A POPULATION PERSPECTIVE

For nations to improve the health of their populations, some have cogently argued, they need to move beyond clinical interventions with high-risk groups. This concept was best articulated by Rose (1992), who noted that “medical thinking has been largely concerned with the needs of sick individuals.” Although this reflects an important mission for medicine and health care, it is a limited one that does little to prevent people from becoming sick in the first place, and it typically has disregarded issues related to disparities in access to and quality of preventive and treatment services. Personal health care is only one, and perhaps the least powerful, of several types of determinants of health, among which are also included genetic, behavioral, social, and environmental factors (IOM, 2000; McGinnis et al., 2002). To modify these, the nation and the intersectoral public health system must identify and exploit the full potential of new options and strategies for health policy and action.

Three realities are central to the development of effective population-based prevention strategies. First, disease risk is currently conceived of as a continuum rather than a dichotomy. There is no clear division between risk for disease and no risk for disease with regard to levels of blood pressure, cholesterol, alcohol consumption, tobacco consumption, physical activity, diet and weight, lead exposure, and other risk factors. In fact, recommended cutoff points for management or treatment of many of these risk factors have changed dramatically and in a downward direction over time (e.g., guidelines for control of “hypertension” and cholesterol), in acknowledgment of the increased risk associated with common moderately elevated levels of a given risk factor. This continuum of risk is also apparent for many social and environmental conditions as well (e.g., socioeconomic status, social isolation, work stress, and environmental exposures). Any population model of prevention should be built on the recognition that there are degrees of risk rather than just two extremes of exposure (i.e., risk and no risk).

The second reality is that most often only a small percentage of any population is at the extremes of high or low risk. The majority of people fall in the middle of the distribution of risk. Rose (1981, 1992) observed that exposure of a large number of people to a small risk can yield a more absolute number of cases of a condition than exposure of a small number of people to a high risk. This relationship argues for the development of strategies that focus on the modification of risk for the entire population rather than for specific high-risk individuals. Rose (1981) termed the preventive approach the “prevention paradox” because it brings large benefits to the community but offers little to each participating individual. In other words, such strategies would move the entire distribution of risk to lower levels to achieve maximal population gains.

The third reality, provided by Rose's (1992) population perspective, is that an individual's risk of illness cannot be considered in isolation from the disease risk for the population to which he or she belongs. Thus, someone in the United States is more likely to die prematurely from a heart attack than someone living in Japan, because the population distribution of high cholesterol in the United States as a whole is higher than the distribution in Japan (i.e., on a graph of the distribution of cholesterol levels in a population, the U.S. mean is shifted to the right of the Japanese mean). Applying the population perspective to a health measure means asking why a population has the existing distribution of a particular risk, in addition to asking why a particular individual got sick (Rose, 1992). This is critical, because the greatest improvements in a population's health are likely to derive from interventions based on the first question. Because the majority of cases of illness arise within the bulk of the population outside the extremes of risk, prevention strategies must be applicable to a broad base of the population. American society experienced this approach to disease prevention and health promotion in the early twentieth century, when measures were taken to promote sanitation and food and water safety (CDC, 1999b), and in more recent policies on seat belt use, unleaded gasoline, vaccination, and water fluoridation, some of which are discussed later in this chapter.

The committee recognizes that achieving the goal of improving population health requires balancing of the strategies aimed at shifting the distribution of risk with other approaches. The committee does, however, endorse a much wider examination, and ultimately the development, of new population-based strategies. Three graphs illustrate different models for risk reduction (see Figure 2–1).

FIGURE 2–1

Models for risk reduction. SOURCE: Data for current distribution from Schwartz and Woloshin, 1999.

These hypothetical models assume etiological links exist among all exposures and disease outcomes. Figure 2–1a shows the effects of an intervention aimed at reducing the risk of those in the highest-risk category. In this example, people with the highest body mass index (BMI)¹ are at in creased risk for cardiovascular heart disease and a plethora of chronic illnesses. Intervening medically, for example, to decrease risk (by lowering levels of obesity, as measured by BMI) ultimately decreases the proportion of the population with the highest BMIs. Such measures among very high-risk individuals may even be endorsed in cases where the “intervention” itself carries a substantial risk of poor outcome or side effects. However, use of such an intervention would be acceptable only in those whose medical risk was very high. Moreover, interventions in high-risk groups may have a limited effect on population outcomes because the greater proportion of those with moderate risk levels may ultimately translate into more chronic disease or other poor health outcomes.

Figure 2–1b illustrates Rose's classic model whereby the greatest benefit is achieved by shifting the entire distribution of risk to a lower level of risk. Because most people are in categories of moderately elevated risk as opposed to very high risk, this strategy offers the greatest benefit in terms of population-attributable risk, assuming that the intervention itself carries little or no risk. The hypothetical example shows what might occur if social policies or other population-wide measures were adopted to promote small decreases in weight in the general population. The committee embraces this kind of model of disease prevention in the case of policies such as seat belt regulation and the reduction of lead levels in gasoline.

