Blacks experience significantly worse mortality and clinical outcomes than white individuals in the general population. These differences are the result of a complex interplay between socioeconomic deprivation, lack of access to health care, overt or latent racial discrimination, and ancestry-related biological differences. The relative contribution of each of these factors is not well defined and may vary according to the studied end point. It is unclear what the clinical outcomes experienced by blacks would be in a system that does not pose the typical barriers to healthcare access seen in the United States. We examined all-cause mortality, incident coronary heart disease, and incident strokes in a cohort of black versus white US veterans with normal estimated glomerular filtration rate. Among US veterans, blacks experienced significantly lower all-cause mortality and incident coronary heart disease but higher incident strokes. These results contrasted the results of a parallel analysis in the National Health and Nutrition Examination Survey (NHANES) 1999 to 2004, which showed higher multivariable-adjusted all-cause mortality in blacks and trends toward higher coronary heart disease and stroke mortality. Our results could be explained by a beneficial effect of free healthcare access on some clinical outcomes in blacks, by selection bias in that black veterans may not be representative of the black community at large, or by a combination of these. Further studies are needed to corroborate the benefits of unhindered access to health care in disadvantaged populations and to uncover potential biological mechanisms that may differentiate individuals who are more resilient within the black community and the unique differential outcomes for coronary heart disease and stroke mortality across races.
Association of Race With Mortality and Cardiovascular Events in a Large Cohort of US Veterans
Abstract
Background—
In the general population, blacks experience higher mortality than their white peers, attributed in part to their lower socioeconomic status, reduced access to care, and possibly intrinsic biological factors. Patients with kidney disease are a notable exception, among whom blacks experience lower mortality. It is unclear if similar differences affecting outcomes exist in patients with no kidney disease but with equal or similar access to health care.
Methods and Results—
We compared all-cause mortality, incident coronary heart disease, and incident ischemic stroke using multivariable-adjusted Cox models in a nationwide cohort of 547 441 black and 2 525 525 white patients with baseline estimated glomerular filtration rate ≥60 mL·min−1·1.73 m−2 receiving care from the US Veterans Health Administration. In parallel analyses, we compared outcomes in black versus white individuals in the National Health and Nutrition Examination Survey (NHANES) 1999 to 2004. After multivariable adjustments in veterans, black race was associated with 24% lower all-cause mortality (adjusted hazard ratio, 0.76; 95% confidence interval, 0.75–0.77; P<0.001) and 37% lower incidence of coronary heart disease (adjusted hazard ratio, 0.63; 95% confidence interval, 0.62–0.65; P<0.001) but a similar incidence of ischemic stroke (adjusted hazard ratio, 0.99; 95% confidence interval, 0.97–1.01; P=0.3). Black race was associated with a 42% higher adjusted mortality among individuals with estimated glomerular filtration rate ≥60 mL·min−1·1.73 m−2 in NHANES (adjusted hazard ratio, 1.42; 95% confidence interval, 1.09–1.87).
Conclusions—
Black veterans with normal estimated glomerular filtration rate and equal access to healthcare have lower all-cause mortality and incidence of coronary heart disease and a similar incidence of ischemic stroke. These associations are in contrast to the higher mortality experienced by black individuals in the general US population.
