|
 |
|
|||||||
|
|||||||||
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
© 2005 American Public Health Association
DOI: 10.2105/AJPH.2004.048165
RESEARCH AND PRACTICE |
Avis J. Thomas, Lynn E. Eberly, and James D. Neaton are with the Coordinating Centers for Biometric Research, Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis. George Davey Smith is with the Department of Social Medicine, University of Bristol, Bristol, England. Jeremiah Stamler is with the Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Ill.
Correspondence: Requests for reprints should be sent to Avis J. Thomas, MS, Coordinating Centers for Biometric Research, University of Minnesota, 2221 University Ave SE, Suite 200, Minneapolis, MN 55414 (e-mail: avist{at}ccbr.umn.edu).
 |
ABSTRACT |
|---|
 TOP ABSTRACT INTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
|---|
Objectives. We explored differences between Black and White men for cardiovascular disease (CVD) mortality across major risk factor levels.
Methods. Major CVD risk factors were measured among 300 647 White and 20 223 Black men aged 35 to 57 years who were screened for the Multiple Risk Factor Intervention Trial (MRFIT). Hazard ratios for CVD deaths for Black and White men over 25 years of follow-up were calculated for subgroups stratified according to risk factor levels.
Results. CVD was responsible for 2518 deaths among Black men and 30772 deaths among White men. The age-adjusted Black-to-White CVD hazard ratio was 1.35 (95% confidence interval [CI]=1.29, 1.40); the risk- and income-adjusted ratio was 1.05 (95% CI=1.01, 1.10). CVD mortality rates were dramatically lower in cases of favorable risk profiles. However, fully adjusted Black-to-White CVD hazard ratios within groups at low, intermediate, high, and very high levels of overall risk were 1.76, 1.20, 1.10, and 0.94, respectively. Similar gradients were evident for individual risk factors.
Conclusions. Higher CVD mortality rates among Black men were largely mediated by risk factors and income. These data underscore the need for sustained primordial risk factor prevention among Black men.
|
INTRODUCTION |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
|---|
During the latter half of the 20th century, life expectancies among Black men and women in the United States were consistently lower than those among White men and women. In 2000, the life expectancy for Black men and women was 71.7 vs 77.4 years for White men and women, a difference of 5.7 years (vs 8.3 in 1950).1 For men, the difference was 6.8 years (vs 7.4 years in 1950). In 2000, age-adjusted US mortality rates from diseases of the circulatory system (codes I00–I99 in the International Statistical Classification of Diseases, 10th Revision2 [ICD-10]) were 50.7 per 10000 among Black men and 39.6 per 10000 among White men.3
Continuing to reduce disparities between mortality rates for Black and White men has been a priority of US federal policymakers since 1985, when the Office of Minority Health was created by the Department of Health and Human Services.4 Eliminating health disparities is one of the 2 overarching goals of the Healthy People 2010 initiative, launched in 2000,5,6 and is a goal of the National Institutes of Health’s strategic research plan for reducing health disparities during fiscal years 2002 through 2006.7 Central to this goal is an enhanced understanding of the nature and causes of mortality differences between Black men and White men.
Between 1973 and 1975, 361662 men (including 317910 White men and 22 792 Black men) aged 35 to 57 years were screened for the Multiple Risk Factor Intervention Trial (MRFIT). Data were collected on cardiovascular disease (CVD) risk factors, race/ethnicity, and zip code of residence. A previous article reported that, after 16 years of follow-up, the age-adjusted Black-to-White hazard ratio for death because of CVD among men screened for the trial was 1.36; further adjustment for estimated household income on the basis of zip code area reduced this ratio to 1.09.8 Similar reductions after adjusting for income were also evident for non-CVD causes of death.
No studies, to our knowledge, have reported differences in CVD mortality rates between Black men and White men according to major risk factor subgroups. Such differences may indicate underlying variations in medical treatment, health behaviors, or genetic characteristics (all of which have been indicated in previous literature) and may also motivate targeted intervention efforts. In our study, we used 25 years of mortality follow-up data from the men screened for MRFIT to compare CVD mortality hazard ratios for Black and White men across several variables: age, blood pressure, serum cholesterol, tobacco use, use of medication for diabetes, previous hospitalization for a myocardial infarction, overall risk, and income. Analyses were conducted with and without adjustment for income to clarify the interplay among race/ethnicity, major risk factors, and income. The null hypotheses tested were that differences in CVD mortality for Black and White men were largely mediated through risk factors and income and that, after adjustment for risk factors and income, they would be constant across risk factor levels.
|
METHODS |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
|---|
Participants
Men screened for the MRFIT at 22 clinical centers in 18 cities across the continental United States provided their name, address, Social Security number, and date of birth.9,10 They were asked to identify their race/ethnicity (White, Black, Oriental, Spanish American, American Indian, or other) and to indicate whether or not they were taking prescription medicine for diabetes, whether they had ever been hospitalized for 2 weeks or more for a heart attack, and how many cigarettes per day they were smoking. Their blood pressure was taken 3 times with a standard mercury sphygmomanometer; we used the average of the second and third readings. A nonfasting blood sample was drawn and total serum cholesterol measured. These clinical data were used to select men at high risk for CVD.
