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. Author manuscript; available in PMC: 2022 May 1.
Published in final edited form as: Neuropharmacology. 2021 Mar 11;188:108518. doi: 10.1016/j.neuropharm.2021.108518

Social vulnerabilities for substance use: Stressors, socially toxic environments, and discrimination and racism

Hortensia Amaro a, Mariana Sanchez b, Tara Bautista c, Robynn Cox d
PMCID: PMC8126433  NIHMSID: NIHMS1683122  PMID: 33716076

Abstract

Applying a social determinants of health framework, this review brings attention to evidence from social sciences and neuroscience on the role of selected social factors in individual and population-level vulnerability to substance use and substance use disorders (SUDs). The understanding that social vulnerability to substance use and SUDs is multifaceted and occurs across different levels of influence (individual, interpersonal, community, and societal) is underscored. We propose that socially based stressors play a critical role in creating vulnerability to substance use and SUDs, and as such, deserve greater empirical attention to further understand how they “get under the skin.” Current knowledge from social sciences and neuroscience on the relationships among vulnerability to substance use resulting from stressors, exposure to socially toxic childhood environments, and racism and discrimination are summarized and discussed, as are implications for future research, practice, and policy. Specifically, we propose using a top-down approach to the examination of known, yet often unexplored, relationships between vulnerability to substance use and SUDs, related inequities, and potential differential effects across demographic groups. Finally, research gaps and promising areas of research, practice, and policy focused on ameliorating social vulnerabilities associated with substance use and SUDs across the lifespan are presented.

1. Introduction

A prodigious scientific literature has documented the multiform influence of life stress on human biology, mental health, and behavioral sequelae. Social determinants are pervasive sources of life stress experienced differentially in social systems. The ramifications of adverse stress exposures are demonstrably injurious to health, yet these exposures have been historically beyond the scope of societal responsibility to redress and outside the official scope of medical care practice. Informed by conceptual frameworks on the social determinants of health (Marmot and Wilkinson, 2006), health disparities (Alvidrez et al., 2019), and stress (Aneshensel, 1992). This paper explores how socially based stressors play a critical role in creating vulnerability to substance use.

Alcohol and drug misuse and related substance use disorders (SUDs) affect millions of individuals in the United States and throughout the world, with significant health consequences and economic costs to societies, communities, families, and individuals (Grant et al., 2015; U.S. Department of Health and Human Services [USDHHS], 2016). Although evident across all sectors of society, the prevalence and negative consequences of alcohol and drug use are generally greater among groups characterized by disenfranchised social status (e.g., poverty, stigmatized identity; Cerdá et al., 2010; Chartier and Caetano, 2010; Glass et al., 2020; Grant et al., 2015; Green and Feinstein, 2012; Reilly et al., 2019; USDHHS, 2016). A robust literature has documented the powerful effects of social environment and context on substance use initiation and disorders, their negative consequences, and treatment access and outcomes (Institute of Medicine [IOM], 2006; National Academies of Sciences, Engineering, and Medicine [NASEM], 2017, 2019a, 2019b; Prom-Wormley et al., 2017). A promising new area of research in the field of SUDs is investigating the interaction between genetic and environmental factors and how slight changes in gene expression can occur over time without altering DNA, known as epigenetics (IOM, 2006; NASEM, 2019a, 2019b; Prom-Wormley et al., 2017).

The vast literature on the neurobiology of substance use has largely focused on a “bottom-up” approach, defined as an approach focused on genetic and biological factors for understanding psychological and behavioral contributors to substance use vulnerability (Camarini et al., 2018; Heilig et al., 2016; Pelloux et al., 2019; Shimamoto, 2018; Tomek and Olive, 2018; Vannan et al., 2018; Yanovich et al., 2018). Only recently has neuroscience employed a “top-down” approach, defined as an approach focused on “diligently looking at the effect of social factors on drug use and addiction in animal models” (Pelloux et al., 2019, p. 1; also see Heilig et al., 2016). Because all human behavior, including and perhaps especially substance use, occurs in a social context (Galea et al., 2004), strict bottom-up approaches present a significant challenge for the translation of basic science to prevention and treatment. Likewise, social science research has much to gain by investigating how “social factors may modify relations between endogenous variables and substance use behavior” (Galea et al., 2004, p. 37). The distinct disciplinary differences in employing bottom-up versus top-down approaches to scientific inquiry on health are not specific to substance use. In fact, this difference was highlighted by the National Academies as a major challenge to interdisciplinary health research and yet key to the advancement of knowledge (IOM, 2006).

Interdisciplinary collaborations between basic and behavioral sciences have facilitated the investigation of biological factors that interact with social and societal factors to place individuals at risk of developing SUDs. Chronic exposure to stressors and a maladaptive stress response to those stressors can influence the development of physical dependence and SUDs. Multiple societal factors outside of an individual’s control may lead to increased exposure and vulnerability to chronic stress, including poverty (Evans et al., 2005), hunger and food insecurity (NASEM, 2019a, 2019b, 2020), homelessness and housing insecurity (NASEM, 2019a, 2019b, 2020), and discrimination (Metzger et al., 2017; NASEM, 2019a, 2020; Williams et al., 1997). Social stress theory (Aneshensel, 1992) suggests that people with disadvantaged social status (such as living below the poverty line and experiencing racial discrimination) are exposed to more stressors (Myers, 2009) and may be more vulnerable to maladaptive responses to stressors due to their chronicity and overwhelmed psychosocial coping resources (Cunliffe, 2016). Such exposure and resulting vulnerability, as explained by social stress theory, may lead to a higher risk of mental illness, including SUDs (Aneshensel, 1992). Approaches focused on downstream factors1 such as individual-level interventions, medical care, and treatment address consequences of these adverse conditions (e.g., substance use vulnerability and disorders). On the other hand, approaches that focus on upstream factors,2 such as those that seek to improve the conditions in which people live via changes in policies, housing, neighborhood conditions, and increased socioeconomic status, could have a more powerful impact at the population level by reducing exposure to modifiable chronic stressors (Braverman et al., 2011; Williams et al., 2008). Such upstream prevention efforts might include structural changes that alter how resources are distributed or how regulatory systems are governed; for example, health care policy and training for providers to assess social determinants of health (housing, access to food, neighborhood safety, chronic stress, and trauma) during medical appointments and a referral process to address areas of concern (NASEM, 2017, 2019a, 2019b).

The current paper brings attention to evidence from social sciences and neuroscience on the role of selected social factors in individual and population-level vulnerability to alcohol and illicit drug use and development of SUDs. We propose that socially based stressors play a critical role in creating vulnerability to substance use and as such, deserve greater empirical to further our understanding of how they “get under the skin.” More specifically, we focus attention on early and ongoing exposure to socially toxic environments, racism, and discrimination as foundational social factors in the psychobiological cascade that creates vulnerability to substance use and its consequences. Using a top-down approach (i.e., in which environmental, external, and human behavioral factors are mapped onto an individual’s psychological response, which can then be observed in biological processes; IOM, 2006), we bring attention to the known and yet often unexplored relationships between vulnerability to substance use and SUDs. Further, we underscore inequities in these foundational social factors and their potential differential effects across race and ethnic groups while noting the moderating and mediating effects of cultural, family, and individual factors.

We first present a conceptual framework to set the discussion in relation to social determinants of health and health disparities. Then, we present evidence from social sciences and neuroscience on the relationships among vulnerability to substance use resulting from stressors, exposure to socially toxic childhood environments, and racism and discrimination. Finally, we highlight research gaps and promising areas of research for deepening the understanding of these factors. Enhancing interdisciplinary approaches is critical to knowledge development because evidence from various disciplines indicates that the etiology of substance use and SUD is multifactorial. These etiological factors include genetic, psychological, and social factors that interact dynamically through the life course at multiple levels to influence vulnerability to substance use at the individual and population levels (Galea et al., 2004; IOM, 2006).

2. Role of social factors and their inequitable distribution in health and substance use

More than 100 years ago, scholar W.E.B. Du Bois (1899) documented the critical role of what today is referred to as social determinants of health, noting that they were systemically and unequally distributed across populations and resulted in health inequities. Differences in health between Whites and non-White populations were largely attributed to the biological inferiority of non-White populations (White, 2011). Dr. Du Bois, a sociologist of significant academic gravitas, challenged these prevailing notions through systematic studies, which provided epidemiological data on the social factors contributing to disease and disparities in the health of Black Americans (White, 2011). Arguably, his legacy informed the framing of our contemporary scientific understanding of how the health of populations is shaped by structural social factors and their cascading and interacting effects on intermediary life conditions they create.

Since that time, volumes of research have verified and expanded the evidence base on social determinants of health (IOM, 2003, 2006; NASEM, 2017, 2019a, 2019b, 2020; Takeuchi and Williams, 2011; White, 2011; Williams and Sternthal, 2010). The role of social factors in health is widely recognized by prestigious and credible scientific and public health organizations. The Institute of Medicine report Genes, Behavior and the Social Environment concluded that research has documented associations between social factors and health, behaviors and health, and genetics and health (IOM, 2006). Likewise, the World Health Organization has recognized the critical role of social factors in health and adopted a social determinants of health framework for understanding and addressing major health conditions and health disparities (Solar and Irwin, 2010). A robust body of scientific work has documented that structural factors (i.e., socioeconomic and political context and socioeconomic position) and intermediary factors (i.e., material circumstances, behavioral and biologic factors, and psychosocial factors) known to affect the health of populations are systematically and disproportionately distributed across population groups (e.g., economic status, gender, race, ethnicity; Adler et al., 2016; Commission on Social Determinants of Health, 2008; IOM, 2006; Marmot and Wilkinson, 1999; NASEM, 2017, 2019a, 2019b, 2020; Singh et al., 2017; Tarlov, 1996).

Applying a social determinants of health framework to the study of vulnerability to substance use, Galea et al. (2004) brought attention to the importance of investigating the contributions of structural determinants; that is, the socioeconomic and political context (i.e., features of governance, macropolitical policies, social and public policies, and cultural and societal values) that affect substance use and related health disparities. Additionally, how do these influence, and get influenced by, socioeconomic position (i.e., social class, occupation, income, gender, racism, education)? This, in turn, is thought to shape intermediary social determinants of health (i.e., material circumstances and behavioral, biological, and psychosocial factors) to affect health outcomes and health equity at the individual and population levels. As a result, these create consequential conditions of health inequities, including vulnerability to substance use (e.g., Boardman et al., 2001; Cave et al., 2020; Earnshaw et al., 2020; Fairman et al., 2020; Galea et al., 2004; Goldstein et al., 2020; Kamiker-Jaffe, 2011; Paradies et al., 2015; Savage and Mezuk, 2014; Spooner and Hetherington, 2004; Thompson et al., 2020; Tsai et al., 2019; Zoorob and Salemi, 2016).

3. Social determinants of health and disparities framework

Building on this framework, more recently, the National Institute on Minority Health and Health Disparities (NIMHD) proposed a more detailed framework (see Figure 1) describing the wide array of determinants that promote or jeopardize health and health disparities (Alvirez et al., 2019). The NIMHD framework relies on an earlier framework proposed by the National Institute on Aging (Hill et al., 2015) and augments it by incorporating multifactorial levels (i.e., from individual to societal levels) of the widely used socioecological model of health and human development introduced by Bronfenbrenner (1977). As shown in Figure 1, the framework is represented as a matrix with domains of influence on health (y-axis) and levels of influence on health (x-axis), in which cells specify unique determinants applicable to the study of a particular health outcome or disparity, such as vulnerability to substance use. For example, stemming from the biological domain of influence in Figure 1, vulnerability to substance use, development of SUD, and related disparities are thought to be driven by multiple levels of influence (Galea et al., 2004). These include genetics (e.g., familial genetic predisposition) and biological factors such as sex and age (individual level), quality of parent–child relationships (interpersonal level), prevalence of SUDs in the community (community level), and availability of alcohol and illicit drugs (societal level). Starting from the sociocultural environment domain of influence, vulnerability to substance use, development of SUD, and related disparities may also be driven by socioeconomic position, cultural identities, and response to discrimination (individual level); social networks or family and peer norms and exposure to discrimination from others (interpersonal level); community norms regarding substance use and community-level structured discrimination including school policies, housing or law enforcement practices, and prevalence of violence and criminal activity (community level); and regional or national norms about substance use, structured discriminatory policies or practices that result in riskier environments, differential treatment of individuals and groups based on race and ethnicity, access to resources such as high-quality or evidence-based substance use prevention and treatment, and access to health insurance for such services (societal level). Importantly, the framework indicates that the domains of influence can act in dynamic and interactive ways through the lifespan and that health outcomes such as substance use may be observed beyond the individual (e.g., initiation of use or SUD diagnosis at an individual level) to the aggregate (e.g., prevalence of SUDs in a school or work setting, communities, or geographic areas; availability of SUD treatment and related rates of treatment utilization and treatment success in municipalities, states, territories, tribes, or nationally).

FIGURE 1 —

FIGURE 1 —

The National Institute on Minority Health and Health Disparities Research Framework: 2017

The NIMHD framework is aspirational and meant to promote greater scientific attention to research on levels of influence beyond the individual. In reality, R01 grants funded by the NIMHD, and arguably by the National Institutes of Health, predominantly focus on individual-level determinants (91%; Alvidrez et al., 2019). Studies that shed light on how socially activated disease-promoting exposures and inequitably structured conditions get under the skin via biological pathways are especially critical in furthering the understanding of how inequities in social determinants of health create, foster, or mitigate substance use vulnerability at individual and population levels.3

4. Public health parable and an illustrative case example

To highlight the importance of addressing upstream factors in health and the important role of social determinants of health and disparities, we turn to the quintessential public health parable and an illustrative case example. The parable shifts the focus from downstream (i.e., individual treatment or approaches) to upstream (i.e., population-level prevention strategies or approaches for the community at large).

In the classic public health parable credited to medical sociologist, Irving Zola, a member of the local community sees a man caught in a river current. She saves the man, only to be drawn to the rescue of more drowning people. After many have been rescued, she walks upstream to investigate why so many people have fallen into the river. She discovers a beautiful overlook along the river’s edge without any warning signs or protective barriers. (National Collaborating Centre for Determinants of Health, 2014, p. 1)

As in this example, addressing substance use and SUDs primarily via individual-level strategies once risk or disease states are evident is insufficient to influence and prevent ongoing exposures at the population level.

A case example specific to substance use is provided here to highlight the complexities and roles of social determinants of health in the etiology of vulnerability to substance use addressed in this paper (see Box 1). To construct this fictitious but likely representative case example, we relied on the work of Dr. John Rich, whose research has clearly elucidated the role of structural factors on the health of his patients and roots of their vulnerability (Rich, 2000, 2009; Rich and Grey, 2005).

Box 1. Case Example.

An emergency room doctor in an inner-city hospital notices that many young Black men are presenting daily with significant physical trauma from gun shots, stabbings, and beatings. He treats them but notices that soon they are back in the ER with similar injuries and that new young men continue to present in the ER with similar injuries. Too often the outcomes are death or disability. He investigates and finds that his patients’ injuries are often a result of becoming involved in drug dealing and drug use and they often have a long history of psychological trauma. He collaborates with colleagues to get the young men into SUD treatment programs, but due to insufficient capacity, the programs cannot accommodate all the young men who continue to present to the ER with similar situations.

As he digs deeper, he finds that his patients come from highly segregated inner-city neighborhoods with concentrated poverty and poorly performing schools. Most of the young men had been expelled from school and subsequently put into the juvenile justice system for minor offences. Upon exiting the system, their lack of job skills makes it difficult to find employment. Left with few life options and faced with the urgency of immediate survival, some get involved in selling drugs and eventually using drugs, whereas others are just in the wrong place at the wrong time. They are essentially “drowning” in a sea of structural and social conditions and stressors. By this point, it is likely that their chronic exposure to multiple adverse life conditions, chronic trauma, and drug use have compromised aspects of their cognitive and psychological functioning and depleted their coping capacity. Upstream solutions are needed for this population-level problem.

Next, we discuss evidence regarding three social factors (i.e., socially determined stressors, exposure to socially toxic environments, and racism and discrimination) as examples of how social determinants of health and their consequential inequities produce vulnerability to alcohol and drug use, SUDs, and related consequences.

5. Role of stress, stressors, and stress response in vulnerability to substance use

The term stress has long been defined as “the nonspecific response of the body to any demand” (Selye, 1974, p. 27). It is important to highlight the critical role of a demand or stimulus (stressor) being present to activate the stress response. The stress response is a multifaceted reaction to a perception of danger or threat, which includes activation of the hypothalamic-pituitary-adrenal (HPA) axis, increased heart rate and blood pressure, decreased function of the digestive system, and tunnel vision; this is sometimes referred to as “fight or flight” (Cannon, 1929). When an acute stressor occurs, this physiological response can help individuals escape dangerous situations, making it advantageous and increasing survival among those with this response (Romero et al., 2015). However, chronic exposure to stressors leading to overactivation of the stress response has been shown to be disruptive to the body and interfere with other functions, such as reward pathways in the brain (Hughes et al., 2017; NASEM, 2019a, 2020; Shin et al., 2009; Thompson et al., 2017) and cardiovascular health and heart rate variability (Kim et al., 2018; Schubert et al., 2009), placing individuals at greater risk of substance use (NASEM, 2020) and developing a SUD (Sinha, 2008).

Social determinants of health such as socially derived chronic and prolonged stressors (e.g., loss or separation from a parent at an early age; incarceration; concentrated poverty; systemically thwarted opportunities due to discrimination; parental divorce or conflict; emotional, physical, or sexual abuse; violence; neglect; isolation; and loss of home to natural disaster) are predictive of SUDs (Balk et al., 2009; Buzawa and Buzawa, 2014; Hughes et al., 2017; NASEM, 2019; Rivas-Rivero et al., 2020; Shin et al., 2009; Sinha, 2008; Thompson et al., 2017). Broadly speaking, social determinants of health and inequities are systemic and structured external forces or stimuli that can result in chronic activation of the stress response. Throughout this section, we use the term stress to refer to the body’s response to stimuli and refer to the stimuli as stressors.

5.1. Stressors and stress response in the social determinants of health framework

As proposed in Figure 1, socioeconomic and political contexts create an environment in which populations are systematically and differentially exposed to varying degrees of risk and protective factors for health and well-being based on their social class, gender, ethnicity, education, occupation, and income (Alvidrez et al., 2019). Based on where someone fits in each of those categories, they have access to material circumstances such as safe living or working conditions, food, or health care. When considering how stressors and the stress response fit in this framework, we can start by acknowledging the stressors associated with the experience of having lower social status than others. In human and animal studies, prior research has found subordinate social status (i.e., lack of respect from peers, perceived as less competent or less important) to be associated with increased pro-inflammatory cytokines (Muscatell et al., 2016), increased risk of chronic social defeat stress (Larrieu et al., 2017), and increased cortisol (Sherman and Mehta, 2020), all of which influence an individual’s health, well-being, and vulnerability to substance use (Sinha, 2008). The presence of these chronically (and sometimes intergenerationally) occurring stressors creates an overactivated stress response, which leads to a feedback loop of challenges in the environment such as access to health care, costs of stress-related illness creating financial hardship, and difficulty obtaining and maintaining employment or completing educational training, reducing one’s social positioning (Howe et al., 2017).

There are many ways to assess stressors and the body’s response to exposure to those stressors. For example, the Perceived Stress Scale is commonly used to assess the degree to which individuals perceive their lives to be stressful and perceived physiological responses to those stressors, such as the frequency of upsetting events happening unexpectedly and the frequency of feeling physically nervous, anxious, or stressed by these events. This scale has been associated with poor self-reported health, elevated blood pressure, depression, and susceptibility to infection (Cohen et al., 1983). To assess the perception of external stressors, it is important to also explore the internalization of these stressors and biological mechanisms of the stress response. For example, many clinical studies use salivary cortisol as a measure of stress; salivary cortisol gives us an estimate of the HPA function (Sampedro-Piquero et al., 2020). Cortisol can also be measured using hair samples, which is thought to be less invasive but requires further investigation in human and animal models before validity and reliability can be concluded (Burnard et al., 2017). Neuroimaging techniques are also used to assess brain region activation in areas such as the nucleus accumbens and the dorsal medial prefrontal cortex during experimental manipulations designed to activate the stress response (Muscatell et al., 2016).

5.2. Epidemiology and population-level research on stress and substance use

The relationship between stress and substance use has been established in the literature from bench and laboratory to clinical and bedside science and is acknowledged as bidirectional and complex, with evidence to support stress leading to greater substance use, and substance use leading to greater stress (Ramchandani et al., 2018). It is also well documented that stress vulnerability is a significant risk factor for initiation of alcohol and drug use and development of SUDs (Sinha, 2001, 2008). Stress vulnerability is a term used to describe a weakened stress response in the brain, possible due to a prolonged disruption of neurotransmitters such as gamma-aminobutyric acid. The stress–vulnerability hypothesis suggests the relationship between stress and substance use is mediated by risk and protective factors, such as social support, history of depression, coping, and self-efficacy (Brown et al., 1990; Brown et al., 1995).

Stressors do not occur in isolation, making it difficult if not impossible to assess a single stressor’s impact on an individual. The response to a single stressor is based on a lifetime of development and adaptions to prior stressors (Lloyd and Turner, 2008). Evolutionarily, it is beneficial for the stress response to be malleable to help people adapt to the environment, learn from dangerous situations, and respond more efficiently. Rodent and human studies have identified similarities in the threat evaluation and response system, which favors (at least in the short term) sensitivity to false positive responses (i.e., evaluating a threat when there is no threat, like running from a shadow that is not a predator) over false negatives (i.e., evaluating no threat when there is a threat, or not running from a predator at the first sight of a shadow; Moscarello and Hartley, 2017). However, in the long term, frequent exposure to stressors, including those that an individual cannot avoid or control, can lead to overactivation of the stress response throughout the lifetime, resulting in a blunted stress response in adulthood and later in life (Lupien et al., 2009; Moscarello and Hartley, 2017). Overactivation of the stress response can lead to higher basal state of arousal (even in the absence of immediate danger) and less functional stress response following exposure to stimuli designed to activate the stress response (Koob, 2009; Sinha, 2008). This pattern of a higher basal level of stress and poorer stress response activation is also present in individuals with SUDs (Blaine et al., 2019). Individuals may be more likely to develop an alcohol use disorder if they consume alcohol as a form of coping with social anxiety or stress during negative social interactions (Sinha, 2001).

