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1.  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
2.  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
3.  Socioeconomic status and prostate cancer incidence and mortality rates among the diverse population of California 
Cancer Causes & Control   2009;20(8):1431-1440.
The racial/ethnic disparities in prostate cancer rates are well documented, with the highest incidence and mortality rates observed among African-Americans followed by non-Hispanic Whites, Hispanics, and Asian/Pacific Islanders. Whether socioeconomic status (SES) can account for these differences in risk has been investigated in previous studies, but with conflicting results. Furthermore, previous studies have focused primarily on the differences between African-Americans and non-Hispanic Whites, and little is known for Hispanics and Asian/Pacific Islanders.
To further investigate the relationship between SES and prostate cancer among African-Americans, non-Hispanic Whites, Hispanics, and Asian/Pacific Islanders, we conducted a large population-based cross-sectional study of 98,484 incident prostate cancer cases and 8,997 prostate cancer deaths from California.
Data were abstracted from the California Cancer Registry, a population-based surveillance, epidemiology, and end results (SEER) registry. Each prostate cancer case and death was assigned a multidimensional neighborhood-SES index using the 2000 US Census data. SES quintile-specific prostate cancer incidence and mortality rates and rate ratios were estimated using SEER*Stat for each race/ethnicity categorized into 10-year age groups.
For prostate cancer incidence, we observed higher levels of SES to be significantly associated with increased risk of disease [SES Q1 vs. Q5: relative risk (RR) = 1.28; 95% confidence interval (CI): 1.25–1.30]. Among younger men (45–64 years), African-Americans had the highest incidence rates followed by non-Hispanic Whites, Hispanics, and Asian/Pacific Islanders for all SES levels. Yet, among older men (75–84 years) Hispanics, following African-Americans, displayed the second highest incidence rates of prostate cancer. For prostate cancer deaths, higher levels of SES were associated with lower mortality rates of prostate cancer deaths (SES Q1 vs. Q5: RR = 0.88; 95% CI: 0.92–0.94). African-Americans had a twofold to fivefold increased risk of prostate cancer deaths in comparison to non-Hispanic Whites across all levels of SES.
Our findings suggest that SES alone cannot account for the greater burden of prostate cancer among African-American men. In addition, incidence and mortality rates of prostate cancer display different age and racial/ethnic patterns across gradients of SES.
Electronic supplementary material
The online version of this article (doi:10.1007/s10552-009-9369-0) contains supplementary material, which is available to authorized users.
PMCID: PMC2746891  PMID: 19526319
Prostate cancer; Socioeconomic status; Disparities; Incidence rates; Mortality rates
4.  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
5.  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
6.  Healthy Eating and Risks of Total and Cause-Specific Death among Low-Income Populations of African-Americans and Other Adults in the Southeastern United States: A Prospective Cohort Study 
PLoS Medicine  2015;12(5):e1001830.
A healthy diet, as defined by the US Dietary Guidelines for Americans (DGA), has been associated with lower morbidity and mortality from major chronic diseases in studies conducted in predominantly non-Hispanic white individuals. It is unknown whether this association can be extrapolated to African-Americans and low-income populations.
Methods and Findings
We examined the associations of adherence to the DGA with total and cause-specific mortality in the Southern Community Cohort Study, a prospective study that recruited 84,735 American adults, aged 40–79 y, from 12 southeastern US states during 2002–2009, mostly through community health centers that serve low-income populations. The present analysis included 50,434 African-Americans, 24,054 white individuals, and 3,084 individuals of other racial/ethnic groups, among whom 42,759 participants had an annual household income less than US$15,000. Usual dietary intakes were assessed using a validated food frequency questionnaire at baseline. Adherence to the DGA was measured by the Healthy Eating Index (HEI), 2010 and 2005 editions (HEI-2010 and HEI-2005, respectively). During a mean follow-up of 6.2 y, 6,906 deaths were identified, including 2,244 from cardiovascular disease, 1,794 from cancer, and 2,550 from other diseases. A higher HEI-2010 score was associated with lower risks of disease death, with adjusted hazard ratios (HRs) of 0.80 (95% CI, 0.73–0.86) for all-disease mortality, 0.81 (95% CI, 0.70–0.94) for cardiovascular disease mortality, 0.81 (95% CI, 0.69–0.95) for cancer mortality, and 0.77 (95% CI, 0.67–0.88) for other disease mortality, when comparing the highest quintile with the lowest (all p-values for trend < 0.05). Similar inverse associations between HEI-2010 score and mortality were observed regardless of sex, race, and income (all p-values for interaction > 0.50). Several component scores in the HEI-2010, including whole grains, dairy, seafood and plant proteins, and ratio of unsaturated to saturated fatty acids, showed significant inverse associations with total mortality. HEI-2005 score was also associated with lower disease mortality, with a HR of 0.86 (95% CI, 0.79–0.93) when comparing extreme quintiles. Given the observational study design, however, residual confounding cannot be completely ruled out. In addition, future studies are needed to evaluate the generalizability of these findings to African-Americans of other socioeconomic status.
Our results showed, to our knowledge for the first time, that adherence to the DGA was associated with lower total and cause-specific mortality in a low-income population, including a large proportion of African-Americans, living in the southeastern US.
In a prospective cohort study, Wei Zheng and colleagues study the association between adherence to dietary guidelines and mortality in low-income US adults, two thirds of whom are African-Americans.
Editors' Summary
Certain parts of the population, including women, children, ethnic and racial minorities, and poor people, are often underrepresented in clinical trials and in epidemiological studies (which examine the patterns, causes, and effects of health and disease conditions). In the US population, the link between diet and health has mostly been studied in non-Hispanic white individuals from middle- and high-income households. Such studies formed the basis for the Dietary Guidelines for Americans (DGA), and more recently have shown that adherence to the DGA is associated with lower levels of obesity, as well as lower risks for diabetes, cardiovascular disease (such as heart attacks and strokes), and certain cancers. To measure adherence to the DGA, the Center for Nutrition Policy and Promotion at the US Department of Agriculture developed the Healthy Eating Index (HEI) in 1995. The DGA and the HEI have been updated several times, and the HEI-2010—the latest version—reflects the 2010 DGA.
Why Was This Study Done?
Because research participants are often not representative of the entire US population, it is unknown whether the results of many studies are valid for all Americans. To remedy this situation, efforts have been made to recruit participants from previously underrepresented parts of the population and to address important health questions in such groups. For this study, the researchers wanted to examine whether adherence to the DGA was associated with better health outcomes in poor people and African-Americans, consistent with the results in wealthier non-Hispanic white individuals.
What Did the Researchers Do and Find?
The researchers analyzed data from the Southern Community Cohort Study (SCCS). The SCCS was funded by the National Cancer Institute and was initiated in 2001 with the goal of addressing unresolved questions about the causes of cancer and other chronic diseases, as well as reasons for health disparities. The SCCS recruited most of its participants from community health centers in 12 states in the southeastern US. These centers serve predominantly poor and uninsured people, including many African-Americans. Of approximately 85,000 SCCS participants, over two-thirds were African-American, and over half were poor, with an annual household income of less than US$15,000.
For this study, the researchers used a food frequency questionnaire that was designed to capture foods commonly consumed in the southeastern US, and from this calculated HEI-2010 scores for each participant. They also collected other health- and lifestyle-related information. They then followed all participants for whom they had complete information (over 77,000) for a number of years (half of them for over 6.2 years). During that period, 6,906 participants died; including 2,244 from cardiovascular disease, 1,794 from cancer, and 2,550 from other diseases. When the researchers tested for a possible association between HEI-2010 and death (controlling for other relevant factors such as age, weight, exercise, smoking, and the presence of specific chronic diseases), they found that participants with a higher HEI-2010 score (reflecting better adherence to the DGA) had a lower risk of dying in the follow-up period. Participants with the healthiest diet (those in the top one-fifth of HEI-2010 scores) had only about 80% of the risk of death of those with the unhealthiest diets (those in the bottom one-fifth of HEI-2010 scores). This reduction in the risk of death by approximately 20% was true for death from any disease, death from cancer, and death from cardiovascular disease.
What Do These Findings Mean?
The results support the validity of the DGA for healthy eating across the US population. However, the study had some limitations. For example, participants were asked only once—when they first joined the SCCS—about their diet, their household income, and other factors that can change over time, such as exercise habits and diseases they have been diagnosed with. Besides such changes, there could be other factors not captured in the study that might influence the association between diet and death. Despite these uncertainties, the findings suggest that adherence to the DGA is associated with lower total mortality and mortality from cancer or cardiovascular disease in poor US Americans in general, and in low-income African-Americans.
