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chapter THREE
Early Childhood Health Disparities,
Biological Embedding, and Life‐Course Health
Daniel Berry
The longevity of a vibrant and productive society depends upon the health of its children.
Over the last two decades, the United States (US) has made gains in some important indicators of child health. Sparked, in part, by the Children’s Health Insurance Program
(CHIP) established in 1997 and the Affordable Care Act’s (ACA) extension of CHIP
through 2015, the percentage of children in the US covered by health insurance has
increased – particularly for families struggling with economic adversity (Rosenbaum &
Kenney, 2014). Fewer children are exposed to tobacco smoke, unsafe drinking water, and
dangerous levels of lead. Mortality rates in infancy and early childhood have declined
(Bloom, Cohen, & Freeman, 2013). This is good news.
Nonetheless, in absolute terms, the US lags behind most industrialized countries on
many critical health indicators. Of the 34 Organisation for Economic Co‐Operation and
Development (OECD) countries, the US is ranked last with respect to health care coverage.
In fact, the US is one of only two OECD countries in which fewer than 80% of its total
population is covered by health insurance (Mexico is the other). Although the obesity epidemic is evident across industrialized countries, the US ranks highest with respect to adult
and childhood obesity (OECD, 2015). In addition, whereas the obesity rate has recently
stabilized in many of the OECD countries, it remains comparatively more positive
in the US (19% for children ages 3–17 in 2011, up from 17% in 2001 and 12% in 1991).
The Wiley Handbook of Early Childhood Development Programs, Practices, and Policies, First Edition.
Edited by Elizabeth Votruba-Drzal and Eric Dearing.
© 2017 John Wiley & Sons, Inc. Published 2017 by John Wiley & Sons, Inc.
36 Berry
Perhaps most strikingly, infant and childhood mortality rates – our most profound
i­ndicator of child health – are higher in the US than almost all other industrialized c­ountries
(more than 60 infant and 9 child deaths per 10,000), ranking 30th and 31st respectively
out of the 34 OECD countries (OECD, 2015).
Within the US, there are marked disparities in child health across the income
d­istribution – children from low‐income contexts fare worse on virtually all key health
indicators available from nationally representative data. Children from poor families are at
greater risk for a variety of negative health conditions, ranging from low birth weight to
chronic conditions such as asthma and hearing, speech and vision problems – with 33%
of poor children compared to 27% of nonpoor children experiencing any of these chronic
health conditions (Case, Lee, & Paxson, 2008; Case, Lubotsky, & Paxson, 2002; Currie &
Lin, 2007). This health disparity – or the “income: health gradient” – is evident even in
the very first years of life. To put this in context, based on the number of children below
the US poverty threshold in 2013 (Jiang, Ekono, & Skinner, 2015), approximately
5.7 million infants, toddlers, and preschoolers begin life at heightened risk for chronic
health problems that could plague them throughout their lives. Moreover, these disparities
are unevenly distributed across children, with racial/ethnic minority children facing the
dual burdens of poverty and ill‐health at far higher rates than their white peers (Olshansky
et al., 2012; Williams, Mohammed, Leavell, & Collins, 2010).
Accumulating evidence across several literatures – ranging from health economics to
psychoneuroimmunology – indicates that social and economic adversity across the
p­renatal to early childhood years1 may have especially salient and far‐reaching impacts on
life‐course health. In this chapter, I introduce theory and empirical findings from across
these literatures. I first describe some of most well‐documented early childhood health
disparities and then highlight recent theoretical and empirical work concerning the
b­iological mechanisms through which early adversity may “get under the skin” to affect
children’s longer term health trajectories. To close, I briefly discuss the implications of this
growing literature for early childhood programs and policy.
Health Disparities in Early Childhood
Income/SES
The income: health gradient is evident in the earliest days of development. Infants born
to poor mothers are more likely to be born pre‐term and/or low birth weight (see
Blumenshine, Egerter, Barclay, Cubbin, & Braveman, 2010) – a known risk factor for
subsequent physical and cognitive problems (Boulet, Schieve, & Boyle, 2011). Infant
mortality rates are highest for those born to mothers with low incomes (e.g., 100% poverty threshold) and/or low levels of education (e.g., < high school; Gage, Fang, O’Neill, &
Di Rienzo, 2013). After infancy, children from low‐income contexts are at greater risk for
unintended accidents (e.g., falls, poisonings; Gielen, McDonald, & Shields, 2015) – the
leading cause of mortality in early childhood – and are more likely to suffer from
chronic conditions, such as asthma, obesity, and hearing, speech and vision problems
Early Childhood Health Disparities 37
(Braveman, Cubbin, Egerter, Williams, & Pamuk, 2010; Case, Lee, & Paxson, 2008;
Case, Lubotsky, & Paxson, 2002; Currie & Lin, 2007). Indeed, nationally representative
data indicate that children from low‐income families tend to show worse health, broadly
conceived. For instance, based on National Health Interview (NHIS) data (1997–2003),
Case and colleagues (2008) find that less affluent families are less likely than their more
affluent peers to rate their children’s health as “very good or excellent” – reflections presumably informed by the finding that these same low‐income children were more likely to
suffer from an array of chronic health problems (e.g., asthma, bronchitis, sinusitis, diabetes;
Case, Lubotsky, & Paxson, 2002). Some work suggests that these early disparities increase
as children age. Even with controls for parental education level, Case and colleagues (Case,
Fertig, & Paxson, 2005; Case, Lubotsky, & Paxson, 2002) found that a doubling of family
income increases the probability that a child is in excellent or very good health by 4% for
ages 0–3, 4.9% for ages 4–8, 5.9% for ages 9–12, and 7.2% for ages 13–17.
Most strikingly, despite long‐term, secular improvements in child health over the last
several decades, the rates of improvement have been considerably slower for children
growing up in low‐income families and communities – yielding increases in socio‐economic
health disparities over time. For example, using somewhat broad indices of cumulative risk
comprising county‐level indicators (e.g., income, education, poverty rate, occupation),
Singh and Kogan (2007) reported that, despite overall population‐level decreases in infant
mortality between 1969 and 2000, the disparity in childhood mortality (ages 1–14)
between the top and bottom SES quintile actually increased over this period. Adjusting for
age and race, the relative risk of all-cause childhood mortality between the bottom and top
SES quintile increased from 38% to 72%, between 1969 and 2000. Remarkably, these
increases were quite consistent across mortality attributed to more common experiential
causes (e.g., unintentional accidents), as well as comparatively rarer biological causes
(e.g., cardiovascular disease, cancer).
Race/Ethnicity
Disparities across adverse birth outcomes, infant mortality, and more general early‐childhood health are also evident across racial/ethnics lines (Olshansky et al., 2012; Williams
et al., 2010). For example, non‐Hispanic Black mothers are more likely to give birth to
preterm and/or low birth weight infants than are White mothers (20% versus 12% for
pre‐term births and 16% versus 8% for low birth weight infants; Martin, Hamilton,
Osterman, Curtin, & Mathews, 2015). In infancy, despite some important historical
improvements, the infant mortality rates for Black infants remain approximately twice
that of White infants (13 versus 5 per 1000 live births; Mathews & MacDorman, 2012).
As they age, Black children are at greater risk for chronic conditions, such as asthma (14%
asthma prevalence rate for Black children versus 7% for White children; CDC, 2011;
McDaniel, Paxson, & Waldfogel, 2006); and both Black and Hispanic children are more
likely to show the types of dramatic early childhood Body Mass Index (BMI) increases
that are thought to underlie chronic obesity (27% of young Black children and 25% of
Hispanic children are at the 95th percentile or above, compared to 7% of White children;
Taveras, Gillman, Kleinman, Rich‐Edwards, & Rifas‐Shiman, 2013).
38 Berry
Notably, the complex intersection of race and social and economic inequality paints a
multi‐factorial and less well‐understood etiological picture. For instance, due to far‐reaching
socio‐historical inequalities, race and income are strongly inter‐twined. Approximately
69% of non‐Hispanic Black and 66% of Hispanic children under the age of six live
in families earning less than 200% of the poverty line, compared with 30% of White
children (Jiang et al., 2015).
