.org/10.1098/rstb.2019.0742

Duim, E., Lima Passos, V., 2020. Highways to Ageing - Linking life
course SEP to multivariate trajectories of health outcomes in older
adults. Arch. Gerontol. Geriatr. 91, 104193.
https://doi.org/10.1016/j.archger.2020.104193

Easterling, M.R., Ellner, S.P., Dixon, P.M., 2000. Size-specific
sensitivity: Applying a new structured population model. Ecology 81,
694--708.
https://doi.org/10.1890/0012-9658(2000)081{[}0694:SSSAAN{]}2.0.CO;2

Elderd, B.D., Miller, T.E.X., 2016. Quantifying demographic uncertainty:
Bayesian methods for Integral Projection Models (IPMs). Ecol. Monogr.
86, 125--144.

Ellis, S., Franks, D.W., Nattrass, S., Cant, M.A., Weiss, M.N., Giles,
D., Balcomb, K.C., Croft, D.P., 2017. Mortality risk and social network
position in resident killer whales: sex differences and the importance
of resource abundance. Proc. Biol. Sci. 284.
https://doi.org/10.1098/rspb.2017.1313

Ellis, S., Snyder-Mackler, N., Ruiz-Lambides, A., Platt, M.L., Brent,
L.J.N., 2019. Deconstructing sociality: The types of social connections
that predict longevity in a group-living primate. Proc. Royal Soc. B
286. https://doi.org/10.1098/rspb.2019.1991

Ellner, S.P., Rees, M., 2006. Integral projection models for species
with complex demography. Am. Nat. 167, 410--428.
https://doi.org/10.1086/499438

Engelman, M., Jackson, H., 2019. Gradual Change, Homeostasis, and
Punctuated Equilibrium: Reconsidering Patterns of Health in Later Life.
Demography 56, 2323--2347. https://doi.org/10.1007/s13524-019-00826-x

Ferraro, K.F., 2018. The gerontological imagination: An integrative
paradigm of aging. Oxford University Press.

Ferraro, K.F., Schafer, M.H., Wilkinson, L.R., 2016. Childhood
Disadvantage and Health Problems in Middle and Later Life: Early
Imprints on Physical Health? Am. Sociol. Rev. 81, 107--133.
https://doi.org/10.1177/0003122415619617

Ferraro, K.F., Shippee, T.P., 2009. Aging and cumulative inequality: How
does inequality get under the skin? Gerontologist 49, 333--343.
https://doi.org/10.1093/geront/gnp034

Ferraro, K.F., Shippee, T.P., Schafer, M.H., 2009. Cumulative inequality
theory for research on aging and the life course. Handbook of Theories
of Aging 413--434. https://doi.org/10.1017/S000748530002229X

Flombaum, J.I., Santos, L.R., 2005. Rhesus monkeys attribute perceptions
to others. Curr. Biol. 15, 447--52.
https://doi.org/10.1016/j.cub.2004.12.076

Franzini, L., Ribble, J.C., Keddie, A.M., 2001. Understanding the
Hispanic paradox. Ethn. Dis. 11, 496--518.

Gager, Y., Gimenez, O., O'Mara, M.T., Dechmann, D.K.N., 2016. Group
size, survival and surprisingly short lifespan in socially foraging
bats. BMC Ecol. 16, 1--12. https://doi.org/10.1186/s12898-016-0056-1

Gavrilov, L.A., Gavrilova, N.S., 2015. New Developments in the
Biodemography of Aging and Longevity. Gerontology 61, 364--371.
https://doi.org/10.1159/000369011

Gill, T.M., Gahbauer, E.A., Han, L., Allore, H.G., 2010. Trajectories of
Disability in the Last Year of Life. NEJM 362, 1173--1180.
https://doi.org/10.1056/nejmoa0909087

Giuliani, C., Garagnani, P., Franceschi, C., 2018. Genetics of human
longevity within an eco-evolutionary nature-nurture framework. Circ.
Res. 123, 745--772. https://doi.org/10.1161/CIRCRESAHA.118.312562

Goldberg, S.B., Bolt, D.M., Davidson, R.J., 2021. Data missing not at
random in mobile health research: Assessment of the problem and a case
for sensitivity analyses. J. Med. Internet. Res. 23, 1--18.
https://doi.org/10.2196/26749

Graham, J.W., 2009. Missing data analysis: Making it work in the real
world. Annu. Rev. Psychol. 60, 549--576.
https://doi.org/10.1146/annurev.psych.58.110405.085530