Additional Information
Please access these websites via the online version of this summary at
Information is available online about the Southern Community Cohort Study
The US Department of Agriculture’s Center for Nutrition Policy and Promotion has information on the Healthy Eating Index, which is based on the Dietary Guidelines for Americans
The World Health Organization provides information on diet as part of its global strategy for diet, physical activity, and health, as well as a factsheet on healthy diet
Wikipedia has a page on race and health in the US (note that Wikipedia is a free online encyclopedia that anyone can edit)
PMCID: PMC4444091  PMID: 26011727
7.  Life expectancy and disparity: an international comparison of life table data 
BMJ Open  2011;1(1):e000128.
To determine the contribution of progress in averting premature deaths to the increase in life expectancy and the decline in lifespan variation.
International comparison of national life table data from the Human Mortality Database.
40 developed countries and regions, 1840–2009.
Men and women of all ages.
Main outcome measure
We use two summary measures of mortality: life expectancy and life disparity. Life disparity is a measure of how much lifespans differ among individuals. We define a death as premature if postponing it to a later age would decrease life disparity.
In 89 of the 170 years from 1840 to 2009, the country with the highest male life expectancy also had the lowest male life disparity. This was true in 86 years for female life expectancy and disparity. In all years, the top several life expectancy leaders were also the top life disparity leaders. Although only 38% of deaths were premature, fully 84% of the increase in life expectancy resulted from averting premature deaths. The reduction in life disparity resulted from reductions in early-life disparity, that is, disparity caused by premature deaths; late-life disparity levels remained roughly constant.
The countries that have been the most successful in averting premature deaths have consistently been the life expectancy leaders. Greater longevity and greater equality of individuals' lifespans are not incompatible goals. Countries can achieve both by reducing premature deaths.
Article summary
Article focus
We examined the relationship between high life expectancy and low life disparity.
We determined the relative importance of premature versus late deaths in increasing life expectancy and reducing life disparity.
We examined whether policies to increase life expectancy were compatible with those to reduce lifespan variation.
Key messages
Most of the gains in life expectancy are the result of reducing disparities in how long people live, by averting premature mortality.
Progress in reducing death rates for people who live longer than average has had little effect on life disparity levels and has contributed only modestly to life expectancy gains.
The countries that have been most successful at reducing premature mortality enjoy the highest life expectancies and the greatest equality in individuals' lifespans.
Strengths and limitations
We are the first to examine this issue using a large, comparable database of 40 developed countries from 1840 to 2009 containing 7056 life tables.
Our analysis was limited to countries with data of high enough quality to be included in the database.
Although this database contains high mortality life tables from historic populations, it is unknown whether the patterns we observed would also be seen in contemporary emerging and developing countries.
PMCID: PMC3191439  PMID: 22021770
8.  Missed opportunities: racial and neighborhood socioeconomic disparities in emergency colorectal cancer diagnosis and surgery 
BMC Cancer  2014;14:927.
Disparities by race and neighborhood socioeconomic status exist for many colorectal cancer (CRC) outcomes, including screening use and mortality. We used population-based data to determine if disparities also exist for emergency CRC diagnosis and surgery.
We examined two emergency CRC outcomes using 1992–2005 population-based U.S. SEER-Medicare data. Among CRC patients aged ≥66 years, we examined racial (African American vs. white) and neighborhood poverty disparities in two emergency outcomes defined as: 1) newly diagnosed CRC or 2) CRC surgery associated with: obstruction, perforation, or emergency inpatient admission. Multilevel logistic regression (patients nested in census tracts) analyses adjusted for sociodemographic, tumor, and clinical covariates.
Of 83,330 CRC patients, 29.1% were diagnosed emergently. Of 55,046 undergoing surgery, 26.0% had emergency surgery. For both outcomes, race and neighborhood poverty disparities were evident. A significant race by poverty interaction (p < .001) was noted: poverty rate was associated with both outcomes among African Americans, but not whites. Compared to whites in low poverty (<10%) neighborhoods, African Americans in high poverty (≥20%) neighborhoods had increased odds of emergency diagnosis (AOR: 1.50, 95% CI: 1.38-1.63) and surgery (AOR: 1.63, 95% CI: 1.47-1.81).
Emergency CRC outcomes are associated with high poverty residence among African Americans in this population-based study, potentially contributing to observed disparities in CRC morbidity and mortality. Targeted efforts to increase CRC screening among African Americans living in high poverty neighborhoods could reduce preventable disparities.
PMCID: PMC4364088  PMID: 25491412
Colorectal cancer; Emergency outcomes; Disparities; Race; Socioeconomic status; SEER-Medicare
9.  The Association Between Neighborhood Characteristics and Body Size and Physical Activity in the California Teachers Study Cohort 
American journal of public health  2011;102(4):689-697.
We considered interactions between physical activity and body mass index (BMI) and neighborhood factors.
We used recursive partitioning to identify predictors of low recreational physical activity (<2.5 hours/week) and overweight and obesity (BMI≥25.0 kg/m2) among 118 315 women in the California Teachers Study. Neighborhood characteristics were based on 2000 US Census data and Reference US business listings.
Low physical activity and being overweight or obese were associated with individual sociodemographic characteristics, including race/ethnicity and age. Among White women aged 36 to 75 years, living in neighborhoods with more household crowding was associated with a higher probability of low physical activity (54% vs 45% to 51%). In less crowded neighborhoods where more people worked outside the home, the existence of fewer neighborhood amenities was associated with a higher probability of low physical activity (51% vs 46%). Among non–African American middle-aged women, living in neighborhoods with a lower socioeconomic status was associated with a higher probability of being overweight or obese (46% to 59% vs 38% in high–socioeconomic status neighborhoods).
Associations between physical activity, overweight and obesity, and the built environment varied by sociodemographic characteristics in this educated population.
PMCID: PMC3410673  PMID: 21852626
10.  Reinterpreting Ethnic Patterns among White and African American Men Who Inject Heroin: A Social Science of Medicine Approach 
PLoS Medicine  2006;3(10):e452.
Street-based heroin injectors represent an especially vulnerable population group subject to negative health outcomes and social stigma. Effective clinical treatment and public health intervention for this population requires an understanding of their cultural environment and experiences. Social science theory and methods offer tools to understand the reasons for economic and ethnic disparities that cause individual suffering and stress at the institutional level.
Methods and Findings
We used a cross-methodological approach that incorporated quantitative, clinical, and ethnographic data collected by two contemporaneous long-term San Francisco studies, one epidemiological and one ethnographic, to explore the impact of ethnicity on street-based heroin-injecting men 45 years of age or older who were self-identified as either African American or white. We triangulated our ethnographic findings by statistically examining 14 relevant epidemiological variables stratified by median age and ethnicity. We observed significant differences in social practices between self-identified African Americans and whites in our ethnographic social network sample with respect to patterns of (1) drug consumption; (2) income generation; (3) social and institutional relationships; and (4) personal health and hygiene. African Americans and whites tended to experience different structural relationships to their shared condition of addiction and poverty. Specifically, this generation of San Francisco injectors grew up as the children of poor rural to urban immigrants in an era (the late 1960s through 1970s) when industrial jobs disappeared and heroin became fashionable. This was also when violent segregated inner city youth gangs proliferated and the federal government initiated its “War on Drugs.” African Americans had earlier and more negative contact with law enforcement but maintained long-term ties with their extended families. Most of the whites were expelled from their families when they began engaging in drug-related crime. These historical-structural conditions generated distinct presentations of self. Whites styled themselves as outcasts, defeated by addiction. They professed to be injecting heroin to stave off “dopesickness” rather than to seek pleasure. African Americans, in contrast, cast their physical addiction as an oppositional pursuit of autonomy and pleasure. They considered themselves to be professional outlaws and rejected any appearance of abjection. Many, but not all, of these ethnographic findings were corroborated by our epidemiological data, highlighting the variability of behaviors within ethnic categories.
Bringing quantitative and qualitative methodologies and perspectives into a collaborative dialog among cross-disciplinary researchers highlights the fact that clinical practice must go beyond simple racial or cultural categories. A clinical social science approach provides insights into how sociocultural processes are mediated by historically rooted and institutionally enforced power relations. Recognizing the logical underpinnings of ethnically specific behavioral patterns of street-based injectors is the foundation for cultural competence and for successful clinical relationships. It reduces the risk of suboptimal medical care for an exceptionally vulnerable and challenging patient population. Social science approaches can also help explain larger-scale patterns of health disparities; inform new approaches to structural and institutional-level public health initiatives; and enable clinicians to take more leadership in changing public policies that have negative health consequences.