Data on race/ethnicity-specific median household incomes for most zip code areas were available from the 1980 US census. Of the men screened, 300 647 White men and 20223 Black men had complete risk measurements and supplied residential zip codes that could be matched to race/ethnicity-specific census data.11,12 These men formed the basis of our study.
We used the participants’ names, dates of birth, and Social Security numbers to obtain mortality data from the Social Security Administration and National Death Index through December 31, 1999. For those who died before 1991, we used Social Security files and clinic records to determine dates of death. We then obtained death certificates from states of residence, and underlying causes of death were coded according to the ICD-9.13 Among those who died in 1991 or later, we obtained the date and primary cause of death (using either the ICD-9 or ICD-10) via the National Death Index Plus Service.
Statistical Analysis
Means and standard deviations (or percentages, when appropriate) were tabulated separately for White men and Black men for the following individual risk factors: age, serum cholesterol level, systolic blood pressure level, diastolic blood pressure level, presence and stage of hypertension,14 cigarette smoking status (smoker/nonsmoker), number of cigarettes smoked per day (among smokers), previous admission to a hospital for a heart attack, use of medicine for diabetes, and race/ethnicity-specific median household income on the basis of zip code area of residence. In addition to separate classifications according to each risk factor, definitions consistent with previous work were used to classify men by level of overall risk.15,16
Men were categorized as being at low risk if their systolic blood pressure level was 120 mm Hg or lower, their diastolic blood pressure level was 80 mm Hg or lower, their cholesterol level was less than 200 mg/dL, they did not smoke, they were not taking medicine for diabetes, and they had no history of hospitalization for heart attack. They were categorized as being at intermediate risk if their systolic blood pressure level was less than 140 mm Hg, their diastolic blood pressure level was less than 90 mm Hg, their cholesterol level was less than 240 mg/dL, they did not smoke, and they failed to meet at least one of the criteria for low risk status.
Men were categorized as being at very high risk if their systolic blood pressure level was 160 mm Hg or above, their diastolic blood pressure level was 100 mm Hg or above, their cholesterol level was 280 mg/dL or above, they smoked more than 30 cigarettes per day, they were taking medicine for diabetes, or they had a history of hospitalization for heart attack. Finally, men were categorized as being at high risk if they were not in the very high risk category but their systolic blood pressure level was 140 mm Hg or above, their diastolic blood pressure level was 90 mm Hg or above, their cholesterol level was 240 mg/dL or above, or they smoked. In Black-to-White linear and logistic regression comparisons involving baseline characteristics, race/ethnicity was used as a predictive variable, and adjustment was made for age and income when appropriate.
Proportional hazards regression analyses17 were used to estimate the associations of race/ethnicity, income, and risk factors with CVD mortality. Analyses were stratified according to clinical site and adjusted for age, income, cholesterol level, systolic blood pressure level, presence of diabetes, history of heart attack, and number of cigarettes smoked per day. Graphical methods were used in testing proportional hazards assumptions, and Black-to-White hazard ratios for 5-year intervals were computed to determine whether differences in CVD mortality rates had changed over the 25-year follow-up period.
Subgroup analyses were conducted to examine ways in which Black-to-White hazard ratios varied according to risk factor and income levels. Hypertension risk categories were based on the classifications outlined by the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure18; cholesterol risk categories were based on the classifications of the National Cholesterol Education Program.19 Regression analyses were adjusted for age, income, and all risk factors except the stratification variable; analyses stratified by overall risk level were adjusted for all of the individual risk factors as well as income. We tested for log-linear trends in Black-to-White hazard ratios across risk and income levels using a fully adjusted regression model that included the predictor in question (as a continuous variable, when possible), race/ethnicity, and the interaction of these two variables. To determine whether trends across risk levels were because of confounding with age, we repeated key analyses separately for men younger than 45 years at screening and for men 45 years or older at screening.