Gueorguieva, R., Sindelar, J.L., Falba, T.A., Fletcher, J.M., Keenan,
P., Wu, R., Gallo, W.T., 2009. The impact of occupation on self-rated
health: Cross-sectional and longitudinal evidence from the health and
retirement survey. J. Gerontol. B Psychol. Sci. Soc. Sci. 64, 118--124.
https://doi.org/10.1093/geronb/gbn006

Guilley, E., Ghisletta, P., Armi, F., Berchtold, A., D'Epinay, C.L.,
Michel, J.P., De Ribaupierre, A., 2008. Dynamics of frailty and ADL
dependence in a five-year longitudinal study of octogenarians. Res.
Aging 30, 299--317. https://doi.org/10.1177/0164027507312115

Gunasekara, F.I., Carter, K., Blakely, T., 2012. Comparing self-rated
health and self-assessed change in health in a longitudinal survey:
Which is more valid? Soc. Sci. Med. 74, 1117--1124.
https://doi.org/10.1016/j.socscimed.2011.11.038

Gutin, I., Hummer, R.A., 2021. Social Inequality and the Future of US
Life Expectancy. Annu. Rev. Sociol. 47, 501--520.
https://doi.org/10.1146/annurev-soc-072320-100249

Hajat, A., Kaufman, J.S., Rose, K.M., Siddiqi, A., Thomas, J.C., 2011.
Long-term effects of wealth on mortality and self-rated health status.
Am. J. Epidemiol. 173, 192--200. https://doi.org/10.1093/aje/kwq348

Hamilton, W.D., 1966. The Moulding of Senescence by Natural Selection.
J. Theor. Biol. 12, 12-45.

Hammers, M., Kingma, S.A., Spurgin, L.G., Bebbington, K., Dugdale, H.L.,
Burke, T., Komdeur, J., Richardson, D.S., 2019. Breeders that receive
help age more slowly in a cooperatively breeding bird. Nat. Commun. 10,
1--10. https://doi.org/10.1038/s41467-019-09229-3

Hart, S.R., Musci, R.J., Ialongo, N., Ballard, E.D., Wilcox, H.C., 2013.
Demographic and clinical characteristics of consistent and inconsistent
longitudinal reporters of lifetime suicide attempts in adolescence
through young adulthood. Depress Anxiety 30, 997--1004.
https://doi.org/10.1002/da.22135

Heinze, J., Giehr, J., 2021. The plasticity of lifespan in social
insects. Philos. Trans. R. Soc. 376, 0--2.
https://doi.org/10.1098/rstb.2019.0734

Hernández-Pacheco, R., Plard, F., Grayson, K.L., Steiner, U.K., 2021.
Demographic consequences of changing body size in a terrestrial
salamander. Ecol. Evol. 11:174--185.

Hernández-Pacheco, R, Rawlins, R.G., Kessler, M.J., et al., 2013.
Demographic variability and density-dependent dynamics of a free-ranging
rhesus macaque population. Am. J. Primatol. 75:1152--1164.

Hirai, H., Kondo, K., Kawachi, I., 2012. Social determinants of active
aging: Differences in mortality and the loss of healthy life between
different income levels among older japanese in the ages cohort study.
Curr. Gerontol. Geriatr. Res 2012. https://doi.org/10.1155/2012/701583

Hjelmborg, J.B., Iachine, I., Skytthe, A., Vaupel, J.W., McGue, M.,
Koskenvuo, M., Kaprio, J., Pedersen, N.L., Christensen, K., 2006.
Genetic influence on human lifespan and longevity. Hum. Genet. 119,
312--321. https://doi.org/10.1007/s00439-006-0144-y

Hoffman, K.L., Gothard, K.M., Schmid, M.C.C., Logothetis, N.K., 2007.
Facial-Expression and Gaze-Selective Responses in the Monkey Amygdala.
Curr. Biol. 17, 766--772. https://doi.org/10.1016/j.cub.2007.03.040

Holt-Lunstad, J., Smith, T.B., Layton, J.B., 2010. Social relationships
and mortality risk: A meta-analytic review. PLoS Med. 7.
https://doi.org/10.1371/journal.pmed.1000316

Hooper, P.L., Gurven, M., Kaplan, H.S., 2014. Social and economic
underpinnings of human biodemography M. Weinstein, M.A. Lane (eds.),
Sociality, Hierarchy, Health: Comparative Biodemography: A Collection of
Papers, National Academies Press, Washington, DC, pp. 169-196

Hughes, K., Bellis, M.A., Hardcastle, K.A., Sethi, D., Butchart, A.,
Mikton, C., Jones, L., Dunne, M.P., 2017. The effect of multiple adverse
childhood experiences on health: a systematic review and meta-analysis.
Lancet Public Health 2, e356--e366.
https://doi.org/10.1016/S2468-2667(17)30118-4