As expected, these socio‐economic differences partially explain racial disparities in
health and mortality in childhood (Woolf & Braveman, 2011). However, the mechanisms underlying racial/ethnic health disparities are seemingly more complex than SES
alone. In the broadest statistical sense, racial/ethnic health differences are evident across
wide‐ranging short‐ and long‐term health outcomes (see Woolf & Braveman, 2011),
even after adjusting for socio‐economic indicators like income and education. Further,
some have proposed that – due to the massive social inequalities experienced by minority
families in the US – gains in broad markers of socio‐economic status (e.g., income,
e­ducation) will yield less-pronounced health benefits for minority compared with majority individuals (i.e., the “diminishing returns” hypothesis; Farmer & Ferraro, 2005).
Indeed, this idea has garnered some empirical support. For example, using data from a
representative population‐based study of recent mothers in California, Braveman and
colleagues (2015) found that the Black‐White difference in the preterm (i.e., < 37 weeks)
birth rate was evident only in families at the high end of the SES distribution (i.e.. here,
a composite of family and neighbor income, parental education, and occupational
p­restige). Specifically, consistent with the “diminishing returns” hypothesis, they found
that the relation between higher SES and lower risk of pre‐term birth was evident for
White but not Black mothers.
The driving forces behind such conditional racial health disparities are unclear;
h­owever, they likely reflect the complex intersection of cumulative social and environmental inequalities. For instance, Black children (and increasingly Hispanic children)
face substantially greater residential segregation than do their White peers (Logan &
Stults, 2011) – segregation linked to a “geographic accumulation” (Acevedo‐Garcia,
Osypuk, McArdle, & Williams, 2008, p. 322) of physical and social health risks. Black
and Hispanic children are more likely to grow up in contexts with higher concentrations
of neighborhood poverty and low levels of educational attainment (Lichter, Parisi, &
Taquino, 2012). Minority children growing up in low‐income contexts tend to live farther away from resources like supermarkets (i.e., “food deserts”) than do their majority
white peers (see Walker, Keane, & Burke, 2010). And, although the findings with respect
to racial differences in physical access to green space and health‐supportive environments (e.g., public playgrounds) have been somewhat mixed (Franzini et al., 2010;
Macintyre, 2007), the quality and safety of these environments tend to be worse in
neighborhoods comprising largely poor, minority children (Franzini et al., 2010).
Minority children are also at greater risk for being exposed to risky health behaviors
(e.g., smoking; Quinto et al., 2013), as well as broad levels of environmental toxicity.
For example, Currie (2011) found that – holding zipcode, income, education, and
s­everal other potential confounds constant – non‐white mothers were more likely to
live within 2,000 meters of a Superfund or Toxic Release Inventory (TRI) site during
pregnancy.
Early Childhood Health Disparities 39
Rurality
Although we often think of these broad‐ranging environmental health risks as occurring
in “inner‐city” centers, increasing evidence suggests that growing up in rural contexts may
carry its own health risks. For instance, in their work considering birth trends in Alabama,
Kent, McClure, Zaitchik, & Gohlke (2013) found that – unlike the secular decreases in
adverse birth outcomes (e.g., pre‐term birth, low birth weight) seen typically between
2005 and 2010 for mothers from more urban contexts – mothers in rural contexts tended
to maintain historic highs in these adverse birth outcomes over this span. National‐level
trends remain undocumented; however, similar findings have been noted in other southern states (e.g., Georgia; Markley & Tu, 2015). At the national level, death rates due to
unintentional injuries are substantially greater for children in rural areas. For instance,
between birth and age 14, the mortality rate due to motor‐vehicle accidents for those
growing up in the most rural counties is more than double that of those in the most urban
(28 vs. 11 per 100,000, respectively; Myers et al., 2013). Albeit attenuated, these estimates
were robust after adjusting for an array of county‐level socio‐demographic covariates.
Data with respect to rurality and more normative health outcomes are only beginning
to emerge. However, descriptive findings at both the state (Crooks, 2000; Davy, Harrell,
Stewart, & King, 2004) and national levels (Davis, Bennett, Befort, & Nollen, 2011;
Lutfiyya, Lipksy, Wisdom‐Behounek, Inpanbutr‐Martinkus, 2007) suggest that children
in rural/nonmetropolitan counties tend to show higher obesity rates (22%) than do their
peers in more urban counties (17%). Interestingly, some of this work suggests that these
relations are evident, despite the fact that children in rural and urban contexts tend to
show very similar patterns of physical activity, screen time, and dietary intake (Davis et al.,
2011). Overall, the links between rurality and obesity found in childhood align with those
established with adults. Indeed, in addition to showing higher rates of obesity (Slack,
Myers, Martin, & Heymsfield, 2014), adults living in rural contexts are more likely to
suffer from chronic health problems (e.g., hypertension, diabetes, cancer; Meit et al.,
2014; Wallace, Grindeanu, & Cirillo, 2004), and higher mortality rates (Singh &
Siahpush, 2014) than do those living in more urban contexts.
Like the effects of income and race/ethnicity, health disparities across rural and more
urban contexts reflect a complex integration of economic, cultural, and social forces. For
instance, rurality is strongly intertwined with poverty and race. Of the 708 US counties
showing persistent childhood poverty in 2015, nearly 80% were rural (US Department of
Agriculture, 2015). The over‐representation of racial/ethnic minority families in rural
poverty is also striking. Nearly 60% of all rural Black families and 32% of all rural Hispanic
families live in counties with high concentrations of poverty (Lichter et al., 2012).
Many of the health risks associated with poverty and race introduced earlier in the
chapter are also likely at play in rural contexts and may well be amplified when considered
in tandem with risk factors that are particular to rural areas. For example, in addition to
the accumulation of physical and social health risks borne of concentrated poverty,
c­hildren growing up in rural contexts also often face “institutional” risk factors, such as
comparatively more limited access to high‐quality health care. In the most basic sense,
there are fewer medical specialists available to children and families living in rural areas.
Nationally, the number of general pediatricians per 100,000 residents is approximately six
40 Berry
times larger in metropolitan (20–25 per 100,000) counties than it is in the most rural
counties (4 per 100,000; Meit et al., 2014). Furthermore, accessing the health care that is
available may be more difficult for those in more rural contexts. At the national level, the
representation of uninsured children is nearly 50% higher for children in the most rural
(15%) compared to the most urban regions (10%; O’Hare, 2009). As such, children
growing up in rural poverty may face double or triple jeopardy – the cumulative physical
and psychosocial stressors of poverty and/or racial/ethnic marginalization, combined with
less access to high‐quality preventative and compensatory health care (Continelli,
McGinnis, & Holmes, 2010; Shi, Lebrun, & Tsai, 2010). However, given the dearth of
empirical work to date, understanding the complexities of rurality and health for young
children remains a pressing area of study.
In summary, early childhood health disparities are unevenly distributed across socio‐
economic, racial, and geographic lines. Although the complex intersection of these factors
in the etiology of these disparities is not fully understood, as introduced later in the chapter,
a convergent empirical and theoretical literature indicates that these early experiences and
health disparities likely have far‐reaching impacts on life‐course health.
Early Adversity and Long‐Term Health: Empirical Findings
Findings from both epidemiological studies of adult mortality and prospective longitudinal studies of life‐course health suggest that socioeconomic adversities experienced in
early childhood likely play unique and important roles in long‐term health. For
instance, Galobardes, Lynch, and Smith’s (2004, 2006, 2008) systematic reviews of the
literature illustrate well‐replicated links between childhood socio‐economic status and
adult mortality and disease morbidity. Of the 33 studies concerning childhood a­dversity
and all‐cause adult mortality (US and Europe) surveyed by these authors, 28 indicated
that adult mortality rates were elevated for those experiencing socio‐economic risk factors in childhood. Although childhood socio‐economic risk was typically measured
using broad‐scale indices such as paternal education, similar relations were evident for
other indicators (e.g., household crowding, poor ventilation). Critically, across several
studies there was evidence that these relations were specific to socio‐economic risk
experienced in childhood – adjusting for socio‐economic (e.g., education, income) and
even health risks (e.g. diastolic blood pressure, cholesterol, body mass index) in adulthood typically only attenuated the long‐term associations. Across studies, the all‐cause
mortality risk was approximately 20–40% higher for those who experienced socio‐
economic risks as children. These relations were remarkably consistent across different
indices of risk, geographic locations, and historical periods in which the c­hildhood risk
occurred.