Introduction
Blacks represent an estimated 13.2% of the US population, which amounts to >41 million individuals.1 Poorer health outcomes in blacks have been well documented.2–6 These outcome differences have been ascribed largely to the substantial socioeconomic disadvantage of blacks, with resultant lower health literacy, decreased disease awareness, suboptimal access to health care, and overt or latent discrimination in receiving recommended healthcare interventions.7
Editorial see p 1519
Clinical Perspective on p 1548
Notwithstanding the validity and importance of these factors, the underlying causes for differences in the health outcomes of blacks are likely even more complex and are affected by genetic differences between individuals of African and European ancestry.8 A notable example for this is the advanced stages of chronic kidney disease (CKD) and end-stage renal disease (ESRD), the incidence and prevalence of which are disproportionately higher in blacks, in part as a result of recently described common genetic polymorphisms in individuals of African ancestry,9–13 but paradoxically, blacks with advanced CKD and ESRD have lower mortality than their white peers.14,15 The markedly different pathogenesis of CKD in blacks could affect race-associated clinical outcomes in patients with kidney disease (eg, by affecting differently the age and comorbidity characteristics of affected patients). It is possible that there are also other CKD-independent biological mechanisms affecting race-specific cardiovascular and other clinical outcomes. The effects of such mechanisms independently of socioeconomic differences could have different impacts on various clinical outcomes as a result of distinct differences in the pathophysiology of each outcome. Atherosclerosis may have different pathophysiological underpinnings in blacks, who develop significantly less vascular calcification compared with white individuals,16–19 which may be the result of (among others) genetic differences in vitamin D and bone metabolism8,20 and could result in differences in cardiovascular morbidity and mortality. Blacks also experience a higher incidence of hypertension, more uncontrolled hypertension, and differences in central aortic blood pressure with potential consequences such as higher rates of left ventricular hypertrophy and stroke.21 The relative contribution of these various factors to the disparities in outcomes seen in blacks and the extent to which they are affected by socioeconomic factors are not well defined and may vary according to the studied end point and the studied population segment. Furthermore, among the complex socioeconomic factors affecting race-specific outcomes in the United States, the relative contribution of poor access to health care is not well defined.
We hypothesized that blacks without advanced CKD or ESRD will have improved outcomes in a healthcare system that allows enrollment independently of race or socioeconomic status. The US Veterans Health Administration (VHA) is a healthcare system that does not impose the typical access barriers of the US healthcare system that may disproportionately impede enrollment of blacks. We compared all-cause mortality and incident cardiovascular event rates in a large contemporary cohort of black and white individuals with an estimated glomerular filtration rate (eGFR) ≥60 mL·min−1·1.73 m−2 followed up in any US VHA facility. We hypothesized that outcome differences between black and white veterans may be attenuated or eliminated by open healthcare access in US VHA facilities.
Methods
Study Design and Participants
We used data from a historic cohort study examining risk factors in patients with incident CKD (Racial and Cardiovascular Risk Anomalies in CKD [RCAV] study).22 The algorithm for cohort definition is shown in Figure 1. US veterans with serum creatinine measurements performed from October 1, 2004, to September 30, 2006, were identified from the national Veterans Affairs (VA) Corporate Data Warehouse LabChem data files.23 Overall, 4 447 691 veterans had at least 1 available serum creatinine measurement, representing ≈94% of all veterans who received VA health care during this time period.24 eGFR was calculated by the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation.25 The RCAV cohort included 3 582 478 patients with eGFR ≥60 mL·min−1·1.73 m−2, of whom we excluded 509 512 patients with race other than black or white (Hispanic, 73 105 [2.0%]; other races, 68 889 [1.9%]; and missing race, 365 489 [10.2%]). The VA collects information on patients’ race primarily from VA Form 10-10EZ (Application for Health Benefits),26 which patients complete at enrollment and is updated as needed. We complemented this self-reported race with race data obtained from Medicare through the VA-Medicare data merge project.26 In case of discrepancies, we used the race determination from Medicare because of its more accurate nature.27 Our final analytic sample consisted of 3 072 966 patients (547 441 black and 2 525 525 white).
Sociodemographic Characteristics, Comorbidities, Medication Use, and Laboratory Variables
Information about sociodemographic characteristics, comorbid conditions, medication use, and laboratory characteristics was obtained as previously described.28,29 Briefly, data on patients’ age, sex, marital status (married, single, divorced or widowed), mean per capita income, service connectedness (a measure indicating whether 1 or more of a patient’s comorbidities were caused by military service, resulting in certain privileges such as preferential access to care and lower copayments), body mass index, systolic and diastolic blood pressures, comorbid conditions, location and frequency of healthcare encounters, and medication use were obtained from various national VA research data files.30 Comorbidities and clinical events were assessed from the VA Inpatient and Outpatient Medical SAS Datasets31,32 by use of International Classification of Diseases, Ninth Revision diagnostic and procedure codes and Current Procedural Terminology codes (online-only Data Supplement). Prevalent comorbidities were defined as the presence of relevant International Classification of Diseases, Ninth Revision and Current Procedural Terminology codes recorded from October 1, 2004, to September 30, 2006.28,29 Prevalent coronary heart disease (CHD) was defined as the presence of diagnostic codes for coronary artery disease, angina, or myocardial infarction or procedure codes for percutaneous coronary interventions or coronary artery bypass grafting. We examined the use of 2 commonly applied medication classes (angiotensin-converting enzyme inhibitors or angiotensin II receptor blockers and statin-type cholesterol-lowering agents); use of healthcare interventions (influenza vaccinations and blood cholesterol level measurements) from October 1, 2004, to September 30, 2006; and the yearly rate of healthcare encounters over the entire follow-up period to identify discrepancies in basic healthcare delivery. Other baseline characteristics were assessed on the date of cohort entry. In addition to data derived from VA sources, we included select socioeconomic indicators using 2004 county typology codes (housing stress, low education, low employment, and persistent poverty; see the Methods section in the online-only Data Supplement) based on the patients’ residential address, obtained from the Area Health Resources Files system issued by the US National Center for Health Workforce Analysis, Bureau of Health Workforce, Health Resources and Services Administration (http://ahrf.hrsa.gov/).
