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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
OCR for page 169
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.
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Representative terms from entire chapter:
african americans