Jackson, H., Engelman, M., 2022. Deaths, Disparities, and Cumulative
(Dis)Advantage: How Social Inequities Produce an Impairment Paradox in
Later Life. J. Gerontol. A Biol. Sci. 77, 392--401.
https://doi.org/10.1093/gerona/glab181

Jackson, H., Engelman, M., Bandeen-Roche, K., 2019. Robust Respondents
and Lost Limitations: The Implications of Nonrandom Missingness for the
Estimation of Health Trajectories. J. Aging Health 31, 685--708.
https://doi.org/10.1177/0898264317747079

Johnson, C.E., 1974. Consistency of reporting ethnic origin in the
current population survey. US Dep. Commerce Tech. Pap. No. 31,
Washington, DC: Bur Census

Johnson, B., Carey, J.R., 2014. Hierarchy and connectedness as
determinants of health and longevity in social insects, in Sociality,
Hierarchy, Health: Comparative Biodemography: A Collection of Papers
(National Academies Press, 2014), pp. 269--293.

Jones, O.R., Scheuerlein, A., Salguero-Gómez, R., Camarda, C.G.,
Schaible, R., Casper, B.B., Dahlgren, J.P., Ehrlén, J., García, M.B.,
Menges, E.S., Quintana-Ascencio, P.F., Caswell, H., Baudisch, A.,
Vaupel, J.W., 2014. Diversity of ageing across the tree of life. Nature
505, 169--173. https://doi.org/10.1038/nature12789

Kaestle, C.E., 2015. Age of smoking milestones: Longitudinal
inconsistencies and recanting. J. Adolesc. Health 56, 382--388.
https://doi.org/10.1016/j.jadohealth.2014.12.005

Kappeler, P.M., Clutton-Brock, T., Shultz, S., Lukas, D., 2019. Social
complexity: patterns, processes, and evolution. Behav. Ecol. Sociobiol.
73, 1--6. https://doi.org/10.1007/s00265-018-2613-4

Kelley, K., Clark, B., Brown, V., Sitzia, J., 2003. Good practice in the
conduct and reporting of survey research. IJQHC 15, 261--266.
https://doi.org/10.1093/intqhc/mzg031

Kennedy, A., Herman, J., Rueppell, O., 2021. Reproductive activation in
honeybee (Apis mellifera) workers protects against abiotic and biotic
stress. Philos. Trans. R. Soc. 376.
https://doi.org/10.1098/rstb.2019.0737

Keown, D., 2003. Patterns of functional decline At the End of Life, in:
Buddhism \& Bioethics. Palgrave Macmillan UK, London, pp. 139--187.
https://doi.org/10.1007/978-1-349-23981-8\_3

Kinnally, E.L., Martinez, S.J., Chun, K., Capitanio, J.P., Ceniceros,
L.C. Early Social Stress Promotes Inflammation and Disease Risk in
Rhesus Monkeys. Sci Rep. 2019;9. pmid:31110226

Korb, J., Heinze, J., 2021. Ageing and sociality: Why, when and how does
sociality change ageing patterns? Philos. Trans. R. Soc. 376.
https://doi.org/10.1098/rstb.2019.0727

Korb, J., Meusemann, K., Aumer, D., Bernadou, A., Elsner, D., Feldmeyer,
B., Foitzik, S., Heinze, J., Libbrecht, R., Lin, S., Majoe, M., Kuhn,
J.M.M., Nehring, V., Negroni, M.A., Paxton, R.J., Séguret, A.C., Stoldt,
M., Flatt, T., 2021. Comparative transcriptomic analysis of the
mechanisms underpinning ageing and fecundity in social insects. Philos.
Trans. R. Soc. 376. https://doi.org/10.1098/rstb.2019.0728

Kramer, B.H., Nehring, V., Buttstedt, A., Heinze, J., Korb, J.,
Libbrecht, R., Meusemann, K., Paxton, R.J., Séguret, A., Schaub, F.,
Bernadou, A., 2021. Oxidative stress and senescence in social insects: A
significant but inconsistent link? Philos. Trans. R. Soc. 376.
https://doi.org/10.1098/rstb.2019.0732

Kroeger, S.B., Blumstein, D.T., Martin, J.G.A., 2021. How social
behaviour and life-history traits change with age and in the year prior
to death in female yellow-bellied marmots. Philos. Trans. R. Soc. 376.
https://doi.org/10.1098/rstb.2019.0745

Lange, E.C., Zeng, S., Campos, F.A., Li, F., Tung, J., Archie, E.A.,
Alberts, S.C., 2022. Early life adversity and adult social relationships
have independent effects on survival in a wild animal model of aging.
bioRixv 1--35.

