NCBI Bookshelf. A service of the National Library of Medicine, National Institutes of Health.

Institute of Medicine (US) Committee on Health and Behavior: Research, Practice, and Policy. Health and Behavior: The Interplay of Biological, Behavioral, and Societal Influences. Washington (DC): National Academies Press (US); 2001.

Cover of Health and Behavior

Health and Behavior: The Interplay of Biological, Behavioral, and Societal Influences.

Show details

4Social Risk Factors

Among the greatest advances in elucidating the determinants of disease over the past two decades has been the identification of social and psychological conditions that seem to influence morbidity and mortality directly through physiological processes and indirectly via behavioral pathways. This chapter examines a set of sociopsychological factors for which substantial evidence exists for effects on health outcomes: socioeconomic status; social support and networks; occupational stress, unemployment, and retirement; social cohesion and social capital, and religious belief Although it was previously believed that some diseases were caused by psychological states with little biological basis and that others were purely “physical,” it is now understood that in almost all cases that distinction is false. Most psychosomatic diseases involve various genetic and environmental determinants, and all states of health and disease are influenced to some extent by psychosocial conditions. Disorders rarely have discrete causes.

This chapter reviews the evidence accumulated during the 1980s and 1990s, identifying strengths and weaknesses and identifying areas for future investigations as they relate to social conditions that are risk related or health promoting.


A strong and consistent finding of epidemiologic research is that there are health differences among socioeconomic groups. Lower mortality, morbidity, and disability rates among socioeconomically advantaged people have been observed for hundreds of years and have been replicated using various indicators of socioeconomic status (SES) and multiple disease outcomes (Kaplan and Keil, 1993; Syme and Berkman, 1976). Educational differentials in mortality have increased over the past three decades in this country (Feldman et al., 1989; Pappas et al., 1993; Tyroler et al., 1993). Moreover, formal comparisons of the mortality differences associated with education show that relationships between educational attainment and mortality are stronger in the United States than they are in most European countries (Kunst and Mackenbach, 1994).

Results from the National Longitudinal Mortality Study (NLMS) are representative of recent research that has documented the link between SES and health. The NLMS is a large national database on the U.S. noninstitutionalized population assembled from survey information collected between 1978 and 1985; deaths were ascertained using the National Death Index for 1979–1989 (Sorlie et al., 1995). Mortality was strongly associated with education, income, and occupation (Rogot et al., 1992; Sorlie et al., 1992, 1995). For example, among those aged 25–64, white men and women with 0–4 total years of education had age-adjusted death rates that were 66% and 44% higher, respectively, than those with 5 or more years of college. For African American men and women, the corresponding increases in mortality were 73% and 78%, respectively. Similar findings were observed when income was used as a proxy for SES. Age-adjusted death rates among white men and women with annual family incomes of less than $5,000 were 80% and 30% higher, respectively, than were those among their counterparts in households with incomes of $50,000 or more. As with education, even greater differentials were seen among African Americans: men in African American households earning less than $5,000 were twice as likely to die during follow-up than were those in families earning $50,000 or more. Poor African American women were 80% more likely to die than were wealthier women.

Socioeconomic differentials in mortality have been observed for many causes of death. The Multiple Risk Factor Intervention Trial (MRFIT) followed 320,909 white and African American men for 16 years (Davey Smith et al., 1996a,b). Median family income in ZIP code of residence was predictive of death from a variety of medical conditions in analyses adjusted for age, smoking status, blood pressure, serum cholesterol, previous myocardial infarction, and treatment for diabetes.

To assess the 11-year mortality risk associated with individual family income, Anderson et al. (1997) linked NLMS data to census tract information on income for 239, 187 persons. Among persons aged 25–64, the mortality rate ratios (that is, the ratio of mortality rate at the low income to the mortality rate at high income) associated with individual family income were 2.03 for white men, 2.10 for African American men, 1.61 for white women, and 1.92 for African American women. The rate ratios associated with median census tract income, adjusting for individual-level income, were 1.26 for white men, 1.49 for African American men, 1.61 for white women, and 1.30 for African American women. Although family income had a stronger association with mortality than did median census tract income, the results indicate that community SES makes an independent contribution to mortality.

With regard to specific disease outcomes, the relationship between SES and cardiovascular disease has received the most attention. SES appears to be an important factor in the development and progression of cardiovascular disease (Kaplan and Keil, 1993), the leading cause of death in this country (National Center for Health Statistics, 1992). The British Whitehall study of civil servants found that those in the lowest grades of employment were at highest risk for heart disease (Marmot et al., 1991) and that low levels of personal control in the work environment could explain much of this association (Bosma et al., 1997; Marmot et al., 1997).

Perhaps the most striking finding that emerges from these analyses is the graded and continuous nature of the association between income and mortality, with differences persisting well into the middle-class range of incomes. This phenomenon also has been observed in several European investigations (Blane et al., 1997; Davey Smith et al., 1990; Macintyre, 1997; Macintyre et al., 1998). For example, in the Whitehall longitudinal studies (Davey Smith et al., 1990; Marmot et al., 1991), each employment grade had worse health and higher mortality than did the grade above it. Executive-grade civil servants (level 2) are not poor by any absolute standard, but they had higher mortality than did administrators (level 1). The fact that socioeconomic differences in health are not confined to segments of the population that are materially deprived in the conventional sense argues against an interpretation of socioeconomic differences simply as a function of absolute poverty. The pathways involved are likely to be complex; diverse explanations for the socioeconomic gradient in health have been proposed and examined.

Material Conditions

SES is clearly associated with the material condition of a person's life. However, there are many examples of people who live in relative deprivation who exhibit greater disease resistance and general health than would be expected from their circumstances. Access to medical care and exposure to specific environmental conditions must be considered.

Distribution of Medical Care

There is ample evidence that SES is strongly related to access to and quality of preventive care, ambulatory care, and high-technology procedures (Kaplan and Keil, 1993). It appears unlikely, however, that these factors account for more than a small percentage of the variation. Because causes of death that are purportedly “not amenable” to medical care show socioeconomic gradients similar to those of potentially treatable causes (Davey Smith et al., 1996a; Mackenbach et al., 1989), it has been argued that differential access to healthcare programs and services is not entirely responsible for socioeconomic differentials in health (Wilkinson, 1996).

Toxic Physical Environments

Despite enormous improvements in sanitary engineering, which have contributed to the sharp increase in life expectancy observed among all socioeconomic groups during the past century, the socioeconomic gradient in health status persists. It has been proposed that the SES gap is still attributable to effects of crowded and unsanitary housing, air and water pollution, inadequate food supply, poor working conditions, and other such deficits that disproportionately affect those in the lower socioeconomic strata. Studies that incorporate assessments of material deprivation and the physical environment will be important to sort out the degree to which this is an important pathway. However, inasmuch as the gradient in morbidity and mortality persists even between middle-class and well-to-do men and women and even in societies in which material conditions are very good, it seems unlikely that gradients are solely the result of these material circumstances.

Psychosocial Risk Factors

Considerable evidence links low SES to adverse psychosocial conditions. People who work in low-paid jobs are not only the most materially disadvantaged, but they also have higher job and financial insecurity; experience more unemployment, work injury, lack of control, and other social and environmental stressors; report fewer social supports; and more frequently have a cynically hostile or fatalistic outlook (Adler et al., 1994; Berkman and Syme, 1979; Bosma et al., 1997; House et al., 1988; Karasek and Theorell, 1990).

Psychosocial Context

The most successful interventions of the many clinical trials incorporated elements of social or organizational change to modify individual behavioral risk factors, such as alcohol and tobacco consumption, diet, and physical activity. Most behaviors are not randomly distributed in the population, but rather are socially patterned and often cluster with one another. Thus, many people who drink also smoke cigarettes, and those who follow health-promoting dietary practices also tend to be physically active. People who are poor, have low levels of education, or are socially isolated are more likely to engage in a wide array of risk-related behaviors and less likely to engage in health-promoting ones (Adler et al., 1994; Matthews et al., 1989). This patterned behavioral response led Link and Phelan (1995) to speak of situations that place individuals “at risk of risks.”

Understanding why “poor people behave poorly” (Lynch et al., 1997a) requires recognition that specific behaviors once thought of as falling exclusively within the realm of individual choice occur in a social context. The social environment influences behavior by shaping norms; enforcing patterns of social control (which can be health promoting or health damaging); providing or not providing environmental opportunities to engage in particular behaviors; and reducing or producing stress, for which engaging in specific behaviors might be an effective short-term coping strategy (Berkman and Kawachi, 2000). Environments, especially social contexts, place constraints on individual choice. Incorporating the social context into behavioral interventions led to a new array of clinical trials that take advantage of communities, schools, and worksites to achieve behavioral change (see Sorensen et al., 1998; Chapter 6, this volume).

Relationship to Health-Related Behaviors and Biological Risk Factors

Given the fact that socioeconomic stressors are disproportionately concentrated in lower socioeconomic groups (McLeod and Kessler, 1990), it is not surprising that many investigations indicate an inverse relationship between SES and adverse health behaviors (such as smoking, physical inactivity, less nutritious diets, and excessive alcohol consumption), and between SES and biological risk factors (such as high blood pressure, high serum cholesterol and fibrinogen, and obesity; Davey Smith et al., 1996a,b; Kaplan and Keil, 1993; Lynch et al., 1997a; Marmot et al., 1991). Statistical adjustment for such biological and behavioral risk factors generally leads to attenuation of excess mortality among lower groups. However, socioeconomic gradients still persist (Davey Smith et al., 1996a,b; 1990; Haan et al., 1987; Marmot et al., 1991). For example, in the MRFIT study (Davey Smith et al., 1996a,b), stratification by smoking status revealed similar gradients in income and coronary heart disease for smokers and nonsmokers.

Conceptualization and Measurement of SES

Commonly used measures of SES in epidemiologic studies include education, income, and occupation (Liberatos et al., 1988; Lynch and Kaplan, 2000; Morgenstern, 1985), but some work suggests that additional measures of wealth might be important and that increased attention should be paid to gender and life course issues (Anderson and Armstead, 1995; Lynch and Kaplan, 2000). In the social sciences, theoretical perspectives focus on different aspects of stratification. Social class as described by Weber (1946) has three domains: (1) class, by which he meant ownership and economic resources; (2) status, by which he meant prestige, community ranking, or honor; and (3) political power. This tripartite definition has led many social scientists to identify multiple indicators of social class. In the United States, these three domains are often assessed by income or wealth to tap economic resources and occupational rankings based on prestige to tap status. Political power per se is rarely assessed. Because occupationally based scales are often unavailable in the United States, most measures are based on income and education. In contrast, in Europe occupationally based scales are the most common indicators of social class. Common measures in Europe include the Erikson-Goldthorpe-Portocarero scheme. This scheme was developed to facilitate international comparisons of social stratification. It is still rarely used in the United States because routine data on the key elements are not commonly collected. (Kunst et al., 1998). Several reviews have outlined common measures of SES in the United States (Berkman and MacIntrye, 1997).

It is possible that different aspects of SES may lead to poor health through different pathways. For instance, income may influence outcomes very directly through material resources whereas occupational-based rankings may impact job-related psychosocial stresses and education may influence health-related behaviors. However, because these aspects of SES are usually highly correlated with each other, these distinct pathways are extremely difficult to identify. Thus, disentangling distinct effects of education or income for example remains a major challenge.

Almost all studies of income and health have measured income at only one point in adulthood. That fails to capture the health effects of sustained exposure to low income, to account for transitions into and out of low-income groups, or to allow for exploration of dynamic interrelationships between health and income. There is considerable volatility in income during adulthood: 26–39% of U.S. residents aged 45–65 experience income reductions of at least 50% in some 11-year period (Duncan et al., 1996), suggesting a need to measure income at multiple points in time (for example, through socioeconomic trajectories or careers). Lynch et al. (1997b) found significantly worse health outcomes among persons with sustained, as opposed to transitory, economic hardship.

General Susceptibility versus Disease Specificity

It has been argued that unfavorable socioeconomic position increases susceptibility to disease in general, and potential biological mechanisms of stress-related immune suppression and neuroendocrine activation have been postulated to account for this phenomenon (McEwen, 1998). However, within the general pattern of increased mortality, there is marked heterogeneity of the strength of the associations observed (Davey Smith et al., 1996a, b). Results from an examination of site-specific cancer mortality (Davey Smith et al., 1991) suggest that, although general susceptibility might be operative, research on disease-specific pathways should not be neglected.