The final hypothetical model (Figure 2–1c), although not discussed by Rose explicitly, illustrates a reduction in the distributions of those at highest and lowest risk with no change in the distribution of those with a mean level of risk. This model is appropriate for illustrating phenomena relating to inequality, where redistribution of some good (e.g., income, education, housing, or health care) reduces inequality without necessarily changing the mean of the distribution of that good. One hypothetical example is the association between low income and poor health. In many cases, there is a curvilinear association between these goods and health outcomes, with decreased health gains experienced by those at the upper bounds of the distribution. For example, data on income suggest that there are large differences in the health gains achieved per dollar earned for those at the lower end of the income distribution and fewer differences in the health gains achieved per dollar earned for those at the upper end. Thus, the curvilinear association, if it were a causal one, would suggest that substantial gains in population-level health outcomes may be achieved by a redistribution of some resources without actual changes in the means.

These graphs help to illustrate three different strategies for improving the health of the population. The nation has often endorsed the first strategy without a critical examination of the other two, especially the second one. The American public has grown accustomed to seeing differences in exposures to risk, both environmental and behavioral, and disparities in health outcomes. Acknowledging these gradients fully will help develop true population-based intervention strategies and help the partners who collaborate to assure the public's health move to take effective actions and make effective policies.

Understanding and ultimately improving a population's health rest not only on understanding this population perspective but also on understanding the ecology of health and the interconnectedness of the biological, behavioral, physical, and socioenvironmental domains. In some ways, conventional public health models (e.g., the agent–host–environment triad) have long emphasized an ecological understanding of disease prevention. Enormous gains in the control and eradication of infectious diseases rested upon a deep understanding of the ecology of specific agents and the power of environmental interventions rather than individual or behavioral interventions to control disease. For example, in areas where sanitation and water purification are poor, individual behaviors, such as hand washing and boiling of water, are emphasized to reduce the spread of disease. However, when environmental controls become feasible, it is easy to move to a more “upstream”² intervention (like municipal water purification) to improve health. The last several decades of research have resulted in a deeper understanding not only of the physical dimensions of the environment that are toxic but also of a broad range of related conditions in the social environment that are factors in creating poor health. These social determinants challenge the discipline of public health to more fully incorporate them.

Over the past decade, several models have been developed to illustrate the determinants of health and the ecological nature of health (e.g., see Dahlgren and Whitehead [1991], Evans and Stoddart [1990], and Appendix A). Many of these models have been developed in the United Kingdom, Canada, and Scandinavia, where population approaches have started to shape governmental and public health policies. The committee has built on the Dahlgren-Whitehead model—which also guided the Independent Inquiry into Inequalities in Health in the United Kingdom—modifying it to reflect special issues of relevance in the United States (see Figure 2–2). This figure serves as a useful heuristic to help us think about the multiple determinants of population health. It may, for instance, help to illustrate how the health sector, which includes governmental public health agencies and the health care delivery system, must work with other sectors of government such as education, labor, economic development, and agriculture to create “healthy” public policy. Furthermore, the governmental sector needs to work in partnership with nongovernmental sectors such as academia, the media, business, community-based organizations and communities themselves to create the intersectoral model of the public health system first alluded to in the 1988 Institute of Medicine (IOM) report and established in this report as critical to effective health action.

FIGURE 2–2

A guide to thinking about the determinants of population health. NOTES: Adapted from Dahlgren and Whitehead, 1991. The dotted lines between levels of the model denote interaction effects between and among the various levels of health determinants (Worthman, (more...)

Most models of health determinants identify macro-level conditions and policies (social, economic, cultural, and environmental) as potent forces in shaping midlevel (working conditions, housing) and proximate (behavioral, biological) determinants of health. Macro-level or upstream determinants (such as policies and societal norms) and micro-level determinants (such as sex or the virulence of a disease agent) interact along complex and dynamic pathways to produce health at a population level. As mentioned above, exposures at the environmental level may have a greater influence on population health than individual vulnerabilities, although at an individual level, personal characteristics including genetic predispositions interact with the environment to produce disease. For instance, smoking is a complex biobehavioral activity with both significant genetic heritability and nongenetic, environmental influences, and many studies have shown an interaction between smoking and specific genes in determining the risk of developing cardiovascular disease and cancers. It is also important to note that developmental and historical conditions change over time at both a societal level (e.g., demographic changes) and an individual level (e.g., life course issues) and that disease itself evolves as agents change in virulence.

In the pages that follow, the committee provides a concise discussion of the key determinants that constitute the ecology of health, including environmental and social determinants, and elaborates in more detail on the social influences on health. This decision was made in recognition of a longer history in studying the ways in which environment shapes population health.

THE PHYSICAL ENVIRONMENT AS A DETERMINANT OF HEALTH

At least since the time of Hippocrates' essay “Air, Water and Places,” written in 400 B.C.E., humans have been aware of the many connections between health and the environment. Improved water, food, and milk sanitation, reduced physical crowding, improved nutrition, and central heating with cleaner fuels were the developments most responsible for the great advances in public health achieved during the twentieth century. These advantages of a developed nation are taken for granted, but in fact, they could deteriorate without adequate support of the governmental public health infrastructure.