To determine whether Black-to-White hazard ratios were affected by regional variations, we ran a separate set of regressions stratified by zip code rather than clinical center. This represented a more general approach than a hierarchical model, which would have imposed a normal distribution on individual zip code effects. To further examine the high Black-to-White hazard ratio among men at low and intermediate risk, we computed separate risk-adjusted hazard ratios for 3 follow-up periods: 1–10 years, 11–20 years, and more than 20 years. We then tested whether the Black-to-White hazard ratio varied significantly over time in a single model with an interaction between race and time. A similar analysis was also conducted for men aged 35 to 39 years at the time of screening (at all risk levels). We used SAS 8.0 (SAS Institute Inc, Cary, NC) software to conduct all analyses. The P values reported were not adjusted for multiple comparisons.
|
RESULTS |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
|---|
Baseline Characteristics
Table 1
summarizes key baseline characteristics for the 20 223 Black and 300 647 White men with complete data. Black men had lower serum cholesterol levels and higher blood pressure levels than did White men, and a higher percentage of Black men were smokers. Fewer Black men than White men were classified as being at low or intermediate risk (4.2% and 17.4%, respectively, for Black men as compared with 6.9% and 26.2%, respectively, for White men), and more were classified as being at high or very high risk (51.1% and 27.3%, respectively, for Black men vs 43.3% and 23.5%, respectively, for White men). Among the men screened for the MRFIT, 14% of Black men and 8% of White men were excluded from this study owing to incomplete data; the men with incomplete data were about 10% more likely to be smokers but were similar in other respects to the men included in the study.
|
Table 2
shows differences in income and risk factor distributions among Black and White men. Eighty-three percent of Black men, as compared with 21% of White men, were in the lowest income quartile. The majority of Black men (65%) were in the lowest income quartile and at high (42%) or very high (23%) risk for CVD mortality. In contrast, only 15% of White men had low incomes and were at high (9%) or very high (6%) risk for CVD mortality.
|
Influence of Income and Risk on Overall CVD Mortality
Over a median follow-up time of 25 years, there were 2518 CVD deaths among Black men and 30772 among White men. Age-adjusted CVD death rates were 58.6 (Black men) and 43.8 (White men) per 10000 person-years. The age-adjusted Black-to-White hazard ratio for CVD death was 1.35 (95% confidence interval [CI] = 1.29, 1.40), and this ratio was similar across 5-year follow-up intervals (1.34, 1.26, 1.41, 1.28, and 1.40 at less than 5 years, 6–10 years, 11–15 years, 16–20 years, and more than 20 years, respectively; P = .47 for log-linear trend over time). Graphical methods also showed that the Black-to-White hazard ratio did not change over time.
Because of the large differences in the income and risk factor distributions between Black and White men, adjustment for age and risk factors reduced the Black-to-White CVD hazard ratio from 1.35 to 1.23 (95% CI = 1.18, 1.28). Adjustment for age and income, without adjustment for risk factors, resulted in a Black-to-White hazard ratio of 1.09 (95% CI = 1.04, 1.14); with further adjustment for risk factors, the Black-to-White hazard ratio was 1.05 (95% CI = 1.01, 1.10).
CVD Mortality Within Risk Factor and Income Levels
To determine whether differences in CVD mortality for Black and White men varied according to risk factor level, age-adjusted CVD death rates were compared for Black and White men in subgroups defined according to screening risk-factor levels. In the case of each of the risk factors, fully adjusted Black-to-White hazard ratios for CVD death decreased as level of risk increased (Table 3). In lower-risk groups, most hazard ratios were significantly greater than 1.0, indicating a greater risk of CVD death among Black men than among White men. For example, Black-to-White hazard ratios were 1.76, 1.20, 1.10, and 0.94 for men at low, intermediate, high, and very high overall risk, respectively. The significance value for the log-linear trend in Black-to-White hazard ratios was less than .0001, indicating marked heterogeneity among the 4 subgroups.
|
The larger Black-to-White hazard ratios among men at low and intermediate risk were explored further across the follow-up periods, although the power of these analyses was limited. The risk-adjusted Black-to-White hazard ratios among the combined low- and intermediate-risk men were 1.35 during the first decade of follow-up, 1.48 during the second decade of follow-up, and 1.56 thereafter. The number of deaths among Black men was small (37, 113, and 118 in the 3 time periods, respectively), and the risk-adjusted significance value for the interaction between race/ethnicity and follow-up time was not statistically significant (P = .41).
Among the higher risk groups, there was less evidence that hazard ratios increased over time (data not shown). Also, among men aged 35 to 39 years at screening (all risk levels included), risk-adjusted Black-to-White hazard ratios were 1.33, 1.44, and 1.86 in the 3 time segments, respectively, with 36, 113, and 100 deaths, respectively, occurring among Black men; the significance value for the interaction between race/ethnicity and follow-up time was .02. Among men who were older at the time of their screening, the trend was attenuated (data not shown).