Outcomes
Outcomes of interest were all-cause mortality, incident CHD, and incident ischemic strokes. Deaths were ascertained from the VA Vital Status Files, the sensitivity and specificity of which (with the US National Death index used as gold standard) are 98.3% and 99.8%, respectively.33 Incident CHD was defined as the composite of a first occurrence of an acute myocardial infarction, percutaneous coronary intervention, or coronary artery bypass grafting; incident ischemic stroke was defined as the first occurrence of an ischemic stroke after October 1, 2006, in patients without such diagnoses before this date.
Statistical Analyses
Data are expressed as means ±SDs, medians (25th–75th percentiles), and proportions. Because of the large sample size, the significance of differences in the main cohort was established on the basis of what we deemed to be biologically or clinically meaningful differences. Differences between variables in the propensity-matched cohort were examined by calculating standardized differences and were regarded as significant if they were >0.1. The start of the follow-up period was the date of cohort entry, which was defined as the date of the first eGFR ≥60 mL·min−1·1.73 m−2 from October 1, 2004, to September 30, 2006. Patients were followed up until death or were censored at the date of the last healthcare or administrative VA encounter, as documented in the VA Vital Status Files, or on July 26, 2013. Sex-specific crude event rates were calculated from the number of event occurrences and patient-years during the follow-up period, and sex-specific age-adjusted event rates were calculated by the direct standardization method using the US 2000 Census data as the standard population (http://www.cdc.gov/nchs/tutorials/NHANES/NHANESAnalyses/agestandardization/age_standardization_intro.htm).
The association of black race with the outcomes of interest was examined in univariable models and after multivariable adjustment. The association of covariates with outcomes was assessed in univariable analyses with the use of Kaplan-Meier curves and log-rank tests or with univariable Cox proportional hazards models and χ2 tests, as appropriate. We included in multivariable models the covariates showing statistically significant associations with outcomes or those that could be associated with outcomes based on theoretical considerations. Cox models were applied to examine the effect confounders, with adjustments implemented incrementally. Model 1 was unadjusted; model 2, adjusted for age, sex, and baseline eGFR; model 3, model 2 variables plus prevalent comorbidities (diabetes mellitus, hypertension, CHD, congestive heart failure, cerebrovascular disease, peripheral vascular disease, chronic lung disease, peptic ulcer disease, hemiplegia, liver disease, dementia, rheumatic disease, malignancy, HIV/AIDS, and depression); model 4, model 3 variables plus baseline body mass index and systolic and diastolic blood pressures; and model 5, model 4 variables plus mean per capita income, marital status, service connectedness, housing stress, low education, low employment, persistent poverty, frequency of healthcare encounters, use of angiotensin-converting enzyme inhibitors or angiotensin II receptor blockers and statins, receipt of influenza vaccination(s), and each patient’s VA healthcare center. Because of previous reports of marked differences in the outcomes of blacks with CKD,14,15 we further examined effect modification by decreased kidney function in subgroup analyses of patients who maintained eGFR ≥60 mL·min−1·1.73 m−2 throughout follow-up and in those who developed incident stage 3 and above CKD.34 In the latter group, the start of follow-up was the date of eGFR used to define incident CKD. We further assessed whether the occurrence of incident CHD or stroke modifies the association of race with outcomes by including the incident events in the models as time-dependent variables and by including multiplicative interaction terms. Proportionality was tested by the use of Schoenfeld and scaled Schoenfeld residuals. We evaluated the fit of the model by using the Cox-Snell residuals.