Environmental health problems, historically local in their effects and short in duration, have changed dramatically within the last 25 years. Today's problems are also persistent and global. Together, global warming, population growth, habitat destruction, loss of green space, and resource depletion have produced a widely acknowledged environmental crisis (NRC, 1999). These long-term environmental problems are not amenable to quick technical fixes, and their resolution will require community and societal engagement. At the local and community levels, environmental issues are equally complex and are also related to a range of socioeconomic factors. A brief look at some of the evidence on environmental determinants of health may help shed some light on why health is not equally shared.

The importance of “place” to health status became increasingly clear in the last decades of the twentieth century. The places in which people work and live have an enormous impact on their health. The characteristics of place include the social and economic environments, as well as the natural environment (e.g., air, water) and the built environment, which may include transportation, buildings, green spaces, roads, and other infrastructure (IOM, 2001b). Environmental hazards in workplaces and communities may range from tobacco smoke to pesticides to toxic housing. Rural areas may present increased health risks from pesticides and other environmental exposures, whereas some environmental threats to health can occur because of urban living conditions.

More than three-quarters of Americans live in urban areas (Bureau of the Census, 1993). Although rural Americans experience certain health-related disadvantages (e.g., health care access issues due to transportation and availability) (Slifkin et al., 2000; NCHS, 2001), some of the health effects of the inner city (i.e., decay and crime) are often dramatic and may be related to broader social issues. The “urban health penalty”—the “greater prevalence of a large number of health problems and risk factors in cities than in suburbs and rural areas” (Leviton et al., 2000: 863)—has been frequently discussed and studied (Lawrence, 1999; Freudenberg, 2000; Geronimus, 2000). A variety of political, socioeconomic, and environmental factors shape the health status of cities and their residents by influencing “health behaviors such as exercise, diet, sexual behavior, alcohol and substance use” (Freudenberg, 2000: 837). The negative environmental aspects of urban living—toxic buildings, proximity to industrial parks, and a lack of parks or green spaces, among others—likely affect those who are already at an economic and social disadvantage because of the concentration of such negative aspects in specific pockets of poverty and deprivation (Lawrence, 1999; Maantay, 2001; Williams and Collins, 2001). Urban dwellers may experience higher levels of air pollution, which is associated with higher levels of cardiovascular and respiratory disease (Hoek et al., 2001; Ibald-Mulli et al., 2001; Peters et al., 2001). People who live in aging buildings and in crowded and unsanitary conditions may also experience increased levels of lead in their blood, as well as asthma and allergies (Pertowski, 1994; Pew Environmental Health Commission, 2000; CDC, 2001a). These examples illustrate some of the profound effects of the physical environment on health. The places where people live may expose them to harmful factors.

THE SOCIAL DETERMINANTS OF HEALTH

Most recently, social epidemiologists and other researchers have focused on identifying the social equivalents of leaded gasoline and environmental tobacco smoke. Among the greatest advances in understanding the factors that shape population health over the last two decades, and clearly since the last Institute of Medicine (IOM, 1988) report on the health of the public, has been the identification of social and behavioral conditions that influence morbidity, mortality, and functioning.

The evidence amassed strongly and consistently points to the importance of these conditions as significant determinants of population health. Because they also feature prominently in the committee's determinants-of-health model, the evidence related to four conditions whose importance is robustly supported is reviewed here: (1) socioeconomic position, (2) race and ethnicity, (3) social networks and social support, and (4) work conditions. Additionally, we discuss the evidence related to a fifth condition that has been and that still is the subject of great interest as well as controversy: ecological-level influences, namely, economic inequality and social capital.³ The present analysis reviews key evidence related to these five conditions that has been presented more extensively in Health and Behavior (IOM, 2001).

Socioeconomic Status and Health

A strong and consistent finding of epidemiological 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 in recent decades, these observations have been replicated using various indicators of socioeconomic status (SES) and multiple disease outcomes (Syme and Berkman, 1976; Kaplan and Keil, 1993). SES is defined in terms of education, income, and occupation. Furthermore, educational differentials in mortality have increased in the United States over the past three decades, leading to a growing inequality, even though mortality rates have dropped for all groups (Feldman et al., 1989; Pappas et al., 1993; Tyroler et al., 1993).

Although it may be measured as level of education or income, SES is a complex phenomenon often based on indicators of relationships to work (occupational position or ranking), social class or status, and access to power. From a policy perspective as well as an etiological perspective, it is important to understand which of the components is critical—for instance, if education is found to be important, the policies that may be implemented would differ from the policies needed if income was found to be the most influential factor. In fact, most research has not tested such competing hypotheses directly, so in the examples that follow, these have not been disaggregated, although the indicators used in each study are explicitly identified.