Lantz, P.M., Golberstein, E., House, J.S., Morenoff, J., 2010.
Socioeconomic and behavioral risk factors for mortality in a national
19-year prospective study of U.S. adults. Soc. Sci. Med. 70, 1558--1566.
https://doi.org/10.1016/j.socscimed.2010.02.003

Layes, A., Asada, Y., Kepart, G., 2012. Whiners and deniers - What does
self-rated health measure? Soc. Sci. Med. 75, 1--9.
https://doi.org/10.1016/j.socscimed.2011.10.030

Lea, A.J., Akinyi, M.Y., Nyakundi, R., Mareri, P., Nyundo, F., Kariuki,
T., Alberts, S.C., Archie, E.A., Tung, J., 2018. Dominance
rank-associated gene expression is widespread, sex-specific, and a
precursor to high social status in wild male baboons. Proc. Natl. Acad.
Sci. USA 115, E12163--E12171. https://doi.org/10.1073/pnas.1811967115

Lehmann, J., Majolo, B., McFarland, R., 2015. The effects of social
network position on the survival of wild Barbary macaques, Macaca
sylvanus. Behav. Ecol. 27, 20--28. https://doi.org/10.1093/beheco/arv169

Levin, S.C., Childs, D.Z., Compagnoni, A., Evers, S., Knight, T.M.,
Salguero-Gómez, R., 2021. ipmr: Flexible implementation of Integral
Projection Models in R. Methods Ecol. Evol. 12, 1826--1834.
https://doi.org/10.1111/2041-210X.13683

Loxton, D., Powers, J., Townsend, N., Harris, M.L., Forder, P., 2019.
Longitudinal inconsistency in responses to survey items that ask women
about intimate partner violence. BMC Med Res. Methodol. 19, 1--8.
https://doi.org/10.1186/s12874-019-0835-4

Lucas, E.R., Keller, L., 2020. The co-evolution of longevity and social
life. Funct. Ecol. 34, 76--87. https://doi.org/10.1111/1365-2435.13445

Machanda, Z.P., Rosati, A.G., 2020. Shifting sociality during primate
ageing: Shifting sociality during primate aging. Philos. Trans. R. Soc.
375. https://doi.org/10.1098/rstb.2019.0620rstb20190620

Majoe, M., Libbrecht, R., Foitzik, S., Nehring, V., 2021. Queen loss
increases worker survival in leaf-cutting ants under paraquat-induced
oxidative stress. Philos. Trans. R. Soc. 376.
https://doi.org/10.1098/rstb.2019.0735

Markides, K.S., Coreil, J., 1986. The health of Hispanics in the
southwestern United States: An epidemiologic paradox. Public Health
Reports 101, 253--265.

Markides, K.S., Rote, S., 2019. The Healthy Immigrant Effect and Aging
in the United States and Other Western Countries. Gerontologist 59,
205--214. https://doi.org/10.1093/geront/gny136

Marmot, M.G., Sapolsky, R., 2014. Of baboons and men: Social
circumstances, biology, and the social gradient in health, in Sociality,
Hierarchy, Health: Comparative Biodemography: A Collection of Papers,
National Academies Press, pp. 365--388.

Massey, J.T., 1980. A comparison of interviewer observed race and
respondent reported race in the National Health Interview Survey. In
Proceedings of the American Statistical Association (pp. 425-428).
Washington, DC: American Statistical Association.

McGue, M., Vaupel, J.W., Holm, N.V., 1993. Harvald, B. Longevity is
moderately heritable in a sample of Danish twins born 1870--1880. J.
Gerontol. A 48, B237--B244.

Mesoudi, A., Thornton, A., 2018. What is cumulative cultural evolution?
Proc. Royal Soc. B. 285:20180712.

Merow, C., Dahlgren, J.P., Metcalf, C.J.E., Childs, D.Z., Evans, M.E.K.,
Jongejans, E., Record, S., Rees, M., Salguero-Gómez, R., Mcmahon, S.M.,
2014. Advancing population ecology with integral projection models: A
practical guide. Methods Ecol. Evol. 5, 99--110.
https://doi.org/10.1111/2041-210X.12146

Mirowsky, J., Reynolds, J.R., 2000. Age, depression, and attrition in
the national survey of families and households. Sociol. Methods Res.
https://doi.org/10.1177/0049124100028004004

Montero, A.P., Williams, D.M., Martin, J.G.A., Blumstein, D.T., 2020.
More social female yellow-bellied marmots, Marmota flaviventer, have
enhanced summer survival. Anim. Behav. 160, 113--119.
https://doi.org/10.1016/j.anbehav.2019.12.013

Morton, P.M., Ferraro, K.F., 2020. Early Social Origins of Biological
Risks for Men and Women in Later Life. J. Health Soc. Behav. 61,
503--522. https://doi.org/10.1177/0022146520966364

Namboodiri, K., Suchindran, C.M., 2013. Life table techniques and their
applications. Academic Press.