Bourgois and colleagues found that the African American and white men in their study had a different pattern of drug use and risk behaviors, adopted different strategies for survival, and had different personal histories.
Editors' Summary
There are stark differences in the health of different ethnic groups in America. For example, the life expectancy for white men is 75.4 years, but it is only 69.2 years for African-American men. The reasons behind these disparities are unclear, though there are several possible explanations. Perhaps, for example, different ethnic groups are treated differently by health professionals (with some groups receiving poorer quality health care). Or maybe the health disparities are due to differences across ethnic groups in income level (we know that richer people are healthier). These disparities are likely to persist unless we gain a better understanding of how they arise.
Why Was This Study Done?
The researchers wanted to study the health of a very vulnerable community of people: heroin users living on the streets in the San Francisco Bay Area. The health status of this community is extremely poor, and its members are highly stigmatized—including by health professionals themselves. The researchers wanted to know whether African American men and white men who live on the streets have a different pattern of drug use, whether they adopt varying strategies for survival, and whether they have different personal histories. Knowledge of such differences would help the health community to provide more tailored and culturally appropriate interventions. Physicians, nurses, and social workers often treat street-based drug users, especially in emergency rooms and free clinics. These health professionals regularly report that their interactions with street-based drug users are frustrating and confrontational. The researchers hoped that their study would help these professionals to have a better understanding of the cultural backgrounds and motivations of their drug-using patients.
What Did the Researchers Do and Find?
Over the course of six years, the researchers directly observed about 70 men living on the streets who injected heroin as they went about their usual lives (this type of research is called “participant observation”). The researchers specifically looked to see whether there were differences between the white and African American men. All the men gave their consent to be studied in this way and to be photographed. The researchers also studied a database of interviews with almost 7,000 injection drug users conducted over five years, drawing out the data on differences between white and African men. The researchers found that the white men were more likely to supplement their heroin use with inexpensive fortified wine, while African American men were more likely to supplement heroin with crack. Most of the white men were expelled from their families when they began engaging in drug-related crime, and these men tended to consider themselves as destitute outcasts. African American men had earlier and more negative contact with law enforcement but maintained long-term ties with their extended families, and these men tended to consider themselves as professional outlaws. The white men persevered less in attempting to find a vein in which to inject heroin, and so were more likely to inject the drug directly under the skin—this meant that they were more likely to suffer from skin abscesses. The white men generated most of their income from panhandling (begging for money), while the African American men generated most of their income through petty crime and/or through offering services such as washing car windows at gas stations.
What Do These Findings Mean?
Among street-based heroin users, there are important differences between white men and African American men in the type of drugs used, the method of drug use, their social backgrounds, the way in which they identify themselves, and the health risks that they take. By understanding these differences, health professionals should be better placed to provide tailored and appropriate care when these men present to clinics and emergency rooms. As the researchers say, “understanding of different ethnic populations of drug injectors may reduce difficult clinical interactions and resultant physician frustration while improving patient access and adherence to care.” One limitation of this study is that the researchers studied one specific community in one particular area of the US—so we should not assume that their findings would apply to street-based heroin users elsewhere.
Additional Information.
Please access these Web sites via the online version of this summary at
The US Centers for Disease Control (CDC) has a web page on HIV prevention among injection drug users
The World Health Organization has collected documents on reducing the risk of HIV in injection drug users and on harm reduction approaches
The International Harm Reduction Association has information relevant to a global audience on reducing drug-related harm among individuals and communities
US-focused information on harm reduction is available via the websites of the Harm Reduction Coalition and the Chicago Recovery Alliance
Canada-focused information can be found at the Street Works Web site
The Harm Reduction Journal publishes open-access articles
The CDC has a web page on eliminating racial and ethnic health disparities
The Drug Policy Alliance has a web page on drug policy in the United States
PMCID: PMC1621100  PMID: 17076569
11.  Years of life lost to prison: racial and gender gradients in the United States of America 
The United States has the highest rate of imprisonment of any country in the world. African Americans and Hispanics comprise a disproportionately large share of the prison population. We applied a "prison life expectancy" to specify differences in exposure to imprisonment by gender and race at the population level.
The impact of imprisonment on life expectancy in the United States was measured for each year from 2000 to 2004, and then averaged. Using the Sullivan method, prison and prison-free life expectancies were estimated by dividing the years lived in each age range of the life table into these two states using prevalence of imprisonment by gender and race.
African American males can expect to spend on average 3.09 years in prison or jail over their lifetime and Hispanic and Caucasian males can spend on average 1.06 and 0.50 years, respectively. African American females, on the other hand, can expect to spend on average 0.23 years in these institutions and Hispanic and Caucasian females can expect to spend on average 0.09 and 0.05 years, respectively. Overall, African American males, the highest risk group, can expect to spend on average 61.80 times longer in prison or jail as compared to Caucasian women, the lowest risk group.
There are clear gender and racial gradients in life expectancy spent in prison in the United States. Future research needs to examine how current imprisonment practice in the United States may influence population health and health disparities.
PMCID: PMC2265700  PMID: 18221538
12.  Neighborhood Context and Substance Use Disorders: A Comparative Analysis of Racial and Ethnic Groups in the United States 
Drug and alcohol dependence  2012;125(Suppl 1):S35-S43.
There is evidence that ethnic/racial minorities are conferred differential risk for substance use problems based on where they live. Despite a burgeoning of research focusing on the role of neighborhood characteristics on health, limited findings are available on substance use. Our study uses nationally representative data (N= 13, 837) to examine: (1) What neighborhood characteristics are associated with risk of substance use disorders?; (2) Do the associations between neighborhood characteristics and substance use disorders remain after adjusting for individual-level factors?; and (3) Do neighborhood characteristics associated with substance use disorders differ by race/ethnicity after adjusting for individual-level factors?
Data were drawn from the Collaborative Psychiatric Epidemiology Studies (CPES-Geocode file) with 836 Census tracts. Analyses included African Americans, Asians, Caribbean Blacks, Latinos, and non-Latino whites. Separate logistic regression models were fitted for any past-year substance use disorder, alcohol use disorder, and drug use disorder.
Living in more affluent and residentially unstable census tracts was associated with decreased risk of past-year substance use disorder, even after adjusting for individual-level factors. However, when we investigated the interaction of race/ethnicity and census latent factors with past-year substance use disorders, we found different associations for the different racial/ethnic groups. We also found different associations between neighborhood affluence, residential instability and any past-year substance use and alcohol disorders by nativity.
Characteristics of the environment might represent differential risk for substance disorders depending on a person’s ethnicity/race and nativity status.
PMCID: PMC3488110  PMID: 22699095
neighborhood context; substance use disorders; alcohol; drugs; racial/ethnic minorities; nativity
13.  The Association between Neighborhood Socioeconomic Status and Clinical Outcomes among Patients 1 Year after Hospitalization for Cardiovascular Disease 
Journal of community health  2013;38(4):690-697.
Residing in lower socioeconomic status neighborhoods is associated with increased risk of morbidity and mortality. Few studies have examined this association for cardiovascular disease (CVD) outcomes in a treated population in New York City (NYC). The purpose of this study was to determine the relationship between neighborhood level poverty and one-year clinical outcomes (rehospitalization and/or death) among hospitalized patients with CVD. Data on rehospitalization and/or death at one-year were collected from consecutive patients admitted at a university medical center in NYC from November 2009 to September 2010. NYC residents totaled 2,198. U.S. Census 2000 zip code data was used to quantify neighborhood SES into quintiles of poverty (Q1=lowest poverty to Q5=highest poverty). Univariate analyses were used to determine associations between neighborhood poverty and baseline characteristics and comorbidities. A logistic regression analysis was used to calculate odds ratios for the association between quintiles of poverty and rehospitalization/death at one year. Fifty-five percent of participants experienced adverse outcomes. Participants in Q5 (9%) were more likely to be female (odds ratio [OR]=0.49,95% confidence interval [CI] 0.33–0.73), younger (OR=0.50,95% CI 0.34–0.74), of minority race/ethnicity (OR=18.24,95% CI 11.12=29.23), and have no health insurance (OR=4.79,95% CI 2.92–7.50). Living in Q5 was significantly associated with increased comorbidities, including diabetes mellitus and hypertension, but was not a significant predictor of rehospitalization/death at one year. Among patients hospitalized with CVD, higher poverty neighborhood residence was significantly associated with a greater prevalence of comorbidities, but not of rehospitalization and/or death. Affordable, accessible resources targeted at reducing the risk of developing CVD and these comorbidities should be available in these communities.