Moreover, there was some evidence of disease specificity. For example, childhood socio‐
economic risk showed rather robust associations with cardiovascular disease morbidity
and mortality in adulthood (e.g., coronary heart disease, stroke) – outcomes often the
result of cumulative and chronic cardiovascular problems. Yet, there was little evidence
that these early risks were associated with overall cancer mortality. Rather, relations
Early Childhood Health Disparities 41
between early socio‐economic risk and cancer mortality were typically limited to cancers
linked to health lifestyles (e.g., smoking).
The methodological limitations of this literature notwithstanding (e.g., retrospective
accounts, broad risk indices, potential endogeneity), collectively, this work suggests both
direct and indirect connections between childhood socio‐economic risk and disease morbidity and mortality in adulthood. However, these reviews left several important questions
unanswered – in particular: (a) whether early childhood serves as an especially salient
developmental span, and (b) whether similar relations extend to more normative health
outcomes.
More recent longitudinal work provides positive support for both possibilities. For
instance, using data from the Panel Study of Income Dynamics (PSID), Ziol‐Guest,
Duncan, and Kalil (2009) found that – for low‐income families – lower income in early
childhood was predictive of higher body mass index (BMI) upwards of 30 to 37 years later.
Similar to prior findings for children’s academic outcomes, the relation was nonlinear,
such that the income effect was most pronounced for the lowest income families. Quite
similar relations were evident for other health outcomes (Ziol‐Guest, Duncan, Kalil, &
Boyce, 2012). Lower income in early childhood was predictive of higher rates of arthritis
and hypertension between the ages of 30 and 41. The magnitudes of these relations were
non‐trivial. In adulthood, the hypertension and arthritis rates for those who experienced
low‐income in early childhood were nearly twice that of those who did not experience this
early risk factor. The timing of these outcomes is also noteworthy. Arthritis and hypertension are typically conditions of old age. Here, the differences were evident when the
p­articipants were still quite young, perhaps indicating the early onset of conditions known
to have origins in chronic inflammatory immune response (see later).
With respect to developmental timing, there was an indication that the income effect
was most salient when low income was experienced between gestation and the first one or
two years of life (see also Duncan, Ziol‐Guest, & Kalil, 2010). Because data regarding
timing effects are quite scant, these findings should be considered preliminary. Collectively,
though, this work suggests that socio‐economic risk experienced in early childhood may
have long‐term consequences for adult health – ranging from more normative (e.g.,
obesity, hypertension) to more profound (e.g., cardiovascular disease, premature death)
health outcomes.
Early Adversity and Long‐Term Health: (Some) Biological Mechanisms
The effects of early childhood experiences on children’s long‐term health can be explained
by a myriad of plausible, intersecting development mechanisms. Muenning (2014) and
Conti (2013) have provided thoughtful accounts of the ways through which early experiences (e.g., better income and resources, lower stress, better early health) likely bolster
cognitive and social abilities that, in turn, have cascading reciprocal effects on health‐
supportive environments (e.g., education, earnings, peers) and behaviors (e.g., diet, exercise,
abstaining from drugs and alcohol) underlying better life‐course health. Rather than
r­eiterate this work, in this section my aim is to extend it by highlighting some plausible
42 Berry
biological mechanisms through which children’s early experience may impact long‐term
health. Poverty is used as an example of early childhood adversity throughout, given that
it is a broad, well‐studied stressor in the lives of families and young children. However,
there is every reason to suspect that the described processes function quite similarly
with respect to experiential stressors caused by the diverse array of experiences introduced
previously (e.g., social and institutional).
We first take a look at the “big picture,” laying out an evolutionary rationale for why
one might expect links between early experience and long‐term health. I then introduce
some of the specific biological mechanisms through which these long‐term effects likely
manifest – the biological embedding of early experience through: (1) the early experiential
coordination of young children’s physiological stress and immunological systems, and
(2) epigenetic alteration.
Evolution, “Fetal Origins,” and Pre-/Perinatal Stress
Drawing from wide‐ranging areas of study – from evolutionary ecology to developmental
psychoneuroimmunology – a convergent literature indicates that children’s experiences
very early in life can become biologically “embedded” in ways that have pronounced
effects on life‐course health. Much of this work is either directly or indirectly informed by
contemporary evolutionary theories of developmental plasticity (West‐Eberhard, 1989;
2003). The basic idea of developmental plasticity is that we (like many organisms) have
evolved mechanisms through which early experiences tune our biological, psychological,
and behavioral processes in ways that maximize the probability of early survival and subsequent reproductive success in that given environment. That is, in the most fundamental
way, we are built to adapt to our experiences.
In evolutionary ecology the instantiations of these inter‐related processes are often
referred to as Life History (LH) Strategies (Belsky, Steinberg, & Draper, 1991; Ellis,
Figueredo, Brumbach, & Schlomer, 2009). In the context of environments that cue threats
to reproductive success, organisms are theorized to adopt “fast” LH strategies. Fast LH
strategies entail metabolic, physiological, and psychological shifts that assure early survival
in the context of physical (e.g., nutrition) and psychological (e.g., stress) risks, as well as
quickly paced maturation and sexual debut. With respect to selection, fast LH strategies
are thought to be adaptive because they maximize early survival and subsequent fecundity
when threats to reproductive success and offspring survival are high. However, central to
Life History Theory, such strategies typically come with tradeoffs. For instance, fast LH
strategies are energetically costly and, therefore, come at the expense of longevity
(Belsky et al., 1991; Ellis et al., 2009).
Slow LH strategies, in contrast, support metabolic, physiological, and psychological
shifts suited to contexts in which threats to early survival and eventual reproductive success
are minimal, such as lower metabolism, slower maturation, and richer physiological and
psychological tools to negotiate the environment. Here, energetic costs are expended over a
protracted developmental span and, thus, exert far less of a toll on longevity. Yet, given that
absolute numbers of offspring are comparatively diminished, slow LH strategies nonetheless
Early Childhood Health Disparities 43
also incur a cost. Indeed, slow LH strategies would incur particularly strong costs, were
environments to become unpredictable, competitive, or dangerous after slow strategies have
been largely established. In the simplest terms “fast” LH strategies are thought to reflect
short‐term investments in offspring quantity – sometimes at the cost of long‐term morbidity; whereas, “slow” LH strategies are thought to reflect longer‐term investments in offspring
quality (Belsky et al., 1991; Del Giudice, Ellis, & Shirtcliff, 2011; Ellis et al., 2009).
What does any of this have to do with early experiences and long‐term health? Given
these tradeoffs, the biological machinery that evolved to allow one to maximize early survival and subsequent reproductive success in the context of physical and psychological
stress (i.e., fast LH strategies) may well be the same biological machinery that underlies
long‐term health problems (i.e., morbidity, premature senescence). That is, if the stressors
faced by children in early childhood act upon this same vestigial biological machinery, one
would expect clear connections between early experience and life‐course health – because
that is the way the machinery was selected to work.
There is good reason to suspect that the early life experiences faced by children growing
up in poverty may often be functionally similar to those experienced on a more evolutionary time‐scale. That is, although we no longer face the same predator–prey, survival, and
reproductive pressures of our ancestral past, the biological mechanisms presumably
selected in these ancestral contexts likely extend to the physical and psychosocial risk
f­actors experienced by low‐income children today (Ellis et al., 2009).
Early work in this area was concerned with the potential long‐term impacts of pre‐ and
perinatal exposure to malnutrition and stress on children’s health – risk factors that are
considerably more common for pregnant women in poverty. For instance, pregnant
women in poverty are more likely to show unhealthy weight gain during pregnancy, with
some under‐gaining and others over‐gaining (Laraia, Epel, & Siega‐Riz, 2013). Both are
associated with greater risk of preterm birth (Nagahawatte & Goldenberg, 2008). Pregnant
women in poverty are also more likely to face a confluence of physical and psychosocial
stressors known to affect the hormonal milieu of the inter‐uterine environment and infant
health (DiPietro, 2004; Schetter, 2011).