Reverse Causation and Social Selection

The idea that poor health might lead to a worsening of SES rather than the other way around suggests a “reverse-causation” or “social-selection” hypothesis. If the less healthy are more likely to experience downward social mobility or are less likely to be upwardly mobile, the result will be a concentration of ill people in the lower social classes. Although evidence of reverse causation is strong for some conditions (most notably schizophrenia and other severe mental illnesses), such selection appears to have a relatively small influence on the overall socioeconomic gradient of health (Black et al., 1988; Marmot et al., 1995, 1987). Commonly cited evidence against the social-selection hypothesis includes the tendency of educational attainment, a measure not affected by illness that occurs after early adulthood, to be as strongly predictive of adult health outcomes as are other SES measures. Moreover, in longitudinal surveys, SES-related mortality differentials generated by social selection would be greatest early in the follow-up period if social selection were operative, but this has not been observed (Fox et al., 1985).

It is nevertheless possible that conditions operating at an early age— say between birth and entry into the workforce—are important in shaping social positions observed in adulthood and in influencing adult health directly (Lynch et al., 1997a). Early influences might shape developmental biology (Chapter 2), the lives people lead, and the environments in which they live and work as adults.


A social network is the web of social relationships that surround an individual and the structural characteristics of that web. Many researchers have measured social networks in a general way that taps the degree to which an individual is integrated into society. Examples include the degree to which an individual participates in voluntary associations or the number of friends a person has. Social support is a distinct function of social relationships; it is clear that not all relationships are supportive. Other functions of networks can influence health outcomes, including patterns of social influence, social engagement, and person-to-person contacts (which can promote the spread of infectious diseases; Berkman and Glass, 2000).

People form ties with others from the moment they are born. The survival of newborns depends on their attachment to and nurturance by others over an extended period (Baumeister and Leary, 1995). The need to belong does not stop in infancy, but rather affiliation and nurture and social relationships are essential for physical and psychological well-being throughout life (Cohen and Syme, 1985; Seeman, 1996). Affirmative social interactions—those that satisfy the need for autonomy, competence, and relatedness—are related to feeling understood and appreciated (Reis and Judd, 2000). Cognitive or interpersonal deficits in childhood and adolescence can further impair individual ability to acquire the social and instrumental skills people need to avoid life stressors and achieve age-appropriate social roles.

Positive Social Relations

Initial assessments of social isolation (or integration) emphasized objective features of social support, such as the size or density of one's social network and frequency of contact with relatives and friends. Subsequent studies elaborated more subjective or functional aspects, such as the perception of emotional and instrumental support or the amount of assistance provided by others (Cohen, 1988; Cohen and Wills, 1985; Vaux, 1988). Research on social support has increasingly differentiated into specific substantive areas, such as the role of social support in stress and coping (Thoits, 1995), social support in family relationships (Pierce et al., 1996), social support and personality (Pierce et al., 1997), and social support in differential survival from particular health challenges, such as myocardial infarction (e,g., Ruberman et al., 1984), or cancer (e,g., Spiegel et al., 1989).


One concept used to explain how social support affects health is buffering. For example, stress-induced decrements in immune function have been shown by research on medical students undergoing exams, but the decline was particularly pronounced for those lacking social buffers—those who reported being lonely (Glaser et al., 1992; Kiecolt-Glaser et al., 1994). Research involving people going through major life transitions (such as loss of a spouse or birth of a child) illustrates that social networks and social support influence the coping process and buffer the effects of stressors on health (Hirsch and Dubois, 1992; Rhodes et al., 1994; Walker et al., 1977).

Promoting Health-Enhancing Behaviors

Other research examines possible mechanisms, such as the extent to which significant others promote and encourage positive health practices (Berkman, 1995; Taylor et al., 1997). For example, social integration could enhance the beneficial effects of restorative behaviors, such as sleep. Sleep is a quintessential active restoration performed without immediate social contact. Although lonely individuals in one study slept as many hours as did socially embedded people, responses to the Pittsburgh Sleep Quality Index (Buysse et al., 1989) revealed that lonely individuals reported poorer sleep quality, longer sleep latency, and greater daytime dysfunction due to sleepiness than did socially embedded individuals. Other data confirm that lonely people sleep less efficiently, take slightly longer to fall asleep, evidence longer rapid eye movement latency, and awaken more frequently during the night than do embedded individuals (Cacioppo et al., 2000). Another study (Lewis and Rook, 1999) found that control in social relationships (that is, influencing and regulating social networks) was associated with more health-enhancing behavior, but with greater distress.

Altering Physiological Processes

Extensive research explores the underlying physiological roots through which social ties influence health (e.g., Berkman, 1995; Cohen and Herbert, 1996; Kang et al., 1998; Kiecolt-Glaser et al., 1994; Seeman, 1996; Seeman and McEwen, 1996; Uchino et al., 1996). Meta-analyses of the experimental literature support the hypothesis that perceived social isolation is associated with physiological adjustments, with the most reliable effects found for blood pressure, catecholamines, and aspects of cellular and humoral immune function (Seeman and McEwen, 1996; Uchino et al., 1996). In a study of carotid arthrosclerosis in middle-aged men, higher intima media thickness of the carotid artery was found in those who lived alone than in those who cohabited—even after controlling for age, health status, education, saturated fat consumption, and smoking (Helminen et al., 1995). The biological effects of loneliness are evident even after controlling for common individual personality differences (e.g., extraversion, neuroticism) in intervention studies designed to reduce social isolation and improve physiological functioning (Cacioppo et al., 2000; Uchino et al., 1996). People's beliefs, attitudes, and values pertaining to others appear to be especially important, as subjective indices of social isolation have been found to be more powerful predictors of stress and health than are objective indices (e.g., Uchino et al., 1996).

The relationship between social ties and the onset and progression of infectious disease has received growing attention recently. Socially supportive relationships appear to have beneficial effects on primary immune system parameters that regulate host resistance (Esterling et al., 1996; Kiecolt-Glaser et al., 1994; Uchino et al., 1996). Cohen et al. (1997) tested their hypothesis that diversity of network ties is related to susceptibility to cold. Participants were given nasal drops containing rhinovirus or placebo and monitored for the development of colds. Those who reported more types of social ties (e.g., spouse, parent, friend, workmate, and so on) were less susceptible to colds, produced less mucus, fought infection more efficiently, and shed less virus; moreover, susceptibility to infection decreased in a linear manner with increasing diversity of the social network. Further evidence that social ties mediate primary immune system parameters comes from a study by Theorell et al, (1995), who tracked the decline in the count of CD4 cells of the immune system over a 5-year period among a cohort of HIV-infected men in Sweden. The count declined more rapidly in men who reported lower “availability of attachments” at baseline.

Although research on the physiological pathways that could link networks to health is just developing, researchers have documented associations among social integration and social support and several physiological mechanisms related to health outcomes, including cardiovascular reactivity and neuroendocrine and immune function (Seeman, 1996; Uchino et al., 1996). In one of the few observational studies to link social support and neuroendocrine measures in humans, Seeman et al. (1994) found that older men and women who reported more frequent emotional support excreted less epinephrine, norepinephrine, and cortisol in their urine.

Several experimental studies have investigated the link between the social relationship and cardiovascular reactivity. Kamarck et al. (1990) found that participants asked to complete a laboratory task alone exhibited significantly greater systolic blood pressure and heart rate reactivity than did those who were allowed to have a friend with them. Lepore et al. (1993) varied the degree of social support available to participants asked to give a speech. The three social conditions were to give the speech alone, to give it in the presence of a nonsupportive confederate, and to give it in the presence of a supportive confederate. Participants in the last group exhibited the smallest increase in systolic pressure, followed by participants who gave their speeches alone. Links between neuroendocrine measures, cardiovascular reactivity, and blood pressure and social relationships might constitute potential pathways by which social networks, support, and engagement influence important health outcomes.

Establishing and Maintaining Long-Term Resources

Researchers looking at attachment in early and later life and at close personal relationships have described some features of deep, meaningful, loving human connections (Ryff and Singer, 2000). Numerous investigators have examined the nature of affect in intimate relationships, its development over time, and related expressions of emotion during marital interaction (e.g., Carstensen et al., 1995 e.g., Carstensen et al., 1996; Gottman, 1994; Gottman and Levenson, 1992). Collectively, research on interpersonal flourishing gives greater attention to the emotional upside of significant social relationships and their consequences for improved health (see Ryff and Singer, 1998, 2000; Taylor et al., 2000). Individuals on positive relationship pathways (positive ties with parents during childhood, intimate ties with spouse in adulthood) are less likely to show high allostatic load than are people on negative relationship pathways, and such relational strengths appear to offer protection against cumulative economic adversity (Singer and Ryff, 1999).

Negative Social Relations


Over the past 20 years, 13 large prospective cohort studies in the United States, Scandinavia, and Japan have shown that people who are isolated or disconnected from others are at increased risk of dying prematurely. For example, in a study in Alameda County, California (Berkman and Syme, 1979), men and women who had few ties to others (assessed using an index of contacts with friends and relatives, marital status, and church and group membership) were 1.9–3.1 times more likely to die in a 9-year follow-up period (1965–1974) than were those who had many more contacts. The relative risks1 associated with social isolation were not centered in one cause of death. Those who had few social ties were at increased risk of dying from ischemic heart disease; cerebrovascular and circulatory disease; cancer; and a final category that included respiratory, gastrointestinal, and all other causes of death. Several other studies, both in the United States and across the world, have replicated the basic observation that social isolation increases the relative risk of mortality (Berkman, 1995; Berkman and Kawachi, 2000; Blazer, 1982; Cohen, 1988; House et al., 1982, 1988; Kaplan et al., 1988; Orth-Gomer and Johnson, 1987; Pennix et al., 1997; Schoenbach et al., 1986; Seeman et al., 1988, 1993, 1996; Sugisawa et al., 1994; Welin et al., 1985).

Powerful epidemiologic evidence consistently supports the notion that social ties, especially intimate ties and emotional support provided by them, promote increased survival and better prognosis among people with serious cardiovascular disease (Berkman et al., 1992; Case et al., 1992; Krumholz et al., 1998; Orth-Gomer et al., 1988; Oxman et al., 1995; Ruberman et al., 1984; Williams et al., 1992). Most studies find that social networks are related more strongly to mortality than to the incidence of myocardial infarction (MI) (Kawachi et al., 1996; Reed et al., 1983; Vogt et al., 1992; but see Orth-Gomer et al., 1993). A similar pattern of associations between social integration and incidence versus recovery from stroke has been observed (Colantonio et al., 1992, 1993; Friedland and McColl, 1987; Glass and Maddox, 1992; McLeroy et al., 1984; Morris et al., 1993). For example, although social integration was not associated with the incidence of stroke in an elderly cohort (Colantonio et al., 1992), poststroke recovery after 6 months was significantly related to prestroke social integration (Colantonio et al., 1993). Socially isolated people exhibited worse functional status 6 months after a stroke (Glass et al., 1993), as measured by impairments in activities of daily living and frequency of nursing home placement.

Adverse Interactions

Being part of a social network, however, can have harmful as well as positive consequences, because the value to the individual of such ties depends upon the character of that network as well as on the strength of those ties. Membership in networks, for example, provides access to domestic, economic, and informational resources (Uehara, 1990). If a person is tied to a tightly knit group, the resources available through that group, especially informational resources, could be limited. Sometimes people who have weak ties have access to more resources than those who are tightly connected to a group with limited means (Pescosolido, 1986, 1991; Uehara, 1990).