Several major studies have ascertained that education, income, and occupation, as indicators of SES, are associated with mortality and with mortality due to certain causes. The National Longitudinal Mortality Study found that mortality was strongly associated with all three measures of SES (Rogot et al., 1992; Sorlie et al., 1992, 1995) (see Box 2–1).

BOX 2–1

Linking SES to Health: Findings from the National Longitudinal Mortality Study. Age-adjusted death rates for white men and women ages 25 to 64 with 0 to 4 total years of education that were 66 and 44 percent higher, respectively, than those for men and (more...)

The Multiple Risk Factor Intervention Trial followed 320,909 white and African-American men for 16 years (Davey Smith et al., 1996a, 1996b) and found that the median family income in one's zip code of residence was predictive of death from a variety of causes. Heart disease, the leading cause of death in the United States, provides a strong example of the association between SES and mortality. Research has documented the relationship between SES and cardiovascular disease (NCHS, 1992; Kaplan and Keil, 1993), and the British Whitehall longitudinal study of civil servants found that those in the lowest grades of employment were at the highest risk for heart disease (Marmot et al., 1991).

A striking finding that emerges from analyses of occupation- and area-based income measures is the graded and continuous nature of the association between socioeconomic position and mortality, with differences persisting well into the middle socioeconomic ranges (Davey Smith et al., 1990; Blane et al., 1997; Macintyre et al., 1998). For example, in the Whitehall studies (Davey Smith et al., 1990; Marmot et al., 1991), the individuals in each employment grade had worse health and a higher rate of mortality than those in the grade above.

Although many of the studies that focused on occupation-, education-, or area-level SES showed a gradient that is virtually linear, studies that focus on income often show somewhat different results. For example, in work by Backlund and colleagues (1996), the association between (increasing) income and (decreasing) mortality is clearly curvilinear, with the decline in the mortality rate with increasing income greatest among those in groups earning less than $25,000 per year but with the decline with increasing income being much less among those earning between $25,000 and $60,000 per year. This curvilinear relationship suggests diminishing returns of income as one approaches the highest income categories, although some association may persist. This curvilinear association between income and health is what lays the framework for findings that more egalitarian societies (i.e., those with a less steep differential between the richest and the poorest) have better average health, because a dollar at the bottom “buys” more health than a dollar at the top. Whether SES has a linear or curvilinear relationship with health has enormous implications for understanding both the etiologic associations and the policy implications of this research. In either case, however, it is important to note that a “threshold” model focused exclusively on the very poorest segments and ignoring others near the bottom and the working poor will not address the relatively poor population health outcomes for the U.S. population as a whole. The major reason for this is because there are groups in the moderate-risk categories of working poor and working class who contribute disproportionately large numbers to death rates and poor health outcomes.

SES is linked to health status through multiple pathways (such as distribution of health care, psychosocial condition, toxic physical environments, and health-related behaviors), but these relationships have not yet been fully elucidated. It is also likely that some degree of reverse causation influences the strength of these associations. Studies in which education rather than income or occupation is used as an indicator of SES are stronger in this regard since most people are not influenced by serious chronic diseases related to cardiovascular disease, stroke, or cancer in ways that inhibit their level of educational attainment in their adolescence and early twenties. Furthermore, although many studies have included a broad range of covariates in their multivariable analyses, it is of course possible that unobserved attributes account for some observed disparities. There is ample evidence that SES is strongly related to access to and the quality of preventive care, ambulatory care, and high-technology procedures (Kaplan and Keil, 1993); but health care appears to account for a small percentage of the variation in health status among different SES groups. It has been argued that differential access to health care programs and services is not entirely responsible for socioeconomic differentials in health (Wilkinson, 1996), because causes of death that apparently are not amenable to medical care show socioeconomic gradients similar to those for potentially treatable causes (Mackenbach et al., 1989; Davey Smith et al., 1996a). Furthermore, similar gradients persist in countries with universal coverage, such as the United Kingdom.

Despite the past century's great advances in sanitation, which have contributed to the sharp increase in life expectancy observed among all socioeconomic groups, the socioeconomic gradient in health status persists. It has been proposed, and to some extent documented, that the gap in health status by SES may still be attributable to the effects of crowded and unsanitary housing, air and water pollution, environmental toxins, an inadequate food supply, poor working conditions, and other such deficits that have historically affected and that still disproportionately affect those in the lower socioeconomic strata (USPHS, 1979; Williams, 1990; Adler et al., 1994; Sargent et al., 1995; McLoyd, 1998). Studies that incorporate assessments of material deprivation and aspects of the physical environment will be important to explicate these important potential pathways.

Considerable evidence links low SES to adverse psychosocial conditions. People in lower socioeconomic positions are not only more materially disadvantaged, but also have higher levels of job and financial insecurity; experience more unemployment, work injuries, lack of control, and other social and environmental stressors; report fewer social supports; and more frequently, have a cynically hostile or fatalistic outlook (Berkman and Syme, 1979; Karasek and Theorell, 1990; Adler et al., 1994; Heaney et al., 1994; Bosma et al., 1997).

