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 48 Berry (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 50 Berry 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 52 Berry 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 54 Berry 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. References Alemayehu, B., & Warner, K. 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