5.3. Biological and clinical research on stress and substance use

The complexity of the relationship between stress and substance use cannot be explained through social environment alone. An interdisciplinary integration of neuroscience and social science is necessary to identify factors that produce increased susceptibility to substance use initiation, addiction, and relapse (Koob and Le Moal, 1997). Neurobiological studies have provided evidence, through rodent models, of genetic vulnerability and sensitivity to stress leading to adaptations in the HPA axis responsiveness related to the development and maintenance of addiction (Koob and Volkow, 2016; Sinha, 2001). These adaptations cause dysregulations, interfere with reward and motivational processes in the brain, negatively influence the ability to cope with stressful events (Cadoni, 2016). Neuroimaging evidence supports a link between the stress response pathways in the brain and the reward pathways involved in complex decision making, memory, learning, and risk taking (Koob, 2008; Sinha, 2001, 2008). Beyond the initiation of substance use, it is well established that the motivation to continue using can be explained by a change in the function of neurotransmitters associated with reinforcing effects, such as dopamine, serotonin, and gamma-aminobutyric acid, which also includes pathways in the brain associated with stress systems and disruption of those systems (Koob and Le Moal, 2005).

Sinha (2008) identified three types of stress vulnerability factors that increase risk of developing SUDs: (1) genetic influences and family history of psychopathology; (2) developmental and individual-level factors such as executive function, negative emotionality, impulsivity, and altered initial sensitivity to rewarding effects of drugs; and (3) early adverse life events, trauma and child maltreatment experiences, and prolonged and chronic stress experiences. These three factors can be seen in the first three columns of Figure 1—individual, intrapersonal, and community factors of health and well-being—each of which influences the others and the rows in the framework across the life course, including biological pathways involved in stress regulation and behavioral control.

Activation of the stress response is related to craving of alcohol and other substances (Cavicchioli et al., 2020). In two samples of individuals with alcohol use disorder, craving mediated the relationship between the activated stress response following exposure to stressors and next-day alcohol intake (Wemm et al., 2019). The relationship between activation of the stress response and craving has also been investigated in animal models; for example, studies using animals with drug or alcohol dependence have shown increases in drug seeking (comparable to craving in humans) when presented with stimuli designed to provoke the stress response (Le et al., 2000; Mantsch et al., 2016). Similar findings emerged in human models when provoking an acute stress response in the laboratory, which resulted in an increase in alcohol and drug craving (Sinha, 2001, 2009; Sinha and Li, 2007).

Animal models have provided evidence of potential protective factors that may help reduce substance use and abuse. For example, Venniro and colleagues (2018) found that rats deprived of social interactions with other rats self-administered available drugs, but when given the opportunity and choice, they preferred social interactions over drug administration. This finding was consistent across drug class, drug dose, gender, sex, and abstinence duration. This research supports the important role of social interaction and support in substance use treatment. Other animal model research has compared enriched environments with deprived environments and found differential changes in brain development among adolescents raised in these environments, specifically documenting negative effects in deprived environments and positive effects in enriched environments regarding the amygdala, hippocampus, and frontal cortex, which all play an important role in stress response and behavioral and impulse control (Lupien et al., 2009).

A promising new area of research in the field of SUDs is investigating the interaction between genetic and environmental factors and how slight changes in gene expression can occur over time without altering DNA, known as epigenetics. A recent literature review discussed how early life experiences and environmental factors can interact to influence gene expressions in addiction phenotypes, which may help explain how behavioral traits are passed between generations (Ajonijebu et al., 2017). The investigation of specific genes involved in epigenetic changes that may be regulated by the environment, such as the proprotein convertase subtilisin/kexin 9 enzyme (PCSK9), may lead to the development of innovative pharmacological treatments and manipulations of gene expressions to create protective phenotypes. Lee and colleagues (2019) found that treatment based on this enzyme attenuated alcohol-induced steatohepatitis in a rat model, which suggests the potential for future novel treatments for humans with liver disease resulting from alcohol use. Although such treatments are critical for individuals with SUDs, using this information to create population-level interventions is also needed to reduce exposure to chronic stressors and create protective factors among the most vulnerable. More research is needed to determine the intergenerational effects of adverse life events altering gene expression and how this information can be used to create long-term upstream prevention programs for vulnerable populations.

6. Role of socially toxic environments in childhood and substance use vulnerability

The foundation for a lifetime of social, emotional, and physical health is set during childhood. Some have argued that these developmental processes begin prenatally through dynamic ongoing interaction between biology and the environment and can have lasting impacts across the life span (Entringer et al., 2011; Oberlander et al., 2008; Paul et al., 2021). Early positive experiences strengthen developing biological systems and cognitive and emotional development, whereas exposure to toxic environments increases morbidity and mortality and limits access to life opportunities (Felitti et al., 1998; Metzler et al., 2017; Shonkoff, 2016). Exposure to socially toxic childhood environments has been linked with most major causes of death in U.S. adults (Campbell et al., 2016), with particularly robust associations with substance use (Hughes et al., 2017) including early initiation (Dube et al., 2003), SUDs (Halpern et al., 2018), and relapse (Hyman et al., 2008).

Socially toxic environments in childhood can be defined as stressful chronic experiences that occur during childhood that either directly (e.g., physical abuse or neglect) or indirectly (e.g., housing, economic instability, community violence, experiences of racism) affect children through their living environments. Various terms have been used to refer to life conditions that pose adversity and threaten well-being. For example, Garbarino (1995) coined the term “socially toxic environment” to refer to social contexts (e.g., violence, poverty, other economic pressures on parents and their children) in which children grow up in a state of degradation that presents serious threats to their healthy psychological and social development. During the same period, the term “adverse childhood experiences” (ACEs) originated in a groundbreaking study of the prevalence of various forms of physical and emotional abuse, neglect, and household dysfunction and their association with negative life outcomes including substance abuse (Felitti et al., 1998). Subsequently, the National Scientific Council on the Developing Child (2004) coined the term “toxic stress” to describe the well-documented effects of excessive activation of stress response systems on a child’s developing brain, immune system, metabolic regulatory systems, and cardiovascular system. Importantly, in addition to sources of adversity captured in ACEs, it also included community and systemic issues (e.g., community violence, experiences with racism and chronic poverty; Center on the Developing Child, 2020; Shonkoff et al., 2009; Shonkoff et al., 2012). In this paper, we use the term “socially toxic environments” to refer to these multiple sources of adversity that begin in childhood and have lifelong consequences for health and vulnerability to substance use.

6.1. Exposure to socially toxic environments in the framework of social determinants of health

In the framework presented in Figure 1, exposure to socially toxic environments is in the sociocultural environment domain of influence. It operates at the individual, interpersonal, community, and societal levels and is associated with health disparities at each level throughout the lifecycle (Amaro et al., 2020; NASEM, 2017). As noted in national reports on key factors affecting childhood health (NASEM, 2019a) and adolescent health (NASEM, 2019b), the manifestations and mechanisms of exposure to socially toxic environments at different socioecological levels are well documented.

Examples of the mechanisms through which socially toxic environments work include individual-level factors like economic insecurity, housing instability (Buu et al., 2009; Cambron et al., 2019), and exposure to child maltreatment (Dubowitz et al., 2020); and interpersonal factors like family conflict (Wadman et al., 2020), parental substance use (Abar et al., 2015), parental separation (Jackson et al., 2016) and perceived discrimination (Chavez et al., 2018; Davis et al., 2019; Rogers et al., 2020). At the community level, mechanisms include concentrated neighborhood disadvantage and crime (Burlew et al., 2009; Handley et al., 2015) and exposure to community violence (Motley et al., 2017), whereas policies that separate immigrant families and limit economically disadvantaged children’s access to high-quality early childhood education are a societal influence (NASEM, 2019a).

6.1.1. Biological perspectives: How toxic environments get “under the skin”

Increasing biomedical evidence supports the plausibility of causal epigenetic changes in people exposed to adversity, many of which have been linked to greater susceptibility to acquiring a SUD (Kundakovic and Champagne, 2015; Iacono et al., 2018; McLaughlin et al., 2019; Shin et al., 2018; Somaini et al., 2011; Teicher et al., 2016). Exposure to toxic stress during childhood can disrupt developing brain architecture and other maturing organs, dysregulate metabolic processes, and excessively activate the stress system (Hughes et al., 2017; Scheidell, 2018; Shonkoff and Garner, 2011). It can also disrupt neurodevelopment and various neurological functions, including cognition, memory, and emotional functioning, that have been linked to drug initiation and continued use (Franke, 2014; Zannas and West, 2014).

McLaughlin and colleagues (2019) conducted a systematic review of 109 studies examining associations between neural development and multiple exposures to toxic environments in children (physical, sexual, and emotional abuse; domestic violence; other forms of violence; multiple forms of deprivation like institutional rearing; physical and emotional neglect; and extreme material deprivation). Findings indicated that children exposed to such threats had (a) reduced amygdala, medial prefrontal cortex (mPFC), and hippocampal volume and heightened amygdala activation to threat; and (b) reduced volume and altered function in frontoparietal regions. There is also some limited evidence for accelerated development in amygdala-mPFC circuits, with most studies suggesting a delay among maturation in children exposed to adversity (McLaughlin et al., 2019).

A substantial body of evidence also suggests that the possible dysregulation of neuroendocrine responses and neurotransmitter function induced by exposure to trauma and neglect during childhood could constitute one of the essential biological changes that link these early adverse experiences with substance abuse vulnerability (Gerra et al., 2008; Heim et al., 2002; Shea et al., 2005). Somaini and colleagues (2011) conducted a review of existing evidence on the associations between biological changes induced by childhood adversity and substance use. Evidence indicated that HPA function based on secretion levels of cortisol is associated with childhood adversity and contributes to addiction vulnerability later in life. Additionally, higher frequency of the serotonin transporter 5-HTT SS genotype is associated with an increased susceptibility in the use of illegal psychotropic drugs among adolescents, suggesting that the low-activity S allele may influence behavioral traits and substance use vulnerability (Gerra et al., 2005), likely prompting an impairment of brain serotonin transmission (Blier et al., 1987; Coccaro et al., 1996). Parental neglect is also associated with higher plasma levels of two HPA hormones linked to drug use. Specifically, studies have found positive associations between basal plasma levels of cortisol and adrenocorticotropic hormone (ACTH) and parental neglect scores on Childhood Experiences of Care and Abuse Questionnaire (Somaini et al., 2011). Interactions between genetic factors and parental behavior have also shown to predict drug use susceptibility among youth exposed to adverse childhood conditions (Somaini et al., 2011). Additionally, studies have found exposure to toxic environments and adversity during childhood can also affect immune function and permanently change the responsiveness of the peripheral immune system to a substance of abuse, such as cocaine (Iacono et al., 2018).

Although there is a burgeoning field of knowledge on the biological mechanisms underlying the associations between socially toxic childhood environments and substance use vulnerability, limitations remain. Notably existing evidence comes primarily from small and heterogeneous samples with cross-sectional research designs that hinder a comprehensive understanding of the neurobiological consequences of socially toxic childhood environments. Future research with larger samples and pooled data across labs, more precise measurements of adversity, and longitudinal studies that track these biological mechanisms over time are needed to further elucidate these associations (McLaughlin et al., 2019).

6.2. Mechanisms linking socially toxic childhood environments to SUD

Various mechanisms have been posited to explain how childhood adversity affects subsequent SUDs. Childhood is a highly formative development period; thus, exposure to traumatic events and co-occurring chronic stress can lead to cognitive and social disruption from an early age. Extant evidence indicates associations between socially toxic childhood environments, unhealthy coping mechanisms, and depression and other mental health disorders, leading to self-medication with drugs in an attempt to regulate negative emotions (Dube et al., 2001; Heim et al., 2009; McCauley et al., 1997). The following sections provide an overview of biological and social factors associated with childhood adversity, limitations in the current body of evidence, and suggestions for future directions of research.

6.2.1. Social perspectives on socially toxic environments in childhood

As previously noted, along with the biological implications associated with socially toxic childhood environments, social factors are related to these conditions across multiple levels of influence. In the following section, we provide a few examples of these risk factors across domains. At the individual level, economic insecurity is often a key driver associated with socially toxic environments in childhood. Socioeconomic disparities exist across race and ethnicity and geographic region. For instance, Black, American Indian or Alaska Native, and Hispanic children are more likely to live in low-income and poor households or in deep poverty (below 50% of the federal poverty threshold). Racial and ethnic disparities also exist in social mobility. Evidence suggests that Black and American Indian or Alaska Native children have the lowest rates of upward mobility, even when controlling for parental income (Chetty et al., 2018). Vast evidence exists linking economic insecurity with numerous adverse health outcomes in youth, including greater vulnerability to substance use (Buu et al., 2009; Lee et al., 2013).

At the interpersonal level, research on resilience has overwhelmingly demonstrated that that a key protective factor for children facing childhood adversity is a safe, stable, and nurturing relationship with at least one caretaker. These parent–child bonds help foster positive outcomes across domains ranging from healthy psychological adjustment to positive peer relationships (Bornstein and Leventhal, 2015; Bronfenbrenner and Morris, 2006; Centers for Disease Control and Prevention, 2014; Luthar, 2006; Masten et al., 2014; National Scientific Council on the Developing Child, 2004). Supportive relationships early in life also play a key role in buffering stress responses, thereby allowing children to more easily confront stressful situations (Hostinar and Gunnar, 2015).

Because most studies have examined individual and interpersonal factors associated with socially toxic environments in childhood, it is important to consider the broader contextual factors that lead to socially toxic environments. Indeed, a child’s ZIP code, which captures upstream features of the social and economic environment and related resources, has been found to be more important than genetics in determining future health and life chances (Lavizzo-Mourey, 2014). Although not an exhaustive list, these community and societal factors include neighborhood and community conditions such as concentrated neighborhood disadvantage (Handley et al., 2015), neighborhood and exposure to community violence (Burlew et al., 2009), economic insecurity, housing instability (Buu et al., 2009), access to health care and early education (Cannon et al., 2017; Jones et al., 2015), immigration-related factors (Schwartz et al., 2015; Unger, 2015), and discrimination (Cave et al., 2020), all of which have been linked with increased substance use vulnerability (Trucco, 2020).

For instance, previous studies found associations between higher unemployment rates and adolescent risk of marijuana use initiation (Tucker et al., 2013). Similarly, neighborhood disadvantage was linked with greater marijuana dependence symptoms, but only among youth exposed to child maltreatment (Handley et al., 2015). Youth exposed to high-risk contexts including neighborhood criminal activity (e.g., neighborhood rates of arrests, robbery, illicit drug use) have shown higher rates of early substance use initiation by middle school (Burlew et al., 2009). Similarly, children exposed to unstable housing and residing in unstable neighborhoods characterized by high residential mobility (i.e., residents moving in and out frequently) were more likely to develop various SUDs during late adolescence (Buu et al., 2009). Moreover, adolescents residing in neighborhoods with greater concentrated disadvantage were more likely to be exposed to substances, which in turn, enhanced availability of and opportunities for substance use (Cerdá et al., 2010). Neighborhood features such as social cohesion (i.e., extent of connectedness and solidarity among groups in a community) and social capital (features of social structures such as interpersonal trust, norms of reciprocity, and mutual aid) vary across neighborhoods and are associated with better health (NASEM, 2017). They serve as resources for individuals, facilitate collective action, and when absent, can contribute to health disparities (Amaro et al., 2020; Kawachi and Berkman, 2000; NASEM, 2017). For example, neighborhoods with low levels of collective efficacy (i.e., residents’ willingness to intervene in the face of neighborhood problems such as criminal activity on behalf of the community)—one aspect of social capital (Amaro et al., 2020; NASEM, 2017)—are less likely to have the capacity to monitor and enforce proper conduct among residents, thereby leading to increased levels of substance use (Handley et al., 2015; NASEM, 2017). Some investigators have suggested that under certain conditions (e.g., low socioeconomic status, high-crime communities), some aspects of social capital may present additional sources of strain and facilitate high-risk behaviors such as substance use via contagion effects (NASEM, 2017). Longitudinal studies have shown the impact of neighborhood-related factors on vulnerability to substance use does not halt in adolescence but can have lasting impacts well into adulthood and throughout the lifespan (Lee et al., 2018; Zimmerman et al., 2017).

Yet another important factor to consider is children’s access to early care and education (ECE). ECE is defined here as nonparental care provided outside the child’s home. ECE services can be delivered in various venues including center- and school-based settings (i.e., a setting other than a child’s home; NASEM, 2018). ECE provides numerous pathways to improving health and achieving health equity, including academic readiness; cognitive and socioemotional development; and overall positive impacts on physical, emotional, and mental health and well-being among youth and into adulthood (Campbell et al., 2014; D’Onise et al., 2010; Hahn et al., 2016; Muennig et al., 2011; NASEM, 2019a). Specifically, engagement in ECE programs has been associated with reduced externalizing and internalizing behaviors (Carney et al., 2015), including reduced substance use (Cannon et al., 2017; Jones et al., 2015). Currently, eligibility for ECE programs is limited. Even among families that are eligible, accessibility is low due to funding deficits and limited availability of programs and services (NASEM, 2018). As such, publicly funded ECE programs do not reach most children, and this is especially true for children exposed to socially toxic environments (NASEM, 2019a). For instance, parents with lower levels of education, income insecurity, and limited English proficiency are less likely to participate in ECE programs. It is also important to note that access to ECE does not guarantee equitable access to quality care, particularly for children from economically disadvantaged homes and racial and ethnic minorities such as Black and Hispanic children, who are more likely to receive lower-quality care compared to White and Asian children (Barnett et al., 2013; Valentino, 2018).

Children from immigrant families also face unique barriers that have the potential to make them particularly vulnerable to socially toxic environments. Although most children in immigrant families are U.S. citizens (90%), approximately one quarter have an unauthorized immigrant parent (Koball et al., 2015). Regardless of their own citizenship status, many are adversely affected by their household context and the sociopolitical context surrounding U.S. immigration policies (Filindra et al., 2011; Koball et al., 2015). Current U.S. immigration policies have become a salient source of toxic stress for these families and their children. For instance, fears of parental deportation among children have been linked with adverse physiological and psychological outcomes including eating and sleeping problems, headaches, stomachaches, depression, and anxiety (Artiga and Ubri, 2017; Rojas-Flores et al., 2017). Dramatic recent increases in immigrant deportation and the detention of parents seeking asylum at the border have resulted in thousands of children being separated from their families (HHS Office of Inspector General, 2019). Additionally, recent immigration policies have affected parents’ willingness to enroll their children in public programs, including ECE settings (Cervantes et al., 2018). These barriers could be exacerbated as increasing efforts to limit access to government services, such as health care, through the public charge rule take effect at the federal or state levels (Kaiser Family Foundation, 2019).

Numerous sociocultural factors have also been linked with elevated prevalence of substance use among youth from immigrant families. For instance, previous research has found increased substance use among Hispanic youth is associated with decreased family cohesion as children adopt individualistic U.S. values more rapidly than their parents; traditional cultural values such as religiosity and familism erode; and families face immigration, cultural, and economic stressors and exposure to ethnic discrimination (Schwartz et al., 2015; Unger, 2015).

In this section, we reviewed a few key factors associated with socially toxic childhood environments and subsequent substance use vulnerabilities. Given the multitude and complexity of factors that trigger such conditions, there remains a pressing need to address socially toxic childhood environments through multisectoral efforts that build comprehensive resources by combining individual, interpersonal, community, and policy efforts and interventions at the local, state, and national levels. Notably, one element of socially toxic childhood environments cuts across all levels of influence: racism and discrimination. Racism and discrimination shape the lived experiences of racial and ethnic minority children and families. It manifests in the unequal distributions of social, economic, and environmental resources that lead to deep-rooted biological, psychological, social, and environmental consequences (NAMEM, 2017, 2018). The following section is dedicated to the impacts of racism and discrimination on social vulnerabilities in substance use.

7. Role of discrimination and racism in vulnerability to substance use

The history of science and the social construction of race has been inextricably connected in the United States. Racialized views and perspectives played an important role in the intellectual freedom of the United States from Europe. As Gould (1996) argued, American polygeny, the belief that human races stem from different species, was a primary theory that gained recognition in the international science arena. Keel (2013) further pointed out that this scientific movement developed right before the American Civil War, during a time of uncertainty when the country was fervent about establishing racial inequalities (Cox, 2020).

The term racism was scarcely used in the early part of the 20th century. It first appeared in an unabridged version of the Merriam-Webster Dictionary in the late 1930s. Since then, the definition has been revised numerous times (most recently in June 2020) to capture its multiple dimensions. According to Merriam-Webster (2020), “racism is a belief that race is a fundamental determinant of human traits and capacities and that racial differences produce an inherent superiority of a particular race, which manifests in behaviors or attitudes that reflect and foster discrimination or prejudice; the systemic oppression of a racial group to the social, economic, and political advantage of another; and a political or social system founded on racism and designed to execute its principles.” In summary, “racism is often defined as individual prejudice, but racism is also systemic, existing in the advantages and disadvantages imprinted in cultural artifacts, ideological discourse, and institutional realities that work together with individual biases” (Salter et al., 2017, p. 1). As noted by Williams and colleagues (2019), racism manifests via institutional, cultural, and interpersonal forms in which individuals or institutions, with or without intent, treat racial and ethnic groups differently, resulting in inequitable access to opportunities and “self-reported discrimination, a subset of these experiences that individuals are aware of” (p. 111). A key aspect of racism is differential power relations, which disadvantages the less powerful group(s) in decisions related to allocation of goods and resources (e.g., placement of environmental hazards in or near minority communities, quality of public services, education and housing quality, employment opportunities, and access to and quality of health care; Adler et al., 2016; IOM, 2006; Jones, 1997; NASEM, 2017, 2019a, 2019b).

