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D Overview of Health Disparities Nancy E. Adler, Ph.D. University of California, San Francisco This paper examines conceptual approaches and current data on health dis- parities in the United States. The concept of health disparities requires some discussion before looking at the data. Health disparities are more than simply differences in health. The fact that some individuals or groups die sooner, or experience a disease more severely, than others is a necessary yet insufficient condition to establish a disparity. As Braveman and Gruskin (2003) noted, the fact that young people are healthier than the elderly is not an unfair difference. A disparity implies that the difference is inequitable and unjust (Carter-Pokras and Baquet, 2002). To determine whether a difference is unjust, one criterion is to question whether that difference is avoidable or immutable. Some definitions question whether the difference is detrimental to groups that are already disad- vantaged, in opportunity or resources. No consensus exists on the definition of health disparities (which are also referred to as health inequalities) or how to measure them. Carter-Pokras and Baquet (2002) noted that definitions of health disparities depend on "who is deciding what is avoidable and unjust and how it is decided." They identified 11 current definitions of disparities and categorized them into three general ap- proaches. Some compare populations based on minority status, asking whether the health of minorities differs from nonminorities. Others compare the health of specific groups with that of the overall population, asking whether a given group Background paper prepared for the Institute of Medicine's Committee on the Review and Assess- ment of the National Institute of Health's (NIH's) Strategic Research Plan to Reduce and Ultimately Eliminate Health Disparities. 121

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122 EXAMINING THE HEALTH DISPARITIES RESEARCH PLAN OF THE NIH has poorer health than the population at large. The third approach is to compare specific groups, asking whether Group X has poorer health than Group Y. Three of the 11 definitions address differences in both health and health care. A markedly different approach to health disparities is to start with an ob- served difference on health indicators and then establish whether this difference constitutes a disparity (i.e., whether it is inequitable or unjust). For example, Murray, Michaud, McKenna, and Marks (1998) reported marked differences in life expectancy within the United States. They identified more than a 40-year gap in life expectancy between the shortest-lived group (Native American and Alas- kan Native males in six counties in South Dakota) and the longest-lived group (Asian American females in Bergen County, New Jersey). At first glance, this approach appears to be purely empirical. However, researchers choose which demographic or spatial characteristics to monitor based on either available data or pre-existing theories or expectations of which groups or places may experience poorer health. Braveman, Starfield, and Geiger (2001) were critical of an ap- proach that simply examined health extremes, without including a comparison of social groups that experience social disadvantage. They argued that although examining extremes in health may provide a good starting point, these additional analyses will be key to understanding disparities. To adequately understand health disparities, researchers need valid and con- sistent measurement of disparities and the variables that shape them. Researchers located in different regions of the world have different traditions in choosing a metric to measure disparities. Occupational level is the most common indepen- dent variable in the United Kingdom, while education or occupation dominates in other European countries, and race/ethnicity is the most common variable in the United States (Murray et al., 1999). The choice of variables examined must be explicitly linked to models or theories of disparities. For example, although the shortest- and longest-lived groups described above differ by gender, ethnicity, and place of residence, they also are likely to differ in education, income, and other factors. The difference in longevity may be due to particular variables and/ or their interactions; some variables also may be markers for other factors that have a more direct causal link. The choice of variables to examine may also be affected by what is considered to be unjust. Just as a consensual definition of disparities remains elusive, so does a shared definition of health. There is no single, summative measure of the state of an individual's health, other than longevity. Length of life is clearly quantifiable. However, even mortality has its limitations as a measure. First, although people would generally prefer to live as long as possible, quality of life also matters. As a result, many researchers use lifespan weighted by quality or disability (quality- adjusted life years or disability-adjusted life years), particularly in doing cost- benefit analyses of various health policies or treatments. These measures, too, are limited. No summative measure is currently available that captures the World

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APPENDIX D 123 Health Organization (1948) definition of health as "a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity." Second, mortality as the end point poses challenges to conducting research that can identify the mechanisms by which disparities operate. Some experiences associated with disadvantages that affect longevity occur in early life. To estab- lish causal effects on mortality, one needs prospective cohort studies such as the British cohort studies of children born in 1946 and in 1958 or the planned U.S. birth cohort study. Even with such cohorts, no single study can capture all the processes involved in health disparities. To conduct more timely research, inter- mediate indicators of health are needed. Third, mortality is a function of multiple factors, including vulnerability and exposure to disease or injury and the quality of diagnosis and treatment, each of which may show different patterns of inequality. For example, the incidence of breast cancer is higher among women with more education and income. How- ever, among women with breast cancer, survival is longer for patients of higher socioeconomic status (SES). Mortality from breast cancer will reflect both of these associations. Studying only disparities in overall mortality will mask the two components of mortality (incidence and survival). Fourth, different diseases, and causes of death, have distinct patterns of disparities. Some diseases (e.g., sickle cell anemia in African Americans) have a strong genetic component, whereas differences in the prevalence of other dis- eases are likely due more directly to social disadvantage. A variety of diseases may share a common pathway. One striking finding is that health disparities can be observed across a wide range of diseases that have different etiologic risk factors. However, specific aspects of disadvantage, and associated mechanisms, have been implicated in some diseases but not others. Adequately addressing health disparities will require identifying both common pathways to multiple diseases and disease-specific mechanisms. An Empirical View Some insight into how researchers are approaching and defining health dis- parities can be gained by examining the types of published studies that use relevant terms. The term health disparity has only recently come into common use. Table D-1 shows the increase in the number of articles published on health disparities as a key term. While only 1 article with this term emerged from a PubMed search of articles from 1985 to 1989 and only 11 and 18 articles in the next two 5-year time periods, respectively, 439 such articles were published from 2000 to 2004. The term health inequalities came into usage slightly earlier (3 articles for 19801984, 11 for 19851989, 34 for 19901994, and 86 for 1995 1999), but in the past 5 years, use of the term health disparities appears to have become more popular. This may partly reflect growing research in the United

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124 EXAMINING THE HEALTH DISPARITIES RESEARCH PLAN OF THE NIH TABLE D-1 Number of Articles Appearing in Medical Literature with Key Term Health Disparity or Health Inequality 19801984 19851989 19901994 19951999 20002004 Health disparity 0 1 11 18 439 Health inequality 3 11 34 86 380 States, where health disparities is more commonly used; researchers in Europe and Great Britain more frequently report on health inequalities. The increase may also reflect the adoption of the term health disparities by the National Institutes of Health (NIH) for this domain of work. The kinds of disparities or inequalities that are being examined can be seen in other key words associated with health disparities. As discussed earlier, there are many definitions for health disparities and the groups or variables being compared. The term health disparities is sometimes used synonymously with racial and ethnic disparities, though most definitions of health disparities include education, income, and geographic location. Table D-2 presents the number of papers published from 2000 to 2004 that use the term health disparities as a key word, along with the terms race, ethnicity, SES, or components of these (e.g., African American or black, Asian, Hispanic or Latino, occupation, education, income) as well as gender or sex and rural. This provides a rough indicator of which aspect of health disparities researchers are examining. As can be seen in Table D-2, relatively few papers use the term SES in relation to health disparities (n = 25), but substantially more report on the specific components of SES, for example, income (n = 56) and education (n = 104). Fifty- seven articles during this time period report on health disparities in conjunction TABLE D-2 Number of Articles Published from 2000 2004 on Health Disparities and Specific Variables Health Disparities and: Socioeconomic status 25 Income 56 Education 104 Occupation 3 Race 57 Ethnicity 35 African American or black 61 Asian 22 Hispanic or Latino 69 Native American or American Indian 17 Rural 21 Gender/sex 50