Analyses were repeated in a propensity score–matched cohort. Propensity scores quantifying the likelihood of black versus white race were calculated by logistic regression, using all variables included in multivariable models and applying a 1-to-1 nearest neighbor matching without replacement in Stata’s psmatch2 command suite. All outcomes were also examined in subgroups divided by baseline age, sex, prevalent CHD, congestive heart failure, diabetes mellitus, hypertension, eGFR, and income level. Unadjusted analyses for CHD and stroke were repeated in sensitivity analyses using competing risk regression, with nonevent deaths treated as competing events.35 To compare outcomes associated with black versus white race in the VA system with those in the general population, we also performed an analysis of all-cause, cardiovascular, and stroke-related mortality overall in individuals with eGFR ≥60 mL·min−1·1.73 m−2 and in various subgroups using data from the NHANES 1999 to 2004 and adjusting all estimates for the complex NHANES survey design (online-only Data Supplement).
Statistical analyses were performed with STATA MP version 12 (STATA Corp, College Station, TX) and SAS version 9.3 (Research Triangle Park, NC). The study protocol was approved by the Research and Development Committees at the Memphis VA Medical Center and Long Beach VA Medical Center.
Results
The mean±SD baseline age of the cohort was 59.9±14.0 years, and 93.6% were men. Baseline characteristics in the overall cohort are shown in Table 1. Compared with whites, blacks were younger, more likely to be female, service connected, hypertensive, and diabetic and to have HIV/AIDS. They were less likely to be married and to have prevalent CHD and chronic lung disease. Blacks also had more frequent healthcare encounters, higher systolic and diastolic blood pressures, and a lower per capita income and were more likely to live in areas with high housing stress, lower education level, and persistent poverty. The use of angiotensin-converting enzyme inhibitors or angiotensin II receptor blockers and the administration of blood cholesterol measurements were similar in black and white veterans. The use of statins and the administration of influenza vaccinations were slightly less common in blacks. Blacks and whites had characteristics similar to each other in the propensity-matched cohort (Table I in the online-only Data Supplement). Differences in baseline characteristics between black and white individuals (when available) were in general similar in the NHANES cohort and the VA cohort (Table II in the online-only Data Supplement).
| Unmatched | |||
|---|---|---|---|
| All(n=3 072 966) | Whites(n=2 525 525, 82%) | Blacks(n=547 441, 18%) | |
| Age, y | 59.9±13.4 | 61.0±13.9 | 54.5±13.2 |
| eGFR, mL·min−1·1.73 m−2 | 84.0±15.7 | 82.3±14.4 | 91.9±18.8 |
| Male sex, n (%) | 2 876 626 (94) | 2 383 874 (94) | 492 752 (90) |
| Hypertension, n (%) | 1 842 120 (60) | 1 503 404 (60) | 338 716 (62) |
| DM, n (%) | 735 372 (24) | 598 022 (24) | 137 350 (25) |
| CHD, n (%) | 359 848 (12) | 321 545 (13) | 38 303 (7) |
| CHF, n (%) | 143 230 (5) | 118 970 (5) | 24 260 (4) |
| CVD, n (%) | 194 493 (6) | 163 514 (6) | 30 979 (6) |
| PAD, n (%) | 174 990 (6) | 149 833 (6) | 25 157 (5) |
| Chronic lung disease, n (%) | 586 672 (19) | 504 170 (20) | 82 502 (15) |
| Dementia, n (%) | 26 253 (0.9) | 21 370 (0.9) | 4883 (0.9) |
| Rheumatologic disease, n (%) | 44 044 (1) | 37 664 (1) | 6380 (1) |
| Peptic ulcer disease, n (%) | 59 130 (2) | 47 734 (2) | 11 396 (2) |
| Liver disease, n (%) | 38 241 (1) | 31 265 (1) | 6976 (1) |
| Hemiplegia, n (%) | 15 458 (0.5) | 12 187 (0.5) | 3271 (0.6) |
| Malignancies, n (%) | 324 508 (11) | 271 282 (11) | 53 226 (10) |
| AIDS/HIV, n (%) | 20 318 (0.7) | 9321 (0.4) | 10 997 (2) |
| Depression, n (%) | 301 777 (10) | 245 141 (10) | 56 636 (10) |
| Per capita income, $ | 22 496 (11 643–35 000) | 24 100 (12 284–37 533) | 16 732 (10 044–29 416) |