There is most often, especially in the United States, a striking and consistent association between SES and risk-related health behaviors such as cigarette smoking, physical inactivity, a less nutritious diet, and heavy alcohol consumption. This patterned behavioral response has led Link and Phelan (1995) to speak of situations that place people “at risk of risks.” Understanding why “poor people behave poorly” (Lynch et al., 1997) requires recognition that specific behaviors formerly attributed exclusively to individual choice have been found to be influenced by the 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 denying opportunities to engage in particular behaviors; and reducing or producing stress, for which engaging in specific behaviors (such as smoking) might be an effective short-term coping strategy (Berkman and Kawachi, 2000). Both physical and social environments place constraints on individual choice. Over time, those with more economic and social resources have tended to adopt health-promoting behaviors and reduce risky behaviors at a faster rate than those with fewer economic resources.

Socioeconomic disparities in health in the United States are large, are persistent, and appear to be increasing over recent decades, despite the general improvements in many health outcomes. The most advantaged American men and women experience levels of longevity that are the highest in the world. However, less advantaged groups experience levels of health comparable to those of average men and women in developing nations of Africa and Asia or to Americans about half a century ago (Berkman and Lochner, 2002). Furthermore, these wide disparities coupled with the large numbers of people in these least-advantaged groups contribute to the low overall health ranking of the United States among developed, industrialized nations. A major opportunity for us to improve the health of the U.S. population rests on our capacity to either reduce the numbers of the most disadvantaged men, women, and children in the highest risk categories or to reduce their risks for poor health.

Ecological-Level Influences: The Importance of Place in Population Health

Social characteristics of individuals are closely related to health. Among the most important findings to emerge from public health research over recent years is the extent to which characteristics of areas exert independent effects on health. This ecological⁴ approach has been rediscovered and is now embedded in a multilevel framework. The major idea is that characteristics of places—neighborhoods, schools, work sites, and even nations— carry with them health risks for the individuals who live in those environments. The health risk conferred by these places is above and beyond the risk that individuals carry with them. Thus, we might view characteristics of physical environments (e.g., parks and buildings) as well as social environments (e.g., levels of inequality and civic trust) as truly properties of places, not individuals. In this section, the committee reviews evidence related to two aspects of places—economic inequality and social capital— that are assessed at an ecological level to examine their effects on health. These findings are relatively new and undoubtedly will be refined with further research. Economic inequality may exert an effect on health in addition to the effect of individual income on health. Such an effect may be particularly robust for people in the United States who are at the lower ends of the distribution.

The United States is among the richest countries in the world, yet it is also one of the most (and increasingly) unequal in terms of the distribution of its wealth as measured by a wide and growing gap between the best-off and the worst-off quintiles (Weinberg, 1996; Jencks, 2002) (see Box 2–2). 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, with the country with the more even distribution having a longer life expectancy overall. Cross-national studies initially supported an association between income equality and population longevity, but more recent research, which includes newer and more accurate data for more countries, suggests that the area-level effects of inequality across nations may not hold over time (Lynch et al., 2001; Gravelle et al., 2002; Rodgers, 2002). Recent studies have shown the cross-national correlation between economic inequality and mortality to be very weak or virtually nonexistent (Kunst et al., 1998). Furthermore, in several countries (Canada, for example), inequalities at the level of provinces or neighborhoods within cities often have been found to be not significant in terms of health status. In the United States, however, data are more consistent in supporting the area-level effect of inequality net of individual effect. For example, Kaplan and colleagues (1996) and Kennedy and colleagues (1996) independently found that the degree of household income inequality in the 50 states was associated with the state-level variation in total mortality, as well as with the state-level variations in infant mortality and rates of death from coronary heart disease, cancer, and homicide. The findings persisted after controlling for urban–rural proportion and for health behavior variables such as cigarette smoking rates.

BOX 2–2

Income Inequality in the United States. In 1968, the wealthiest 20 percent of U.S. households earned an average of $73,754, whereas the poorest 20 percent of households earned $7,202 (Atkinson et al., 1995). In 1994, the inflation-adjusted average income (more...)

Lynch and colleagues (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 explained entirely by the fact that places that exhibit income inequality have greater concentrations of poor people, who in turn have a higher risk of mortality (compositional effects). There is also evidence of a contextual effect of income inequality directly on individual health (Wilkinson, 1992; Kennedy et al., 1998; Soobader and LeClere, 1999). Kennedy and colleagues (1998) reported that people residing in states with the greatest income inequality were 1.25 times more likely to report being in 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 levels.