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APPENDIX D 125 with race and 35 with ethnicity, with comparable numbers reporting health dis- parities associated with specific groups: African Americans (n = 61), Asians (n = 22), Hispanics or Latinos (n = 69), and Native Americans or American Indians (n = 17). A small number of articles report on health disparities and rural health (n = 21) and somewhat more on sex or gender and health disparities (n = 50). There may be some overlap among these categories, but these data provide a rough order of magnitude of the studies and trends in using the term health disparity. Importantly, the data suggest no single category dominates the empiri- cal work being reported on health disparities. This snapshot of key words reveals a spread of papers reporting on disparities associated with SES and its compo- nents, race/ethnicity, gender, and rural health. The parallel snapshot looking at health inequalities is a bit different. As noted earlier, studies using this term are more likely to come from Europe, particularly Great Britain, where researchers have focused more on health differ- ences associated with socioeconomic factors, rather than with racial and ethnic factors. As a result, relatively more papers report on health inequalities in con- junction with SES, rather than with race or ethnicity. Table D-2 reports only on the number of papers using the term health dis- parities in relation to SES and race/ethnicity. A much larger literature on the association of these sociodemographic factors and health exists, though it does not explicitly identify these factors in key words as a health disparity. Paralleling the marked increase in research on health disparities, the number of articles reporting on sociodemographic factors and health (without using health disparities as a key word) has increased exponentially, as seen in Table D-3. Articles on SES and health increased from 337 in 19751979 to nearly 1900 in 20002004. Articles on race and health, or ethnicity and health, increased from 182 and 35, respectively, in 19751979, to 4172 and 2913, respectively, in 2000 2004. The largest category by far is education and health, but a number of these articles may be reporting on health education and not necessarily on the associa- tion of educational attainment and health. The pattern of increase, however, is similar to the other categories, and some part of the growth in publications re- flects increasing research on the health effects of education, outside of specific health education. There is also a substantial literature on sex/gender and health and on rural health. Defining Health Disparity Groups The Minority Health and Health Disparities Research and Education Act of 2000 defines health disparity populations (or groups) as those for which "there is a significant disparity in the overall rate of disease incidence, prevalence, mor- bidity, mortality or survival rates." As discussed earlier, which group is identified as being a disparity group will differ depending on which of the above health indicators is used. For example, men could be viewed as a disparity group relative

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126 EXAMINING THE HEALTH DISPARITIES RESEARCH PLAN OF THE NIH TABLE D-3 Number of Articles Appearing in Medical Literature on Sociodemographic Factors and Health (Without Using Health Disparities as a Key Word) 1975 1980 1985 1990 1995 2000 Topic 1979 1984 1989 1994 1999 2004 Socioeconomic status and health 337 734 1189 1657 1801 1898 Race and health 182 316 678 1661 2750 4172 Ethnicity and health 35 82 167 594 1468 2913 Income and health 850 1148 1946 3351 5149 6491 Education and health 13,333 12,971 17,658 27,733 37,827 44,989 Occupation and health 116 211 446 777 989 1263 African American or black and health 322 498 1105 2746 4284 6290 Asian and health 76 178 373 766 1482 2674 Hispanic or Latino and health 86 207 448 1205 1885 2994 Native American and health 5 10 31 99 224 265 American Indian and health 25 6 48 124 225 345 Rural and health 3067 3186 3822 6723 8340 9892 Sex or gender and health 2199 2935 5836 12,840 21,117 30,201 to women if mortality rates are used, but women would be seen as a disparity group if some measures of morbidity are used. Similarly, differences in disease incidence or prevalence depend on the disease examined. Some differences be- tween men and women derive from biology: There is a greater prevalence of breast cancer among women than men. Only women experience cervical or ova- rian cancer; only men experience prostate cancer. Differences in these disease rates do not fit the definition of a disparity because they are unavoidable. When examining other diseases, the question of whether a difference in prevalence represents a disparity becomes more complex. For example, women have a lower prevalence of cardiovascular disease than men. It can be debated whether this difference represents a disparity. The female advantage may reflect the protective effects of female hormones--a biological difference that is not modifiable by social policy. However, the extent of the male-female difference could also be due to modifiable conditions that reflect social disadvantage. It is possible that greater hardships faced by women as the result of discrimination in the workplace, exposure to sexual harassment and abuse, and so forth lessen the biological advantage they might otherwise enjoy. To the extent that this is the case, the disparity is the reduction in the "natural" female advantage. Others might argue that men face greater lifetime stresses than women and that the difference in cardiovascular diseases represents some combination of an un- avoidable biological difference and a modifiable difference. However, given that, on average, men have greater social advantages than women, this would not fit the definition of a health disparity, if one assumes that only avoidable differences experienced by disadvantaged groups qualify as a disparity.

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APPENDIX D 127 Lung cancer is an example of a disease whose patterns have grown more similar between women and men, but one would not see this as a reduction in disparities. Men once had a relatively greater prevalence of lung cancer, com- pared with women, than they do today. The change in the relative prevalence reflects changes in rates of smoking among men and women: Over time, as women's rate of smoking has increased, so has their rate of lung cancer. The difference in the prevalence of smoking, and the resulting difference in lung cancer rates, meet the criterion of an avoidable difference. However, the per- ceived fairness of this change depends upon one's explanation for the greater rates of smoking among men than women in the 20th century. Ironically, the greater social equality of women may have provided more opportunity for women to take on male risk factors, such as smoking. The example of gender suggests that a further definition of health disparities may be useful. Even under ideal social and environmental conditions, there will be differences in rates of some diseases and in longevity due to genetic, and other biological, factors. Both individuals and groups may differ in vulnerability to specific diseases, due to this variation. Eventually, medical treatments for genetic risk may equalize individuals' capacities for a healthy lifespan. If so, failures to reach the same end point could be considered a disparity because anything less would be potentially avoidable and inequitable. Thus, disparities could be de- fined as the extent to which individuals, or segments of the population, fail to achieve their highest potential state of health, at a given age, given currently available medical treatments. Current Approaches to Disparity Groups: Race/Ethnicity Several definitions of health disparities equate disparities with differences among racial and ethnic groups. The NIH Strategic Plan Volume 1 (2002, pp. 19 20) presents data on health among several selected populations. These data are reproduced in Table D-4. They show marked differences in such diverse health indicators as infant mortality, cancer mortality, coronary heart disease mortality, and the prevalence of diabetes, end-stage renal disease, and stroke. More recent data are available from Health, United States, 2004 (National Center for Health Statistics, 2004), recently released by the Centers for Disease Control and Pre- vention (CDC). Table D-5 presents the overall death rate, as well as death rates for the two leading causes of death--heart disease and malignant neoplasms. Table D-6 presents data on infant deaths. There are two clear observations that can be made about the data pre- sented in both of these tables. One is that African Americans show more adverse health outcomes on each one of the indicators. They have the greatest morbidity and mortality on every reported indicator, and the gap is often substantial. For example, compared with Asians or Pacific Islanders who experience 4.8 deaths for every 1,000 live births, African Americans experi-

