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1.  The Promise of Prevention: The Effects of Four Preventable Risk Factors on National Life Expectancy and Life Expectancy Disparities by Race and County in the United States 
PLoS Medicine  2010;7(3):e1000248.
Majid Ezzati and colleagues examine the contribution of a set of risk factors (smoking, high blood pressure, elevated blood glucose, and adiposity) to socioeconomic disparities in life expectancy in the US population.
There has been substantial research on psychosocial and health care determinants of health disparities in the United States (US) but less on the role of modifiable risk factors. We estimated the effects of smoking, high blood pressure, elevated blood glucose, and adiposity on national life expectancy and on disparities in life expectancy and disease-specific mortality among eight subgroups of the US population (the “Eight Americas”) defined on the basis of race and the location and socioeconomic characteristics of county of residence, in 2005.
Methods and Findings
We combined data from the National Health and Nutrition Examination Survey and the Behavioral Risk Factor Surveillance System to estimate unbiased risk factor levels for the Eight Americas. We used data from the National Center for Health Statistics to estimate age–sex–disease-specific number of deaths in 2005. We used systematic reviews and meta-analyses of epidemiologic studies to obtain risk factor effect sizes for disease-specific mortality. We used epidemiologic methods for multiple risk factors to estimate the effects of current exposure to these risk factors on death rates, and life table methods to estimate effects on life expectancy. Asians had the lowest mean body mass index, fasting plasma glucose, and smoking; whites had the lowest systolic blood pressure (SBP). SBP was highest in blacks, especially in the rural South—5–7 mmHg higher than whites. The other three risk factors were highest in Western Native Americans, Southern low-income rural blacks, and/or low-income whites in Appalachia and the Mississippi Valley. Nationally, these four risk factors reduced life expectancy at birth in 2005 by an estimated 4.9 y in men and 4.1 y in women. Life expectancy effects were smallest in Asians (M, 4.1 y; F, 3.6 y) and largest in Southern rural blacks (M, 6.7 y; F, 5.7 y). Standard deviation of life expectancies in the Eight Americas would decline by 0.50 y (18%) in men and 0.45 y (21%) in women if these risks had been reduced to optimal levels. Disparities in the probabilities of dying from cardiovascular diseases and diabetes at different ages would decline by 69%–80%; the corresponding reduction for probabilities of dying from cancers would be 29%–50%. Individually, smoking and high blood pressure had the largest effect on life expectancy disparities.
Disparities in smoking, blood pressure, blood glucose, and adiposity explain a significant proportion of disparities in mortality from cardiovascular diseases and cancers, and some of the life expectancy disparities in the US.
Please see later in the article for the Editors' Summary
Editors' Summary
Life expectancy (a measure of longevity and premature death) and overall health have increased steadily in the United States over recent years. New drugs, new medical technologies, and better disease prevention have all helped Americans to lead longer, healthier lives. However, even now, some Americans live much longer and much healthier lives than others. Health disparities—differences in how often certain diseases occur and cause death in groups of people classified according to their ethnicity, geographical location, sex, or age—are extremely large and persistent in the US. On average, black men and women in the US live 6.3 and 4.5 years less, respectively, than their white counterparts; the gap between life expectancy in the US counties with the lowest and highest life expectancies is 18.4 years for men and 14.3 years for women. Disparities in deaths (mortality) from chronic diseases such as cardiovascular diseases (for example, heart attacks and stroke), cancers, and diabetes are known to be the main determinants of these life expectancy disparities.
Why Was This Study Done?
Preventable risk factors such as smoking, high blood pressure, excessive body fat (adiposity), and high blood sugar are responsible for many thousands of deaths from chronic diseases. Exposure to these risk factors varies widely by race, state of residence, and socioeconomic status. However, the effects of these observed disparities in exposure to modifiable risk factors on US life expectancy disparities have only been examined in selected groups of people and it is not known how multiple modifiable risk factors affect US health disparities. A better knowledge about how disparities in risk factor exposure contribute to health disparities is needed to ensure that prevention programs not only improve the average health status but also reduce health disparities. In this study, the researchers estimate the effects of smoking, high blood pressure, high blood sugar, and adiposity on US life expectancy and on disparities in life expectancy and disease-specific deaths among the “Eight Americas,” population groups defined by race and by the location and socioeconomic characteristics of their county of residence.
What Did the Researchers Do and Find?
The researchers extracted data on exposure to these risk factors from US national health surveys, information on deaths from different diseases in 2005 from the US National Center for Health Statistics, and estimates of how much each risk factor increases the risk of death from each disease from published studies. They then used modeling methods to estimate the effects of risk factor exposure on death rates and life expectancy. The Asian subgroup had the lowest adiposity, blood sugar, and smoking rates, they report, and the three white subgroups had the lowest blood pressure. Blood pressure was highest in the three black subgroups, whereas the other three risk factors were highest in Western Native Americans, Southern rural blacks, and whites living in Appalachia and the Mississippi Valley. The effects on life expectancy of these factors were smallest in Asians and largest in Southern rural blacks but, overall, these risk factors reduced the life expectancy for men and women born in 2005 by 4.9 and 4.1 years, respectively. Other calculations indicate that if these four risk factors were reduced to optimal levels, disparities among the subgroups in deaths from cardiovascular diseases and diabetes and from cancers would be reduced by up to 80% and 50%, respectively.
What Do These Findings Mean?
These findings suggest that disparities in smoking, blood pressure, blood sugar, and adiposity among US racial and geographical subgroups explain a substantial proportion of the disparities in deaths from cardiovascular diseases, diabetes, and cancers among these subgroups. The disparities in risk factor exposure also explain some of the disparities in life expectancy. The remaining disparities in deaths and life expectancy could be the result of preventable risk factors not included in this study—one of its limitations is that it does not consider the effect of dietary fat, alcohol use, and dietary salt, which are major contributors to different diseases. Thus, suggest the researchers, reduced exposure to preventable risk factors through the implementation of relevant policies and programs should reduce life expectancy and mortality disparities in the US and yield health benefits at a national scale.
Additional Information
Please access these Web sites via the online version of this summary at
The US Centers for Disease Control and Prevention, the US Office of Minority Health, and the US National Center on Minority Health and Health Disparities all provide information on health disparities in the US
MedlinePlus provides links to information on health disparities and on healthy living (in English and Spanish)
The US Centers for Disease Control and Prevention provides information on all aspects of healthy living
The American Heart Association and the American Cancer Society provide information on modifiable risk factors for patients and caregivers
Healthy People 2010 is a national framework designed to improve the health of people living in the US
The US National Health and Nutrition Examination Survey (NHANES) and the Behavioral Risk Factor Surveillance System (BRFSS) collect information on risk factor exposures in the US
PMCID: PMC2843596  PMID: 20351772
2.  The Fall and Rise of US Inequities in Premature Mortality: 1960–2002 
PLoS Medicine  2008;5(2):e46.
Debates exist as to whether, as overall population health improves, the absolute and relative magnitude of income- and race/ethnicity-related health disparities necessarily increase—or derease. We accordingly decided to test the hypothesis that health inequities widen—or shrink—in a context of declining mortality rates, by examining annual US mortality data over a 42 year period.
Methods and Findings
Using US county mortality data from 1960–2002 and county median family income data from the 1960–2000 decennial censuses, we analyzed the rates of premature mortality (deaths among persons under age 65) and infant death (deaths among persons under age 1) by quintiles of county median family income weighted by county population size. Between 1960 and 2002, as US premature mortality and infant death rates declined in all county income quintiles, socioeconomic and racial/ethnic inequities in premature mortality and infant death (both relative and absolute) shrank between 1966 and 1980, especially for US populations of color; thereafter, the relative health inequities widened and the absolute differences barely changed in magnitude. Had all persons experienced the same yearly age-specific premature mortality rates as the white population living in the highest income quintile, between 1960 and 2002, 14% of the white premature deaths and 30% of the premature deaths among populations of color would not have occurred.
The observed trends refute arguments that health inequities inevitably widen—or shrink—as population health improves. Instead, the magnitude of health inequalities can fall or rise; it is our job to understand why.
Nancy Krieger and colleagues found evidence of decreasing, and then increasing or stagnating, socioeconomic and racial inequities in US premature mortality and infant death from 1960 to 2002.
Editors' Summary
One of the biggest aims of public health advocates and governments is to improve the health of the population. Improving health increases people's quality of life and helps the population be more economically productive. But within populations are often persistent differences (usually called “disparities” or “inequities”) in the health of different subgroups—between women and men, different income groups, and people of different races/ethnicities, for example. Researchers study these differences so that policy makers and the broader public can be informed about what to do to intervene. For example, if we know that the health of certain subgroups of the population—such as the poor—is staying the same or even worsening as the overall health of the population is improving, policy makers could design programs and devote resources to specifically target the poor.
To study health disparities, researchers use both relative and absolute measures. Relative inequities refer to ratios, while absolute inequities refer to differences. For example, if one group's average income level increases from $1,000 to $10,000 and another group's from $2,000 to $20,000, the relative inequality between the groups stays the same (i.e., the ratio of incomes between the two groups is still 2) but the absolute difference between the two groups has increased from $1,000 to $10,000.
Examining the US population, Nancy Krieger and colleagues looked at trends over time in both relative and absolute differences in mortality between people in different income groups and between whites and people of color.
Why Was This Study Done?
There has been a lot of debate about whether disparities have been widening or narrowing as overall population health improves. Some research has found that both total health and health disparities are getting better with time. Other research has shown that overall health gains mask worsening disparities—such that the rich get healthier while the poor get sicker.
Having access to more data over a longer time frame meant that Krieger and colleagues could provide a more complete picture of this sometimes contradictory story. It also meant they could test their hypothesis about whether, as population health improves, health inequities necessarily widen or shrink within the time period between the 1960s through the 1990s during which certain events and policies likely would have had an impact on the mortality trends in that country.
What Did the Researchers Do and Find?
In order to investigate health inequities, the authors chose to look at two common measures of population health: rates of premature mortality (dying before the age of 65 years) and rates of infant mortality (death before the age of 1).