Furthermore, not all social connections are beneficial While both positive and negative interactions can affect psychological well-being, the negative interactions are generally more strongly linked (Ingersoll-Dayton et al., 1997; Rook, 1984). Studies of adult relationships have examined not only their contributions to intimacy (Berscheid and Reis, 1998) and well-being (Meyers and Diener, 1995; Sternberg and Hojat, 1997), but also their adverse consequences: divorce and bereavement (Kiecolt-Glaser et al., 1998), poor interpersonal relationships (Baumeister and Leary, 1995), and dispositional and cognitive factors that contribute to loneliness and depression (Marangoni and Ickes, 1989). One study of older adults in long-term marriages, for example, showed that 30 minutes of conflict discussion was associated with changes in cortisol, adrenocorticotropic hormone, and norepinephrine in women, but not in men (Kiecolt-Glaser et al., 1997). Other studies linked marital conflict and high blood pressure (Ewart et al., 1991), elevated plasma catecholamine concentrations (Malarkey et al., 1994), and autonomic activation (Levenson et al., 1993). Caregivers of relatives with progressive dementia are characterized by impaired wound-healing compared with controls matched for age and family income (Kiecolt-Glaser et al., 1995, 1998). Social conflicts have been shown to increase susceptibility to infection (Cohen et al., 1998; Glaser et al., 1999).

Family characteristics that could undermine the health of children and adolescents include a family environment that is conflictual, angry, violent, or abusive; parent/child relationships that are unresponsive and lacking in cohesiveness, warmth, and emotional support; and parenting styles that are either overly controlling and dominating or that offer little imposition of rules and structure (Taylor et al., 1997). Long-term exposure to such conditions contributes to deficits in emotional understanding, difficulties with appropriate expression of emotion, increased emotional reactivity to conflict, and maladaptive coping strategies for managing stressful events in general.


The workplace is an important source of adverse and protective health effects alike. A consistent body of research has emerged over the past two decades to show that work conditions (job demands, control, latitude) and trends in work, such as downsizing and unemployment, are related to health (Karasek and Theorell, 1990). Workplace investigations also have identified protective factors—such as the ability to develop social ties at work—that help guard against the adverse mental and health effects of work stress (Buunk and Verhoeven, 1991).

Job Strain

Since Karasek introduced the “demand/control” model to characterize the psychosocial work environment (Karasek and Theorell, 1990), many empirical studies have tested the predictive validity of the model with respect to the physical health of workers. Job strain—the combination of a psychologically demanding workplace and low job control—is hypothesized as leading to adverse health outcomes. Studies using both dimensions generally have provided better predictions than studies using either dimension alone. However, job control—the opportunity to use and develop skills and to exert authority over workplace decisions— emerged as the more robust component of a health-promoting work environment.

In reviewing the literature on the relationship between job strain and cardiovascular disease, Schnall et al. (1994) reported that 17 of 25 studies found that lack of job control significantly predicted adverse outcomes, whereas only 8 of 23 studies found that high psychological demands did so. Studies published in the past 5 years bolster the importance of job control (for example, Bosma et al., 1997, 1998; Johnson et al., 1996; North et al., 1996; Theorell et al., 1998). A recent population-based case/control study found that low job control was associated with incidence of first MI among employed Swedish men 45–64 years old, although the association was somewhat weakened by adjustment for social class (blue- or white-collar status; Theorell et al., 1998). A decrease in job control during the 10 years preceding MI was also significantly predictive of increased cardiovascular risk, as was job strain, even after adjustment for multiple covariates, including history of chest pain and shift, night, or overtime work. High psychological demand did not consistently predict MI onset in this cohort. In agreement with findings from other studies, the relationships between low job control and MI were strongest among younger respondents (under 55 years old) and among blue-collar workers. Empirical evidence of the adverse effect of low job control among women is sparse because few studies have been done among women.

Siegrist (1996) recently developed an effort/reward imbalance model of job stress that postulates that high-effort conditions (characterized by high job demands and psychological immersion in work) are balanced against three sources of rewards: money, esteem, and “occupational-status control” (promotion prospects and job security). The emphasis is on the balance between work-related costs and gains rather than on specific job task characteristics, as in the demand/control model. Effort/reward imbalance and low job control were independent predictors of incident coronary heart disease among British civil servants in models adjusted for age, employment grade, negative affectivity, and coronary risk profile (Bosma et al., 1998). Relative risk ratios for high-effort and low-reward conditions ranged from 2.59 to 3.63, depending on sex and specific coronary end point.


Several longitudinal epidemiologic studies have examined the relationship between unemployment and mortality (Kasl and Jones, 2000). Three reports from a national survey by the British Office of Population Censuses and Surveys were based on a 10-year follow-up of British men (Moser et al., 1984, 1986, 1987). Men seeking work during the week before the 1971 census had a higher age-adjusted mortality than would be expected from the rates in the total sample; after adjustment for social class, the standardized mortality ratio2 (SMR) was 121. Particularly high mortality was observed for suicide (SMR, 169). Statistical adjustment for possible prior differences in health status was not possible. A shorter follow-up of men after the 1981 census confirmed the earlier findings but obtained a somewhat lower adjusted SMR of 112.

Additional studies using similar methods have been conducted in Sweden (Stefansson, 1991), Finland (Martikainen, 1990), Denmark (Iversen et al., 1987), and Italy (Costa and Segnan, 1987). Unemployment appears to be associated with SMRs of 150–200, adjusted for age and social class. Cause-specific analyses suggest that suicides, accidents, violent deaths, and alcohol-related deaths tend to be especially high, but that they do not completely account for the excess mortality. Adjustments for various indicators of health status reduce estimates much less than do adjustments for sociodemographic characteristics, but available health status indicators in these studies are quite limited. Sex differences were examined in two of the studies: the Danish data showed no difference in magnitude of effect attributable to unemployment, whereas the Swedish data showed a much weaker effect on women (SMR, 114). Results from the U.S. National Longitudinal Mortality Study (Sorlie and Rogot, 1990) are not consistent with the European data. Among those aged 45–64, the age-, education-, and income-adjusted SMRs due to unemployment were 107 for men and 81 for women; neither statistic was significantly different from the null value of 100. The discrepancy is not easily explained, inasmuch as the “social safety net” that protects the unemployed is believed to be stronger in Europe than in the United States (Kasl and Jones, 2000).

The British Regional Heart Study (Morris et al., 1994) followed men 40–59 years old who had been continuously employed for at least 5 years before initial screening. The respondents were contacted again 5 years later and asked about changes in employment since the initial screening. They were then followed for an additional 5.5 years for mortality. Compared with the continuously employed men, those with some unemployment (but not due to illness, according to self-report) had an age-adjusted relative risk of 1.59. Further adjustment for social class, smoking, alcohol use, and preexisting disease at initial screening slightly reduced the relative risk to 1.47. Men who were “unemployed or retired due to illness” had an adjusted relative risk of 3.14; this high relative risk reveals the inadequacy of using standard adjustments for health status measurements made while the men were all continuously employed. Baseline health status probably should be updated in such studies or, at a minimum, supplemented with reports of illness-related reasons for not working.

As discussed for SES, the causation-versus-selection question is raised here as well; it is not clear whether unemployment causes excess mortality or whether background variables (such as social class and poor pre-existing health) cause both unemployment and mortality (Kasl and Jones, 2000). To add further complexity, some studies find weaker associations between unemployment and mortality when the regional unemployment rate is high (Iversen et al., 1987; Martikainen and Valkonen, 1996).

Physical Morbidity

Studies of unemployment and physical morbidity introduce a new concern not applicable to mortality studies: the measurement of health status outcomes (Kasl and Jones, 2000). Physical symptoms and complaints, for instance, might result from psychological distress rather than from some underlying physical condition. Conversely, distress could lower the threshold for reporting existing physical symptoms. Moreover, measures based on seeking or receiving medical care could indicate differences in illness behavior rather than in underlying illness.

Morris and Cook (1991) reviewed longitudinal studies of factory closures. Their findings show that the job loss experience exerts a negative effect on physical health. In a prospective study of closure of a sardine factory in Norway (Westin, 1990; Westin et al., 1988, 1989), the rates of disability pension over a 10-year follow-up period were higher than were rates at a similar factory nearby that remained open. The pensions were supposedly “granted for medical conditions only,” but it is difficult to know exactly what was being assessed and what health status differences would have been observed with other types of measurements. In a Canadian study of factory closure (Grayson, 1989), former employees reported about 2.5 times more ailments during a 27-month follow-up than the expected average. The higher prevalence was for a wide range of conditions, such as headaches, acute respiratory ailments, ulcers, arthritis, vision and hearing disorders, and dental troubles. Only heart disease, asthma, and endocrine diseases showed no significant differences. The authors offered the interpretation that the data indicated higher levels of stress that produce “a series of symptoms that people mistake for illness itself.”

Biological and Behavioral Risk Factors

Studies of biological indicators of stress reactivity and cardiovasculardisease risk (Kasl and Jones, 2000) provide consistent evidence of their acute sensitivity to some aspect of the unemployment experience, particularly anticipation of job loss. However, chronic adverse changes in neuroendocrine and cardiovascular measures in relation to enduring unemployment are infrequently documented (Kasl and Jones, 2000).

Evidence on the impact of unemployment on health behaviors is mixed. Longitudinal data from the British Regional Heart Study (Morris et al., 1992, 1994) showed only an increase in weight attributable to unemployment; there was no evidence of an effect on cigarette or alcohol use. Higher levels of smoking and heavy drinking were in fact predictive of later unemployment in this study. But analysis of panel data from the U.S. Epidemiologic Catchment Area study suggested that the 1-year incidence of clinically significant alcohol abuse was greater among those who had been laid off than among those who had not been laid off (Catalano et al., 1993). The available evidence does not distinguish between the causation theory (unemployment led to alcohol use) and the selection theory (those who used alcohol were more likely to be laid off) (Dooley et al., 1992; Kasl and Jones, 2000).

Threatened Job Loss

The impact of threatened job loss has received increased attention recently. Foremost among the recent investigations are those of the Whitehall II cohort of British civil servants (Ferrie et al., 1995, 1998). White-collar workers under threat of major organizational change (elimination or transfer to the private sector) showed adverse changes in self-rated health, long-standing illness, sleep patterns, number of physical symptoms, and minor psychiatric morbidity. Only health-related behaviors did not show an adverse change. Longitudinal data on male Swedish shipyard workers threatened with job loss and on stably employed controls (Mattiasson et al., 1990) showed that serum cholesterol concentrations increased significantly among the former group. The increase was greater among those with increases in cardiovascular risk factors, particularly weight and blood pressure. However, no significant differential trends over time were seen for weight, blood pressure, or blood glucose. In a study of Finnish government workers (Vahtera et al., 1997), downsizing was associated with increased medically certified sick leave. Among American automobile workers (Heaney et al., 1994), extended periods of job insecurity were associated with increased physical symptoms. However, workers who remain in an organization after a downsizing do not experience a decline in well-being despite an increase in work demands (Parker et al., 1997).


Negative health consequences associated with retirement have not been demonstrated (Kasl and Cobb, 1980; McGoldrick, 1989; Minkler, 1981; Moen, 1996). To the contrary, the evidence shows an absence of an adverse effect (Kasl and Jones, 2000).

Older studies (Palmore et al., 1984) tended to show neither adverse effects nor benefits. Some specific variables, such as subjective global evaluations of one's health, might show improvement, but this was seen as a function of reinterpreting one's health in the absence of physical demands on the job. More recent studies (Gall et al., 1997; Midanik et al., 1995; Ostberg and Samuelsson, 1994; Salokangas and Jowkamaa, 1991) tend to show some benefits of retirement, primarily in the psychological domain and in health behaviors. A study of older steelworkers forced to retire early because of downsizing did not show any adverse effects on their health (Gall et al., 1997; Gillanders et al., 1991). Loss of a job close to normal retirement age might have only small negative effects, if any.

There is no question that poor health leads to early or involuntary retirement (McGoldrick, 1989; Moen, 1996). This makes it difficult to test the proposition that, although planned and “on-schedule” retirement does not have negative consequences, it is the unplanned, involuntary, “off-schedule” retirement that should have adverse effects, because the downward health status trajectory that precipitated the retirement manifests itself as poor health status after retirement.

Men who choose to continue working well beyond the conventional retirement age are an unusual group, in good health and with a strong work commitment (Parnes and Sommers, 1994). It would be of interest to study the effects of mandatory retirement in this group rather than in blue-collar workers, who usually prefer to retire early (and usually do so if retirement benefits are adequate). But members of such occupational groups as doctors, judges, and farmers who continue working beyond typical retirement age are not easily recruited into a study of mandatory retirement (Kasl and Jones, 2000).


Health and economic status are closely related (Wilkinson, 1996); indicators of health worsen as affluence decreases. The existence of inequality—a property of the population in question—has important consequences for the health of individuals and groups.