To assess whether the relationship between lower risk factor levels and higher Black-to-White hazard ratios was simply because of their mutual association with age, the proportional hazards regressions by overall risk level were repeated separately for men who were younger than 45 years and men who were 45 years or older at screening. Results across risk levels within each age group were similar to those observed when all ages were combined.
We also analyzed CVD mortality differences for Black and White men according to income level (Table 3
). Results showed that age-adjusted CVD death rates varied according to income level, with higher death rates at lower income levels. However, in contrast to all of the risk factor effects, the age- and risk-adjusted Black-to-White hazard ratios did not exhibit a significant income level trend.
In general, age-adjusted mortality rates varied more by risk factor level than by race/ethnicity or income. For instance, mortality rates among Black men varied by a factor of 4 across levels of overall risk, whereas mortality rates among White men varied by a factor of 7.
Additional Sensitivity Testing
Cause-specific CVD deaths.
Among Black men, there were 321 deaths attributed to stroke, 1430 deaths attributed to coronary heart disease (CHD), and 767 deaths attributed to other CVD causes. The corresponding numbers of deaths among White men were 2987, 21518, and 6267, respectively. Black-to-White hazard ratios adjusted by age, income, and risk factors were 1.37 (95% CI = 1.20, 1.57) for stroke, 0.86 (95% CI = 0.81, 0.91) for CHD, and 1.58 (95% CI = 1.44, 1.72) for other CVD causes. Overall risk differences were also investigated for these varying causes of death, although the power of these analyses was limited. Fully adjusted Black-to-White hazard ratios for stroke deaths were 4.38, 1.17, 1.41, and 1.25 among men at low, intermediate, high, and very high risk, respectively, (P = .47 for trend). Corresponding hazard ratios were 0.93, 1.01, 0.91, and 0.76, respectively, (P = .12 for trend) for CHD deaths and 2.72, 1.75, 1.61, and 1.41, respectively, (P = .49 for trend) for other CVD deaths. The results for different types of stroke were similar.
Regional variation in CVD deaths. To ascertain whether there were regional differences in CVD mortality unaccounted for by our previous models, we performed a separate analysis of the 228863 men (71% of our study cohort) who lived in zip code areas from which both Black and White participants had been recruited, and we considered proportional hazards regressions stratified by zip code rather than clinical center. The estimated Black-to-White hazard ratio adjusted for age and risk but not income decreased from 1.23 to 1.15 (95% CI = 1.09, 1.22); the hazard ratio adjusted for age, risk, and income increased from 1.05 to 1.10 (95% CI = 1.01, 1.19).
|
DISCUSSION |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
|---|
The large MRFIT database provided a unique opportunity for examining the interacting effects of risk factor levels, income, and race/ethnicity on CVD mortality rates for men. The principal findings of our investigation were as follows: (1) more than 4 times as many Black men than White men had elevated risk factor levels and low incomes; (2) adjustment for age, income, and risk factor differences explained most of the 35% excess CVD mortality for Black men that persisted over the 25-year follow-up period; (3) risk-level variations in age-adjusted mortality rates were greater than racial/ethnic or income variations; and (4) relative differences in CVD mortality for Black and White men varied according to risk factor level, with low-risk Black men at significantly greater relative risk than low-risk White men, even after adjustment for other risk factors and income. This was true both for each risk factor considered and for overall risk. In contrast, after adjustment for risk level, there was no consistent pattern in CVD mortality differences for Black and White men according to income level.
As shown in other research, the higher overall CVD mortality rate among Black men than among White men was largely explained by differences in zip code area incomes and risk factor levels.8 Income and risk factor levels are strong predictors of CVD mortality, and their distributions in this study differed substantially according to race/ethnicity. Black men were much more concentrated than were White men at the high and very high overall risk levels and in the lowest income quartile (66% vs 15%). Adjustment for income level reduced the Black-to-White CVD hazard ratio from 1.35 to 1.09. This suggests that low income may be associated with decreased access to or use of health care services, lower quality of services,20 or detrimental health-related circumstances, behaviors, or beliefs.21 Further adjustment for risk factors reduced the Black-to-White CVD hazard ratio from 1.09 to 1.05. Income inequities and higher risk factor levels formed a lethal combination for Black men.
Among men at lower risk levels, we observed higher Black-to-White CVD hazard ratios that increased over the years of follow-up. Other researchers have shown that Black men have a greater propensity to show increases in risk levels over time. This could be owing to Black men having less access to screening or preventive health care,22–24 and hence being less likely to be aware of health concerns and respond appropriately as risk factors develop. Possible additional explanations for risk factor differences involve differences in health-related behaviors,25–28 the cumulative effects of stress and other psychosocial factors,29–32 and genetic differences.33 Blood pressure, in particular, may increase with age at a more rapid rate among Black men than among White men.