| Married, n (%) | 1 609 343 (54) | 1 400 099 (58) | 209 244 (40) |
| Service connected, n (%) | 1 273 171 (41) | 1 009 039 (40) | 264 132 (48) |
| BMI, kg/m2 | 29.2±5.8 | 29.2±5.7 | 29.0±6.0 |
| SBP, mm Hg | 135.4±19.2 | 135.2±18.9 | 136.8±20.5 |
| DBP, mm Hg | 77.2±11.9 | 76.6±11.6 | 79.9±12.8 |
| ACEI/ARB use, n (%) | 1 636 622 (22) | 1 342 705 (23) | 293 917 (20) |
| Statin use, n (%) | 1 688 623 (15) | 1 417 215 (16) | 271 408 (9) |
| Influenza vaccination, n (%) | 2 006 550 (30) | 1 672 423 (31) | 334 127 (26) |
| Cholesterol measurement, n (%) | 2 866 616 (79) | 2 355 044 (80) | 511 572 (76) |
| Healthcare encounters >1/mo, n (%) | 1 696 067 (56) | 1 347 435 (54) | 348 632 (64) |
| Living in area with high housing stress, n (%) | 1 014 255 (34) | 770 143 (31) | 244 112 (47) |
| Living in area with low education, n (%) | 312 812 (11) | 238 346 (10) | 74 466 (14) |
| Living in area with low employment, n (%) | 275 108 (9) | 218 818 (9) | 56 290 (11) |
| Living in area of persistent poverty, n (%) | 275 108 (5) | 102 782 (4) | 37 247 (7) |
Data are presented as mean±SD, median (25th–75th percentiles), or number (percent of total). ACEI/ARB indicates angiotensin-converting enzyme inhibitors/angiotensin receptor blockers; BMI, body mass index; CHD, coronary heart disease; CHF, chronic heart failure; CVD, cerebrovascular disease; DBP, diastolic blood pressure; DM, diabetes mellitus; eGFR, estimated glomerular filtration rate; PAD, peripheral arterial disease; and SBP, systolic blood pressure.
Mortality
A total of 638 536 patients died overall (crude rate, 30.16 per 1000 patient-years; 95% confidence interval [CI], 30.09–30.24) during a median follow-up of 7.9 years. There were 551 208 deaths in white patients (crude rate, 31.87 per 1000 patient-years; 95% CI, 31.79–31.96) during a median follow-up of 7.8 years and 87 328 deaths in black patients (crude rate, 22.53 per 1000 patient-years; 95% CI, 22.38–22.68) during a median follow-up of 8.0 years. Table 2 shows sex-specific crude and age-adjusted mortality rates, indicating lower crude mortality rates in blacks for both men and women. This difference disappeared after adjustment for age in men and diminished but remained slightly lower in black women. Compared with whites, blacks had an overall crude mortality hazard ratio (HR) of 0.70 (95% CI, 0.69–0.71; P<0.001; model 1, Figure 2). Adjustment for age, sex, and baseline eGFR resulted in the attenuation of the black mortality advantage (model 2 HR, 0.99; 95% CI, 0.98–0.99; P<0.001), but further adjustment for additional covariates resulted in a gradual decrease in the mortality risk associated with black race (HR, 0.76; 95% CI, 0.75–0.77; P<0.001; model 5, Figure 2). Incident CHD and stroke modified the association between race and mortality. Compared with white patients who did not experience incident CHD or stroke, blacks without CHD or stroke had significantly lower mortality, whereas mortality rates in white and black patients after an incident CHD or stroke were similar and significantly higher compared with rates in patients without incident events (Figure 2). Adjusted all-cause mortality was higher in black versus white individuals both overall (HR, 1.51; 95% CI, 1.19–1.92) and in individuals with eGFR ≥60 mL·min−1·1.73 m−2 in NHANES (HR, 1.42; 95% CI, 1.09–1.87; Figure 3). Mortality in NHANES was also higher in blacks versus whites categorized by age (adjusted HR for age 18–49 versus ≥50 years, 1.74 [95% CI, 0.70–4.32] and 1.45 [1.10–1.93], respectively), sex (adjusted HR for men and women, 1.32 [95% CI, 0.91–1.91] and 1.67 [95% CI, 1.19–2.32], respectively), and poverty level (adjusted HR for poverty level ≥200% and <200%, 1.30 [95% CI, 0.84–2.00] and 1.45 [95% CI, 1.08–1.94], respectively). Cardiovascular and stroke-related mortality was similar in blacks and whites in NHANES, although the low number of stroke events resulted in imprecise risk estimates (Figure 3).