People and Places

The United States is among the richest countries in the world, yet it is also one of the least equal in distribution of its wealth (Atkinson et al., 1995). In 1968, the wealthiest 20% of U.S. households earned on average $73,754, compared with $7,202 earned by households in the bottom 20%. In 1994, the inflation-adjusted average income of the top 20% had jumped to $105,945, whereas the average income of the bottom 20% had grown to only $7,762 (Brown et al., 1997). The best-off 1% of the American population owns 40–50% of the nation's wealth (Hacker, 1997; Wolff, 1995). The poverty rate at the bottom of the economic hierarchy has remained stable during the past three decades; today, some 36.5 million Americans (13.7%) are officially poor.

At a national level, the hypothesis linking income inequalities and health would predict that two countries with the same average income but different income distributions would experience different patterns of mortality; the country with the more equitable distribution having a higher life expectancy overall. Cross-national studies support an association between income equality and population longevity. For example, in a cross-sectional examination of 11 countries belonging to the Organisation for Economic Co-operation and Development (OECD), Wilkinson (1986) found a strong negative correlation (R =–0.81, P < .0001) between income inequality, as measured by the Gini coefficient, and life expectancy. The Gini coefficient is the most widely used measure of income distribution and theoretically ranges from 0 (perfect equality) to 1 (perfect inequality). Similarly, a high positive correlation (R=0.86, P < .001) was found between the life expectancy of nine OECD countries and the proportion of national income accruing to the least well-off 70% of the population (Wilkinson, 1992). By itself, the gross national product per capita3 could explain less than 10% of the variance in life expectancy (Wilkinson, 1992).

Income inequality within the United States has been linked to adverse health outcomes. Kaplan et al. (1996) and Kennedy et al. (1996) independently examined the relationship between degree of household income inequality in the 50 states and state-level variation in mortality. Kaplan et al. (1996) used as their measure of income distribution the share of total income earned by the bottom 50% of households in each state. If all incomes were equivalent, the bottom half of households would account for half the aggregate income. In reality, the income earned by the bottom half ranged only from 17.5% to 23.6% of the total income. A strong correlation (R=–0.62, P < 001) was found between this measure of inequality and age standardized mortality. The association was observed in men and women and in whites and African Americans. Kennedy et al. (1996) examined two measures of income distribution: the Gini coefficient4 and the Robin Hood index. The Robin Hood index is the proportion of aggregate income that must be redistributed from rich to poor households to attain perfect equality of incomes. Both measures were strongly correlated with age-adjusted total and cause-specific mortality. Adjusting for poverty rates and median income, a 1% increase in the Robin Hood index was associated with an excess mortality of 21.7 per 100,000 (95% CI, 6.6–36.7), which suggests that even a modest reduction in inequality could have important public health consequences. Income inequality was associated not only with higher total mortality but also with infant mortality and rates of death from coronary heart disease, cancer, and homicide. The findings persisted after controlling for urban rural proportion and for such health behavior variables as cigarette-smoking rates. Lynch et al, (1998) observed a relationship between income inequality and mortality at the level of U.S. metropolitan areas.

Although income inequality is strongly correlated with poverty (R = 0.73), the adverse effect of income inequality on health outcomes does not appear to be entirely explained by a compositional effect (places that exhibit income inequality have greater concentrations of poor people, who in turn have higher mortality risk). There is also evidence of a contextual effect of income inequality directly on individual health (Kennedy et al., 1998; Soobader and LeClere, 1999; Wilkinson, 1992). Kennedy et al. (1998) conducted a multilevel (individual ecologic) analysis of the effects of income inequality on individual self-rated health, adjusting for individual household income and other characteristics, such as educational attainment, smoking, overweight, and access to health care. People residing in states with the greatest income inequality were 1.25 times more likely to report being in only fair or poor health than were those living in the most egalitarian states. The effect of income inequality was statistically significant and independent of absolute income.

At least three pathways have been proposed to account for the relationship between income inequality and health: underinvestment in human capital (Kaplan, 1996); disruption of social cohesion by income disparities, which leads to disinvestment in social capital (Kawachi and Kennedy, 1997; Kawachi et al., 1997a); and direct psychological pathways, for example, frustration and envy created by invidious social comparisons (the relative-deprivation hypothesis) (Kawachi et al., 1994; Wilkinson, 1996). These are briefly described below.

Underinvestment in Human Capital

Kaplan (1996) reported striking correlations between degree of income inequality and indicators of human capital investment. States with the highest income inequality (as measured by the proportion of total household income received by the less well-off 50%) spent a smaller proportion of their budgets on education and showed poorer educational outcomes, ranging from worse reading and mathematics proficiency to higher high school dropout rates.

Erosion of Social Cohesion

Kawachi et al, (1997b) tested the association between income inequality and social cohesion at the population level. People living in states with high income disparities tend to be more mistrustful of each other (R =0.71) and to belong to fewer civic organizations (R=–0.41). Both indicators were strongly correlated with age-adjusted mortality (R = 0.79 for social mistrust, R=–0.49 for civic-association membership, P < .05 for both). The authors speculated that income inequality erodes social cohesion, with adverse consequences for public health.

Relative Deprivation

Few epidemiologic studies directly connect frustrated expectations to health outcomes. Dressler (1996) coined the term “cultural consonance in lifestyle” to refer to the degree to which individuals succeed in achieving the lifestyle considered customary for their community. To the extent that individuals strive for and fail to meet the cultural ideal, negative health consequences follow. The degree of departure from cultural consonance is a strong predictor of systolic blood pressure, even after adjustment for established clinical risk factors for hypertension, including age, sex, obesity, occupation, education, and income (Dressler, 1996). The adverse consequences of relative deprivation are not confined to the psychological realm. As societies become more prosperous, material needs increase not just because people think they need more when their neighbors have more, but also for practical reasons. Many consumer goods introduced as luxury items (such as automobiles and telephones) gradually become necessities. In the early 1900s, when cities were organized on the assumption that residents would get around on foot or by streetcar, the automobile was considered a luxury. As car ownership became more prevalent, public transportation atrophied, and many employers, businesses, and families relocated to areas that were accessible only by car. In many places today it is extremely difficult, if not impossible, to work, shop, or socialize without a car (Jencks, 1992).

Race and Discrimination

Although whites and African Americans experienced substantial improvements in life expectancy at all ages throughout the 20th century, substantial gaps remain in life expectancy, morbidity, and functional status. The data suggest a temporal lag in life expectancies between the 2 groups in the United States. Life expectancy at birth for African Americans in 1990 was the same as it was for whites in 1950. Even after controlling for income, African American men and women have lower life expectancy than do whites at every income level (see for example Anderson et al., 1997; Geronimus et al., 1996).

Those differences, which are often substantial across a diversity of health outcomes, are commonly reduced but remain significant when indicators of socioeconomic status are considered. This phenomenon has led researchers to investigate the health effects of discrimination itself. Aspects of discrimination might influence health through any number of mechanisms, including socioeconomic position. However, conceptualizing discrimination (whether it applies to racial or ethnic minorities, women, homosexuals, or groups of different ages) as a stressful experience that can influence disease processes is a major advance in scientific thinking over the past decade.

Discrimination is defined as “the process by which a member, or members, of a socially defined group is, or are, treated differently (especially unfairly) because of his/her/their membership of that group” (Jary and Jary, 1995). Conceptually, the pathways by which discrimination can affeet health involve exposure, susceptibility, and response to economic and social deprivation, toxic substances and hazardous conditions (physical, chemical, and biological agents), socially inflicted trauma (mental, physical, or sexual, ranging from verbal to violent), targeted marketing of legal and illegal psychoactive substances and other commodities, and inadequate health care by facilities and by specific providers (including access to care, diagnosis, and treatment) (Krieger, 2000).

Public health researchers have only recently begun to quantify the health effects of discrimination. Krieger (2000) outlines three approaches to studying these effects: indirect comparison of health outcomes of subordinate versus dominant groups without having specific information on discrimination; self-reported discrimination and its relation to health outcomes; and assessment of population-level experience of discrimination and health. The second and third approaches, although not without problems, can shed light on the specific aspects of discrimination with health consequences.

Krieger and Sidney (1996) investigated the relationship between self-reported racial discrimination and blood pressure among 4086 African American and white 25–34'year-old participants in the Coronary Artery Risk Development in Young Adults study, a prospective, multisite, community-based investigation. Among African Americans, systolic blood pressure was significantly increased, by 2–4 mm Hg (millimeters of mercury), among working-class men and women; in professional women reporting substantial discrimination; and among working-class men and women reporting no discrimination, when compared with those reporting moderate discrimination. Conversely, among professional men, blood pressure was more than 4 mm Hg lower among those reporting no discrimination.

One interpretation of why a self-report of no racial discrimination was associated with increased blood pressure among working-class African American women and men and professional African American women but lower blood pressure among professional African American men is that the meaning of “no discrimination” could be related to social position, in this case, sex and class. For people with more power and resources, a no might truly mean no. Among more disenfranchised people, a no might reflect internalized oppression. In such cases, a disjuncture between words and somatic evidence could be an instance of a pathogenic manifestation of experiences that people cannot readily describe. Adding plausibility to that interpretation are the results of two smaller studies, both of which found higher blood pressure among members of groups subjected to discrimination (African American women in one, white gay men in the other) who said that they had experienced no versus moderate discrimination (Krieger, 1990; Krieger and Sidney, 1997).

The third approach, measuring population-level experiences of discrimination and health effects, is illustrated by a study on the relationship of African American residential segregation and political empowerment with infant postneonatal mortality (the death rate of infants 2–12 months old) (LaVeist, 1992). Degree of residential segregation was assessed with a widely used index, the percentage of African Americans who would need to relocate to make the ratio of African Americans to whites in every neighborhood the same as that for the city as a whole. Relative political power was defined as the ratio of the proportion of African American representatives on the city council to the proportion of the voting-age population that was African American. Direct political power was defined as the percentage of city council members who were African American. Increased neonatal mortality was independently associated with higher levels of segregation, with poverty, and with lower levels of relative (but not direct) political power, even when controlling for intra city allocation of municipal resources (for example, per capita spending by neighborhoods on health, public safety, firefighting, streets, and sewers). Population measures of economic participation and political empowerment developed for other subordinate groups (for example, United Nations Development Programme, 1996) have not yet been used in epidemiologic studies. Other neighborhood effects also have been reported in relation to blood pressure (Diez-Roux et al., 1997).

Multilevel analyses have not examined discrimination and health. It is plausible that residential segregation could modify perceptions and effects of individually reported experiences of discrimination. This would be an important new phase of research.

Social Cohesion and Social Capital

Social integration can be conceived of as both an individual and a societal characteristic (Kawachi and Kennedy, 1997). A socially integrated individual has many social connections, in the form of intimate social contacts (spouse, friends, relatives) and more extended connections (membership in religious groups and other voluntary associations). At the group level, a socially cohesive society is one that is endowed with stocks of “social capital,” which consists partly of moral resources, such as trust between citizens and norms of reciprocity.

More socially integrated societies seem to have lower rates of crime, suicide, mortality from all causes, and better overall quality of life (Kawachi and Berkman, 2000; Kawachi and Kennedy, 1997; Wilkinson, 1996). Kawachi et al. (1997b) analyzed social capital indicators across the United States in relation to state-level death rates. Social capital indicators were created from data gathered in the General Social Surveys conducted of the National Opinion Research Center in 1986–1990. Respondents in 39 states were asked to count the number of memberships in a variety of voluntary organizations, including church groups, sports groups, hobby groups, fraternal organizations, and labor unions. The per capita density of membership in voluntary groups was inversely correlated with age-adjusted mortality from all causes (R=–0.49, P < .0001). Adjusting for household poverty, an increase in average per capita group membership by 1 unit was associated with a decrease in age-adjusted mortality of 66.8 deaths per 100,000. Density of civic association membership and levels of interpersonal trust (percentage of citizens endorsing the expectation that altruistic behaviors will be returned in kind at some future time) were also important indicators of social capital. Level of distrust was strikingly correlated with age-adjusted mortality (R=0.79, P < .0001). Lower levels of trust were associated with higher rates of most major causes of death, including coronary heart disease, cancer, cerebrovascular disease, unintentional injury, and infant mortality.