In particular, Barker’s (1998) seminal “fetal‐origins” hypothesis raised the generative
idea that exposure to early physical (e.g., nutrition) and physiological stressors (e.g., stress
hormones) alter the biological set‐points that regulate physiological and metabolic
responses to changing internal and external demands (Gluckman, Hanson, Cooper, &
Thornburg, 2008). In turn, these early adaptations were posited to have long‐term cascading effects on health. For instance, malnutrition and physiological stress in utero are theorized to initiate metabolic changes in the developing fetus in ways that maximize the
body’s ability to store fatty acids and insulin (Barker, 2004). From an evolutionary perspective, this biological plasticity to early nutritional or physiological stress is thought to
confer adaptive advantages because it essentially calibrates these nascent systems to function in ways that are optimally aligned with survival in the c­urrent environment. In this
case, these metabolic changes are critical for supporting the immense energetic demands
of the developing prenatal central nervous system (Gluckman et al., 2008).
To the extent to which nutritional quality was also compromised in the post‐natal
environment, these prenatal compensatory adaptations also confer secondary benefits – for
example, postnatal growth in the context of limited caloric availability. However, this same
44 Berry
adaptation may also confer risks. For instance, such early metabolic changes also place
young children at risk for early insulin resistance and obesity (Stout, Espel, Sandman,
Glynn, & Davis, 2015; Vickers, Breier, Cutfield, Hofman, & Gluckman, 2000). This is
likely particularly the case for children growing up in present‐day poverty, whose diets
often comprise low‐cost, high‐energy foods (Drewnowski & Darmon, 2005).
At face value, one would typically consider insulin resistance and obesity to be negative
health outcomes. Indeed, with regard to modern‐day longevity they are undeniably
n­egative (Park, Falconer, Viner, & Kinra, 2012). Yet, from a Life History perspective, early
nutritional or physiological threats experienced early in development may trigger a range
of biological and behavioral changes that are – in an evolutionary sense – quite adaptive,
despite their negative impacts on long‐term health (Del Guidice et al., 2011; Ellis et al.,
2009). For instance, early adaptations underlying heightened adiposity/obesity are also
known to trigger earlier sexual maturation (e.g., Ong et al., 2009; Sloboda, Hart, Doherty,
Pennell, & Hickey, 2007; Terry, Ferris, Tehranifar, Wei, & Flom, 2009). Early sexual
maturation is a critical component of fast LH strategies that maximize the likelihood of
fecundity in the context of limited or threatening environments. As such, an adaptation
that once carried substantial fitness value can nonetheless be maladaptive when considered
in the context of present‐day lifespans.
Using low birth/infant weight as a proxy for early biological adaptation, considerable
epidemiological evidence shows long‐term associations between birth/infant weight and
long‐term health (Barker, 1998; 2004; Hanson & Gluckman, 2014). Similar effects are also
evident in “natural experiments” (Almond & Mazumder, 2005; Roseboom et al., 2001;
Stein, Zybert, Van der Pal‐de Bruin, & Lumey, 2006). For example, between 1944 and
1945 the Nazi regime cut food and fuel supplies from reaching a highly populated region of
western Netherlands. The ensuing famine led to catastrophic declines in daily caloric intake
in the region during this span (~400–800 calories; Roseboom et al., 2001). Researchers have
leveraged this malevolent action as a plausibly exogenous shock, studying the causal impacts
of prenatal caloric restriction and physiological stress on long‐term health. For instance,
comparing those who experienced this nutritional (and presumably stressful) event in utero
to those who were in utero just prior to or just after the event, Roseboom and colleagues
(2001) found that by the age of 30 those who experienced the famine prenatally fared worse
on several physiological health indicators (e.g., glucose tolerance, lipid profiles, BMI). Using
sibling‐fixed effects methods, Stein and colleagues (Stein et al., 2006) found that the famine
had similar effects on weight and adiposity in women nearly 60 years after exposure.
Although the mechanisms remain unclear, this work suggests that health insults occurring
very early in life may have far‐reaching effects on life‐course health.
Poverty and Stress in Early Childhood
More recent theoretical and empirical work highlights the idea that the relation between
early experience and long‐term health extends beyond the prenatal period and into early
childhood. Several evolutionarily‐informed models propose that experiential cues – such
as the less predictable, responsive, and emotionally warm social relations that tend to
occur in the context of poverty – play a direct role in organizing young children’s nascent
Early Childhood Health Disparities 45
physiological, affective, and cognitive systems (Blair & Raver, 2012; Boyce & Ellis, 2005;
Miller, Chen, & Parker, 2011; Del Guidice et al., 2011; Karatoreos & McEwen, 2013;
Parker, Buckmaster, Sundlass, Schatzberg, & Lyons, 2006; Porges, 2011). In particular,
early experiences comprising chronically unsupportive, unpredictable, or challenging
social experiences are proposed to “tune” children’s developing autonomic (parasympathetic and sympathetic) and adrenocortical (e.g., HPA‐axis) systems, such that they are
particularly “vigilant” and responsive to environmental change (e.g., perceived threat).
These systemic physiological shifts toward vigilance are thought to confer (short‐term)
adaptive advantages by supporting the organism to effectively regulate environmental and
psychological challenges. Given the vestiges of their evolutionary origins, they are also
theorized to underlie physiological, metabolic and behavioral profiles reflecting “fast” life‐
history strategies that would have carried fitness benefits in our ancestral past – yet, as
described earlier, accrue health risks in the long term.
There is good evidence that the physical and psychosocial environments faced by young
children in poverty play an important role in their developing physiological stress systems.
For instance, the cumulative stress of poverty can undermine parents’ abilities to e­ffectively
and consistently read, interpret, and respond to their children’s affective needs (Blair &
Raver, 2012; Conger, Conger, & Martin, 2010). In turn, both experimental work with
animals and observational studies with children suggest that compromised parenting can
impact autonomic nervous system (ANS) and hypothalamic pituitary adrenal (HPA)
axis functioning – the two main human physiological stress systems (Calkins, Propper, &
Mills‐Koonce, 2013; Del Guidice et al., 2011; Gunnar & Herrera, 2013; Hostinar &
Gunnar, 2013). Lower quality parenting is commonly predictive of atypical (i.e., over‐ or
under‐responsive) physiological profiles. Indeed, there is some direct support for such
cascading effects in early childhood. For example, Blair and colleagues (2011) found that
low family income experienced early in life was predictive of subsequently elevated cortisol
levels – the end‐product hormone produced by the HPA axis – in early childhood.
Moreover, they showed that this relation was explained partially by the way low income
“trickled down” to negatively affect parenting quality.
Beyond psychosocial risks, the physical environments experienced by children in poverty have been implicated in shaping children’s developing physiological stress systems
(Evans, 2004; Evans & Wachs, 2010). For example, children in low‐income families are
more likely to be exposed to environments that are more densely populated, noisy, disorganized, and unpredictable (Evans & English, 2002; Evans, 2004). Collectively, these
aspects of the environment are often considered under the umbrella term, household chaos.
A growing literature suggests that chaotic environments may alter children’s ANS
(Evans & English, 2002) and HPA axis functioning (Evans & English, 2002; Berry, Blair,
Vernon‐Feagans, Willoughby, & Granger, 2015).
Stress, Inflammation, and Health in Early Childhood
The idea that chronic physiological stress has negative impacts on health is not new
(e.g., Seyle, 1950). However, plausible biological accounts for how stress exposure early in
life translates to health effects that manifest several decades later have only recently begun
46 Berry
to emerge (e.g., Danese & McEwen, 2012; Miller et al., 2011). Miller and colleagues’
(2011) model of biological embedding provides a particularly compelling theoretical
framework for clarifying these developmental processes. Aligned with the evolutionarily‐informed models introduced earlier in the chapter, they argue that the physiological
stress systems (e.g., ANS, HPA axis) and innate immune systems are intimately linked,
given the functional selection advantages borne of pairing metabolic adjustments and
tissue repair to physiological cues of caloric need and physical danger (see also Sapolsky,
Romero, & Munck, 2000). However, in the long term, they posit that the v­igilant/reactive physiological and behavioral profiles that often manifest in the context of chronic
stress also initiate immunological changes that put one at heightened risk for health problems later in life. As described earlier, this is simply another tradeoff of fast
LH strategies.