| White | Black | |||||||
|---|---|---|---|---|---|---|---|---|
| Male | Female | Male | Female | |||||
| Crude Rate (95% CI) | Age-Adjusted Rate (95% CI) | Crude Rate (95% CI) | Age-Adjusted Rate (95% CI) | Crude Rate (95% CI) | Age-Adjusted Rate (95% CI) | Crude Rate (95% CI) | Age-Adjusted Rate (95% CI) | |
| Mortality | 33.13(33.03–33.22) | 18.74(18.67–18.81) | 11.06(10.85–11.27) | 13.35(13.07–13.62) | 24.53(24.36–24.69) | 19.31(19.16–19.46) | 5.13(4.92–5.36) | 11.48(10.66–12.31) |
| CHD | 3.76(3.73–3.79) | 2.38(2.36–2.41) | 1.17(1.10–1.24) | 1.08(1.01–1.15) | 2.97(2.91–3.03) | 2.11(2.06–2.16) | 0.82(0.74–0.92) | 0.94(0.75–1.13) |
| Stroke | 3.01(2.98–3.03) | 1.83(1.8–1.85) | 1.33(1.26–1.41) | 1.32(1.24–1.41) | 3.75(3.68–3.82) | 2.69(2.63–2.75) | 1.32(1.21–1.44) | 2.03(1.7–2.36) |
| AMI | 2.52(2.50–2.55) | 1.66(1.64–1.69) | 0.92(0.86–0.99) | 0.87(0.8–0.93) | 2.25(2.20–2.31) | 1.65(1.6–1.69) | 0.67(0.59–0.76) | 0.79(0.61–0.97) |
| PCI | 1.10(1.09–1.12) | 0.7(0.68–0.71) | 0.28(0.25–0.31) | 0.24(0.21–0.27) | 0.79(0.76–0.82) | 0.53(0.51–0.56) | 0.21(0.17–0.27) | 0.24(0.15–0.32) |
| CABG | 0.76(0.75–0.77) | 0.43(0.42–0.44) | 0.11(0.09–0.13) | 0.10(0.08–0.12) | 0.41(0.39–0.43) | 0.26(0.25–0.28) | 0.07(0.05–0.10) | 0.05(0.03–0.07) |
Event rates are presented as number per 1000 patient-years and 95% confidence intervals. AMI indicates acute myocardial infarction; CABG, coronary artery bypass grafting; CHD, coronary heart disease; CI, confidence interval; and PCI, percutaneous coronary intervention.
Incident CHD
A total of 63 808 patients experienced an incident CHD event (crude rate, 3.43 per 1000 patient-years; 95% CI, 3.40–3.46), with 53 988 events in whites (crude rate, 3.60 per 1000 patient-years; 95% CI, 3.57–3.63) and 9820 events in blacks (crude rate, 2.73 per 1000 patient-years; 95% CI, 2.68–2.79). Incident CHD rates in blacks versus whites were lower in both men and women after adjustment for age (Table 2). Both crude (HR, 0.75; 95% CI, 0.74–0.77) and adjusted (HR, 0.63; 95% CI, 0.62–0.65) risks of incident CHD and of the individual components (acute myocardial infarction, coronary artery bypass grafting, and percutaneous coronary intervention) were lower in blacks (Figure 4). The risk of incident CHD was higher in individuals after strokes compared with those who did not have an incident stroke, but incident strokes did not modify the association between race and incident CHD, which remained lower in black versus white patients with or without incident strokes (Figure 4).
