Reproduced from: sci-hub.tw/10.1007/s40572-018-0191-2
FOOD, HEALTH, AND THE ENVIRONMENT (KE NACHMAN AND D LOVE, SECTION EDITORS)
An Integrated Socio-Environmental Model of Health and Well-Being: a Conceptual Framework Exploring the Joint Contribution of Environmental and Social Exposures to Health and Disease Over the Life Span
Hector A. Olvera Alvarez1 & Allison A. Appleton2 & Christina H. Fuller3 & Annie Belcourt4 & Laura D. Kubzansky5
Hector A. Olvera Alvarez and Allison Appleton are co-leading authors.
This article is part of the Topical Collection on Food, Health, and the Environment
1Hector A. Olvera Alvarez email@example.com
School of Nursing, University of Texas El Paso, 500 W. University Ave, El Paso, TX 79968, USA
3Christina H. Fuller firstname.lastname@example.org
School of Public Health, Division of Environmental Health, Georgia State University, P.O. Box 3995, Atlanta, GA 30302, USA
5Laura D. Kubzansky email@example.com
Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA
© Springer International Publishing AG, part of Springer Nature 2018
Purpose of the review Environmental and social determinants of health often co-occur, particularly among socially disadvantaged populations, yet because they are usually studied separately, their joint effects on health are likely underestimated. Building on converging bodies of literature, we delineate a conceptual framework to address these issues.
Recent findings Previous models provided a foundation for study in this area, and generated research pointing to additional important issues. These include a stronger focus on biobehavioral pathways, both positive and adverse health outcomes, and intergenerational effects. To accommodate the expanded set of issues, we put forward the Integrated Socio-Environmental Model of Health and Well-Being (ISEM), which examines how social and environmental factors combine and potentially interact, via multi-factorial pathways, to affect health and well-being over the life span. We then provide applied examples including the study of how food environments affect dietary behavior.
Summary The ISEM provides a comprehensive, theoretically informed framework to guide future research on the joint contribution of social and environmental factors to health and well-being across the life span.
Keywords Total environment . Social determinants . Cumulative exposures . Life course . Health disparities
Researchers evaluating social and environmental determinants of disease have historically worked in parallel with little crossover despite a common goal of promoting and safeguarding human health. Social and environmental risks do not exist in isolation but rather co-occur and disproportionately affect socially disadvantaged individuals and populations experiencing health disparities. As such, traditional siloed research likely underestimates or misidentifies the joint complex effects of social and environmental exposures on health. As social and environmental exposures may have synergistic health effects that accumulate and drive disparities over the life span (from conception to death) [1, 2], major granting and professional organizations like the National Institute of Environmental Health Sciences and the Environmental Protection Agency are increasingly calling for research and services that jointly address the health consequences of social and environmental exposures [3, 4]. This emergent interdisciplinary research approach focuses on the totality of factors within the environment that impact health. This includes conceptualizing not only social and environmental factors that increase risk but also factors that are protective, and focusing on both disease and healthy states. Moreover, identifying mechanisms through which these processes affect health over the life span is critical for recognizing opportunities for intervention during potential sensitive periods. To promote this perspective, we have developed a conceptual model we refer to as the Integrated Socio-Environmental Model of Health and Well-being (ISEM). The model examines how environmental and social factors may jointly contribute to health and well-being across the life span. It is designed to have broad applicability across populations and life stages, and to facilitate the study of biologic and behavioral mechanisms linking exposures to disease and health disparities, while explicitly considering the role of positive factors in promoting health and well-being.
Here, we identify the components of ISEM, while also reviewing prior frameworks and empirical evidence that informed the development of this model. Next, we illustrate how ISEM might guide research on how food environments affect dietary behavior, and further provide two additional examples of how ISEM can guide the integrated study of social and environmental factors across a range of health problems. We conclude by discussing the limitations of the ISEM and identifying needs and priorities for future research in this area.
Previous Models and Frameworks
A number of conceptual frameworks and models describe how environmental (e.g., air pollution, access to parks, climate change) [5, 6] or social (e.g., early-life adversity, harsh family conditions) [7•, 8] factors independently affect physical and/or mental health outcomes [1, 9]. These frameworks often provide detailed descriptions of the pathophysiologic or sociocultural pathways through which each factor impacts health. However, the co-occurrence and potential interactions between environmental and social factors have received less attention. Other frameworks have explicitly considered the totality of environmental exposures via perspectives described as total environment, cumulative risk, and the exposome [1,10••, 11••]. These perspectives share key attributes in that they consider multiple exposures simultaneously, incorporate both physical and social factors, and often seek to describe health disparity-related processes [12–15].
Two of these perspectives have been particularly influential to the development of ISEM. Both focus specifically on child and maternal health [10••, 11••]. Morello-Frosch and Shenassa (2006) proposed a model of how place-based stressors (e.g., poverty, low social capital) increase an individual’s susceptibility to environmental pollutants and adversely impact maternal and child health outcomes [10••]. The model explicitly considers multi-level interactions between community- and individual-level stressors and buffers, and outlines a mechanistic pathway linking exposure to environmental pollutants to adverse health outcomes. Clougherty and Kubzansky (2009) drew on this model to further delineate pathways through which combined exposure to air pollution and psychosocial stress might affect respiratory health [16•]. In contrast, a model proposed by Tulve and colleagues [11••] takes a more system-based approach to describe child health as the result of intrinsic biological factors, activities and behavior, and exposure to social and environmental factors. This model provides a total environment perspective that encompasses both the physical and social environments, defines chemical and non-chemical stressors, and integrates proximal and distal effects across time (life course) and space (e.g., individual, community), but has less focus on underlying mechanisms.
Research in this emergent field has grown to encompass more complex pathways, interactions between multiple exposures, cultural factors, resources and susceptibilities occurring at the individual and community level, positive and adverse health outcomes, and differences in effects across the life course. Existing frameworks may not accommodate this expanded set of issues because they do not capture one or more of following domains: (a) clear testable mechanistic pathways, (b) multiple pathways through which social and environmental factors could interact, (c) behavioral pathways, (d) positive health outcomes or potential health-promoting factors, and (e) potential intergenerational transmission of effects. The ISEM was developed drawing on insights of earlier models, with the purpose of expanding applicability to many populations and contexts.
The Integrated Socio-Environmental Model of Health and Well-Being
The ISEM was developed through a yearlong series of meetings and discussions among an interdisciplinary team of environmental health scientists, epidemiologists, and social scientists; collaborative activities to develop the model were supported by the Harvard JPB Environmental Health Fellowship Program, an interdisciplinary program for researchers working at the intersection of environmental and social determinants of health. The team reviewed prior conceptual models and framework papers, conducted literature reviews, and identified gaps in the evidence base to develop the model. A preliminary version of the ISEM was presented at a JPB Fellowship supported workshop titled “What is the joint contribution of environmental and social determinants of health in explaining health and disease over the life course? Building the field and identifying research priorities” (October 2017; Saratoga Springs, NY). The model was revised according to feedback provided by workshop attendees, which included experts in fields such as social epidemiology, environmental health, epigenetics, and exposure science.
The ISEM illustrates how environmental and social factors combine and potentially interact, via multi-directional pathways, to affect health and well-being over the life span and across generations (Fig. 1). The ISEM starts by characterizing individuals via intra-individual factors (e.g., sex, age) and as part of a community which is further embedded in a larger social and physical environment shaped by both policy and culture. Communities are comprised of culturally based norms and practices that can shape and be shaped by the larger social and environmental contexts. Thus, intra-individual, community-level, social, and physical environmental factors independently and jointly play a role in the health of individuals. At any given moment, individuals are both exposed and responding to an array of interrelated physical environmental factors (e.g., natural, built) and social-structural factors (e.g., race, socioeconomic position). These factors in turn jointly or independently influence biologic (e.g., inflammation) and behavioral (e.g., diet) responses that ultimately drive health or disease. Furthermore, biologic and behavioral responses to exposures can be independent or may also be interrelated.
Resources and susceptibilities, produced either by intrinsic or extrinsic factors, can shape biologic and behavioral responses and mediate and/or modulate effects of social and physical environmental factors on health. Adding complexity, these resources and susceptibilities are also patterned by physical and social environmental factors at individual and community levels. For example, accessibility and availability of healthy food may be more limited in lower- versus higher-income communities which in turn affects dietary behaviors with down-stream implications for health. Overtime, interactions between access and behaviors involved in diet and nutrition can influence the cultural practices of a community. One example, from the experience of indigenous communities, stems from the introduction of the Food Distribution Program on Indian Reservations authorized under Section 4(b) of the Food and Nutrition Act of 2008 and Section 4(a) of the Agriculture and Consumer Protection Act of 1973. These foods tended to be of lower nutritional quality (high in carbohydrates, fat, and sugar). Foods made from these products have since come to be viewed as traditional foods, thereby adding to the complex set of risk factors facing many Native communities.
In addition, ISEM explicitly notes that biological and behavioral responses can have either adverse effects and contribute to disease onset and ill being, or protective effects and contribute to positive health and well-being. Finally, ISEM recognizes that exposures and outcomes may be connected by dynamic processes that are evident at and continually operate across all life stages and may transcend generations. Model components are described in detail in the following sections.
The Total Environment
The ISEM adopts a total environmental perspective and brings a sharp focus on how individuals are embedded within communities and what aspects of these communities may be useful to consider in more detail [11••]. The model further notes that culture wraps around all aspects of the environment and often provides the lens through which policies and experiences are interpreted. Culture refers to collectively held behavioral norms, traditions, worldviews, and beliefs, which may shape socio-environmental factors and their health effects in systematic ways. Culture can shape the ways communities view causes of health problems as well as potential solutions . Cultural factors are perhaps the least well studied in this arena, but represent a unique source of either strength or potential susceptibilities within communities and individuals .
The ISEM also distinguishes the physical and social environments, and further subdivides the physical environment into two domains, natural and built. We define the physical environment as constructed foundational, chemical, and biological stressors that emerge from both built (i.e., anthropogenic) and natural processes. A wide range of factors that emerge from the built environment, such as air pollution, have been linked to adverse health outcomes including cardiovascular disease (CVD), respiratory disease, and mortality [19, 20]. Stressors originating from the natural environment have been less well studied, but are also important [21, 22]. For example, extreme heat events are associated with increases in mortality and there appear to be regional differences, with stronger effects seen in low-income areas [23, 24].
The social environment encompasses those determinants tied to collective human interactions and activities that influence health through culture, norms, and controls [25, 26]. The social environment is shaped by social structures that impact resource distribution and allocation within a society, which in turn pattern exposure to stressors or protective factors. Perhaps one of the best studied structural factors is a person’s position in the social hierarchy, typically referred as socioeconomic position (SEP) [27, 28]. Lower SEP has been linked to adverse outcomes including reduced life expectancy as well as higher incidence of many chronic conditions, such as cancer, respiratory disease, and CVD . Another common social environmental exposure is psychosocial stress, which has been independently associated with adverse outcomes such as depression, CVD, and life expectancy [29–31]. Psychosocial stress results when external conditions overwhelm an individual’s capacity to manage negative effects of external stressors . Other social environmental exposures include factors like income distribution, social capital, and discrimination.
Finally, the ISEM explicitly recognizes that policy shapes various aspects of both the social and physical environment and provides a context in which the downstream components of the model take place. For example, neighborhood zoning policy could directly affect the density of alcohol outlets, availability of grocery stores, and access to safe and affordable housing in communities, which may in turn drive individual-level behaviors and subsequent health outcomes.
Resources and Susceptibilities
We define resources as factors (e.g., biological, sociocultural, emotional, and economic) that help individuals adaptively cope with socio-environmental challenges, and define susceptibilities as factors that impair individuals’ ability to cope adaptively. Resources and susceptibilities may be shaped by aspects of the total environment (e.g., SEP, climate, and culture) and can be identified at the individual and community levels. Resources may be considered protective and restorative factors and susceptibilities as risk-inducing factors. Accordingly, resources and susceptibilities can mediate or modulate biological and behavioral responses to physical and social environmental exposures and ultimately their effect on health and well-being.
In addition, resources and susceptibilities may be either intrinsic to the individual or extrinsic. Examples of intrinsic factors include genetic susceptibilities, mental health factors, and cognitive and psychological abilities. Examples of extrinsic factors include differential exposures to biological toxins, chemical pollutants, social support, or stigma. Resource and susceptibility factors may modulate effects of socio-environmental exposures by altering the magnitude and trajectory of biologic and behavioral responses to exposures. For instance, an intrinsic factor like genetic propensity for certain diseases can amplify the adverse effect of a psychosocial stressor (e.g., financial stress) on health, while another intrinsic factor, effective stress coping skills, could protect against it . Similarly, an extrinsic factor like social support may mitigate adverse effects of the built environment, for example, protecting against exposure to food deserts (low availability to healthy foods) by providing the means (e.g., transportation) to access healthy foods, reducing the risk of obesity, and the sequelae of health problems .
Biologic and Behavioral Responses
Numerous biologic processes and behaviors are sensitive to social and environmental exposures, and in turn contribute to health. We define response as any behavioral or biologic alteration that is attributable to upstream exposures and stimuli, and which may function as a mechanism linking exposures to health outcomes. Following this broad definition, many biologic processes are candidates. For example, elevated blood pressure may occur in response to higher levels of perceived stress and also to air pollution exposure, and these joint effects in turn contribute to hypertensive disease [34, 35]. Numerous other processes, including inflammation, alterations to the epigenome, and microbiome composition, have been identified as potential mechanisms . Likewise, numerous health behaviors such as smoking, diet, and physical activity may be responses to socio-environmental stimuli and thus may lie on the pathway linking socio-environmental exposures and health outcomes. For example, environmental (e.g., availability and accessibility of supermarkets) and social (e.g., SEP) factors might jointly influence intake of healthy fruits and vegetables, and thereby contribute to disease risk [37–41].
Health and Well-Being Versus Disease and Ill-Being
ISEM posits that both positive and negative health outcomes are a product of the interactions between socio-environmental factors. The model goes beyond a deficit and disease-based approach to health to consider positive health outcomes and provides a framework for identifying not only risk but also protective, restorative, and health-promoting factors.
Applications of ISEM
Identifying Novel Pathways
The ISEM can facilitate the identification of novel pathways for exploration by providing a broad scope as well as an emphasis on mechanisms underlying the synergistic effects of the social and physical environment on health. Critical to note is that more detailed and fine-grained aspects of the ISEM, such as feedback loops between various systems or specific bio-behavioral processes, were purposely left out to allow for the key model elements to be presented in a clear and parsimonious way. In this regard, the ISEM is intended to serve as a framework from which more detailed models can be derived. For example, the model promotes consideration of behavioral and biological responses along with resources and susceptibilities, thereby signaling the critical importance of delineating underlying mechanisms. However, detailed discussion of mechanisms would more likely be available in models inspired by ISEM, but specific to a particular research question. Here, we consider examples of two mechanisms that can be explored with the ISEM, epigenetics, and the microbiome.
The burgeoning field of epigenetics indicates that the switches on our genes are heavily influenced by social and environmental exposures and the resulting pattern in gene expression can in turn affect health outcomes. The sensitivity of the epigenome is perhaps greatest during fetal development, with evidence rapidly accumulating that environmental and social exposures can modulate the epigenome and influence later health. For example, several epigenome-wide association studies have linked prenatal exposure to neurotoxic metals (e.g., arsenic, mercury) to methylation across a number of loci [42–46], including sites related to central nervous system development and behavioral disorders [47, 48]. Other work has linked methylation at these sites to adverse child neurodevelopment  and lower birthweight , both considered sentinels of cognitive-behavioral problems and later life health [49, 50]. Likewise, studies have found that depression and related markers of social adversity during pregnancy contribute to epigenome-wide DNA methylation , and in particular to genes involved in the hypothalamic-pituitary-adrenal (HPA) axis [52–56], which regulates the body’s response to stress. Such gestational HPA-related epigenetic changes are associated with restricted fetal growth , and contribute to poor neurodevelopment and dysregulation of the child’s stress response systems [56, 58–62]. Although studies rarely consider combined effects of social and environmental exposures on the epigenome, taken together, these parallel lines of research highlight the epigenome’s sensitivity to both social and environmental exposures and suggest that epigenetics may provide a fruitful avenue for research on the joint effects of these exposures to lifelong health.
The human microbiome, which comprises all microorganisms (e.g., bacteria, archaea, and fungi) found in human tissues and biofluids, is shaped by social and environmental factors. The microbiome plays an integral role in optimal human health. Dysbiosis, meaning microbial imbalance or maladaptation, affects host immunity  and has been associated with the pathology of several illnesses including asthma [64, 65], inflammatory bowel disease , multiple sclerosis , anxiety , and depression . The composition of the microbiome is shaped by exposures at multiple levels, including individual-level diet or medication use, as well as psychosocial factors (e.g., psychological stress, social interactions) [70–73]. Moreover, composition and diversity of the gut microbiota also vary according to aspects of the social and physical environment such as ethnicity and geographical location, partially due to dietary customs (i.e., an aspect of the sociocultural environment) and food availability (i.e., and aspect of the natural environment), and to other environmental exposures (e.g., heavy metals) [74–76]. These variations have in turn been linked with health outcomes. For instance, among populations with Western diets (e.g., high in animal protein, sugar, fat), a depleted microbial diversity in the gut has been linked to rising incidence of obesity, coronary heart disease, metabolic syndrome, and certain types of cancer . Conversely, gut microbes can have protective properties by modulating human toxicity of environmental pollutants via metabolic processes . Taken together, these findings suggest that the microbiome may serve as a critical pathway connecting joint exposure to socio-environmental factors to health and well-being.
Identifying Joint Contributions of Multiple Aspects of the Total Environment: Guiding Diverse Research Questions
Research guided by ISEM would start with the premise that both social and environmental factors matter, and considering effects of these factors in combination is likely to be fruitful. Here, we consider how the model might guide research aimed at understanding how social and environmental factors might jointly influence diet and obesity; we then provide two additional examples of research questions ISEM might inform.
Example 1 — Food Environment, Dietary Behavior, and Obesity
Investigators seeking to understand the role of the environment in risk of obesity have identified the food environment as critical. Research has consistently demonstrated that a poor neighborhood food environment (defined according to level of availability and accessibility to healthy food ) as well as the quality of food are significant risk factors for obesity [41, 78]. In contrast, greater availability of and accessibility to supermarkets and higher quality of foods are associated with higher consumption of fruits and vegetables and overall healthier diets [79, 80]. However, recent reviews suggest that effects of the food environment on obesity risk are mixed or have effects that are relatively small in magnitude . The ISEM framework suggests that considering the food environment in isolation as a risk factor for poor dietary behaviors and obesity may be insufficient.
Other work has demonstrated that a variety of other upstream social structural factors influence the food environment and subsequent dietary behaviors [79, 82]. For example, low-income and racial/ethnic minority groups are more likely to be exposed to poor food environments characterized by an increased availability and accessibility to fast-food restaurants and convenience stores [83, 84], whereas individuals living in higher-income neighborhoods have increased availability and access to supermarkets and grocery stores [80, 83, 85]. Moreover, psychosocial resources and susceptibilities that are patterned according to race/ethnicity and SES may modulate effects of food environmental exposures on dietary behaviors, which in turn influence obesity risk. For example, psychosocial stress and depression are more prevalent among those with low SES , and these experiences are also associated with greater consumption of unhealthy foods and obesity [87–89]. Thus, individuals with low SES may experience the “double jeopardy” of being more prone to consume more unhealthy foods as well as having less access to healthy food. Absent considering these modulating factors, the role of the larger environment in obesity risk may be under-appreciated. The ISEM framework can encourage examination of the multilevel determinants, moderators, and pathways through which the food environment increases the risk of obesity.
Example 2 — Childhood Adversity, Inflammatory Pathways, and Vulnerability to Air Pollution Exposure
Some investigators have recently hypothesized that severe stress exposure early in life promotes a pro-inflammatory phenotype (PIP) that heightens individuals’ reactivity and responsiveness to factors that upregulate inflammation [90••]. The ISEM promotes consideration of how these kinds of toxic early life social exposures might interact with other environmental exposures to exacerbate risk of chronic disease later in life. Thus, early life stress might promote a PIP, which then increases vulnerability to other exposures that can separately induce inflammation, a major risk factor for numerous chronic diseases, including asthma, cardiovascular disease, depression, and certain types of cancer.
Across their life span, socially disadvantaged individuals are disproportionately more likely to experience severe psychosocial stress, and are also more likely to live in environments with more air pollution. Both psychosocial stress and air pollution have been linked to chronically elevated inflammation. For example, in an observational study with healthy adults, exposure to chronic stress was associated with increased expression of NF-κB and glucocorticoid receptor-β . Likewise, toxicologic work mostly in animals has shown that the inflammatory response to air pollution is mediated by the activation of NF-κB via oxidative stress [92, 93]. Studies in both humans and animals have demonstrated that increased expression of glucocorticoid receptor-β can lead to cellular insensitivity to glucocorticoids, including in airway cells, creating a physiologic environment that favors the production of pro-inflammatory cytokines and increases systemic inflammation [94, 95]. Taken together, this work suggests that children who are exposed to high levels of early life stress may be more likely to have increased expression of NF-κB as well as glucocorticoid receptor-β and a propensity to produce proinflammatory cytokines, which could in turn potentiate a subsequent inflammatory response to air pollution . The ISEM framework may suggest important next steps for testing this hypothesis; for instance, investigators might consider whether children who experience early adversity and exhibit epigenetic modulation also appear to be more susceptible to developing higher levels of chronic inflammation after exposure to air pollution.
Example 3 — Indoor Pollution and Health Disparities Among American Indian and Alaska Native Communities
Guided by ISEM, research seeking to identify factors that either potentiate or buffer health risks associated with toxic environmental exposures might start with an explicit consideration of community; this perspective may provide novel insights into the myriad pathways by which social disadvantage may influence population health. For example, American Indian and Alaska Native (AIAN) populations experience disproportionate health effects of social disadvantage [96–99]. Numerous studies have also documented that AIAN communities are highly exposed to multiple stressors within cultural (e.g., high levels of discrimination), environmental (e.g., high levels of indoor pollution), and psychosocial (e.g., high levels of unemployment) domains [9, 100–105]. The ISEM might suggest considering cultural and social factors as moderating factors that increase risk of adverse health outcomes within AIAN populations [106, 107]. For example, previous research has identified elevated indoor PM2.5 concentrations in AIAN communities [108, 109]. A number of cultural and social factors contribute to sustaining high levels of indoor air pollution in AIAN and rural communities including the use of woodstoves to heat homes [108, 110, 111]. Severe economic limitations and geographic location often limit members of the AIAN community access to electricity or natural gas as does geographic location (many AIAN communities live in colder regions). Traditional cultural (ceremonial use of wood stoves or plant medicinals) and other economic (in rural communities, it can be less expensive to gather wood than pay for electricity and natural gas) factors are also at play. Further contributing to indoor air pollution among AIAN is the high prevalence of smoking commercial forms of tobacco. Cultural and ceremonial uses of tobacco likely shaped current social norms around tobacco, which in turn resulted in less restrictive policies (e.g., smoke-free policies), as compared to other communities. Finally, other historical traditions in many tribal communities include burning certain plants for ceremonial and spiritual purposes that can also increase indoor air pollution.
The ISEM model suggests that considering potential synergistic effects of these community-related factors may provide additional insight into effects on health and potential interventions to mitigate these exposures. Some AIAN community research collaborations have started working to promote best burn practices, use of indoor air filtration units, and implementation of commercial tobacco cessation projects and policies, while still recognizing the importance of continuing spiritual traditions [108, 111]. However, as suggested by the ISEM model, more factors may be at play. For example, given high levels of social stress in these communities, the role of various forms of stress (e.g., experiences of discrimination, psychological distress, or social support) in maintaining or changing practices that influence indoor air quality and the health sequelae may be worth a closer look.
Next Steps and Future Directions
One challenge to implementing research guided by ISEM is the intense data requirements. Data collected at multiple levels (e.g., individual, community, environment), data on social and physical exposures, and longitudinal and spatially resolved data are required. A particularly informative design would be prospective cohort studies that collect data on as many elements delineated by ISEM as possible, for prolonged periods of time. Such studies would include a wide range of social and environmental factors that tend to typically co-occur, evaluate interacting bio-behavioral pathways across the life span, and consider effects of both adverse and protective factors on health and well-being.
As such data-intense research may not always be feasible, a strategic alternative could be to focus research on specific processes that connect joint exposures to socio-environmental factors to health and well-being. Key elements of the ISEM can be used to define these sub-areas of research focus (see Table 1). Specifically, we suggest that future research focus on characterizing the interdependence and interplay of the physical and social environmental factors. Some of these interactions are complex and take place through bi-directional pathways that extend across the life span, instead of primarily resulting from concurrent exposures over short periods of time. Thus, such research will also need to incorporate a life course perspective. An example would be the interaction between childhood adversity, inflammatory pathways, and vulnerability to air pollution exposure discussed in the previous section. A fruitful research approach to tackling these questions could consist of strategically designed experimental research to test specific causal hypotheses regarding whether and how interactions between social and environmental factors directly alter biological functioning and behavior. Both human studies and animal models may be informative. For example, in humans, natural experiments (e.g., near highway or commuter exposure) can be used to determine if stress across the life span exacerbates the biological responses (e.g., inflammation) to air pollution exposure. While in animals, control-exposure experimental designs can be used to study the interactions of stress and air pollution exposure in more detail and discern relevant mechanistic pathways. For example, one study exposed 24 rats to either air pollution only, stress only, or air pollution and stress, and found that stress mediated the effect of air pollution on respiratory patterns . While few studies in this area have been conducted, greater insight may be gained with more work like this; such studies can more directly test causal hypotheses related to the interplay between stress and environmental toxicants on specific health outcomes.
Research that focuses on potential protective or mitigating effects of combined socio-environmental exposures is also greatly overdue, as is exploring the role of culture. In this regard, the ISEM is ideally structured to support the study of pathways of resilience, specifically to delineate the processes or factors that can change the path of a joint exposure to socio-environmental factors ways that determines if an outcome is positive or not. To this end, we propose that future studies focus not only on identifying protective factors but also on exploring the pathways that lead to positive outcomes.
|Strengths of ISEM||Priorities for future research|
|Explicitly considers the interdependence and joint contribution of physical and social environmental factors to health outcomes||Characterize the nature of the relationship between physical and social environmental factors with health outcomes (e.g., synergies, mediation, moderation)|
|Adopts an ecological perspective in which the individual is nested within communities and embedded in a larger culture||Explore the role of culture in the exposure-outcome continuum at the individual and community level|
|Incorporates moderating effects by resources and susceptibilities that can be intrinsic to the individual as well as shaped by the environment (contextual)||Determine how social and physical environmental factors can interact by shaping the resources and susceptibilities that could augment or diminish the response to each other|
|Outlines a progression connecting exposure to outcome to support study of pathways and mechanisms of action||Construct and test plausible bio-behavioral pathways through which exposure to specific social and environmental factors impact health and well-being|
|Recognizes that environmental factors can lead to either adverse or protective outcomes depending on the pathway||Distill modifiable bio-behavioral factors that can cause a joint socio-environmental exposure to lead to a positive outcome (e.g., pathways of resilience)|
|The applicability of the model extends to processes that are relevant across the life span or transgenerationally||Understand how physical and social environmental factors can interact to engrain susceptibilities that drive health disparity across generations|
Understanding the mechanisms underlying how the total environment embeds lifelong susceptibilities that drive health disparities across generations is imperative. Given the increasing technical capability for “omics” research and for obtaining such data via sensors and monitors, as well as the rapidly burgeoning literature suggesting the insights to be gained from these processes, one important first step is to begin to assess the role of the epigenome and microbiome as central mechanisms underlying synergistic effects of social and environmental factors and on health and well-being. This type of work is valuable, as it will increase our understanding of how joint socio-environmental factors can produce susceptible or resilient phenotypes that shape health and well-being throughout the life span. Finally, studies are urgently needed that focus on how combined exposures to socio-environmental factors can imprint lifelong and even trans-generational susceptibilities that drive and potentially perpetuate health disparities across social strata.
The ISEM draws from multiple research disciplines to build a broad perspective on how social and physical environmental factors may jointly impact health and well-being across the life span. Building on existing frameworks, the ISEM contributes to this emerging field of research by delineating clear testable pathways (e.g., mechanistic, behavioral) through which interacting social and environmental factors could induce both adverse and positive health outcomes, including across generations. In this regard, the ISEM expands the applicability of previous frameworks to many populations and contexts. With a more comprehensive framework that can accommodate increasingly sophisticated technical and analytic capacity, this model can help to establish a solid foundation for rapid advancements that can inform policy, public health, and practice.
Acknowledgments This work was funded by the JPB Environmental Health Fellowship Program granted by The JPB Foundation and managed by the Harvard T.H. Chan School of Public Health. The authors thank the JPB fellows for the helpful discussion in developing the ideas put forward here. The authors also thank Nicolle Tulve (EPA), Rachel Morello-Frosch (UC-Berkeley), Madeleine Scammell (Boston University), and Jose Ricardo Suarez (SDSU) for their helpful comments on the manuscript.
Compliance with Ethical Standards
Conflict of Interest Hector A. Olvera Alvarez, Allison A. Appleton, Christina H. Fuller, Annie Belcourt, and Laura D. Kubzansky declare that they have no conflict of interest.
Human and Animal Rights and Informed Consent This article does not contain any studies with human or animal subjects performed by any of the authors.
Papers of particular interest, published recently, have been highlighted as:
• Of importance
•• Of major importance
- Appleton AA, Holdsworth EA, Kubzansky LD. A systematic review of the interplay between social determinants and environmental exposures for early-life outcomes. Curr Environ Health Rep. 2016;3(3):287–301.
- Clougherty JE, Schmool JLC, Kubzansky LD. The role of non-chemical stressors in mediating socioeconomic susceptibility to environmental chemicals. Curr Environ Health Rep. 2014;1: 302–13. https://doi.org/10.1007/s40572-014-0031-y.
- U. S. Department of Health and Human Services NIoH, National Institute of Environmental Health Sciences. Advancing science, improving health: a plan for environmental health research. Bethesda: US Government Printing Office; 2012.
- Payne-Sturges DC, Korfmacher KS, Cory-Slechta DA, Jimenez M, Symanski E, Carr Shmool JL, et al. Engaging communities in research on cumulative risk and social stress-environment interactions: lessons learned from EPA’s STAR program. Environ Justice. 2015;8(6):203–12.
- Cascio WE. Proposed pathophysiologic framework to explain some excess cardiovascular death associated with ambient air particle pollution: insights for public health translation. Biochim Biophys Acta. 2016;1860(12):2869–79.
- Bedimo-Rung AL, Mowen AJ, Cohen DA. The significance of parks to physical activity and public health: a conceptual model. Am J Prev Med. 2005;28(2 Suppl 2):159–68.
- • Nusslock R, Miller GE. Early-life adversity and physical and emotional health across the lifespan: a neuroimmune network hypothesis. Biol Psychiatry. 2016;80(1):23–32. Proposes a detailed framework of biological mechanisims relevant to the connection between social and physical environments.
- Repetti RL, Taylor SE, Seeman TE. Risky families: family social environments and the mental and physical health of offspring. Psychol Bull. 2002;128(2):330–66.
- Beals J, Manson SM, Whitesell NR, Spicer P, Novins DK, Mitchell CM. Prevalence of DSM-IV disorders and attendant help-seeking in two American Indian reservation populations. Arch Gen Psychiatry. 2005;62:99–108.
- •• Morello-Frosch R, Shenassa ED. The environmental “riskscape” and social inequality: implications for explaining maternal and child health disparities. Environ Health Perspect. 2006;114(8): 1150–3. Provides a streamlined framework for evaluating joint effects between environmental and social factors.
- •• Tulve N. Development of a conceptual framework depicting a childs total (built, natural, social) environment in order to optimize health and well-being. J Environ Health Sci. 2016;2(2):1–8. Relies on a systems-based approach to delineate a comprehensive framework for examining joint exposures to social and environmental factors.
- Gravlee CC. How race becomes biology: embodiment of social inequality. Am J Phys Anthropol. 2009;139(1):47–57.
- Juarez PD, Matthews-Juarez P, Hood DB, Im W, Levine RS, Kilbourne BJ, et al. The public health exposome: a population-based, exposure science approach to health disparities research. Int J Environ Res Public Health. 2014;11(12):12866–95.
- Diez Roux AV. Conceptual approaches to the study of health disparities. Annu Rev Public Health. 2012;33:41–58.
- Diez Roux AV, Mair C. Neighborhoods and health. Ann N YAcad Sci. 2010;1186(1):125–45.
- • Clougherty JE, Kubzansky LD. A framework for examining social stress and susceptibility to air pollution in respiratory health. Environ Health Perspect. 2009;117(9):1351–8. It provides and applied example of one existing framework.
- Kleinman A. Patients and healers in the context of culture: an exploration of the borderland between anthropology, medicine, and psychiatry. Berkeley: University of California Press; 1980.
- Beals J, Manson SM, Mitchell CM, Spicer P. AI-SUPERPFP team. Cultural specificity and comparison in psychiatric epidemiology: walking the tightrope in American Indian research. Culture. Med Psychiatry. 2003;27:259–89.
- Brook RD, Franklin B, Cascio W, Hong Y, Howard G, Lipsett M, et al. Air pollution and cardiovascular disease: a statement for healthcare professionals from the expert panel on population and prevention science of the American Heart Association. Circulation. 2004;109(21):2655–71.
- Brook RD, Rajagopalan S, Pope CA 3rd, Brook JR, Bhatnagar A, Diez-Roux AV, et al. Particulate matter air pollution and cardiovascular disease: an update to the scientific statement from the American Heart Association. Circulation. 2010;121(21):2331–78.
- Gascon M, Triguero-Mas M, Martinez D, Dadvand P, Rojas-Rueda D, Plasencia A, et al. Residential green spaces and mortality: a systematic review. Environ Int. 2016;86:60–7.
- Markevych I, Schoierer J, Hartig T, Chudnovsky A, Hystad P, Dzhambov AM, et al. Exploring pathways linking greenspace to health: theoretical and methodological guidance. Environ Res. 2017;158:301–17.
- Benmarhnia T, Grenier P, Brand A, Fournier M, Deguen S, Smargiassi A. Quantifying vulnerability to extreme heat in time series analyses: a novel approach applied to neighborhood social disparities under climate change. Int J Environ Res Public Health. 2015;12(9):11869–79.
- Gronlund CJ, Zanobetti A, Schwartz JD, Wellenius GA, O’Neill MS. Heat, heat waves, and hospital admissions among the elderly in the United States, 1992–2006. Environ Health Perspect. 2014;122(11):1187–92.
- Oakes JM, Kaufman JS. Methods in social epidemiology. 2nd ed. San Francisco: Jossey-Bass; 2006. 478 p
- Berkman LF, Kawachi I. Social epidemiology. USA: Oxford University Press; 2000.
- Mendelson T, Thurston RC, Kubzansky LD. Affective and cardiovascular effects of experimentally-induced social status. Health Psychol. 2008;27(4):482–9.
- Krieger N, Williams DR, Moss NE. Measuring social class in US public health research: concepts, methodologies, and guidelines. Annu Rev Public Health. 1997;18:341–78.
- Brunner E. Biology and health inequality. PLoS Biol. 2007;5(11): e267.
- Clougherty JE, Shmool JLC, Kubzansky LD. The role of non-chemical stressors in mediating socioeconomic susceptibility to environmental chemicals. Curr Environ Health Rep. 2014;1(4):302– 13.
- Cohen S, Janicki-Deverts D, Miller GE. Psychological stress and disease. JAMA. 2007;298(14):1685–7.
- Kendler KS, Kessler RC, Walters EE, MacLean C, Neale MC, Heath AC, et al. Stressful life events, genetic liability, and onset of an episode of major depression in women. Am J Psychiatry. 1995;152(6):833–42.
- Walker RE, Keane CR, Burke JG. Disparities and access to healthy food in the United States: a review of food deserts literature. Health Place. 2010;16(5):876–84.
- Lim YH, Bae HJ, Yi SM, Park E, Lee BE, Hong YC. Vascular and cardiac autonomic function and PM2.5 constituents among the elderly: a longitudinal study. Sci Total Environ. 2017;607-608:847–54.
- Shin CN, Soltero E, Mama SK, Sunseri C, Lee RE. Association of discrimination and stress with cardiometabolic risk factors in ethnic minority women. Clin Nurs Res. 2017;26(6):694–712.
- Majnik AV, Lane RH. The relationship between early-life environment, the epigenome and the microbiota. Epigenomics. 2015;7(7): 1173–84.
- Bodor JN, Rose D, Farley TA, Swalm C, Scott SK. Neighbourhood fruit and vegetable availability and consumption: the role of small food stores in an urban environment. Public Health Nutr. 2008;11(4):413–20.
- Curl CL, Beresford SA, Hajat A, Kaufman JD, Moore K, Nettleton JA, et al. Associations of organic produce consumption with socioeconomic status and the local food environment: multiethnic study of atherosclerosis (MESA). PLoS One. 2013;8(7): e69778.
- Mehta NK, Chang VW. Weight status and restaurant availability a multilevel analysis. Am J Prev Med. 2008;34(2):127–33.
- Morland KB, Evenson KR. Obesity prevalence and the local food environment. Health Place. 2009;15(2):491–5.
- Penney TL, Almiron-Roig E, Shearer C, McIsaac JL, Kirk SF. Modifying the food environment for childhood obesity prevention: challenges and opportunities. Proc Nutr Soc. 2014;73(2): 226–36.
- Cardenas A, Koestler DC, Houseman EA, Jackson BP, Kile ML, Karagas MR, et al. Differential DNA methylation in umbilical cord blood of infants exposed to mercury and arsenic in utero. Epigenetics. 2015;10(6):508–15.
- Bakulski KM, Lee H, Feinberg JI, Wells EM, Brown S, Herbstman JB, et al. Prenatal mercury concentration is associated with changes in DNA methylation at TCEANC2 in newborns. Int J Epidemiol. 2015;44(4):1249–62.
- Kippler M, Engstrom K, Mlakar SJ, Bottai M, Ahmed S, Hossain MB, et al. Sex-specific effects of early life cadmium exposure on DNA methylation and implications for birth weight. Epigenetics. 2013;8(5):494–503.
- Kile ML, Houseman EA, Baccarelli AA, Quamruzzaman Q, Rahman M, Mostofa G, et al. Effect of prenatal arsenic exposure on DNA methylation and leukocyte subpopulations in cord blood. Epigenetics. 2014;9(5):774–82.
- Green BB, Karagas MR, Punshon T, Jackson BP, Robbins DJ, Houseman EA, et al. Epigenome-wide assessment of DNA methylation in the placenta and arsenic exposure in the New Hampshire birth cohort study (USA). Environ Health Perspect. 2016;124(8):1253–60.
- Maccani JZ, Koestler DC, Houseman EA, Armstrong DA, Marsit CJ, Kelsey KT. DNA methylation changes in the placenta are associated with fetal manganese exposure. Reprod Toxicol (Elmsford, NY). 2015;57:43–9.
- Maccani JZ, Koestler DC, Lester B, Houseman EA, Armstrong DA, Kelsey KT, et al. Placental DNA methylation related to both infant toenail mercury and adverse neurobehavioral outcomes. Environ Health Perspect. 2015;123(7):723–9.
- Liu J, Bann C, Lester B, Tronick E, Das A, Lagasse L, et al. Neonatal neurobehavior predicts medical and behavioral outcome. Pediatrics. 2010;125(1):e90–8.
- Appleton AA, Murphy MA, Koestler DC, Lesseur C, Paquette AG, Padbury JF, et al. Prenatal programming of infant neurobehaviour in a healthy population. Paediatr Perinat Epidemiol. 2016;30(4):367–75.
- Rijlaarsdam J, Pappa I, Walton E, Bakermans-Kranenburg MJ, Mileva-Seitz VR, Rippe RC, et al. An epigenome-wide association meta-analysis of prenatal maternal stress in neonates: a model approach for replication. Epigenetics. 2016;11(2):140–9.
- Mulligan CJ, D’Errico NC, Stees J, Hughes DA. Methylation changes at NR3C1 in newborns associate with maternal prenatal stress exposure and newborn birth weight. Epigenetics. 2012;7(8):853–7.
- Appleton AA, Armstrong DA, Lesseur C, Lee J, Padbury JF, Lester BM, et al. Patterning in placental 11-B hydroxysteroid dehydrogenase methylation according to prenatal socioeconomic adversity. PLoS One. 2013;8(9):e74691.
- Hompes T, Izzi B, Gellens E, Morreels M, Fieuws S, Pexsters A, et al. Investigating the influence of maternal cortisol and emotional state during pregnancy on the DNA methylation status of the glucocorticoid receptor gene (NR3C1) promoter region in cord blood. J Psychiatr Res. 2013;47(7):880–91.
- Stroud LR, Papandonatos GD, Rodriguez D, McCallum M, Salisbury AL, Phipps MG, et al. Maternal smoking during pregnancy and infant stress response: test of a prenatal programming hypothesis. Psychoneuroendocrinology. 2014;48:29–40.
- Appleton AA, Lester BM, Armstrong DA, Lesseur C, Marsit CJ. Examining the joint contribution of placental NR3C1 and HSD11B 2 m ethylation f or infant neurobehavior. Psychoneuroendocrinology. 2015;52:32–42.
- Marsit CJ, Maccani MA, Padbury JF, Lester BM. Placental 11-beta hydroxysteroid dehydrogenase methylation is associated with newborn growth and a measure of neurobehavioral outcome. PLoS One. 2012;7(3):e33794.
- Conradt E, Lester BM, Appleton AA, Armstrong DA, Marsit CJ. The roles of DNA methylation of NR3C1 and 11beta-HSD2 and exposure to maternal mood disorder in utero on newborn neurobehavior. Epigenetics. 2013;8(12):1321–9.
- Oberlander TF, Weinberg J, Papsdorf M, Grunau R, Misri S, Devlin AM. Prenatal exposure to maternal depression, neonatal methylation of human glucocorticoid receptor gene (NR3C1) and infant cortisol stress responses. Epigenetics. 2008;3(2):97–106.
- Monk C, Feng T, Lee S, Krupska I, Champagne FA, Tycko B. Distress during pregnancy: epigenetic regulation of placenta glucocorticoid-related genes and fetal neurobehavior. Am J Psychiatry. 2016;173(7):705–13.
- Paquette AG, Houseman EA, Green BB, Lesseur C, Armstrong DA, Lester B, et al. Regions of variable DNA methylation in human placenta associated with newborn neurobehavior. Epigenetics. 2016;11(8):603–13.
- Lesseur C, Paquette AG, Marsit CJ. Epigenetic regulation of infant neurobehavioral outcomes. Med Epigenet. 2014;2(2):71–9.
- Belkaid Y, Harrison OJ. Homeostatic immunity and the microbiota. Immunity. 2017;46(4):562–76.
- Hahn DL, Dodge RW, Golubjatnikov R. Association of Chlamydia pneumoniae (strain TWAR) infection with wheezing, asthmatic bronchitis, and adult-onset asthma. JAMA. 1991;266(2):225–30.
- Huang YJ, Boushey HA. The microbiome in asthma. J Allergy Clin Immunol. 2015;135(1):25–30.
- Halfvarson J, Brislawn CJ, Lamendella R, Vazquez-Baeza Y, Walters WA, Bramer LM, et al. Dynamics of the human gut microbiome in inflammatory bowel disease. Nat Microbiol. 2017;2:17004.
- Jangi S, Gandhi R, Cox LM, Li N, von Glehn F, Yan R, et al. Alterations of the human gut microbiome in multiple sclerosis. Nat Commun. 2016;7:12015.
- Schnorr SL, Bachner HA. Integrative therapies in anxiety treatment with special emphasis on the gut microbiome. Yale J Biol Med. 2016;89(3):397–422.
- Akkasheh G, Kashani-Poor Z, Tajabadi-Ebrahimi M, Jafari P, Akbari H, Taghizadeh M, et al. Clinical and metabolic response to probiotic administration in patients with major depressive disorder: a randomized, double-blind, placebo-controlled trial. Nutrition (Burbank, Los Angeles County, Calif). 2016;32(3):315–20.
- Breton J, Massart S, Vandamme P, De Brandt E, Pot B, Foligne B. Ecotoxicology inside the gut: impact of heavy metals on the mouse microbiome. BMC Pharmacol Toxicol. 2013;14(1):62.
- Claus SP, Guillou H, Ellero-Simatos S. The gut microbiota: a major player in the toxicity of environmental pollutants? NPJ Biofilms Microbiomes. 2016;2:16003.
- Cresci GA, Bawden E. Gut microbiome: what we do and don’t know. Nutr Clin Pract. 2015;30(6):734–46.
- Ribiere C, Peyret P, Parisot N, Darcha C, Dechelotte PJ, Barnich N, et al. Oral exposure to environmental pollutant benzo[a]pyrene impacts the intestinal epithelium and induces gut microbial shifts in murine model. Sci Rep. 2016;6:31027.
- Meakins TS, Jackson AA. Salvage of exogenous urea nitrogen enhances nitrogen balance in normal men consuming marginally inadequate protein diets. Clin Sci (Lond). 1996;90(3):215–25.
- Prideaux L, Kang S, Wagner J, Buckley M, Mahar JE, De Cruz P, et al. Impact of ethnicity, geography, and disease on the microbiota in health and inflammatory bowel disease. Inflamm Bowel Dis. 2013;19(13):2906–18.
- Yatsunenko T, Rey FE, Manary MJ, Trehan I, Dominguez-Bello MG, Contreras M, et al. Human gut microbiome viewed across age and geography. Nature. 2012;486(7402):222–7
- Charreire H, Casey R, Salze P, Simon C, Chaix B, Banos A, et al. Measuring the food environment using geographical information systems: a methodological review. Public Health Nutr. 2010;13 (11):1773–85.
- Holsten JE. Obesity and the community food environment: a systematic review. Public Health Nutr. 2009;12(3):397–405.
- Zenk SN, Lachance LL, Schulz AJ, Mentz G, Kannan S, Ridella W. Neighborhood retail food environment and fruit and vegetable intake in a multiethnic urban population. Am J Health Promot. 2009;23(4):255–64.
- Robinson PL, Dominguez F, Teklehaimanot S, Lee M, Brown A, Goodchild M. Does distance decay modelling of supermarket accessibility predict fruit and vegetable intake by individuals in a large metropolitan area? J Health Care Poor Underserved. 2013;24 (1 Suppl):172–85.
- Cobb LK, Appel LJ, Franco M, Jones-Smith JC, Nur A, Anderson CA. The relationship of the local food environment with obesity: a systematic review of methods, study quality, and results. Obesity (Silver Spring). 2015;23(7):1331–44.
- Grigsby-Toussaint DS, Zenk SN, Odoms-Young A, Ruggiero L, Moise I. Availability of commonly consumed and culturally specific fruits and vegetables in African-American and Latino neighborhoods. J Am Diet Assoc. 2010;110(5):746–52.
- Lee H. The role of local food availability in explaining obesity risk among young school-aged children. Soc Sci Med. 2012;74(8): 1193–203.
- Fleischhacker SE, Evenson KR, Rodriguez DA, Ammerman AS. A systematic review of fast food access studies. Obes Rev. 2011;12(5):e460–71.
- Apparicio P, Cloutier MS, Shearmur R. The case of Montreal’s missing food deserts: evaluation of accessibility to food supermarkets. Int J Health Geogr. 2007;6(1):4.
- Baum A, Garofalo JP, Yali AM. Socioeconomic status and chronic stress. Does stress account for SES effects on health? Ann N Y Acad Sci. 1999;896:131–44.
- Blaine B. Does depression cause obesity?: a meta-analysis of longitudinal studies of depression and weight control. J Health Psychol. 2008;13(8):1190–7.
- Darmon N, Drewnowski A. Does social class predict diet quality? Am J Clin Nutr. 2008;87(5):1107–17.
- de Wit L, Luppino F, van Straten A, Penninx B, Zitman F, Cuijpers P. Depression and obesity: a meta-analysis of community-based studies. Psychiatry Res. 2010;178(2):230–5.
- •• Olvera HA, Kubzansky LD, Campen MJ, Slavich GM. Early life stress, air pollution, inflammation, and disease: an integrative review and immunologic model of social-environmental adversity and lifespan health. 2017. Submitted for publication. Provides a detail example of how the ISEM can be applied to examine mechanisms of joint effects of social and environmental exposures.
- Miller GE, Chen E, Fok AK, Walker H, Lim A, Nicholls EF, et al. Low early-life social class leaves a biological residue manifested by decreased glucocorticoid and increased proinflammatory signaling. Proc Natl Acad Sci U S A. 2009;106(34):14716–21.
- Bennett JM, Gillie BL, Lindgren ME, Fagundes CP, Kiecolt-Glaser JK. Inflammation through a psychoneuroimmunological lens. Handbook of systems and complexity in health. New York: Springer; 2013. p. 279–99.
- Fiordelisi A, Piscitelli P, Trimarco B, Coscioni E, Iaccarino G, Sorriento D. The mechanisms of air pollution and particulate matter in cardiovascular diseases. Heart Fail Rev. 2017;22(3):337–47. https://doi.org/10.1007/s10741-017-9606-7.
- Cain DW, Cidlowski JA. Specificity and sensitivity of glucocorticoid signaling in health and disease. Best Pract Res Clin Endocrinol Metab. 2015;29(4):545–56.
- Hamid QA, Wenzel SE, Hauk PJ, Tsicopoulos A, Wallaert B, Lafitte JJ, et al. Increased glucocorticoid receptor beta in airway cells of glucocorticoid-insensitive asthma. Am J Respir Crit Care Med. 1999;159(5 Pt 1):1600–4.
- Centers for Disease Control and Prevention (CDC). Diabetes prevalence among American Indians and Alaska Natives and the overall population — United States, 1994–2002. MMWR Morb Mortal Wkly Rep. 2003;52(30):702–4.
- Barnes PM, Adams PF, Powell-Griner E. Health characteristics of the American Indian or Alaska native adult population: United States, 2004–2008. Natl Health Stat Rep. 2010;(20):1–22.
- Holm JE, Vogeltanz-Holm N, Poltavski D, McDonald L. Assessing health status, behavioral risks, and health disparities in American Indians living on the northern plains of the U.S. Public Health Rep. 2010;125(1):68–78.
- MacDorman MF. Race and ethnic disparities in fetal mortality, preterm birth, and infant mortality in the United States: an overview. Semin Perinatol. 2011;35(4):200–8.
- Beals J, Belcourt-Dittloff A, Garroutte EM, Croy C, Jervis LL, Whitesell NR, et al. Trauma and conditional risk of posttraumatic stress disorder in two American Indian reservation communities. Soc Psychiatry Psychiatr Epidemiol. 2012;8:8.
- Walters SP, Thebo AL, Boehm AB. Impact of urbanization and agriculture on the occurrence of bacterial pathogens and stx genes in coastal waterbodies of Central California. Water Res. 2011;45 (4):1752–62.
- Noonan CW, Brown BD, Bentley B, Conway K, Corcoran M, Four Star K, et al. Variability in childhood asthma and body mass index across Northern Plains American Indian communities. J Asthma. 2010;47(5):496–500.
- Belcourt-Dittloff A, Swaney G, Belcourt G. Tribal borders: confronting health disparities & accessible care. Northwest Public Health. 2011;28(1):18–9.
- Indian Health Service. Facts on Indian health disparities Rockville, MD 2006 [Available from: http://info.ihs.gov/Files/DisparitiesFacts-Jan2006.pdf.
- U. S. Department of Health and Human Services. Mental health: culture, race, and ethnicity. Rockville: Public Health Service, Office of the Surgeon General; 2001.
- Walters KL, Beltran R, Evans-Campbell T, Simoni JM. Keeping our hearts from touching the ground: HIV/AIDS in American Indian and Alaska native women. Womens Health Issues. 2011;21(6 Suppl):S261–5.
- Garroutte EM, Goldberg J, Beals J, Herrell R, Manson SM. Spirituality and attempted suicide among American Indians. Soc Sci Med. 2003;56(7):1571–9.
- Ward T, Boulafentis J, Simpson J, Hester C, Moliga T, Warden K, et al. Lessons learned from a woodstove changeout on the Nez Perce reservation. Sci Total Environ. 2011;409(4):664–70.
- Semmens E, Noonan C, Ward T, Weiler E, Boulafentis J, editors. Effectiveness of interventions in improving indoor and outdoor air quality: preliminary results from a randomized trial of woodsmoke and asthma. 23rd Annual Conference of the International Society for Environmental Epidemiology; 2011 September 13–16; Barcelona, Spain.
- Noonan C, Ward T, Navidi W, Sheppard L, Bergauff M, Palmer C. Assessing the impact of a wood stove replacement program on air quality and children’s health. Health Effects Institute: Boston; 2011.
- Ward TJ, Palmer C, Bergauff M, Hooper K, Noonan CW. Results of a residential indoor PM2.5 sampling program before and after a woodstove changeout. Indoor Air. 2008;18(5):408–15.
- Clougherty JE, Rossi CA, Lawrence J, Long MS, Diaz EA, Lim RH, et al. Chronic social stress and susceptibility to concentrated ambient fine particles in rats. Environ Health Perspect. 2010;118 (6):769–75.