In the Coronary Artery Risk Development in Young Adults (CARDIA) study, high blood pressure incidence rates over a 10-year period were 16.4% among Black men and 7.8% among White men.34 Even an increase of 2 mm Hg in the blood pressure difference between Black and White men over a 20-year age span could account for a 10% greater risk of CVD mortality among Black men.35 Other researchers have explained blood pressure differences in Black and White men by taking into account experiences of racial/ethnic discrimination and the mechanisms through which individuals respond to unfair treatment.36,37 It is also possible that other unknown risk factors or unmeasured confounders that affect Black and White men differently were not measured in this study or that adjustment for social status was incomplete.
For example, Black men may receive less health care at early stages of disease development, possibly owing to employment, insurance, or other circumstances that effectively limit their access. There is evidence that Black and White men are highly segregated within the US health care system, with Black men more likely to visit doctors who are not board certified and less likely to have access to high-quality specialists, high-quality diagnostic imaging, and other important ancillary resources.38 If Black men do in fact show greater increases in risk factor levels over time, this would indicate a need for increased public health interventions targeted toward this population to prevent risk factor development. For example, doctors could receive training in providing preventive interventions to symptom-free individuals and families. Such efforts might also include public health interventions with school-aged children to promote good nutrition, increase physical activity, and discourage tobacco use. The importance of primordial preventive efforts has been noted by others.39
In contrast to the CVD results, we found no evidence after risk and income adjustment of an elevated CHD risk among Black men, similar to the 16-year findings previously reported.8 This result may reflect the fact that all of the CHD distal risk processes act through a limited range of proximal factors: smoking and adverse dietary habits in combination with their consequent effects on serum cholesterol, blood pressure, weight, and diabetic status.40 This may not be the case with stroke and other non-CHD types of CVD mortality. For example, socioeconomic deprivation in early life (more likely experienced by Black than White MRFIT participants) has been shown to have a strong association with stroke risk apart from the influence of the aforementioned risk factors.41,42 Among men at low risk factor levels, the influence of early life deprivation (perhaps fetal growth restriction, poor nutrition in childhood, or infections acquired in childhood) may be particularly important.
Limitations of our study include the use of zip code–based, race/ethnicity-specific median household income rather than individual socioeconomic status.43,44 Because zip code areas can be large and diverse, zip code–based income measures can be less precise than measures based on actual family incomes. However, to the extent that one’s neighborhood itself is a partial determinant of one’s socioeconomic status and affects one’s health,45,46 such neighborhood-based income estimates make a valuable contribution to multivariate models and also have the advantage of being less susceptible to spurious fluctuations resulting from unemployment or illness. Adjusting for zip code–based income strongly affected Black-to-White hazard ratios, further verifying the usefulness of this measure. The validity and limitations of race-specific, zip code–based income measures have been explored elsewhere.7
It has been shown that the standard National Death Index matching algorithm omits more Black deaths than White deaths47; using these underdetection estimates as a guide, we may be missing data on 2.0% of the deaths that occurred among Black men. Thus, the true Black-to-White hazard ratios were at least as high as those described here. Also, changes in lifestyle since the early 1970s, increased prevalence of insurance (which increases access to health care among those who are covered), and improved public awareness of CVD health risks may reduce the applicability of our results to contemporary US populations.
We were limited by having data only on mortality and not data on morbidity or patterns of health care use; this limitation reduces our capacity for inferring causal pathways. Furthermore, our results may not generalize to women. However, the risk factor characteristics of the men screened in MRFIT were very similar to those of the US adult male population at the time with regard to both overall levels and differentials between Black and White men.48–51
The racial/ethnic differences in CVD mortality observed in this cohort have persisted for 25 years and have largely been mediated by differences in major risk factors and income at screening. However, among men at lower risk factor levels, income and risk factor adjustment did not fully explain the mortality differential between Black and White men; this difference may be because Black men have a greater tendency to develop risk factors over time. Our findings emphasize that preventing risk factors from developing and reducing economic inequalities are keys in reaching Healthy People 2010’s goal of eliminating health disparities.
|
Acknowledgments |
|---|
The Multiple Risk Factor Intervention Trial was conducted under a contract with the National Heart, Lung, and Blood Institute. This work was supported by the National Heart, Lung, and Blood Institute (grants R01-HL-43232 and R01-HL-68140).