Kawachi et al. (1999) also carried out a multilevel study of the relationship between the above indicators of state-level social capital and individual self-rated health. A strength of this study was the availability of information on individual medical and behavioral confounding variables, including health insurance coverage, cigarette-smoking and overweight, and on sociodemographic characteristics, such as household income, education and whether one lived alone. Even after adjustment for those variables, people residing in states with low social capital were more likely to report fair or poor health. The odds ratio5 for fair or poor health associated with living in areas with the lowest as opposed to the highest levels of interpersonal trust was 1.41.

There are several plausible mechanisms by which social cohesion might influence health through contextual effects (Kawachi and Berkman, 2000). At the neighborhood level, social capital might influence health behaviors by promoting more rapid diffusion of health information, increasing the likelihood that healthy norms of behavior are adopted, and exerting control over deviant health-related behavior. Sampson et al. (1997) provide evidence that “collective efficacy,” or the extent to which neighbors are willing to exert social control over deviant behavior, plays an important role in preventing crime and delinquency. A similar process might operate to prevent other forms of unhealthy behavior, such as adolescent smoking, drinking, and drug abuse. Neighborhood social capital also could affect health by increasing access to local services and amenities; evidence from criminology suggests that socially cohesive neighborhoods are more successful at uniting to ensure that budget cuts do not disrupt local services (Sampson et al., 1997). Finally, neighborhood social capital could influence health through direct psychosocial pathways by providing social support and acting as the source of self-esteem and mutual respect, for example. Variations in the availability of psychosocial resources at the community level might explain the anomalous finding that individuals with few social ties but who reside in socially cohesive communities—such as East Boston (Seeman et al., 1993), African Americans in rural Georgia (Schoenbach et al., 1986), or Japanese Americans in Hawaii (Reed et al., 1983)—do not appear to suffer the same adverse health consequences as do socially isolated people living in less cohesive communities (Kawachi and Berkman, 2000). At the state level, it appears that more cohesive states produce more egalitarian patterns of political participation, which result in policies that ensure the security of all residents (Kawachi and Kennedy, 1997).


Longitudinal studies published in the past decade demonstrate the health benefits of religious involvement. For example, among residents of Alameda County, California, attendance at places of worship was associated with a lower 28-year mortality (Strawbridge et al., 1997). Residents of religious kibbutzim in Israel had a 40% lower 16-year mortality from cardiovascular disease than did those living on secular kibbutzim (Kark et al., 1996). The 6-month mortality after elective open-heart surgery was significantly lower among patients with strong religious faith than it was among their nonreligious counterparts (Oxman et al., 1995). In a community-based sample of elderly residents of New Haven, Connecticut, religious group membership protected elderly Christians and Jews against death in the month before their religious holidays during a 6-year period (Idler and Kasl, 1992). In that cohort, those who never or rarely attended religious services had nearly twice the stroke rate of those who attended weekly during the same period (Colantonio et al., 1992). Frequent religious attendance also predicted better physical function 8–12 years later, even after controlling for baseline function (Idler and Kasl, 1997).

Those longitudinal studies show that lower mortality and morbidity rates among those frequently attending religious services are partly but not entirely explained by improved health practices and increased social contacts deriving from attendance or by confounding due to baseline health status (i.e., selection). Nevertheless, evidence from some of the more methodologically sound studies indicates that the health-promoting effects of private worship or spirituality (such as prayer or scripture-reading at home, or subjective feelings of religious commitment) appears to be weaker than that of attendance at services (e.g., Idler and Kasl, 1997). However, this field is relatively new and more research is needed to determine how religious attendance is associated with good health. This complex topic is only briefly touched on here; a comprehensive analysis of the impact of spirituality and religion on health is beyond the scope of this report.