National Center for Health, 2016. National Center for Health
Statistics.~Health, United States 2015: with Special Feature on Racial
and Ethnic Health Disparities.

Negroni, M.A., MacIt, M.N., Stoldt, M., Feldmeyer, B., Foitzik, S.,
2021. Molecular regulation of lifespan extension in fertile ant workers.
Philos. Trans. R. Soc. 376. https://doi.org/10.1098/rstb.2019.0736

Noren Hooten, N., Pacheco, N.L., Smith, J.T., Evans, M.K., 2022. The
accelerated aging phenotype: The role of race and social determinants of
health on aging. Ageing Res. Rev. 73, 101536.
https://doi.org/10.1016/j.arr.2021.101536

Nuñez, C.M.V., Adelman, J.S., Rubenstein, D.I., 2015. Sociality
increases juvenile survival after a catastrophic event in the feral
horse (Equus caballus). Behav. Ecol. 26, 138--147.
https://doi.org/10.1093/beheco/aru163

Oeppen, J., Vaupel, J.W., 2002. Broken Limits to Life Expectancy.
Science 296, 1029--1031. https://doi.org/10.1126/science.1069675

Palma-Gudiel, H., Fañanás, L., Horvath, S., Zannas, A.S., 2020.
Psychosocial stress and epigenetic aging, 1st ed, Int. Rev. Neurobiol.
Elsevier Inc. https://doi.org/10.1016/bs.irn.2019.10.020

Paniw, M., Cozzi, G., Sommer, S., Ozgul, A., 2021. Demographic processes
in socially strutured populations, in: Salguero-Gómez, R. and Gamelon,
M. (eds.), Demographic methods across the tree of life. Oxford, Oxford
Uiversity Press, pp. 341-350.

Patterson, S.K., Hinde, K., Bond, A.B., Trumble, B.C., Strum, S.C.,
Silk, J.B., 2021. Effects of early life adversity on maternal effort and
glucocorticoids in wild olive baboons. Behavioral Ecol. Sociobiol. 75:
114.

Pavez-Fox, M.A., Negron-Del Valle, J.E., Thompson, I.J., Walker, C.S.,
Bauman, S.E., Gonzalez, O., Compo, N., Ruiz-Lambides, A., Martinez,
M.I., Platt, M.L., Montague, M.J., Higham, J.P., Snyder-Mackler, N.,
Brent, L.J.N., 2021. Sociality predicts individual variation in the
immunity of free-ranging rhesus macaques. Physiol. Behav. 241.
https://doi.org/10.1016/j.physbeh.2021.113560

Pfefferle, D., Kazem, A.J.N., Brockhausen, R.R., Ruiz-Lambides, A. V.,
Widdig, A., 2014. Monkeys Spontaneously Discriminate Their Unfamiliar
Paternal Kin under Natural Conditions Using Facial Cues. Curr. Biol. 24,
1806--1810. https://doi.org/10.1016/j.cub.2014.06.058

Phillips, K.A., Bales, K.L., Capitanio, J.P., Conley, A., Czoty, P.W.,
't Hart, B.A., Hopkins, W.D., Hu, S.L., Miller, L.A., Nader, M.A.,
Nathanielsz, P.W., Rogers, J., Shively, C.A., Voytko, M. Lou, 2014. Why
primate models matter. Am. J. Primatol. 76, 801--827.
https://doi.org/10.1002/ajp.22281

Plard, F., Turek, D., Gru\selectlanguage{english}ebler, M.U., Schaub, M., 2019. Ipm2: Towards
better under-standing and forecasting of population dynamics. Ecol.
Monogr., 89, e01364.

Purdie, D.M., Dunne, M.P., Boyle, F.M., Cook, M.D., Najman, J.M., 2002.
Health and demographic characteristics of respondents in an Australian
national sexuality survey: Comparison with population norms. J.
Epidemiol. Community Health (1978) 56, 748--753.
https://doi.org/10.1136/jech.56.10.748

Qui\selectlanguage{ngerman}ñones, A.R., Liang, J., Bennett, J.M., Xu, X., Ye, W., 2011. How does
the trajectory of multimorbidity vary across black, white, and mexican
americans in middle and old age? J Gerontol B Psychol Sci Soc Sci. 66 B,
739--749. https://doi.org/10.1093/geronb/gbr106

Rau, V., Korb, J., 2021. The effect of environmental stress on ageing in
a termite species with low social complexity. Philos. Trans. R. Soc.
376. https://doi.org/10.1098/rstb.2019.0739

Rees, M., Childs, D.Z., Ellner, S.P., 2014. Building integral projection
models: A user's guide. J. Anim. Ecol. 83, 528--545.
https://doi.org/10.1111/1365-2656.12178

\selectlanguage{english}Riley, J.C., 2001. Rising Life Expectancy: A Global History. Cambridge,
Cambridge University Press.