These findings pose the challenge of explaining why the effects of inequality are more significant and conclusive in the United States than in other developed nations. Some (Kawachi and Kennedy, 1997) have argued that inequality is associated with a lack of investment in education, development, and social services and is also related to weak civic and social bonds—or a lack of trust—between people (Wilkinson, 1996; Kawachi et al., 1997; Kawachi and Berkman, 2000). Some countries buffer the effects of inequality with stronger social service programs. Investigators have argued that U.S. analyses have not adequately considered other state-level or country-level social and demographic factors (e.g., racial composition) that may not be “downstream” in the causal chain linking inequality to health (Deaton and Lubotsky, 2001). These questions remain challenges to a new field. However, it is important to note that these studies are all examining the contextual or area-level effects of inequality, net of individual or “compositional” effects. No one has disputed the strong and consistent effects of SES on individual health. New research on area-level efforts related to neighborhoods, work sites, and states and even across countries poses considerable methodological challenges (Deaton, 2002). Nonetheless, such research holds great potential to help us understand the ways in which both the social and the physical (built and natural) environments may affect health and behavior.

Social participation and integration can also be conceived of as both individual and societal characteristics (Kawachi and Kennedy, 1997). Some investigators have started to conceptualize these dimensions at an ecological or group level. At the group level, a socially cohesive society, or one in which most citizens are socially integrated, 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. This has led investigators to examine the area-level effects of these domains. Particular interest has been focused on the relationship between social capital and health. At a group level, more socially integrated societies seem to have lower rates of crime, suicide, and mortality from all causes and a better overall quality of life (Wilkinson, 1996; Kawachi and Kennedy, 1997; Kawachi and Berkman, 2000).

Kawachi and colleagues (1997) analyzed social capital indicators across the United States in relation to state-level death rates. The per capita density of membership in voluntary groups was inversely correlated with age-adjusted mortality from all causes. Density of civic association, group membership, and levels of interpersonal trust (i.e., percentage of citizens endorsing the expectation that altruistic behaviors will be returned in kind at some future time) were also associated with lower mortality. Kawachi and colleagues (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 being overweight, and on sociodemographic characteristics, such as household income, education, and whether one lived alone. Even after adjustment for these variables, people residing in states with low levels of social capital were more likely to report fair or poor health. The odds ratio for fair or poor health in association with living in areas with the lowest levels of interpersonal trust 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. At the neighborhood level, social capital might influence health behaviors by promoting the more rapid diffusion of health information. Sampson and colleagues (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. Neighborhood social capital also could affect health by increasing access to local services and amenities (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.

Although there has been a great deal of interest in these area-level studies of social capital, there has also been a fair amount of skepticism regarding their validity. Several social scientists (Portes and Landolt, 1996; Sandefur and Laumann, 1998; Durlauf, 1999) have voiced concerns about the ambiguity of the concept, the potential for social capital to lead to undesirable outcomes related to the exclusion of certain groups, and insufficient attention to the determinants of social capital itself or the causal patterning between it and other social conditions. Future studies will be strengthened with the addition of items tapping the conceptual richness of the domain of social capital and the capacity to distinguish it from other closely related constructs of social networks and SES.

POPULATION-LEVEL PREVENTIVE INTERVENTIONS

The evidence presented and discussed in this chapter aims to demonstrate that taking into account the environmental and social determinants of health is essential to creating effective population-level interventions for health improvement. Health risk is related to a complex of social, economic, and political factors that both surpass and powerfully interact with “downstream” elements such as individual behaviors, biological traits, and access to health care services. There have been few empirical tests of population-based approaches to health promotion that focused on risk-related social conditions, but in an effort to understand how such approaches might work, several examples are presented to illustrate the effectiveness and efficiency (e.g., cost) of population-based interventions to prevent disease and promote health.

Preventive interventions at the population level may be classified as universal, selective, and indicated, borrowing the classification developed by Gordon (IOM, 1994b). A universal measure is one that would be desirable for everyone in an eligible population. It would focus on shifting the entire population distribution rather than on targeting only relatively high-risk individuals, as illustrated earlier in this chapter. It would likely involve an agreed-on public policy requiring broad-based public understanding and political support. A selective preventive measure is one that is desirable only when an individual is a member of a subgroup of the population whose risk of becoming ill is above average. These are the more traditional population-oriented public health education interventions targeted toward the high-risk segments of the population. Finally, an indicated preventive measure is one that is applicable to persons who, on examination, manifest a risk factor, condition, or abnormality that identifies them individually as being at high risk for the future development of a disease. This type of intervention, usually provided in the context of clinical practice, deals only with individuals diagnosed with a disease, not with the nameless statistical subset of a population as in selective preventive measures. For example, a universal preventive measure for heart disease could include the provision of general advice to consume a diet low in fat accompanied by a regulatory policy requiring food labeling. A selective intervention could include a program focusing on diet and behavioral changes for overweight individuals who do not exercise regularly, and an indicated preventive measure might include antihypertensive medication for those diagnosed as hypertensive.