7.1. Racism in the framework of social determinants of health

In the framework presented in Figure 1, racism is identified as part of the sociocultural environment domain of influence. It operates at the intrapersonal, interpersonal, community, and societal levels and is associated with differential health outcomes and disparities at each level and throughout the lifecycle (Alvidrez et al., 2019; NASEM, 2017). As noted in various national reports (IOM, 2003; NASEM, 2017, 2019a, 2019b, 2020), the manifestations of racism are vast and its mechanisms at different socioecological levels are well documented.

Not intended to be comprehensive, the following list provides some examples of mechanisms through which racism works. These mechanisms include stereotype threat, which manifests as self-doubt and can undermine performance (Aronson et al., 2013; Spencer et al., 2016) and have other negative consequences (see review by Williams et al., 2019); implicit racial bias or unconscious cognitive biases (Dasgupta, 2013; Dovidio et al., 2002; FitzGerald and Hurst, 2017; Gershenson and Papageorge, 2018; Maina et al., 2018; Williams and Mohammed, 2013) that shape attitudes and behaviors toward members of racial and ethnic minority groups (interpersonal level); and racial profiling by police (Fagan et al., 2010; Fagan et al., 2014; Geller and Toch, 1996; Plant and Peruche 2005), inequitable practices by the criminal justice (Kovera 2019; Kurlychek and Johnson, 2019) and child welfare (Dettlaff et al., 2020; Gourdine, 2019; Maguire-Jack et al., 2020) systems, and differential disciplinary practices in schools (Amemiya et al., 2020; Okonofua and Eberhardt, 2015; Riddle and Sinclair, 2019) and health care institutions (IOM, 2003; Johnson, 2020; NASEM, 2017, 2019a, 2019b, 2020; institutional level). In addition, at the social environment level, it operates via systemic and interactive mechanisms facilitated by policies and practices such as residential or neighborhood and school segregation, disproportionate exposure to environmental toxins, discriminatory banking and lending practices, gerrymandering, and redlining voter suppression (community, state, and federal levels; see Bell and Ebisu, 2012; Bravo et al., 2016; Williams et al., 2019 for reviews; also see NASEM, 2017). As such, “racism is considered a fundamental cause of adverse health outcomes for racial/ethnic minorities and racial/ethnic inequities in health” (Williams et al., 2019, p. 1).

7.2. Epidemiology of racism and discrimination and manifestation of differential vulnerability

Although a robust literature has documented significant racial and ethnic disparities in many physical health indicators (Adler et al., 2016; IOM, 2003, 2006; NASEM, 2017, 2019a, 2019b, 2020; Williams and Collins, 2001; Williams and Jackson, 2005), the evidence on racial and ethnic disparities in mental health, including SUDs, indicates a more complex relationship (Carter, 2007; Chartier and Caetano, 2010; Mezuk et al., 2010). The epidemiological evidence indicates that the prevalence of substance use and SUDs varies by race and ethnicity (USDHHS, 2016). The latest data from the Monitoring the Future national survey show that in the eighth and 12th grades, Hispanic students have the highest use rates for most drugs compared to other groups, whereas African American students have lower levels of use for certain drugs other than heroin and bath salts (Johnston et al., 2019; note the report does not provide data for other racial and ethnic groups). Data from the 2015 National Survey on Drug use and Health suggest that members of minority groups aged 12 or older have similar or lower rates of binge drinking and alcohol use disorder as Whites—with the exception of American Indians or Alaska Natives, who have higher rates (USDHHS, 2016). Past-30-day illicit drug use is similar or only slightly higher among most minority groups compared to Whites. Exceptions are American Indians, Alaska Natives, Native Hawaiians, and other Pacific Islanders, who have higher rates of drug use disorder (USDHHS, 2016). However, important within-group variations exist in substance use and SUDs—for example, factors related to ethnic heritage, immigration, cultural affiliation and identity, acculturation processes, and acculturative stress are associated with substance use among both youth and adults (Abraido-Lanza et al., 2016; Ahmmad and Adkins, 2020; Blanco et al., 2013; Cano et al., 2017; Fish et al., 2017; Lui and Zamboanga, 2018; Meca et al., 2017; Meca et al., 2019; Myers, 2009; Pittman et al., 2017; Salas-Wright et al., 2018; Savage and Mezuk, 2014; Unger et al., 2014; Vilsaint et al., 2019).

Notably, a different pattern emerges if substance use and substance-related disorders are analyzed at different stages of the life course. Specifically, noninstitutionalized Blacks have lower levels of substance use (Johnston et al., 2020) and substance use-related disorders (Wu et al., 2011) during adolescence compared to Hispanics and Whites, and this also seems true among juvenile justice-involved youth (Welty et al., 2016). However, after age 25, a racial and ethnic crossover effect has been documented, primarily among Blacks, featuring an increase in the prevalence of substance use and SUD (Banks and Zapolski, 2018). For example, Watt (2008) found that after age 35, Black men report a higher prevalence of overall illicit drug use and Black women report a higher prevalence of heavy drinking compared to Hispanics or Whites. Study findings also suggest the crossover effect reverses after controlling for compositional characteristics (all factors influenced by racism and discrimination) such as socioeconomic status, education, employment, social support, and drug exposure. Further, numerous studies report significant associations between experience of discrimination and substance use and SUD among minorities (Ornelas et al., 2011; Otiniano Verissimo, Gee et al., 2014; Otiniano Verissimo, Grella et al., 2014). These findings suggest that the compounded and systemic effects of racial and ethnic discrimination may lead to increased substance use and SUDs later in life (Assari et al., 2017).

Reports of race- and ethnicity-based discrimination are ubiquitous among minorities (e.g., Assari, 2020; Bleich et al., 2019; Casey et al., 2019; Findling et al., 2019; Lee et al., 2019). The effects of social exclusion can affect almost every aspect of well-being. For example, racially segregated entrenched schools (Reardon and Owens, 2014) and neighborhoods (Hardy et al., 2018) have been linked with decreased intergenerational mobility (Andrews et al., 2017). In addition, higher education does not confer the same health benefits for Blacks compared to Whites (Shuey and Willson, 2008), and the compounded effects of the widening racial wealth gap (Darity et al., 2018) have been associated with poorer health (Shuey and Willson, 2008).

Racial and ethnic disparities in employment are also pervasive. Black workers are two times as likely to be unemployed as White workers at virtually every level of education. Blacks are also more likely to be underemployed and earn substantially less when employed compared to Whites (Pettit and Ewert, 2009; Western and Petit, 2005; Williams and Wilson, 2019). Currently, unemployment rates are higher in all minority groups compared to Whites (Pew Research Center, 2020). These disparities are further exacerbated when considering the effect of criminal records on employment access and earnings among racial and ethnic minorities, and this is further exacerbated by incarceration (Holzer et al., 2005; Lyons and Pettit, 2011; Pager, Bonikowski, et al., 2009; Pager, Western, et al., 2009).

Prior research has also documented the deleterious effects of racism on health outcomes, including access to health care, quality of health care, health care policies and practices, and ultimately, the overall health of people of color. A robust body of evidence has established a link between discrimination experiences and negative health outcomes in children, youth, and adults. The cumulative negative effects of discrimination on health using varied health outcomes have also been documented (see review by Williams et al., 2019). Further, previous research on discrimination in health care settings has also shown that minorities experience discrimination significantly more than non-Hispanic Whites (Williams et al., 2019) and that such experiences are associated with more negative patient experiences of health services, including distrust, dissatisfaction, and communication barriers and related outcomes such as delaying or not getting health care and lack of adherence to treatment (see meta-analysis by Ben et al., 2017). Research on personal experiences of discrimination or racism among individuals with SUDs is limited. Such studies have shown that active drug users experience high rates of discrimination and stigma based on their drug use and many other factors such as race and ethnicity, criminal justice history, and mental health problems (Ahern et al., 2007; Kulesza et al., 2013; Minior et al., 2003; Otiniano Verissimo, Grella et al., 2014; Matsumoto et al., 2020; Yang et al., 2017; Young et al., 2005).

Arguably, no indicators of the cumulative disadvantage of racial discrimination are more obvious than racial disparities in health and criminal justice system involvement, and these two are intricately connected regarding substance use and misuse. However, when it comes to substance use treatment and prevention, more emphasis has been placed on individual, behavioral, or criminal justice approaches, with little attention to social, structural, and institutional factors that might lead to greater incidence, prevalence, and morbidity of the disease.

7.3. How racism gets under the skin to create vulnerability to substance use

This section focuses on the role of the structural factor of racism in initiation and severity of substance use; as such, it conceptualizes SUDs in a racial health disparities framework. Race and ethnicity are thought to primarily influence health through racism and discrimination. As previously mentioned, racism creates social and economic barriers that lead to an inequitable distribution of resources, which ultimately affects health and health behaviors. Although many studies have focused on perceived discrimination, it should be noted that racial and ethnic discrimination does not have to be perceived to negatively affect well-being (Harrell et al., 2011).

The direct and indirect effects of racism have been linked to higher allostatic load (Ong et al., 2017), accelerated cellular (biological) aging (Geronimus et al., 2010; Rewak et al., 2014), poor birth outcomes for mother and child (Alio et al., 2010; David and Collins, 1997; Turner, 2009), chronic illnesses and mortality (Chae et al., 2015; Jackson et al., 2010; Turner, 2009), and substance use initiation and misuse (Gilbert and Zemore, 2016; Turner, 2009).

The relationship between race and health in general, and substance use in particular, is often modeled through psychosocial stressors and coping channels resulting from cultural, structural, or interpersonal discrimination. This approach posits that racism leads to greater psychological stress, economic barriers, and changes in behavior and psychobiological processes, which could also affect future generations (Brondolo et al., 2009; Mezuk et al., 2010). These models are based on the stress response model, which has been the dominant framework to understand how social factors affect mental health (Turner, 2009). The stress process model links social characteristics to physical, mental, and overall well-being through social stress (Turner, 2009). Nonetheless, the relationship between racism discrimination and health is not always straightforward. For example, even though morbidity and mortality are worse for Black Americans, noninstitutionalized Blacks have been found to have surprisingly lower rates of major mental disorders than non-Hispanic Whites (Mezuk et al., 2013). Although this may seem paradoxical, Jackson and colleagues proposed a stress-coping model that demonstrates that African Americans may engage in unhealthy behaviors such as overeating and substance use to relieve the symptoms of stress in the short run at the expense of their long-run health (Jackson, and Knight, 2006; Jackson et al., 2010).

The environmental affordances model, which builds on the stress-coping framework of Jackson and Knight (2006), uses a transdisciplinary approach to investigate how individual actions relate to environmental resources and circumstances to understand racial gaps in physical and mental health (Mezuk et al., 2013). The benefit of this model is it relates social circumstances, such as residential segregation, which is also affected by systemic racism (Rothstein, 2017), to health behaviors, the social environment, and mental and physical well-being. Moreover, the model explicitly considers the role of race in this relationship by postulating that “the motivation for and availability to engage in poor health behaviors as stress-coping or self-regulation strategies is influenced by social structures and contexts (e.g., poverty, segregation, access to goods) and that these social structures are in turn differentially distributed across racial groups)” (Mezuk et al., 2013, p. 83). Previously discussed is a social concept that developed from phenotypical differences, race in this context is an indicator of experiences, not variations in genetic, epigenetic, or neurobiological factors. The model postulates that environmental circumstances provide stress and affordances (or occasions to relieve stress); that coping behaviors are influenced by cultural, social, and contextual factors; and that all organisms engage in actions to immediately relieve distress from stressful events.

As proposed by Mezuk and colleagues (2013), the social environment can increase contact with chronic stress for marginalized populations and the relationship between environmental circumstances and the variety of health-related coping activities. These coping behaviors can mitigate the negative effects of stress on mental health in the short run but lead to discrepancies in physical health in the long run. These poor health behaviors can mediate the relationship between stress and the occurrence of psychopathology. Furthermore, environmental, social, and economic inequities worsen the effects of these coping strategies on physical well-being. Overall, chronic stress directly and indirectly (through participation in negative health behaviors) can lead to large differences in morbidity and mortality over the life course (Mezuk et al., 2013). Walters et al. (2002) developed a similar “Indigenist” stress-coping model. They model discrimination as a form of traumatic stress and explicitly incorporate historical trauma and unresolved grief into their framework, which are highly relevant to groups such as American Indians, Alaska Natives, and Blacks, who have a deep-rooted history of subjugation and trauma in the United States with insufficient reconciliation and atonement regarding past injustices. Historical trauma, unresolved grief from this historical trauma, and continued discrimination can result in greater substance use through its effect on mental health.

Neurologically, stress activates the HPA axis to release corticotropin-releasing hormone (CRH) from the hypothalamus. This, in turn, causes ACTH to discharge from the pituitary gland, which then travels to the adrenal cortex to cause the secretion of glucocorticoids. These glucocorticoids then travel back to the hypothalamus and pituitary gland to stop the discharge of ACTH and CRH through a negative feedback loop (Charmandari et al., 2005). Chronic stress disrupts the cortisol’s negative feedback, leading to continued secretion of CRH (Charmandari et al., 2005; Jackson et al., 2010). Chronic activation of this system leads to increased anxiety and ultimately, poor mental health outcomes (Marin et al., 2011). Jackson and Knight (2006) hypothesized that negative health behaviors such as substance use or misuse can protect against the negative psychological effects of chronic activation of this system caused by chronic exposure to negative adverse environmental factors and stressors, such as racial and ethnic discrimination. Specifically, this mechanism is triggered through substance use by the release of dopamine and beta-endorphins, which relieve feelings of anxiety and stress while also reinforcing addictive behavior through the reward system in the brain (Volkow et al., 2017). Therefore, although substance use can lessen feelings of stress and anxiety, it also reinforces the behavior as linked to stress relief through its impact on the brain’s reward system (Volkow and Boyle, 2018; Volkow et al., 2016).

Research has found that unhealthy behaviors strengthen the relationship between stress and depression for Whites but attenuate the relationship for Blacks. In other words, Blacks who engage in unhealthy behaviors have a lower likelihood of depression than Blacks who do not (Jackson et al., 2010; Mezuk et al., 2010). Clark (2014) also tested the stress-coping model among Blacks and found that depressive symptoms completely mediated the relationship between perceived discrimination and recent substance use and partially mediated the relationship between perceived discrimination and lifetime substance use. Another study by Gerrard et al. (2012) found that experiencing discrimination led to a greater willingness to initiate substance use and perceived discrimination was positively associated with substance use among Black adolescents and young adults who approve of “substance use-as-coping.” A recent qualitative study of young adults who identify as lesbian, gay, transgender, bisexual, and queer (LGBTQ) found that some LGBTQ youth initiated substance use to cope with stressors such as interpersonal and structural discrimination (Felner et al., 2020). Interestingly, in a group of Asian and Pacific Islander college and graduate students, perceived racism was associated with inferior mental health but not substance use; however, no direct test of the stress-coping framework was conducted (Chia-Chen et al., 2014).

A positive association has also been found between self-reported and perceived racism and discrimination among Black Americans (Borrell et al., 2007), U.S.-born Latinos (Otiniano Verissimo, Gee et al., 2014; Otiniano Verissimo, Grella et al., 2014), Asian Americans (Yoo et al., 2010), and African, South East Asian, and Hispanic immigrants (Tran et al., 2010) and substance use. In addition, perceived discrimination among Hispanics during adolescence and early adulthood is associated with initiation of substance use (Rogers et al., 2020). In a study of LGB adults, those who reported sexual orientation, race, and gender discrimination had four times greater odds of reported SUD in the past year than LGB adults who reported no discrimination (McCabe et al., 2010). More recent research by Glass et al. (2020) found racial and ethnic discrimination was associated with increasing severity of alcohol used disorder along both extensive and intensive margins. In other words, the experience of racial and ethnic discrimination and the number of events experienced were both associated with more severe alcohol use disorder.

Although animal studies have not directly tested the relationship between the social stressor of racism and substance use, studies have investigated the role of social stress, social subordination, and positive social interactions. For example, studies have found that chronic subordination and negative social experiences can lead to increased initiation of substance use and self-medicating (Neisewander et al., 2012). In addition, studies of aggressive social interactions (like racism and discrimination) found greater drug use, relapse, and drug-seeking behaviors among rats and mice with “acute and repeated social defeat exposure” (Logrip et al., 2012; Pelloux et al., 2019). Finally, studies have found benefits of positive social interactions can overcome drug craving in animal subjects (Pelloux et al., 2019; Venniro et al., 2018). These findings suggest that negative social interactions and social oppression stemming from racism are risk factors for substance use initiation and misuse, whereas social inclusion may be protective. Heilig et al. (2016) proposed that the neuroscience addiction model could be made more translational to clinical treatment by explicitly incorporating social exclusion into addiction neuroscience.

The stress-coping model also implies that the environmental effects of racial and ethnic discrimination (e.g., greater access to negative coping mechanisms, such as via alcohol and illicit drugs, and limited access to resources that encourage healthy behaviors, such as parks and gyms), make it more likely for minorities to use coping strategies to manage chronic stress through choices that offer immediate relief (such as substance use initiation or abuse). For example, low-income Black neighborhoods tend to have higher concentrations of liquor stores and other alcohol outlets, and this higher concentration has been associated with greater susceptibility of risky alcohol use among Black drinkers (Theall et al., 2011). Animal research on rats and primates has also found that availability of substances affects use (Heilig et al., 2016; Nader and Banks, 2014).

7.4. Institutional racism example: Criminal justice system, racism, and substance use

Of particular importance to this discussion on racial and ethnic discrimination and health is an understanding of the role of institutional racism and its negative effect on health and health behaviors. The United States has the dubious distinction of having the highest imprisonment rate (655 per 100,000) in the world (International Center for Prison Studies, 2018), accompanied by disproportionate representation of incarcerated minorities (Alexander, 2012; Center on Addiction and Substance Use [CASA], 2010), high proportion of incarcerated individuals with SUDs, and little capacity to provide SUD treatment (Alexander, 2012; CASA, 2010; Nowotny, 2015). Minorities have disproportionately greater exposure to the criminal justice system in general and incarceration in particular (Bonczar, 2003; Hinton, 2016; Wildeman and Wang, 2017), resulting in severe health outcomes at the individual, family, and community levels (Lorvick et al., 2018; Wildeman and Wang, 2017). Structural racism has a long history of manifestations via institutional policies, including criminal justice policies (Bailey et al., 2017; Hinton et al., 2018). Relatedly, policy changes in the last four decades, not increases in criminal behavior, have led to the expansion of the carceral state and extensive reach of the criminal justice system in the lives of low-income and marginalized communities (Alexander, 2012; Raphael and Stoll, 2013). Included in these policies is the War on Drugs, which not only treated substance use as a crime instead of an illness, but also targeted drugs used by minorities and widened the net of the criminal justice system, thereby increasing racial disparities in this system (Cox and Cunningham, 2021). These disparities partially stem from government policies that disproportionately target communities of color. For instance, intergovernmental grant programs, together with U.S. Supreme Court decisions authorizing police with extraordinary powers to stop and search residents with minimal to no probable cause, often in the name of drug policy, have contributed to the disproportionate policing and imprisonment of people of color (Alexander, 2012; Benson and Rasmussen, 1996; Blumenson and Nilsen, 1998; Cox and Cunningham, 2021; Holcomb et al., 2018; Russell, 1998; Sandy, 2002; Tieger, 1971).

Given the impact of criminal justice policies on minority communities and their direct and indirect effects on health, it is not possible to discuss racial health disparities without considering the impact of the criminal justice system on the health outcomes of minorities in general and substance use in particular. Moreover, given the indirect negative effects of concentrated incarceration on families and communities, it is necessary to take an intergenerational life course approach to understand how these policies might affect outcomes of certain communities across time and space, including substance use initiation and abuse.

The criminal justice system plays a central role in the lives of marginalized communities of color and is an important component of the racism these communities experience (Kwate and Goodman, 2015). At every level of the criminal justice system, racial disparities and biases are present (Alexander, 2012; Bailey et al., 2017; Cox, 2020; Hinton et al., 2018; Wildeman and Wang, 2017). Criminal justice system contact has been associated with diminished mental health in general (Sugie and Turney, 2017) and increased emotional distress, anxiety, trauma, and posttraumatic stress disorder in particular (Geller et al., 2014; Hirschtick et al., 2020; Jackson et al., 2019). Contact with the criminal justice system has also been associated with mental distress of communities and family members and is a regular occurrence in communities of color (Bor et al., 2018; DeVylder et al., 2018; Lee et al., 2014; Naser and Visher, 2006; Wildeman et al., 2012), which could contribute to intergenerational transmission of substance use and SUDs. Because minorities have disproportionately higher contact with this system, they are also more likely to experience health consequences resulting from the additional stress of being marked with a criminal record, the stigma of incarceration, or having a family member incarcerated (see Cox, 2018). Although drug use is common in the incarcerated population, there is reverse causality in this relationship. That is, drug use can lead to incarceration. Conversely, the negative aforementioned direct psychosocial effects of incarceration and the indirect effects of the mark of a criminal record and incarceration after release can also limit the ability to be a productive, law-abiding citizen, which can contribute to substance use initiation, disorder, and relapse. Although there is scant longitudinal research investigating the effect of incarceration on substance use, Genberg et al. (2015) found that the odds of injection drug use among people who had stopped injecting drugs prior to their incarceration increased after their release. Mowen and Visher (2015) reported that family conflict during incarceration is associated with substance use. To the extent that incarceration places additional strain on family relationships, it is another mechanism through which incarceration can affect drug use. Other research has found social support (e.g., supportive parenting) can protect against chronic stress, such as racism (Gibbons et al., 2010). Animal models have also demonstrated the importance of social engagement and support. For example, in a review by Neisewander et al. (2012), negative social experiences in general, and chronic social subordination in particular, increased vulnerability to drug use and abuse. This means we should expect greater substance use in the incarcerated population in general, with larger effects among racial minorities dealing with the intersectionality of their race and criminal status. Given that stress may differentially promote initiation and relapse of substance use among women (Torres and O’Dell, 2016), this may be especially true for incarcerated women, who are disproportionately racial and ethnic minorities and typically more disadvantaged than men (Cox, 2012).