  1. Adler N, Boyce T, Chesney M, Cohen S, Folkman S, Kahn R, Syme L. Socioeconomic status and health: the challenge of the gradient. American Psychologist. 1994;49:15–24. [PubMed: 8122813]
  2. Anderson NB, Armstead CA. Toward understanding the association of socioeconomic status and health: A new challenge for the biopsychosocial approach. Psychosomatic Medicine. 1995;57:213–225. [PubMed: 7652122]
  3. Anderson RT, Sorlie P, Backlund E, Johnson N, Kaplan GA. Mortality effects of community socioeconomic status. Epidemiology. 1997;8:42–47. [PubMed: 9116094]
  4. Atkinson AB, Rainwater L, Smeeding TM. Income Distribution in OECD Countries: Evidence from the Luxembourg Income Study. Paris: Organization for Economic Cooperation and Development; 1995.
  5. Baumeister RF, Leary MR. The need to belong: Desire for interpersonal attachments as a fundamental human motivation. Psychological Bulletin. 1995;117:497– 529. [PubMed: 7777651]
  6. Berkman L. The role of social relations in health promotion. Psychosomatic Medicine. 1995;57(3):245–254. [PubMed: 7652125]
  7. Berkman L, Glass T. Social integration, social networks, social support and health. In: Berkman L, Kawachi I, editors. Social Epidemiology. New York: Oxford University Press; 2000.
  8. Berkman L, Kawachi I, editors. Social Epidemiology. New York: Oxford University Press; 2000.
  9. Berkman L, Syme S. Social networks, host resistance, and mortality: A nine-year follow-up of Alameda County residents. American Journal of Epidemiology. 1979;109:186–204. [PubMed: 425958]
  10. Berkman LF. The role of social relations in health promotion. Psychosomatic Medicine. 1995;57:245–254. [PubMed: 7652125]
  11. Berkman LF, Macintyre S. The measurement of social class in health studies: old measures and new formulations. In: Kogevinas M, Pearce N, Susser M, Boffetta P, editors. Social Inequalities and Cancer, IARC Scientific Publications No. 138. Lyon: International Agency for Research on Cancer; 1997. pp. 51–64. [PubMed: 9353663]
  12. Berkman LF, Leo-Summers L, Horwitz RI. Emotional support and survival after myocardial infarction: A prospective, population-based study of the elderly. Annals of Internal Medicine. 1992;117:1003–1009. [PubMed: 1443968]
  13. Berscheid E, Reis HT. Attraction and close relationships. In: Gilbert DT, Fiske ST, Lindzey G, editors. The Handbook of Social Psychology. 4th edition. Vol. 2. Boston: McGraw-Hill; 1998. pp. 193–281.
  14. Black D, Morris JN, Smith C, Townsend P, Whitehead M. Inequalities in Health: The Black Report; the Health Divide. London: Penguin Group; 1988.
  15. Blane D, Bartley M, Davey Smith G. Disease aetiology and materialistic explanations of socioeconomic mortality differentials. European Journal of Public Health. 1997;7:385–391.
  16. Blazer D. Social support and mortality in an elderly community population. American Journal of Epidemiology. 1982;115:684–694. [PubMed: 7081200]
  17. Bosma H, Marmot MG, Hemingway H, Nicholson AC, Brunner E, Stansfeld SA. Low job control and risk of coronary heart disease in Whitehall II (prospective cohort) study. British Medical Journal. 1997;314:558–565. [PMC free article: PMC2126031] [PubMed: 9055714]
  18. Bosma H, Peter R, Siegrist J, Marmot M. Two alternative job stress models and the risk of coronary heart disease. American Journal of Public Health. 1998;88:68–74. [PMC free article: PMC1508386] [PubMed: 9584036]
  19. Brown LR, Renner M, Flavin C. Vital Signs 1997. New York: W. W. Norton and Company; 1997.
  20. Buunk BP, Verhoeven K. Companionship and support at work: A microanalysis of the stress-reducing features of social interactions. Basic and Applied Social Psychology. 1991;12:243–258.
  21. Buysse DJ, Reynolds CF 3rd, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Research. 1989;28:193–213. [PubMed: 2748771]
  22. Cacioppo JT, Ernst JM, Burleson MH, McClintock MK, Malarkey WB, Hawkley LC, Kowalewski RB, Paulsen A, Hobson JA, Hugdahl K, Spiegel D, Bernston GG. Lonely traits and concomitant physiological processes: The MacArthur social neuroscience studies. International Journal of Psychophysiology. 2000;35:143–154. [PubMed: 10677643]
  23. Carstensen LL, Gottman JM, Levenson RW. Emotional behavior in long-term marriage. Psychology and Aging. 1995;10:140–149. [PubMed: 7779311]
  24. Carstensen LL, Levenson RW, Gottman JM. Affect in intimate relationships: The developmental course of marriage. In: Magai C, McFadden SH, editors. Handbook of Emotion, Adult Development, and Aging. San Diego, CA: Academic Press; 1996. pp. 227–247.
  25. Case RB, Moss AJ, Case N, McDermott M, Eberly S. Living alone after myocardial infarction. Journal of the American Medical Association. 1992;267:515–519. [PubMed: 1729573]
  26. Catalano R, Dooley D, Wilson G, Hough R. Job loss and alcohol abuse: A test using data from the Epidemiologic Catchment Area Project. Journal of Health and Social Behavior. 1993;34:215–225. [PubMed: 7989666]
  27. Cohen S. Psychosocial models of the role of social support in the etiology of physical disease. Health Psychology. 1988;7:269–297. [PubMed: 3289916]
  28. Cohen S, Herbert TB. Health psychology: Psychological factors and physical disease from the perspective of human psychoneuroimmunology. Annual Review of Psychology. 1996;47:113–142. [PubMed: 8624135]
  29. Cohen S, Syme L, editors. Social support and health. Orlando, FL: Academic Press; 1985.
  30. Cohen S, Wills TA. Stress, social support, and the buffering hypothesis. Psychology Bulletin. 1985;98:310–357. [PubMed: 3901065]
  31. Cohen S, Doyle WJ, Skoner DP, Rabin BS, Gwaltney JM Jr. Social ties and susceptibility to the common cold. Journal of the American Medical Association. 1997;277:1940–1944. [PubMed: 9200634]
  32. Cohen S, Frank E, Doyle WJ, Skoner DP, Rabin BS, Gwaltney JM Jr. Types of stressors that increase susceptibility to the common cold in healthy adults. Health Psychology. 1998;17:214–223. [PubMed: 9619470]
  33. Colantonio A, Kasl S, Ostfeld A. Depressive symptoms and other psychosocial factors as predictors of stroke in the elderly. American Journal of Epidemiology. 1992;136:884–894. [PubMed: 1442754]
  34. Colantonio A, Kasl SV, Ostfeld AM, Berkman LF. Psychosocial predictors of stroke outcomes in an elderly population. Journal of Gerontology. 1993;48:S261–S268. [PubMed: 8366275]
  35. Costa G, Segnan N. Unemployment and mortality. British Medical Journal (Clinical Research Edition). 1987;294:1550–1551. [PMC free article: PMC1246699] [PubMed: 3111631]
  36. Davey Smith G, Leon D, Shipley MJ, Rose G. Socioeconomic differentials in cancer among men. International Journal of Epidemiology. 1991;20:339–345. [PubMed: 1917232]
  37. Davey Smith G, Neaton JD, Wentworth D, Stamler R, Stamler J. Socioeconomic differentials in mortality risk among men screened for the Multiple Risk Factor Intervention Trial. II. Black men. American Journal of Public Health. 1996a;86:497–504. [PMC free article: PMC1380549] [PubMed: 8604779]
  38. Davey Smith G, Shipley MJ, Rose G. Magnitude and causes of Socioeconomic differentials in mortality: Further evidence from the Whitehall Study. Journal of Epidemiology and Community Health. 1990;44:265–270. [PMC free article: PMC1060667] [PubMed: 2277246]
  39. Davey Smith G, Wentworth D, Neaton JD, Stamler R, Stamler J. Socioeconomic differentials in mortality risk among men screened for the Multiple Risk Factor Intervention Trial. I. White men. American Journal of Public Health. 1996b;86:486–496. [PMC free article: PMC1380548] [PubMed: 8604778]
  40. Diez-Roux AV, Nieto FJ, Muntaner C, Tyroler HA, Comstock GW, Shahar E, Cooper LS, Watson RL, Szklo M. Neighborhood environments and coronary heart disease: A multilevel analysis. American Journal of Epidemiology. 1997;146:48–63. [PubMed: 9215223]
  41. Dooley D, Catalano R, Hough R. Unemployment and alcohol disorder in 1910 and 1990: Drift versus social causation. Journal of Occupational and Organizational Psychology. 1992;65:277–290.
  42. Dressler WW. Culture and blood pressure: Using consensus analysis to create a measurement. Cultural Anthropology Methods. 1996;8:6–8.
  43. Duncan C, Jones K, Moon G. Health related behavior in context—a multi level modelling approach. Social Science and Medicine. 1996;42:817–830. [PubMed: 8778995]
  44. Esterling BA, Kiecolt-Glaser JK, Glaser R. Psychosocial modulation of cytokine-induced natural killer cell activity in older adults. Psychosomatic Medicine. 1996;58:264–272. [PubMed: 8771626]
  45. Ewart CK, Taylor CB, Kraemer HC, Agras WS. High blood pressure and marital discord: Not being nasty matters more than being nice. Health Psychology. 1991;10:155–163. [PubMed: 1879387]
  46. Feldman J, Makuc D, Kleinman J, Cornoni-Huntley J. National trends in educational differentials in mortality. American Journal of Epidemiology. 1989;129:919– 933. [PubMed: 2705434]
  47. Ferrie J, Shipley M, Marmot M, Stansfeld S, Davey Smith G. Health effects of anticipation of job change and non-employment: Longitudinal data from the Whitehall II study. British Medical Journal. 1995;311:1264–1269. [PMC free article: PMC2551182] [PubMed: 7496235]
  48. Ferrie J, Shipley M, Marmot M, Stansfeld S, Davey Smith G. The health effects of major organisational change and job insecurity. Social Science and Medicine. 1998;46:243–254. [PubMed: 9447646]
  49. Fox AJ, Goldblatt PO, Jones DR. Social class mortality differentials: Artefact, selection or life circumstances? Journal of Epidemiology and Community Health. 1985;39:1–8. [PMC free article: PMC1052392] [PubMed: 3989429]
  50. Friedland J, McColl M. Social support and psychosocial dysfunction after stroke: Buffering effects in a community sample. Archives of Physical Medicine and Rehabilitation. 1987;68:475–480. [PubMed: 3619609]
  51. Gall T, Evans D, Howard J. The retirement adjustment process: Changes in the well-being of male retirees across time. Journals of Gerontology. Series B, Psychological Sciences and Social Sciences. 1997;52:110–117. [PubMed: 9158562]
  52. Geronimus AT, Bound J, Waidmann TA, Hillemeier MM, Burns PB. Excess mortality among blacks and whites in the United States. New England Journal of Medicine. 1996;335:1552–1558. [PubMed: 8900087]
  53. Gillanders W, Buss T, Wingard E, Gemmel D. Long-term health impacts of forced early retirement among steelworkers. Journal of Family Practice. 1991;32:401– 405. [PubMed: 2010740]
  54. Glaser R, Kiecolt-Glaser JK, Bonneau RH, Malarkey W, Kennedy S, Hughes J. Stress-induced modulation of the immune response to recombinant hepatitis B vaccine. Psychosomatic Medicine. 1992;54:22–29. [PubMed: 1553399]
  55. Glaser R, Rabin B, Chesney M, Cohen S, Natelson B. Stress-induced immunomodulation: Implications for infectious diseases? Journal of the American Medical Association. 1999;281:2268–2270. [PubMed: 10386538]
  56. Glass T, Maddox GL. The quality and quantity of social support: Stroke recovery as psycho-social transition. Social Science and Medicine. 1992;34:1249–1261. [PubMed: 1641684]
  57. Glass TA, Matchar DB, Belyea M, Feussner JR. Impact of social support on outcome in first stroke. Stroke. 1993;24:64–70. [PubMed: 8418553]
  58. Gottman JM. What Predicts Divorce? The Relationship Between Marital Processes and Marital Outcomes. Hillsdale, NJ: Lawrence Erlbaum Associates; 1994.
  59. Gottman JM, Levenson RW. Marital processes predictive of later dissolution: behavior, physiology, and health. Journal of Personality and Social Psychology. 1992;63:221–233. [PubMed: 1403613]
  60. Grayson J. Reported illness after CGE closure. Canadian Journal of Public Health. 1989;80:16–19. [PubMed: 2702538]
  61. Haan M, Kaplan G, Camacho T. Poverty and health: Prospective evidence from the Alameda County Study. American Journal of Epidemiology. 1987;125:989–998. [PubMed: 3578257]
  62. Hacker A. Money: Who Has How Much and Why. New York: Scribner; 1997.
  63. Heaney C, Israel B, House J. Chronic job insecurity among automobile workers: effects on job satisfaction and health. Social Science and Medicine. 1994;38:1431– 1437. [PubMed: 8023192]
  64. Helminen A, Rankinen T, Mercuri M, Rauramaa R. Carotid atherosclerosis in middle-aged men. Relation to conjugal circumstances and social support. Scandinavian Journal of Social Medicine. 1995;23:167–172. [PubMed: 8602486]
  65. Hirsch BJ, DuBois DI. The Relation of Peers Social Support and Psychological Symptomatology during the Transition to Junior High School: A Two-year Longitudinal Analysis. American Journal of Community Psychology. 1992;20:333–347. [PubMed: 1415031]
  66. House J, Robbins C, Metzner H. The association of social relationships and activities with mortality: Prospective evidence from the Tecumseh Community Health Study. American Journal of Epidemiology. 1982;116:123–140. [PubMed: 7102648]
  67. House JS, Landis KR, Umberson D. Social relationships and health. Science. 1988;241:540–545. [PubMed: 3399889]
  68. Idler E, Kasl S. Religion, disability, depression and the timing of death. American Journal of Sociology. 1992;97:1052–1079.
  69. Idler E, Kasl S. Religion among disabled and nondisabled persons. II. Attendance at religious services as a predictor of the course of disability. Journal of Gerontology. Series B, Psychological Sciences and Social Sciences. 1997;52:S306–S316. [PubMed: 9403524]
  70. Ingersoll-Dayton B, Morgan D, Antonucci T. The effects of positive and negative social exchanges on aging adults. Journals of Gerontology. Series B, Psychological Sciences and Social Sciences. 1997;52:S190–S199. [PubMed: 9224447]
  71. Iversen L, Andersen O, Andersen P, Christoffersen K, Keiding N. Unemployment and mortality in Denmark, 1970–80. British Medical Journal. 1987;295:879–884. [PMC free article: PMC1247928] [PubMed: 3119084]
  72. Jary D, Jary J. Collins Dictionary of Sociology. 2nd edition. Glasgow, UK: Harper Collins; 1995.
  73. Jencks C. Rethinking Social Policy: Race, Poverty, and the Underclass. Cambridge: Harvard University Press; 1992.
  74. Johnson JV, Stewart W, Hall EM, Fredlund P, Theorell T. Long-term psychosocial work environmental and cardiovascular mortality among Swedish men. American Journal of Public Health. 1996;86:324–331. [PMC free article: PMC1380510] [PubMed: 8604756]
  75. Kamarck TW, Manuck SB, Jennings JR. Social support reduces cardiovascular reactivity to psychological challenge: A laboratory model. Psychosomatic Medicine. 1990;52:42–58. [PubMed: 2305022]
  76. Kang DH, Coe CL, Karaszewski J, McCarthy DO. Relationship of social support to stess responses and immune function in healthy and asthmatic adolescents. Research in Nursing and Health. 1998;21:11–28. [PubMed: 9535404]
  77. Kaplan G. People and places: Contrasting perspectives on the association between social class and health. International Journal of Health Services. 1996;26:507–519. [PubMed: 8840199]
  78. Kaplan G, Salonen J, Cohen R, Brand R, Syme S, Puska P. Social connections and mortality from all causes and cardiovascular disease: Prospective evidence from eastern Finland. American Journal of Epidemiology. 1988;128:370–380. [PubMed: 3394703]
  79. Kaplan GA, Keil JE. Socioeconomic factors and cardiovascular disease: A review of the literature. Circulation. 1993;88:1973–1998. [PubMed: 8403348]
  80. Kaplan GA, Pamuk E, Lynch JW, Cohen RD, Balfour JL. Inequality in income and mortality in the United States: Analysis of mortality and potential pathways. British Medical Journal. 1996;312:999–1003. [PMC free article: PMC2350835] [PubMed: 8616393]
  81. Karasek R, Theorell T. Healthy Work. New York: Basic Books; 1990.
  82. Kark J, Shemi G, Friedlander Y, Martin O, Manor O, Blondheim S. Does religious observance promote health? Mortality in secular vs. religious kibutzim in Israel. American Journal of Public Health. 1996;86:341–346. [PMC free article: PMC1380514] [PubMed: 8604758]
  83. Kasl S, Cobb S. The experience of losing a job: Some effects on cardiovascular functioning. Psychotherapy and Psychosomatics. 1980;34:88–109. [PubMed: 7220773]
  84. Kasl S, Jones B. The impact of job loss and retirement on health. In: Berkman L, Kawachi I, editors. Social Epidemiology. New York: Oxford University Press; 2000.
  85. Kawachi I, Berkman L. Social cohesion, social capital and health. In: Berkman L, Kawachi I, editors. Social Epidemiology. New York: Oxford University Press; 2000.
  86. Kawachi I, Kennedy BP. Socioeconomic determinants of health: Health and social cohesion: Why care about income inequality? British Medical Journal. 1997;314:1037–1040. [PMC free article: PMC2126438] [PubMed: 9112854]
  87. Kawachi I, Colditz GA, Ascherio A, Rimm EB, Giovannucci E, Stampfer MJ, Willett WC. A prospective study of social networks in relation to total mortality and cardiovascular disease in men in the U.S.A. Journal of Epidemiology and Community Health. 1996;50:245–251. [PMC free article: PMC1060278] [PubMed: 8935453]
  88. Kawachi I, Kennedy B, Lochner K, Prothrow-Stith D. Social capital, income inequality, and mortality. American Journal of Public Health. 1997b;87:1491–1498. [PMC free article: PMC1380975] [PubMed: 9314802]
  89. Kawachi I, Kennedy BP, Lochner K. Long live community: Social capital as public health. The American Prospect. 1997a;35:56–59.
  90. Kawachi I, Kennedy BP, Glass R. Social capital and self-rated health: A contextual analysis. American Journal of Public Health. 1999;89:1187–1193. [PMC free article: PMC1508687] [PubMed: 10432904]
  91. Kawachi I, Levine S, Miller SM, Lasch K, Amick BCI. Income Inequality and Life Expectancy: Theory, Research, and Policy. Boston: The Health Institute, New England Medical Center; 1994.
  92. Kennedy BP, Kawachi I, Prothrow-Stith D. Income distribution and mortality: Cross-sectional ecologic study of the Robin Hood Index in the United States. British Medical Journal. 1996;312:1004–1007. [PMC free article: PMC2350807] [PubMed: 8616345]
  93. Kennedy BP, Kawachi I, Glass R, Prothrow-Stith D. Income distribution, socioeconomic status, and self rated health in the United States: Multilevel analysis. British Medical Journal. 1998;317:917–921. [PMC free article: PMC28675] [PubMed: 9756809]
  94. Kiecolt-Glaser JK, Glaser R, Cacioppo JT. Marital conflict in older adults: Endocrinological and immunological correlates. Psychosomatic Medicine. 1997;59:339–349. [PubMed: 9251151]
  95. Kiecolt-Glaser JK, Malarkey WB, Cacioppo JT, Glaser R. Stressfulpersonal relationships: Immune and endocrine function. In: Glaser R, Kiecolt-Glaser J, editors. Handbook of Human Stress and Immunity. San Diego: Academic Press; 1994. pp. 321–339.
  96. Kiecolt-Glaser JK, Marucha PT, Malarkey WB, Mercado AM, Glaser R. Slowing of wound healing by psychological stress. Lancet. 1995;346:1194–1196. [PubMed: 7475659]
  97. Kiecolt-Glaser JK, Page GG, Marucha PT, MacCallum RC, Glaser R. Psychological influences on surgical recovery. Perspectives from psychoneuroimmunology. American Psychologist. 1998;53:1209–1218. [PubMed: 9830373]
  98. Krieger N. Racial and gender discrimination: Risk factors for high blood pressure? Social Science and Medicine. 1990;30:1273–1281. [PubMed: 2367873]
  99. Krieger N. Discrimination and health: A U.S. perspective on concepts, methods, and measures for epidemiologic research on health consequences of embodying racism, sexism, and other forms of social inequality. In: Berkman L, Kawachi I, editors. Social Epidemiology. New York: Oxford University Press; 2000.
  100. Krieger N, Sidney S. Racial discrimination and blood pressure: The CARDIA study of young black and white adults. American Journal of Public Health. 1996;86:1370– 1378. [PMC free article: PMC1380646] [PubMed: 8876504]
  101. Krieger N, Sidney S. Prevalence and health implications of anti-gay discrimination: A study of black and white women and men in the CARDIA cohort. Coronary Artery Risk Development in Young Adults. International Journal of Health Services. 1997;27:157–176. [PubMed: 9031018]
  102. Krumholz HM, Butler J, Miller J, Vaccarino V, Williams C, Mendes CF, de Leon CF, Seeman TE, Kasl SV, Berkman LF. The prognostic importance of emotional support for elderly patients hospitalized with heart failure. Circulation. 1998;97:958–964. [PubMed: 9529263]
  103. Kunst AE, Groenhof F, Mackenbach JP. EU Working Group on socioeconomic inequalities in Health. Occupational class and cause specific mortality in middle aged men in 11 European countries: Comparison of population based studies. British Medical Journal. 1998;316:1636–1642. [PMC free article: PMC28562] [PubMed: 9603745]