Human Participant Protection
This study was approved by the human subjects committee of the University of Minnesota. All individuals screened consented to having the data they provided used for scientific purposes, and at the time of screening signed a written consent form to that effect.
|
Footnotes |
|---|
Peer Reviewed
Contributors
A. J. Thomas completed all of the analyses and wrote the article. L. E. Eberly supervised the project. J. D. Neaton, G. Davey Smith, and J. Stamler originated the study and provided feedback and suggestions throughout. All of the authors helped to develop ideas and interpret findings.
Accepted for publication October 12, 2004.
|
References |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
|---|
1. Centers for Disease Control and Prevention. United States life table, 2000. Available at: http://www.cdc.gov/nchs/products/pubs/pubd/nvsr/51/51_03.htm. Accessed May 9, 2005.
2. International Classification of Diseases, Tenth Revision. Geneva, Switzerland: World Health Organization; 1992.
3. Centers for Disease Control and Prevention. Age-adjusted death rates for 358 selected causes, United States, 1999–2000, Table GMWK13. Available at: http://www.cdc.gov/nchs/datawh/statab/unpubd/mortabs/gmwk13_10.htm. Accessed May 9, 2005.
4. US Dept of Health and Human Services. Office of Minority Health Web site. Available at: http://www.omhrc.gov/omhrc. Accessed May 9, 2005.
5. US Dept of Health and Human Services. Healthy People 2010 initiative. Available at: http://www.healthypeople.gov/About/goals.htm. Accessed May 9, 2005.
6. Marwick C. Healthy People 2010 initiative launched. JAMA. 2000;283:989–900.
7. Zerhouni EA, Ruffin J. National Institutes of Health Strategic Research Plan and Budget to Reduce and Ultimately Eliminate Health Disparities, Volume 1, Fiscal Years 2002–2006. Washington, DC: US Dept of Health and Human Services; 2002.
8. Davey Smith G, Neaton JD, Wentworth D, Stamler R, Stamler J. Mortality differences between black and white men in the USA: contribution of income and other risk factors among men screened for MRFIT. Lancet. 1998;351:934–939.[Web of Science][Medline]
9. Neaton JD, Kuller LH, Wentworth D, Borhani NO. Total and cardiovascular mortality in relation to cigarette smoking, serum cholesterol concentration, and diastolic blood pressure among black and white males followed for up to five years. Am Heart J. 1984;108:759–769.[CrossRef][Web of Science][Medline]
10. Harrison HH, Morgan J. Quality control of screening procedures in the Multiple Risk Factor Intervention Trial. Control Clin Trials. 1986;7:91S–108S.[CrossRef][Medline]
11. Davey Smith G, Neaton JD, Wentworth D, Stamler R, Stamler J. Socioeconomic differentials in mortality risk among men screened for the Multiple Risk Factor Intervention Trial: part I, results for 300,685 white men. Am J Public Health. 1996;86:486–496.
12. Davey Smith G, Wentworth D, Neaton JD, Stamler R, Stamler J. Socioeconomic differentials in mortality risk among men screened for the Multiple Risk Factor Intervention Trial: part II, results for 20,224 black men. Am J Public Health. 1996;86:497–504.
13. International Classification of Diseases, Ninth Revision, North American Clinical Modification. Vol. 1. Ann Arbor, Mich: Edwards Brothers Inc; 1981.
14. Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. Sixth report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. Arch Intern Med. 1997;157:2413–2446.
15. Stamler J, Stamler R, Neaton JD, et al. Low risk-factor profile and long-term cardiovascular and non-cardiovascular mortality and life expectancy: findings for 5 large cohorts of young adult and middle-aged men and women. JAMA. 1999;282:2012–2018.
16. Greenland P, Knoll MD, Stamler J, et al. Major risk factors as antecedents of fatal and non-fatal coronary heart disease events. JAMA. 2003;290:891–897.
17. Cox DR. Regression models and life tables with discussion. J R Stat Soc Ser B. 1972;34:187–220.
18. Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. The seventh report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. JAMA. 2003;289:2560–2572.
19. Expert Panel on Detection, Evaluation and Treatment of High Blood Cholesterol in Adults. Executive summary of the third report of the National Cholesterol Educational Program (NCEP). JAMA. 2001;285:2486–2497.
20. Fiscella K, Franks P, Gold M, Clancy C. Inequality in quality: addressing socioeconomic, racial, and ethnic disparities in health care. JAMA. 2000;289:2579–2584.
21. Strike PC, Steptoe A. Psychosocial factors in the development of coronary artery disease. Prog Cardiovasc Dis. 2004;46:337–347.[CrossRef][Web of Science][Medline]
22. Otten MW, Teutsch SM, Williamson DF, Marks JS. The effect of known risk factors on the excess mortality of black adults in the United States. JAMA. 1990; 263:845–850.