PMCID: PMC3706565  PMID: 23468321
cardiovascular disease; neighborhood; socioeconomic; prevention
14.  Neighborhood socioeconomic status and fruit and vegetable intake among Whites, Blacks, and Mexican-Americans in the United States 
Socioeconomic and racial/ethnic disparities in health status across the United States are large and persistent. Obesity rates are rising faster in Black and Hispanic populations than in Whites and foreshadow even greater disparities in chronic diseases such as diabetes and cardiovascular disease in years to come. Factors that influence dietary intake of fruits and vegetables in these populations are only partly understood.
We examined associations between fruit and vegetable intake and neighborhood socioeconomic status (NSES), analyzed whether NSES explains racial differences in intake, and explored the extent to which NSES has differential effects by race/ethnicity of United States (U.S.) adults.
Using geocoded residential addresses from the Third National Health and Nutrition Examination Survey (NHANES III), we merged individual-level data with county and census-tract level U.S. Census data. We estimated three-level hierarchical models predicting fruit and vegetable intake with individual characteristics and an index of neighborhood SES as explanatory variables.
Neighborhood SES was positively associated with fruit and vegetable intake: a one standard deviation increase in the neighborhood SES index was associated with consumption of nearly 2 additional servings of fruit and vegetables per week. Neighborhood SES explained some of the Black-White disparity in fruit and vegetable intake and was differentially associated with fruit and vegetable intake among Whites, Blacks, and Mexican-Americans.
The positive association of neighborhood SES with fruit and vegetable intake is one important pathway through which the social environment of neighborhoods affects population health and nutrition for Whites, Blacks and Hispanics in the United States.
PMCID: PMC3829689  PMID: 18541581
Neighborhood Socioeconomic Status; Race/Ethnicity; Fruit and Vegetable Consumption
15.  Heart Disease and Stroke Statistics—2011 Update 
Circulation  2010;123(4):e18-e209.
Each year, the American Heart Association (AHA), in conjunction with the Centers for Disease Control and Prevention, the National Institutes of Health, and other government agencies, brings together the most up-to-date statistics on heart disease, stroke, other vascular diseases, and their risk factors and presents them in its Heart Disease and Stroke Statistical Update. The Statistical Update is a valuable resource for researchers, clinicians, healthcare policy makers, media professionals, the lay public, and many others who seek the best national data available on disease morbidity and mortality and the risks, quality of care, medical procedures and operations, and costs associated with the management of these diseases in a single document. Indeed, since 1999, the Statistical Update has been cited more than 8700 times in the literature (including citations of all annual versions). In 2009 alone, the various Statistical Updates were cited ≈1600 times (data from ISI Web of Science). In recent years, the Statistical Update has undergone some major changes with the addition of new chapters and major updates across multiple areas. For this year’s edition, the Statistics Committee, which produces the document for the AHA, updated all of the current chapters with the most recent nationally representative data and inclusion of relevant articles from the literature over the past year and added a new chapter detailing how family history and genetics play a role in cardiovascular disease (CVD) risk. Also, the 2011 Statistical Update is a major source for monitoring both cardiovascular health and disease in the population, with a focus on progress toward achievement of the AHA’s 2020 Impact Goals. Below are a few highlights from this year’s Update.
Death Rates From CVD Have Declined, Yet the Burden of Disease Remains High
The 2007 overall death rate from CVD (International Classification of Diseases 10, I00–I99) was 251.2 per 100 000. The rates were 294.0 per 100 000 for white males, 405.9 per 100 000 for black males, 205.7 per 100 000 for white females, and 286.1 per 100 000 for black females. From 1997 to 2007, the death rate from CVD declined 27.8%. Mortality data for 2007 show that CVD (I00–I99; Q20–Q28) accounted for 33.6% (813 804) of all 2 243 712 deaths in 2007, or 1 of every 2.9 deaths in the United States.
On the basis of 2007 mortality rate data, more than 2200 Americans die of CVD each day, an average of 1 death every 39 seconds. More than 150 000 Americans killed by CVD (I00–I99) in 2007 were <65 years of age. In 2007, nearly 33% of deaths due to CVD occurred before the age of 75 years, which is well before the average life expectancy of 77.9 years.
Coronary heart disease caused ≈1 of every 6 deaths in the United States in 2007. Coronary heart disease mortality in 2007 was 406 351. Each year, an estimated 785 000 Americans will have a new coronary attack, and ≈470 000 will have a recurrent attack. It is estimated that an additional 195 000 silent first myocardial infarctions occur each year. Approximately every 25 seconds, an American will have a coronary event, and approximately every minute, someone will die of one.
Each year, ≈795 000 people experience a new or recurrent stroke. Approximately 610 000 of these are first attacks, and 185 000 are recurrent attacks. Mortality data from 2007 indicate that stroke accounted for ≈1 of every 18 deaths in the United States. On average, every 40 seconds, someone in the United States has a stroke. From 1997 to 2007, the stroke death rate fell 44.8%, and the actual number of stroke deaths declined 14.7%.
In 2007, 1 in 9 death certificates (277 193 deaths) in the United States mentioned heart failure.
Prevalence and Control of Traditional Risk Factors Remains an Issue for Many Americans
Data from the National Health and Nutrition Examination Survey (NHANES) 2005–2008 indicate that 33.5% of US adults ≥20 years of age have hypertension (Table 7-1). This amounts to an estimated 76 400 000 US adults with hypertension. The prevalence of hypertension is nearly equal between men and women. African American adults have among the highest rates of hypertension in the world, at 44%. Among hypertensive adults, ≈80% are aware of their condition, 71% are using antihypertensive medication, and only 48% of those aware that they have hypertension have their condition controlled.
Despite 4 decades of progress, in 2008, among Americans ≥18 years of age, 23.1% of men and 18.3% of women continued to be cigarette smokers. In 2009, 19.5% of students in grades 9 through 12 reported current tobacco use. The percentage of the nonsmoking population with detectable serum cotinine (indicating exposure to secondhand smoke) was 46.4% in 1999 to 2004, with declines occurring, and was highest for those 4 to 11 years of age (60.5%) and those 12 to 19 years of age (55.4%).
An estimated 33 600 000 adults ≥20 years of age have total serum cholesterol levels ≥240 mg/dL, with a prevalence of 15.0% (Table 13-1).
In 2008, an estimated 18 300 000 Americans had diagnosed diabetes mellitus, representing 8.0% of the adult population. An additional 7 100 000 had undiagnosed diabetes mellitus, and 36.8% had prediabetes, with abnormal fasting glucose levels. African Americans, Mexican Americans, Hispanic/Latino individuals, and other ethnic minorities bear a strikingly disproportionate burden of diabetes mellitus in the United States (Table 16-1).
The 2011 Update Expands Data Coverage of the Obesity Epidemic and Its Antecedents and Consequences
The estimated prevalence of overweight and obesity in US adults (≥20 years of age) is 149 300 000, which represents 67.3% of this group in 2008. Fully 33.7% of US adults are obese (body mass index ≥30 kg/m2). Men and women of all race/ethnic groups in the population are affected by the epidemic of overweight and obesity (Table 15-1).
Among children 2 to 19 years of age, 31.9% are overweight and obese (which represents 23 500 000 children), and 16.3% are obese (12 000 000 children). Mexican American boys and girls and African American girls are disproportionately affected. Over the past 3 decades, the prevalence of obesity in children 6 to 11 years of age has increased from ≈4% to more than 20%.
Obesity (body mass index ≥30 kg/m2) is associated with marked excess mortality in the US population. Even more notable is the excess morbidity associated with overweight and obesity in terms of risk factor development and incidence of diabetes mellitus, CVD end points (including coronary heart disease, stroke, and heart failure), and numerous other health conditions, including asthma, cancer, degenerative joint disease, and many others.
The prevalence of diabetes mellitus is increasing dramatically over time, in parallel with the increases in prevalence of overweight and obesity.
On the basis of NHANES 2003–2006 data, the age-adjusted prevalence of metabolic syndrome, a cluster of major cardiovascular risk factors related to overweight/obesity and insulin resistance, is 34% (35.1% among men and 32.6% among women).