Incident Stroke
In total, 59 734 patients experienced an incident stroke (crude rate, 3.02 per 1000 patient-years; 95% CI, 2.99–3.04), with 46 984 events in whites (crude rate, 2.91 per 1000 patient-years; 95% CI, 2.88–2.93) and 12 750 events in blacks (crude rate, 3.49 per 1000 patient-years; 95% CI, 3.43–3.55). Incident stroke rates in blacks versus whites remained higher in both men and women after adjustment for age (Table 2). Crude stroke risk was higher in blacks (HR, 1.18; 95% CI, 1.16–1.21), but this was attenuated after multivariable adjustments, especially for socioeconomic characteristics in model 5 (HR, 0.99; 95% CI, 0.97–1.01; Figure 5). Incident CHD did not modify the association between race and incident stroke, which was similar in black and white patients with or without CHD, even though the risk of stroke was higher in patients who experienced an incident CHD event compared with those who did not (Figure 5).
Sensitivity Analyses
The mortality risk associated with black race was also lower in propensity score–matched analyses (HR, 0.86; 95% CI, 0.85–0.87; Table III in the online-only Data Supplement), in patients who maintained eGFR ≥60 mL·min−1·1.73 m−2 (n=2 732 494; HR, 0.83; 95% CI, 0.82–0.84), and in various examined subgroups (Figure 6), but it was similar in those who developed incident eGFR <60 mL·min−1·1.73 m−2 (n=328 221; HR, 0.99; 95% CI, 0.96–1.01; Figures I and II in the online-only Data Supplement).
The risk of incident CHD was lower in blacks versus whites in the overall propensity-matched cohort (HR, 0.68; 95% CI, 0.66–0.70; Table III in the online-only Data Supplement), in patients who developed incident eGFR <60 mL·min−1·1.73 m−2 (HR, 0.79; 95% CI, 0.75–0.84), and in those who maintained eGFR ≥60 mL·min−1·1.73 m−2 throughout follow-up (HR, 0.63; 95% CI, 0.61–0.65; Figures III and IV in the online-only Data Supplement), as well as in all examined subgroups except for patients ≥80 years old (Figure 6) and in a competing-risk regression model (Table IV in the online-only Data Supplement).
Stroke risk was also higher in blacks in the overall propensity-matched cohort (HR, 1.09; 95% CI, 1.06–1.12; Table III in the online-only Data Supplement), in patients who developed incident CKD (HR, 1.17; 95% CI, 1.10–1.24), and in those who maintained eGFR ≥60 mL·min−1·1.73 m−2 (HR, 1.05; 95% CI, 1.02–1.09; Figures V and VI in the online-only Data Supplement), as well as in competing-risk regression (Table IV in the online-only Data Supplement). Blacks experienced higher stroke risk among older individuals and women and in subgroups with prevalent CHF, lower eGFR, and lower income (Figure 6).
Discussion
In this large cohort of >3 million contemporary US veterans with baseline eGFR ≥60 mL·min−1·1.73 m−2, we found significant differences in major clinical outcomes between blacks and whites not traditionally reported in the general population. We found substantially lower incident CHD rates and, most surprising, lower all-cause mortality in blacks compared with whites. Differences in demographic, comorbidity, and socioeconomic characteristics accounted for some but not all of the difference in mortality and CHD. Contrasting the lower mortality seen in US veterans, our analyses of NHANES 1999 to 2004 showed higher all-cause mortality in blacks versus whites. Similar to previous reports,36 incident stroke rates were higher in black veterans, but differences were attenuated to nonsignificant after adjustment for socioeconomic characteristics.
Worse health outcomes in blacks have been well described.37 These have included a variety of outcomes transcending age and sex categories.2–6 The socioeconomic deprivation of blacks has provided a plausible explanation for these observations7 and points to the importance of breaking down the many remaining barriers faced by the black community. In today’s typical US healthcare environment, it is difficult to separate the effects on outcomes of poor healthcare access and race-based discrimination in healthcare delivery from the effects of biological mechanisms. This would require not only an egalitarian healthcare system but also a society that does not directly or indirectly discriminate. In a community-based standardized healthcare system, Karter et al38 found similar or even reduced rates of diabetes-related complications in minority compared with white enrollees, with the exception of developing ESRD. The Department of Defense and VHA are open-access healthcare systems in the United States in that they provide comprehensive health care based on a military or veteran status. Thus, it is less likely that institutional barriers or cost would disproportionately prevent black veterans from obtaining health care. In an analysis of Department of Defense enrollees, Gao et al39 found similar rates of quality care indicators for black and white patients with stages 3 and 4 CKD. Previous studies examining all-cause mortality in hospitalized patients40 and outcomes associated with various health conditions such as congestive heart failure,41 Pneumocystis carinii pneumonia,42 and colorectal and lung cancer43,44 have also reported better outcomes in black veterans compared with those typically seen in nonveterans. Furthermore, our examination of basic healthcare metrics such as the administration of common health screening procedures and medications suggests that basic health care delivered after enrollment in a contemporary VHA facility is not racially discriminatory. Also supporting this notion, a previous study examining unmet healthcare needs in patients of various races and ethnicities showed that the use of VA ambulatory care eliminated the disparity in the ability to obtain needed healthcare services between black and white veterans.45 Finally, among participants in the UK Prospective Diabetes Study (UKPDS), Afro-Caribbean patients experienced substantially reduced risk of all-cause and diabetes-related mortality, myocardial infarctions, but not strokes,46,47 suggesting that receiving similar care in a controlled system outside the United States may also result in benefits for minorities that are similar to those reported in our study.