Specifically, these authors propose that exposure to chronic physiological stress in early
childhood leads to cumulative changes in the way the innate immune system regulates
inflammation. In the context of normative (i.e., modest/moderate) levels of physiological
stress, inflammation plays a key role in supporting cellular health. When tissues are
damaged, inflammatory processes coordinate cells of the innate immune system to remove
pathogens and repair or remove damaged tissue (Koh & DiPietro, 2011). A key component of this process entails the recruitment of monocytes – white blood cells that
a­ccumulate in damaged tissue. Upon reaching tissue, monocytes subsequently divide and
differentiate into macrophages and dendritic cells that are critical to effective cellular
repair (Koh & DiPietro, 2011; Miller et al., 2011). These complex cellular processes are
driven by molecules called pro‐inflammatory cytokines (e.g., interleukin‐1β [IL‐1 β];
interleukin‐6 [IL‐6], and tumor necrosis factor‐α [TNF]), which, in turn, transact with
proteins (e.g., C Reactive Protein) and white blood cells associated with the acquired
immune system (e.g., T Cells) to support effective acute responses to infection and injury.
In the short term, acute inflammatory responses are critical for survival.
In contrast, inflammation can play a more sinister role when activated chronically.
A well‐developed literature shows that chronic inflammation underlies wide‐ranging
m­etabolic problems, including insulin resistance, type II diabetes, obesity, atherosclerosis,
hypertension, and stroke (see Gregor & Hotamisligil, 2011; Hotamisligil, 2006; Libby,
DiCarli, & Weissleder 2010). Recent work has also implicated inflammation as a key
mechanism in the hypothalamic regulation of these metabolic processes (Valdearcos, Xu, &
Koliwad, 2015). Chronic inflammation is thought to play a role in premature aging
(e.g., weakness, immobility, declining immunological and endocrine functioning; Chung
et al., 2009; Jenny, 2012). Further, although inflammation has been found to show both
pro‐ and anti‐tumor effects in cancer pathology, increasing evidence suggests that inflammation often promotes the initiation and progression of tumor growth (Galdiero &
Mantovani, 2015). Indeed, recent estimates suggest that approximately 25% of cancers
may be associated with chronic inflammation (Brennecke, Allavena, Laface, Mantovani,
Bottazzi, 2015; Mantovani, Allavena, Sica, Balkwill, 2008).
Informed by well‐developed neuroimmunological literature (see Irwin & Cole, 2011),
Miller and colleagues (2011) propose that the chronic activation of the two major physiological stress system – the ANS and the HPA axis – early in life initiate cellular modifications that lead to elevated inflammatory response and weakened inhibitory regulation of
Early Childhood Health Disparities 47
these inflammation processes. That is, owing to the complex feed‐forward and feedback
processes linking the ANS, HPA axis, and immunological systems, the chronic release of
catecholamines (e.g., epinephrine, norepinephrine) from the sympathetic branch of the
ANS and cortisol from the HPA axis create a biological milieu that promotes the pro‐
inflammatory tendencies of macrophages and other white blood cells that drive innate
immune response. Over time, these processes are theorized to culminate in increasingly
chronic states of inflammation.
Although longitudinal data are scarce, evidence from the Dunedin Multidisciplinary
Health and Development Study (DMHDS) is consistent with this model. The DMHDS
comprises data from a birth cohort of children sampled in from Dunedin, New Zealand
in 1972–1973, who were then subsequently followed prospectively through adulthood.
Based on these data, Danese and colleagues (2009) found that several indicators of experiential stress in childhood – child maltreatment, low SES, and social isolation – were
independently predictive of high levels of a C Reactive Protein (a biomarker of heightened
inflammation) as well as heightened risk of cumulative metabolic problems (i.e., high
blood pressure, high total cholesterol, low high‐density lipoprotein cholesterol, high
g­lycated hemoglobin, and low maximum oxygen consumption) at age 32. These effects
were robust after adjusting for familial health risk, as well as contemporaneous SES and
heath behaviors.
Similar findings are evident from cross‐sectional, retrospective studies. For instance,
Coehlo and colleagues’ (Coelho, Viola, Walss‐Bass, Brietzke, & Grassi‐Oliveira, 2014)
recent systematic review of this literature (~20 studies, after exclusion) suggests a rather
robust positive relation between child maltreatment and concentrations of biological indicators of inflammation in adulthood. Other work indicates that these relations do not
appear to be restricted to the more “extreme” stress of maltreatment. For example, Miller
and colleagues (Miller et al., 2009) sampled 25–40‐year‐olds who – based on parental
occupational prestige – were rated as being low versus high SES in early childhood.
Adjusting for contemporaneous SES and other potential confounds, they found that those
from low‐SES families tended to output higher levels of cortisol across the day. In addition, when they exposed the participants’ extracted white blood cells to viral and bacterial
stimuli known to evoke immune response (i.e., ex vivo), they found that the pro‐inflammatory
response was stronger for those from low‐SES backgrounds.
Of course, one should keep the considerable caveats with respect to potential endogeneity problems in mind (e.g., unobserved confounds; inaccurate retrospective accounts).
Collectively, though, as part of a growing literature (see Danese & McEwan, 2012; Miller
et al., 2011), this work is consistent with the following ideas: (1) early life stress may have
long‐term effects on physiological stress systems, (2) perhaps, by virtue of this internal
physiological milieu – these early experiences are associated with heighted inflammatory
immune response at a cellular level, and (3) these processes collectively manifest in disease
states much later in life.
Notably, a critical question remains: How do early states of physiological stress and
chronic inflammation become embedded biologically? What early biological changes
might catalyze developmental effects on health that may be rather minimal early in life,
yet become substantial with age? Although this area of study is largely in its infancy, several
possibilities have been advanced, including cumulative structural changes to chromosomes
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(e.g., telomere erosion; Cohen et al. 2013; Haussmann & Marchetto, 2010) and systematic,
long‐term changes to tissues within the stress systems and brain (e.g., tissue remodeling;
Miller et al., 2011). As introduced next, however, one of the most developed literatures
concerns the plausible role of epigenetic alteration – (largely) stable modifications to genetic
expression that occur without changes to the actual DNA sequence.
Epigenetic Mechanisms of Early Adversity and Life‐Course Health
Epigenetic mechanisms play a role in an array of biological processes, such as cellular
d­ifferentiation, imprinting parental origin of DNA, X chromosome silencing, as well as
cellular pathologies (e.g., cancer). Importantly, as sparked by Meaney and colleagues’
(see Meaney & Szyf, 2005) seminal research program with rodents, increasing evidence
indicates that epigenetic modification may serve as an important molecular mechanism
through which experience impacts long‐term developmental outcomes. Indeed, although
evidence suggests epigenetic modifications can be pharmacologically reversed (Weaver
et al., 2004; Weaver, Meaney, & Szyf., 2006), epigenetic changes are often stable over long
developmental spans and are heritable across generations (Heard & Martienssen, 2014;
Szyf, 2015). Thus, epigenetic processes serve as a particularly plausible mechanism through
which the effects of early adversity can manifest much later in life – and, perhaps, across
generations (Heard & Martienssen, 2014).
Epigenetic modifications can be driven by several molecular events (see Gräff, Kim,
Dobbin, & Tsai, 2011). Many involve altering the availability of the particular DNA
sequences that can be read (i.e., transcribed) and subsequently expressed as RNA, amino
acids, and (ultimately) proteins. When we think of DNA, we often think of the ladder‐like
structure of the double‐helix. Notably, this double‐helix is wrapped tightly around p­roteins
called histones, which help to provide structure to the DNA strands. In turn, individual
histones are clustered in tight 8‐histone (octamer) groupings called nucleosomes. The nucleosomes are then bound together to form a macromolecule called chromatin – the physical
packaging of the DNA within a chromosome. This compact binding of DNA is necessary
because it allows the DNA to fit within the nucleus of eukaryotic cells, where it is housed.
However, the compactness of chromatin structure is dynamic and flexible and affected by
the chemical signals it receives. Changes in the density of the chromatin structure (i.e.,
tight to loose) have pronounced effects on genetic expression – that is, the degree to which
the gene is turned on or off – because the tightness or looseness of the c­hromatin affects
the extent to which DNA are visible for transcription and, thereby, the extent to which the
gene is expressed.
One epigenetic mechanism, histone modification, affects genetic expression by chemically
altering the “tails” of histones in ways that impact the chromatin structure. The specific
process by which this occurs (e.g., acetylation, methylation, phosphorylation, ubiquitination)
and the extent to which it up‐ or down‐regulates expression is complex (Gräff et al., 2011).
However, as one example, acetylation of the histone tail often initiates a molecular
c­ascade that relaxes the chromatin structure, such that it can be more effectively t­ranscribed
and ultimately expressed.