Although many studies have looked at the effectiveness of preventive measures, few have studied universal, population-level strategies. In some cases, however, such as tobacco use prevention and automobile-related injury prevention population-based strategies (e.g., laws) have been used successfully, largely because of recognition of the broad determinants of health. Results of these interventions indicate that, at least in some cases, a population-level strategy or, to use Gordon's classification, a universal measure may be more optimal and cost-effective than interventions targeted further downstream (i.e., at the individual level). Acting on the most upstream level of determinants of health typically means the level of national policy. This may help shift national norms and values that lead to the passage, adoption, and ultimately, success of the respective legislation, as in the case of seat belt legislation, which has steadily and gradually normalized this behavior across America, or tobacco policy, which has curbed the use of tobacco (e.g., through changes in the social landscape of outdoor advertising and sanctions on smoking in the workplace and public places such as restaurants). Alternately, upstream policy interventions may also refer to modifying the broader, social determinants of health such as income (e.g., through the provision of earned income tax credits and minimum wage increases), education, and social connectedness.

Policy making at the national, state, and local levels has the potential to positively shape population health by addressing specific elements of the determinants of health, such as inadequate housing, unavailability of family-friendly social and work policies, lack of public transportation, lack of safe public spaces, and so forth. The health consequences of contaminated water and leaded gasoline have been well elaborated. It is now time to determine their social equivalents—elements of the social environment that influence health status—and take action to shape them in support of population health. Such action may focus not only on education, decent housing, and a living wage but also on the political choices that move the broad (social and other) determinants of health in a positive direction. For example, certain health care disparities (e.g., disparities in access and quality) are created in part by political choices and by allowing public and private insurance programs to limit coverage for preventive health care and for conditions related to mental health, substance abuse, and oral health.

THE PUBLIC HEALTH SYSTEM IN ACTION: A SCENARIO

In this section, the committee uses the specific scenario of a risk to population health—namely, obesity—to present and discuss the contributions that communities, the health care delivery system, employers and business, the media, academia, and the governmental public health infrastructure can make to improve health. Food and eating are integral to the life of individuals and communities. Families, friends, and neighbors gather around meals; and both the process of eating and the food itself are heavily imbued with cultural, social, and even emotional meaning. This helps explain why eating and nutrition perfectly exemplify the influence of multiple determinants on health. The development of obesity itself is influenced by multiple determinants of health, from the genetic to the social and environmental, and the public health partners must consider these many dimensions in formulating their responses.

Obesity: Magnitude and Future Trends

The problems of overweight and obesity in America have reached epidemic proportions and threaten the health and quality of life of millions of adults and children. According to CDC's 1999 National Health and Nutri tion Examination Survey, more than 61 percent of adults are either overweight or obese and at least 13 percent of children ages 6 to 11 and 14 percent of adolescents ages 12 to 19 are overweight. The prevalence of obesity among adults has grown by nearly 20 percent over the past 30 years, and the number of children who are overweight has tripled in the same period (see Figure 2–3) (CDC, 2001a).

FIGURE 2–3

Obesity trends among U.S. adults: Behavioral Risk Factor Surveillance System (BRFSS), 1991, 1995, 2000. NOTE: Obesity is BMI ≥ 30, or –30 lbs overweight for 5′4″ woman.

Obesity is a growing concern because it poses a higher risk and results in a higher incidence of health conditions such as diabetes, cardiovascular disease, stroke hypertension, osteoarthritis, and certain cancers than other risk factors (NIH, 1998; Allison et al., 1999; Must et al., 1999; Williamson, 1999; Tataranni and Bogardus, 2001; Tuomilehto et al., 2001).

The human and economic costs are impossible to ignore. Every year an estimated 300,000 U.S. adults die of causes that may be attributed to obesity; in addition, others suffer from chronic disease and an impaired quality of life (Allison et al., 1999; Mokdad et al., 2000). According to another estimate, as many as 309,000 to 582,000 deaths in 1990 were associated with poor diet and inadequate physical activity (McGinnis and Foege, 1993). Should the number of overweight and obese Americans continue to grow at its current rate, obesity will surpass tobacco as the most preventable cause of death and illness in the United States. Additionally, the estimated direct and indirect costs associated with obesity are $100 billion annually (Wolf and Colditz, 1998), and this figure does not take into consideration the cost of treating the uninsured or the personal impact of obesity on quality of life.

The fact that the prevalence and incidence of type II diabetes mellitus (see Figure 2–4) have increased exemplifies the association between weight and health. The rise in the rate of obesity during the past decade has been paralleled by a 25 percent increase in the rate of type II diabetes (Harris et al., 1998). The rapid and large rate of increase in obesity among children is especially alarming, given that childhood obesity is clearly associated with obesity in adulthood and subsequent health problems. For instance, until recently, type I diabetes was the most prevalent type of diabetes among children. However, new studies have shown that the rate of type II diabetes is increasing dramatically and that 85 percent of children with type II diabetes are either overweight or obese (ADA, 2000). Approximately 63 percent of the total direct costs associated with obesity are related to type II diabetes (Wolf and Colditz, 1998).

FIGURE 2–4

Diabetes and Gestational Diabetes trends among adults in the United States: BRFSS 1990, 1995, and 1999. SOURCES: CDC (www.cdc.gov/diabetes/pubs/glance.htm) and Mokdad et al. (2000).