To determine mortality rates, the authors used death statistics data from different counties, which are routinely collected by state and national governments. To be able to rank mortality rates for different income groups, they used data on the median family incomes of people living within those counties (meaning half the families had income above, and half had incomes below, the median value). They calculated mortality rates for the total population and for whites versus people of color. They used data from 1960 through 2002. They compared rates for 1966–1980 with two other time periods: 1960–1965 and 1981–2002. They also examined trends in the annual mortality rates and in the annual relative and absolute disparites in these rates by county income level.
Over the whole period 1960–2002, the authors found that premature mortality (death before the age of 65) and infant mortality (death before the age of 1) decreased for all income groups. But they also found that disparities between income groups and between whites and people of color were not the same over this time period. In fact, the economic disparities narrowed then widened. First, they shrank between 1966 and 1980, especially for Americans of color. After 1980, however, the relative health inequities widened and the absolute differences did not change. The authors conclude that if all people in the US population experienced the same health gains as the most advantaged did during these 42 years (i.e., as the whites in the highest income groups), 14% of the premature deaths among whites and 30% of the premature deaths among people of color would have been prevented.
What Do These Findings Mean?
The findings provide an overview of the trends in inequities in premature and infant mortality over a long period of time. Different explanations for these trends can now be tested. The authors discuss several potential reasons for these trends, including generally rising incomes across America and changes related to specific diseases, such as the advent of HIV/AIDS, changes in smoking habits, and better management of cancer and cardiovascular disease. But they find that these do not explain the fall then rise of inequities. Instead, the authors suggest that explanations lie in the social programs of the 1960s and the subsequent roll-back of some of these programmes in the 1980s. The US “War on Poverty,” civil rights legislation, and the establishment of Medicare occurred in the mid 1960s, which were intended to reduce socioeconomic and racial/ethnic inequalities and improve access to health care. In the 1980s there was a general cutting back of welfare state provisions in America, which included cuts to public health and antipoverty programs, tax relief for the wealthy, and worsening inequity in the access to and quality of health care. Together, these wider events could explain the fall then rise trends in mortality disparities.
The authors say their findings are important to inform and help monitor the progress of various policies and programmes, including those such as the Healthy People 2010 initiative in America, which aims to increase the quality and years of healthy life and decrease health disparities by the end of this decade.
Additional Information.
Please access these Web sites via the online version of this summary at 0050046.
Healthy People 2010 was created by the US Department of Health and Human Services along with scientists inside and outside of government and includes a comprehensive set of disease prevention and health promotion objectives for the US to achieve by 2010, with two overarching goals: to increase quality and years of healthy life and to eliminate health disparities
Johan Mackenbach and colleagues provide an overview of mortality inequalities in six Western European countries—Finland, Sweden, Norway, Denmark, England/Wales, and Italy—and conclude that eliminating mortality inequalities requires that more cardiovascular deaths among lower socioeconomic groups be prevented, as well as more attention be paid to rising death rates of lung cancer, breast cancer, respiratory disease, gastrointestinal disease, and injuries among women and men in the lower income groups.
The WHO Health for All program promotes health equity
A primer on absolute versus relative differences is provided by the American College of Physicians
PMCID: PMC2253609  PMID: 18303941
3.  Future Research Directions for Understanding Neighborhood Contributions to Health Disparities 
This paper proposes several promising future directions for neighborhood research to address health inequalities. First, there is a need to apply a Geography of Opportunity framework to understand how vast spatial (neighborhood, regional) inequality translates into health inequality. Such a framework highlights inequality that unfolds across an entire region, as well as the continuing significance of race/ethnicity for producing disparities in health and in the social determinants of health. The Geography of Opportunity framework also points to some of the methodological limitations of current neighborhood health studies, given the structure of neighborhood racial inequality in the US for estimating how important neighborhoods are for producing racial health disparities. Second, there is a need to incorporate life course concepts, data, and methods, including to model residential histories, neighborhood temporal change and residential mobility, starting early in life. A life course focus would help inform when in life neighborhoods matter most for health and health inequalities, as well as improve exposure assessment of residential contexts. Third, we must model mechanisms linking neighborhoods and health, including the role of individual and household socioeconomic status (SES). Lastly, we need to more meaningfully integrate social determinants of health, including drawing on policy evaluations that aim to improve neighborhood environments or that aim to expand household neighborhood choice. Doing so would inform how specific modifiable neighborhood exposures stimulated by policy may influence health and health disparities.
PMCID: PMC3714396  PMID: 23660539
health inequality; health disparities; racial/ethnic health disparities; neighborhood effects; geography; life course; social determinants of health; social policy
4.  Health Behaviours, Socioeconomic Status, and Mortality: Further Analyses of the British Whitehall II and the French GAZEL Prospective Cohorts 
PLoS Medicine  2011;8(2):e1000419.
Further analysis of data from two prospective cohorts reveals differences in the extent to which health behaviors attenuate associations between socioeconomic position and mortality outcomes.
Differences in morbidity and mortality between socioeconomic groups constitute one of the most consistent findings of epidemiologic research. However, research on social inequalities in health has yet to provide a comprehensive understanding of the mechanisms underlying this association. In recent analysis, we showed health behaviours, assessed longitudinally over the follow-up, to explain a major proportion of the association of socioeconomic status (SES) with mortality in the British Whitehall II study. However, whether health behaviours are equally important mediators of the SES-mortality association in different cultural settings remains unknown. In the present paper, we examine this issue in Whitehall II and another prospective European cohort, the French GAZEL study.
Methods and Findings
We included 9,771 participants from the Whitehall II study and 17,760 from the GAZEL study. Over the follow-up (mean 19.5 y in Whitehall II and 16.5 y in GAZEL), health behaviours (smoking, alcohol consumption, diet, and physical activity), were assessed longitudinally. Occupation (in the main analysis), education, and income (supplementary analysis) were the markers of SES. The socioeconomic gradient in smoking was greater (p<0.001) in Whitehall II (odds ratio [OR]  = 3.68, 95% confidence interval [CI] 3.11–4.36) than in GAZEL (OR  = 1.33, 95% CI 1.18–1.49); this was also true for unhealthy diet (OR  = 7.42, 95% CI 5.19–10.60 in Whitehall II and OR  = 1.31, 95% CI 1.15–1.49 in GAZEL, p<0.001). Socioeconomic differences in mortality were similar in the two cohorts, a hazard ratio of 1.62 (95% CI 1.28–2.05) in Whitehall II and 1.94 in GAZEL (95% CI 1.58–2.39) for lowest versus highest occupational position. Health behaviours attenuated the association of SES with mortality by 75% (95% CI 44%–149%) in Whitehall II but only by 19% (95% CI 13%–29%) in GAZEL. Analysis using education and income yielded similar results.
Health behaviours were strong predictors of mortality in both cohorts but their association with SES was remarkably different. Thus, health behaviours are likely to be major contributors of socioeconomic differences in health only in contexts with a marked social characterisation of health behaviours.
Please see later in the article for the Editors' Summary
Editors' Summary
The influence of the socioeconomic environment on the health of individuals and populations is well known, giving rise to the so-called social determinants of health. The social determinants of health are the conditions in which people are born, grow, live, work, and age, including the health system. These circumstances are shaped by the distribution of money, power, and resources at global, national, and local levels, which are themselves influenced by policy choices. The social determinants of health are mostly responsible for health inequities—the unfair and avoidable differences in health status seen within and between countries. In addition, health-damaging behaviors are often strongly socially patterned. For example, material constraints, lack of knowledge, and limited opportunities to follow health promoting messages often act as barriers that prevent those from lower socioeconomic groups to adopt a healthy lifestyle. Yet the extent to which health behaviors explain social inequalities in health remains unclear and can range from 12% to 72% according to some studies.
Why Was This Study Done?
In a recently published paper using data from the British Whitehall II cohort, the researchers showed that longitudinal assessment of health behaviors accounted for socioeconomic differences in mortality better than a single baseline assessment as used in most previous studies. (The Whitehall II study started in 1985 to examine the socioeconomic gradient in health among 10,308 London-based civil servants [6,895 men and 3,413 women] aged 35–55).
However, it is not clear whether health behaviors are equally important mediators of the socioeconomic-health association in different cultural settings. In this study, the researchers examine this issue by comparing their recent findings of the Whitehall II study with another European cohort, the French GAZEL study. (The GAZEL study started in 1989 among employees of the French national gas and electricity company totaling 20,625 employees [15,011 men and 5,614 women], aged 35–50.) The Whitehall II study and the GAZEL study have comparable designs in the way both assess socioeconomic status, health behaviors, and mortality and have a similar age range and follow-up period.
What Did the Researchers Do and Find?
The researchers included 9,771 participants from the Whitehall II study and 17,760 from the GAZEL study—mean follow up for Whitehall II was 19.5 years and for GAZEL was 16.5 years. The researchers used occupation as the main marker of socioeconomic status, and education and income as supplementary markers of socioeconomic status. Apart from a few exceptions, the researchers analyzed each cohort separately and used statistical techniques to calculate: the mortality rates per 1000 person-years for each socioeconomic group; the age- and sex-adjusted prevalence rates of smoking, heavy alcohol consumption, unhealthy diet, and physical inactivity, at the first and the last follow-up of the study for each socioeconomic group; and the differences in health behaviors prevalence between lowest and highest occupational position. Then the researchers used a statistical model to deduce the contribution of all health behaviors.
The researchers found that the socioeconomic gradient in smoking, unhealthy diet, and physical inactivity was greater in Whitehall II than in GAZEL. Socioeconomic differences in mortality were similar in the two cohorts, a hazard ratio of 1.62 in Whitehall II and 1.94 in GAZEL for lowest versus highest occupational position. Health behaviors weakened the association between socioeconomic status and mortality by 75% in Whitehall II but only by 19% in GAZEL. The supplementary analysis the researchers conducted using education and income as socioeconomic markers gave similar results.
What Do These Findings Mean?
These results suggest that the social patterning of unhealthy behaviors differs between countries. Although in both cohorts socioeconomic status and health behaviors were strong predictors of mortality, major differences in the social patterning of unhealthy behaviors in the two cohorts meant that the causal chains leading from socioeconomic status to health behaviors to mortality were different. Therefore it may be that health behaviors are likely to only be major contributors of socioeconomic differences in health in contexts with a marked social characterization of those behaviors. In order to identify the common and unique determinants of social inequalities in health in different populations, there needs to be further comparative research on the relative importance of different pathways linking socioeconomic status to health.