Early Childhood Health Disparities 49
To date, the most relevant work with respect to children’s early experiences and epigenetic alteration has largely concerned the methylation of the DNA itself. Methylation
occurs when a methyl group binds with a cytosine DNA nucleotide. This typically occurs
in sites called CpG islands, which comprise rich segments of adjacent cytosine‐phosphate‐
guanine dinucleotides (hence, CpG). CpG islands are commonly located in or near the
promoter regions of a gene – non‐coding segments of DNA that help regulate gene
t­ranscription (Christensen et al., 2009). Methylation of the promotor typically blocks
transcription factors from being able to initiate transcription. It also often sets off a series
of events that lead to the deacetylation of histone tails (Fuks, 2005). Deacetylation is the
inverse of acetylation; it leads to the compaction of the chromatin structure. As such,
methylation typically down‐regulates genetic expression by virtue of multiple chemical
processes. It may also serve as an important biochemical mechanism through which early
experience impacts long‐term outcomes.
Emerging Developmental Evidence from Non‐Human Animal Studies
There is good evidence that epigenetic processes, such as methylation, serve as important
chemical mechanisms through which information about the caregiving environment is
transmitted to offspring. Meaney and colleagues’ (Meaney & Szyf, 2005) elegant cross‐
fostering experiments with rodents provided much of the groundwork in this area of
study. In their work, they have shown that normative differences in maternal behavior,
such as licking and grooming (LG) by rat dams, can have long‐lasting effects on the infant
rat pups’ developing physiological stress systems (Caldji, Diorio, & Meaney, 2000; Caldji
et al., 1998; Weaver et al., 2004) and, in turn, behavior (Weaver et al., 2006). Critically,
these cascading effects of parenting on stress physiology and behavior were explained partially by the fact that maternal LG behavior manifested in different methylation patterns
in glucocorticoid receptor (GR) genes (e.g., NR3C1) extracted from the hippocampal
brain cells of the cross‐fostered rat pups. Rat pups cross‐fostered to low LG dams showed
highly methylated GR receptors in the hippocampal area of the brain; whereas those of rat
pups cross‐fostered to high LG dams were hypomethylated (McGowan et al., 2011;
Weaver et al., 2004). The fact that rat pups were cross‐fostered is non‐trivial because
it rules out the possibility that any parenting effects are actually explained by genetic
(i.e., DNA) differences and/or DNA‐environment correlations.
With respect to long‐term health, these epigenetic changes in GR‐related genes may be
critical. Glucocorticoid receptors provide the main pathways through which cortisol – the
end‐product hormone of the HPA axis – impacts the brain. Specifically, when cortisol
binds to GRs it initiates a negative feedback loop between the GR and the hypothalamus,
which tells the hypothalamus to stop the HPA‐axis cascade (i.e., end the stress response;
Gunnar & Quevedo, 2007). As such, with high LG caregiving hypomethylation of GRs
leads to greater GR expression and, in turn, more efficient negative feedback messaging
within the HPA axis. In contrast, low LG caregiving methylates the GR promoter, which
leads to low levels of GR expression, and less efficient negative feedback signaling within
the HPA axis. As introduced earlier, this likely plays an important role in long‐term health
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because the over activation of the HPA axis is thought to underlie the chronic inflammatory states known to cause wide‐ranging negative effects on health. Notably, because
cortisol also typically shows similar negative feedback effects on inflammation (i.e., more
cortisol, less inflammation), the effect of chronic cortisol on heightened inflammation
likely manifests by desensitizing of this acute inhibitory effect of cortisol on inflammation
(Miller et al., 2011; see ex vivo findings later in the chapter).
Increasing evidence also suggests that life stress may affect the expression of genes
a­ffiliated with both stress and immune responses. Experimental work with non‐human
primates, for example, has shown that exposure to early life stress is predictive of systematic
differences in the methylation patterns in cell‐lines known to underlie stress and immune
response (Cole et al., 2012; Provencal et al., 2012; Tung et al. 2012). And, there is evidence
suggesting that the effects of early rearing on these epigenetic alterations are direct, as
opposed to a downstream effect occurring later in development (Provencal et al., 2012).
The distal impacts of these epigenetic effects on genetic expression and, ultimately, stress
and immune function are unclear from these data. Collectively, nonetheless, the evidence
from studies of non‐human animals provides support for the idea that early experiential
stress may leave a biological signature on the developing stress and immune systems – perhaps,
even in very first few weeks of life.
Emerging Developmental Evidence from Human Studies
Developmental studies of epigenetic processes are beginning to extend into human populations. Indeed, some work suggests that such embedding processes begin prenatally. Based
on (virtually) the same sample of surviving adults from the 1944 Dutch famine described
previously, Tobi and colleagues (2009) found that individuals (~60 years old) who were
exposed to the famine around the time of their conception (or to a lesser degree in late
gestation) showed higher methylation levels across several candidate genes linked with
immunological functioning (e.g., inflammation, ATP, leptin) compared to their own siblings who were not exposed to the famine. Interestingly, some (but not all) of the methylation patterns were similar to those found with genetic homologues evidenced in studies of
non‐human primates (e.g., Provencal et al., 2012). Other work has used mixed PBMCs
from newborns’ umbilical cords as a proxy measure for epigenetic changes that occurred
in utero. For example, infants born to mothers suffering from depression in their third
trimester have been found to show more highly methylated regions of the glucocorticoid
receptor (NR3C1) than those of non‐depressed mothers (Oberlander et al., 2008). This is
strikingly consistent with the early NR3C1findings from studies of rat pups fostered by
low LG mothers (Meaney & Szyf, 2005), particularly given the cell‐line differences
between the studies (i.e., blood cell versus hippocampal brain cell). Indeed, the extent to
which one expects meaningful epigenetic consistencies across cell‐types is still largely
unclear (though, Provencal et al., 2012; also see later).
An increasingly convergent literature suggests that related epigenetic effects extend to
children’s experiences later in childhood. For example, retrospective accounts of childhood maltreatment and parental loss have been linked to NR3C1 methylation patterns in
Early Childhood Health Disparities 51
adulthood that are quite similar to those found perinatally (Tyrka, Price, Marsit, Walters, &
Carpenter, 2012). Related findings are also evident when considered at a genome‐wide
level. Naumova and colleagues (2012) compared the genome‐wide methylation profiles
between a sample of Russian children (7–10 years old) raised in institutionalized
care – which entails substantial early deprivation and social isolation – with those from an
income‐matched comparison group of Russian children raised by their biological parents.
Based on data from mixed PMBCs they found noteworthy differences in the methylation
patterns between groups. Children raised in institutionalized care typically showed methylation levels that were much higher than the matched comparison group. Bioinformatic
analyses suggested the methylated genetic loci tended to cluster in genes known to impact
physiological stress system functioning (e.g., arginine/vasopressin, glucocorticoid and
steroid biosynthesis), the neural transmission of stress‐ and reward‐relevant information
(e.g,, the dopaminergic system, serotonin biosynthesis and receptor activity), and immune
functioning (e.g., inflammatory response, cytokine activity, antigen processing, toll‐like
receptor signaling).
With respect to this clustering of stress and immunologically relevant genes, remarkably similar trends have been shown with samples of foster children in the US. Based on
T‐Cells extracted from mixed PMBCs – blood cells that are particularly relevant to
acquired immune‐system functioning – Bick and colleagues (2012) compared the genome‐
wide methylation patterns of children exposed to foster care in childhood with those who
had not. As young adults, those who were exposed to the foster care system in childhood
showed higher methylation patterns across 72 genes and lower methylation across 101
genes. Although the exact methylation pattern varied somewhat from the findings with
the Russian sample, their bioinformatic analysis, again, showed that the largest methylation differences across the foster versus non‐foster groups typically occurred in genes
underlying immunological functioning (e.g., ubiquitin‐mediated modulation of inflammation response, antibody activity). Using a candidate‐gene approach with these same
data, these authors found that mother‐reported ratings of affection and warmth toward
the child measured 5 to10 years prior to the collection of the blood samples was strongly
negatively (‐.81) correlated with methylation of the GR (NR3C1) gene, as well as the
immunologically‐relevant gene (MIF) known to mitigate the link between glucocorticoids
on inflammation (‐.80). This likely means that (like the early findings with rodents)
warm parenting is predictive of more efficient down‐regulation of the HPA axis (via GR
­expression/function), as well as, perhaps, immunological mechanisms that regulate connections between the HPA axis and inflammation.