Riosmena, F., Everett, B.G., Rogers, R.G., Dennis, J.A., 2014. Negative
acculturation and nothing more? cumulative disadvantage and mortality
during the immigrant adaptation process among Latinos in the United
States. International Migration Review 49, 443--478.
https://doi.org/10.1111/imre.12102

Rosati, A.G., Arre, A.M., Platt, M.L., Santos, L.R., 2018. Developmental
shifts in social cognition: socio-emotional biases across the lifespan
in rhesus monkeys. Behav. Ecol. Sociobiol. 72, 163.
https://doi.org/10.1007/s00265-018-2573-8

Rosati, A.G., Arre, A.M., Platt, M.L., Santos, L.R., 2016. Rhesus
monkeys show human-like changes in gaze following across the lifespan.
Proc. Royal Soc. B 283. https://doi.org/10.1098/rspb.2016.0376

Rosati, A.G., Hagberg, L., Enigk, D.K., Otali, E., Emery Thompson, M.,
Muller, M.N., Wrangham, R.W., Machanda, Z.P., 2020. Social selectivity
in aging wild chimpanzees. Science, 370: 473-476

Rosenbaum, S., Zeng, S., Campos, F.A., Gesquiere, L.R., Altmann, J.,
Alberts, S.C., Li, F., Archie, E.A., 2020. Social bonds do not mediate
the relationship between early adversity and adult glucocorticoids in
wild baboons. Proc. Natl Acad. Sci. USA 117:052--20 062.

Salazar, M.K., 1990. Interviewer bias. How it affects survey research.
AAOHN J 38, 567--72. https://doi.org/10.1177/216507999003801203

Santos, L.R., Nissen, A.G., Ferrugia, J.A., 2006. Rhesus
monkeys,\emph{Macaca mulatta} , know what others can and cannot hear.
Anim. Behav. 71, 1175--1181.
https://doi.org/10.1016/j.anbehav.2005.10.007

Santos, L.R., Platt, M.L., 2014. Evolutionary Anthropological Insights
into Neuroeconomics. Neuroeconomics 109--122.
https://doi.org/10.1016/b978-0-12-416008-8.00007-3

Sapolsky, R.M., 2004. Social Status and Health in Humans and Other
Animals. Annu. Rev. Anthropol. 33, 393--418.
https://doi.org/10.1146/annurev.anthro.33.070203.144000

Sauerteig, M.R., Ferraro, K.F., Bauldry, S., 2022. Life Course Stressors
and Functional Limitations in Later Life among White, Black, and
Hispanic Adults: Deleterious, Hardening, or Benign? J. Gerontol. B
Psychol. Sci. Soc. Sci. 77, 249--259.
https://doi.org/10.1093/geronb/gbab066

Stearns, S.C., 1992. The Evolution of Life Histories. Oxford, Oxford
University Press.

Schoen, R., 2006. Dynamic Multistate Models. Dynamic Population Models.
Springer, Dordrecht. 161-202.

Shah, M., Sachdeva, M., Dodiuk-Gad, R.P., 2020. COVID-19 and racial
disparities. J. Am. Acad. Dermatol .83, e35.
https://doi.org/10.1016/j.jaad.2020.04.046

Shively, C.A., Wilson, M.E., 2016. Social Inequalities in Health in
Nonhuman Primates, The Biology Gradient. Switzerland, Springer
International Publishing.

Silk, J.B., 2014. Evolutionary perspectives on the links between close
social bonds, health, and fitness. In Sociality, Hierarchy, Health:
Comparative Biodemography, in: Weinstein, M., Lane, M.A. (Eds.), pp.
121--143. National Academies Press, Washington.