  104. Kunst AE, Mackenbach JP. The size of mortality differences associated with educational level in nine industrialized countries. American Journal of Public Health. 1994;84:932–937. [PMC free article: PMC1614960] [PubMed: 8203689]
  105. LaVeist T. The political empowerment and health status of African-Americans: Mapping a new territory. American Journal of Sociology. 1992;97:1080–1095.
  106. Lepore SJ, Mata Allen KA, Evans GW. Social support lowers cardiovascular reactivity in an acute stressor. Psychosomatic Medicine. 1993;55:518–524. [PubMed: 8310112]
  107. Levenson RW, Carstensensen LL, Gottman JM. Long-term marriage: Age, gender and satisfaction. Psychology and Aging. 1993;8:301–313. [PubMed: 8323733]
  108. Lewis MA, Rook KS. Social control in personal relationships: Impact on health behaviors and psychological distress. Health Psychology. 1999;18:63–71. [PubMed: 9925047]
  109. Liberatos P, Link BG, Kelsey JL. The measurement of social class in epidemiology. Epidemiologic Reviews. 1988;10:87–121. [PubMed: 3066632]
  110. Link B, Phelan J. Social conditions as fundamental causes of disease. Journal of Health and Social Behavior (Spec No.). 1995:80–94. [PubMed: 7560851]
  111. Lynch J, Kaplan GA. Socioeconomic position. In: Berkman LF, Kawachi I, editors. Social Epidemiology. New York: Oxford University Press; 2000.
  112. Lynch J, Kaplan GA, Pamuk ER, Cohen RD, Heck KE, Balfour JL, Yen IH. Income inequality and mortality in metropolitan areas of the United States. American Journal of Public Health. 1998;88:1074–1080. [PMC free article: PMC1508263] [PubMed: 9663157]
  113. Lynch JW, Kaplan GA, Salonen JT. Why do poor people behave poorly? Variation in adult health behaviors and psychological characteristics by stages of the socioeconomic life course. Social Science and Medicine. 1997a;44:809–819. [PubMed: 9080564]
  114. Lynch JW, Kaplan GA, Shema SJ. Cumulative impact of sustained economic hardship on physical, cognitive, psychological, and social functioning. New England Journal of Medicine. 1997b;337:1889–1895. [PubMed: 9407157]
  115. Macintyre S. The Black Report and beyond: What are the issues? Social Science and Medicine. 1997;44:723–746. [PubMed: 9080558]
  116. Macintyre S, Ellaway A, Der G, Ford G, Hunt K. Do housing tenure and car access predict health because they are simply markers of income or self-esteem? A Scottish study. Journal of Epidemiology and Community Health. 1998;52:657–664. [PMC free article: PMC1756620] [PubMed: 10023466]
  117. Mackenbach JP, Stronks K, Kunst AE. The contribution of medical care to inequalities in health: differences between socio-economic groups in decline of mortality from conditions amenable to medical intervention. Social Science and Medicine. 1989;29:369–376. [PubMed: 2762863]
  118. Malarkey WB, Kiecolt-Glaser JK, Pearl D, Glaser R. Hostile behavior during marital conflict alters pituitary and adrenal hormones. Psychosomatic Medicine. 1994;56:41–51. [PubMed: 8197314]
  119. Marangoni C, Ickes W. Loneliness: A theoretical review with implications for measurement. Journal of Personal and Social Relationships. 1989;6:93–128.
  120. Marmot MG, Bobak M, Davey Smith G. Explanations for social inequalities in health. In: Amick B, Levine S, Tarlov AR, Chapman Walsh D, editors. Society and Health. New York: Oxford University Press; 1995. pp. 172–210.
  121. Marmot MG, Bosma H, Hemingway H, Brunner E, Stansfeld S. Contribution of job control and other risk factors to social variations in coronary heart disease incidence. Lancet. 1997;350:235–239. [PubMed: 9242799]
  122. Marmot MG, Davey Smith G, Stansfield S, Patel C, North F, Head J, White I, Brunner E, Feeney A. Health inequalities among British civil servants: The Whitehall II Study. Lancet. 1991;337:1387–1393. [PubMed: 1674771]
  123. Marmot MG, Kogevinas M, Elston MA. Social/economic status and disease. Annual Review of Public Health. 1987;8:111–135. [PubMed: 3555518]
  124. Martikainen P. Unemployment and mortality among Finnish men, 1981–5. British Medical Journal. 1990;301:407–411. [PMC free article: PMC1663697] [PubMed: 2282395]
  125. Martikainen P, Valkonen T. Excess mortality of unemployed men and women during a period of rapidly increasing unemployment. Lancet. 1996;348:909–912. [PubMed: 8843808]
  126. Matthews K, Kelsey S, Meilahn E, Kuller LH, Wing RR. Educational attainment and behavioral and biologic risk factors for coronary heart disease in middle-aged women. American Journal of Epidemiology. 1989;129:1132–1144. [PubMed: 2729252]
  127. Mattiasson I, Lindegarde F, Nilsson J, Theorell T. Threat of unemployment and cardiovascular risk factors: Longitudinal study of quality of sleep and serum cholesterol concentrations in men threatened with redundancy. British Medical Journal. 1990;301:461–465. [PMC free article: PMC1663764] [PubMed: 2207398]
  128. McEwen BS. Protective and damaging effects of stress mediators. New England Journal of Medicine. 1998;338:171–179. [PubMed: 9428819]
  129. McGoldrick A. Stress, early retirement, and health. In: Markides K, Cooper C, editors. Aging, Stress and Health. Chichester, UK: John Wiley; 1989. pp. 91–118.
  130. McLeod J, Kessler R. Socioeconomic status differences in vulnerability to undesirable life events. Journal of Health and Social Behavior. 1990;31:162–172. [PubMed: 2102495]
  131. McLeroy KB, DeVellis R, DeVellis B, Kaplan B, Toole J. Social support and physical recovery in a stroke population. Journal of Social and Personal Relationships. 1984;1:395–413.
  132. Meyers DJ, Diener E. Who is happy? Psychological Science. 1995;6:10–19.
  133. Midanik L, Soghikian K, Ransom L, Tekawa I. The effect of retirement on mental health and health behaviors: The Kaiser Permanente Retirement Study. Journals of Gerontology. Series B, Psychological Sciences and Social Sciences. 1995;50:S59– S61. [PubMed: 7757831]
  134. Minkler M. Research on the health effects of retirement: An uncertain legacy. Journal of Health and Social Behavior. 1981;22:117–130. [PubMed: 7240711]
  135. Moen P. A life course perspective on retirement, gender and well-being. Journal of Occupational Health Psychology. 1996;1:131–144. [PubMed: 9547042]
  136. Morgenstern H. Socioeconomic factors: Concepts, measurement and health effects. In: Ostfeld A, Eaker E, editors. Measuring Psychosocial Variables in Epidemiological Studies of Cardiovascular Disease. Bethesda, MD: National Institutes of Health; 1985. pp. 3–36.
  137. Morris J, Cook D. A critical review of the effect of factory closures on health. British Journal of Industrial Medicine. 1991;48:1–8. [PMC free article: PMC1035303] [PubMed: 1993153]
  138. Morris J, Cook D, Shaper A. Non-employment and changes in smoking, drinking, and body weight. British Medical Journal. 1992;304:536–541. [PMC free article: PMC1881409] [PubMed: 1559056]
  139. Morris J, Cook D, Shaper A. Loss of employment and mortality. British Medical Journal. 1994;308:1135–1139. [PMC free article: PMC2540120] [PubMed: 8173455]
  140. Morris P, Robinson R, Andrzejewski P, Samuels J, Price T. Association of depression with 10-year poststroke mortality. American Journal of Psychiatry. 1993;150:124–129. [PubMed: 8417554]
  141. Moser K, Fox A, Jones D. Unemployment and mortality in the OCPS Longitudinal Study. Lancet. 1984;2(8415):1324–1329. [PubMed: 6150333]
  142. Moser K, Fox A, Jones D, Goldblatt P. Unemployment and mortality: Further evidence from the OCPS Longitudinal Study 1971–1981. Lancet. 1986;1(8477):365–367. [PubMed: 2868304]
  143. Moser K, Goldblatt P, Fox A, Jones D. Unemployment and mortality: comparison of the 1971 and 1981 longitudinal study census samples. British Medical Journal. 1987;294:86–90. [PMC free article: PMC1245095] [PubMed: 3105667]
  144. National Center for Health Statistics. Vital Statistics of the United States, 1992. Washington, DC: U.S: Government Printing Office; 1992.
  145. North FM, Syme SL, Feeney A, Shipley M, Marmot M. Psychosocial work environment and sickness absence among British civil servants: The Whitehall II study. American Journal of Public Health. 1996;86:332–340. [PMC free article: PMC1380513] [PubMed: 8604757]
  146. Orth-Gomer K, Johnson J. Social network interaction and mortality: A six year follow-up study of a random sample of the Swedish population. Journal of Chronic Diseases. 1987;40:949–957. [PubMed: 3611293]
  147. Orth-Gomer K, Rosengren A, Wilhelmsen L. Lack of social support and incidence of coronary heart disease in middle-aged Swedish men. Psychosomatic Medicine. 1993;55:37–43. [PubMed: 8446739]
  148. Orth-Gomer K, Unden AL, Edwards ME. Social isolation and mortality in ischemic heart disease. Acta Medica Scandinavica. 1988;224:205–215. [PubMed: 3239448]
  149. Ostberg H, Samuelsson S. Occupational retirement in women due to age: Health aspects. Scandinavian Journal of Social Medicine. 1994;22:90–96. [PubMed: 8091161]
  150. Oxman TE, Freeman DH Jr, Manheimer ED. Lack of social participation or religious strength and comfort as risk factors for death after cardiac surgery in the elderly. Psychosomatic Medicine. 1995;57:5–15. [PubMed: 7732159]
  151. Palmore E, Fillenbaum G, George L. Consequences of retirement. Journal of Gerontology. 1984;39:109–116. [PubMed: 6690582]
  152. Pappas G, Queen S, Hadden W, Fisher G. The increasing disparity in mortality between socioeconomic groups in the U.S. 1960–1986. New England Journal of Medicine. 1993;329:103–109. [PubMed: 8510686]
  153. Parker S, Chmiel N, Wall T. Work characteristics and employee well-being within a context of strategic downsizing. Journal of Occupational Health Psychology. 1997;2:289–303. [PubMed: 9552298]
  154. Parnes H, Sommers D. Shunning retirement: Work experience of men in their seventies and early eighties. Journal of Gerontology. 1994;49:S117–S124. [PubMed: 8169346]
  155. Pennix BW, van Tilburg T, Kriegsman DM, Deeg DJ, Boeke AJ, van Eijk JT. Effects of social support and personal coping resources on mortality in olderage: The Longitudinal Aging Study, Amsterdam. American Journal of Epidemiology. 1997;146:510–519. [PubMed: 9290512]
  156. Pescosolido B. Migration, medical care and the lay referral system: A network theory of adult socialization. American Journal of Sociology. 1986;51:523–590.
  157. Pescosolido B. Illness careers and network ties: A conceptual model of utilization and compliance. Advances in Medical Sociology. 1991;2:161–184.
  158. Pierce GR, Lakey B, Sarason IG, Sarason BR, editors. Sourcebook of Social Support and Personality. New York: Plenum Press; 1997.
  159. Pierce GR, Sarason BR, Sarason IG, editors. Handbook of Social Support and the Family. New York: Plenum Press; 1996.
  160. Reed D, McGee D, Yano K, Feinleib M. Social networks and coronary heart disease among Japanese men in Hawaii. American Journal of Epidemiology. 1983;117:384–396. [PubMed: 6837553]
  161. Reis HT, Judd CM, editors. Handbook of Research Methods in Social and Personality Psychology. New York: Cambridge University Press; 2000.
  162. Rhodes JE, Contreras JM, Mangelsdorf SC. Natural mentor relationships among Latina adolescent mothers: Psychological adjustment, moderating processes, and the role of early parental acceptance. American Journal of Community Psychology. 1994;22:211–227. [PubMed: 7977178]
  163. Rogot E, Sorlie P, Johnson N. Life expectancy by employment status, income and education in the National Longitudinal Mortality Study. Public Health Reports. 1992;107:457–461. [PMC free article: PMC1403677] [PubMed: 1641443]
  164. Rook KS. The negative side of social interaction: impact on psychological wellbeing. Journal of Personality and Social Psychology. 1984;46:1097–1108. [PubMed: 6737206]
  165. Ruberman W, Weinblatt E, Goldberg J, Chaudhary B. Psychosocial influences on mortality after myocardial infarction. New England Journal of Medicine. 1984;311:552–559. [PubMed: 6749228]
  166. Ryff CD, Singer B. The contours of positive human health. Psychological Inquiry. 1998;9:1–28.
  167. Ryff CD, Singer B, editors. Emotion, Social Relationships, and Health. New York: Oxford University Press; 2000.
  168. Salokangas R, Jowkamaa M. Physical and mental health changes in retirement age. Psychotherapy and Psychosomatics. 1991;55:100–107. [PubMed: 1891555]
  169. Sampson RJ, Raudenbush SW, Earls F. Neighborhoods and violent crime: A multilevel study of collective efficacy. Science. 1997;277:918–924. [PubMed: 9252316]
  170. Schnall P, Landsbergis P, Baker D. Job strain and cardiovascular disease. Annual Review of Public Health. 1994;15:381–411. [PubMed: 8054091]
  171. Schoenbach V, Kaplan B, Freedman L, Kleinbaum D. Social ties and mortality in Evans County, Georgia. American Journal of Epidemiology. 1986;123:577–591. [PubMed: 3953538]
  172. Seeman T. Social ties and health: the benefits of social integration. Annals of Epidemiology. 1996;6:442–451. [PubMed: 8915476]
  173. Seeman T, Berkman L, Blazer D, Rowe J. Social ties and support as modifiers of neuroendocrine function. Annals of Behavioral Medicine. 1994;16:95–106.
  174. Seeman T, Berkman L, Kohout F, LaCroix A, Glynn R, Blazer D. Intercommunity variation in the association between social ties and mortality in the elderly: a comparative analysis of three communities. Annals of Epidemiology. 1993;3:325–335. [PubMed: 8275207]
  175. Seeman T, Kaplan G, Knudsen L, Cohen R, Guralnik J. Social network ties and mortality among the elderly in the Alameda County Study. American Journal of Epidemiology. 1988;126:714–723. [PubMed: 3631060]
  176. Seeman TE, McEwen BS. Impact of social environment characteristics on neuroendocrine regulation. Psychosomatic Medicine. 1996;58:459–471. [PubMed: 8902897]
  177. Siegrist J. Adverse health effects of high-effort/low-reward conditions. Journal of Occupational Health Psychology. 1996;1:27–41. [PubMed: 9547031]
  178. Singer B, Ryff CD. Hierarchies of life histories and associated health risks. Annals of the New York Academy of Science. 1999;896:96–115. [PubMed: 10681891]
  179. Soobader MJ, LeClere FB. Aggregation and the measurement of income inequality: Effects on morbidity. Social Science and Medicine. 1999;48:733–744. [PubMed: 10190636]
  180. Sorensen G, Emmons K, Hunt MK, Johnston D. Implications of the results of community intervention trials. Annual Review of Public Health. 1998;19:379–416. [PubMed: 9611625]
  181. Sorlie P, Rogot E. Mortality by employment status in the National Longitudinal Mortality Study. American Journal of Epidemiology. 1990;132:983–992. [PubMed: 2239913]
  182. Sorlie P, Backlund E, Keller JB. U.S. mortality by economic, demographic and social characteristics: the National Longitudinal Mortality Study. American Journal of Public Health. 1995;585:949–956. [PMC free article: PMC1615544] [PubMed: 7604919]
  183. Sorlie P, Rogot E, Anderson R, Johnson N, Backlund E. Black-white mortality differences by family income. Lancet. 1992;340:346–350. [PubMed: 1353813]
  184. Spiegel D, Bloom JR, Kraemer HC, Gottheil E. Effect of psychosocial treatment on survival of patients with metastatic breast cancer. Lancet. 1989;2(8668):888–891. [PubMed: 2571815]
  185. Stefansson C. Long-term unemployment and mortality in Sweden, 1980–1986. Social Science and Medicine. 1991;32:419–423. [PubMed: 2024157]
  186. Sternberg RJ, Hojat M, editors. Satisfaction in Close Relationships. New York: Guilford; 1997.
  187. Strawbridge W, Cohen R, Shema S, Kaplan G. Frequent attendance at religious services and mortality over 28 years. American Journal of Public Health. 1997;87:957–961. [PMC free article: PMC1380930] [PubMed: 9224176]
  188. Sugisawa H, Liang J, Liu X. Social networks, social support and mortality among older people in Japan. Journal of Gerontology. 1994;49:S3–13. [PubMed: 8282987]
  189. Syme S, Berkman L. Social class, susceptibility and sickness. American Journal of Epidemiology. 1976;104:1–8. [PubMed: 779462]
  190. Taylor SE, Kemeny ME, Reed GM, Bower JE, Gruenewald TL. Psychological resources, positive illusions and health. American Psychologist. 2000;55:99–109. [PubMed: 11392870]
  191. Taylor SE, Repetti RL, Seeman T. Health psychology: What is an unhealthy environment and how does it get under the skin? Annual Review of Psychology. 1997;48:411–447. [PubMed: 9046565]
  192. Theorell T, Blomkvist V, Jonsson H, Schulman S, Berntrop E, Stigendal L. Social support and the development of immune function in human immunodeficiency virus infection. Psychosomatic Medicine. 1995;57:32–36. [PubMed: 7732156]
  193. Theorell T, Tsutsumi A, Hallquist J, Reuterwall C, Hogstedt C, Fredlund P, Emlund N, Johnson JV. Decision latitude, job strain, and myocardial infarction: A study of working men in Stockholm. The SHEEP Study Group. Stockholm Heart epidemiology Program. American Journal of Public Health. 1998;88:382–388. [PMC free article: PMC1508348] [PubMed: 9518968]
  194. Thoits PA. Stress, coping, and social support processes: Where are we? What next? Journal of Health and Social Behavior, Extra Issue. 1995:53–79. [PubMed: 7560850]
  195. Tyroler HA, Wing S, Knowles MG. Increasing inequality in coronary heart disease mortality in relation to educational achievement profiles of places of residence, United States, 1962 to 1987. Annals of Epidemiology. 1993;3:S51–S54.
  196. Uchino BN, Cacioppo JT, Kiecolt-Glaser JK. The relationship between social support and physiological processes: A review with emphasis on underlying mechanisms and implications for health. Psychological Bulletin. 1996;119:488–531. [PubMed: 8668748]
  197. Uehara E. Dual exchange theory, social networks, and informal social support. American Journal of Sociology. 1990;96:521–557.
  198. United Nations Development Programme (UNDP). Human Development Report,1996. New York: Oxford University Press; 1996.
  199. Vahtera J, Kivimaki M, Pentti J. Effect of organizational downsizing on health of employees. Lancet. 1997;350:1124–1128. [PubMed: 9343499]
  200. Vaux A. Social and emotional loneliness: The role of social and personal characteristics. Personality and Social Psychology Bulletin. 1988;14:722–734.
  201. Vogt TM, Mullooly JP, Ernst D, Pope CR, Hollis JF. Social networks as predictors of ischemic heart disease, cancer, stroke, and hypertension: incidence, survival and mortality. Journal of Clinical Epidemiology. 1992;45:659–666. [PubMed: 1607905]
  202. Walker KN, MacBride A, Vachon MLS. Social support networks and the crisis of bereavement. Social Science and Medicine. 1977;11:35–41. [PubMed: 887956]
  203. Weber M. Class, status, party. In: Mills CW, editor; Gerth H, translator. Max Weber: Essays in Sociology. New York: Oxford University Press; 1946. pp. 180–195.
  204. Welin L, Tibblin G, Svardsudd K, Tibblin B, Ander-Peciva S, Larsson B, Wilhelmsen L. Prospective study of social influences on mortality: The study of men born in 1913 and 1923. Lancet. 1985;1(8434):915–918. [PubMed: 2858755]
  205. Westin S. The structure of a factory closure: Individual responses to job loss and unemployment in a 10-year controlled follow-up study. Social Science and Medicine. 1990;31:1301–1311. [PubMed: 2287959]
  206. Westin S, Norum D, Schlesselman J. Medical consequences of a factory closure: Illness and disability in a four-year follow-up study. International Journal of Epidemiology. 1988;17:153–161. [PubMed: 2968325]
  207. Westin S, Schlesselman J, Korper M. Long-term effects of a factory closure: unemployment and disability during ten years' follow-up. Journal of Clinical Epidemiology. 1989;42:435–441. [PubMed: 2732771]
  208. Wilkinson RG. Income distribution and life expectancy. British Medical Journal. 1992;304:165–168. [PMC free article: PMC1881178] [PubMed: 1637372]
  209. Wilkinson RG. Unhealthy Societies: The Afflictions of Inequality. London: Routledge; 1996.
  210. Wilkinson RG, editor. Class And Health: Research and Longitudinal Data. London: Tavistock; 1986.
  211. Williams R, Barefoot J, Califf R, Haney T, Saunders W, Pryor D, Hlatky MA, Siegler IC, Mark DB. Prognostic importance of social and economic resources among medically treated patients with angiographically documented coronary artery disease. Journal of the American Medical Association. 1992;267:520–524. [PubMed: 1729574]
  212. Wolff E. Top Heavy: A Study of the Increasing Inequality of Wealth in America. New York: 20th Century Fund; 1995.