Importantly, these findings do not appear to be limited to children experiencing
extreme levels of social adversity (e.g., maltreatment, social isolation). Longitudinal work
using the 1958 British Birth Cohort Study showed long‐term associations between broader
measures of SES between birth and 7 and the genome‐wide methylation patterns found in
the children’s mixed PMBCs as adults (~45 years old; Borghol et al., 2012). Methylation
differences between children experiencing low and high SES in early childhood were evident in genes linked to a broad array of functions – ranging from intra‐ and extra‐cellular
signaling to sensation and perception. Notably, there was a sizable representation of genes
thought to underlie immunological functioning (e.g., inflammation, cytokines), including
some of the same processes noted by others (e.g., ubiquitin‐mediated modulation of
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inflammation; Bick et al., 2012). Further, this work indicated that these relations were:
(1) evident after adjusting for levels of contemporaneous SES in adulthood, and (2) that
there was very little overlap in the methylation patterns associated with early versus later
SES. These findings raise the provocative idea that epigenetic effects that emerge early in
life may be quite distinct and, perhaps, considerably more salient than those occurring
later in life.
Indeed, the findings from a growing number of retrospective studies similarly highlight
the importance of environmental stress experienced in early childhood. In particular, a
comprehensive series of studies by Chen, Cole, Kobor, and Miller provide compelling
evidence that early‐life SES is predictive of differences in epigenetic methylation and
genetic expression in adulthood, after adjusting for contemporaneous SES. For example,
based on mixed PMBC samples from adults (~25–40) selected to have experienced either
high or low SES (operationalized as parental occupational prestige) between birth and the
age of 5, these authors reported differences in expression across 140 genes between the two
early‐life SES groups (73 up‐regulated, 67 down‐regulated). Bioinformatic analyses
revealed that these expression differences were associated with the up‐regulation of genes
linked to the adrenergic functioning and immunological signaling, as well as the down‐
regulation of genes linked with glucocorticoid receptor functioning (though, not NR3C1,
specifically; Miller et al., 2009).
Again, this is consistent with the idea that chronic experiential stress is predictive
of over‐active physiological and immune response, as well as the less‐efficient down‐
regulation of these systems. These inferences were also evident empirically. On average,
those from low‐SES contexts in early childhood were found to show higher levels of
overall cortisol output over the course of the day as adults. Further, ex vivo analyses of
the response of these cells to toll‐like receptor stimulation – a chemical trigger that
initiates cellular immune response to pathogen/injury – showed that the cells from
adults from the low‐SES group showed a considerably more substantial immunological
response (i.e., interleukin‐6 cytokine).
In subsequent work with this same sample, this group found rather weak correlations
between methylation and genetic expression (Lam et al., 2012). This is somewhat c­ ounter‐
intuitive, given the common interpretation that methylation down‐regulates genetic
expression. However, it likely underscores that fact that these processes are far more complex, heterogeneous, and dynamic across individuals, cell‐types, and loci than considered
typically (Gräff et al., 2014). Nonetheless, the findings with respect to methylation are
strikingly similar to those for expression. On average, low SES in early childhood, but not
adulthood, was predictive of higher levels of CpG methylation as adults and higher levels
of methylation were, in turn, predictive of higher levels of diurnal cortisol output and
self‐reported levels of stress in adulthood. Further, ex vivo analyses of cellular function
showed that higher levels of methylation were associated with more pronounced cellular
immune response (i.e., cytokine interleukin 6) to toll‐receptor stimulation – quite similar
to the relation already noted for genetic expression.
Indeed, more recent work from this group has provided provocative cross‐species
e­vidence of the close tie between the sympathetic branch of the ANS and immunological
function (Powell et al., 2013). Based on findings from a repeated social defeat stress
p­aradigm conducted with mice, they show that the effect of experiential stress on immune
Early Childhood Health Disparities 53
function is driven, in part, by the effects of sympathetic stress response on the expression
of genes in bone marrow cells that produce monocytes and other immune‐relevant white
blood cells (myelopoiesis). Recall that monocytes are the critical workhorse leukocytes that
help orchestrate a cascade of cellular immunological changes (e.g., inflammation) that
drive the immune response. That is, in addition the regulatory cross‐talk between the
physiological stress systems and the immune systems, sympathetic innervation of myelopoiesis may also lead to a baseline over‐production of the leukocytes that drive the chronic
states of inflammation. Critically, they show that the genetic expression differences
between stressed and non‐stressed mice (in monocyte cells) show notable overlap (~32%)
with the genetic expression differences (in mixed RBNCs) found between adults who
experienced either low versus high levels of early‐life SES. As such, it seems plausible that
part of the effects of early stress on long‐term health may manifest via the effects of stress
on the very biological machinery that undergirds the production of white blood cells – the
backbone of inflammatory response of the innate immune system.
Putting the Pieces Together
The first section of this chapter briefly reviewed the considerable literature suggesting that
health disparities are evident across socio‐economic, racial, and geographic lines from the
earliest stages of development – prenatally, perinatally, and throughout early childhood.
These disparities persist – and may grow stronger – into adulthood. The etiologies of
these short‐ and long‐term disparities are complex and multifactorial. As posited by
“s­ystems” (e.g., Gottlieb, Wahlsten, & Lickliter, 1998) and “cascade” models of development (Masten & Cicchetti, 2010), as well as econometric models of “dynamic complementarity” (Conti & Heckman, 2014), they likely reflect the ongoing, reciprocal, and
self‐organizing transaction of one’s health strengths/liabilities (biologically and behaviorally)
and health‐related experiences (opportunities and choices) over time.
Rather than attempt to articulate these complex dynamics in full, the aim was to introduce the idea that these long‐term developmental dynamics are likely explained, in part,
by the way early experience shapes the organization of several biological systems underlying life‐course health. Specifically, I highlighted contemporary evolutionary thinking
behind much of this work, recent theory implicating the roles of stress and chronic inflammation as critical mechanisms explaining these long‐term relations, and epigenetic alteration as one possible biological process through which these inflammation‐mediated effects
may emerge. My intent was not to privilege biological mechanisms over experiential and
psychological mechanisms in any substantive way. Well‐rounded coverage of the latter has
recently been provided by others (Muennig, 2015; Conti, 2013). Indeed, I hope that the
brief foray into epigenetics accentuates the inextricable connection between experience
and biology.
With respect to developmental processes, the epigenetic literature is arguably the most
well‐developed and provides a compelling biological explanation for how early experience
can feasibly show far‐reaching developmental effects. However, several other processes –
such as telomere erosion and tissue remodeling – are garnering increased interest and
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theoretical and empirical support (Cohen et al. 2013; Miller et al., 2011) and will
undoubtedly add to a more comprehensive understanding of these long‐term developmental processes. In addition, it is worth noting that epigenetic research is still in its
infancy. As noted, some consistencies are beginning to emerge across studies (sometimes
even species); however, there are also notable inconsistencies across studies. Indeed, inconsistencies are going to be common. Genome‐wide expression/methylation analyses typically make comparisons across tens of thousands to millions of loci (e.g., expression
quantitative loci [eQTLs]), and bioinformatic analyses cross‐reference increasingly large
collections of data. The fact that there is often a substantial lack of overlap between studies
and samples is likely partially due to the reality that many of the findings are probably
false‐positives. Like molecular genetics (i.e., DNA) and fMRI, epigenetics fights an uphill
battle against Type I error – disentangling meaningful heterogeneity across predictors,
outcomes, samples, cell‐lines, methods and development from noise and false‐positives
remains a challenge across these areas of inquiry.