The proportion of youth (≤18 years of age) who report engaging in no regular physical activity is high, and the proportion increases with age. In 2007, among adolescents in grades 9 through 12, 29.9% of girls and 17.0% of boys reported that they had not engaged in 60 minutes of moderate-to-vigorous physical activity, defined as any activity that increased heart rate or breathing rate, even once in the previous 7 days, despite recommendations that children engage in such activity ≥5 days per week.
Thirty-six percent of adults reported engaging in no vigorous activity (activity that causes heavy sweating and a large increase in breathing or heart rate).
Data from NHANES indicate that between 1971 and 2004, average total energy consumption among US adults increased by 22% in women (from 1542 to 1886 kcal/d) and by 10% in men (from 2450 to 2693 kcal/d; see Chart 19-1).
The increases in calories consumed during this time period are attributable primarily to greater average carbohydrate intake, in particular, of starches, refined grains, and sugars. Other specific changes related to increased caloric intake in the United States include larger portion sizes, greater food quantity and calories per meal, and increased consumption of sugar-sweetened beverages, snacks, commercially prepared (especially fast food) meals, and higher energy-density foods.
The 2011 Update Provides Critical Data Regarding Cardiovascular Quality of Care, Procedure Utilization, and Costs
In light of the current national focus on healthcare utilization, costs, and quality, it is critical to monitor and understand the magnitude of healthcare delivery and costs, as well as the quality of healthcare delivery, related to CVDs. The Update provides these critical data in several sections.
Quality-of-Care Metrics for CVDs
Chapter 20 reviews many metrics related to the quality of care delivered to patients with CVDs, as well as healthcare disparities. In particular, quality data are available from the AHA’s “Get With The Guidelines” programs for coronary artery disease and heart failure and the American Stroke Association/ AHA’s “Get With the Guidelines” program for acute stroke. Similar data from the Veterans Healthcare Administration, national Medicare and Medicaid data and National Cardiovascular Data Registry Acute Coronary Treatment and Intervention Outcomes Network - “Get With The Guidelines” Registry data are also reviewed. These data show impressive adherence with guideline recommendations for many, but not all, metrics of quality of care for these hospitalized patients. Data are also reviewed on screening for cardiovascular risk factor levels and control.
Cardiovascular Procedure Utilization and Costs
Chapter 21 provides data on trends and current usage of cardiovascular surgical and invasive procedures. For example, the total number of inpatient cardiovascular operations and procedures increased 27%, from 5 382 000 in 1997 to 6 846 000 in 2007 (National Heart, Lung, and Blood Institute computation based on National Center for Health Statistics annual data).
Chapter 22 reviews current estimates of direct and indirect healthcare costs related to CVDs, stroke, and related conditions using Medical Expenditure Panel Survey data. The total direct and indirect cost of CVD and stroke in the United States for 2007 is estimated to be $286 billion. This figure includes health expenditures (direct costs, which include the cost of physicians and other professionals, hospital services, prescribed medications, home health care, and other medical durables) and lost productivity resulting from mortality (indirect costs). By comparison, in 2008, the estimated cost of all cancer and benign neoplasms was $228 billion ($93 billion in direct costs, $19 billion in morbidity indirect costs, and $116 billion in mortality indirect costs). CVD costs more than any other diagnostic group.
The AHA, through its Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current data available in the Statistics Update. The 2007 mortality data have been released. More information can be found at the National Center for Health Statistics Web site,
Finally, it must be noted that this annual Statistical Update is the product of an entire year’s worth of effort by dedicated professionals, volunteer physicians and scientists, and outstanding AHA staff members, without whom publication of this valuable resource would be impossible. Their contributions are gratefully acknowledged. Véronique L. Roger, MD, MPH, FAHAMelanie B. Turner, MPHOn behalf of the American Heart Association Heart Disease and Stroke Statistics Writing Group
Note: Population data used in the compilation of NHANES prevalence estimates is for the latest year of the NHANES survey being used. Extrapolations for NHANES prevalence estimates are based on the census resident population for 2008 because this is the most recent year of NHANES data used in the Statistical Update.
PMCID: PMC4418670  PMID: 21160056
AHA Statistical Update; cardiovascular diseases; epidemiology; risk factors; statistics; stroke
16.  Lung Cancer Occurrence in Never-Smokers: An Analysis of 13 Cohorts and 22 Cancer Registry Studies  
PLoS Medicine  2008;5(9):e185.
Better information on lung cancer occurrence in lifelong nonsmokers is needed to understand gender and racial disparities and to examine how factors other than active smoking influence risk in different time periods and geographic regions.
Methods and Findings
We pooled information on lung cancer incidence and/or death rates among self-reported never-smokers from 13 large cohort studies, representing over 630,000 and 1.8 million persons for incidence and mortality, respectively. We also abstracted population-based data for women from 22 cancer registries and ten countries in time periods and geographic regions where few women smoked. Our main findings were: (1) Men had higher death rates from lung cancer than women in all age and racial groups studied; (2) male and female incidence rates were similar when standardized across all ages 40+ y, albeit with some variation by age; (3) African Americans and Asians living in Korea and Japan (but not in the US) had higher death rates from lung cancer than individuals of European descent; (4) no temporal trends were seen when comparing incidence and death rates among US women age 40–69 y during the 1930s to contemporary populations where few women smoke, or in temporal comparisons of never-smokers in two large American Cancer Society cohorts from 1959 to 2004; and (5) lung cancer incidence rates were higher and more variable among women in East Asia than in other geographic areas with low female smoking.
These comprehensive analyses support claims that the death rate from lung cancer among never-smokers is higher in men than in women, and in African Americans and Asians residing in Asia than in individuals of European descent, but contradict assertions that risk is increasing or that women have a higher incidence rate than men. Further research is needed on the high and variable lung cancer rates among women in Pacific Rim countries.
Michael Thun and colleagues pooled and analyzed comprehensive data on lung cancer incidence and death rates among never-smokers to examine what factors other than active smoking affect lung cancer risk.
Editors' Summary
Every year, more than 1.4 million people die from lung cancer, a leading cause of cancer deaths worldwide. In the US alone, more than 161,000 people will die from lung cancer this year. Like all cancers, lung cancer occurs when cells begin to divide uncontrollably because of changes in their genes. The main trigger for these changes in lung cancer is exposure to the chemicals in cigarette smoke—either directly through smoking cigarettes or indirectly through exposure to secondhand smoke. Eighty-five to 90% of lung cancer deaths are caused by exposure to cigarette smoke and, on average, current smokers are 15 times more likely to die from lung cancer than lifelong nonsmokers (never smokers). Furthermore, a person's cumulative lifetime risk of developing lung cancer is related to how much they smoke, to how many years they are a smoker, and—if they give up smoking—to the age at which they stop smoking.
Why Was This Study Done?
Because lung cancer is so common, even the small fraction of lung cancer that occurs in lifelong nonsmokers represents a large number of people. For example, about 20,000 of this year's US lung cancer deaths will be in never-smokers. However, very little is known about how age, sex, or race affects the incidence (the annual number of new cases of diseases in a population) or death rates from lung cancer among never-smokers. A better understanding of the patterns of lung cancer incidence and death rates among never-smokers could provide useful information about the factors other than cigarette smoke that increase the likelihood of not only never-smokers, but also former smokers and current smokers developing lung cancer. In this study, therefore, the researchers pooled and analyzed a large amount of information about lung cancer incidence and death rates among never smokers to examine what factors other than active smoking affect lung cancer risk.
What Did the Researchers Do and Find?
The researchers analyzed information on lung cancer incidence and/or death rates among nearly 2.5 million self-reported never smokers (men and women) from 13 large studies investigating the health of people in North America, Europe, and Asia. They also analyzed similar information for women taken from cancer registries in ten countries at times when very few women were smokers (for example, the US in the late 1930s). The researchers' detailed statistical analyses reveal, for example, that lung cancer death rates in African Americans and in Asians living in Korea and Japan (but not among Asians living in the US) are higher than those in people of the European continental ancestry group. They also show that men have higher death rates from lung cancer than women irrespective of racial group, but that women aged 40–59 years have a slightly higher incidence of lung cancer than men of a similar age. This difference disappears at older ages. Finally, an analysis of lung cancer incidence and death rates at different times during the past 70 years shows no evidence of an increase in the lung cancer burden among never smokers over time.
What Do These Findings Mean?