Siracusa, E.R., Higham, J.P., Snyder-Mackler, N., Brent, L.J.N., 2022.
Social ageing: Exploring the drivers of late-life changes in social
behaviour in mammals. Biol. Lett. 18.
https://doi.org/10.1098/rsbl.2021.0643

Snyder-Mackler, N., Burger, J.R., Gaydosh, L., Belsky, D.W., Noppert,
G.A., Campos, F.A., Bartolomucci, A., Yang, Y.C., Aiello, A.E., O'Rand,
A., Harris, K.M., Shively, C.A., Alberts, S.C., Tung, J., 2020. Social
determinants of health and survival in humans and other animals. Science
368. https://doi.org/10.1126/SCIENCE.AAX9553

Snyder-Mackler, N., Sanz, J., Kohn, J.N., Brinkworth, J.F., Morrow, S.,
Shaver, A.O., Grenier, J.C., Pique-Regi, R., Johnson, Z.P., Wilson,
M.E., Barreiro, L.B., Tung, J., 2016. Social status alters immune
regulation and response to infection in macaques. Science 354,
1041--1045. https://doi.org/10.1126/science.aah3580

Snyder-Mackler, N., Sanz, J., Kohn, J.N., Voyles, T., Pique-Regi, R.,
Wilson, M.E., Barreiro, L.B., Tung, J., 2018. Social status alters
chromatin accessibility and the gene regulatory response to
glucocorticoid stimulation in rhesus macaques. Proc. Natl. Acad. Sci.
U.S.A. 116, 1219--1228.

Sorlie, P.D., Rogot, E., Johnson, N.J., 1992. Validity of demographic
characteristics on the death certificate. Epidemiology 3, 181--184.
https://doi.org/10.1097/00001648-199203000-00018

Tasaki, E., Takata, M., Matsuura, K., 2021. Why and how do termite kings
and queens live so long? Philos. Trans. R. Soc. 376.
https://doi.org/10.1098/rstb.2019.0740

Thompson, N.A., Cords, M., 2018. Stronger social bonds do not always
predict greater longevity in a gregarious primate. Ecol. Evol. 8,
1604--1614. https://doi.org/10.1002/ece3.3781

Thorslund, M., W\selectlanguage{ngerman}ärneryd, B., 1990. Surveying the elderly about health,
medical care and living conditions. Some issues of response
inconsistency. Arch Gerontol.. Geriatr. 11, 161--173.
https://doi.org/10.1016/0167-4943(90)90009-U

Tuljapurkar, S., Zuo, W., Coulson, T., Horvitz, C., Gaillard, J.M.,
(2020). Skewed distributions of lifetime reproductive success: Beyond
mean and variance. Ecology Letters, 23, 748-- 756.

Tung, J., Archie, E.A., Altmann, J., Alberts, S.C., 2016. Cumulative
early life adversity predicts longevity in wild baboons. Nat Commun 7,
11181. https://doi.org/10.1038/ncomms11181

Vable, A.M., Gilsanz, P., Kawachi, I., 2019. Is it possible to overcome
the ``long arm'' of childhood socioeconomic disadvantage through upward
socioeconomic mobility? J. Public Health (Oxf) 41, 566--574.
https://doi.org/10.1093/pubmed/fdz018

Vágási, C.I., Vincze, O., Lemaître, J.F., Pap, P.L., Ronget, V.,
Gaillard, J.M., 2021. Is degree of sociality associated with
reproductive senescence? A comparative analysis across birds and
mammals. Philos. Trans. R. Soc. 376.
https://doi.org/10.1098/rstb.2019.0744

Van Beijsterveldt, C.E.M., Van Boxtel, M.P.J., Bosma, H., Houx, P.J.,
Buntinx, F., Jolles, J., 2002. Predictors of attrition in a longitudinal
cognitive aging study: The Maastricht Aging Study (MAAS). J. Clin.
Epidemiol. 55, 216--223. https://doi.org/10.1016/S0895-4356(01)00473-5

Vaupel, J.W., 2010. Biodemography of human ageing. Nature 464, 536--542.
https://doi.org/10.1038/nature08984

Vaupel, J.W., 2004. The Biodemography of Aging. Population and
Development Review

30, Supplement: Aging, Health, and Public Policy (2004), pp. 48-62.

Vincze, O., Colchero, F., Lemaître, JF. et al., 2022. Cancer risk across
mammals. Nature 601, 263--267.
https://doi.org/10.1038/s41586-021-04224-5

Vuolo, M., Ferraro, K.F., Morton, P.M., Yang, T.Y., 2014. Why Do Older
People Change Their Ratings of Childhood Health? Demography 51,
1999--2023. https://doi.org/10.1007/s13524-014-0344-3

Wachter, K.W., 2008. Biodemography comes of Age. Demographic Res. 19,
1501--1512. https://doi.org/10.4054/DemRes.2008.19.40

Wallace, S.P., 2015. Equity and social determinants of health among
older adults. Generations, 38, 6--11.