Additional Information
Please access these websites via the online version of this summary at
WHO provides information on social determinants of health
University College London provides information on the Whitehall study
The GAZEL study is available in an online open access format
PMCID: PMC3043001  PMID: 21364974
5.  Understanding How Race/Ethnicity and Gender Define Age-Trajectories of Disability: An Intersectionality Approach 
Social science & medicine (1982)  2011;72(8):1236-1248.
A number of studies have demonstrated wide disparities in health among racial/ethnic groups and by gender, yet few have examined how race/ethnicity and gender intersect or combine to affect the health of older adults. The tendency of prior research to treat race/ethnicity and gender separately has potentially obscured important differences in how health is produced and maintained, undermining efforts to eliminate health disparities. The current study extends previous research by taking an intersectionality approach (Mullings & Schulz, 2006), grounded in life course theory, conceptualizing and modeling trajectories of functional limitations as dynamic life course processes that are jointly and simultaneously defined by race/ethnicity and gender. Data from the nationally representative 1994–2006 US Health and Retirement Study and growth curve models are utilized to examine racial/ethnic/gender differences in intra-individual change in functional limitations among White, Black and Mexican American Men and Women, and the extent to which differences in life course capital account for group disparities in initial health status and rates of change with age. Results support an intersectionality approach, with all demographic groups exhibiting worse functional limitation trajectories than White Men. Whereas White Men had the lowest disability levels at baseline, White Women and racial/ethnic minority Men had intermediate disability levels and Black and Hispanic Women had the highest disability levels. These health disparities remained stable with age—except among Black Women who experience a trajectory of accelerated disablement. Dissimilar early life social origins, adult socioeconomic status, marital status, and health behaviors explain the racial/ethnic disparities in functional limitations among Men but only partially explain the disparities among Women. Net of controls for life course capital, Women of all racial/ethnic groups have higher levels of functional limitations relative to White Men and Men of the same race/ethnicity. Findings highlight the utility of an intersectionality approach to understanding health disparities.
PMCID: PMC3087305  PMID: 21470737
Health Disparities; Functional Limitations; Race/Ethnicity; Gender; Intersectionality; Life Course; Older Adults; USA; disability
6.  Eight Americas: Investigating Mortality Disparities across Races, Counties, and Race-Counties in the United States 
PLoS Medicine  2006;3(9):e260.
The gap between the highest and lowest life expectancies for race-county combinations in the United States is over 35 y. We divided the race-county combinations of the US population into eight distinct groups, referred to as the “eight Americas,” to explore the causes of the disparities that can inform specific public health intervention policies and programs.
Methods and Findings
The eight Americas were defined based on race, location of the county of residence, population density, race-specific county-level per capita income, and cumulative homicide rate. Data sources for population and mortality figures were the Bureau of the Census and the National Center for Health Statistics. We estimated life expectancy, the risk of mortality from specific diseases, health insurance, and health-care utilization for the eight Americas. The life expectancy gap between the 3.4 million high-risk urban black males and the 5.6 million Asian females was 20.7 y in 2001. Within the sexes, the life expectancy gap between the best-off and the worst-off groups was 15.4 y for males (Asians versus high-risk urban blacks) and 12.8 y for females (Asians versus low-income southern rural blacks). Mortality disparities among the eight Americas were largest for young (15–44 y) and middle-aged (45–59 y) adults, especially for men. The disparities were caused primarily by a number of chronic diseases and injuries with well-established risk factors. Between 1982 and 2001, the ordering of life expectancy among the eight Americas and the absolute difference between the advantaged and disadvantaged groups remained largely unchanged. Self-reported health plan coverage was lowest for western Native Americans and low-income southern rural blacks. Crude self-reported health-care utilization, however, was slightly higher for the more disadvantaged populations.
Disparities in mortality across the eight Americas, each consisting of millions or tens of millions of Americans, are enormous by all international standards. The observed disparities in life expectancy cannot be explained by race, income, or basic health-care access and utilization alone. Because policies aimed at reducing fundamental socioeconomic inequalities are currently practically absent in the US, health disparities will have to be at least partly addressed through public health strategies that reduce risk factors for chronic diseases and injuries.
US mortality rates were calculated according to "race-county" units and divided into the "eight Americas", across which there are enormous disparities in life expectancy.
Editors' Summary
It has been recognized for a long time that the number of years that people in the United States can expect to live (“life expectancy”) varies enormously. For example, white Americans tend to live longer than black Americans, and life expectancy is much greater in some of the roughly 3,000 counties of the US than it is in others. However, there is a lack of information and understanding on how big a part is played in “health inequalities” by specific diseases and injuries, by risk factors (such as tobacco, alcohol, and obesity), and by variations in access to effective health care.
Why Was This Study Done?
The researchers wanted to find a way of dividing the people of the US into groups based on a small number of characteristics—such as location of county of residence, race, and income—that would help demonstrate the most important factors accounting for differences in life expectancy.
What Did the Researchers Do and Find?
The researchers used figures from the US Census Bureau and the National Center for Health Statistics to calculate mortality (death) rates for the years 1982–2001. They took note of the county of residence and of the race of all the people who died during that period of time. This enabled them to calculate the mortality rates for all 8,221 “race-county units” (all of the individuals of a given race in a given county). They experimented with different ways of combining the race-counties into a small and manageable number of groups. They eventually settled on the idea of there being “eight Americas,” defined on the basis of race-county, population density, income, and homicide rate. Each group contains millions or tens of millions of people. For each of the eight groups the researchers estimated life expectancy, the risk of mortality from specific diseases, the proportion of people who had health insurance, and people's routine encounters with health-care services. (The researchers also created maps of life expectancies for the US counties.) They describe their eight Americas as follows: Asians, northland low-income rural whites, Middle America, low-income whites in Appalachia and the Mississippi Valley, western Native Americans, black Middle America, low-income southern rural blacks, and high-risk urban blacks.
Many striking differences in life expectancy were found between the eight groups. For example, in 2001, the life expectancy gap between the 3.4 million high-risk urban black males and the 5.6 million Asian females was nearly 21 years. Within the sexes, the life expectancy gap between the best-off and the worst-off groups was 15.4 years for males (Asians versus high-risk urban blacks) and 12.8 years for females (Asians versus low-income rural blacks in the South). The causes of death that were mainly responsible for these variations were various chronic diseases and injury. The gaps between best-off and worst-off were similar in 2001 to what they were in 1987.
What Do These Findings Mean?
Health inequalities in the US are large and are showing no sign of reducing. Social and economic reforms would certainly help change the situation. At the same time, the public health system should also improve the way in which it deals with risk factors for chronic diseases and injuries so that groups with the highest death rates receive larger benefits.
Additional Information.
Please access these Web sites via the online version of this summary at
A Perspective article by Gregory Pappas in this issue of PLoS Medicine (DOI: 10.1371/journal.pmed.0030357) discusses the methods of this piece of research and the findings
The American Medical Students' Association deals with the question “What are Health Disparities?” on its web site
The National Institutes of Health's “Strategic Research Plan to Reduce and Ultimately Eliminate Health Disparities” may be seen at the NIH web site
The Office of Minority Health at the Centers for Disease Control and Prevention has a Web page called “Eliminating Racial and Ethnic Health Disparities”
The issue of health inequalities in the US has also been dealt with by the Robert Wood Johnson Foundation
PMCID: PMC1564165  PMID: 16968116
7.  Racial disparities in hematopoietic cell transplantation in the United States 
Bone marrow transplantation  2011;47(11):10.1038/bmt.2011.214.
Hematopoietic cell transplantation (HCT) is a highly specialized, expensive and resource-intense medical procedure that can be associated with racial disparities. We review the prevailing literature on racial disparities in HCT in the United States and describe areas for future research and interventions. We discuss the complexity of interpreting race as a biological and social determinant of disease in biomedical research, especially as it relates to HCT. In the United States, race is often a surrogate for socioeconomic, education and health insurance status. We also discuss some of the nuances to consider while reviewing the literature on racial disparities. Disparities by race exist in three areas related to HCT: donor availability, access to HCT and outcomes of HCT. African-Americans/Blacks have a lower likelihood of finding an unrelated donor. Race and ethnicity definitions are country-specific and reconciling race data can represent significant challenges to unrelated donor registries worldwide. African-Americans/Blacks do not have the same access to autologous and allogeneic HCT as Whites. Racial disparities in outcomes of HCT are more prevalent among allogeneic HCT than autologous HCT recipients. More research is required to understand the biological, social, cultural, medical and financial aspects of race that may influence access to HCT and survival after transplantation. Better understanding of racial disparities will minimize inequities, inform health policy, guide development of interventions targeted to eliminate disparities and ensure equitable access to HCT for all populations.
PMCID: PMC3848311  PMID: 22056642
race/ethnicity; hematopoietic cell transplantation; allogeneic transplant; autologous transplant; access; health-care disparities
8.  Does the response to alcohol taxes differ across racial/ethnic groups? Some evidence from 1984-2009 Behavioral Risk Factor Surveillance System 
Excessive alcohol use remains an important lifestyle-related contributor to morbidity and mortality in the U.S. and worldwide. It is well documented that drinking patterns differ across racial/ethnic groups, but not how those different consumption patterns would respond to tax changes. Therefore, policy makers are not informed on whether the effects of tax increases on alcohol abuse are shared equally by the whole population, or policies in addition to taxation should be pursued to reach certain sociodemographic groups.
Aims of the Study
To estimate differential demand responses to alcohol excise taxes across racial/ethnic groups in the U.S.
Individual data from the Behavioral Risk Factor Surveillance System 1984-2009 waves (N= 3,921,943, 39.3% male; 81.3% White, 7.8% African American, 5.8% Hispanic, 1.9% Asian or Pacific Islander, 1.4% Native American, and 1.8% other race/multi-race) are merged with tax data by residential state and interview month. Dependent variables include consumption of any alcohol and number of drinks consumed per month. Demand responses to alcohol taxes are estimated for each race/ethnicity in separate regressions conditional on individual characteristics, state and time fixed effects, and state-specific secular trends.
The null hypothesis on the identical tax effects among all races/ethnicities is strongly rejected (P < 0.0001), although pairwise comparisons using t-test are often not statistically significant due to a lack of precision. Our point estimates suggest that the tax effect on any alcohol consumption is largest among White and smallest among Hispanic. Among existing drinkers, Native American and other race/multi-race are most responsive to tax effects while Hispanic least. For all races/ethnicities, the estimated tax effects on consumption are large and significant among light drinkers (1-40 drinks per month), but shrink substantially for moderate (41-99) and heavy drinkers (≥ 100).
Extensive research has been conducted on overall demand responses to alcohol excise taxes, but not on heterogeneity across various racial/ethnic groups. Only one similar prior study exists, but used a much smaller dataset. The authors did not identify differential effects. With this much larger dataset, we found some evidence for different responses across races/ethnicities to alcohol taxes, although we lack precision for individual group estimates. Limitations of our study include the absence of intrastate tax variations, no information on what type of alcohol is consumed, lack of controls for subgroup baseline alcohol consumption rates, and measurement error in self-reported alcohol use data.
Implications for Health Policies
Tax policies aimed to reduce alcohol-related health and social problems should consider whether they target the most harmful drinking behaviors, affect subgroups in unintended ways, or influence some groups disproportionately. This requires information on heterogeneity across subpopulations. Our results are a first step in this direction and suggest that there exists a differential impact across races/ethnicities, which may further increase health disparities. Tax increases also appear to be less effective among the heaviest consumers who are associated with highest risk.
Implications for Further Research
More research, including replications in different settings, is required to obtain better estimates on differential responses to alcohol tax across races/ethnicities. Population heterogeneity is also more complex than our first cut by race/ethnicity and needs more fine-grained analyses and model structures.
PMCID: PMC3089007  PMID: 21552394
9.  The concept of race and health status in America. 
Public Health Reports  1994;109(1):26-41.
Race is an unscientific, societally constructed taxonomy that is based on an ideology that views some human population groups as inherently superior to others on the basis of external physical characteristics or geographic origin. The concept of race is socially meaningful but of limited biological significance. Racial or ethnic variations in health status result primarily from variations among races in exposure or vulnerability to behavioral, psychosocial, material, and environmental risk factors and resources. Additional data that capture the specific factors that contribute to group differences in disease must be collected. However, reductions in racial disparities in health will ultimately require change in the larger societal institutions and structures that determine exposure to pathogenic conditions. More attention needs to be given to the ways that racism, in its multiple forms, affects health status. Socio-economic status is a central determinant of health status, overlaps the concept of race, but is not equivalent to race. Inadequate attention has been given to the range of variation in social, cultural, and health characteristics within and between racial or ethnic minority populations. There is a growing emphasis, both within and without the Federal Government, on the collection of racial or ethnic identifiers in health data systems, but noncoverage of the Asian and Pacific Islander population, Native Americans, and subgroups of the Hispanic population is still a major problem. However, for all racial or ethnic groups, we need not only more data but better data. We must be more active in directly measuring the health-related aspects of belonging to these social categories.
PMCID: PMC1402239  PMID: 8303011
10.  The Reversal of Fortunes: Trends in County Mortality and Cross-County Mortality Disparities in the United States  
PLoS Medicine  2008;5(4):e66.
Counties are the smallest unit for which mortality data are routinely available, allowing consistent and comparable long-term analysis of trends in health disparities. Average life expectancy has steadily increased in the United States but there is limited information on long-term mortality trends in the US counties This study aimed to investigate trends in county mortality and cross-county mortality disparities, including the contributions of specific diseases to county level mortality trends.
Methods and Findings
We used mortality statistics (from the National Center for Health Statistics [NCHS]) and population (from the US Census) to estimate sex-specific life expectancy for US counties for every year between 1961 and 1999. Data for analyses in subsequent years were not provided to us by the NCHS. We calculated different metrics of cross-county mortality disparity, and also grouped counties on the basis of whether their mortality changed favorably or unfavorably relative to the national average. We estimated the probability of death from specific diseases for counties with above- or below-average mortality performance. We simulated the effect of cross-county migration on each county's life expectancy using a time-based simulation model. Between 1961 and 1999, the standard deviation (SD) of life expectancy across US counties was at its lowest in 1983, at 1.9 and 1.4 y for men and women, respectively. Cross-county life expectancy SD increased to 2.3 and 1.7 y in 1999. Between 1961 and 1983 no counties had a statistically significant increase in mortality; the major cause of mortality decline for both sexes was reduction in cardiovascular mortality. From 1983 to 1999, life expectancy declined significantly in 11 counties for men (by 1.3 y) and in 180 counties for women (by 1.3 y); another 48 (men) and 783 (women) counties had nonsignificant life expectancy decline. Life expectancy decline in both sexes was caused by increased mortality from lung cancer, chronic obstructive pulmonary disease (COPD), diabetes, and a range of other noncommunicable diseases, which were no longer compensated for by the decline in cardiovascular mortality. Higher HIV/AIDS and homicide deaths also contributed substantially to life expectancy decline for men, but not for women. Alternative specifications of the effects of migration showed that the rise in cross-county life expectancy SD was unlikely to be caused by migration.
There was a steady increase in mortality inequality across the US counties between 1983 and 1999, resulting from stagnation or increase in mortality among the worst-off segment of the population. Female mortality increased in a large number of counties, primarily because of chronic diseases related to smoking, overweight and obesity, and high blood pressure.
Majid Ezzati and colleagues analyze US county-level mortality data for 1961 to 1999, and find a steady increase in mortality inequality across counties between 1983 and 1999.
Editors' Summary
It has long been recognized that the number of years that distinct groups of people in the United States would be expected to live based on their current mortality patterns (“life expectancy”) varies enormously. For example, white Americans tend to live longer than black Americans, the poor tend to have shorter life expectancies than the wealthy, and women tend to outlive men. Where one lives might also be a factor that determines his or her life expectancy, because of social conditions and health programs in different parts of the country.
Why Was the Study Done?
While life expectancies have generally been rising across the United States over time, there is little information, especially over the long term, on the differences in life expectancies across different counties. The researchers therefore set out to examine whether there were different life expectancies across different US counties over the last four decades. The researchers chose to look at counties—the smallest geographic units for which data on death rates are collected in the US—because it allowed them to make comparisons between small subgroups of people that share the same administrative structure.
What Did the Researchers Do and Find?
The researchers looked at differences in death rates between all counties in US states plus the District of Columbia over four decades, from 1961 to 1999. They obtained the data on number of deaths from the National Center for Health Statistics, and they obtained data on the number of people living in each county from the US Census. The NCHS did not provide death data after 2001. They broke the death rates down by sex and by disease to assess trends over time for women and men, and for different causes of death.
Over these four decades, the researchers found that the overall US life expectancy increased from 67 to 74 years of age for men and from 74 to 80 years for women. Between 1961 and 1983 the death rate fell in both men and women, largely due to reductions in deaths from cardiovascular disease (heart disease and stroke). During this same period, 1961–1983, the differences in death rates among/across different counties fell. However, beginning in the early 1980s the differences in death rates among/across different counties began to increase. The worst-off counties no longer experienced a fall in death rates, and in a substantial number of counties, mortality actually increased, especially for women, a shift that the researchers call “the reversal of fortunes.” This stagnation in the worst-off counties was primarily caused by a slowdown or halt in the reduction of deaths from cardiovascular disease coupled with a moderate rise in a number of other diseases, such as lung cancer, chronic lung disease, and diabetes, in both men and women, and a rise in HIV/AIDS and homicide in men. The researchers' key finding, therefore, was that the differences in life expectancy across different counties initially narrowed and then widened.
What Do these Findings Mean?
The findings suggest that beginning in the early 1980s and continuing through 1999 those who were already disadvantaged did not benefit from the gains in life expectancy experienced by the advantaged, and some became even worse off. The study emphasizes how important it is to monitor health inequalities between different groups, in order to ensure that everyone—and not just the well-off—can experience gains in life expectancy. Although the “reversal of fortune” that the researchers found applied to only a minority of the population, the authors argue that their study results are troubling because an oft-stated aim of the US health system is the improvement of the health of “all people, and especially those at greater risk of health disparities” (see, for example
Additional Information.
Please access these Web sites via the online version of this summary at
A study by Nancy Krieger and colleagues, published in PLoS Medicine in February 2008, documented a similar “fall and rise” in health inequities. Krieger and colleagues reported that the difference in health between rich and poor and between different racial/ethnic groups, as measured by rates of dying young and of infant deaths, shrank in the US from 1966 to 1980 then widened from 1980 to 2002
Murray and colleagues, in a 2006 PLoS Medicine article, calculated US mortality rates according to “race-county” units and divided into the “eight Americas,” and found disparities in life expectancy across them
The US Centers for Disease Control has an Office of Minority Health and Health Disparities. The office “aims to accelerate CDC's health impact in the US population and to eliminate health disparities for vulnerable populations as defined by race/ethnicity, socioeconomic status, geography, gender, age, disability status, risk status related to sex and gender, and among other populations identified to be at-risk for health disparities”
Wikipedia has a chapter on health disparities (note that Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
In 2001 the US Agency for Healthcare Research and Quality sponsored a workshop on “strategies to reduce health disparities”
PMCID: PMC2323303  PMID: 18433290
11.  Expressed racial identity and hypertension in a telephone survey sample from Toronto and Vancouver, Canada: do socioeconomic status, perceived discrimination and psychosocial stress explain the relatively high risk of hypertension for Black Canadians? 
Canadian research on racial health inequalities that foregrounds socially constructed racial identities and social factors which can explain consequent racial health inequalities is rare. This paper adopts a social typology of salient racial identities in contemporary Canada, empirically documents consequent racial inequalities in hypertension in an original survey dataset from Toronto and Vancouver, Canada, and then attempts to explain the inequalities in hypertension with information on socioeconomic status, perceived experiences with institutionalized and interpersonal discrimination, and psychosocial stress.
Telephone interviews were conducted in 2009 with 706 randomly selected adults living in the City of Toronto and 838 randomly selected adults living in the Vancouver Census Metropolitan Area. Bivariate analyses and logistic regression modeling were used to examine relationships between racial identity, hypertension, socio-demographic factors, socioeconomic status, perceived discrimination and psychosocial stress.
The Black Canadians in the sample were the most likely to report major and routine discriminatory experiences and were the least educated and the poorest. Black respondents were significantly more likely than Asian, South Asian and White respondents to report hypertension controlling for age, immigrant status and city of residence. Of the explanatory factors examined in this study, only educational attainment explained some of the relative risk of hypertension for Black respondents. Most of the risk remained unexplained in the models.
Consistent with previous Canadian research, socioeconomic status explained a small portion of the relatively high risk of hypertension documented for the Black respondents. Perceived experiences of discrimination both major and routine and self-reported psychosocial stress did not explain these racial inequalities in hypertension. Conducting subgroup analyses by gender, discerning between real and perceived experiences of discrimination and considering potentially moderating factors such as coping strategy and internalization of racial stereotypes are important issues to address in future Canadian racial inequalities research of this kind.
PMCID: PMC3520873  PMID: 23061401
Canada; Racial identity; Hypertension; Socioeconomic status; Perceived discrimination; Psychosocial stress
Social science & medicine (1982)  2010;71(2):251-258.
Although social stratification persists in the US, differentially influencing the well-being of ethnically defined groups, ethnicity concepts and their implications for health disparities remain under-examined. Ethnicity is a complex social construct that influences personal identity and group social relations. Ethnic identity, ethnic classification systems, the groupings that compose each system and the implications of assignment to one or another ethnic category are place-, time- and context-specific. In the US, racial stratification uniquely shapes expressions of and understandings about ethnicity. Ethnicity is typically invoked via the term, ‘race/ethnicity’; however, it is unclear whether this heralds a shift away from racialization or merely extends flawed racial taxonomies to populations whose cultural and phenotypic diversity challenge traditional racial classification. We propose that ethnicity be conceptualized as a two-dimensional, context-specific, social construct with an attributional dimension that describes group characteristics (e.g., culture, nativity) and a relational dimension that indexes a group’s location within a social hierarchy (e.g., minority vs. majority status). This new conceptualization extends prior definitions in ways that facilitate research on ethnicization, social stratification and health inequities. While federal ethnic and racial categories are useful for administrative purposes such as monitoring the inclusion of minorities in research, and traditional ethnicity concepts (e.g., culture) are useful for developing culturally appropriate interventions, our relational dimension of ethnicity is useful for studying the relationships between societal factors and health inequities. We offer a new conceptualization of ethnicity and outline next steps for employing socially meaningful measures of ethnicity in empirical research. Ethnicity is both increasingly complex and increasingly central to social life; therefore, improving its conceptualization and measurement is crucial for advancing research on ethnic health inequities.
PMCID: PMC2908006  PMID: 20488602
USA; ethnic groups; ethnicity; health disparities; race relations; social epidemiology; social stratification; concepts
13.  Reliability of Internet- Versus Telephone-Administered Questionnaires in a Diverse Sample of Smokers 
Smoking is more prevalent among lower-income individuals and certain racial/ethnic minorities. Addressing tobacco cessation among diverse populations is an urgent public health priority. As Internet use continues to rise among all segments of the US population, Web-based interventions have enormous potential to reach priority populations. Conducting Web-based smoking cessation research in priority populations requires psychometrically sound measurement instruments. To date, only one published study has examined the psychometric properties of Internet-administered measures commonly used in Web-based cessation trials. However, the sample was homogeneous with regard to race/ethnicity and income. We sought to replicate and extend these findings in a more diverse sample of smokers.
The aim was to examine the internal consistency and test-retest reliability of measures commonly used in smoking cessation clinical trials among racial/ethnic minorities and smokers with lower income.
Participants were enrolled in a randomized trial of the efficacy of an Internet smoking cessation program between June 2005 and September 2006. Following a baseline telephone assessment and randomization into the parent trial, participants were recruited to the reliability substudy. In phase I of recruitment, all participants in the parent trial were recruited to the substudy; in phase II, all consecutive racial/ethnic minority participants in the parent trial were recruited. Race and ethnicity were assessed via self-report using two standard items from the US Office of Management and Budget. An email was sent 2 days after the telephone assessment with a link to the Internet survey. Measures examined were quit methods, perceived stress, depression, social support, smoking temptations, alcohol use, perceived health status, and income. Internal consistency and test-retest reliability of Internet- versus telephone-administered measures were examined within four strata defined by race/ethnicity (non-Hispanic White, racial/ethnic minority) and annual household income (US $40,000 or less, more than $40,000).
Of the 442 individuals invited, 319 participated (72% response rate): 52.4% were non-Hispanic White, 22.9% Black, 11.6% Hispanic, 7.8% Asian, 4.4% American Indian / Alaska Native, and 1% Native Hawaiian / Other Pacific Islander. About half (49.4%) reported an annual household income of US $40,000 or less, and 25.7% had a high school degree or less. Test-retest reliability was satisfactory to excellent across all strata for the majority of measures examined: 9 of 12 continuous variables had intraclass correlation coefficients ≥ 0.70, and 10 of 18 binary variables and both ordinal variables had kappa coefficients ≥ 0.70. Test-retest reliability of several quit methods varied across strata.
Race/ethnicity and income do not affect the psychometric properties of most Internet-administered measures examined. This knowledge adds to the confidence of conducting Web-based smoking cessation research and strengthens the scientific rigor of collecting information via the Internet on racial/ethnic minority and low-income subgroups.
Trial registration NCT00282009 (parent trial)
PMCID: PMC2483847  PMID: 18364345
Reliability; smoking; Internet; diversity; measurement; psychometrics; minority groups; questionnaires; socioeconomic factors; social class; poverty; African Americans; Hispanic Americans
14.  Disparities in Breast Cancer Treatment and Outcomes: Biological, Social, and Health System Determinants and Opportunities for Research 
The Oncologist  2013;18(9):986-993.
This article summarizes existing literature exploring reasons for racial disparities in breast cancer mortality, with an emphasis on treatment disparities and opportunities for future research. Recognition that variation in cancer care quality may be correlated with race (and socioeconomic and health system factors) may assist policy makers in identifying strategies to more equally distribute clinical expertise and health infrastructure across multiple user populations.
Racial disparities in breast cancer mortality have been widely documented for several decades and persist despite advances in receipt of mammography across racial groups. This persistence leads to questions about the roles of biological, social, and health system determinants of poor outcomes. Cancer outcomes are a function not only of innate biological factors but also of modifiable characteristics of individual behavior and decision making as well as characteristics of patient-health system interaction and the health system itself. Attempts to explain persistent racial disparities have mostly been limited to discussion of differences in insurance coverage, socioeconomic status, tumor stage at diagnosis, comorbidity, and molecular subtype of the tumor. This article summarizes existing literature exploring reasons for racial disparities in breast cancer mortality, with an emphasis on treatment disparities and opportunities for future research. Because breast cancer care requires a high degree of multidisciplinary team collaboration, ensuring that guideline recommended treatment (such as endocrine therapy for hormone receptor positive patients) is received by all racial/ethnic groups is critical and requires coordination across multiple providers and health care settings. Recognition that variation in cancer care quality may be correlated with race (and socioeconomic and health system factors) may assist policy makers in identifying strategies to more equally distribute clinical expertise and health infrastructure across multiple user populations.
PMCID: PMC3780646  PMID: 23939284
Breast cancer; Disparities; Cancer care quality; Race; Access to care
15.  Conceptualizing and Categorizing Race and Ethnicity in Health Services Research 
Health Services Research  2005;40(5 Pt 2):1658-1675.
Veterans Affairs (VA) patient populations are becoming increasingly diverse in race and ethnicity. The purpose of this paper is to (1) document the importance of using consistent standards of conceptualizing and categorizing race and ethnicity in health services research, (2) provide an overview of different methods currently used to assess race and ethnicity in health services research, and (3) suggest assessment methods that could be incorporated into health services research to ensure accurate assessment of disease prevalence and incidence, as well as accounts of appropriate health services use, in patients with different racial and ethnic backgrounds.
A critical review of published literature was used.
Principal Findings
Race is a complex, multidimensional construct. For some individuals, institutionalized racism and internalized racism are intertwined in the effects of race on health outcomes and health services use. Ethnicity is most commonly used as a social–political construct and includes shared origin, shared language, and shared cultural traditions. Acculturation appears to affect the strength of the relationships among ethnicity, health outcomes, and health services use.
Improved and consistent methods of data collection need to be developed for use by VA researchers across the country. VA research sites with patients representing specific population groups could use a core set of demographic items in addition to expanded modules designed to assess the ethnic diversity within these population groups. Improved and consistent methods of data collection could result in the collection of higher-quality data, which could lead to the identification of race- and ethnic-specific health services needs. These investigations could in turn lead to the development of interventions designed to reduce or eliminate these disparities.
PMCID: PMC1361221  PMID: 16179001
Race and ethnicity; measurement; health disparities
16.  Disparities in access to care and satisfaction among U.S. children: the roles of race/ethnicity and poverty status. 
Public Health Reports  2005;120(4):431-441.
OBJECTIVES: The study assessed the progress made toward reducing racial and ethnic disparities in access to health care among U.S. children between 1996 and 2000. METHODS: Data are from the Household Component of the 1996 and 2000 Medical Expenditure Panel Survey. Bivariate associations of combinations of race/ethnicity and poverty status groups were examined with four measures of access to health care and a single measure of satisfaction. Logistic regression was used to examine the association of race/ethnicity with access, controlling for sociodemographic factors associated with access to care. To highlight the role of income, we present models with and without controlling for poverty status. RESULTS: Racial and ethnic minority children experience significant deficits in accessing medical care compared with whites. Asians, Hispanics, and blacks were less likely than whites to have a usual source of care, health professional or doctor visit, and dental visit in the past year. Asians were more likely than whites to be dissatisfied with the quality of medical care in 2000 (but not 1996), while blacks and Hispanics were more likely than whites to be dissatisfied with the quality of medical care in 1996 (but not in 2000). Both before and after controlling for health insurance coverage, poverty status, health status, and several other factors associated with access to care, these disparities in access to care persisted between 1996 and 2000. CONCLUSIONS: Continued monitoring of racial and ethnic differences is necessary in light of the persistence of racial/ethnic and socioeconomic disparities in access to care. Given national goals to achieve equity in health care and eliminate racial/ ethnic disparities in health, greater attention needs to be paid to the interplay of race/ethnicity factors and poverty status in influencing access.
PMCID: PMC1497738  PMID: 16025723
17.  Explaining Racial/ethnic Disparities in children’s dental health: a decomposition analysis 
American Journal of Public Health  2012;102(5):859-866.
To measure racial/ethnic inequalities in child dental health and quantify the contribution of several household, neighborhood, and geographic effects to these disparities using a decomposition analysis.
Using data from the 2007 National Survey of Children’s Health, we measured and decomposed racial/ethnic disparities in selected child dental health and dental preventive care outcomes. We employed a decomposition model to quantify the extent to which demographic, socioeconomic, maternal health, health insurance, neighborhood, and geographic effects explain these disparities.
Significant racial/ethnic disparities in dental health were observed. Hispanic children had the poorest dental health and lowest preventive dental care use, followed by African-American children, compared to Whites. The model explanatory variables accounted for a large proportion of the disparities in dental health (58–77%) and for most of the disparities in preventive dental care (89–100%). Socioeconomic status (maternal education and household poverty level) was the single most relevant factor for explaining these disparities and accounted for 71% of the gap between African-American and white children in preventive dental care, and 55% of that gap between Hispanic and white children. Other relevant factors for explaining disparities included maternal health, age, and marital status, neighborhood safety and social capital, and state of residence.
Racial/ethnic disparities in child dental health in the US are mostly socioeconomically driven involving household and neighborhood contributors. Reducing these disparities requires policies that recognize the multilevel pathways underlying these disparities.
PMCID: PMC3402215  PMID: 22420801
18.  Drivers of Inequality in Millennium Development Goal Progress: A Statistical Analysis 
PLoS Medicine  2010;7(3):e1000241.
David Stuckler and colleagues examine the impact of the HIV and noncommunicable disease epidemics on low-income countries' progress toward the Millennium Development Goals for health.
Many low- and middle-income countries are not on track to reach the public health targets set out in the Millennium Development Goals (MDGs). We evaluated whether differential progress towards health MDGs was associated with economic development, public health funding (both overall and as percentage of available domestic funds), or health system infrastructure. We also examined the impact of joint epidemics of HIV/AIDS and noncommunicable diseases (NCDs), which may limit the ability of households to address child mortality and increase risks of infectious diseases.
Methods and Findings
We calculated each country's distance from its MDG goals for HIV/AIDS, tuberculosis, and infant and child mortality targets for the year 2005 using the United Nations MDG database for 227 countries from 1990 to the present. We studied the association of economic development (gross domestic product [GDP] per capita in purchasing-power-parity), the relative priority placed on health (health spending as a percentage of GDP), real health spending (health system expenditures in purchasing-power-parity), HIV/AIDS burden (prevalence rates among ages 15–49 y), and NCD burden (age-standardised chronic disease mortality rates), with measures of distance from attainment of health MDGs. To avoid spurious correlations that may exist simply because countries with high disease burdens would be expected to have low MDG progress, and to adjust for potential confounding arising from differences in countries' initial disease burdens, we analysed the variations in rates of change in MDG progress versus expected rates for each country. While economic development, health priority, health spending, and health infrastructure did not explain more than one-fifth of the differences in progress to health MDGs among countries, burdens of HIV and NCDs explained more than half of between-country inequalities in child mortality progress (R2-infant mortality  = 0.57, R2-under 5 mortality  = 0.54). HIV/AIDS and NCD burdens were also the strongest correlates of unequal progress towards tuberculosis goals (R2 = 0.57), with NCDs having an effect independent of HIV/AIDS, consistent with micro-level studies of the influence of tobacco and diabetes on tuberculosis risks. Even after correcting for health system variables, initial child mortality, and tuberculosis diseases, we found that lower burdens of HIV/AIDS and NCDs were associated with much greater progress towards attainment of child mortality and tuberculosis MDGs than were gains in GDP. An estimated 1% lower HIV prevalence or 10% lower mortality rate from NCDs would have a similar impact on progress towards the tuberculosis MDG as an 80% or greater rise in GDP, corresponding to at least a decade of economic growth in low-income countries.
Unequal progress in health MDGs in low-income countries appears significantly related to burdens of HIV and NCDs in a population, after correcting for potentially confounding socioeconomic, disease burden, political, and health system variables. The common separation between NCDs, child mortality, and infectious syndromes among development programs may obscure interrelationships of illness affecting those living in poor households—whether economic (e.g., as money spent on tobacco is lost from child health expenditures) or biological (e.g., as diabetes or HIV enhance the risk of tuberculosis).
Please see later in the article for the Editors' Summary
Editors' Summary
In 2000, 189 countries adopted the United Nations (UN) Millennium Declaration, which commits the world to the eradication of extreme poverty by 2015. The Declaration lists eight Millennium Development Goals (MDGs), 21 quantifiable targets, and 60 indicators of progress. So, for example, MDG 4 aims to reduce child mortality (deaths). The target for this goal is to reduce the number of children who die each year before they are five years old (the under-five mortality rate) to two-thirds of its 1990 value by 2015. Indicators of progress toward this goal include the under-five mortality rate and the infant mortality rate. Because poverty and ill health are inextricably linked—ill health limits the ability of individuals and nations to improve their economic status, and poverty contributes to the development of many illnesses—two other MDGs also tackle public health issues. MDG 5 sets a target of reducing maternal mortality by three-quarters of its 1990 level by 2015. MDG 6 aims to halt and begin to reverse the spread of HIV/AIDS, malaria, and other major diseases such as tuberculosis by 2015.
Why Was This Study Done?
Although progress has been made toward achieving the MDGs, few if any of the targets are likely to be met by 2015. Worryingly, low-income countries are falling furthest behind their MDG targets. For example, although child mortality has been declining globally, in many poor countries there has been little or no progress. What is the explanation for this and other inequalities in progress toward the health MDGs? Some countries may simply lack the financial resources needed to combat epidemics or may allocate only a low proportion of their gross domestic product (GDP) to health. Alternatively, money allocated to health may not always reach the people who need it most because of an inadequate health infrastructure. Finally, coexisting epidemics may be hindering progress toward the MDG health targets. Thus, the spread of HIV/AIDS may be hindering attempts to limit the spread of tuberculosis because HIV infection increases the risk of active tuberculosis, and ongoing epidemics of diabetes and other noncommunicable diseases (NCDs) may be affecting the attainment of health MDGs by diverting scarce resources. In this study, the researchers investigate whether any of these possibilities is driving the inequalities in MDG progress.
What Did the Researchers Do and Find?
The researchers calculated how far 227 countries were from their MDG targets for HIV, tuberculosis, and infant and child mortality in 2005 using information collected by the UN. They then used statistical methods to study the relationship between this distance and economic development (GDP per person), health spending as a proportion of GDP (health priority), actual health system expenditures, health infrastructure, HIV burden, and NCD burden in each country. Economic development, health priority, health spending, and health infrastructure explained no more than one-fifth of the inequalities in progress toward health MDGs. By contrast, the HIV and NCD burdens explained more than half of inequalities in child mortality progress and were strongly associated with unequal progress toward tuberculosis goals. Furthermore, the researchers calculated that a 1% reduction in the number of people infected with HIV or a 10% reduction in rate of deaths from NCDs in a population would have a similar impact on progress toward the tuberculosis MDG target as a rise in GDP corresponding to at least a decade of growth in low-income countries.
What Do These Findings Mean?
These findings are limited by the quality of the available data on health indicators in low-income countries and, because the researchers used country-wide data, their findings only reveal possible drivers of inequalities in progress toward MDGs in whole countries and may mask drivers of within-country inequalities. Nevertheless, as one of the first attempts to analyze the determinants of global inequalities in progress toward the health MDGs, these findings have important implications for global health policy. Most importantly, the finding that unequal progress is related to the burdens of HIV and NCDs in populations suggests that programs designed to achieve health MDGs must consider all the diseases and factors that can trap households in vicious cycles of illness and poverty, especially since the achievement of feasible reductions in NCDs in low-income countries could greatly enhance progress towards health MDGs.
Additional Information
Please access these Web sites via the online version of this summary at
The United Nations Millennium Development Goals website provides detailed information about the Millennium Declaration, the MDGs, their targets and their indicators
The Millennium Development Goals Report 2009 and its progress chart provide an up-to-date assessment of progress towards the MDGs
The World Health Organization provides information about poverty and health and health and development
PMCID: PMC2830449  PMID: 20209000
19.  Investigating the Relationship between Socially-Assigned Ethnicity, Racial Discrimination and Health Advantage in New Zealand 
PLoS ONE  2013;8(12):e84039.
While evidence of the contribution of racial discrimination to ethnic health disparities has increased significantly, there has been less research examining relationships between ascribed racial/ethnic categories and health. It has been hypothesized that in racially-stratified societies being assigned as belonging to the dominant racial/ethnic group may be associated with health advantage. This study aimed to investigate associations between socially-assigned ethnicity, self-identified ethnicity, and health, and to consider the role of self-reported experience of racial discrimination in any relationships between socially-assigned ethnicity and health.
The study used data from the 2006/07 New Zealand Health Survey (n = 12,488), a nationally representative cross-sectional survey of adults 15 years and over. Racial discrimination was measured as reported individual-level experiences across five domains. Health outcome measures examined were self-reported general health and psychological distress.
The study identified varying levels of agreement between participants' self-identified and socially-assigned ethnicities. Individuals who reported both self-identifying and being socially-assigned as always belonging to the dominant European grouping tended to have more socioeconomic advantage and experience less racial discrimination. This group also had the highest odds of reporting optimal self-rated health and lower mean levels of psychological distress. These differences were attenuated in models adjusting for socioeconomic measures and individual-level racial discrimination.
The results suggest health advantage accrues to individuals who self-identify and are socially-assigned as belonging to the dominant European ethnic grouping in New Zealand, operating in part through socioeconomic advantage and lower exposure to individual-level racial discrimination. This is consistent with the broader evidence of the negative impacts of racism on health and ethnic inequalities that result from the inequitable distribution of health determinants, the harm and chronic stress linked to experiences of racial discrimination, and via the processes and consequences of racialization at a societal level.
PMCID: PMC3877153  PMID: 24391876
20.  Socioeconomic Inequalities in Lung Cancer Treatment: Systematic Review and Meta-Analysis 
PLoS Medicine  2013;10(2):e1001376.
In a systematic review and meta-analysis, Lynne Forrest and colleagues find that patients with lung cancer who are more socioeconomically deprived are less likely to receive surgical treatment, chemotherapy, or any type of treatment combined, compared with patients who are more socioeconomically well off, regardless of cancer stage or type of health care system.
Intervention-generated inequalities are unintended variations in outcome that result from the organisation and delivery of health interventions. Socioeconomic inequalities in treatment may occur for some common cancers. Although the incidence and outcome of lung cancer varies with socioeconomic position (SEP), it is not known whether socioeconomic inequalities in treatment occur and how these might affect mortality. We conducted a systematic review and meta-analysis of existing research on socioeconomic inequalities in receipt of treatment for lung cancer.
Methods and Findings
MEDLINE, EMBASE, and Scopus were searched up to September 2012 for cohort studies of participants with a primary diagnosis of lung cancer (ICD10 C33 or C34), where the outcome was receipt of treatment (rates or odds of receiving treatment) and where the outcome was reported by a measure of SEP. Forty-six papers met the inclusion criteria, and 23 of these papers were included in meta-analysis. Socioeconomic inequalities in receipt of lung cancer treatment were observed. Lower SEP was associated with a reduced likelihood of receiving any treatment (odds ratio [OR] = 0.79 [95% CI 0.73 to 0.86], p<0.001), surgery (OR = 0.68 [CI 0.63 to 0.75], p<0.001) and chemotherapy (OR = 0.82 [95% CI 0.72 to 0.93], p = 0.003), but not radiotherapy (OR = 0.99 [95% CI 0.86 to 1.14], p = 0.89), for lung cancer. The association remained when stage was taken into account for receipt of surgery, and was found in both universal and non-universal health care systems.
Patients with lung cancer living in more socioeconomically deprived circumstances are less likely to receive any type of treatment, surgery, and chemotherapy. These inequalities cannot be accounted for by socioeconomic differences in stage at presentation or by differences in health care system. Further investigation is required to determine the patient, tumour, clinician, and system factors that may contribute to socioeconomic inequalities in receipt of lung cancer treatment.
Please see later in the article for the Editors' Summary
Editors' Summary
Lung cancer is the most commonly occurring cancer worldwide and the commonest cause of cancer-related death. Like all cancers, lung cancer occurs when cells begin to grow uncontrollably because of changes in their genes. The most common trigger for these changes in lung cancer is exposure to cigarette smoke. Most cases of lung cancer are non-small cell lung cancer, the treatment for which depends on the “stage” of the disease when it is detected. Stage I tumors, which are confined to the lung, can be removed surgically. Stage II tumors, which have spread to nearby lymph nodes, are usually treated with surgery plus chemotherapy or radiotherapy. For more advanced tumors, which have spread throughout the chest (stage III) or throughout the body (stage IV), surgery generally does not help to slow tumor growth and the cancer is treated with chemotherapy and radiotherapy. Small cell lung cancer, the other main type of lung cancer, is nearly always treated with chemotherapy and radiotherapy but sometimes with surgery as well. Overall, because most lung cancers are not detected until they are quite advanced, less than 10% of people diagnosed with lung cancer survive for 5 years.
Why Was This Study Done?
As with many other cancers, socioeconomic inequalities have been reported for both the incidence of and the survival from lung cancer in several countries. It is thought that the incidence of lung cancer is higher among people of lower socioeconomic position than among wealthier people, in part because smoking rates are higher in poorer populations. Similarly, it has been suggested that survival is worse among poorer people because they tend to present with more advanced disease, which has a worse prognosis (predicted outcome) than early disease. But do socioeconomic inequalities in treatment exist for lung cancer and, if they do, could these inequalities contribute to the poor survival rates among populations of lower socioeconomic position? In this systematic review and meta-analysis, the researchers investigate the first of these questions. A systematic review uses predefined criteria to identify all the research on a given topic; a meta-analysis is a statistical approach that combines the results of several studies.
What Did the Researchers Do and Find?
The researchers identified 46 published papers that studied people with lung cancer in whom receipt of treatment was reported in terms of an indicator of socioeconomic position, such as a measure of income or deprivation. Twenty-three of these papers were suitable for inclusion in a meta-analysis. Lower socioeconomic position was associated with a reduced likelihood of receiving any treatment. Specifically, the odds ratio (chance) of people in the lowest socioeconomic group receiving any treatment was 0.79 compared to people in the highest socioeconomic group. Lower socioeconomic position was also associated with a reduced chance of receiving surgery (OR = 0.68) and chemotherapy (OR = 0.82), but not radiotherapy. The association between socioeconomic position and surgery remained after taking cancer stage into account. That is, when receipt of surgery was examined in early-stage patients only, low socioeconomic position remained associated with reduced likelihood of surgery. Notably, the association between socioeconomic position and receipt of treatment was similar in studies undertaken in countries where health care is free at the point of service for everyone (for example, the UK) and in countries with primarily private insurance health care systems (for example, the US).
What Do These Findings Mean?
These findings suggest that patients in more socioeconomically deprived circumstances are less likely to receive any type of treatment, surgery, and chemotherapy (but not radiotherapy) for lung cancer than people who are less socioeconomically deprived. Importantly, these inequalities cannot be explained by socioeconomic differences in stage at presentation or by differences in health care system. The accuracy of these findings may be affected by several factors. For example, it is possible that only studies that found an association between socioeconomic position and receipt of treatment have been published (publication bias). Moreover, the studies identified did not include information regarding patient preferences, which could help explain at least some of the differences. Nevertheless, these results do suggest that socioeconomic inequalities in receipt of treatment may exacerbate socioeconomic inequalities in the incidence of lung cancer and may contribute to the observed poorer outcomes in lower socioeconomic position groups. Further research is needed to determine the system and patient factors that contribute to socioeconomic inequalities in lung cancer treatment before clear recommendations for changes to policy and practice can be made.
Additional Information
Please access these Web sites via the online version of this summary at
The US National Cancer Institute provides information about all aspects of lung cancer for patients and health care professionals (in English and Spanish); a monograph entitled Area Socioeconomic Variations in U. S. Cancer Incidence, Mortality, Stage, Treatment, and Survival, 19751999 is available
Cancer Research UK also provides detailed information about lung cancer and links to other resources, such as a policy statement on socioeconomic inequalities in cancer and a monograph detailing cancer and health inequalities in the UK
The UK National Health Service Choices website has a page on lung cancer that includes personal stories about diagnosis and treatment
MedlinePlus provides links to other US sources of information about lung cancer (in English and Spanish)
PMCID: PMC3564770  PMID: 23393428
21.  Exploring Health Disparities in Integrated Communities: Overview of the EHDIC Study 
Journal of Urban Health   2007;85(1):11-21.
Progress in understanding the nature of health disparities requires data that are race-comparative while overcoming confounding between race, socioeconomic status, and segregation. The Exploring Health Disparities in Integrated Communities (EHDIC) study is a multisite cohort study that will address these confounders by examining the nature of health disparities within racially integrated communities without racial disparities in socioeconomic status. Data consisted of a structured questionnaire and blood pressure measurements collected from a sample of the adult population (age 18 and older) of two racially integrated contiguous census tracts. This manuscript reports on baseline results from the first EHDIC site, a low-income urban community in southwest Baltimore, Maryland (EHDIC-SWB). In the adjusted models, African Americans had lower rates of smoking and fair or poor self-rated health than whites, but no race differences in obesity, drinking, or physical inactivity. Our findings indicate that accounting for race differences in exposure to social conditions reduces or eliminates some health-related disparities. Moreover, these findings suggest that solutions to the seemingly intractable health disparities problem that target social determinants may be effective, especially those factors that are confounded with racial segregation. Future research in the area of health disparities should seek ways to account for confounding from SES and segregation.
PMCID: PMC2430138  PMID: 17999196
Health disparities; Confounding; Race; Socioeconomic status; Segregation; Integration; Urban; Community
22.  Ethnicity/race, ethics, and epidemiology. 
Ethnicity/race is a much-studied variable in epidemiology. There has been little consensus about what self-reported ethnicity/race represents, but it is a measure of some combination of genetic, socioeconomic, and cultural factors. The present article will attempt to: 1.) Elucidate the limitations of contemporary discourse on ethnicity/race that emphasizes the genetic and socioeconomic dimensions as competing explanatory frameworks; 2.) Demonstrate how considerable attention to the cultural dimension facilitates understanding of race differences in health-related outcomes; and 3.) Discuss interpretations of disparities in health status of African Americans versus European Americans from an ethical perspective. A major challenge to the discourse on ethnicity/race and health being limited to socioeconomic and genetic considerations is the lack of attention to the third alternative of a cultural perspective. The combined cultural ideologies of individualism and racism undermine the utility of epidemiologic research in health promotion and disease prevention campaigns aimed at reducing the racial gaps in health status. An ethical analysis supplements the cultural perspective. Ethics converge with culture on the notion of values influencing the study of ethnicity/race in epidemiology. A cultural approach to the use of ethnicity/race in epidemiologic research addresses methodological limitations, public health traditions, and ethical imperatives.
PMCID: PMC2594561  PMID: 12934873
23.  Non-hispanic whites have higher risk for pulmonary impairment from pulmonary tuberculosis 
BMC Public Health  2012;12:119.
Disparities in outcomes associated with race and ethnicity are well documented for many diseases and patient populations. Tuberculosis (TB) disproportionately affects economically disadvantaged, racial and ethnic minority populations. Pulmonary impairment after tuberculosis (PIAT) contributes heavily to the societal burden of TB. Individual impacts associated with PIAT may vary by race/ethnicity or socioeconomic status.
We analyzed the pulmonary function of 320 prospectively identified patients with pulmonary tuberculosis who had completed at least 20 weeks standard anti-TB regimes by directly observed therapy. We compared frequency and severity of spirometry-defined PIAT in groups stratified by demographics, pulmonary risk factors, and race/ethnicity, and examined clinical correlates to pulmonary function deficits.
Pulmonary impairment after tuberculosis was identified in 71% of non-Hispanic Whites, 58% of non-Hispanic Blacks, 49% of Asians and 32% of Hispanics (p < 0.001). Predictors for PIAT varied between race/ethnicity. PIAT was evenly distributed across all levels of socioeconomic status suggesting that PIAT and socioeconomic status are not related. PIAT and its severity were significantly associated with abnormal chest x-ray, p < 0.0001. There was no association between race/ethnicity and time to beginning TB treatment, p = 0.978.
Despite controlling for cigarette smoking, socioeconomic status and time to beginning TB treatment, non-Hispanic White race/ethnicity remained an independent predictor for disproportionately frequent and severe pulmonary impairment after tuberculosis relative to other race/ethnic groups. Since race/ethnicity was self reported and that race is not a biological construct: these findings must be interpreted with caution. However, because race/ethnicity is a proxy for several other unmeasured host, pathogen or environment factors that may contribute to disparate health outcomes, these results are meant to suggest hypotheses for further research.
PMCID: PMC3305434  PMID: 22325005
24.  Facilitating the Recruitment of Minority Ethnic People into Research: Qualitative Case Study of South Asians and Asthma 
PLoS Medicine  2009;6(10):e1000148.
Aziz Sheikh and colleagues report on a qualitative study in the US and the UK to investigate ways to bolster recruitment of South Asians into asthma studies, including making inclusion of diverse populations mandatory.
There is international interest in enhancing recruitment of minority ethnic people into research, particularly in disease areas with substantial ethnic inequalities. A recent systematic review and meta-analysis found that UK South Asians are at three times increased risk of hospitalisation for asthma when compared to white Europeans. US asthma trials are far more likely to report enrolling minority ethnic people into studies than those conducted in Europe. We investigated approaches to bolster recruitment of South Asians into UK asthma studies through qualitative research with US and UK researchers, and UK community leaders.
Methods and Findings
Interviews were conducted with 36 researchers (19 UK and 17 US) from diverse disciplinary backgrounds and ten community leaders from a range of ethnic, religious, and linguistic backgrounds, followed by self-completion questionnaires. Interviews were digitally recorded, translated where necessary, and transcribed. The Framework approach was used for analysis. Barriers to ethnic minority participation revolved around five key themes: (i) researchers' own attitudes, which ranged from empathy to antipathy to (in a minority of cases) misgivings about the scientific importance of the question under study; (ii) stereotypes and prejudices about the difficulties in engaging with minority ethnic populations; (iii) the logistical challenges posed by language, cultural differences, and research costs set against the need to demonstrate value for money; (iv) the unique contexts of the two countries; and (v) poorly developed understanding amongst some minority ethnic leaders of what research entails and aims to achieve. US researchers were considerably more positive than their UK counterparts about the importance and logistics of including ethnic minorities, which appeared to a large extent to reflect the longer-term impact of the National Institutes of Health's requirement to include minority ethnic people.
Most researchers and community leaders view the broadening of participation in research as important and are reasonably optimistic about the feasibility of recruiting South Asians into asthma studies provided that the barriers can be overcome. Suggested strategies for improving recruitment in the UK included a considerably improved support structure to provide academics with essential contextual information (e.g., languages of particular importance and contact with local gatekeepers), and the need to ensure that care is taken to engage with the minority ethnic communities in ways that are both culturally appropriate and sustainable; ensuring reciprocal benefits was seen as one key way of avoiding gatekeeper fatigue. Although voluntary measures to encourage researchers may have some impact, greater impact might be achieved if UK funding bodies followed the lead of the US National Institutes of Health requiring recruitment of ethnic minorities. Such a move is, however, likely in the short- to medium-term, to prove unpopular with many UK academics because of the added “hassle” factor in engaging with more diverse populations than many have hitherto been accustomed to.
Please see later in the article for the Editors' Summary
Editors' Summary
In an ideal world, everyone would have the same access to health care and the same health outcomes (responses to health interventions). However, health inequalities—gaps in health care and in health between different parts of the population—exist in many countries. In particular, people belonging to ethnic minorities in the UK, the US, and elsewhere have poorer health outcomes for several conditions than people belonging to the ethnic majority (ethnicity is defined by social characteristics such as cultural tradition or national origin). For example, in the UK, people whose ancestors came from the Indian subcontinent (also known as South Asians and comprising in the main of people of Indian, Pakistani, and Bangladeshi origin) are three times as likely to be admitted to hospital for asthma as white Europeans. The reasons underpinning ethnic health inequalities are complex. Some inequalities may reflect intrinsic differences between groups of people—some ethnic minorities may inherit genes that alter their susceptibility to a specific disease. Other ethnic health inequalities may arise because of differences in socioeconomic status or because different cultural traditions affect the uptake of health care services.
Why Was This Study Done?
Minority ethnic groups are often under-represented in health research, which could limit the generalizability of research findings. That is, an asthma treatment that works well in a trial where all the participants are white Europeans might not be suitable for South Asians. Clinicians might nevertheless use the treatment in all their patients irrespective of their ethnicity and thus inadvertently increase ethnic health inequality. So, how can ethnic minorities be encouraged to enroll into research studies? In this qualitative study, the investigators try to answer this question by talking to US and UK asthma researchers and UK community leaders about how they feel about enrolling ethnic minorities into research studies. The investigators chose to compare the feelings of US and UK asthma researchers because minority ethnic people are more likely to enroll into US asthma studies than into UK studies, possibly because the US National Institute of Health's (NIH) Revitalization Act 1993 mandates that all NIH-funded clinical research must include people from ethnic minority groups; there is no similar mandatory policy in the UK.
What Did the Researchers Do and Find?
The investigators interviewed 16 UK and 17 US asthma researchers and three UK social researchers with experience of working with ethnic minorities. They also interviewed ten community leaders from diverse ethnic, religious and linguistic backgrounds. They then analyzed the interviews using the “Framework” approach, an analytical method in which qualitative data are classified and organized according to key themes and then interpreted. By comparing the data from the UK and US researchers, the investigators identified several barriers to ethnic minority participation in health research including: the attitudes of researchers towards the scientific importance of recruiting ethnic minority people into health research studies; prejudices about the difficulties of including ethnic minorities in health research; and the logistical challenges posed by language and cultural differences. In general, the US researchers were more positive than their UK counterparts about the importance and logistics of including ethnic minorities in health research. Finally, the investigators found that some community leaders had a poor understanding of what research entails and about its aims.
What Do These Findings Mean?
These findings reveal a large gap between US and UK researchers in terms of policy, attitudes, practices, and experiences in relation to including ethnic minorities in asthma research. However, they also suggest that most UK researchers and community leaders believe that it is both important and feasible to increase the participation of South Asians in asthma studies. Although some of these findings may have been affected by the study participants sometimes feeling obliged to give “politically correct” answers, these findings are likely to be generalizable to other diseases and to other parts of Europe. Given their findings, the researchers warn that a voluntary code of practice that encourages the recruitment of ethnic minority people into health research studies is unlikely to be successful. Instead, they suggest, the best way to increase the representation of ethnic minority people in health research in the UK might be to follow the US lead and introduce a policy that requires their inclusion in such research.
Additional Information
Please access these Web sites via the online version of this summary at
Families USA, a US nonprofit organization that campaigns for high-quality, affordable health care for all Americans, has information about many aspects of minority health in the US, including an interactive game about minority health issues
The US Agency for Healthcare Research and Quality has a section on minority health
The UK Department of Health provides information on health inequalities and a recent report on the experiences of patients in Black and minority ethnic groups
The UK Parliamentary Office of Science and Technology also has a short article on ethnicity and health
Information on the NIH Revitalization Act 1993 is available
NHS Evidences Ethnicity and Health has a variety of policy, clinical, and research resources on ethnicity and health
PMCID: PMC2752116  PMID: 19823568
25.  An Examination of Racial/Ethnic Disparities in Children’s Oral Health in the United States 
Assess the extent apparent racial/ethnic disparities in children’s oral health and oral health care are explained by factors other than race/ethnicity.
Data Source
2007 National Survey of Children’s Health, for children 2–17 years (N=82,020). Outcomes included parental reports of child’s oral health status, receipt of preventive dental care, and delayed dental care/unmet need. Model-based survey data analysis examined racial/ethnic disparities, controlling for other child, family, and community/state (contextual) factors.
Unadjusted results show large oral health disparities by race/ethnicity. Compared to non-Hispanic Whites, Hispanics and non-Hispanic Blacks were markedly more likely to be reported in only fair/poor oral health (odds ratios (ORs) [95% confidence intervals] 4.3 [4.0–4.6], 2.2 [2.0–2.4], respectively), lack preventive care (ORs 1.9 [1.8–2.0], 1.4 [1.3–1.5]), and experience delayed care/unmet need (ORs 1.5 [1.3–1.7], 1.4 [1.3–1.5]). Adjusting for child, family, and community/state factors reduced or eliminated racial/ethnic disparities. Adjusted ORs (AORs) for Hispanics and non-Hispanic Blacks attenuated for fair/poor oral health, to 1.6 [1.5–1.8] and 1.2 [1.1–1.4], respectively. Adjustment eliminated disparities in each group for lacking preventive care (AORs 1.0 [0.9–1.1], 1.1 [1.1–1.2]), and in Hispanics for delayed care/unmet need (AOR 1.0). Among non-Hispanic Blacks, adjustment reversed the disparity for delayed care/unmet need (AOR 0.6 [0.6–0.7]).
Racial/ethnic disparities in children’s oral health status and access were found to be attributable largely to determinants such as socioeconomic and health insurance factors. Efforts to decrease disparities may be more efficacious if targeted at the social, economic, and other factors associated with minority racial/ethnic status, and may also have collateral positive effects on sectors of the majority population who share similar social, economic and cultural characteristics.
PMCID: PMC3702186  PMID: 22970900
children; oral health; race/ethnic disparities

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