Although some of the findings described above have been hinted at in previous, smaller studies, these and other findings provide a much more accurate picture of lung cancer incidence and death rates among never smokers. Most importantly the underlying data used in these analyses are now freely available and should provide an excellent resource for future studies of lung cancer in never smokers.
Additional Information.
Please access these Web sites via the online version of this summary at
The US National Cancer Institute provides detailed information for patients and health professionals about all aspects of lung cancer and information on smoking and cancer (in English and Spanish)
Links to other US-based resources dealing with lung cancer are provided by MedlinePlus (in English and Spanish)
Cancer Research UK provides key facts about the link between lung cancer and smoking and information about all other aspects of lung cancer
PMCID: PMC2531137  PMID: 18788891
17.  Life Expectancies of South African Adults Starting Antiretroviral Treatment: Collaborative Analysis of Cohort Studies 
PLoS Medicine  2013;10(4):e1001418.
Leigh Johnson and colleagues estimate the life expectancies of HIV positive South African adults who are taking antiretroviral therapy by using information from 6 programmes between 2001 and 2010.
Few estimates exist of the life expectancy of HIV-positive adults receiving antiretroviral treatment (ART) in low- and middle-income countries. We aimed to estimate the life expectancy of patients starting ART in South Africa and compare it with that of HIV-negative adults.
Methods and Findings
Data were collected from six South African ART cohorts. Analysis was restricted to 37,740 HIV-positive adults starting ART for the first time. Estimates of mortality were obtained by linking patient records to the national population register. Relative survival models were used to estimate the excess mortality attributable to HIV by age, for different baseline CD4 categories and different durations. Non-HIV mortality was estimated using a South African demographic model. The average life expectancy of men starting ART varied between 27.6 y (95% CI: 25.2–30.2) at age 20 y and 10.1 y (95% CI: 9.3–10.8) at age 60 y, while estimates for women at the same ages were substantially higher, at 36.8 y (95% CI: 34.0–39.7) and 14.4 y (95% CI: 13.3–15.3), respectively. The life expectancy of a 20-y-old woman was 43.1 y (95% CI: 40.1–46.0) if her baseline CD4 count was ≥200 cells/µl, compared to 29.5 y (95% CI: 26.2–33.0) if her baseline CD4 count was <50 cells/µl. Life expectancies of patients with baseline CD4 counts ≥200 cells/µl were between 70% and 86% of those in HIV-negative adults of the same age and sex, and life expectancies were increased by 15%–20% in patients who had survived 2 y after starting ART. However, the analysis was limited by a lack of mortality data at longer durations.
South African HIV-positive adults can have a near-normal life expectancy, provided that they start ART before their CD4 count drops below 200 cells/µl. These findings demonstrate that the near-normal life expectancies of HIV-positive individuals receiving ART in high-income countries can apply to low- and middle-income countries as well.
Please see later in the article for the Editors' Summary
Editors' Summary
According to the latest figures, more than 34 million people worldwide currently live with HIV/AIDS. In 2011, an estimated 2.5 million people were newly infected with HIV, and in the same year 1.7 million people died from AIDS. Since the beginning of the epidemic in the 1980s, more than 60 million people have contracted HIV and nearly 30 million have died of HIV-related causes. Despite the stark statistics, the life expectancy for people infected with the AIDS virus has dramatically improved over the past decade since the introduction of an effective combination of antiretroviral drugs. In high-income countries, people who are HIV-positive can expect a near-normal life expectancy if they take these drugs (as antiretroviral treatment—ART) throughout their life.
Why Was This Study Done?
Recent studies investigating the life expectancy of people living with HIV have mostly focused on the situation in high-income settings. The situation in low- and middle-income countries is vastly different. People who are diagnosed with HIV are often late in starting treatment, treatments regimes are sometimes interrupted, and a large proportion of patients are lost to follow-up. It is important to gain a realistic estimate of life expectancy in low- and middle-income countries so patients can be given the best information. So in this study the researchers used a model to estimate the life expectancy of patients starting ART in South Africa, using data from several ART programs.
What Did the Researchers Do and Find?
The researchers used data collected from six programs in South Africa based in Western Cape, Gauteng, and KwaZulu-Natal between 2001 and 2010. The researchers calculated the observation time from the time of ART initiation to the date of death or to the end of the study. Then the researchers used a relative survival approach to model the excess mortality attributable to HIV, relative to non-HIV mortality rates in South Africa, over different periods from ART initiation.
Using these methods, the researchers found that over the time period, 37,740 adults started ART and 2,066 deaths were recorded in patient record systems. Of the 16,250 patients who were lost to follow-up, the researchers identified 2,947 further deaths in the population register. When they inputted these figures into their model, the researchers estimated that the mortality rate was 83.2 per 1,000 person-years of observation (PYO), and was higher in males (99.8 per 1,000 PYO) than in females (72.6 per 1,000 PYO). The researchers also found that the most significant factor determining the life expectancy of treated patients was their age at ART initiation: the average life expectancy of men starting ART varied between 27.6 years at age 20 and 10.1 years at age 60, while corresponding estimates in women were 36.8 and 14.4, respectively. Life expectancies were also significantly influenced by baseline CD4 counts; life expectancies in patients with baseline CD4 counts ≥200 cells/µl were between 70% and 86% of those of HIV-negative adults of the same age and sex, while patients starting ART with CD4 counts of <50 cells/µl had life expectancies that were between 48% and 61% of those of HIV-negative adults. Importantly, the researchers found that life expectancies were also 15%–20% higher in patients who survived their first 24 months after starting ART than in patients of the same age who had just started therapy.
What Do These Findings Mean?
These findings suggest that in South Africa, patients starting ART have life expectancies around 80% of normal life expectancy, provided that they start treatment before their CD4 count drops below 200 cells/µl. Although these results are encouraging, this study highlights that health services must overcome major challenges, such as dealing with late diagnosis, low uptake of CD4 testing, loss from pre-ART care, and delayed ART initiation, if near-normal life expectancies are to be achieved for the majority of HIV-positive South Africans. With the anticipated increase in the fraction of patients starting ART at higher CD4 counts in the future, long-term survival can be expected to increase even further. It is therefore critical that appropriate funding systems and innovative ways to reduce costs are put in place, to ensure the long-term sustainability of ART delivery in low- and middle-income countries.
Additional Information
Please access these websites via the online version of this summary at
The International Epidemiologic Databases to Evaluate AIDS has more statistical information from world regions
amfAR, the Foundation for AIDS Research, works with health care workers and AIDS organizations in developing countries to create and implement effective HIV research, treatment, prevention, and education strategies
PMCID: PMC3621664  PMID: 23585736
18.  Calculating expected years of life lost for assessing local ethnic disparities in causes of premature death 
BMC Public Health  2008;8:116.
A core function of local health departments is to conduct health assessments. The analysis of death certificates provides information on diseases, conditions, and injuries that are likely to cause death – an important outcome indicator of population health. The expected years of life lost (YLL) measure is a valid, stand-alone measure for identifying and ranking the underlying causes of premature death. The purpose of this study was to rank the leading causes of premature death among San Francisco residents, and to share detailed methods so that these analyses can be used in other local health jurisdictions.
Using death registry data and population estimates for San Francisco deaths in 2003–2004, we calculated the number of deaths, YLL, and age-standardized YLL rates (ASYRs). The results were stratified by sex, ethnicity, and underlying cause of death. The YLL values were used to rank the leading causes of premature death for men and women, and by ethnicity.
In the years 2003–2004, 6312 men died (73,627 years of life lost), and 5726 women died (51,194 years of life lost). The ASYR for men was 65% higher compared to the ASYR for women (8971.1 vs. 5438.6 per 100,000 persons per year). The leading causes of premature deaths are those with the largest average YLLs and are largely preventable. Among men, these were HIV/AIDS, suicide, drug overdose, homicide, and alcohol use disorder; and among women, these were lung cancer, breast cancer, hypertensive heart disease, colon cancer, and diabetes mellitus. A large health disparity exists between African Americans and other ethnic groups: African American age-adjusted overall and cause-specific YLL rates were higher, especially for homicide among men. Except for homicide among Latino men, Latinos and Asians have comparable or lower YLL rates among the leading causes of death compared to whites.
Local death registry data can be used to measure, rank, and monitor the leading causes of premature death, and to measure and monitor ethnic health disparities.
PMCID: PMC2386472  PMID: 18402698
19.  Ethnic Differences in In-Hospital Place of Death Among Older Adults in California 
Medical care  2009;47(2):138-145.