Wilkinson, R., Marmot, M., 2003. Social Determinants of Health The Solid
Facts, in: Chronic Illness Care. Springer International Publishing,
Cham, pp. 435--449. https://doi.org/10.1007/978-3-319-71812-5\_36

Williams, D.R., Chiquita, C., 1995. US Socioeconomic and Racial
Differences in Health: Patterns and Explanations. Annu. Rev. Sociol. 21,
349--386. https://doi.org/10.1146/annurev.soc.21.1.349

Williams, D.R., Lawrence, J.A., Davis, B.A., 2019. Racism and Health:
Evidence and Needed Research. Annu. Rev. Public Health 40, 105--125.
https://doi.org/10.1146/annurev-publhealth-040218-043750

Yashin, A.I., Stallard, E., Land, K.C.~2016. Biodemography of aging:
determinants of healthy life span and longevity.~Springer

Young, A.F., Powers, J.R., Bell, S.L., 2006. Attrition in longitudinal
studies: Who do you lose? Aust N Z J Public Health 30, 353--361.
https://doi.org/10.1111/j.1467-842X.2006.tb00849.x

Zipple, M.N., Archie, E.A., Tung, J., Altmann, J., Alberts, S.C., 2019.
Intergenerational effects of early adversity on survival in wild
baboons. Elife 8, 1--14. https://doi.org/10.7554/eLife.47433.001

\par\null\par\null

\textbf{Table 1} . Basic demographic information for the construction of
integral projection models (IPMs). The construction of IPMs depends on
four fundamental relationships (phenotype-demographic functions) that
describe the associations between the phenotype and survival, the
phenotype and the ontogeny of the phenotype (change) among survivors,
the phenotype and fertility, and the probability density distribution of
offspring phenotypes given parental phenotypes (i.e., heritability).
Variables in bracket represent those in the proposed IPM.\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
\textbf{Vital Rates} & \textbf{Dependency at present time (\emph{t})} &
\textbf{Proposed dependencies}\tabularnewline
\midrule
\endhead
Survival & State, age & Sociality {[}s{]}, health {[}z{]}, age
{[}a{]}\tabularnewline
Phenotype ontogeny (state change) & State, age & Sociality {[}s{]},
health {[}z{]}, age {[}a{]}\tabularnewline
Number of offspring & State, age & Sociality {[}s{]}, health {[}z{]},
age {[}a{]}\tabularnewline
Kind of offspring (state distribution) & Inheritance -- parental state,
parental age & Parental sociality {[}s{]}, parental health {[}z{]},
parental age {[}a{]}\tabularnewline
\bottomrule
\end{longtable}

\par\null\par\null

\textbf{Fig 1}. Comparison of mixed-effects regression and multi-state
model predictions of individual health histories from simulated survey
data. Survey data: colored trajectories represent 10 randomly chosen
individual histories from the total pool of trajectories. Mixed-effects:
black trajectories represent 10 randomly chosen predicted individual
histories from the total pool of trajectories (gray). For this, the
survey data (gray dots) was modeled using a linear mixed-effect model.
Multi-state model: black trajectories represent 10 randomly chosen
predicted individual histories from the total pool of trajectories
(gray). For this, the survey data (gray dots) was discretized into five
health states and modeled using a 5 x 5 matrix population model.

\par\null\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.98\columnwidth]{figures/Screen-Shot-2023-01-11-at-10-01-34-AM/Screen-Shot-2023-01-11-at-10-01-34-AM}
\caption{{This is a caption
{\label{626348}}%
}}
\end{center}
\end{figure}

\par\null

\textbf{Fig 2.~} Comparison of mixed-effects regression and multi-state
model predictions of individual health histories from simulated survey
data. Survey data: colored trajectories represent 10 randomly chosen
individual histories from the total pool of trajectories. Mixed-effects:
black trajectories represent 10 randomly chosen predicted individual
histories from the total pool of trajectories (gray). For this, the
survey data (gray dots) was modeled using a linear mixed-effect model.
Multi-state model: black trajectories represent 10 randomly chosen
predicted individual histories from the total pool of trajectories
(gray). For this, the survey data (gray dots) was discretized into five
health states and modeled using a 5 x 5 matrix population model.

\par\null\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=1.00\columnwidth]{figures/image7/image7}

\end{center}
\end{figure}

\textbf{Fig 3.} Integral projection model output across population
health and age structures needed to evaluate phenotype-specific
selection gradients on demographic and life history parameters through
sensitivity analysis. Sociality is defined by social status as a
categorical variable of two levels, \(s\) and
\(s^{\prime}\). Sex structure is not shown.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.98\columnwidth]{figures/image8/image8}

\end{center}
\end{figure}

\selectlanguage{english}
\FloatBarrier
\end{document}