Although the prevalence and growth of obesity affect all segments of the population, there are notable trends among certain segments. Men are more likely to be overweight, whereas women are more likely to be obese. Obesity also increases with age until the mid-60s; this may be partly because Americans over the age of 65 are more likely to be malnourished. The prevalence of obesity among African Americans and Hispanics, especially women, is significantly higher than that among other populations. People with lower education and income levels also have increased rates of obesity, as do people living in the southeastern region of the United States (CDC, 2000).

The causes of obesity are both complex and multifaceted. Although genetic factors do play a role in determining whether a person has a propensity for obesity, nutritional, behavioral, and environmental factors are more significant (Hill and Peters, 1998). Americans are consuming more calories, eating higher-calorie foods, and exercising less than ever before. Work is more likely to be sedentary than in the past; and more time is spent watching television, playing video games, or using computers. High-calorie food choices are widely available and easily affordable as more Americans turn to fast food and prepared meals on a daily basis. Furthermore, food portions have become “supersized,” surpassing the Department of Agriculture and Department of Health and Human Services (DHHS) recommended dietary guidelines for how much food Americans should consume each day. At the same time, the number of physical education programs in schools is declining, and people in some communities lack access to affordable and nutritional food options and safe areas in which to exercise (Hill and Peters, 1998; CDC, 1999a; Crespo et al., 2001; Dietz, 2001; French et al., 2001).

The association between obesity and socioeconomic factors illustrates the value of a population perspective and the need to consider the multiple determinants of health in addressing population health improvement.

CONCLUDING OBSERVATIONS

In the previous sections, the committee has outlined what population health means in terms of understanding not only the risks of various exposures to individuals but also the importance of incorporating concepts related to the distribution of risk in populations. This chapter has outlined a way to understand how social, environmental, and biological conditions shape population health. Population-based interventions, such as tobacco control policies, show that it is possible to implement cost-effective strategies that benefit society as a whole and improve the health of many segments of the population. Population health improvements will have to focus attention on both overall improvement in the nation's health and reduction of the disparities in health, as recommended by Healthy People 2010. To achieve this goal, the nation will have to develop innovative strategies for interventions for a broad set of health determinants. Many of the determinants of health are part of the broad economic and social context and, thus, beyond the direct control of administrators in public- and private-sector health care organizations. Action on these strategies at the national level will require an alignment of public policy in the agriculture, commerce, education, and treasury sectors of government, among others, to promote health. This includes providing resources to support the population-based research necessary to further our understanding of the social etiology of disease and disability. Efforts to curb tobacco use have a 50-year history from the appearance of the first surgeon general's report. There is now a high level of consensus and political will to act. Effective public health systems must be vehicles to accelerate such efforts to save thousands of lives that will otherwise be lost. The special role of the governmental public health infrastructure is to advocate for and educate others about the evidence to support such policy actions and to assure that the public health system—the diverse array of individuals and organizations that must act together for health at the community level—is both committed to and equipped for implementation of a coordinated set of strategies to attain the highest levels of health for the nation. The next chapters of this report explore the potential role of each component of the public health system in achieving this goal.

Assurance is one of the three core public health functions put forth by the 1988 IOM report The Future of Public Health. The special role of the governmental public health infrastructure in this mission will be that of steward, facilitator, and supporter rather than actor in every situation, because assuring a healthy nation cannot be accomplished through a single plan of action or through the efforts of a single governmental agency or sector of the economy. Instead, it will require a coordinated set of strategies that must be implemented by all Americans—as individuals, families and community members, businesses and workers, and citizens. With each sector of society assuming a portion of the responsibility for improving the health of the nation, it becomes increasingly possible to achieve the nation's true potential for a population with excellent health and the fewest possible disparities.

The chapters that follow discuss several partners within a redefined public health system that has as its backbone the governmental public health infrastructure but that includes the resources, perspectives, and actions of other stakeholders who are partners in the public health system. Each of these partners can increase the relevance of its contributions to population health by considering the multiple determinants of health, especially those that contribute to unequal opportunities for good health.

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Footnotes

1

Body mass index is a measure of body fat based on height and weight (kilograms divided by meters squared, kg/m²). A person with a BMI of between 18.5 and 24.9 would be considered of normal weight, whereas a person with a BMI of between 25 and 29.9 would be considered overweight, and someone with a BMI of 30 or greater would be classified as obese. BMIs above normal are associated with an increased risk of morbidity and mortality. A person's BMI is influenced by genes, behavior, the environment, and interactions among these factors.

2

Upstream refers to determinants of health that are somewhat removed from the more “downstream” biological and behavioral bases for disease. Such upstream determinants include “social relations, neighborhoods and communities, institutions, and social and economic policies” (IOM, 2000).

3

“Social capital is defined as the resources available to individuals and to society through social relationships,” ranging from material to psychosocial resources (Kawachi et al., 2002).

4

“Ecological” refers to the ecology or the combined characteristics (e.g., the social and economic characteristics) of places.