More substantively, the field is only beginning to get a handle on how best to interpret
epigenetic modifications. A common concern is the extent to which epigenetic alterations
are specific to cell‐type. For instance, is there good reason to expect that early experiences
will be reflected similarly in the methylation patterns of GR genes that are extracted from
brain cells affiliated with different regions of the limbic‐HPA axis (e.g., frontal cortex,
hypothalamus, hippocampus)? Moreover, should we expect commonalities across these
brain cells and, say, blood cells or buccal mucosa cells extracted from cheek swabs – common
cell types used in studies of human epigenetics? Although there seems to be some overlap
across cell‐types (Provencal et al., 2012; Smith et al., 2015), the intricacies and ultimate
implication of differences remain somewhat unclear. This is an obvious limitation for
studies of humans, for whom brain tissue is not readily available (though Alt et al., 2010;
McGowan et al., 2008). Indeed, even in the context of immune‐system functioning in
which one has ready access to the cells that are doing the actual biological legwork (i.e.,
white blood cells), increasing findings suggest that methylation/expression profiles may
show important differences across different types of white blood cells (e.g., CD3+ T Cells,
CD14 + monocytes; Lam et al., 2012). In addition, as illustrated earlier, there is no 1‐to‐1
correspondence between methylation and expression – which is arguably one of the most
clearly articulated epigenetic alterations. This is not terribly surprising given the complex,
dynamic connections likely occurring within and across genes, specific loci, cells, and
individuals. However, it does suggest that – despite the exciting new contributions that
this area of research has made to developmental science – there is much left to learn.
Does Any of This Matter for Early Childhood Policy?
There is credible evidence that children’s early experiences can have long‐term impacts on
health. This has non‐trivial implications for early childhood policy. There is increasingly
good reason to suspect that these long‐term effects are driven, in part, by dynamic
d­evelopmental connections between children’s emerging physiological stress and immunological systems (see Miller et al., 2011) – a partially vestigial remnant of our ancestral
Early Childhood Health Disparities 55
past (Del Giudice et al., 2011). Although the theorized evolutionary back‐story may seem
tangential with respect to “real-world” applications, it is likely quite relevant. Theory
s­uggests that chronic stress doesn’t function like a bull in an immunological china shop,
leaving random destruction in its wake. Rather, chronic stress may well calibrate cross‐
s­ystem integration in internally consistent and predictable ways. On one hand, this has
troubling implications. If this is accurate, then the present‐day link between early adversity and later health problems is likely partially driven by the fact that normative connections between stress and immune functioning are, in fact, normative because of their
remarkably efficient capacity to maximize short‐term success at the cost of long‐term
health. In other words, in evolutionary terms, these systems are working as intended
(i.e., selected) – contemporary humans just live too long.
On the other, it suggests that under conditions of moderate, surmountable, and intermittent stress – such as those occurring in the context of warm, responsive, and stable
adult‐child relationships and consistent and predictable home environments – these same
mechanisms can be leveraged to initiate stress‐immune processes underlying “slow” life
history strategies thought to reflect effective emotional and behavioral self‐regulatory
control, the perpetuation of positive parenting, and better life‐course health.
Consistent with economically-minded perspectives (e.g., Conti & Heckman, 2014)
the biological data also point to the possibility that experiences occurring in early childhood may have particularly salient developmental effects on children’s long‐term outcomes. For instance, although some of the methodological limitations are noteworthy
(e.g., retrospective, observational data; small, non‐representative samples), several findings
indicated that the long‐term relations evident between early childhood adversity and
genetic expression, methylation, and immunological functioning were robust after adjusting for contemporaneous levels of economic adversity in adulthood. The findings from
experimental studies of rodents and non‐human primates indicate that these experiential
effects can occur very early in development and remain stable across the lifespan (Cole
et al., 2012; Meaney & Szyf, 2005; Provencal et al., 2012). Indeed, epigenetic changes can
be heritable and transmitted across generations (Szyf, 2015). It is also worth noting, however, that increasing evidence suggests that epigenetic modification can be reversed
(at least, pharmacologically with rodents; Weaver et al., 2004; 2006). Even more importantly, epigenetic effects on physiology (e.g., HPA axis) and broader behavioral phenotypes (e.g., anxiety, cognition) can be reversed in the context of subsequently supportive
environments, even without affecting the epigenetic modification itself. That is, just like
the effects of genetic (i.e., DNA) variation, the long‐term effects of the epigenetic variation
are subject to experiential modification.
Nonetheless, questions regarding developmental timing of particularly sensitive periods or “thresholds” underlying links between experience and children’s subsequent health
remain largely unanswered. With some recent exceptions (e.g., Miller & Wherry, 2014;
Ziol‐Gest et al., 2009; 2012), very few studies of humans have adopted designs that allow
the disaggregation of potentially meaningful developmental thresholds. This is often a
thorny design problem, given the typical within‐child stability in broad indicators of SES
over time. On one hand there is every reason to suspect that good health begets good
health (Conti & Heckman, 2014). Thus, children’s experiences in early childhood likely
play a particularly salient role in life‐course health. This certainly aligns with biological
56 Berry
perspectives, which tend to view early childhood as a critical developmental “switch point”
in the biological embedding of experience. On the other hand, it is also worth noting that
contemporary theory tends to view early childhood as one of multiple salient developmental switch points in which the complex cross‐system dynamics underlying experience and
health are especially plastic to re‐organization and change – developmental apertures that
change but don’t close (e.g. see Del Giudice et al., 2011).
A final implication of this work concerns the estimation of policy costs and benefits.
The efficacy of public policy is commonly measured as the benefits produced by the policy
relative to cost. The evidence introduced in this chapter would suggest that the validity of
these analyses with respect to early childhood policy and health will be contingent upon
the accurate assessment of long‐term health impacts. Benefit‐to‐cost ratios will likely be
negatively biased, to the extent to which long‐term health impacts are excluded. This is
noteworthy; the benefits of good health (and the costs of bad health) accrue at far greater
rates in adulthood because health problems tend to be comparatively more chronic and
costly than earlier in life. On average, in the US we tend to incur about 8% of our lifetime
average health costs in childhood (0–19), whereas we incur almost a third of our total
lifetime health costs in middle age (40–64; Alemayehu & Warner, 2004). In addition,
health disparities have benefit‐relevant effects on productivity that manifest only upon
joining the workforce. For example, type 2 diabetes – a chronic and largely preventable
health condition linked to chronic inflammation – is predictive of unemployment and
lower wages (Minor, 2013). Such health effects carry both personal and social costs.
For instance, in the US, diabetes‐related (combined type 1 and 2) absenteeism and
p­roductivity losses were estimated to cost approximately $5 billion and $20.8 billion,
respectively, in 2012.
By virtue of their potential organizing effects on children’s nascent stress and immune
systems (e.g. inflammation), health‐relevant programs/policies (e.g., Earned Income Tax
Credit [EITC], State Child Health Insurance Program [SCHIP], Women Infants and
Children [WIC]; Head Start, etc.) that evince even seemingly modest and/or transient
effects on health in early childhood may well have clinically and economically meaningful
impacts in the long term. Indeed, when considered in light of the presumably massive
positive externalities garnered by long‐term health benefits (e.g., Cawley & Meyerhoefer,
2012; Minor, 2013), these programs quite likely pay for themselves. Of course, this
remains a conjecture to be tested directly. However, it has certainly been the case for early
childhood interventions such as the Perry Preschool and Abecederian Programs, which
have shown marked social returns per dollar spent (e.g., Barnett & Masse, 2007; Heckman,
Moon, Pinto, Savelyev, & Yavitz, 2010). Indeed, these social returns are themselves likely
downwardly biased, given that (to my knowledge) they have yet to account for the impacts
of these programs on long‐term health (Campbell et al., 2014; Muennig, Schweinhart,
Montie, & Neidell, 2009).
In sum, our growing understanding of the psychoneuroimmunology of early childhood
suggests that children’s early experiences play a critical role in the organization and dynamics of their nascent autonomic, adrenocortical, and immune systems. In particular, exposures to ongoing stressors – such as those experienced by children facing socio‐economic
adversity – have been theorized to create an internal hormonal milieu leading to states
of chronic inflammation. In the short term, inflammation plays a vital role in the body’s
Early Childhood Health Disparities 57
ability to support effective acute responses to infection and injury. However, when chronic
inflammation accumulates over time, it is known to underlie a litany of debilitating long‐
term health problems, ranging from type 2 diabetes and cardiovascular disease to tumor
growth. As such, the seeds of long‐term health are sewn early. From a policy perspective,
this work suggests that the true impacts of health‐relevant early childhood policies will
require a life‐course approach; the most dramatic health impacts likely manifest in the
long term. Given the extreme personal and social costs of ill‐health in adulthood, there is
good reason to suspect that the long‐term benefits may well exceed short‐ and long‐term
costs. Collectively, this work suggests that clarifying the biopsychosocial processes underlying early experience and health are vital to understanding the long‐term implications of
early childhood policy.
Note
1. “Early childhood” is used throughout as a catch‐all term for this period, unless stated
otherwise.
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