Substantial ethnic differences have been reported in the probability that death will occur in a hospital setting rather than at home, in a hospice, or in a nursing home. To date, no study has investigated the role of both individual characteristics and contextual characteristics, including local health care environments, to explain ethnic differentials in end-of-life care.
The study purpose is to examine ethnic differences in the association between death as a hospital in-patient and individual and contextual characteristics, as well as medical resource supply.
Research Design
This study employed a secondary data analysis.
We used data from the California Death Statistical Master file for the years 1999–2001, which included 472,382 complete cases. These data were geocoded and linked to data from the US Census Bureau and the American Hospital Association.
Death as an in-patient was most common for Asian (54%) and Hispanic immigrants (49%) and least common for non-Hispanic whites (36%) and US-born Asians (41%). Medical resource supply variables are of considerable importance in accounting for ethnic differentials in the probability of dying in a hospital. Residual differences in in-hospital site of death were largest for immigrant populations.
There are sizeable ethnic differentials in the probability that a death will occur in a hospital in California. These differences are substantially mediated by sociodemographic characteristics of the decedent and local medical care supply. One implication of these findings is that variation exists in the efficiency and quality of end of life care delivered to ethnic minorities.
PMCID: PMC4006956  PMID: 19169113
place of death; ethnicity; medical resource supply
20.  Improvements in Survival After Follicular Lymphoma by Race/Ethnicity and Socioeconomic Status: A Population-Based Study 
Journal of Clinical Oncology  2009;27(18):3044-3051.
A recent report suggested improvements in survival after follicular lymphoma (FL), but not for all racial/ethnic groups. To better understand the reasons for these FL survival differences, we examined the joint influences of diagnostic period, race/ethnicity, and neighborhood socioeconomic status (SES) on survival in a large population-based case series.
All patients (n = 15,937) diagnosed with FL between 1988 and 2005 in California were observed for vital status through November 2007. Overall and FL-specific survival were analyzed with Kaplan-Meier and Cox proportional hazards regression. Neighborhood SES was assigned from United States Census data using residence at diagnosis.
Overall and FL-specific survival improved 22% and 37%, respectively, from 1988 to 1997 to 1998 to 2005, and were observed in all racial/ethnic groups. Asian/Pacific Islanders had better survival than non-Hispanic white, Hispanic, and black patients who had similar outcomes. Lower neighborhood SES was associated with worse survival in patients across all stages of disease (P for trend < .01). Patients with the lowest SES quintile had a 49% increased risk of death from all causes (hazard ratio [HR] = 1.49, 95% CI, 1.30 to 1.72) and 31% increased risk of death from FL (HR = 1.31; 95% CI, 1.06 to 1.60) than patients with the highest SES.
Evolving therapies have likely led to improvements in survival after FL. Although improvements have occurred within all racial/ethnic groups, lower neighborhood SES was significantly associated with substantially poorer survival.
PMCID: PMC2702236  PMID: 19451447
21.  Disparities in survival after Hodgkin lymphoma: a population-based study 
Cancer causes & control : CCC  2009;20(10):1881-1892.
Survival after Hodgkin lymphoma (HL) is generally favorable, but may vary by patient demographic characteristics. The authors examined HL survival according to race/ethnicity and neighborhood socioeconomic status (SES), determined from residential census block group at diagnosis. For 12,492 classical HL patients ≥15 years diagnosed in California during 1988-2006 and followed through 2007, we determined risk of overall and HL-specific death using Cox proportional hazards regression; analyses were stratified by age and Ann Arbor stage. Irrespective of disease stage, patients with lower neighborhood SES had worse overall and HL-specific survival than patients with higher SES. Patients with the lowest quintile of neighborhood SES had a 64% (patients aged 15-44 years) and 36% (≥45 years) increased risk of HL-death compared to patients with the highest quintile of SES; SES results were similar for overall survival. Even after adjustment for neighborhood SES, blacks and Hispanics had increased risks of HL-death 74% and 43% (15-44 years) and 40% and 17% (≥45 years), respectively, higher than white patients. The racial/ethnic differences in survival were evident for all stages of disease. These data provide evidence for substantial, and probably remediable, racial/ethnic and neighborhood SES disparities in HL outcomes.
PMCID: PMC2888633  PMID: 19557531
Hodgkin disease; survival; mortality; social class; census
22.  Racial/ethnic and sexual behavior disparities in rates of sexually transmitted infections, San Francisco, 1999-2008 
BMC Public Health  2010;10:315.
Racial/ethnic minorities and men who have sex with men (MSM) represent populations with disparate sexually transmitted infection (STI) rates. While race-specific STI rates have been widely reported, STI rates among MSM is often challenging given the absence of MSM population estimates. We evaluated the race-specific rates of chlamydia and gonorrhea among MSM and non-MSM in San Francisco between 1999-2008.
2000 US Census data for San Francisco was used to estimate the number of African-American, Asian/Pacific Islander, Hispanic, and white males. Data from National HIV Behavioral Surveillance (NHBS) MSM 1, conducted in 2004, was used to estimate the total number of MSM in San Francisco and the size of race/ethnic sub-populations of MSM. Non-MSM estimates were calculated by subtracting the number of estimated MSM from the total number of males residing in San Francisco. Rates of MSM and non-MSM gonorrhea and chlamydia reported between 1999 and 2008 were stratified by race/ethnicity. Ratios of MSM and non-MSM rates of morbidity were calculated by race/ethnicity.
Between 1999-2008, MSM accounted for 72% of gonorrhea cases and 51% of chlamydia cases. Throughout the study period, African-American MSM had the highest chlamydia rate with 606 cases per 100,000 in 1999 increasing to 2067 cases per 100,000 in 2008. Asian/Pacific Islander MSM consistently had the lowest rate among MSM with1003 cases per 100,000 in 2008. The ratio of MSM/non-MSM for chlamydia was highest among whites 11.6 (95% CI: 8.8-14.4) and Asian/Pacific Islanders 8.6 (95% CI: 6.2-11), and lowest among African-Americans 1.53 (95% CI: 1.2-1.9) and Hispanics 4.43 (95% CI: 2.8-6.0). Gonorrhea rates were similar for African-American, white, and Hispanic MSM between 2137-2441 cases per 100,000 in 2008. Asian/Pacific Islander MSM had the lowest gonorrhea rate with 865 cases per 100,000 in 2008. The ratio of MSM/non-MSM for gonorrhea was highest among whites 11.6 (95% CI: 8.8-14.4) and Asian/Pacific Islanders 8.6 (95% CI: 6.2-11), and lowest among African-Americans 1.53 (95% CI: 1.2-1.9) and Hispanics 4.43 (95% CI: 2.8-6.0).
For all racial/ethnic groups in San Francisco, MSM carried a substantially higher burden of STIs compared to non-MSM except among African-American men. These racial and sexual behavior disparities warrant further public health attention and resources.
PMCID: PMC2903517  PMID: 20525397
23.  Impact of neighborhood and individual socioeconomic status on survival after breast cancer varies by race/ethnicity: The Neighborhood and Breast Cancer Study 
Research is limited on the independent and joint effects of individual- and neighborhood-level socioeconomic status (SES) on breast cancer survival across different racial/ethnic groups.
We studied individual-level SES, measured by self-reported education, and a composite neighborhood SES (nSES) measure in females (1,068 non-Hispanic whites, 1,670 Hispanics, 993 African-Americans, and 674 Asian-Americans), aged 18–79 years and diagnosed 1995–2008, in the San Francisco Bay Area. We evaluated all-cause and breast cancer-specific survival using stage-stratified Cox proportional hazards models with cluster adjustment for census block groups.
In models adjusting for education and nSES, lower nSES was associated with worse all-cause survival among African-Americans (p-trend=0.03), Hispanics (p-trend=0.01) and Asian-Americans (p-trend=0.01). Education was not associated with all-cause survival. For breast cancer-specific survival, lower nSES was associated with poorer survival only among Asian-Americans (p-trend=0.01). When nSES and education were jointly considered, women with low education and low nSES had 1.4 to 2.7-times worse all-cause survival than women with high education and high nSES across all races/ethnicities. Among African-Americans and Asian-Americans, women with high education and low nSES had 1.6 to 1.9-times worse survival, respectively. For breast cancer-specific survival, joint associations were found only among Asian-Americans with worse survival for those with low nSES regardless of education.