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128 EXAMINING THE HEALTH DISPARITIES RESEARCH PLAN OF THE NIH TABLE D-4 Health Disparities of Certain Conditions in Selected Populations Index in Selected Populations American Asian or Indian or Health Condition and African Hispanic Pacific Alaska Specific Example White American or Latino Islander Native Infant mortality rate, 5.9 13.9 5.8 5.1 9.1 per 1,000 live births Cancer mortality rate, 199.3 255.1 123.7 124.2 129.3 per 100,000 Lung cancer, age- 38.3 46.0 13.6 17.2 25.1 adjusted death rate Female breast cancer, 18.7 26.1 12.1 9.8 10.3 age-adjusted death rate Coronary heart disease 206 252 145 123 126 mortality, rate per 100,000 Stroke mortality, rate 58 80 39 51 38 per 100,000 Diabetes diagnosed, 36 74 61 DSU DSU rate per 100,000 End-stage renal disease, 218 873 DNA 344 589 rate per 1,000,000 NOTE: DSU, data are statistically unreliable; DNA, data have not been analyzed. SOURCE: National Institutes of Health, 2002. TABLE D-5 Death Rates per 100,000 by Race/Ethnicity, 2002 Non- African American Hispanic White American Indian Asian Hispanic White All causes 829 1083.3 677.4 474.4 629.3 837.5 Heart disease 236.7 308.4 157.4 134.6 180.5 239.2 Malignant neoplasm 191.7 238.8 125.4 113.6 128.4 195.6 SOURCE: National Center for Health Statistics, 2004. ence 13.6 deaths. The next highest group, American Indians or Alaskan Na- tives, have a rate of 8.9 deaths. The second observation is that no other group shows consistently poor health outcomes across indicators. Whites show poorer outcomes than groups other than African Americans on most of the reported health indicators (e.g., overall cancer

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APPENDIX D 129 TABLE D-6 Infant Deaths per 1,000 Live Births, 20002002, Overall by Education Race/Ethnicity Overall White 5.7 Non-Hispanic white 5.7 Black 13.5 Non-Hispanic black 13.6 American Indian/Alaskan Native 8.9 Asian/Pacific Islander 4.8 Chinese 3.2 Japanese 4.5 Filipino 5.7 Hawaiian 8.7 Other 4.8 Hispanic or Latino 5.5 Mexican 5.4 Puerto Rican 8.3 Cuban 4.2 Central/South American 4.9 Other 6.7 SOURCE: National Center for Health Statistics, 2004, Tables 19 and 20. mortality as well as death rate for breast and lung cancer, coronary heart disease and stroke mortality, and prevalence of AIDS). American Indians or Alaskan Natives have the second highest rates of infant mortality, and Hispanics or Latinos have the second highest prevalence of diabetes. Asian Americans or Pacific Islanders show the most favorable profile. They experience the lowest rates of infant mortality, overall cancer mortality and death from lung and breast cancer, and coronary heart disease mortality. They have a markedly lower prevalence of AIDS than any group other than Native Americans/Alaskan Natives. They show intermediate rates of stroke mortality and end-stage renal disease. One problem with the conclusions reached above is that they are based on large groupings by race and ethnicity. These broad categories may mask substan- tial variation in health within some of the groups. Members of the same ethnic group from different countries and areas of origin have different degrees of disadvantage and health risk. For example, as shown in Table D-6, Asians/Pacific Islanders as a group have the lowest rate of infant deaths (4.8 per 1,000 births) compared with other groups. However, this masks substantial variation among Asians and Pacific Islanders. The rate for Hawaiians (8.7) is more than double that of Chinese (3.2), with intermediate rates shown by Japanese (4.5) and Filipi- nos (5.7). A similarly large span in outcomes is shown among Hispanics and Latinos. As a group, they show the second lowest rates of infant deaths (5.5 per

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130 EXAMINING THE HEALTH DISPARITIES RESEARCH PLAN OF THE NIH 1,000 births). Within this category, however, Puerto Ricans experience 8.3 deaths, whereas Cubans experience 4.2 deaths per 1,000, with intermediate rates shown by Central and South Americans and by Mexicans. Within both whites and blacks, removal of Hispanics has little impact on rates, probably because Hispanics make up a relatively small proportion of the larger group. Recent analyses reported by Zsembik and Fennell (2005) using National Health Interview data from 1997 to 2001 compared a number of medical conditions, functional impairment, and overall self-rated health for Mexicans, Puerto Ricans, Cubans, and Dominicans in the United States along with blacks and whites. The pattern of health advantage depended, in part, on the health outcome examined; however, overall, Mexicans reported better health outcomes than others, while Puerto Ricans reported poorer outcomes. The relative position of Cubans and Dominicans differed by outcome. Data from Palaniappan, Wang, and Fortmann (2004) also show variation in disparities when examining subgroups in relation to specific diseases. They examined rates of death from coronary heart disease and from all causes broken down into more precise subgroups of Asians. Although Asian Indians had the lowest rates of all-cause mortality, as can be seen in Table D-7, they had rela- tively high rates of coronary heart disease compared with other Asian groups. Among blacks as well, subgroups vary substantially. Fang, Madhavan and Alderman (1996) reported significant differences in the rates of mortality from cardiovascular disease among blacks born in different parts of the United States or in the Caribbean. Mortality rates among blacks residing in New York City were markedly higher than among those residing in the South, intermedi- ate among those born in the Northeast, and lowest among those born in the Caribbean. These data illustrate the importance of looking at subgroups within large ethnic categories. However, it is often difficult to obtain adequate data to evaluate health disparities in these subgroups because of their relatively small numbers. This becomes even more acute when studying smaller populations, such as those from specific countries or ethnic groups. For example, Yang, Mills, and Riordan TABLE D-7 Mortality Ratios for Coronary Heart Disease (CHD) and All- Cause Mortality in California, 19962000 Men Women All Causes CHD All Causes CHD Non-Hispanic white 107 111 109 107 Non-Hispanic black 156 124 155 160 Hispanic 75 67 70 74 Chinese 58 48 56 47 Japanese 64 60 59 46 Asian Indian 53 92 59 97 SOURCE: Palaniappan et al., 2004.

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APPENDIX D 131 (2004) reported markedly higher incidence rates and mortality from cervical cancer among Hmong women than among other Asians/Pacific Islanders, and Cho and Hummer (2001) reported substantial variations in disability status among subpopulations of Asians/Pacific Islanders, with the Hmong, Laotians, and Cam- bodians showing the poorest outcomes. These groups differ by SES, as well. For example, within the Asian immigrant group, more than 60 percent of those from India or Taiwan are college graduates, compared with roughly 5 percent of those from Cambodia or Laos (Rumbaut, 1996). Even within a given group, subpopu- lations may experience greater disadvantage and poorer health (e.g., Native Americans living on reservations versus other Native Americans). Further complicating ethnic group differences in health, health status ap- pears to vary by length of time in the United States. First-generation immigrants appear to have a health advantage across virtually every group (Singh and Miller, 2004). This may be due, in part, to the healthy immigrant effect, in which there is differential selection for those who have the characteristics (including better health) that allows them to immigrate to the United States (Thomas and Karagas, 1996). It may also reflect protective effects of traditional diets, supportive social networks, or other health practices of first-generation immigrants. Supporting this view, Eschbach, Ostir, Patel, Markides, and Goodwin (2004) report lower mortality among older Mexican Americans living in neighborhoods with a high density of Mexican Americans. They attributed this difference to the protective effects of the concentration, which may buffer Mexican Americans from "un- healthful aspects of U.S. culture" (Eschbach et al., 2004, p. 1810). Finally, as shown in recent analyses by Williams (2005), the extent of dis- parities also varies depending on the measure used. Disparities will differ not only between different diseases, but also within mortality rates, depending on the measure. For example, Williams showed greater disparities between African Americans and whites when age-specific comparisons were made, rather than age-adjusted comparisons. Looking at age-specific rates also shows differences that occur only at some points across the lifespan. The approach to disparities suggested earlier--which frames disparity as the gap between current health status and biologically feasible health--suggests a strategy of using the group with the best health outcome as the comparison group. This group presumably represents the highest achievable outcome under current social and health care conditions, though one would need to evaluate potential genetic factors. Research could then be directed to understanding the other fac- tors responsible for the gap between the optimal outcome and the groups with the poorest outcomes. These may be disease-specific mechanisms. At the same time, the large, persistent, and consistent disadvantage suffered by African Americans across diseases suggests that some common mechanisms systematically affect this group's health. It also suggests that more attention should be paid to cross- cutting factors that systematically affect African Americans' health. Potential factors are described below.

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164 EXAMINING THE HEALTH DISPARITIES RESEARCH PLAN OF THE NIH research in reducing health disparities. They suggest at least five research consid- erations. First, each model depicts the importance of considering multiple levels of influence on health. Second, individuals, families, and communities are em- bedded in these multiple levels. This means that examination of only one level in isolation will be less successful in developing successful interventions and poli- cies to eliminate disparities. Third, there is temporal continuity across levels and the life course. Fourth, there is intergenerational transmission of social and health capital. This means that individuals, communities, and populations carry the accumulated results of the balance of resources and stressors experienced at the multiple levels across time. Fifth, there are direct and indirect effects of variables (e.g., socioeconomic, racial/ethnic, gender, etc.), as well as interaction effects. All the models described above, and the literature on which they are based, share a common perspective: health-enhancing opportunities and health- damaging exposures are socially patterned, with that patterning influenced by SES, race, class, and the roles associated with institutions such as the family, educational institutions, and occupational settings. The accumulated impact of multiple physical and social influences, starting during gestation, affects not only birth outcomes and childhood health but also adult morbidity and mortality. For example, most studies evaluating timing of exposure have found that child- hood socioeconomic circumstances have an inverse relationship with cardiovas- cular morbidity or mortality, independent of subsequent adult social position, thus suggesting that some underlying causes of cardiovascular disease may strike early in life (Davey Smith et al., 1997; Gliksman et al., 1995; Hasle, 1990; Kaplan and Salonen, 1990; Lynch et al., 1994; Vagero and Leon, 1994; Wannamethee et al., 1996). Remaining Issues This review provides one overview of the vast data on health disparities. Different perspectives undoubtedly highlight different issues. This review dem- onstrated that the term health disparities is not being used in any single way. The papers on health disparities encompass research on socioeconomic factors, race and ethnicity, sex/gender, and rural health. The data point to the difficulty in specifying specific disparity groups. In examining a range of reports on overall mortality and on the prevalence of specific diseases, including the data used in the NIH Health Disparities Strategic Plan, Fiscal Years 20042008, the only racial/ethnic group that shows consistently poorer health across a range of indica- tors is African Americans. A caveat, however, is that the available data may not provide a full and accurate estimate of disparities. For some groups, for example, ethnicity may not be accurately captured in mortality data or in surveys, and this may lead to an undercounting of deaths or disease prevalence in these groups. There may be biases introduced for specific populations (e.g., a Mexican Ameri- can health advantage may be due, in part, to the return of those who are ill or

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APPENDIX D 165 dying to Mexico). Also, as noted earlier, the identification of race and ethnicity into broad categories (e.g., Asian) may miss specific groups (e.g., Hmong) whose health status is markedly worse. This argues for greater attention paid to the nature of the data and the sampling being used to establish the degree of health disparities. The data on disparities among other groups shows some consistency, al- though here, too, finer differentiations could yield more informative findings. Health in rural areas appears to be poorer than in more populated areas, though finer differentiators may be helpful in examining suburban versus urban areas. Health is also worse for those who are poorer and less educated, as well as those in low-SES occupations, and these factors account for much, but not all, of the health disadvantage experienced by different racial and ethnic groups and those in rural areas. Policies to eliminate health disparities need to be informed by scientific understanding of their causes. The empirical and conceptual approaches to date have revealed that the poorer health of African Americans is largely, but not wholly, accounted for by socioeconomic disadvantage. This raises questions. What accounts for the remaining effect? One candidate is exposure to discrimina- tion and racism, which may increase stress responses with their attendant health effects. Another candidate is inadequate measurement of socioeconomic disad- vantage and the implications of SES for a range of environmental exposures. A second question: What aspects of socioeconomic disadvantage contribute to health disparities (for those that account for racial/ethnic disparities as well as those that operate for members of all racial and ethnic groups)? SES includes various aspects, each of which confers different resources and has different im- plications for health. In addition to individual-level factors (e.g., income, educa- tion, wealth/assets, occupation), both race/ethnicity and SES shape the area of residence and work environments, each of which has an additional effect on health. For example, residential segregation of African Americans has resulted in areas of concentrated poverty that have health-damaging effects. At the same time, recent research suggests that for Latinos, the barrio effect of greater ethnic density may be health-protective, despite the greater poverty in these areas (Eschbach et al., 2004). Within the work environment, physical conditions may contribute to the risk of injury or disease, as does the social organization of work and particularly the degree of control over demands (Bosma et al., 1997). It should be noted that virtually all research on health disparities shows associations but does not establish causality. The challenges of establishing cau- sality differ for various sociodemographic variables. Race/ethnicity and sex are determined at birth, and it is not plausible that these are affected by their own health status. With regard to socioeconomic factors, however, mutual causation between SES and health is possible, especially for income. When people become ill, they not only incur medical expenses but may also be less able to work. Smith (1999) demonstrated the adverse effect of poor health on income among partici-

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166 EXAMINING THE HEALTH DISPARITIES RESEARCH PLAN OF THE NIH pants in the Health and Retirement Study. In this sample of older adults, the most important factor in diminished income and wealth was early retirement due to health problems. Reverse causation is less plausible for education; disease later in life does not change earlier educational attainment. However, the life course models presented above suggest that health disparities early in life may, in turn, affect educational attainment that could both limit later SES and affect adult health. This suggests a reciprocal causal chain between SES and health, but one in which the socioeconomic factors are likely to be more fundamental (Link and Phelan, 1995). There are some innovative approaches to establishing causal direction by forming links to experimental programs in which specific aspects of social disad- vantage are reduced, and the health impact can be examined. For example, sev- eral Central and Latin American countries are embarking on anti-poverty pro- grams that may yield health benefits. The PROGRESA project (now called OPORTUNIDADES) in Mexico has shown in a randomized social experiment that income supplementation tied to incentives for health-promoting behaviors-- such as using prenatal care and pediatric check-ups and additional cash incentives to keep one's children in school--has resulted in improved growth and decreased anemia in children (Gertler, 2004). Programs in other countries may tease apart the beneficial effects of income supplementation versus income supplementation linked to behavioral incentives. In the United States, the Moving to Opportunity program called for randomized housing-project residents to receive a voucher to allow them to move elsewhere only to a low-poverty area, or to a control condi- tion with no voucher. Both children and adults randomized to the low-poverty neighborhood condition subsequently showed better mental health outcomes but not other health outcomes; there were more favorable outcomes for girls than for boys (Kling et al., 2004). Other social experiments have not explicitly examined health effects but could be used to do so. For example, a few early childhood education programs, such as the Perry Preschool, had a randomized design that showed economic and social benefits for the children randomized to the experimental condition (Barnett, 1996; Reynolds et al., 2001). However, long-term health effects of enriched early education have not yet been demonstrated. Social experiments such as those described above require collaboration across sectors and links between health research and housing, education, labor, and so forth. With the exception of massive national programs like PROGRESA, these will necessarily be on a small scale because they are expensive to implement. In addition, there will be a continuing need for sophisticated and creative approaches to examining causal effects in the context of observational studies, which will likely comprise the bulk of research. In this work, longitudinal studies will be important to help establish temporal ordering, as well as cross-sectional studies to provide initial evidence of associations and identify possible mediators. If the National Children's Study is launched, it will be critical to have adequate mea-

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APPENDIX D 167 sures of sociodemographic factors at each time point of data collection and measures of the psychosocial and environmental factors likely to shape health disparities in this population. In such research, explicit models of health disparities should be specified. This will guide not only the selection of independent and mediating variables but also the health outcome to be studied. The latter may include mortality rates, life expectancy at birth or at different ages, the incidence or prevalence of specific diseases, functional status, and/or self-rated health. One of the questions in such research is whether to examine single diseases or multiple outcomes. Under- standing disease-specific pathways is useful for delineating pathophysiological processes. However, data showing similar disparities across a range of diseases suggest that there may be some common pathways to multiple health outcomes. Some of the models and research reviewed above propose that exposure to stress is one such common pathway. Recent research linking greater stress to cell aging (Epel et al., 2004) provides some evidence that chronic stress may, indeed, lead to a type of accelerated aging that can increase risk for a number of diseases. Risk factors such as tobacco use and obesity (which are more common in more disad- vantaged groups) may also serve as a common risk factor, as may environmental exposures. The linkage across diseases points to the need for greater cooperation across NIH institutes in supporting disparities research. At the same time that research is needed on common pathways to multiple outcomes, some mechanisms may be unique to specific diseases. Not every dis- ease shows the same associations with race/ethnicity, SES, etc. For some diseases, such as breast cancer and malignant melanoma, the usual SES gradient is reversed; these diseases are actually more common among more advantaged groups. One unexplained finding is why African Americans show more adverse outcomes in relation to physical health but often show lower rates of mental illness. The pattern of associations with SES and race/ethnicity can also vary for different stages of disease. For example, higher-SES women are more likely to be diagnosed with breast cancer than women who are less well educated or affluent; this is a real difference in rates of onset and not simply due to better diagnosis. However, once diagnosed, higher-SES women have a greater length of survival, even when controlling for the stage of disease at diagnosis. Thus, it may be useful to look at predictors of different components of mortality associated with a given disease and take into account disparities in incidence and survival. More common than reversals in associations is the finding that the degree of disparities varies for different diseases. For example, the SES gradient is steeper for cardiovascular disease than for many cancers. Within cancer, the gradient is steeper for cervical cancer than for other types of cancer. As researchers identify disease-specific pathways that may account for disparities, they may also learn much by compar- ing the nature and degree of disparities across diseases. Finally, new approaches to measuring health outcomes also exist. Social disadvantage has a pervasive

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168 EXAMINING THE HEALTH DISPARITIES RESEARCH PLAN OF THE NIH impact on a variety of risk factors and diseases. In addition to identifying com- mon pathways, it would be helpful to have a valid measure of health capital, a summative measure of the overall health and functioning of individuals that could be aggregated to assess the health stock of groups. This would require operationalizing the World Health Organization (1948) definition of health. There are early efforts to do so, primarily by health services researchers who have developed instruments such as the 36-Item Short-Form Health Survey (SF-36). The next generation of measures should be done with an eye to their applicability to evaluating health disparities. Collaboration between NIH and the Agency for Healthcare Research and Quality (AHRQ), which uses such measures more fre- quently, may also prove beneficial. Implications of This Analysis for the NIH Research Agenda Researchers would find it difficult to implement the idea that the healthiest group be taken as the standard against which other groups could be evaluated, in terms of the extent of their health disparity. However, this strategy may be worth discussing. It shares common ground with that suggested by Murray et al. (1999), but it incorporates a focus on groups that may address the concern raised by Braveman et al. (2001) that the health status of disadvantaged groups could be overlooked. Such an approach may have the potential to stimulate novel research and provide information on the strengths of groups that could help inform others (e.g., understanding the Hispanic paradox may provide clues to health-protective social and cultural processes). The data presented in this paper underline the importance of collaboration across NIH institutes, because health disparities cross-cut multiple diseases and populations. These data also suggest that a strategy based on disparity groups is not as likely to be fruitful as one based on disparity processes. Specifically, understanding the interrelationships and interactions among different sources of social disadvantage (which includes race/ethnicity, SES, gender, and area of residence) will provide a fuller explanation of the mechanisms by which dispari- ties occur. The existing data suggest that socioeconomic disadvantage is a key pathway by which racial/ethnic disparities emerge. At the same time, African Americans show poorer health outcomes even when SES is adjusted for. There may be more impact from research on socioeconomic disadvantage because it is the more powerful effect and is more amenable to intervention. However, it is also important to understand what it is about the experiences of African Ameri- cans that places them at heightened risk above and beyond that associated with their socioeconomic position. This review makes clear the importance of encom- passing the measurement of race/ethnicity, SES, and gender in research. To achieve the dual goals of Healthy People 2010 (U.S. Department of Health and Human Services, 2000), we will need more research--and, impor- tantly, more sophisticated research--on understanding the pathways by which

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APPENDIX D 169 health disparities are created. This work will be facilitated by greater inclusion of appropriate measures of SES as well as race and ethnicity in national data sets and public health monitoring measures in addition to gender and area of resi- dence. Additionally, to the extent possible, measures of psychosocial and behav- ioral variables that are likely to mediate these effects should be included. Strate- gies that involve the measurement of risk factors and preclinical indicators of disease states will be particularly important, as these may provide information on common underlying pathways to multiple diseases, as well as information on disease-specific risk states that can suggest strategies for earlier intervention. The examination of common pathways to multiple diseases underlines the impor- tance of coordinating health disparities research across the NIH institutes, as well as the AHRQ. REFERENCES Backlund E, Sorlie PD, Johnson NJ. 1999. A comparison of the relationships of education and income with mortality: The national longitudinal mortality study. Social Science and Medicine 49:13731384. Barnett S. 1996. Lives in the Balance: Age-27 Benefit-Cost Analysis of the High/Scope Perry Pre- school Program. Monographs of the High/Scope Educational Research Foundation, Number 11, Ypsilanti, Michigan: High/Scope Press. Baum A, Garofalo JP, Yali AM. 1999. Socioeconomic status and chronic stress: Does stress account for SES effects on health? In, Adler N, Marmot M, McEwen BS, Stewart J, eds. Socioeconomic Status and Health in Industrial Nations. Vol. 896. New York: Annals of the New York Acad- emy of Sciences. Pp. 131144. Berkman LF, Glass TA. 2000. Social integration, social networks, social support, and health. In, Berkman LF, Kawachi I, eds. Social Epidemiology. New York: Oxford University Press. Pp. 137173. Bosma H, Marmot MG, Hemingway H, Nicholson AG, Brunner E, Stansfeld A. 1997. Low job control and risk of coronary heart disease in Whitehall II (prospective cohort) study. British Medical Journal 314:558565. Braveman P, Gruskin S. 2003. Defining equality in health. Journal of Epidemiology and Community Health 54:254258. Braveman P, Starfield B, Geiger HJ. 2001. World Health Report 2000: How it removes equity from the agenda for public health monitoring and policy. British Medical Journal 323(7314): 678681. Britten N. 1981. Models of intergenerational class mobility: Findings from the National Survey of Health and Development. British Journal of Sociology 32:224238. Brunner E, Marmot M. 1999. Social organization, stress and health. In, Marmot M, Wilkinson RG, eds. Social Determinants of Health. Oxford, UK: Oxford University Press. Pp. 1743. Carter-Pokras O, Baquet C. 2002. What is a "health disparity"? Public Health Reports 117:426434. CDC (Centers for Disease Control and Prevention). 2001. Obesity Trends, 19912001. Prevalence of Obesity Among U.S. Adults, by Characteristics: Behavioral Risk Factor Surveillance System (19912001); Self Reported Data. [Online]. Available: http://www.cdc.gov/nccdphp/dnpa/ obesity/trend/prev_char.htm [accessed June 14, 2004]. Cho Y, Hummer RA. 2001. Disability status differentials across fifteen Asian and Pacific Islander groups and the effect of nativity and duration of residence in the U.S. Social Biology 48: 171195.

OCR for page 121
170 EXAMINING THE HEALTH DISPARITIES RESEARCH PLAN OF THE NIH Claudio L, Tulton L, Doucette J, Landrigan PJ. 1999. Socioeconomic factors and asthma hospitaliza- tion rates in New York City. Journal of Asthma 36:343350. Cohen DA, Farley TA, Mason K. 2003. Why is poverty unhealthy? Social and physical mediators. Social Science and Medicine 57:16311641. Cohen DA, Mason K, Bedimo A, Scribner R, Basolo V, Farley TA. 2003. Neighborhood physical conditions and health. American Journal of Public Health 93(3):467471. Cooper R, Rotimi C, Ataman S, McGee D, Osotimehin B, Kadiri S, et al. 1997. The prevalence of hypertension in seven populations of West African origin. American Journal of Public Health 87:160168. Coughlin SS, Thompson TD, Seeff L, Richards T, Stallings F. 2002. Breast, cervical, and colorectal carcinoma screening in a demographically defined region of the southern U.S. Cancer 95:2211 2222. Dallman MF, Pecoraro N, Akana SF, La Fleur SE, Gomez F, Houshyar H, et al. 2003. Chronic stress and obesity: A new view of "comfort food." Proceedings of the National Academy of Sciences 100:1169611701. Davey Smith G, Hart C, Blane D, Gillis C, Hawthorne V. 1997. Lifetime socioeconomic position and mortality: Prospective observational study. British Medical Journal 314:547552. DeNavas-Walt C, Proctor BD, Mills RJ. 2004. U.S. Census Bureau, Current Population Reports Income, Poverty, and Health Insurance Coverage in the United States: 2003. Washington, DC: U.S. Government Printing Office. Eberhardt MS, Pamuk ER. 2004. The importance of place of residence: Examining health in rural and nonrural areas. American Journal of Public Health 94:16821686. Epel ES, Blackburn EH, Lin, J, Dhabhar FS, Adler NE, Morrow J, et al. 2004. Accelerated telomere shortening in response to life stress. Proceedings of the National Academy of Sciences 101(49): 1731217315. Eschbach K, Ostir GV, Patel KV, Markides KS, Goodwin JS. 2004. Neighborhood context and mortality among older Mexican Americans: Is there a barrio advantage? American Journal of Public Health 94:18071812. Evans GW. 2001. Environmental stress and health. In, Baum A, Revenson T, Singer JE, eds. Hand- book of Health Psychology. Mahwah, NJ: Erlbaum. Pp. 365385. Evans GW, Lepore SJ. 1992. Conceptual and analytic issues in crowding research. Journal of Envi- ronmental Psychology 12:163173. Fang J, Madhaven S, Alderman MH. 1996. The association between birthplace and mortality from cardiovascular causes among black and white residents of New York City. New England Jour- nal of Medicine 335:15451551. Friedman HS, Tucker JS, Schwartz JE, Tomlinson-Keasey C, Martin LR, Wingard DL, et al. 1995. Psychosocial and behavioral predictors of longevity. American Psychologist 50:6978. Gertler PJ. 2004. Do conditional cash transfers improve child health? Evidence from PROGRESA's controlled randomized experiment. American Economics Review 94(2):331336. Gliksman MD, Kawachi I, Hunter D, Colditz GA, Manson JE, Stampfer MJ, et al. 1995. Childhood socioeconomic status and risk of cardiovascular disease in middle aged US women: A prospec- tive study. Journal of Epidemiology and Community Health 49:1015. Goldthorpe JH. 1980. Social Mobility and Class Structure in Modern Britain. Oxford, UK: Clarendon Press. Guyll M, Matthews KA, Bromberger JT. 2001. Discrimination and unfair treatment: Relationship to cardiovascular reactivity among African American and European American women. Health Psychology 20(5):315325. Haan M, Kaplan GA. 1985. The Contribution of Socioeconomic Position to Minority Health. Report of the Secretary's Task Force on Black and Minority Health, Volume II: Crosscutting Issues in Minority Health. Rockville, MD: U.S. Department of Health and Human Services.

OCR for page 121
APPENDIX D 171 Halsey AH, Heath AF, Ridge JM. 1980. Origins and Destinations: Family, Class and Education in Modern Britain. Oxford, UK: Clarendon Press. Harrell JP, Hall S, Taliaferro J. 2003. Physiological responses to racism and discrimination: An assessment of the evidence. American Journal of Public Health 93(2):243247. Hartley D. 2004. Rural health disparities, population health, and rural culture. American Journal of Public Health 94:16751678. Hasle H. 1990. Association between living conditions in childhood and myocardial infarction. Brit- ish Medical Journal 300:512513. Healthy People 2010. (November 2000). Understanding and Improving Health (Second Edition). Department of Health and Human Services. Hertzman C. 1999. Population health and human development. In, Keating DP, Hertzman C, eds. Developmental Health and the Wealth of Nations: Social, Biological, and Educational Dynam- ics. New York: Guildford Press. Pp. 2140. Hill CV, Neighbors HW, Gayle HD. 2004. The relationship between racial discrimination and health for black Americans: Measurement challenges and the realities of coping. African American Research Perspectives 10(1):8998. House JS. 2002. Understanding social factors and inequalities in health: 20th century progress and 21st century prospects. Journal of Health and Social Behavior 43:125142. House JS, Williams D. 2000. Understanding and reducing socioeconomic and racial/ethnic dispari- ties in health. In, Smedley, BD, Syme, SL, eds. Promoting Health: Intervention Strategies from Social and Behavioral Research. Washington, DC: National Academy Press. Pp. 81124. House JS, Landis KR, Umberson D. 1988. Social relationships and health. Science 241(4865): 540545. Johnson P, Reed H. 1996. Two Nations? The Inheritance of Poverty and Affluence [Institute for Fiscal Studies Commentary No 53]. London: Institute for Fiscal Studies. Kaplan GA. 1999. Part III summary: What is the role of the social environment in understanding inequalities in health? In, Adler NE, Marmot M, McEwen B, Stewart J, eds. Socioeconomic Status and Health in Industrialized Nations. New York: Annals of the New York Academy of Sciences. Pp. 116119. Kaplan GA. 2004. What's wrong with social epidemiology, and how can we make it better? Epide- miologic Reviews 26:124135. Kaplan GA, Salonen JT. 1990. Socioeconomic conditions in childhood and ischaemic heart disease during middle age. British Medical Journal 301:11211123. Kaplan GA, Pamuk ER, Lynch JW, Cohen RD, Balfour JL. 1996. Inequality in income and mortality in the United States: Analysis of mortality and potential pathways. British Medical Journal 312:9991003. Kawachi I, Berkman LF. 2000. Social cohesion, social capital, and health. In, Berkman LF, Kawachi I, eds. Social Epidemiology. New York: Oxford University Press. Pp. 174190. Kennedy BP, Kawachi I, Prothrow-Smith D. 1996. Income distribution and mortality: Cross-sectional ecological study of the Robin Hood index in the United States. British Medical Journal 312:10041007. (Correction in British Medical Journal 312:1194.) Klinenberg E. 2002. Heat Wave: A Social Autopsy of Disaster in Chicago. Chicago: University of Chicago Press. Kling JR, Liebman, JB, Katz LF, Sanbonmatsu L. 2004. Moving to opportunity and tranquility: Neighborhood effects on adult economic self-sufficiency and health from a randomized housing voucher experiment. KSG Working Paper No. RWP04-035. [Online]. Available: http://ssrn.com/ abstract=588942 [accessed June 14, 2004]. Krebs-Smith SM, Cook A, Subar AF, Cleveland L, Friday J. 1995. U.S. adults' fruit and vegetable intakes, 1989 to 1991: A revised baseline for the Healthy People 2000 objective. American Journal of Public Health 85:16231629.

OCR for page 121
172 EXAMINING THE HEALTH DISPARITIES RESEARCH PLAN OF THE NIH Krieger N, Sidney S. 1996. Racial discrimination and blood pressure: The CARDIA study of young black and white adults. American Journal of Public Health 86:13701378. Kuh D, Ben-Shlomo Y, eds. 1997. A Life Course Approach to Chronic Disease Epidemiology. Oxford, UK: Oxford University Press. Kuh D, Wadsworth M. 1991. Childhood influences on adult male earnings in a longitudinal study. British Journal of Sociology 42:537555. Kuh D, Power C, Blane D, Bartley M. 1997. Social pathways between childhood and adult health. In, Kuh D, Ben-Shlomo Y, eds. A Life Course Approach to Chronic Disease Epidemiology. Ox- ford, UK: Oxford University Press. Pp. 169200. Lee P, Paxman D. 1997. Reinventing public health. Annual Review of Public Health 18:135. Lin CC, Rogot E, Johnson NJ, Sorlie PD, Arias E. 2003. A further study of life expectancy by socioeconomic factors in the National Longitudinal Mortality Study. Ethnicity and Disease 13:240247. Link BG, Phelan J. 1995. Social conditions as fundamental causes of disease. Journal of Health and Social Behavior Spec. No:8094. Lochner KA, Kawachi I, Brennan RT, Buka SL. 2003. Social capital and neighborhood mortality rates in Chicago. Social Science and Medicine 56:17971805. Lynch JW, Kaplan GA, Cohen RD, Kauhanen J, Wilson TW, Smith NL, et al. 1994. Childhood and adult socioeconomic status as predictor of mortality in Finland. Lancet 343:524527. Marmot MG, Rose G, Shipley M, Hamilton PJS. 1978. Employment grade and coronary heart dis- ease in British civil servants. Journal of Epidemiology and Community Health 32:244249. McEwen BS. 1998a. Protective and damaging effects of stress mediators. New England Journal of Medicine 338(3):171179. McEwen BS. 1998b. Stress, adaptation, and disease: Allostasis and allostatic load. Annals of the New York Academy of Sciences 840:3344. McEwen B, Seeman T. 1999. Protective and damaging effects of mediators of stress: Elaborating and testing the concepts of allostasis and allostatic load. Annals of the New York Academy of Sciences 896:3047. McEwen BS, Stellar E. 1993. Stress and the individual: Mechanisms leading to disease. Archives of Internal Medicine 153:20932101. McGinnis JM, Williams-Russo P, Knickman JR. 2002. The case for more active policy attention to health promotion. Health Affairs 21(2):7893. Morland K, Wing S, Diez Roux A, Poole C. 2002. Neighborhood characteristics associated with the location of food stores and food service places. American Journal of Preventive Medicine 22(1):2329. Murray CJL, Michaud CM, McKenna M, Marks J. 1998. US Patterns of Mortality by County and Race: 19651994. Boston: Harvard Center for Population and Development Studies. Murray CJL, Gakidou EE, Frenk J. 1999. Health inequalities and social group differences: What should we measure? Bulletin of the World Health Organization 77:537543. National Center for Health Statistics. 2001. Health, United States, 2001 with Urban and Rural Health Chartbook. Hyattsville, MD: National Center for Health Statistics. National Center for Health Statistics. 2004. Health, United States, 2004 with Chartbook on Trends in the Health of Americans. Hyattsville, MD: National Center for Health Statistics. NIH (National Institutes of Health). 2002. NIH Health Disparities Strategic Plan, Fiscal Years 20022006. Vol. 1. Washington, DC: NIH, U.S. Dept. of Health and Human Services. Palaniappan L, Wang Y, Fortmann SP. 2004. Coronary heart disease mortality for six ethnic groups in California, 19902000. Annals of Epidemiology 14:499506. Pamuk E, Makuc D, Heck K, Reuben C, Lochner K. 1998. Socioeconomic Status and Health Chartbook: Health United States, 1998. Hyattsville, MD: National Center for Health Statistics.

OCR for page 121
APPENDIX D 173 Peek-Asa C, Zwerling C, Stallones L. 2004. Acute traumatic injuries in rural populations. American Journal of Public Health 94:16891693. Pierce JP, Fiore MC, Novotny TE, Hatziandreu EJ, Davis RM. 1989. Trends in cigarette smoking in the United States: Education differences are increasing. Journal of the American Medical Asso- ciation 26:5660. Probst JC, Moore CG, Glover SH, Samuels ME. 2004. Person and place: The compounding effects of race/ethnicity and rurality on health. American Journal of Public Health 94:16951703. Reynolds AJ, Temple JA, Robertson DL, Mann EA. 2001. Long-term effects of an early childhood intervention on educational achievement and juvenile arrest: A 15-year follow-up of low-income children in public schools. Journal of the American Medical Association 285:23392346. Rumbaut RG. 1996. Origins and destinies: Immigration, race and ethnicity in contemporary America. In, Pedraza S, Rumbaut RG, eds. Origins and Destinies; Immigration, Race, and Ethnicity in America. Belmont, CA: Wadsworth Publishing Company. Pp. 2142. Sampson RJ, Raudenbush SW, Earls F. 1997. Neighborhoods and violent crime: A multilevel study of collective efficacy. Science 277:918924. Seeman TE, McEwen B, Rowe J, Singer B. 2001. Allostatic load as a marker of cumulative biologi- cal risk: MacArthur studies of successful aging. Proceedings of the National Academy of Sci- ences 98(8):47704775. Singh GK, Miller BA. 2004. Health, life expectancy, and mortality patterns among immigrant popu- lations in the United States. Canadian Journal of Public Health 95:114121. Smedley BD, Stith AY, Nelson AR, eds. 2003. Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care. Washington, DC: The National Academies Press. Smith JP. 1999. Healthy bodies and thick wallets: The dual relationship between health and eco- nomic status. Journal of Economic Perspectives 13(2):145166. Stoddard JL, Johnson CA, Sussman S, Dent C, Boley-Cruz T. 1998. Tailoring outdoor tobacco advertising to minorities in Los Angeles County. Journal of Health Communication 3:137146. Thomas DB, Karagas MR. 1996. Migrant studies. In, Schottenfeld D, Fraumeni JF, eds. Cancer Epidemiology and Prevention. 2nd ed. New York: Oxford University Press. Pp. 236254. U.S. Census Bureau, American Fact Finder. 2000. Sex by Age by Educational Attainment for the Population 18 Years and Over [Census 2000 Summary File 4Sample Data]. [Online]. Avail- able: http://factfinder.census.gov [accessed January 19, 2005]. U.S. Department of Health and Human Services, Agency for Healthcare Research and Quality. 2003. National Healthcare Disparities Report. Rockville, MD: Agency for Healthcare Research and Quality. Vagero D, Leon D. 1994. Effect of social class in childhood and adulthood on adult mortality. Lancet 343:12241225. Wannamethee SG, Whincup PH, Shaper G, Walker M. 1996. Influence of father's social class on cardiovascular disease in middle-aged men. Lancet 348:12591263. Weeks WB, Kazis LE, Shen Y, Cong Z, Ren XS, Miller D, et al. 2004. Differences in health-related quality of life in rural and urban veterans. American Journal of Public Health 94:17621767. Wilkinson RG. 1996. Unhealthy Societies: The Afflictions of Inequality. London: Routledge. Williams DR. 2005. The health of U.S. racial and ethnic populations. Journal of Gerontology-- Series B Psychological Sciences and Social Sciences 60(Spec. Iss. Oct.): 5362. Williams DR, Collins C. 2001. Racial residential segregation: A fundamental cause of racial dispari- ties in health. Public Health Reports 116:404416. Wolfson M, Kaplan G, Lynch J, Ross N, Backlund E. 1999. Relation between income inequality and mortality: Empirical demonstration. British Medical Journal 319:953955. World Health Organization. 1948. Official Records of the World Health Organization, No. 2 (p. 100), Entered into Force on 7 April 1948. Preamble to the Constitution of the World Health Organiza- tion as Adopted by the International Health Conference, New York, 1922 June, 1946; Signed on 22 July 1946 by the Representatives of 61 States.

OCR for page 121
174 EXAMINING THE HEALTH DISPARITIES RESEARCH PLAN OF THE NIH Yang RC, Mills PK, Riordan DG. 2004. Cervical cancer among Hmong women in California, 1988 to 2000. American Journal of Preventive Medicine 24:132138. Zsembik BA, Fennell D. 2005. Ethnic variation in health and the determinants of health among Latinos. Social Science and Medicine 61:5363.