23. Adler NE, Ostrove JM. Socioeconomic status and health: what we know and what we don’t. Ann N Y Acad Sci. 1999;896:3–15.[CrossRef][Web of Science][Medline]
24. Freeman HP. Poverty, race, racism, and survival. Ann Epidemiol. 1993;3:145–149.[Medline]
25. Cooper RS. Health and the social status of Blacks in the United States. Ann Epidemiol. 1993;3:137–144.[Medline]
26. Sorlie P, Rogeot E, Anderson R, Johnson NJ, Backlund E. Black–White mortality differences by family income. Lancet. 1992;340:346–350.[CrossRef][Web of Science][Medline]
27. Navarro V. Race or class versus race and class: mortality differentials in the United States. Lancet. 1990;336:1238–1240.[CrossRef][Web of Science][Medline]
28. Warrens RC. The morbidity/mortality gap: what is the problem? Ann Epidemiol. 1993;3:127–129.[Medline]
29. Brodish PH, Massing M, Tyroler HA. Income inequality and all-cause mortality in the 100 counties of North Carolina. South Med J. 2000;93:386–391.[Web of Science][Medline]
30. Krantz DS, McCeney MK. Effects of psychological and social factors on organic disease: a critical assessment of research on coronary heart disease. Annu Rev Psychol. 2002;53:341–369.[CrossRef][Web of Science][Medline]
31. Link BG, Phelan J. Social conditions as fundamental causes of disease. J Health Soc Behav. 1995; 35(suppl):80–94.
32. Yan LL, Liu K, Matthews KA, Daviglus ML, Ferguson TF, Kiefe CI. Psychosocial factors and risk of hypertension. JAMA. 2003;290:2138–2148.
33. Cooper R, Rotimi C, Ataman S, et al. The prevalence of hypertension in seven populations of West African origin. Am J Public Health. 1997;87:160–168.
34. Dyer AH, Liu K, Walsh M, Kiefe C, Jacobs DR, Bild DE. Ten-year incidence of elevated blood pressure and its predictors: the CARDIA study. J Hum Hypertens. 1999;13:13–20.[CrossRef][Web of Science][Medline]
35. Prospective Studies Collaboration. Age-specific relevance of usual blood pressure to vascular disease mortality: a meta-analysis of one million adults in 61 prospective studies. Lancet. 2003;360:1903–1913.
36. Krieger N, Sidney S. Racial discrimination and blood pressure: the CARDIA study of young Black and White adults. Am J Public Health. 1996;86:1370–1378.
37. Knox SS, Hausdorff J, Markovitz JH. Reactivity as a predictor of subsequent blood pressure: racial differences in the Coronary Artery Risk Development in Young Adults (CARDIA) study. Hypertension. 2002;40:914–919.
38. Bach PB, Pham HH, Schrag D, Tate RC, Hargraves JL. Primary care physicians who treat Black men and White men. N Engl J Med. 2004;351:575–584.
39. Whelton PK, He J, Appel LJ, et al. Primary prevention of hypertension: clinical and public health advisory from the National High Blood Pressure Education Program. JAMA. 2002;288:1882–1888.
40. Beaglehole MP. The real contribution of the major risk factors to the coronary epidemics: time to end the "only 50%" claim. Arch Intern Med. 2001;161:2657–2660.
41. Davey Smith G, Hart C, Blane D, Hole D. Adverse socioeconomic conditions in childhood and cause specific adult mortality: prospective observational study. BMJ. 1998;316:1631–1635.
42. Hart CL, Hole DJ, Davey Smith G. Influence of socioeconomic circumstances in early and later life on stroke risk among men in a Scottish cohort study. Stroke. 2000;31:2093–2097.
43. Geronimus AT, Bound J, Neidert LJ. On the validity of using census geocode characteristics to proxy individual socioeconomic characteristics. J Am Stat Assoc. 1996;91:529–537.[CrossRef][Web of Science]
44. Roux AV, Merkin SS, Arnett D, et al. Neighborhood of residence and incidence of coronary heart disease. N Engl J Med. 2001;345:99–136.
45. Franzini L, Spears W. Contributions of social context to inequalities in years of life lost to heart disease in Texas, USA. Soc Sci Med. 2003;57:1847–1861.[CrossRef][Web of Science][Medline]
46. Krieger N, Chen JT, Waterman P, Rehkopf D, Subramanian SV. Race/ethnicity, gender, and monitorial socioeconomic gradients in health: a comparison of area-based socioeconomic measures—the Public Health Disparities Geocoding Project. Am J Public Health. 2003;93:1655–1671.
47. Curb JD, Ford CE, Pressel S, Palmer M, Babcock C, Hawkins CM. Ascertainment of vital status through the National Death Index and the Social Security Administration. Am J Epidemiol. 1985;121:754–766.