Both neighborhood and individual SES are associated with survival after breast cancer diagnosis, but these relationships vary by race/ethnicity.
A better understanding of the relative contributions and interactions of SES with other factors will inform targeted interventions towards reducing long-standing disparities in breast cancer survival.
PMCID: PMC4018239  PMID: 24618999
breast cancer survival; neighborhood socioeconomic status; education; race/ethnicity
24.  Personalized Prediction of Lifetime Benefits with Statin Therapy for Asymptomatic Individuals: A Modeling Study 
PLoS Medicine  2012;9(12):e1001361.
In a modeling study conducted by Myriam Hunink and colleagues, a population-based cohort from Rotterdam is used to predict the possible lifetime benefits of statin therapy, on a personalized basis.
Physicians need to inform asymptomatic individuals about personalized outcomes of statin therapy for primary prevention of cardiovascular disease (CVD). However, current prediction models focus on short-term outcomes and ignore the competing risk of death due to other causes. We aimed to predict the potential lifetime benefits with statin therapy, taking into account competing risks.
Methods and Findings
A microsimulation model based on 5-y follow-up data from the Rotterdam Study, a population-based cohort of individuals aged 55 y and older living in the Ommoord district of Rotterdam, the Netherlands, was used to estimate lifetime outcomes with and without statin therapy. The model was validated in-sample using 10-y follow-up data. We used baseline variables and model output to construct (1) a web-based calculator for gains in total and CVD-free life expectancy and (2) color charts for comparing these gains to the Systematic Coronary Risk Evaluation (SCORE) charts. In 2,428 participants (mean age 67.7 y, 35.5% men), statin therapy increased total life expectancy by 0.3 y (SD 0.2) and CVD-free life expectancy by 0.7 y (SD 0.4). Age, sex, smoking, blood pressure, hypertension, lipids, diabetes, glucose, body mass index, waist-to-hip ratio, and creatinine were included in the calculator. Gains in total and CVD-free life expectancy increased with blood pressure, unfavorable lipid levels, and body mass index after multivariable adjustment. Gains decreased considerably with advancing age, while SCORE 10-y CVD mortality risk increased with age. Twenty-five percent of participants with a low SCORE risk achieved equal or larger gains in CVD-free life expectancy than the median gain in participants with a high SCORE risk.
We developed tools to predict personalized increases in total and CVD-free life expectancy with statin therapy. The predicted gains we found are small. If the underlying model is validated in an independent cohort, the tools may be useful in discussing with patients their individual outcomes with statin therapy.
Please see later in the article for the Editors' Summary
Editors' Summary
Cardiovascular disease (CVD) affects the heart and/or the blood vessels and is a major cause of illness and death worldwide. In the US, for example, coronary heart disease—a CVD in which narrowing of the heart's blood vessels by fatty deposits slows the blood supply to the heart and may eventually cause a heart attack—is the leading cause of death, and stroke—a CVD in which the brain's blood supply is interrupted—is the fourth leading cause of death. Established risk factors for CVD include smoking, high blood pressure, obesity, and high blood levels of a fat called low-density lipoprotein (“bad cholesterol”). Because many of these risk factors can be modified by lifestyle changes and by drugs, CVD can be prevented. Thus, physicians can assess a healthy individual's risk of developing CVD using a CVD prediction model (equations that take into account the CVD risk factors to which the individual is exposed) and can then recommend lifestyle changes and medications to reduce that individual's CVD risk.
Why Was This Study Done?
Current guidelines recommend that asymptomatic (healthy) individuals whose likely CVD risk is high should be encouraged to take statins—cholesterol-lowering drugs—as a preventative measure. Statins help to prevent CVD in healthy people with a high predicted risk of CVD, but, like all medicines, they have some unwanted side effects, so it is important that physicians can communicate both the benefits and drawbacks of statins to their patients in a way that allows them to make an informed decision about taking these drugs. Telling a patient that statins will reduce his or her short-term risk of CVD is not always helpful—patients really need to know the potential lifetime benefits of statin therapy. That is, they need to know how much longer they might live if they take statins. Here, the researchers use a mathematical model to predict the personalized lifetime benefits (increased total and CVD-free life expectancy) of statin therapy for individuals without a history of CVD.
What Did the Researchers Do and Find?
The researchers used the Rotterdam Ischemic Heart Disease & Stroke Computer Simulation (RISC) model, which simulates the life courses of individuals through six health states, from well through to CVD or non-CVD death, to estimate lifetime outcomes with and without statin therapy in a population of healthy elderly individuals. They then used these outcomes and information on baseline risk factors to develop a web-based calculator suitable for personalized prediction of the lifetime benefits of statins in routine clinical practice. The model estimated that statin therapy increases average life expectancy in the study population by 0.3 years and average CVD-free life expectancy by 0.7 years. The gains in total and CVD-free life expectancy associated with statin therapy increased with blood pressure, unfavorable cholesterol levels, and body mass index (an indicator of body fat) but decreased with age. Notably, the web-based calculator predicted that some individuals with a low ten-year CVD risk might achieve a similar or larger gain in CVD-free life expectancy with statin therapy than some individuals with a high ten-year risk. So, for example, both a 55-year-old non-smoking woman with a ten-year CVD mortality risk of 2% (a two in a hundred chance of dying of CVD within ten years) and a 65-year-old male smoker with a ten-year CVD mortality risk of 15% might both gain one year of CVD-free life expectancy with statin therapy.
What Do These Findings Mean?
These findings suggest that statin therapy can lead on average to small gains in total life expectancy and slightly larger gains in CVD-free life expectancy among healthy individuals, and show that life expectancy benefits can be predicted using an individual's risk factor profile. The accuracy and generalizability of these findings is limited by the assumptions included in the model (in particular, the model did not allow for the known side effects of statin therapy) and by the data fed into it—importantly, the risk prediction model needs to be validated using an independent dataset. If future research confirms the findings of this study, the researchers' web-based calculator could provide complementary information to the currently recommended ten-year CVD mortality risk assessment. Whether communication of personalized outcomes will ultimately result in better clinical outcomes remains to be seen, however, because patients may be less likely to choose statin therapy when provided with more information about its likely benefits.
Additional Information
Please access these websites via the online version of this summary at
The web-based calculator for personalized prediction of lifetime benefits with statin therapy is available (after agreement to software license)
The American Heart Association provides information about many types of cardiovascular disease for patients, carers, and professionals, including information about drug therapy for cholesterol and a heart attack risk calculator
The UK National Health Service Choices website provides information about cardiovascular disease and about statins
Information is available from the British Heart Foundation on heart disease and keeping the heart healthy; information is also available on statins, including personal stories about deciding to take statins
The US National Heart Lung and Blood Institute provides information on a wide range of cardiovascular diseases
The European Society of Cardiology's cardiovascular disease risk assessment model (SCORE) is available
MedlinePlus provides links to many other sources of information on heart diseases, vascular diseases, stroke, and statins (in English and Spanish)
PMCID: PMC3531501  PMID: 23300388
25.  Neighborhood Disadvantage and Ischemic Stroke: The Cardiovascular Health Study (CHS) 
Background and Purpose
Neighborhood characteristics may influence the risk of stroke and contribute to socioeconomic disparities in stroke incidence. The objectives of this study were to examine the relationship between neighborhood socioeconomic status (NSES) and incident ischemic stroke and examine potential mediators of these associations.
We analyzed data from 3834 whites and 785 African Americans enrolled in the Cardiovascular Health Study, a multicenter, population-based, longitudinal study of adults ages ≥65 years from four U.S. counties. The primary outcome was adjudicated incident ischemic stroke. NSES was measured using a composite of six census tract variables. Race-stratified multilevel Cox proportional hazard models were constructed, adjusted for sociodemographic, behavioral, and biologic risk factors.
Among whites, in models adjusted for sociodemographic characteristics, stroke hazard was significantly higher among residents of neighborhoods in the lowest compared to the highest NSES quartile (Hazard Ratio [HR] =1.32; 95% CI 1.01-1.72), with greater attenuation of the HR after adjustment for biologic risk factors (HR=1.16; 0.88-1.52) than for behavioral risk factors (HR=1.30; 0.99-1.70). Among African Americans, we found no significant associations between NSES and ischemic stroke.
Higher risk of incident ischemic stroke was observed in the most disadvantaged neighborhoods among whites, but not among African Americans. The relationship between NSES and stroke among whites appears to be mediated more strongly by biologic than behavioral risk factors.
PMCID: PMC3781011  PMID: 21940966

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