7.5. Racism and discrimination in SUD treatment

Although there is a dearth of quantitative studies on perceived discrimination or personal experiences of discrimination stemming from the SUD treatment system, evidence of stigma and discrimination of people with SUDs (Tsai et al., 2019) and inequities in SUD treatment access and outcomes has been documented. Studies using data from large SUD treatment systems have revealed that Black and Latino clients wait longer to be admitted to treatment and are less likely to receive the services they need and complete a course of treatment (Guerrero et al., 2013; Marsh et al., 2009; Mennis et al., 2019). In part, these differences are associated with program characteristics such as lack of Medicaid payment acceptance and culturally and linguistically appropriate services (Guerrero et al., 2017). In the criminal justice system, disparities have also been noted wherein incarcerated Blacks and Hispanics with SUD are less likely to receive evidence-based treatment compared to their White counterparts (CASA, 2010). Although more detailed studies of incarcerated populations (e.g., Nowotny, 2015) found no Black–White disparities in utilization of SUD treatment during incarceration, they found that Hispanics were significantly less likely to utilize such treatment and that overall treatment access, especially to evidence-based treatment, was extremely low. However, a study in California documented racial and ethnic disparities in diversion to SUD treatment in lieu of incarceration and noted that Whites are more likely to receive such diversion compared to Blacks and Latinos (Nicosia et al., 2013). Moreover, Whites are more likely to have access to community-based treatment and co-occurring disorder treatment than minorities (Hedden et al., 2021). Due to the overwhelming prevalence of SUDs among incarcerated populations (64.5%; CASA, 2010) and the significant overrepresentation of minorities in incarcerated populations, lack of SUD treatment for incarcerated populations has a further disproportionate impact on racial and ethnic disparities in SUD treatment access. Despite evidence that SUD treatment for incarcerated populations is efficacious and cost-effective (Wakeman and Rich, 2015), stigma related to addiction and prevailing attitudes favoring punishment versus rehabilitation among policy makers contribute to the lack of systemic implementation of SUD treatment and related services in the criminal justice system (CASA, 2010).

Other research on hospitalized patients with SUDs has documented that such patients often experience stigma and discrimination from health care providers and other hospital staff members (Biancarelli et al., 2019; Horner et al., 2019; McNeil et al., 2014; Simon et al., 2019). Prompted by the pressing need to increase service capacity due to the opioid epidemic, a recent qualitative study (Priest et al., 2020) of board-certified or board-eligible addiction physicians from 16 U.S. acute care hospitals sought to identify major barriers and facilitators of development and administration of addiction consultation services. They found that stigma and discrimination of drug users and SUD treatment among hospital health personnel and administrators was a critical barrier to the development and operations of such services.

In summary, racism and discrimination and its various institutional, cultural, and interpersonal forms result in significant threats to well-being. As such, it is critical to consider social factors related to vulnerability to substance use. Emerging data from animal studies provide relevant findings suggesting that factors related to racism and discrimination, such as social subordination and exposure to aggressive social interactions, create vulnerability to substance use. Epidemiological and social science studies have documented the harmful health and other effects of exposure to racism and discrimination. Less is known about how exposure to racism and discrimination interacts with other stressors and adversities to influence substance use vulnerability.

8. Limitations and promising areas for future directions

Although significant scientific progress has taken place in the basic and social sciences regarding the role of stressors, environmentally toxic social contexts, and racism and discrimination in substance use vulnerability, limitations and gaps require further investigation. Next, we highlight some of those limitations and promising areas for future research.

8.1. Increased research focus on community and societal factors that affect social vulnerability to substance use at the individual and community levels of influence

Although substantial gains have been made in recent decades in understanding how stress and development interact to create vulnerability to substance use, research that expands on the study of vulnerabilities is needed. Most approaches to date have focused on mitigating individual-level factors, whereas less has been done to investigate community and societal factors that may interact to directly and indirectly affect youth’s exposure to adversity (Lorenc, 2020). Nor have they included aspects of urban neighborhood context, including overpolicing and environmental pollutants, or experiences driven by low income, such as food insecurity, eviction, overcrowded housing, juvenile justice contact, homelessness, and household stressors of single parents with weak support networks (Wade et al., 2016). Future research that measures a wider range of factors associated with socially toxic environments in childhood is needed to provide a comprehensive understanding of how social determinants of health directly and indirectly influence the association between such exposure and substance abuse. By doing so, the scientific community will produce more sensitive and representative indicators of childhood adversity that more accurately gauge its social distribution in and across populations and communities. This knowledge can lead to the development of more upstream social or community-level policy actions and interventions that address socially toxic environments among children and families, reduce structural risk factors, and strengthen social supports (McEwen and Gregerson, 2019).

8.2. Longitudinal studies of social vulnerability, brain development, and substance use trajectories

As noted in recent national consensus reports (NASEM, 2019a, 2019b, 2020), more longitudinal studies on brain development are needed (Jernigan et al., 2018; Lisdahl et al., 2018). This includes studies assessing if and how alcohol and drug use results in adaptations to the stress response system or exacerbates pre-existing stress dysregulation (Wemm and Sinha, 2019). Data from recent large longitudinal studies, combined with previous work from retrospective analyses of the early life experiences of adults, are providing new opportunities to examine normal brain development and cognitive trajectories from childhood to young adulthood and how these are shaped by biological, psychological, and social and physical environmental factors. An overarching aim is to identify how these factors influence vulnerabilities to SUD. One such initiative is the Adolescent Brain Cognitive Development (ABCD) Study (ABCD Research Consortium, 2020). Funded by multiple institutes at the National Institutes of Health, it is the largest longitudinal study of brain development and child health in the United States. It is currently following the biological and behavioral trajectories of nearly 12,000 children for 10 years beginning at ages 9–10 through adolescence into early adulthood. The study aims to track exposure to substance use and how this may affect developing brain structure and function in terms of mental health and cognitive abilities. In addition to brain imaging and biospecimen collection for genetic and epigenetic analyses, the ABCD Study protocol consists of standardized and harmonized assessments of neurocognition, physical and mental health, social and emotional functions, and culture and environment. The ABCD Study will provide an unprecedented opportunity to examine how factors across multiple domains (i.e., biological, behavioral, sociocultural) and levels of influence (i.e., individual, interpersonal, community) interact across developmental stages to affect substance use trajectories among youth (Jernigan et al., 2018; Zucker et al., 2018). The design and methodology of the ABCD Study holds unparalleled promise for advancing science regarding the role of social vulnerability to substance use and related health conditions. This includes sufficient sampling of various racial and ethnic groups to allow detailed analyses and sound psychometric scales for measuring exposure to socially toxic environments, including discrimination and racism; protective, risk, and cultural factors; and numerous biological measures.

The HEALthy Brain and Child Development (HBCD) Study is another ongoing 10-year longitudinal study similar to the ABCD Study but that aims to assess brain development from infancy into preadolescence. Specifically, the HBCD Study will establish a large cohort of pregnant women across various regions of the United States who have been substantially affected by the opioid crisis and follow these women and their children for at least 10 years. Study findings are expected to provide researchers with a greater understanding of normal brain development in childhood and the long-term impacts of prenatal and postnatal opioid, other drug, and environmental exposures (National Institutes of Health, 2020). Data from the ABCD and HBCD studies can be used to inform interventions at all levels of influence that can reduce exposure to toxic environments and mitigate their impacts on substance use vulnerability.

Both studies have an open science model that makes the data freely available through an open access platform to the broad research community. This ensures that the data can be used to answer a broad range of research questions and facilitates collaboration between behavioral and basic scientists to gain a better understanding of the mechanisms involving social factors and their interaction with biology.

8.3. Development and implementation of population-level upstream prevention approaches

The literature on prevention of substance use has largely focused on downstream approaches (i.e., individual, family, and school-level substance use prevention interventions), many of which address social factors that contribute to substance use vulnerability. However, akin to the public health parable and case study presented earlier, population-level strategies are needed to address upstream root and fundamental causes that create conditions of adversity and social vulnerability (NASEM, 2017). A recently published report by NASEM (2019a) supports our call for upstream approaches to improving child health and advancing health equity in children that can substantially lead to the reduction of socially toxic childhood environments. The report’s recommendations include support from policy makers at the federal, state, local, and tribal levels and philanthropic organizations to (1) develop interventions that are culturally sensitive and tailored to meet the needs of vulnerable children, including those living in chronic poverty and from immigrant backgrounds; (2) reform the content of prenatal, postpartum, and pediatric care to ensure ongoing access to quality health care; (3) have public housing authorities increase the supply of high-quality affordable housing available to families, especially those with young children; and (4) expand access to comprehensive, high-quality, and affordable ECE programs across multiple settings to all eligible children, prioritizing those from vulnerable and underserved populations (NASEM, 2019a).

Without population-level strategies that address root causes, we will forever be swimming against the tide of impacts underlying social vulnerability. Agent-based modeling has recently been applied in public health (Tracy et al., 2018) and holds promise for increased understanding of population-level health outcomes, such as substance use. Although not free of limitations, this approach could be useful “for visualizing, analyzing, and informing complex dynamic systems” (Tracy et al., 2018, p. 78), such as substance use vulnerability, at the population level and modeling potential benefits of interventions under varied scenarios. Agent-based models have proven useful, for example, in assessing disease endpoints at the population level, risk behaviors, and policy impacts and in social epidemiological research and studies on the mechanisms through which upstream social factors contribute to health and disparities (Tracy et al., 2018).

8.4. Advancement of theoretical models and methods in animal models and human research on upstream social factors

Venniro and colleagues (2018) aptly identified a current limitation of current neuroscience research stemming from not incorporating volitional social factors, which play a critical role in human addiction—thereby limiting translation of animal research to prevention and treatment in humans. Future neuroscientific studies may consider how to incorporate volitional social factors that mimic real-world interactions in communities and how these social factors influence neurobiological mechanisms of substance use, addiction, craving, and relapse.

In addition, a neurobiological model of racism and discrimination suggests that ongoing experiences of discrimination result in impaired function of the HPA axis and prefrontal cortex, similar to impairments seen in cases of chronic stress and heightened prolonged stress reactivity, which may ultimately lead to an increased risk of negative mental health outcomes (Berger and Sarnyai, 2015). For this reason, it is imperative to consider discrimination as an acute or chronic psychosocial stressor, and more effort should be directed toward the development of a useful neurobiological framework for understanding its damaging effects on physical and mental health (Busse et al., 2017). Moreover, research is needed to understand how the type of social exclusion generated by racism and discrimination affects neurobiology and substance use.

Similarly, research is needed on how racism and discrimination act across all levels of influence associated with individual, environmental, and structural determinants of substance use and SUDs (individual, interpersonal, community, and societal). And interventions at all these levels need to be developed and tested to identify efficacious strategies to reduce exposure and mitigate impacts of racism and discrimination on health, including substance use.

8.5. Improving treatment availability, access, and outcomes

We have stressed the role of social determinants of health in exposure to factors that increase vulnerability to substance use and called for research on upstream factors and upstream interventions at the population level. However, as in the public health parable and case study presented, there is also a need to advance scientific understanding that leads to more efficacious approaches for those already affected by SUD. Although SUD treatment could be improved in many ways, the most immediate is to test and implement strategies that enable expansion of the SUD treatment capacity and reach with efficacious approaches. This includes access to efficacious treatment in the community, in the criminal justice system, and for all population groups. Treatment itself can be an important aspect of prevention, because parental SUDs are a major risk factor for children.

8.6. Summary

This review provided evidence that socially based stressors play a critical role in creating vulnerability to substance use and as such, deserve greater empirical attention to further our understanding of how they get under the skin. We focused attention on the role of stressors, particularly early and ongoing exposure to socially toxic childhood environments, and racism and discrimination as foundational social factors in the psychobiological cascade that creates vulnerability to substance use and its consequences. Using a top-down approach, we brought attention to some known yet often unexplored relationships between vulnerability to substance use and SUDs and understudied inequities and their potential differential effects across minority populations. Finally, research gaps and promising areas of research, practice, and policy focused on ameliorating social vulnerabilities associated with substance use and SUD across the lifespan were presented. These future efforts can take the form of enhanced interdisciplinary approaches that consider biological and social factors that interact across multiple levels to influence vulnerability to substance use at the individual and population levels.

ACKNOWLEDGEMENTS:

We would like to thank Eric Lindberg for editorial assistance, and Victoria Molinari, Vicky Vazquez, Christie Kirchoff, E. Valerie Daniel, and Ian Lee for their assistance with bibliographic search.

Funding sources:

This work was supported by the following National Institutes of Health Awards: National Institute on Minority Health and Health Disparities (1 S21MD0106830, PI: Gil & De La Rosa) - Mariana Sanchez; National Center for Advancing Translational Science (5TL1TR001864, PI: Cantley, Sinha, & Shapiro); and National Institute on Drug Abuse (5R25DA026401, PI: Valdez)

– Tara Bautista.

Footnotes

Declarations of interest: All authors declare no conflicts of interest.

1

These are factors that are temporally and spatially close to health effects (and hence, relatively apparent), but are influenced by upstream factors (Braverman et al., 2011).

2

These are fundamental causes that set in motion causal pathways leading to (often temporally and spatially distant) health effects through downstream factors (Braverman et al., 2011).

3

Population health is defined as the health outcomes of a group of individuals, including the distribution of such outcomes in the group. Populations can be defined as geographic populations such as states, nations, communities, members in a health system, employees, ethnic groups, disabled persons, prisoners, or any other defined group (Kindig and Stoddart, 2003). Distinct from individual-level approaches to understanding health and disparities, population health focuses on interrelated conditions and factors that influence the health of populations over the life course, identifies systematic variations in patterns of occurrence by groups and geographies, and applies the resulting knowledge to develop and implement policies and actions to improve the health and well-being of those populations (Kindig and Stoddart, 2003; Warnecke et al., 2008) while improving efficiencies and reducing costs.

This manuscript has not been published elsewhere and is not under consideration in any other journal. All authors have approved the manuscript in its current form. If accepted the manuscript will not be published elsewhere in the same form, in English or in any other language, including electronically without the written consent of the copyright-holder.

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

References

  1. Abar CC, Jackson KM, Colby SM, Barnett NP, 2015. Parent-child discrepancies in reports of parental monitoring and their relationship to adolescent alcohol-related behaviors. J. Youth Adolesc 44(9), 1688–1701. 10.1007/s10964-014-0143-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. ABCD Research Consortium. (2020). ABCD Study. https://abcdstudy.org/ (accessed 10 October 2020).
  3. Abraído-Lanza AF, Echeverría SE, Flórez KR, 2016. Latino immigrants, acculturation, and health: Promising new directions in research. Annu. Rev. Public Health 37, 219–236. 10.1146/annurev-publhealth-032315-021545 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Adler NE, Cutler DM, Jonathan JE, Galea S, Glymour M, Koh HK, Satcher D, 2016. Addressing social determinants of health and health disparities. Discussion Paper, Vital Directions for Health and Health Care Series. National Academy of Medicine, Washington, DC. https://nam.edu/wp-content/uploads/2016/09/addressing-social-determinants-of-health-and-health-disparities.pdf (accessed 2 February, 2021). [Google Scholar]
  5. Ahern J, Stuber J, Galea S, 2007. Stigma, discrimination and the health of illicit drug users. Drug Alcohol Depend. 88(2–3), 188–196. 10.1016/j.drugalcdep.2006.10.014 [DOI] [PubMed] [Google Scholar]
  6. Ahmmad Z, Adkins DE, 2020. Ethnicity and acculturation: Asian American substance use from early adolescence to mature adulthood. J. Ethn. Migr. Stud 10.1080/1369183X.2020.1788927 [DOI] [Google Scholar]
  7. Ajonijebu DC, Abboussi O, Russell VA, Mabandla MV, Daniels WM, 2017. Epigenetics: A link between addiction and social environment. Cell. Mole. Life Sci 74(15), 2735–2747. 10.1007/s00018-017-2493-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Alexander M, 2012. The New Jim Crow: Mass Incarceration in the Age of Colorblindness. New York: The New Press. [Google Scholar]
  9. Alio AP, Richman AR, Clayton HB, Jeffers DF, Washington DJ, Salihu HM, 2010. An ecological approach to understanding Black–White disparities in perinatal mortality. Matern. Child Health J 14(4), 557–566. 10.1007/s10995-009-0495-9 [DOI] [PubMed] [Google Scholar]
  10. Alvidrez J, Castille D, Laude-Sharp M, Rosario A, Tabor D, 2019. The National Institute on Minority Health and Health Disparities Research Framework. Am. J. Public Health 109(S1), S16–S20. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6356129 (accessed 9 March 2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Amaro HA, Garcia S, Arnaudova I, Jollies M, 2020. Social environments and health disparities research. In National Institute of Minority Health and Health Disparities: The Science of Health Disparities Research and Applications. New York: Wiley. [Google Scholar]
  12. Amemiya J, Mortenson E, Wang M-T, 2020. Minor infractions are not minor: School infractions for minor misconduct may increase adolescents’ defiant behavior and contribute to racial disparities in school discipline. Am. Psychol 75(1), 23–36. [DOI] [PubMed] [Google Scholar]
  13. Andrews R, Casey M, Hardy BL, Logan TD, 2017. Location matters: Historical racial segregation and intergenerational mobility. Econ. Letters 158, 67–72. 10.1016/j.econlet.2017.06.018 [DOI] [Google Scholar]
  14. Aneshensel CS, 1992. Social stress: Theory and research. Annu. Rev. Sociol 18(1), 15–38. 10.1146/annurev.so.18.080192.000311 [DOI] [Google Scholar]
  15. Aronson J, Burgess D, Phelan SM, Juarez L, 2013. Unhealthy interactions: The role of stereotype threat in health disparities. Am. J. Public Health 103, 50–56. 10.2105/AJPH.2012.300828 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Artiga S, Ubri P, 2017. Living in an immigrant family in America: How fear and toxic stress are affecting daily life, well-being, and health. Menlo Park, CA: Kaiser Family Foundation. http://resource.nlm.nih.gov/101727557. (accessed 5 October 2020). [Google Scholar]
  17. Assari S, Moazen-Zadeh E, Caldwell CH, Zimmerman MA, 2017. Racial discrimination during adolescence predicts mental health deterioration in adulthood: Gender differences among blacks. Front. Public Health 5, 104. 10.3389/fpubh.2017.00104 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Assari S (2020). Social epidemiology of perceived discrimination in the United States: Role of race, educational attainment, and income. Int. J. Epidemiol. Res 7(3), 136. 10.34172/ijer.2020.24 [DOI] [PubMed] [Google Scholar]
  19. Bailey ZB, Krieger N, Agénor M, Graves J, Linos N, Bassett MT, 2017. Structural racism and health inequities in the USA: Evidence and interventions. The Lancet. 389(10077), 1453–1463. 10.1016/S0140-6736(17)30569-X [DOI] [PubMed] [Google Scholar]
  20. Balk E, Lynskey MT, Agrawal A, 2009. The association between DSM-IV nicotine dependence and stressful life events in the National Epidemiologic Survey on Alcohol and related conditions. Am. J. Drug Alcohol Abuse 35(2), 85–90. 10.1080/00952990802585430 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Banks DE, Zapolski TC, 2018. The crossover effect: A review of racial/ethnic variations in risk for substance use and substance use disorder across development. Curr. Addict. Rep 5(3), 386–395. 10.1007/s40429-018-0220-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Barnett S, Carolan M, Johns D, 2013. Equity and excellence: African-American children’s access to quality preschool. New Brunswick, NJ: Center on Enhancing Early Learning Outcomes. [Google Scholar]
  23. Bell ML, Ebisu K, 2012. Environmental inequality in exposures to airborne particulate matter components in the United States. Environ. Health Perspect 120(12), 1699–1704. 10.1289/ehp.1205201 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Ben J, Cormack D, Harris R, Paradies Y, 2017. Racism and health service utilisation: A systematic review and meta-analysis. PLOS One. 12(12), e0189900. 10.1371/journal.pone.0189900 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Benson BL, Rasmussen DW, 1996. Predatory public finance and the origins of the War on Drugs 1984–1989. The Indep. Rev 1(2), 163–189. [Google Scholar]
  26. Berger M, Sarnyai Z, 2015. “More than skin deep”: Stress neurobiology and mental health consequences of racial discrimination. Stress. 18(1), 1–10. 10.3109/10253890.2014.989204 [DOI] [PubMed] [Google Scholar]
  27. Biancarelli DL, Biello KB, Childs E, Drainoni M, Salhaney P, Edeza A, et al. , 2019. Strategies used by people who inject drugs to avoid stigma in healthcare settings. Drug Alcohol Depend. 198, 80–86. 10.1016/j.drugalcdep.2019.01.037 [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Blaine SK, Nautiyal N, Hart R, Guarnaccia JB, Sinha R, 2019. Craving, cortisol and behavioral alcohol motivation responses to stress and alcohol cue contexts and discrete cues in binge and non-binge drinkers. Addict. Biol 24(5), 1096–1108. 10.1111/adb.12665 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Blanco C Morcillo C, Alegría M, Dedios MC, Fernández-Navarro P, Regincos R, Wang S, 2013. Acculturation and drug use disorders among Hispanics in the U.S., J. Psychiatr. Res 47(2), 226–232. 10.1016/j.jpsychires.2012.09.019 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Bleich SN, Findling MG, Casey LS, Blendon RJ, Benson JM, SteelFisher GK, et al. , 2019. Discrimination in the United States: Experiences of black Americans. Health Serv. Res 54, 1399–1408. 10.1111/1475-6773.13220 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Blier P, de Montigny C, Chaput Y, 1987. Modifications of the serotonin system by antidepressant treatments: Implications for the therapeutic response in major depression. J. Clin. Psychopharmacol 7(6 Suppl.), 24S–35S. [PubMed] [Google Scholar]
  32. Blumenson E, Nilsen E, 1998. Policing for profit: The drug war’s hidden economic agenda. The Univ. Chic. Law Rev 35–114. [Google Scholar]
  33. Boardman JD, Finch BK, Ellison CG, Williams DR Jackson JS, 2001. Neighborhood disadvantage, stress, and drug use among adults. J. Health Soc. Behav 42, 151–165. [PubMed] [Google Scholar]
  34. Bonczar TP, 2003. Prevalence of imprisonment in the US population, 1974–2001. Washington, DC: U.S. Department of Justice. https://www.bjs.gov/content/pub/pdf/piusp01.pdf (accessed 5 September 2020). [Google Scholar]
  35. Bor J, Venkataramani AS, Williams DR, Tsai AC, 2018. Police killings and their spillover effects on the mental health of black Americans: A population-based, quasi-experimental study. The Lancet. 392(10144), 302–310. 10.1016/s0140-6736(18)31130-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Bornstein MH, Leventhal T, 2015. Children in bioecological landscapes of development. In: Lerner RM, ed. Handbook of child psychology and developmental science, 7th ed. Vol. 4. Hoboken, NJ: Wiley; pp. 1–5. [Google Scholar]
  37. Borrell LN, Jacobs DR Jr., Williams DR, Pletcher M, Houston TK, Kiefe CI, 2007. Self-reported racial discrimination and substance use in the Coronary Artery Risk Development in Adults Study. Am. J. Epidemiol 166(9), 1068–1079. 10.1093/aje/kwm180 [DOI] [PubMed] [Google Scholar]
  38. Braverman P, Egert S, Williams DR, 2011. The social determinants of health: Coming of age. Annu. Rev. Public Health 32:381–398. 10.1146/annurev-publhealth-031210-101218 [DOI] [PubMed] [Google Scholar]
  39. Bravo MA, Anthopolos R, Bell ML, Miranda ML, 2016. Racial isolation and exposure to airborne particulate matter and ozone in understudied US populations: Environmental justice applications of downscaled numerical model output. Environ. Int 92, 247–255. 10.1016/j.envint.2016.04.008 [DOI] [PubMed] [Google Scholar]
  40. Brondolo E, Gallo LC, Myers HF, 2009. Race, racism and health: Disparities, mechanisms, and interventions. J. Behav. Med 32(1), 1. 10.1007/s10865-008-9190-3 [DOI] [PubMed] [Google Scholar]
  41. Bronfenbrenner U, 1977. Toward an experimental ecology of human development. Am. Psychol 32(7):513–531. 10.1037/0003-066X.32.7.513 [DOI] [Google Scholar]
  42. Bronfenbrenner U, Morris PA, 2006. The bioecological model of human development. In Lerner RM, ed. Handbook of child psychology and developmental science, 7th ed. Vol. 1. New York: Wiley; pp. 793–828. [Google Scholar]
  43. Brown SA, Vik PW, McQuaid JR, Patterson TL, Irwin MR, Grant I, 1990. Severity of psychosocial stress and outcome of alcoholism treatment. J. Abnorm. Psychol 99, 344–348. 10.1037//0021-843x.99.4.344 [DOI] [PubMed] [Google Scholar]
  44. Brown SA, Vik PW, Patterson TL, Grant I, Schuckit MA, 1995. Stress, vulnerability and adult alcohol relapse. J. Stud. Alcohol 56(5), 538–545. 10.15288/jsa.1995.56.538 [DOI] [PubMed] [Google Scholar]
  45. Burlew AK, Johnson CS, Flowers AM, Peteet BJ, Griffith-Henry KD, Buchanan ND, 2009. Neighborhood risk, parental supervision and the onset of substance use among African American adolescents. J. Child Fam. Stud 18(6), 680–689. 10.1007/s10826-009-9273-y [DOI] [Google Scholar]
  46. Burnard C, Ralph C, Hynd P, Edwards JH, Tilbrook A, 2017. Hair cortisol and its potential value as a physiological measure of stress response in human and non-human animals. Anim. Prod. Sci 57(3), 401–414. 10.1071/AN15622 [DOI] [Google Scholar]
  47. Busse D, Yim IS, Campos B, 2017. Social context matters: Ethnicity, discrimination and stress reactivity. Psychoneuroendocrinology. 83, 187–193. 10.1016/j.psyneuen.2017.05.025 [DOI] [PubMed] [Google Scholar]
  48. Buu A, Dipiazza C, Wang J, Puttler LI, Fitzgerald HE, Zucker RA, 2009. Parent, family, and neighborhood effects on the development of child substance use and other psychopathology from preschool to the start of adulthood. J. Stud. Alcohol Drugs 70(4), 489–498. 10.15288/jsad.2009.70.489 [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Buzawa E, Buzawa C, 2014. Intimate partner violence-response to Matjasko et al. J. Policy Analysis Manage 32(1), 139–141. 10.1002/pam.21669 [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Cadoni C, 2016. Fischer 344 and Lewis rat strains as a model of genetic vulnerability to drug addiction. Front. Neurosci 10, 13. 10.3389/fnins.2016.00013 [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Camarini R, Marianno P, Rae M, 2018. Social factors in ethanol sensitization. Int. Rev. Neurobiol 140, 53–80. 10.1016/bs.irn.2018.07.003 [DOI] [PubMed] [Google Scholar]
  52. Cambron C, Kosterman R, Rhew IC, Catalano RF, Guttmannova K, Hawkins JD, 2019. Neighborhood structural factors and proximal risk for youth substance use. Prev. Sci 21, 508–518. 10.1007/s11121-019-01072-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Campbell F, Conti G, Heckman JJ, Moon SH, Pinto R, Pungello E, Pan Y, 2014. Early childhood investments substantially boost adult health. Sci. 343(6178), 1478–1485. 10.1126/science.1248429 [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Campbell JA, Walker RJ, Egede LE, 2016. Associations between adverse childhood experiences, high-risk behaviors, and morbidity in adulthood. Am. J. Prev. Med 50(3), 344–352. 10.1016/j.amepre.2015.07.022 [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Cannon JS, Kilburn MR, Karoly LA, Mattox T, Muchow AN, Buenaventura M, 2017. Investing early: Taking stock of outcomes and economic returns from early childhood programs. Rand Health Q. 7(4), 6. [PMC free article] [PubMed] [Google Scholar]
  56. Cannon WB, 1929. Bodily changes in pain, hunger, fear and rage. New York: D. Appleton & Co. [Google Scholar]
  57. Cano M, Sanchez M, Trepka M, Dillon FR, Sheehan DM, Rojas P, et al. , 2017. Immigration stress and alcohol use severity among recently immigrated Hispanic adults: Examining moderating effects of gender, immigration status and social support. J. Clin. Psychol 73(3), 294–307. 10.1002/jclp.22330 [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Carney R, Stratford B, Moore K, Rojas A, Daneri M, 2015. What works for reducing problem behaviors in early childhood: Lessons from experimental evaluations. Publication #2015–32. Bethesda, MD: Child Trends. [Google Scholar]
  59. Carter RT, 2007. Racism and psychological and emotional injury. The Couns. Psychol 35(1), 13–105. 10.1177/0011000006292033 [DOI] [Google Scholar]
  60. Casey LS, Reisner SL, Findling MG, Blendon RJ, Benson JM, Sayde JM, Miller C, 2019. Discrimination in the United States: Experiences of lesbian, gay, bisexual, transgender, and queer Americans. Health Serv. Res 54, 1454–1466. 10.1111/1475-6773.13229 [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Cave L, Cooper MN, Zubrick SR, Shepherd CCJ, 2020. Racial discrimination and child and adolescent health in longitudinal studies: A systematic review published online ahead of print. Soc. Sci. Med 250, 112864. 10.1016/j.socscimed.2020.112864 [DOI] [PubMed] [Google Scholar]
  62. Cavicchioli M, Vassena G, Movalli M, Maffei C, 2020. Is craving a risk factor for substance use among treatment-seeking with individuals with alcohol and other drugs use disorders? A meta-analytic review. Drug Alcohol Depend. 212, 108002. 10.1016/j.drugalcdep.2020.108002 [DOI] [PubMed] [Google Scholar]
  63. Centers for Disease Control and Prevention, 2014. Essentials for childhood: Steps to create safe, stable, and nurturing relationships and environments. Atlanta, GA: Centers for Disease Control and Prevention. https://www.cdc.gov/violenceprevention/childabuseandneglect/essentials.html [Google Scholar]
  64. Center on Addiction and Substance Abuse. (2010). Behind Bars II: Substance Abuse and America’s Prison Population. February, 153. http://nicic.gov/library/024443 [Google Scholar]
  65. Center on the Developing Child (2020). ACEs and toxic stress: frequently asked questions. Retrieved from https://developingchild.harvard.edu/resources/aces-and-toxic-stress-frequently-asked-questions (accessed 8 August 2020). [Google Scholar]
  66. Cerdá M, Diez-Roux AV, Tchetgen ET, Gordon-Larsen P, Kiefe C, 2010. The relationship between neighborhood poverty and alcohol use: Estimation by marginal structural models. Epidemiology. 21(4), 482–489. 10.1097/ede.0b013e3181e13539 [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Cervantes W, Ullrich R, Matthews H, 2018. Our children’s fear: Immigration policy’s effects on young children. Washington, DC: Center for Law and Social Policy. [Google Scholar]
  68. Chae DH, Clouston S, Hatzenbuehler ML, Kramer MR, Cooper HL, Wilson SM, et al. , 2015. Association between an internet-based measure of area racism and black mortality. PLOS One. 10(4), e0122963. 10.1371/journal.pone.0122963 [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Charmandari E, Tsigos C, Chrousos G, 2005. Endocrinology of the stress response. Annu. Rev. Physiol 67, 259–284. 10.1146/annurev.physiol.67.040403.120816 [DOI] [PubMed] [Google Scholar]
  70. Chartier K, Caetano R, 2010. Ethnicity and health disparities in alcohol research. Alcohol Res. Health 33(1–2), 152–160. [PMC free article] [PubMed] [Google Scholar]
  71. Chavez KE, Palfai TP, Squires LE, Cheng DM, Lloyd-Travaglini C, Saitz R, 2018. Perceived discrimination and drug involvement among black primary care patients who use drugs. Addict. Behav 77, 63–66. 10.1016/j.addbeh.2017.08.029 [DOI] [PMC free article] [PubMed] [Google Scholar]
  72. Chetty R, Hendren N, Jones MR, Porter SR, 2018. Race and economic opportunity in the United States: An intergenerational perspective. NBER Working Paper No. 24441. [Google Scholar]
  73. Cambridge MA: National Bureau of Economic Research. [Google Scholar]
  74. Chia-Chen A, Szalacha LA, Menon U, 2014. Perceived discrimination and its associations with mental health and substance use among Asian American and Pacific Islander undergraduate and graduate students. J. Am. College Health. 62(6), 390–398. 10.1080/07448481.2014.917648 [DOI] [PubMed] [Google Scholar]
  75. Clark TT, 2014. Perceived discrimination, depressive symptoms, and substance use in young adulthood. Addict. Behav 39(6), 1021–1025. [DOI] [PMC free article] [PubMed] [Google Scholar]
  76. Coccaro EF, Kavoussi RJ, Sheline YI, Lish JD, Csernansky JG, 1996. Impulsive aggression in personality disorder correlates with tritiated paroxetine binding in the platelet. Arch. Gen. Psychiatr 53(6), 531–536. 10.1001/archpsyc.1996.01830060075010 [DOI] [PubMed] [Google Scholar]
  77. Cohen S, Kamarck T, Mermelstein R, 1983. A global measure of perceived stress. J. Health and Soc. Behav 24, 385–396. 10.2307/2136404 [DOI] [PubMed] [Google Scholar]
  78. Commission on Social Determinants of Health, 2008. Closing the gap in a generation: Health equity through action on the social determinants of health. Geneva, World Health Organization. [DOI] [PubMed] [Google Scholar]
  79. Cox RJ, 2012. The impact of mass incarceration on the lives of African American women. The Rev. Black Polit. Econ 39(2), 203–212. 10.1007/s12114-011-9114-2 [DOI] [Google Scholar]
  80. Cox R, 2018. Mass incarceration, racial disparities in health, and successful aging. Generations. 42(2), 48–55. [Google Scholar]
  81. Cox R, 2020. Applying the theory of social good to mass incarceration and civil rights. Res. Soc. Work Pract 30(2), 205–218. 10.1177/1049731519872838 [DOI] [Google Scholar]
  82. Cox R, Cunningham JP, 2021. Financing the war on drugs: The impact of law enforcement grants on racial disparities in drug arrests. J. Policy Anal. Manage 40(1), 191–224. 10.1002/pam.22277 [DOI] [Google Scholar]
  83. Cunliffe VT, 2016. The epigenetic impacts of social stress: How does social adversity become biologically embedded? Epigenomics. 8(12), 1653–1669. 10.2217/epi-2016-0075 [DOI] [PMC free article] [PubMed] [Google Scholar]
  84. D’Onise K, Lynch JW, Sawyer MG, McDermott RA, 2010. Can preschool improve child health outcomes? A systematic review. Soc. Sci. Med 70(9), 1423–1440. 10.1016/j.socscimed.2009.12.037 [DOI] [PubMed] [Google Scholar]
  85. Darity W Jr., Hamilton D, Paul M, Aja A, Price A, Moore A, Chiopris C, 2018. What we get wrong about closing the racial wealth gap. Samuel DuBois Cook Center on Social Equity and Insight Center for Community Economic Development. https://socialequity.duke.edu/wp-content/uploads/2020/01/what-we-get-wrong.pdf (accessed 8 August 2020). [Google Scholar]
  86. Dasgupta N, 2013. Implicit attitudes and beliefs adapt to situations: A decade of research on the malleability of implicit prejudice, stereotypes, and the self-concept. Adv. Exp. Soc. Psychol 47, 233–279. 10.1016/B978-0-12-407236-7.00005-X [DOI] [Google Scholar]
  87. David RJ, Collins JW, 1997. Differing birth weight among infants of U.S.-born Blacks, African-born Blacks, and U.S.-born Whites. N. E. J. Med 337(17), 1209–1214. 10.1056/nejm199710233371706 [DOI] [PubMed] [Google Scholar]
  88. Davis SR, Prince MA, Hallgren KA, Johnson N, Stanley LR, Swaim RC, 2019. Classes of drinking motives among American Indian youth drinkers. Psychol. Addict. Behav 33(4), 392. 10.1037/adb0000469 [DOI] [PMC free article] [PubMed] [Google Scholar]
  89. Dettlaff AJ, Weber K, Pendleton M, Boyd R, Bettencourt B, Burton L, 2020. It is not a broken system, it is a system that needs to be broken: The upEND movement to abolish the child welfare system. J. Public Child Welfare 14(5), 500–517. 10.1080/15548732.2020.1814542 [DOI] [Google Scholar]
  90. DeVylder JE, Jun HJ, Fedina L, Coleman D, Anglin D, Cogburn C, et al. , 2018. Association of exposure to police violence with prevalence of mental health symptoms among urban residents in the United States. JAMA Netw. Open. 1(7), e184945. 10.1001/jamanetworkopen.2018.4945 [DOI] [PMC free article] [PubMed] [Google Scholar]
  91. Dovidio JF, Kawakami K, Gaertner SL, 2002. Implicit and explicit prejudice and interracial interaction. J. Pers. Soc. Psychol 82(1), 62–68. 10.1037//0022-3514.82.1.62 [DOI] [PubMed] [Google Scholar]
  92. Dube SR, Anda RF, Felitti VJ, Chapman DP, Williamson DF, Giles WH, 2001. Childhood abuse, household dysfunction, and the risk of attempted suicide throughout the life span: Findings from the adverse childhood experiences study. J. Am. Med. Assoc 286(24), 3089–3096. 10.1001/jama.286.24.3089 [DOI] [PubMed] [Google Scholar]
  93. Dube SR, Felitti VJ, Dong M, Chapman DP, Giles WH, Anda RF, 2003. Childhood abuse, neglect, and household dysfunction and the risk of illicit drug use: The adverse childhood experiences study. Pediatrics. 111(3), 564–572. [DOI] [PubMed] [Google Scholar]
  94. Dubowitz H, Roesch S, Lewis T, 2020. Child maltreatment, early adult substance use, and mediation by adolescent behavior problems. Child Maltreat. 31(4), 526–547. 10.1177/1077559520941919 [DOI] [PubMed] [Google Scholar]
  95. Du Bois WEB (1899). The Philadelphia negro: A social study. New York: Schocken Books. [Google Scholar]
  96. Earnshaw VA, Eaton LA, Collier ZK, Watson RJ, Maksut JL, Rucinski KB, et al. , 2020. HIV stigma, depressive symptoms, and substance use. AIDS Patient Care STDs. 34(6), 275–280. 10.1089/apc.2020.0021 [DOI] [PMC free article] [PubMed] [Google Scholar]
  97. Entringer S, Epel ES, Kumsta R, Lin J, Hellhammer DH, Blackburn EH, et al. , 2011. Stress exposure in intrauterine life is associated with shorter telomere length in young adulthood. Proc. Natl. Acad. Sci 108(33), E513–E518. 10.1073/pnas.1107759108 [DOI] [PMC free article] [PubMed] [Google Scholar]
  98. Evans GW, Gonnella C, Marcynyszyn LA, Gentile L, Salpekar N, 2005. The role of chaos in poverty and children’s socioemotional adjustment. Psychol. Sci 16, 560–565. 10.1111/j.0956-7976.2005.01575.x [DOI] [PubMed] [Google Scholar]
  99. Fagan J, Geller A, Davies G, West V, 2010. Street stops and broken windows revisited: The demography and logic of proactive policing in a safe and changing city. In Rice SK, White MD, eds. Race, ethnicity, and policing: New and essential readings. New York, NY: New York University Press. https://scholarship.law.columbia.edu/faculty_scholarship/1579 (accessed 8 August 2020). [Google Scholar]
  100. Fagan J, Conyers G, Ayres I, 2014. No runs, few hits and many errors: Street stops, bias and proactive policing. Berkeley, CA: Conferences on Empirical Legal Studies. [Google Scholar]
  101. Fairman BJ, Goldstein RB, Simons-Morton BG, Haynie DI, Lin D, Hingson WE, Gilman S, 2020. Neighborhood context and binge drinking from adolescence into early adulthood in a US national cohort. Int. J. Epidemiol 49(1), 103–112. 10.1093/ije/dyz133 [DOI] [PMC free article] [PubMed] [Google Scholar]
  102. Felitti VJ, Anda RF, Nordenberg D, Edwards V, Koss MP, Marks JS, 1998. Relationship of childhood abuse and household dysfunction to many of the leading causes of death in adults: The Adverse Childhood Experiences (ACE) Study. Am. J. Prev. Med 14(4):245–258. 10.1016/S0749-3797(98)00017-8 [DOI] [PubMed] [Google Scholar]
  103. Felner JK, Wisdom JP, Williams T, Katuska L, Haley SJ, Jun HJ, Corliss HL, 2020. Stress, coping, and context: Examining substance use among LGBTQ young adults with probable substance use disorders. Psychiatr. Serv 71(2), 112–120. [DOI] [PMC free article] [PubMed] [Google Scholar]
  104. Filindra A, Blanding D, García C, 2011. The power of context: State-level policies and politics and the educational performance of the children of immigrants in the United States. Harv. Educ. Rev 81(3), 407–438. 10.17763/haer.81.3.n306607254h11281 [DOI] [Google Scholar]
  105. Findling MG, Bleich SN, Casey LS, Blendon RJ, Benson JM, Sayde JM, Miller C, 2019. Discrimination in the United States: Experiences of Latinos. Health Serv. Res 54, 1409–1418. 10.1111/1475-6773.13216 [DOI] [PMC free article] [PubMed] [Google Scholar]
  106. Fish J, Osberg TM, Syed M, 2017. “This is the way we were raised”: Alcohol beliefs and acculturation in relation to alcohol consumption among Native Americans. J. Ethn. Subst. Abuse 16(2), 219–245. 10.1080/15332640.2015.1133362 [DOI] [PubMed] [Google Scholar]
  107. FitzGerald C, Hurst S, 2017. Implicit bias in healthcare professionals: A systematic review. BMC Med. Ethics 18, 19. 10.1186/s12910-017-0179-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  108. Franke HA, 2014. Toxic stress: Effects, prevention and treatment. Children. 1, 390–402. 10.3390/children1030390 [DOI] [PMC free article] [PubMed] [Google Scholar]
  109. Galea S, Nandi A, Vlahov D, 2004. The social epidemiology of substance use. Epidemiol. Rev 26, 36–52. 10.1093/epirev/mxh007 [DOI] [PubMed] [Google Scholar]
  110. Garbarino J, 1995. Raising children in a socially toxic environment. San Francisco, CA: Jossey-Bass. [Google Scholar]
  111. Geller A, Fagan J, Tyler T, Link BG, 2014. Aggressive policing and the mental health of young urban men. Am. J. Public Health 104(12), 2321–2327. 10.2105/AJPH.2014.302046 [DOI] [PMC free article] [PubMed] [Google Scholar]
  112. Geller WA, Toch H, 1996. Understanding and controlling police abuse of force. Geller WA, Toch H, eds. Police violence: Understanding and controlling police abuse of force. New Haven, CT: Yale University Press; pp. 292–328. [Google Scholar]
  113. Genberg BL, Astemborski J, Vlahov D, Kirk GD, Mehta SH, 2015. Incarceration and injection drug use in Baltimore, Maryland. Addiction. 110, 1152–1159. 10.1111/add.12938 [DOI] [PMC free article] [PubMed] [Google Scholar]
  114. Gerra G, Garofano L, Castaldini L, Rovetto F, Zaimovic A, Moi G, et al. , 2005. Serotonin transporter promoter polymorphism genotype is associated with temperament, personality traits and illegal drugs use among adolescents. J. Neural Transm 112(10), 1397–1410. 10.1007/s00702-004-0268-y [DOI] [PubMed] [Google Scholar]
  115. Gerra G, Leopardi C, Cortese E, Zaimovic A, Dell’Agnello G, Manfredini M, et al. (2008). Adrenocorticotropic hormone and cortisol plasma levels directly correlate with childhood neglect and depression measures in addicted patients. Addict. Biol 13(1), 95–104. 10.1111/j.1369-1600.2007.00086.x [DOI] [PubMed] [Google Scholar]
  116. Geronimus AT, Hicken MT, Pearson JA, Seashols SJ, Brown KL, Cruz TD, 2010. Do US black women experience stress-related accelerated biological aging? Hum. Nature 21(1), 19–38. 10.1007/S12110-010-9078-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  117. Gerrard M, Stock ML, Roberts ME, Gibbons FX, O’Hara RE, Weng CY, Wills TA, 2012. Coping with racial discrimination: The role of substance use. Psychol. Addict. Behav 26(3), 550. [DOI] [PMC free article] [PubMed] [Google Scholar]
  118. Gershenson S, Papageorge N, 2018. The power of teacher expectations: How racial bias hinders student attainment. Educ. Next 18(1), 64–71. [Google Scholar]
  119. Gibbons FX, Etcheverry PE, Stock ML, Gerrard M, Weng CY, Kiviniemi M, O’Hara RE (2010). Exploring the link between racial discrimination and substance use: What mediates? What buffers? J. Pers. Soc. Psychol 99(5), 785. 10.1037/a0019880 [DOI] [PMC free article] [PubMed] [Google Scholar]
  120. Gilbert PA, Zemore SE, 2016. Discrimination and drinking: A systematic review of the evidence. Soc. Sci. Med 161, 178–194. 10.1016/j.socscimed.2016.06.009 [DOI] [PMC free article] [PubMed] [Google Scholar]
  121. Glass JE, Williams EC, Oh H, 2020. Racial/ethnic discrimination and alcohol use disorder severity among United States adults. Drug Alcohol Depend. 216, 108203. 10.1016/j.drugalcdep.2020.108203 [DOI] [PubMed] [Google Scholar]
  122. Goldstein R, 2020. The rise of health disparities in the United States: An investigation into Medicaid expansionary policies. Soc. Impact Res. Exp 82. https://repository.upenn.edu/sire/82 (accessed 20 August 2020). [Google Scholar]
  123. Gourdine RM, 2019. We treat everybody the same: Race equity in child welfare. Soc. Work Public Health 34(1), 75–85. 10.1080/19371918.2018.1562400 [DOI] [PubMed] [Google Scholar]
  124. Gould SJ, 1996. The mismeasure of man. New York: WW Norton & Company. [Google Scholar]
  125. Grant BF, Goldstein RB, Saha TD, Chou SP, Jung J, Zhang H, et al. , 2015. Epidemiology of DSM-5 alcohol use disorder: Results from the National Epidemiologic Survey on Alcohol and Related Conditions III. JAMA Psychiatry. 72(8), 757–766. 10.1001/jamapsychiatry.2015.0584 [DOI] [PMC free article] [PubMed] [Google Scholar]
  126. Green KE, Feinstein BA, 2012. Substance use in lesbian, gay, and bisexual populations: An update on empirical research and implications for treatment. Psychol. Addict. Behav 26, 265–278. 10.1037/a0025424 [DOI] [PMC free article] [PubMed] [Google Scholar]
  127. Guerrero EG, Marsh JC, Khachikian T, Amaro H, Vega WA, 2013. Disparities in Latino substance use, service use, and treatment: Implications for culturally and evidence-based interventions under health care reform. Drug Alcohol Depend. 133(3), 805–813. 10.1016/j.drugalcdep.2013.07.027. [DOI] [PubMed] [Google Scholar]
  128. Guerrero EG, Garner BR, Cook B, Kong Y, Vega WA, Gelberg L, 2017. Identifying and reducing disparities in successful addiction treatment completion: Testing the role of Medicaid payment acceptance. Subst. Abuse Treat. Prev. Policy 12, 27. 10.1186/s13011-017-0113-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  129. Hahn RA, Barnett WS, Knopf JA, Truman BI, Johnson RL, Fielding JE, et al. , 2016. Early childhood education to promote health equity: A community guide systematic review. J. Public Health Manage. Pract 22(5), e1–e8. 10.1097/phh.0000000000000378 [DOI] [PMC free article] [PubMed] [Google Scholar]
  130. Halpern SC, Schuch FB, Scherer JN, Sordi AO, Pachado M, Dalbosco C, et al. , 2018. Child maltreatment and illicit substance abuse: A systematic review and meta-analysis of longitudinal studies. Child Abuse Rev. 27(5), 344–360. 10.1002/car.2534 [DOI] [Google Scholar]
  131. Handley ED, Rogosch FA, Guild DJ, Cicchetti D, 2015. Neighborhood disadvantage and adolescent substance use disorder: The moderating role of maltreatment. Child Maltreat. 20(3), 193–202. 10.1177/1077559515584159. [DOI] [PMC free article] [PubMed] [Google Scholar]
  132. Hardy B, Logan TD, Parman J, 2018. The historical role of race and policy for regional inequality. https://www.hamiltonproject.org/papers/the_historical_role_of_race_and_policy_for_regional_inequality (accessed 20 August 2020). [Google Scholar]
  133. Harrell CJP, Burford TI, Cage BN, Nelson TM, Shearon S, Thompson A, Green S, 2011. Multiple pathways linking racism to health outcomes. Du Bois Rev. Soc. Sci. Res. Race 8(1), 143. 10.1017/S1742058X11000178 [DOI] [PMC free article] [PubMed] [Google Scholar]
  134. Hedden BJ, Comartin E, Hambrick N, Kubiak S, 2021. Racial disparities in access to and utilization of jail-and community-based mental health treatment in 8 US midwestern jails in 2017. Am. J. Public Health 111(2), 277–285. 10.2105/AJPH.2020.305992 [DOI] [PMC free article] [PubMed] [Google Scholar]
  135. Heilig M, Epstein DH, Nader MA, Shaham Y, 2016. Time to connect bringing social context into addiction neuroscience. Nat. Rev. Neurosci 17, 592–599. 10.1038/nrn.2016.67 [DOI] [PMC free article] [PubMed] [Google Scholar]
  136. Heim C, Bradley B, Mletzko TC, Deveau TC, Musselman DL, Nemeroff CB, et al. , 2009. Effect of childhood trauma on adult depression and neuroendocrine function: Sex-specific moderation by CRH receptor 1 gene. Front. Behav. Neurosci 3, 41. 10.3389/neuro.08.041.2009 [DOI] [PMC free article] [PubMed] [Google Scholar]
  137. Heim C, Newport DJ, Wagner D, Wilcox MM, Miller AH, Nemeroff CB, 2002. The role of early adverse experience and adulthood stress in the prediction of neuroendocrine stress reactivity in women: A multiple regression analysis. Depress. Anxiety 15(3), 117–125. 10.1002/da.10015 [DOI] [PubMed] [Google Scholar]
  138. HHS Office of Inspector General, 2019. Separated children placed in office of refugee resettlement care. Washington, DC: U.S. Department of Health and Human Services. https://oig.hhs.gov/oei/reports/oei-BL-18-00511.asp (accessed 7 July 2020). [Google Scholar]
  139. Hill CV, Perez-Stable EJ, Anderson NA, Bernard MA, 2015. The National Institute on Aging Health Disparities Research framework. Ethn. Dis 25(3), 245–254. 10.18865/ed.25.3.245 [DOI] [PMC free article] [PubMed] [Google Scholar]
  140. Hinton EK, 2016. From the war on poverty to the war on crime: The making of mass incarceration in America. Cambridge, MA: Harvard University Press. [Google Scholar]
  141. Hinton E, Henderson L, Reed C, 2018. An unjust burden: The disparate treatment of Black Americans in the criminal justice system. New York: Vera Institute of Justice. [Google Scholar]
  142. Hirschtick JL, Homan SM, Rauscher G, Rubin LH, Johnson TP, Peterson CE, Persky VW, 2020. Persistent and aggressive interactions with the police: Potential mental health implications. Epidemiol. Psychiatr. Sci 29, e19. 10.1017/S2045796019000015 [DOI] [PMC free article] [PubMed] [Google Scholar]
  143. Holcomb JE, Williams MR, Hicks WD, Kovandzic TV, Meitl MB, 2018. Civil asset forfeiture laws and equitable sharing activity by the police. Criminol. Public Policy 17(1), 101–127. 10.1111/1745-9133.12341 [DOI] [Google Scholar]
  144. Holzer HJ, Offner P, Sorensen E, 2005. Declining employment among young black less□ educated men: The role of incarceration and child support. J. Policy Anal. Manage 24(2), 329–350. 10.1002/pam.20092 [DOI] [Google Scholar]
  145. Horner G, Daddona J, Burke DJ, Cullinane J, Skeer M, Wurcel AG, 2019. “You’re kind of at war with yourself as a nurse”: Perspectives of inpatient nurses on treating people who present with a comorbid opioid use disorder. PLOS One. 14(10), 1–16. 10.1371/journal.pone.0224335e0224335. [DOI] [PMC free article] [PubMed] [Google Scholar]
  146. Hostinar CE, Gunnar MR, 2015. Social support can buffer against stress and shape brain activity. AJOB Neurosci. 6(3), 34–42. 10.1080/21507740.2015.1047054 [DOI] [PMC free article] [PubMed] [Google Scholar]
  147. Howe GW, Cimporescu M, Seltzer R, Neiderhiser JM, Moreno F, Weihs K, 2017. Combining stress exposure and stress generation: Does neuroticism alter the dynamic interplay of stress, depression, and anxiety following job loss? J. Pers 85(4), 553–564. 10.1111/jopy.12260 [DOI] [PMC free article] [PubMed] [Google Scholar]
  148. Hughes K, Bellis MA, Hardcastle KA, Sethi D, Butchart A, Mikton C, et al. , 2017. The effect of multiple adverse childhood experiences on health: A systematic review and meta-analysis. The Lancet Public Health. 2(8), e356–e366. 10.1016/s2468-2667(17)30118-4 [DOI] [PubMed] [Google Scholar]
  149. Hyman SM, Paliwal P, Chaplin TM, Mazure CM, Rounsaville BJ, Sinha R, 2008. Severity of childhood trauma is predictive of cocaine relapse outcomes in women but not men. Drug Alcohol Depend. 92(1–3), 208–216. 10.1016/j.drugalcdep.2007.08.006 [DOI] [PMC free article] [PubMed] [Google Scholar]
  150. Iacono LL, Catale C, Martini A, Valzania A, Viscomi MT, Chiurchiù V, et al. , 2018. From traumatic childhood to cocaine abuse: The critical function of the immune system. Biol. Psychiatry 84(12), 905–916. 10.1016/j.biopsych.2018.05.022 [DOI] [PubMed] [Google Scholar]
  151. Institute of Medicine, 2003. Unequal treatment: Confronting racial and ethnic disparities in health care. Washington, DC: The National Academies Press. 10.17226/12875 [DOI] [PubMed] [Google Scholar]
  152. Institute of Medicine, 2006. Genes, behavior, and the social environment: Moving beyond the nature/nurture debate. Washington, DC: The National Academies Press. 10.17226/11693 [DOI] [PubMed] [Google Scholar]
  153. International Center for Prison Studies. (2018). World prison brief. https://www.prisonstudies.org/highest-to-lowest/prison-population-total?field_region_taxonomy_tid=All (accessed 10 October 2020). [Google Scholar]
  154. Jackson DB, Fahmy C, Vaughn MG, Testa A, 2019. Police stops among at-risk youth: Repercussions for mental health. J. Adolesc. Health 65(5), 627–632. 10.1016/j.jadohealth.2019.05.027 [DOI] [PubMed] [Google Scholar]
  155. Jackson JS, Knight KM, Rafferty JA, 2010. Race and unhealthy behaviors: Chronic stress, the HPA axis, and physical and mental health disparities over the life course. Am. J. Public Health 100(5), 933–939. 10.2105/AJPH.2008.143446 [DOI] [PMC free article] [PubMed] [Google Scholar]
  156. Jackson JS, Knight KM, 2006. Race and self-regulatory behaviors: The role of the stress response and HPA axis in physical and mental health disparities In: Carstensen LL, Schale KW, eds. Social structure, aging, and self-regulation in the elderly. New York, NY: Springer. [Google Scholar]
  157. Jackson KM, Rogers ML, Sartor CE, 2016. Parental divorce and initiation of alcohol use in early adolescence. Psychol. Addict. Behav 30(4), 450–461. 10.1037/adb0000164 [DOI] [PMC free article] [PubMed] [Google Scholar]
  158. Jernigan TL, Brown SA, Dowling GJ, 2018. The Adolescent Brain Cognitive Development Study. J. Res. Adolesc 28(1), 154–156. 10.1111/jora.12374 [DOI] [PMC free article] [PubMed] [Google Scholar]
  159. Johnson TJ, 2020. Intersection of bias, structural racism, and social determinants with health care inequities. Pediatrics. 146(2), e2020003657. 10.1542/peds.2020-003657 [DOI] [PubMed] [Google Scholar]
  160. Johnston LD, Miech RA, O’Malley PM, Bachman JG, Schulenberg JE, Patrick ME, 2019. Monitoring the Future national survey results on drug use 1975–2018: Overview, key findings on adolescent drug use. Ann Arbor, MI: Institute for Social Research, University of Michigan. [Google Scholar]
  161. Johnston LD, Miech RA, O’Malley PM, Bachman JG, Schulenberg JE, Patrick ME, 2020. Demographic subgroup trends among adolescents in the use of various licit and illicit drugs, 1975–2019. Monitoring the Future Occasional Paper No. 94. Ann Arbor, MI: Institute for Social Research, University of Michigan. [Google Scholar]
  162. Jones JM, 1997. Prejudice and racism. 2nd ed. New York, NY: McGraw-Hill. [Google Scholar]
  163. Jones DE, Greenberg M, Crowley M (2015). Early social-emotional functioning and public health: The relationship between kindergarten social competence and future wellness. Am. J. Public Health 105(11), 2283–2290. 10.2105/AJPH.2015.302630 [DOI] [PMC free article] [PubMed] [Google Scholar]
  164. Kaiser Family Foundation. (2019). Changes to “public charge” inadmissibility rule: implications for health and health coverage. https://www.kff.org/racial-equity-and-health-policy/fact-sheet/public-charge-policies-forimmigrants-implications-for-health-coverage/ (accessed 22 July 2020).
  165. Kamiker-Jaffe KJ, 2011. Areas of disadvantage: A systematic review of effects of area-level socioeconomic status on substance use outcomes. Drug Alcohol Rev. 30, 84–95. 10.1111/j.1465-3362.2010.00191.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  166. Kawachi I, Berkman L, 2000. Social cohesion, social capital, and health. Soc. Epidemiol 174(7). 10.1093/med/9780195377903.003.0008 [DOI] [Google Scholar]
  167. Keel TD, 2013. Religion, polygenism and the early science of human origins. Hist. Hum. Sci 26(2), 3–32. 10.1177/0952695113482916 [DOI] [Google Scholar]
  168. Kim HG, Cheon EJ, Bai DS, Lee YH, Koo BH, 2018. Stress and heart rate variability: A meta-analysis and review of the literature. Psychiatry Invest. 15(3), 235. 10.30773/pi.2017.08.17 [DOI] [PMC free article] [PubMed] [Google Scholar]
  169. Kindig D, Stoddart G, 2003. What is population health? Am. J. Public Health 93(3), 380–383. 10.2105/ajph.93.3.380 [DOI] [PMC free article] [PubMed] [Google Scholar]
  170. Koball H, Capps R, Hooker S, Perreira K, Campetella A, Pedroza JM, Monson W, Huerta S, 2015. Health and social service needs of U.S. citizen children with detained or deported immigrant parents. Washington, DC: Migration Policy Institute. [Google Scholar]
  171. Koob GF, 2008. A role for brain stress systems in addiction. Neuron. 59, 11–34. 10.1016/j.neuron.2008.06.012 [DOI] [PMC free article] [PubMed] [Google Scholar]
  172. Koob GF, 2009. Brain stress systems in the amygdala and addiction. Brain Res. 1293, 61–75. [DOI] [PMC free article] [PubMed] [Google Scholar]
  173. Koob GF, Le Moal M, 1997. Drug abuse: Hedonic homeostatic dysregulation. Science. 278(5335), 52–58. 10.1126/science.278.5335.52 [DOI] [PubMed] [Google Scholar]
  174. Koob GF, Le Moal M, 2005. Plasticity of reward neurocircuitry and the ‘dark side’ of drug addiction. Nature Neurosci. 8(11), 1442–1444. [DOI] [PubMed] [Google Scholar]
  175. Koob GF, Volkow ND, 2016. Neurobiology of addiction: A neurocircuitry analysis. Lancet Psychiatry. 3, 760–773. 10.1016/S2215-0366(16)00104-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  176. Kovera MB, 2019. Racial disparities in the criminal justice system: Prevalence, causes, and a search for solutions. J. Soc. Issues 75(4), 1139–1164. 10.1111/josi.12355 [DOI] [Google Scholar]
  177. Kulesza M, Larimer ME, Rao D, 2013. Substance use related stigma: What we know and the way forward. J. Addict. Behav. Ther. Rehab 2, 2. 10.4172/2324-9005.1000106 [DOI] [PMC free article] [PubMed] [Google Scholar]
  178. Kundakovic M, Champagne FA, 2015. Early-life experience, epigenetics, and the developing brain. Neuropsychopharmacology. 40, 141–53. 10.1038/npp.2014.140 [DOI] [PMC free article] [PubMed] [Google Scholar]
  179. Kurlychek MC, Johnson BD, 2019. Cumulative disadvantage in the American criminal justice system. Annu. Rev. Criminol 2, 291–319. 10.1146/annurev-criminol-011518-024815 [DOI] [Google Scholar]
  180. Kwate NOA, Goodman MS, 2015. Cross-sectional and longitudinal effects of racism on mental health among residents of Black neighborhoods in New York City. Am. J. Public Health 105(4), 711–718. 10.2105/FAJPH.2014.302243 [DOI] [PMC free article] [PubMed] [Google Scholar]
  181. Larrieu T, Cherix A, Duque A, Rodrigues J, Lei H, Gruetter R, Sandi C, 2017. Hierarchical status predicts behavioral vulnerability and nucleus accumbens metabolic profile following chronic social defeat stress. Curr. Biol 27(14), 2202–2210. 10.1016/j.cub.2017.06.027 [DOI] [PubMed] [Google Scholar]
  182. Lavizzo-Mourey R, 2014. Why health, poverty, and community development are inseparable. https://www.frbsf.org/community-development/files/lavizzo-mourey.pdf (accessed 7 Juy 2020). [Google Scholar]
  183. Le AD, Harding S, Juzytsch W, Watchus J, Shalev U, Shaham Y, 2000. The role of corticotrophin-releasing factor in stress-induced relapse to alcohol-seeking behavior in rats. Psychopharmacology. 150(3), 317–324. 10.1007/s002130000411 [DOI] [PubMed] [Google Scholar]
  184. Lee CT, McClernon FJ, Kollins SH, Prybol K, Fuemmeler BF, 2013. Childhood economic strains in predicting substance use in emerging adulthood: Mediation effects of youth self-control and parenting practices. J. Pediatr. Psychol 38(10), 1130–1143. 10.1093/jpepsy/jst056 [DOI] [PMC free article] [PubMed] [Google Scholar]
  185. Lee H, Wildeman C, Wang EA, Matusko N, Jackson JS, 2014. A heavy burden: The cardiovascular health consequences of having a family member incarcerated. Am. J. Public Health 104(3), 421–427. 10.2105/ajph.2013.301504 [DOI] [PMC free article] [PubMed] [Google Scholar]
  186. Lee JO, Jones TM, Kosterman R, Cambron C, Rhew IC, Herrenkohl TI, Hill KG, 2018. Childhood neighborhood context and adult substance use problems: The role of socio-economic status at the age of 30 years. Public Health. 165, 58–66. 10.1016/j.puhe.2018.09.011 [DOI] [PMC free article] [PubMed] [Google Scholar]
  187. Lee JS, Mukhopadhyay P, Matyas C, Trojnar E, Paloczi J, Yang YR, et al. , 2019. PCSK9 inhibition as a novel therapeutic target for alcoholic liver disease. Sci. Rep 9(1), 1–16. 10.1038/s41598-019-53603-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  188. Lisdahl KM, Sher KJ, Conway KP, Gonzalez R, Ewing SWF, Nixon SJ, et al. , 2018. Adolescent brain cognitive development (ABCD) study: Overview of substance use assessment methods. Dev. Cogn. Neurosci 32, 80–96. 10.1016/j.dcn.2018.02.007 [DOI] [PMC free article] [PubMed] [Google Scholar]
  189. Lloyd DA, Turner RJ, 2008. Cumulative lifetime adversities and alcohol dependence in adolescence and young adulthood. Drug Alcohol Depend. 93(3), 217–226. 10.1016/j.drugalcdep.2007.09.012 [DOI] [PMC free article] [PubMed] [Google Scholar]
  190. Logrip ML, Zorrilla EP, Koob GF, 2012. Stress modulation of drug self-administration: Implications for addiction comorbidity with post-traumatic stress disorder. Neuropharmacology, 62(2), 552–564. 10.1016/j.neuropharm.2011.07.007 [DOI] [PMC free article] [PubMed] [Google Scholar]
  191. Lorenc T, Lester S, Sutcliffe K, Stansfield C, Thomas J, 2020. Interventions to support people exposed to adverse childhood experiences: Systematic review of systematic reviews. BMC Public Health. 20, 1–10. 10.1186/s12889-020-08789-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  192. Lorvick J, Comfort M, Kral AH, Lambdin BH, 2018. Exploring lifetime accumulation of criminal justice involvement and associated health and social outcomes in a community-based sample of women who use drugs. J. Urban Health. 95, 584–593. [DOI] [PMC free article] [PubMed] [Google Scholar]
  193. Lui PP, Zamboanga BL, 2018. Acculturation and alcohol use among Asian Americans: A meta-analytic review. Psychol. Addict. Behav 32(2), 173–186. 10.1037/adb0000340 [DOI] [PubMed] [Google Scholar]
  194. Lupien SJ, McEwen BS, Gunnar MR, Heim C, 2009. Effects of stress throughout the lifespan on the brain, behaviour and cognition. Nature Rev. Neurosci 10(6), 434–445. [DOI] [PubMed] [Google Scholar]
  195. Luthar S, 2006. Resilience in development: A synthesis of research across five decades. Dev. Psychopathol 3(2), 740–795. [Google Scholar]
  196. Lyons CJ, Pettit B, 2011. Compounded disadvantage: Race, incarceration, and wage growth. Soc. Prob 58(2), 257–280. 10.1525/sp.2011.58.2.257 [DOI] [Google Scholar]
  197. Maguire-Jack K, Font SA, Dillard R, 2020. Child protective services decision-making: The role of children’s race and county factors. Am. J. Orthopsychiatry 90(1), 48–62. [DOI] [PMC free article] [PubMed] [Google Scholar]
  198. Maina IW, Belton TD, Ginzberg S, Singh A, Johnson TJ, 2018. A decade of studying implicit racial/ethnic bias in healthcare providers using the implicit association test. Soc. Sci. Med 199, 219–229. [DOI] [PubMed] [Google Scholar]
  199. Mantsch JR, Baker DA, Funk D, Lê AD, Shaham Y, 2016. Stress-induced re-instatement of drug seeking: 20 years of progress. Neuropsychopharmacology. 41, 335–356. 10.1038/npp.2015.142 [DOI] [PMC free article] [PubMed] [Google Scholar]
  200. Marin MF, Lord C, Andrews J, Juster RP, Sindi S, Arsenault-Lapierre G, et al. , 2011. Chronic stress, cognitive functioning and mental health. Neurobiol. Learn. Memory 96(4), 583–595. 10.1016/j.nlm.2011.02.016 [DOI] [PubMed] [Google Scholar]
  201. Marmot MG, Wilkinson RG, 1999. Social determinants of health. Oxford, England: Oxford University Press. [Google Scholar]
  202. Marmot M, Wilkinson RG, 2006. Social determinants of health. 2nd ed. Oxford, England: Oxford University Press. [Google Scholar]
  203. Marsh JC, Cao D, Guerrero E, Shin HC, 2009. Need-service matching in substance abuse treatment: Racial/ethnic differences. Eval. Prog. Plan 32(1), 43–51. 10.1016/j.evalprogplan.2008.09.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  204. Masten AS, Cutuli JJ, Herbers JE, Hinz E, Obradović J, Wenzel AJ, 2014. Academic risk and resilience in the context of homelessness. Child Dev. Perspect 8(4), 201–206. 10.1111/cdep.12088 [DOI] [PMC free article] [PubMed] [Google Scholar]
  205. Matsumoto A, Santelices C, Lincoln AK, 2020. Perceived stigma, discrimination and mental health among women in publicly funded substance abuse treatment. Stigma Health. 10.1037/sah0000226 [DOI] [Google Scholar]
  206. McCabe SE, Bostwick WB, Hughes TL, West BT, Boyd CJ, 2010. The relationship between discrimination and substance use disorders among lesbian, gay, and bisexual adults in the United States. Am. J. Public Health 100(10), 1946–1952. 10.2105/AJPH.2009.163147 [DOI] [PMC free article] [PubMed] [Google Scholar]
  207. McCauley J, Kern DE, Kolodner K, Dill L, Schroeder AF, DeChant HK, et al. , 1997. Clinical characteristics of women with a history of childhood abuse: Unhealed wounds. J. Am. Med. Assoc 277(17), 1362–1368. [PubMed] [Google Scholar]
  208. McEwen CA, Gregerson SF, 2019. A critical assessment of the adverse childhood experiences study at 20 years. Am. J. Prev. Med 56(6), 790–794. 10.1016/j.amepre.2018.10.016 [DOI] [PubMed] [Google Scholar]
  209. Mclaughlin KA, Weissman D, Bitrán D, 2019. Childhood adversity and neural development: A systematic review. Annu. Rev. Dev. Psychol 1(1), 277–312. 10.1146/annurev-devpsych-121318-084950 [DOI] [PMC free article] [PubMed] [Google Scholar]
  210. McNeil R, Small W, Wood E, Kerr T, 2014. Hospitals as a “risk environment”: An ethno-epidemiological study of voluntary and involuntary discharge from hospital against medical advice among people who inject drugs. Soc. Sci. Med 105:59–66. 10.1016/j.socscimed.2014.01.010 [DOI] [PMC free article] [PubMed] [Google Scholar]
  211. Meca A, Reinke LG, Scheier LM, 2017. Acculturation and tobacco/illicit drug use in Hispanic youth. In Schwartz SJ, Unger JB, eds. The Oxford handbook of acculturation and health. New York, NY: Oxford University Press; pp. 281–299. [Google Scholar]
  212. Meca A, Zamboanga BL, Lui PP, Schwartz SJ, Lorenzo-Blanco EI, Gonzales-Backen MA, et al. , 2019. Alcohol initiation among recently immigrated Hispanic adolescents: Roles of acculturation and sociocultural stress. Am. J. Orthopsychiatry 89(5), 569–578. 10.1037/ort0000352 [DOI] [PMC free article] [PubMed] [Google Scholar]
  213. Mennis J, Stahler GJ, Abou El Magd S, Baron DA, 2019. How long does it take to complete outpatient substance use disorder treatment? Disparities among Blacks, Hispanics, and Whites in the US. Addict. Behav 93, 158–165. 10.1016/j.addbeh.2019.01.041 [DOI] [PubMed] [Google Scholar]
  214. Merriam-Webster, 2020. Racism. https://www.merriam-webster.com/dictionary/racism (accessed 30 September 2020).
  215. Metzger IW, Cooper SM, Ritchwood TD, Onyeuku C, Griffin CB, 2017. Profiles of African American college students’ alcohol use and sexual behaviors: Associations with stress, racial discrimination, and social support. J. Sex Res 54(3), 374–385. 10.1080/00224499.2016.1179709 [DOI] [PMC free article] [PubMed] [Google Scholar]
  216. Metzler M, Merrick MT, Klevens J, Ports KA, Ford DC, 2017. Adverse childhood experiences and life opportunities: Shifting the narrative. Child. Youth Serv. Rev 72, 141–149. 10.1016/j.childyouth.2016.10.021 [DOI] [PMC free article] [PubMed] [Google Scholar]
  217. Mezuk B, Abdou CM, Hudson D, Kershaw KN, Rafferty JA, Lee H, Jackson JS, 2013. “White box” epidemiology and the social neuroscience of health behaviors: The environmental affordances model. Soc. Ment. Health 3(2), 79–95. [DOI] [PMC free article] [PubMed] [Google Scholar]
  218. Mezuk B, Rafferty JA, Kershaw KN, Hudson D, Abdou CM, Lee H, et al. , 2010. Reconsidering the role of social disadvantage in physical and mental health: Stressful life events, health behaviors, race, and depression. Am. J. Epidemiol 172(11), 1238–1249. 10.1093/aje/kwq283 [DOI] [PMC free article] [PubMed] [Google Scholar]
  219. Minior T, Galea S, Stuber J, Ahern J, Ompad DC, 2003. Racial differences in discrimination experiences and responses among minority substance users. Ethn. Dis 13(4), 521–527. [PubMed] [Google Scholar]
  220. Moscarello JM, Hartley CA, 2017. Agency and the calibration of motivated behavior. Trends Cogn. Sci 21(10), 725–735. 10.1016/j.tics.2017.06.008 [DOI] [PubMed] [Google Scholar]
  221. Motley R, Sewell W, Chen YC, 2017. Community violence exposure and risk-taking behaviors among black emerging adults: A systematic review. J. Community Health 42(5), 1069–1078. 10.1007/s10900-017-0353-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  222. Mowen TJ, Visher CA, 2015. Drug use and crime after incarceration: The role of family support and family conflict. Just. Q. 32(2), 337–359. 10.1080/07418825.2013.771207 [DOI] [Google Scholar]
  223. Muennig P, Robertson D, Johnson G, Campbell F, Pungello EP, Neidell M, 2011. The effect of an early education program on adult health: The Carolina Abecedarian Project randomized controlled trial. Am. J. Public Health 101(3), 512–516. 10.2105/AJPH.2010.200063 [DOI] [PMC free article] [PubMed] [Google Scholar]
  224. Muscatell KA, Dedovic K, Slavich GM, Jarcho MR, Breen EC, Bower JE, et al. , 2016. Neural mechanisms linking social status and inflammatory responses to social stress. Soc. Cogn. Affect. Neurosci 11(6), 915–922. 10.1093/scan/nsw025 [DOI] [PMC free article] [PubMed] [Google Scholar]
  225. Myers HF, 2009. Ethnicity- and socio-economic status-related stresses in context: An integrative review and conceptual model. J. Behav. Med 32, 9–19. 10.1007/s10865-008-9181-4 [DOI] [PubMed] [Google Scholar]
  226. Nader MA, Banks ML, 2014. Environmental modulation of drug taking: Nonhuman primate models of cocaine abuse and PET neuroimaging. Neuropharmacology. 76, 510–517. 10.1016/j.neuropharm.2013.05.044 [DOI] [PMC free article] [PubMed] [Google Scholar]
  227. Naser RL, Visher CA, 2006. Family members’ experiences with incarceration and reentry. West. Crim. Rev 7(2), 20–31. [Google Scholar]
  228. National Academies of Sciences, Engineering, and Medicine, 2017. Communities in action: Pathways to health equity. Washington, DC: The National Academies Press. 10.17226/24624 [DOI] [PubMed] [Google Scholar]
  229. National Academies of Sciences, Engineering, and Medicine, 2018. Transforming the financing of early care and education. Washington, DC: The National Academies Press. 10.17226/24984 [DOI] [PubMed] [Google Scholar]
  230. National Academies of Sciences, Engineering, and Medicine, 2019a. Vibrant and healthy kids: Aligning science, practice, and policy to advance health equity. Washington, DC: The National Academies Press. 10.17226/25466 [DOI] [PubMed] [Google Scholar]
  231. National Academies of Sciences, Engineering, and Medicine, 2019b. The promise of adolescence: Realizing opportunity for all youth. Washington, DC: The National Academies Press. 10.17226/25388 [DOI] [PubMed] [Google Scholar]
  232. National Academies of Sciences, Engineering, and Medicine, 2020. Promoting positive adolescent health behaviors and outcomes: Thriving in the 21st century. Washington, DC: The National Academies Press. 10.17226/25552 [DOI] [PubMed] [Google Scholar]
  233. National Collaborating Centre for Determinants of Health, 2014. Let’s talk: Moving upstream. Antigonish, NS: National Collaborating Centre for Determinants of Health, St. Francis Xavier University. [Google Scholar]
  234. National Institutes of Health, 2020. HEALthy Brain and Child Development Study. https://heal.nih.gov/research/infants-and-children/healthy-brain (Accessed 10 October 2020). [Google Scholar]
  235. National Scientific Council on the Developing Child, 2004. Young children develop in an environment of relationships. Working Paper 1. Cambridge, MA: Harvard University. https://46y5eh11fhgw3ve3ytpwxt9r-wpengine.netdna-ssl.com/wp-content/uploads/2004/04/Young-Children-Develop-in-an-Environment-of-Relationships.pdf (accessed 2 July 2020). [Google Scholar]
  236. Neisewander JL, Peartree NA, Pentkowski NS, 2012. Emotional valence and context of social influences on drug abuse-related behavior in animal models of social stress and prosocial interaction. Psychopharmacology. 224(1), 33–56. 10.1007/s00213-012-2853-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  237. Nicosia N, MacDonald JM, Arkes J, 2013. Disparities in criminal court referrals to drug treatment and prison for minority men. Am. J. Public Health 103(6), e77–e84. [DOI] [PMC free article] [PubMed] [Google Scholar]
  238. Nowotny KM, 2015. Race/ethnic disparities in the utilization of treatment for drug dependent inmates in U.S. state correctional facilities. Addict. Behav 40, 148–153. 10.1016/j.addbeh.2014.09.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
  239. Oberlander TF, Weinberg J, Papsdorf M, Grunau R, Misri S, Devlin AM, 2008. Prenatal exposure to maternal depression, neonatal methylation of human glucocorticoid receptor gene (NR3C1) and infant cortisol -stress responses. Epigenetics. 3(2), 97–106. 10.4161/epi.3.2.6034 [DOI] [PubMed] [Google Scholar]
  240. Okonofua JA, Eberhardt JL, 2015. Two strikes: Race and the disciplining of young students. Psychol. Sci 26(5), 617–624. [DOI] [PubMed] [Google Scholar]
  241. Ong AD, Williams DR, Nwizu U, Gruenewald TL, 2017. Everyday unfair treatment and multisystem biological dysregulation in African American adults. Cult. Divers. Ethnic Minor. Psychol 23(1), 27. [DOI] [PMC free article] [PubMed] [Google Scholar]
  242. Ornelas IJ, Eng E, Perreira KM, 2011. Perceived barriers to opportunity and their relation to substance use among Latino immigrant men. J. Behav. Med 34(3), 182–191. [DOI] [PMC free article] [PubMed] [Google Scholar]
  243. Otiniano Verissimo AD, Gee GC, Ford CL, Iguchi MY, 2014. Racial discrimination, gender discrimination, and substance abuse among Latina/os nationwide. Cult. Divers. Ethnic Minor. Psychol 20(1), 43–51. 10.1037/a0034674 [DOI] [PMC free article] [PubMed] [Google Scholar]
  244. Otiniano Verissimo AD, Grella CE, Amaro H, Gee GC, 2014. Discrimination and substance use disorders among Latinos: The role of gender, nativity, and ethnicity. Am. J. Public Health 104(8), 1421–1428. [DOI] [PMC free article] [PubMed] [Google Scholar]
  245. Pager D, Bonikowski B, Western B, 2009. Discrimination in a low-wage labor market: A field experiment. Am. Sociol. Rev 74(5), 777–799. 10.1177/000312240907400505 [DOI] [PMC free article] [PubMed] [Google Scholar]
  246. Pager D, Western B, Sugie N, 2009. Sequencing disadvantage: Barriers to employment facing young black and white men with criminal records. Ann. Am. Acad. Polit. Soc. Sci 623(1), 195–213. 10.1177/0002716208330793 [DOI] [PMC free article] [PubMed] [Google Scholar]
  247. Paradies Y, Ben J, Denson N, Elias A, Priest N, Pieterse A, et al. , 2015. Racism as a determinant of health: A systematic review and meta-analysis. PLOS One. 10(9), e0138511. 10.1371/journal.pone.0138511 [DOI] [PMC free article] [PubMed] [Google Scholar]
  248. Paul SE, Hatoum AS, Fine JD, Johnson EC, Hansen I, Karcher NR, et al. , 2021. Associations between prenatal cannabis exposure and childhood outcomes: Results from the ABCD Study. JAMA Psychiatry. 78(1), 64–76. 10.1001/jamapsychiatry.2020.2902 [DOI] [PMC free article] [PubMed] [Google Scholar]
  249. Pelloux Y, Giorla E, Montanari C, Baunez C, 2019. Social modulation of drug use and drug addiction. Neuropharmacology. 159, 107545. 10.1016/j.neuropharm.2019.02.027 [DOI] [PubMed] [Google Scholar]
  250. Pettit B, Ewert S, 2009. Employment gains and wage declines: The erosion of black women’s relative wages since 1980. Demography. 46(3), 469–492. 10.1353/dem.0.0061 [DOI] [PMC free article] [PubMed] [Google Scholar]
  251. Pew Research Center, 2020. Unemployment rate is higher than officially recorded, more so for women and certain other groups. https://www.pewresearch.org/fact-tank/2020/06/30/unemployment-rate-is-higher-than-officially-recorded-more-so-for-women-and-certain-other-groups (accessed 10 October 2020).
  252. Pittman DM, Cho Kim S, Hunter CD, Obasi EM, 2017. The role of minority stress in second-generation Black emerging adult college students’ high-risk drinking behaviors. Cult. Divers. Ethn. Minor. Psychol 23(3), 445–455. 10.1037/cdp0000135 [DOI] [PMC free article] [PubMed] [Google Scholar]
  253. Plant EA, Peruche BM, 2005. The consequences of race for police officers’ responses to criminal suspects. Psychol. Sci 16(3), 180–183. 10.1111/j.0956-7976.2005.00800.x [DOI] [PubMed] [Google Scholar]
  254. Priest H, Englander H, McCarty D, 2020. “Now hospital leaders are paying attention”: A qualitative study of internal and external factors influencing addiction consult services. J. Subst. Abuse Treat. 110, 59–65. 10.1016/j.jsat.2019.12.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  255. Prom-Wormley EC, Ebejer J, Dick DM, Bowers MS, 2017. The genetic epidemiology of substance use disorder: A review. Drug Alcohol Depend. 180, 241–259. 10.1016/j.drugalcdep.2017.06.040 [DOI] [PMC free article] [PubMed] [Google Scholar]
  256. Ramchandani VA, Stangl BL, Blaine SK, Plawecki MH, Schwandt ML, Kwako LE, et al. , 2018. Stress vulnerability and alcohol use and consequences: From human laboratory studies to clinical outcomes. Alcohol. 72, 75–88. 10.1016/j.alcohol.2018.06.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
  257. Raphael S, Stoll MA, 2013. Why are so many Americans in prison? New York, NY: Russell Sage Foundation. [Google Scholar]
  258. Reardon SF, Owens A, 2014. 60 years after Brown: Trends and consequences of school desegregation. Annu. Rev. Sociol 40, 199–218. 10.1146/annurev-soc-071913-043152 [DOI] [Google Scholar]
  259. Reilly KH, Bartley K, Paone D, Tuazon E, 2019. Alcohol-related emergency department visits and income inequality in New York City, USA: An ecological study. Epidemiol. Health 41, e2019041. 10.4178/epih.e2019041 [DOI] [PMC free article] [PubMed] [Google Scholar]
  260. Rewak M, Buka S, Prescott J, De Vivo I, Loucks EB, Kawachi I, et al. , 2014. Race-related health disparities and biological aging: Does rate of telomere shortening differ across blacks and whites? Biol. Psychol 99, 92–99. 10.1016/j.biopsycho.2014.03.007 [DOI] [PMC free article] [PubMed] [Google Scholar]
  261. Rhodes T, Lilly R, Fernández C, Giorgino E, Kemmesis UE, Ossebaard HC, et al. , 2003. Risk factors associated with drug use: The importance of ‘risk environment.’ Drugs Educ. Prev. Policy 10(4), 303–329. 10.1080/0968763031000077733 [DOI] [Google Scholar]
  262. Rich JA, 2000. The health crisis of young black men in the inner city. In: The crisis of the young African American male in the inner cities: A consultation of the United States Commission on Civil Rights. Vol. 1. Washington, DC: U.S. Commission on Civil Rights; pp. 132–142. [Google Scholar]
  263. Rich JA, 2009. Wrong place, wrong time: Trauma and violence in the lives of young Black men. Baltimore, MD: Johns Hopkins University Press. [Google Scholar]
  264. Rich JA, Grey M, 2005. Pathways to recurrent trauma among young Black men: Traumatic stress, substance use, and the “code of the street.” Am. J. Public Health 95, 816–824. 10.2105/AJPH.2004.044560 [DOI] [PMC free article] [PubMed] [Google Scholar]
  265. Riddle T, Sinclair S, 2019. Racial disparities in school-based disciplinary actions are associated with county-level rates of racial bias. Proc. Natl. Acad. Sci 116(17), 8255–8260. 10.1073/pnas.1808307116 [DOI] [PMC free article] [PubMed] [Google Scholar]
  266. Rivas-Rivero E, Bonilla-Algovia E, Vázquez JJ, 2020. Risk factors associated with substance use in female victims of abuse living in a context of poverty. Ann. Psychol 36(1), 173–180. 10.6018/analesps.362541 [DOI] [Google Scholar]
  267. Rogers CJ, Forster M, Vetrone S, Unger JB, 2020. The role of perceived discrimination in substance use trajectories in Hispanic young adults: A longitudinal cohort study from high school through emerging adulthood. Addict. Behav 103, 106253. 10.1016/j.addbeh.2019.106253 [DOI] [PMC free article] [PubMed] [Google Scholar]
  268. Rojas-Flores L, Clements ML, Koo JH, London J, 2017. Trauma and psychological distress in Latino citizen children following parental detention and deportation. Psychol. Trauma Theory Res. Pract. Policy 9(3), 352–361. 10.1037/tra0000177 [DOI] [PubMed] [Google Scholar]
  269. Romero LM, Platts SH, Schoech SJ, Wada H, Crespi E, Martin LB, Buck CL, 2015. Understanding stress in the healthy animal–potential paths for progress. Stress. 18(5), 491–497. 10.3109/10253890.2015.1073255 [DOI] [PubMed] [Google Scholar]
  270. Rothstein R, 2017. The color of law: A forgotten history of how our government segregated America. New York, NY: Liveright. [Google Scholar]
  271. Russell KK, 1998. Driving while Black: Corollary phenomena and collateral consequences. Boston Coll. Law Rev 40, 717. 10.2139/ssrn.3625185 [DOI] [Google Scholar]
  272. Salas-Wright CP, Vaughn MG, Trenette T, Goings C, Córdova D Schwartz SJ, 2017. Substance use disorders among immigrants in the United States: A research update, Addict. Behav 76, 169–173. 10.1016/j.addbeh.2017.08.014. [DOI] [PubMed] [Google Scholar]
  273. Salter PS, Adams G, Perez MJ, 2018. Racism in the structure of everyday worlds: A cultural-psychological perspective. Curr. Direct. Psychol. Sci 27(3) 150–155. [Google Scholar]
  274. Sampedro-Piquero P, Vicario S, Pérez-Rivas A, Venero C, Baliyan S, Santín LJ, 2020. Salivary cortisol levels are associated with craving and cognitive performance in cocaine-abstinent subjects: A pilot study. Brain Sci. 10(10), 682. 10.3390/brainsci10100682 [DOI] [PMC free article] [PubMed] [Google Scholar]
  275. Sandy KR, 2002. Discrimination inherent in America’s drug war: Hidden racism revealed by examining the hysteria over crack. Ala. Law Rev 54, 665. [Google Scholar]
  276. Savage JE, Mezuk B, 2014. Psychosocial and contextual determinants of alcohol and drug use disorders in the National Latino and Asian American Study. Drug Alcohol Depend. 139, 71–78. 10.1016/j.drugalcdep.2014.03.011 [DOI] [PMC free article] [PubMed] [Google Scholar]
  277. Scheidell JD, Quinn K, McGorray SP, Frueh BC, Beharie NN, Cottler LB, Khan MR, 2018. Childhood traumatic experiences and the association with marijuana and cocaine use in adolescence through adulthood. Addiction. 113(1), 44–56. 10.1111/add.13921 [DOI] [PMC free article] [PubMed] [Google Scholar]
  278. Schubert C, Lambertz M, Nelesen RA, Bardwell W, Choi JB, Dimsdale JE, 2009. Effects of stress on heart rate complexity—a comparison between short-term and chronic stress. Biol. Psychol 80(3), 325–332. 10.1016/j.biopsycho.2008.11.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
  279. Schwartz SJ, Unger JB, Baezconde-Garbanati L, Zamboanga BL, Lorenzo-Blanco EI, Des Rosiers SE, et al. , 2015. Trajectories of cultural stressors and effects on mental health and substance use among Hispanic immigrant adolescents. J. Adolesc. Health 56(4), 433–439. 10.1016/j.jadohealth.2014.12.011 [DOI] [PMC free article] [PubMed] [Google Scholar]
  280. Selye H, 1974. Stress without distress. New York, NY: New American Library. [Google Scholar]
  281. Shea A, Walsh C, Macmillan H, Steiner M, 2005. Child maltreatment and HPA axis dysregulation: Relationship to major depressive disorder and post-traumatic stress disorder in females. Psychoneuroendocrinology. 30(2), 162–178. 10.1016/j.psyneuen.2004.07.001 [DOI] [PubMed] [Google Scholar]
  282. Sherman GD, Mehta PH, 2020. Stress, cortisol, and social hierarchy. Curr. Opin. Psychol 33, 227–232. 10.1016/j.copsyc.2019.09.013 [DOI] [PubMed] [Google Scholar]
  283. Shimamoto A, 2018. Social defeat stress, sex, and addiction-like behaviors. Int. Rev. Neurobiol 140, 271–313. 10.1016/bs.irn.2018.07.009 [DOI] [PubMed] [Google Scholar]
  284. Shin SH, Edwards HE, Heeren T, Amodeo M, 2009. Relationship between multiple forms of maltreatment by a parent or guardian and adolescent alcohol use. Am. J. Addict 18(3), 226–234. [DOI] [PubMed] [Google Scholar]
  285. Shin SH, McDonald SE, Conley D, 2018. Patterns of adverse childhood experiences and substance use among young adults: A latent class analysis. Addict. Behav 78, 187–192. 10.1016/j.addbeh.2017.11.020 [DOI] [PMC free article] [PubMed] [Google Scholar]
  286. Shonkoff JP, 2016. Capitalizing on advances in science to reduce the health consequences of early childhood adversity. JAMA Pediatr. 170(10), 1003–1007. 10.1001/jamapediatrics.2016.1559 [DOI] [PubMed] [Google Scholar]
  287. Shonkoff JP, Boyce WT, McEwen BS, 2009. Neuro-science, molecular biology, and the childhood roots of health disparities: Building a new framework for health promotion and disease prevention. J. Am. Med. Associ 301(21), 2252–2259. 10.1001/jama.2009.754 [DOI] [PubMed] [Google Scholar]
  288. Shonkoff JP, Garner AA, 2011. Toxic stress, brain development, and the early childhood foundations of lifelong health. Pediatrics. 129(1), e224–e231. 10.1542/peds.2011-2662 [DOI] [PubMed] [Google Scholar]
  289. Shonkoff JP, Garner AS, Siegel BS, Dobbins MI, Earls MF, Garner AS, et al. , 2012. The lifelong effects of early childhood adversity and toxic stress. Pediatrics. 129, e232–e246. 10.1542/peds.2011-2663 [DOI] [PubMed] [Google Scholar]
  290. Shuey KM, Willson AE, 2008. Cumulative disadvantage and black-white disparities in life-course health trajectories. Res. Aging 30(2), 200–225. 10.1177/0164027507311151 [DOI] [Google Scholar]
  291. Simon R, Snow R, Wakeman S, 2020. Understanding why patients with substance use disorders leave the hospital against medical advice: A qualitative study. Subst. Abuse 41(4), 519–525. 10.1080/08897077.2019.1671942 [DOI] [PubMed] [Google Scholar]
  292. Singh GK, Daus GP, Allenden M, Ramey CT, Martin EK, Perry C, De Los Reyes A, 2017. Social determinants of health in the United States: Addressing major health inequality trends for the nation, 1935–2016. Int. J. MCH AIDS. 6(2), 139–164. 10.21106/ijma.236 [DOI] [PMC free article] [PubMed] [Google Scholar]
  293. Sinha R, 2001. How does stress increase risk of drug abuse and relapse? Psychopharmacology. 158(4), 343–359. 10.1007/s002130100917 [DOI] [PubMed] [Google Scholar]
  294. Sinha R, 2008. Chronic stress, drug use, and vulnerability to addiction. Ann. NY Acad Sci 1141, 105. 10.1196/annals.1441.030 [DOI] [PMC free article] [PubMed] [Google Scholar]
  295. Sinha R, 2009. Modeling stress and drug craving in the laboratory: Implications for addiction treatment development. Addict. Biol 14(1), 84–98. 10.1111/j.1369-1600.2008.00134.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  296. Sinha R, Li CS, 2007. Imaging stress-and cue-induced drug and alcohol craving: Association with relapse and clinical implications. Drug Alcohol Rev. 26(1), 25–31. 10.1080/09595230601036960 [DOI] [PubMed] [Google Scholar]
  297. Solar O, Irwin A, 2010. A conceptual framework for action on the social determinants of health. Geneva, Switzerland: World Health Organization. https://www.who.int/sdhconference/resources/ConceptualframeworkforactiononSDH_eng.pdf (accessed 2 June 2020). [Google Scholar]
  298. Somaini L, Donnini C, Manfredini M, Raggi M, Saracino M, Gerra M, et al. , 2011. Adverse childhood experiences (ACEs), genetic polymorphisms and neurochemical correlates in experimentation with psychotropic drugs among adolescents. Neurosci. Biobehav. Rev 35(8), 1771–1778. 10.1016/j.neubiorev.2010.11.008 [DOI] [PubMed] [Google Scholar]
  299. Spencer SJ, Logel C, Davies PG, 2016. Stereotype threat. Annu. Rev. Psychol 67, 415–437. 10.1146/annurev-psych-073115-103235 [DOI] [PubMed] [Google Scholar]
  300. Spooner C, Hetherington K, 2004. Social determinants of drug use. Technical Report Number 228. Sydney, Australia: National Drug and Alcohol Research Centre, University of New South Wales. [Google Scholar]
  301. Sugie NF, Turney K, 2017. Beyond incarceration: Criminal justice contact and mental health. Am. Sociol. Rev 82(4), 719–743. [Google Scholar]
  302. Takeuchi DT, Williams DR, 2011. Past insights, future promises: Race and health in the twenty-first century. Du Bois Rev. 8(1), 1–3. [Google Scholar]
  303. Tarlov A, 1996. Social determinants of health: The sociobiological translation. In: Blane D, Brunner E, Wilkerson R, eds. Health and social organization. London, England: Routledge. [Google Scholar]
  304. Teicher MH, Samson JA, 2016. Annual research review: Enduring neurobiological effects of childhood abuse and neglect. J. Child Psychol. Psychiatry 57, 241–266. 10.1111/jcpp.12507 [DOI] [PMC free article] [PubMed] [Google Scholar]
  305. Theall KP, Lancaster BP, Lynch S, Haines RT, Scribner S, Scribner R, Kishore V, 2011. The neighborhood alcohol environment and at-risk drinking among African-Americans. Alcohol. Clin. Exp. Res 35(5), 996–1003. 10.1111/j.1530-0277.2010.01430.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  306. Thompson JR, Creasy SL, Mair CF, Burke JG, 2020. Drivers of opioid use in Appalachian Pennsylvania: Cross-cutting social and community-level factors. Int. J. Drug Policy 78, 102706. 10.1016/j.drugpo.2020.102706 [DOI] [PMC free article] [PubMed] [Google Scholar]
  307. Thompson R, Lewis T, Neilson EC, English DJ, Litrownik AJ, Margolis B, Dubowitz H, 2017. Child maltreatment and risky sexual behavior. Child Maltreat., 22, 69–78. [DOI] [PMC free article] [PubMed] [Google Scholar]
  308. Tieger JH, 1971. Police discretion and discriminatory enforcement. Duke Law J. 1971(4), 717–743. [Google Scholar]
  309. Tomek SE, Olive F, 2018. Social influences in animal models of opiate addiction. Int. Rev. Neurobiol 140, 81–107. 10.1016/bs.irn.2018.07.004 [DOI] [PubMed] [Google Scholar]
  310. Torres OV, O’Dell LE, 2016. Stress is a principal factor that promotes tobacco use in females. Prog. Neuropsychopharmacol. Biol. Psychiatry 65, 260–268. 10.1016/j.pnpbp.2015.04.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
  311. Tracy M, Cerda M, Keyes K, 2018. Agent-based modeling in public health: Current applications and future directions. Ann. Rev. Public Health 39, 77–94. 10.1146/annurev-pubhealth-040617-014317 [DOI] [PMC free article] [PubMed] [Google Scholar]
  312. Tran AG, Lee RM, Burgess DJ, 2010. Perceived discrimination and substance use in Hispanic/Latino, African-born Black, and Southeast Asian immigrants. Cult. Divers. Ethn. Minor. Psychol 16(2), 226. 10.1037/a0016344 [DOI] [PubMed] [Google Scholar]
  313. Trucco EM, 2020. A review of psychosocial factors linked to adolescent substance use. Pharmacol. Biochem. Behav 196, 172969. 10.1016/j.pbb.2020.172969 [DOI] [PMC free article] [PubMed] [Google Scholar]
  314. Tsai AC, Alegria M, Strathdee SA, 2019. Addressing the context and consequences of substance use, misuse, and dependence: A global imperative. PLOS Med. 16(11), e10030001. 10.1371/journal.pmed.1003000 [DOI] [PMC free article] [PubMed] [Google Scholar]
  315. Tucker JS, Pollard MS, de la Haye K, Kennedy DP, Green HD Jr., 2013. Neighborhood characteristics and the initiation of marijuana use and binge drinking. Drug Alcohol Depend. 128(1–2), 83–89. 10.1016/j.drugalcdep.2012.08.006 [DOI] [PMC free article] [PubMed] [Google Scholar]
  316. Turner RJ, 2009. Understanding health disparities: The promise of the stress process model. In Advances in the conceptualization of the stress process. New York, NY: Springer; pp. 3–21. [Google Scholar]
  317. Unger JB, Schwartz SJ, Huh J, Soto DW, Baezconde-Garbanati L, 2014. Acculturation and perceived discrimination: Predictors of substance use trajectories from adolescence to emerging adulthood among Hispanics. Addict. Behav 39(9), 1293–1296. [DOI] [PMC free article] [PubMed] [Google Scholar]
  318. Unger JB, 2015. Preventing substance use and misuse among racial and ethnic minority adolescents: Why are we not addressing discrimination in prevention programs? Subst. Use Misuse. 50(8–9), 952–955. 10.3109/10826084.2015.1010903 [DOI] [PubMed] [Google Scholar]
  319. U.S. Department of Health and Human Services, 2016. Facing addiction in America: The surgeon general’s report on alcohol, drugs, and health. https://addiction.surgeongeneral.gov/sites/default/files/surgeon-generals-report.pdf (accessed 5 June 2020). [PubMed]
  320. Valentino R, 2018. Will public pre-K really close achievement gaps? Gaps in prekindergarten quality between students and across states. Am. Educ. Res. J 55(1), 79–116. 10.3102/0002831217732000 [DOI] [Google Scholar]
  321. Vannan A, Powel GL, Scott SN, Pagni BA, Neisewander JL, 2018. Animal models of impact of social stress on cocaine use disorders. Int. Rev. Neurobiol 140, 131–169. 10.1016/bs.irn.2018.07.005 [DOI] [PubMed] [Google Scholar]
  322. Venniro M, Zhang M, Caprioli D, Hoots JK, Golden SA, Heins C, et al. , 2018. Volitional social interaction prevents drug addiction in rat models. Nature Neurosci. 21(11), 1520–1529. 10.1038/s41593-018-0246-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  323. Vilsaint CL, NeMoyer A, Fillbrunn M, Sadikova E, Kessler RC, Sampson NS, et al. , 2019. Racial/ethnic differences in 12-month prevalence and persistence of mood, anxiety, and substance use disorders: Variation by nativity and socioeconomic status, Compr. Psychiatry 89, 52–60. [DOI] [PMC free article] [PubMed] [Google Scholar]
  324. Volkow ND, Boyle M, 2018. Neuroscience of addiction: Relevance to prevention and treatment. Am. J. Psychiatry 175(8), 729–740. 10.1176/appi.ajp.2018.17101174 [DOI] [PubMed] [Google Scholar]
  325. Volkow ND, Koob GF, McLellan AT, 2016. Neurobiologic advances from the brain disease model of addiction. N. Engl. J. Med 374(4), 363–371. 10.1056/NEJMra1511480 [DOI] [PMC free article] [PubMed] [Google Scholar]
  326. Volkow ND, Wise RA, Baler R, 2017. The dopamine motive system: Implications for drug and food addiction. Nature Rev. Neurosci 18(12), 741–752. 10.1038/nrn.2017.130 [DOI] [PubMed] [Google Scholar]
  327. Wade R Jr., Cronholm PF, Fein JA, Forke CM, Davis MB, Harkins-Schwarz M, et al. , 2016. Household and community-level adverse childhood experiences and adult health outcomes in a diverse urban population. Child Abuse Negl. 52, 135–145. 10.1016/j.chiabu.2015.11.021 [DOI] [PubMed] [Google Scholar]
  328. Wadman R, Hiller RM, St Clair MC, 2020. The influence of early familial adversity on adolescent risk behaviors and mental health: Stability and transition in family adversity profiles in a cohort sample. Dev. Psychopathol 32(2), 437–454. 10.1017/S0954579419000191 [DOI] [PMC free article] [PubMed] [Google Scholar]
  329. Wakeman SE, Rich JD, 2015. Addiction treatment within U.S. correctional facilities: Bridging the gap between current practice and evidence-based care. J. Addict. Dis 34(2–3), 220–225. 10.1080/10550887.2015.1059217 [DOI] [PubMed] [Google Scholar]
  330. Walters KL, Simoni JM, Evans-Campbell T, 2002. Substance use among American Indians and Alaska natives: Incorporating culture in an “indigenist” stress-coping paradigm. Public Health Rep. 117(Suppl. 1), S104. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1913706/ [PMC free article] [PubMed] [Google Scholar]
  331. Warde B, 2017. Inequality in US social policy: An historical analysis. New York, NY: Routledge. [Google Scholar]
  332. Warnecke RB, Oh A, Breen N, Gehlert S, Paskett E, Tucker KL, et al. , 2008. Approaching health disparities from a population perspective: The National Institutes of Health Centers for Population Health and Health Disparities. Am. J. Public Health 98(9), 1608–1615. 10.2105/AJPH.2006.102525 [DOI] [PMC free article] [PubMed] [Google Scholar]
  333. Watt TT, 2008. The race/ethnic age crossover effect in drug use and heavy drinking. J. Ethn. Subst. Abuse 7(1), 93–114. 10.1080/15332640802083303 [DOI] [PubMed] [Google Scholar]
  334. Welty LJ, Harrison AJ, Abram KM, Olson ND, Aaby DA, McCoy KP, et al. , 2016. Health disparities in drug-and alcohol-use disorders: A 12-year longitudinal study of youths after detention. Am. J. Public Health 106(5), 872–880. 10.2105/AJPH.2015.303032 [DOI] [PMC free article] [PubMed] [Google Scholar]
  335. Wemm SE, Larkin C, Hermes G, Tennen H, Sinha R, 2019. A day-by-day prospective analysis of stress, craving and risk of next day alcohol intake during alcohol use disorder treatment. Drug Alcohol Depend. 204, 107569. 10.1016/j.drugalcdep.2019.107569 [DOI] [PMC free article] [PubMed] [Google Scholar]
  336. Wemm SE, Sinha R, 2019. Drug-induced stress responses and addiction risk and relapse. Neurobiol. Stress 10, 100148. 10.1016/j.ynstr.2019.100148. [DOI] [PMC free article] [PubMed] [Google Scholar]
  337. Western B, Pettit B, 2005. Black-white wage inequality, employment rates, and incarceration. Am. J. Sociol 111(2), 553–578. [Google Scholar]
  338. White K, 2011. The sustaining relevance of W.E.B. DuBois to health disparities research. Du Bois Rev. 8(1), 285–293. 10.1017/S1742058X11000233 [DOI] [Google Scholar]
  339. Wildeman C, Schnittker J, Turney K, 2012. Despair by association? The mental health of mothers with children by recently incarcerated fathers. Am. Sociol. Rev 77(2), 216–243. 10.1177/2F0003122411436234 [DOI] [Google Scholar]
  340. Wildeman C, Wang EA, 2017. Mass incarceration, public health, and widening inequality in the USA. The Lancet. 389(10077), 1464–1474. 10.1016/s0140-6736(17)30259-3 [DOI] [PubMed] [Google Scholar]
  341. Williams DR, Collins C, 2001. Racial residential segregation: A fundamental cause of racial disparities in health. Public Health Rep. 116(5), 404–416. 10.1093/phr/116.5.404. [DOI] [PMC free article] [PubMed] [Google Scholar]
  342. Williams DR, Costa MV, Odunlami AO, 2008. Moving upstream: How interventions that address the social determinants of health can improve health and reduce disparities. J. Public Health Manag. Pract 14(Suppl.), S8–S17. 10.1097/01.PHH.0000338382.36695.42 [DOI] [PMC free article] [PubMed] [Google Scholar]
  343. Williams DR, Jackson PB, 2005. Social sources of racial disparities in health. Health Aff. 24(2), 325–334. 10.1377/hlthaff.24.2.325 [DOI] [PubMed] [Google Scholar]
  344. Williams DR, Lawrence JA, Davis BA, 2019. Racism and health: Evidence and needed research. Am. Rev. Public Health 40, 105–125. [DOI] [PMC free article] [PubMed] [Google Scholar]
  345. Williams DR, Mohammed SA, 2013. Racism and health I: Pathways and scientific evidence. Am. Behav. Sci 57, 1152–1173. 10.1177/0002764213487340 [DOI] [PMC free article] [PubMed] [Google Scholar]
  346. Williams DR, Sternthal M, 2010. Understanding racial-ethnic disparities in health. J. Health Soc. Behav 51, 107–119. 10.1177/0022146510383838 [DOI] [PMC free article] [PubMed] [Google Scholar]
  347. Williams DR, Yu Y, Jackson JS, Anderson NB, 1997. Racial differences in physical and mental health: Socio-economic status, stress and discrimination. J. Health Psychol 2(3), 335–351. 10.1177/135910539700200305 [DOI] [PubMed] [Google Scholar]
  348. Williams J, Wilson V, 2019. Black workers endure persistent racial disparities in employment outcomes. Economic Policy Institute. https://www.epi.org/publication/labor-day-2019-racial-disparities-in-employment (accessed 6 June 2020). [Google Scholar]
  349. Wu LT, Woody GE, Yang C, Pan JJ, Blazer DG, 2011. Racial/ethnic variations in substance-related disorders among adolescents in the United States. Arch. Gen. Psychiatry 68(11), 1176–1185. 10.1001/archgenpsychiatry.2011.120 [DOI] [PMC free article] [PubMed] [Google Scholar]
  350. Yang LH, Wong LY, Grivel MM, Hasin DS, 2017. Stigma and substance use disorders: An international phenomenon. Curr. Opin. Psychiatry 30(5), 378–388. 10.1097/YCO.0000000000000351 [DOI] [PMC free article] [PubMed] [Google Scholar]
  351. Yanovich C, Kirby ML, Michaelevski I, Yadid G, Pinhasov A, 2018. Social rank-associated stress vulnerability predisposes individuals to cocaine attraction. Sci. Rep 8(1), 1759. 10.1038/s41598-018-19816-x. 10.1038/s41598-018-19816-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  352. Yoo HC, Gee GC, Lowthrop CK, Robertson J, 2010. Self-reported racial discrimination and substance use among Asian Americans in Arizona. J. Immigr. Minor. Health 12(5), 683–690. 10.1007/s10903-009-9306-z [DOI] [PubMed] [Google Scholar]
  353. Young M, Stuber J, Ahern J, Galea S, 2005. Interpersonal discrimination and the health of illicit drug users. Am. J. Drug Alcohol Abuse 31, 371–391. 10.1081/ADA-200056772 [DOI] [PubMed] [Google Scholar]
  354. Zannas AS, West AE, 2014. Epigenetics and the regulation of stress vulnerability and resilience. Neuroscience. 264, 157–170. 10.1016/j.neuroscience.2013.12.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  355. Zimmerman GM, Kushner M, 2017. Examining the contemporaneous, short-term, and long-term effects of secondary exposure to violence on adolescent substance use. J. Youth Adolesc 46(9), 1933–1952. 10.1007/s10964-017-0694-4 [DOI] [PubMed] [Google Scholar]
  356. Zoorob MJ, Salemi JL, 2016. Bowling alone, dying together: The role of social capital in mitigating the drug overdose epidemic in the United States. Drug Alcohol Depend. 173, 1–9. 10.1016/j.drugalcdep.2016.12.011 [DOI] [PubMed] [Google Scholar]
  357. Zucker RA, Gonzalez R, Feldstein-Ewing S, Paulus M, Arroyo J, Fuligni A, et al. , 2018. Assessment of culture and environment in the adolescent brain and cognitive development study: Rationale, description of measures, and early data. Dev. Cogn. Neurosci 32, 107–120. 10.1016/j.dcn.2018.03.004 [DOI] [PMC free article] [PubMed] [Google Scholar]

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