Relative risk is the proportion of diseased people among those exposed to the relevant risk factor divided by the proportion of diseased people among those not exposed to the risk factor.


Standardized mortality ratio is the ratio of the observed to the expected number of deaths multiplied by 100.


Gross national product per capita is the dollar value of a country's yearly output of goods and services divided by its population. It reflects the average income of a country's citizens.


The Gini Coefficient is a measure of inequality of a frequency distribution calculated from the ratio of the area between the Lorenz curve and the 45-degree line and the area above the 45-degree line. The Lorenz curve plots the cumulative percentage of a population against the cumulative percentage of a variable such as income. A straight line indicates perfect equality and would have a Gini coefficient of zero.


The odds ratio is a comparison of the presence of risk factors for a disease in a sample of diseased subjects and non diseased controls; the number of people with disease who were exposed to a risk factor (Ie) over those with disease who were not exposed (Io) divided by those without diseases who were exposed (Ne) over those without who were not exposed (No): (Ie/Io)/(Ne/No).

Copyright © 2001, National Academy of Sciences.
Bookshelf ID: NBK43750


  • PubReader
  • Print View
  • Cite this Page
  • PDF version of this title (5.9M)

Related information

  • PMC
    PubMed Central citations
  • PubMed
    Links to PubMed

Recent Activity

Your browsing activity is empty.

Activity recording is turned off.

Turn recording back on

See more...