48. Blood Pressure Levels in Persons 18–74 Years of Age in 1976–80, and Trends in Blood Pressure From 1960 to 1980 in the United States. Washington, DC: National Center for Health Statistics; 1986. DHHS publication PHS 86-1864.
49. Prevalence of Diagnosed Diabetes, Undiagnosed Diabetes, and Impaired Glucose Tolerance in Adults 20–74 Years of Age, United States, 1976–80. Washington, DC: National Center for Health Statistics; 1987. DHHS publication PHS 87-1687.
50. Prevalence of Selected Chronic Conditions, United States, 1979–81. Washington, DC: National Center for Health Statistics; 1986. DHHS publication PHS 86-1583.
51. Serum Cholesterol Levels of Persons 4–74 Years of Age by Socioeconomic Characteristics, United States, 1971–74. Washington, DC: National Center for Health Statistics; 1980. DHHS publication PHS 80-1667.
This article has been cited by other articles:
|
|
 |
|
  K. Fiscella and P. Franks Vitamin D, Race, and Cardiovascular Mortality: Findings From a National US Sample Ann. Fam. Med, January 1, 2010; 8(1): 11 - 18. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 A. V. Chobanian The Hypertension Paradox -- More Uncontrolled Disease despite Improved Therapy N. Engl. J. Med., August 27, 2009; 361(9): 878 - 887. [Full Text] [PDF]
|
|
|
|
 |
|
 A. R. Folsom, K. Yamagishi, A. Hozawa, L. E. Chambless, and for the Atherosclerosis Risk in Communities Study Absolute and Attributable Risks of Heart Failure Incidence in Relation to Optimal Risk Factors Circ Heart Fail, January 1, 2009; 2(1): 11 - 17. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 C Power, K Atherton, and O Manor Co-occurrence of risk factors for cardiovascular disease by social class: 1958 British birth cohort J Epidemiol Community Health, December 1, 2008; 62(12): 1030 - 1035. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 T. Stephens, H. Braithwaite, L. Johnson, C. Harris, S. Katkowsky, and A. Troutman Cardiovascular risk reduction for African-American men through health empowerment and anger management Health Education Journal, September 1, 2008; 67(3): 208 - 218. [Abstract] [PDF]
|
|
|
|
 |
|
 S. Capewell Will screening individuals at high risk of cardiovascular events deliver large benefits? No BMJ, August 28, 2008; 337(aug28_2): a1395 - a1395. [Full Text]
|
|
|
|
 |
|
 R. Mehrotra, D. Kermah, L. Fried, S. Adler, and K. Norris Racial Differences in Mortality Among Those with CKD J. Am. Soc. Nephrol., July 1, 2008; 19(7): 1403 - 1410. [Full Text] [PDF]
|
|
|
|
 |
|
 L. J. Shaw, R. E. Shaw, C. N. B. Merz, R. G. Brindis, L. W. Klein, B. Nallamothu, P. S. Douglas, R. J. Krone, C. R. McKay, P. C. Block, et al. Impact of Ethnicity and Gender Differences on Angiographic Coronary Artery Disease Prevalence and In-Hospital Mortality in the American College of Cardiology-National Cardiovascular Data Registry Circulation, April 8, 2008; 117(14): 1787 - 1801. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 S. H. Woolf Future Health Consequences of the Current Decline in US Household Income JAMA, October 24, 2007; 298(16): 1931 - 1933. [Full Text] [PDF]
|
|
|
|
 |
|
 J. Stamler Low Risk--and the "No More Than 50%" Myth/Dogma Arch Intern Med, March 26, 2007; 167(6): 537 - 539. [Full Text] [PDF]
|
|
|
|
|
|
A. Hozawa, A. R. Folsom, A. R. Sharrett, and L. E. Chambless Absolute and Attributable Risks of Cardiovascular Disease Incidence in Relation to Optimal and Borderline Risk Factors: Comparison of African American With White Subjects--Atherosclerosis Risk in Communities Study Arch Intern Med, March 26, 2007; 167(6): 573 - 579. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 J O'Loughlin, E Dugas, K Maximova, and N Kishchuk Reporting of ethnicity in research on chronic disease: update Postgrad. Med. J., November 1, 2006; 82(973): 737 - 742. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
L. H. Kuller, B. I. Freedman, L. E. Wagenknecht, D. W. Bowden, K. G. Keppel, J. N. Pearcy, J. S. Weissman, B. E. Akpunonu, A. B. Mutgi, S. A. Khuder, et al. Trends in racial disparities in care. N. Engl. J. Med., November 10, 2005; 353(19): 2081 - 2085. [Full Text] [PDF]
|
|
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |