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Institute of Medicine (US) Committee on Assessing Interactions Among Social, Behavioral, and Genetic Factors in Health; Hernandez LM, Blazer DG, editors. Genes, Behavior, and the Social Environment: Moving Beyond the Nature/Nurture Debate. Washington (DC): National Academies Press (US); 2006.

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Genes, Behavior, and the Social Environment: Moving Beyond the Nature/Nurture Debate.

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2The Impact of Social and Cultural Environment on Health


Health is determined by several factors including genetic inheritance, personal behaviors, access to quality health care, and the general external environment (such as the quality of air, water, and housing conditions). In addition, a growing body of research has documented associations between social and cultural factors and health (Berkman and Kawachi, 2000; Marmot and Wilkinson, 2006). For some types of social variables, such as socioeconomic status (SES) or poverty, robust evidence of their links to health has existed since the beginning of official record keeping. For other kinds of variables—such as social networks and social support or job stress—evidence of their links to health has accumulated over the past 30 years. The purpose of this chapter is to provide an overview of the social variables that have been researched as inputs to health (the so-called social determinants of health), as well as to describe approaches to their measurement and the empirical evidence linking each variable to health outcomes.

It should be emphasized at the outset that the social determinants of health can be conceptualized as influencing health at multiple levels throughout the life course. Thus, for example, poverty can be conceptualized as an exposure influencing the health of individuals at different levels of organization—within families or within the neighborhoods in which individuals reside. Moreover, these different levels of influence may co-occur and interact with one another to produce health. For example, the detrimental health impact of growing up in a poor family may be potentiated if that family also happens to reside in a disadvantaged community (where other families are poor) rather than in a middle-class community. Furthermore, poverty may differentially and independently affect the health of an individual at different stages of the life course (e.g., in utero, during infancy and childhood, during pregnancy, or during old age).

In short, the influence of social and cultural variables on health involves dimensions of both time (critical stages in the life course and the effects of cumulative exposure) as well as place (multiple levels of exposure). The contexts in which social and cultural variables operate to influence health outcomes are called, generically, the social and cultural environment.


In recent years, social scientists and social epidemiologists have turned their attention to a growing range of social and cultural variables as antecedents of health. These variables include SES, race/ethnicity, gender and sex roles, immigration status and acculturation, poverty and deprivation, social networks and social support, and the psychosocial work environment, in addition to aggregate characteristics of the social environments such as the distribution of income, social cohesion, social capital, and collective efficacy. Comprehensive surveys of current areas of research in the social determinants of health can be found in existing textbooks (Marmot and Wilkinson, 2006; Berkman and Kawachi, 2000). This chapter focuses on presenting the key research findings for a few selected social variables—SES, the psychosocial work environment, and social networks/ social support. These variables are highlighted because of their robust associations with health status and their well-documented and reliable methods of measuring these variables, and because there are good reasons to believe that these variables interact with both behavioral as well as inherited characteristics to influence health. Race/ethnicity, another set of important variables with robust associations to health, is addressed in Chapter 5.

SES and Health

An association between SES and health has been recognized for centuries (Antonovsky, 1967). Socioeconomic differences in health are large, persistent, and widespread across different societies and for a diverse range of health outcomes. In the social sciences, SES has been measured by three different indicators, taken either separately or in combination: educational attainment, income, and occupational status. Although these measures are moderately correlated, each captures distinctive aspects of social position, and each potentially is related to health and health behaviors through distinct mechanisms.

Educational Attainment

Education is usually assessed by the use of two standard questions that ask about the number of years of schooling completed and the educational credentials gained. The quality of education also may be relevant to health, but it is more difficult to assess accurately. An extensive literature has linked education to health outcomes, including mortality, morbidity, health behaviors, and functional limitations. The relationship between lower educational attainment and worse health outcomes occurs throughout the life course. For example, infants born to Caucasian mothers with fewer than 12 years of schooling are 2.4 times more likely to die before their first birthday than infants born to mothers with 16 or more years of education (NCHS, 1998). The pattern of association between maternal education and infant mortality has been described as a “gradient,” with higher mortality risk occurring with successively lower levels of educational attainment (NCHS, 1998). A similar pattern of educational disparities is apparent for all racial/ ethnic groups, including African American, Hispanic, American Indian, and Asian/Pacific Islander infants (NCHS, 1998). Steep educational gradients also are observed for children’s health (e.g., cigarette smoking, sedentarism and obesity, elevated blood lead levels), health in midlife (e.g., mortality rates between the ages of 25 and 64), and at older ages (the prevalence of activity limitations resulting from chronic conditions such as diabetes and hypertension) (NCHS, 1998).

An association between education and health in observational data does not necessarily imply causation. For example, an association between lower educational attainment and an increased risk of premature mortality during midlife (even in longitudinal study designs) may partly reflect the influence of reverse causation—that is, lower educational attainment in adulthood may have been the consequence of serious childhood illness that truncated the ability of a given individual to complete his/her desired years of schooling (and which independently placed that person at higher risk of premature mortality). Alternatively, the association between education and health may partly reflect confounding by a third variable, such as ability, which is a prior common cause of both educational attainment and health status. Although highly unlikely, in the extreme case, if the association between education and health is entirely accounted for by confounding bias, then improving the individual’s level of schooling would do nothing to improve his/her health chances.

The totality of the evidence suggests, nonetheless, that education is a causal variable in improving health. Natural policy experiments—such as the passage of compulsory schooling legislation at different times in different localities within the United States—suggest that higher levels of education are associated with better health (lower mortality) (Lleras-Muney, 2002). In addition, randomized trials of preschool education, such as the High/Scope Perry Preschool Project, indicate beneficial outcomes even in adolescence and adulthood, such as fewer teenage pregnancies, lower rates of high-school drop-out, and better earnings and employments prospects (which may independently improve health chances) (Parks, 2000; Reynolds et al., 2001). It is therefore likely that the association between schooling and health reflects both a causal effect of education on health, as well as an interaction between the level of schooling and inherited characteristics.

Several causal pathways have been hypothesized through which higher levels of schooling can improve health outcomes. They include the acquisition of knowledge and skills that promote health (e.g., the adoption of healthier behaviors); improved “health literacy” and the ability to navigate the health care system; higher status and prestige, as well as a greater sense of mastery and control, associated with a higher level of schooling (a psychosocial mechanism); as well as the indirect effects of education on earnings and employment prospects (Cutler and Lleras-Muney, 2006). Although it is not established which of these pathways matter more for health, they each are likely to contribute to the overall pattern of higher years of schooling being associated with better health status. Moreover, the evidence points to the importance of improving access to preschool education as a means of enhancing the health prospects of disadvantaged children (Acheson, 1998).


The measurement of income is more complex than assessing educational attainment. Survey-based questions inquiring about income must minimally specify the following components: (a) time frame—for example monthly, annually, or over a lifetime (in general, the shorter the time frame for the assessment of income, the greater the measurement error); (b) sources, such as wages and salary, self-employment income, rent, interest and dividends, pensions and social security, unemployment benefits, alimony and near-cash sources such as food stamps; (c) unit of measurement, that is, whether income is assessed for the individual or the household (with appropriate adjustments for household size in the latter case); and (d) whether it is gross or disposable income (i.e., taking account of taxes and transfer payments). In addition to the higher rate of measurement error for income (as compared to educational attainment), this variable also is associated with higher refusal rates in surveys that are administered to the general population.

As with education, an extensive literature has documented the association between income and health. For example, even after controlling for educational attainment and occupational status, post-tax family income was associated with a 3.6-fold mortality risk among working-age adults in the Panel Study of Income Dynamics, comparing the top (>$70,000 in 1984 dollars) to the bottom (<$15,000) categories of income (Duncan et al., 2002). The association between income and mortality also has been described as a “gradient” (Adler et al., 1994). That is, the excess risks of poor health are not confined simply to individuals below the official poverty threshold of income. Rather, an individual’s chances of having good health (e.g., avoiding premature mortality) improve with each incremental rise in income (although the relationship is also steepest at lower levels of income and tends to flatten out beyond incomes that are about twice the median level).

Also, as with education, the causal direction of an association between income and health does not entirely run from income → health. That is, the relationship between the two variables is acknowledged to be dynamic and reciprocal. Ill health is a potent cause of job loss and reduction in income. Indeed, income as an indicator of SES is more susceptible to reverse causation than education, which tends to be completed in early adult life prior to the onset of major causes of morbidity and functional limitations.

Nonetheless, tests of the income/health relationship in different datasets suggest that lower income is likely to be a cause of worse health status. For example, children do not normally contribute to household incomes, yet their health is strongly associated with levels of household income in both the Panel Study of Income Dynamics and the National Health Interview Surveys (Case et al., 2002). Furthermore, the adverse health effects of lower income accumulate over children’s lives, so that the relationship between income and children’s health becomes more pronounced as children grow older (Case et al., 2002).

An alternative possibility is that the relationship between income and health is explained by a third variable—such as inherited ability—that is associated with both socioeconomic mobility and the adoption of health maintenance behaviors. However, even inherited ability is unlikely to entirely account for the income/health association. If inherited ability is the sole explanation for the income/health relationship, we would not expect to find any association between family income and health among children who are adopted soon after birth by nonbiological parents (assuming that adoptive parents do not get to choose the children they will adopt based on their background, including their socioeconomic circumstances). Yet, in the National Health Interview Survey, the impact of family income on child health has been found to be similar among children who were adopted by nonbiological parents compared to children who were reared by their biological parents (Case et al., 2002). Other types of tests of the income/health association—such as the use of instrumental variable estimation (Ettner, 1996) and the observation of natural experiments that resulted in exogenous increases in income (Costello et al., 2003)—similarly have led to the conclusion that the effect of higher incomes on improved health status is likely to be causal.

The causal pathways linking income to health are likely to be different from those linking education to health. Most obviously, income enables individuals to purchase various goods and services (e.g., nutrition, heating, health insurance) that are necessary for maintaining health. Additionally, secure incomes may provide individuals with a psychological sense of control and mastery over their environment. (See Chapter 4 for a detailed discussion of psychological factors and health.) That said, it has also been observed that higher incomes are associated with healthier behaviors (such as wearing seatbelts and refraining from smoking in homes) that do not, in themselves, cost money (Case and Paxson, 2002). Although the causal mechanisms underlying these relationships are not clear, it has been speculated that “the lack of adequate resources strips parents of the energy necessary to wrestle children into seat belts. Poorer parents may also smoke to buffer themselves from poverty-related stress and depression” (Case and Paxson, 2002).

Debate also exists in the literature concerning whether it is absolute income or relative income that matters for health (Kawachi and Kennedy, 2002). The absolute income theory posits that an individual’s level of wellbeing is determined by his/her own (absolute) level of income, and only his/her own income. Many definitions of poverty, for example, are based upon the concept of the failure to meet a minimal standard of living defined in absolute terms (e.g., the inability to afford food). By contrast, the relative income theory posits that individual health is determined by the relative distance (or gap) between a given individual’s income and that of others around him/her (Kawachi and Kennedy, 2002).

The concept of relative income has been operationalized in empirical research by measures of relative deprivation (at the individual level) as well as by aggregate measures of income inequality (at the community level). Measures of relative deprivation involve assessments of the income distance between individuals and their comparison (or reference) group—that is defined by others who are alike with respect to age group, occupational class, or community of residence. The causal mechanisms underlying the relationship between absolute income and health are linked to the ability to access material goods and services necessary for the maintenance of health. Relative income is hypothesized to be linked to health through psychosocial stresses generated by invidious social comparisons as well as by the inability to participate fully in society because of the failure to attain normative standards of consumption. Growing evidence has suggested an association between relative deprivation (measured among individuals) and poor health outcomes (Aberg Yngwe et al., 2003; Eibner et al., 2004). A related literature has attempted to link the societal distribution of income (as an aggregate index of relative deprivation) to individual health outcomes, although the findings in this area remain contested (Subramanian and Kawachi, 2004; Lynch et al., 2004).

Variables other than household income also may be useful for health research—such as assets including inherited wealth, savings, or ownership of homes or motor vehicles (Berkman and Macintyre, 1997). While income represents the flow of resources over a defined period, wealth captures the stock of assets (minus liabilities) at a given point in time, and thus indicates economic reserves. Measuring wealth is particularly salient for studies that involve subjects towards the end of the life course, a time when many individuals have retired and depend on their savings. In the Panel Study of Income Dynamics, for example, only a weak association was seen between post-tax family income and mortality among post-retirement-age subjects, while measures of wealth continued to indicate a strong association with mortality risk (Duncan et al., 2002).

Finally, measures of income, poverty, and deprivation have been extended to incorporate the dimension of place. Growing research, utilizing multilevel study designs, has conceptualized economic status as an attribute of neighborhoods (Kawachi and Berkman, 2003). These studies have revealed that residing in a disadvantaged (or high-poverty) neighborhood imposes an additional risk to health beyond the effects of individual SES. A recent Department of Housing and Urban Development randomized experiment in neighborhood mobility, the so-called Moving To Opportunity study, found results consistent with observational data: Moving from a poor to a wealthier neighborhood was associated with significant improvements in adult mental health and rates of obesity (Kling et al., 2004). Disadvantaged neighborhoods are often characterized by adverse physical, social, and service environments, including exposure to more air pollution via proximity to heavy traffic, a lack of local amenities such as grocery stores, health clinics, and safe venues for physical activity, and exposure to signs of social disorder (Kawachi and Berkman, 2003). In other words, the relevant social and cultural “environments” for the production of health include not only an individual’s immediate personal environment (e.g., his/ her family), but also the broader social contexts such as the community in which a person resides.

Occupational Status

The third standard component of SES that typically is measured by social scientists is occupational status, which summarizes the levels of prestige, authority, power, and other resources that are associated with differ ent positions in the labor market. Occupational status has the advantage over income of being a more permanent marker of access to economic resources.

Three main traditions can be discerned in the way in which different disciplines have approached the measurement of aspects of occupations relevant to health. In the traditional occupational health field, researchers have focused on the physical aspects of the job, such as exposure to chemical toxins or physical hazards of injury (Slote, 1987). In the fields of occupational health psychology and social epidemiology, researchers have focused on characterizing the psychosocial work environment, including measures of job security, psychological job demands and stress, and decision latitude (control over the work process) (Karasek and Theorell, 1990). Finally, the sociological tradition has tended to focus on occupational status, which includes both objective indicators (e.g., educational requirements associated with different jobs) as well as subjective indicators (e.g., the level of prestige associated with different jobs in the occupational hierarchy) (Berkman and Macintyre, 1997).

Several alternative approaches currently exist for the measurement of occupational status. For a detailed description, see Berkman and Macintyre (1997) as well as Lynch and Kaplan (2000). For example, the Edwards classification (U.S. Census Bureau, 1963) is a scheme based upon the conceptual distinction between manual and nonmanual occupations. The Edwards classification was used to demonstrate that individuals who grew up in manual (as compared to nonmanual) households during childhood and adolescence were at increased risk of developing heart disease in later adult life, independently of the individual’s own attained SES (Gliksman et al., 1995). An alternative and commonly used measure of occupational status is the Duncan Socioeconomic Index (SEI), which combines subjective ratings of occupational prestige with objective measures of education and incomes associated with each occupation. SEI scores, which range from 0 to 100, were originally constructed by Duncan (1961) using data from the 1947 National Opinion Research Center study, which provided public opinions about the relative prestige rankings of representative occupations. These prestige rankings were then combined with U.S. Census information on the levels of education and incomes associated with each Census-defined occupation. The resulting SEI scores have been updated several times (Burgard et al., 2003). In the Wisconsin Longitudinal Survey of men and women who graduated from Wisconsin high schools in 1957 (53 or 54 years old in 1992-1993), Duncan SEI scores were inversely associated with self-reported health, depression, psychological well-being, and smoking status (Marmot et al., 1997).

As is the case with both education and income, an association between occupational status and health may partly reflect reverse causation. That is, ill health (e.g., depression or alcoholism) is a major cause of downward occupational mobility, as well as a constraint on upward social mobility. An individual’s choice of occupation also may reflect unmeasured variables (such as ability) that simultaneously influence health status. Although the adverse health impact of job loss (e.g., through factory closure studies) is widely accepted (Kasl and Jones, 2000), fewer studies have convincingly demonstrated a causal effect of variables such as occupational prestige on health outcomes. As noted above, existing measures of occupational status such as the Duncan SEI combine measures of prestige with indicators of education and income that are thought to affect health independently. In addition, there are uncertainties regarding the optimal time point for measuring occupational status, especially since individuals change occupations over their life course. Job changes that occur earlier in people’s careers are often associated with upward social mobility, while late-career changes may be related to a diminished capacity to function within demanding occupations (Burgard et al., 2003). For this reason, the frequently used “final occupation”—that is the occupation of an individual at the time of death or at the onset of disease—may not be an optimal indicator of the occupational conditions experienced over the individual’s life course. Few studies have examined the health effects of occupational status over an individual’s entire life course (Burgard et al., 2003), although some evidence suggests that persistently low occupational status measured at multiple time points or downward status mobility over time may be associated with worse health outcomes (Williams, 1990).

The potential pathways linking occupational status to health outcomes are again distinct from those linking either education or income to health. First, higher status (and nonmanual) occupations are less likely to be associated with hazardous exposures to chemicals, toxins, and risks of physical injury. Higher status jobs also are more likely to be associated with a healthier psychosocial work environment (Karasek and Theorell, 1990), including higher levels of control (decision latitude) as well as a greater range of skill utilization (lack of monotony). A greater sense of control in turn implies improved ability to cope with daily stress, including a reduced likelihood of deleterious coping behaviors such as smoking or alcohol abuse. Undoubtedly, a major intervening pathway between occupational status and health is through the indirect effects of higher incomes and access to a wider range of resources such as powerful social connections.

In summary, there is good evidence linking each of the major indicators of SES to health outcomes. Together, education, income, and occupation mutually influence and interact with one another over the life course to shape the health outcomes of individuals at multiple levels of social organization (the family, neighborhoods, and beyond).

Social Networks, Social Support, and Health

An independent social determinant of health is the extent, strength, and quality of our social connections with others. Recognition of the importance of social connections for health dates back as far as the work of Emile Durkheim. More recently John Bowlby (1969) maintained that secure attachments are not only necessary for food, warmth, and other material resources, but also because they provide love, security, and other nonmaterial resources that are necessary for normal human development (Berkman and Glass, 2000). Certain periods during the life course may be critical for the development of bonds and attachment (Fonagy, 1996). According to attachment theory, secure attachments during infancy satisfy a universal human need to form close affective bonds (Bowlby, 1969).

Two social variables are of particular interest in characterizing social relationships: social networks and social support. Social networks are defined as the web of person-centered social ties (Berkman and Glass, 2000). Its assessment includes the structural aspects of social relationships, such as size (the number of network members), density (the extent to which members are connected to one another), boundedness (the degree to which ties are based on group structures such as work and neighborhood), and homogeneity (the extent to which individuals are similar to one another). Its assessment also may extend to aspects including frequency of contact, extent of reciprocity, and duration. Social support refers to the various types of assistance that people receive from their social networks and can be further differentiated into three types: instrumental, emotional, and informational support. Instrumental support refers to the tangible resources (such as cash loans, labor in kind) that people receive from their social networks, while emotional support includes less tangible (but equally important) forms of assistance that make people feel cared for and loved (such as sharing confidences, talking over problems). Informational support refers to the social support that people receive in the form of valuable information, such as advice about healthy diets or tips about a new cancer screening test.

A variety of pencil-and-paper instruments exist to measure both social networks and social support; for a detailed guide, see Cohen et al. (2000). Several of these instruments have been psychometrically validated and indicate good internal consistency and test-retest reliability. However, one criticism of measurement in this area has been the lack of an established “gold standard.” The variety of different measures currently in use makes it difficult to compare results across studies (Seeman, 1998).

A substantial body of epidemiological evidence has linked social networks and social support to positive physical and mental health outcomes throughout the life course (Stansfeld, 1999). Social connectedness is be lieved to confer generalized host resistance to a broad range of health outcomes, ranging from morbidity and mortality to functional outcomes (Cassel, 1976). Prospective epidemiological studies in adult populations have found consistently that social networks predict the risk of all-cause and cause-specific mortality (including cardiovascular disease, cancer, and traumatic causes of death) (Berkman and Glass, 2000). For mental health outcomes, a wealth of evidence indicates that social support buffers the effects of stressful life events and helps to prevent the onset of psychiatric disorders, particularly depression (Kawachi and Berkman, 2001). Both social networks and social support have been linked to better prognoses and survival following major illnesses, such as myocardial infarction, stroke, and certain types of cancer, including melanoma (Berkman and Glass, 2000). Some experimental evidence in the field of psychoneuroimmunology has suggested that social connectedness may confer host resistance against the development of infections (Cohen et al., 2000). In addition, a growing body of research has linked social support to neuroendocrine regulation. For example, the presence of a supportive caregiver among children has been shown to lower hypothalamic-pituitary-adrenal (HPA) reactivity (as measured by salivary cortisol levels) to maternal separation (Gunnar et al., 1992). Among adults, social support predicts lower levels of HPA axis and sympathetic nervous system reactivity in laboratory-based challenge paradigms (Seeman and McEwen, 1996).

The relationship between social networks/social support and health is bidirectional in two ways. First, major illnesses (such as a diagnosis of depression or HIV) can be a potent trigger of changes in social networks and social support. Depression typically results in social withdrawal, while newly diagnosed patients with HIV may find that members of their social network either avoid them (because of the associated stigma) or rally to their support. Second, social networks/social support can be both a positive and negative influence on health outcomes simultaneously. For example, it may not be health promoting to belong to one’s intimate network if that network happens to be one of injection drug users. Similarly, abusive partners or abusive parents are sources of negative social support. The association between social networks/social support and health also may reflect confounding by a third variable, such as temperament or personality. (See Chapter 4 for a detailed discussion of personality and temperament.)

The most rigorous approach to overcoming the threats to causal inference (caused by endogeneity or omitted variable bias) is to conduct a randomized controlled trial. To date, however, the results of randomized trials of social support provision have been mixed. For example, recent large-scale randomized trials following major illnesses, such as myocardial infarction (Writing Committee for the ENRICHD Investigators, 2003), stroke (Glass et al., 2004), and metastatic breast cancer (Goodwin et al., 2001), have not found beneficial effects on clinical outcomes (improved survival or functional recovery). However, it is premature to conclude on the basis of these intervention trials that social support has no causal effect on health. For example, it has been pointed out that most of the observational evidence on social support has focused on support received from naturally occurring networks, while most interventions have attempted to bolster social support through strangers (e.g., patient support groups) (Cohen et al., 2000). The typical “treatment” in intervention studies also may have been of insufficient “dose” or duration to affect clinical outcomes. The bottom line seems to be that effective interventions to strengthen social support (to affect clinical outcomes) have yet to be devised (Cohen et al., 2000).

From the standpoint of mechanisms, recent research suggests that affiliative behavior has a basis in biology. Animal models point to the role of the neuropeptide oxytocin in facilitating various social behaviors such as maternal attachment and pair bonding (Zak et al., 2004). Social support and the administration of oxytocin have been shown to reduce stress responses during a public speaking task (Heinrichs et al., 2003). In the emerging field of neuroeconomics, it was recently demonstrated that the intranasal administration of oxytocin causes a substantial increase in trust among humans, thereby greatly increasing the benefits from social interactions (Kosfeld et al., 2005). If oxytocin is indeed the biological substrate for prosocial behavior, these preliminary findings suggest promising experimental and laboratory-based approaches for investigating gene-environment interactions in the association of social support and health.

The investigation of the health effects of social networks/social support can be further extended to the community level. The concept of social capital has been defined as the resources that are available to members of communities and other social contexts (e.g., workplaces) by virtue of the existence of a rich network of social interactions (Kawachi et al., 2004). Measures of social capital typically emphasize two components, both measured (or aggregated) to the community level. The structural component of social capital includes the extent and intensity of associational links and activity in society (e.g., density of civic associations; measures of informal sociability; indicators of civic engagement). The cognitive component assesses people’s perceptions of trust, sharing, and reciprocity (Harpham et al., 2002). A growing number of multilevel studies have found an association between community stocks of social capital and individual health outcomes (e.g., mortality, self-rated health, some health behaviors) net of the influence of individual socioeconomic characteristics (Kawachi et al., 2004). Although causality in this area is still contested (Pearce and Smith, 2003), there are plausible grounds for supposing that a more socially cohesive community (evidenced by higher stocks of social capital) would be better able to protect the health of its members. For example, higher stocks of social capital are associated with the improved ability of communities to exercise informal social control over deviant behaviors (such as smoking and drinking by minors), as well as to undertake collective action for mutual benefit (e.g., passage of local ordinances to restrict smoking in public places). Social capital and social cohesion are therefore potentially important characteristics of the “social and cultural environment” that ultimately influence patterns of health achievement.

The Psychosocial Work Environment and Health

The psychosocial work environment—particularly exposure to job stress—has been linked to the onset of several conditions, including cardiovascular disease, musculoskeletal disorders, and mental illness (Marmot and Wilkinson, 2006). Two models of job stress have received particular attention in the literature: the job demand-control model (Karasek and Theorell, 1990) and the effort-reward imbalance model (Siegrist et al., 1986). The demand-control model posits that it is the combination of high psychological demands and low level of control (low decision authority and skill utilization) that leads to high physiological strain among workers and hence to the onset of disease (such as hypertension and cardiovascular disease) (Marmot and Wilkinson, 2006). A pencil-and-paper questionnaire to measure job demands and job control has been developed and validated for use in population-based studies (and can be accessed at

In contrast to the demand-control model of job stress, the effort-reward imbalance model developed by Siegrist maintains that working conditions produce adverse health outcomes when the costs associated with the job (e.g., high level of effort) exceed its rewards (money, esteem, and career opportunities) (Siegrist et al, 1986). As with the demand-control model, a self-administered questionnaire has been developed and validated. Both the demand-control model and the effort-reward imbalance model have been shown to predict the incidence of cardiovascular disease and other health outcomes in longitudinal observational studies (Marmot and Wilkinson, 2006).

The relationship between job stress and health is likely to be reciprocal, however. For example, the onset of subtle illness symptoms may result in the worker switching to a less demanding job. In theory, this issue could be addressed in longitudinal studies through careful and repeated assessments of workers’ health symptoms over time. On the other hand, other problems, such as omitted variable bias, can present formidable challenges to causal inference in this field. For example, some individuals may “select into” certain occupations based on temperament, personality, and innate “hardiness;” while others may “select out” of stressful jobs for the same reasons. If these third variables (temperament, hardiness) remain unmeasured, their omission may result in biased estimates of the effect of psychosocial working conditions on health outcomes. Future research in psychosocial work environment should therefore attempt to control for these variables and investigate the potential interactions between inherited individual characteristics and the psychosocial work environment in producing differential patterns of health and disease.


Social variables potentially affect health outcomes throughout the entire spectrum of etiology: from disease onset (beginning prenatally and accumulating in their effects throughout the life course) to disease progression and survival. During each stage of the disease continuum, social-environmental variables can influence outcomes in a variety of different ways. Prior to the onset of disease, social variables might influence the risk of prenatal infections, the adoption of risky or health-promoting behaviors, or the ability to cope with adverse circumstances. Subsequent to the development of illness, social variables may determine the rate of progression of disease (or recovery) through differential rates of access to treatment, treatment adherence, coping behaviors, or “direct” effects on immune surveillance and tissue repair.

It is important to note, however, that the relevance and magnitude of the associations between social-environmental variables and health outcomes can vary at different points of the disease process. For example, the incidence of some cancers, notably breast cancer and melanoma, is higher among more advantaged SES groups, reflecting in part the underlying socioeconomic distribution of their risk factors. For breast cancer, the increased incidence among higher SES women is in part explained by reproductive factors, including earlier age at menarche, later age at first birth, and lower fertility.1 On the other hand, survival following the diagnosis of breast cancer consistently favors higher SES women, due, among other things, to earlier detection and better access to effective treatment (Lochner and Kawachi, 2000). Likewise, observational evidence suggests the strong role of social support in improving survival and functional recovery following major diseases (such as stroke or heart attack), but the evidence is less consistent for preventing the incidence of disease (where social networks appear to have a stronger role) (Seeman, 1998).

There also may be critical stages in the life course during which the social environment has a stronger impact on later life health outcomes. For example, the Barker hypothesis implicates the prenatal period as being particularly relevant for the later development of coronary heart disease and some cancers (Barker and Bagby, 2005). In addition, social-environmental conditions often cumulate over the life course, so that for example, persistent poverty may be more detrimental to health than transient poverty, and studying the dynamic trajectories of social variables is likely to be of additional interest in explaining patterns of health. Finally, social-environmental conditions may be reproduced across generations, because parents “pass on” their disadvantage to their children. For example, poor households are more likely to have sick children (Cutler and Lleras-Muney, 2006). Childhood illness can in turn truncate the educational and occupational mobility of the affected individuals. This constitutes a social mechanism—separate from a genetic mechanism—for the inheritance or transmission of disease risk. There may, of course, be gene-environment interactions involved in the ways in which these two separate influences shape the patterns of health across the life course.


The current state of research on social variables demonstrates incredible potential for improving our understanding of health. It also provides an excellent backdrop for contributing to the development research and the research agenda on gene-environment interactions. Specifically, benefits may result from the increased interest in understanding gene-environment interactions that may include insights into the social variables that represent important sources of variance and increased understanding about how physiological pathways for some disease processes might be modified, constrained, or moderated by environmental influences. For example, if one were interested in how stress is related to drug abuse, given the higher levels of chronic social stress, an ethnically diverse sample would be of great benefit to drawing conclusions about extremes of the stress continuum by studying African Americans who have experienced psychosocial sources such as racism and discrimination (e.g., Clark et al., 1999). Additionally, how the accumulation of stressful experiences over a lifetime impacts the relationship between stress, SES, and drug abuse would provide important additional information about how genetic mechanisms work.


There remain important unanswered questions in understanding the contribution of the social and cultural environment to health. Given the burgeoning interest in examining gene-environment interactions in health, there exists an opportunity to make a major investment in new research initiatives—parallel to current investments in genetics and molecular science—to expand our understanding of social and cultural influences on health. A research agenda for expanding the scope of such research has already been outlined by previous National Research Council reports.2 This chapter has presented an overview of the state of the field in the measurement of social-environmental variables and our empirical understanding of the mechanisms by which these variables influence disease onset and progression. Significant opportunities are at hand to bridge the gaps in our understanding of how social and genetic factors interact and mutually influence health outcomes. The next chapter discusses the relationship of genetics and health.


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It should be noted that genetic factors also may apparently vary by socioeconomic group. For example, the prevalence of the BRCA1 gene mutations is higher among women of Ashkenazi Jewish descent than among other women. In turn, Americans of Ashkenazi Jewish origin tend to have a higher than average socioeconomic position than the average. Disentangling the various contributions of genes and social factors is therefore challenging (McClain et al., 2005).


Promoting Health: Intervention Strategies from Social and Behavioral Research, 2000; New Horizons in Health: An Integrative Approach, 2001; and Understanding Racial and Ethnic Differences in Health and Late Life, 2004.

Copyright © 2006, National Academy of Sciences.
Bookshelf ID: NBK19924


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