One possible explanation for the observed racial discrepancies in outcomes is the biological differences between blacks and whites that are overwhelmed by the socioeconomic disparities in the general population but uncovered in a system that provides open access to health care. There is now mounting evidence that some blacks have distinctly unique genetic characteristics linked to their African ancestry that have a direct impact on health outcomes.8 Aside from the above-mentioned common genetic polymorphisms responsible for excess CKD and ESRD in blacks,9–13 there may be additional ones affecting cardiovascular pathophysiology and outcomes.48–50 We limited our analysis to patients with eGFR ≥60 mL·min−1·1.73 m−2 to examine patients in whom clinical outcomes would less likely be affected by genetic differences leading to kidney disease or the biological effect of azotemia, which could directly or indirectly affect the lower mortality previously described by us in black veterans with CKD.15 Our results showing lower mortality and CHD incidence but higher stroke incidence in blacks independently of their level of eGFR suggest that such differences could be a result of genetic or other differences in susceptibility to various cardiovascular processes. Recent findings that blacks experience significantly less vascular calcification compared with white individuals,16–19 perhaps owing to genetic differences in various physiological processes such as vitamin D and bone metabolism,8,20 support such a hypothesis. Other race-specific biological differences that could affect other cardiovascular outcomes such as strokes include a higher incidence of hypertension, more uncontrolled hypertension, and differences in central aortic blood pressure and prevalence of left ventricular hypertrophy in blacks.21 The presence of divergent clinical outcomes (lower CHD incidence but higher stroke incidence) could be indicative of distinctly different biological processes underlying these outcomes, with some portending a favorable but others an unfavorable outcome in blacks. Our finding that mortality after incident cardiovascular events in our cohort was similar in blacks and whites and the lack of difference in cardiovascular and stroke-related mortality between blacks and whites in NHANES suggest that most of the race-based differences could affect the development of cardiovascular lesions, and less so their secondary deleterious consequences.
Our study is notable for the very large number of studied individuals and for its US-wide distribution. Our study also has several limitations. Our cohort consisted predominantly of men; hence, our conclusions may not apply to women. Previous studies have described important sex differences in race-based outcomes.51 However, our findings were similar in female compared with male veterans, and despite the low percentage of women in our cohort, their absolute number was substantial (>150 000 patients) and eclipsed the number of women examined in most or all previous studies. We used self-identified race as our predictor, which is biologically inferior to gene-based determination of ancestry; however, the former captures social constructs and the latter method is not yet available for large-scale epidemiological studies. Our cohort consisted of US veterans with distinct demographic and clinical characteristics; hence, it is unclear whether our findings can be applied to nonveterans. Enrollment in the US armed services and subsequently into the VHA may include distinct populations of blacks and whites. Although we cannot discount this possibility, the basic characteristics of our cohort suggest that differences between blacks and whites seen in the general population were indeed present in our cohort (eg, differences in income, marital status, and certain comorbidities). Thus, it is less plausible that the observed differences in outcomes were due solely to selection bias. Furthermore, higher stroke rates52 and lower incidence of CHD47,51 in populations of African ancestry have previously been reported in nonveterans, which also suggests that our findings are not limited to US veterans alone. We examined clinical events recorded during care received in a VA facility and would not have captured similar events recorded at non-VA facilities. We captured clinical events using diagnostic codes, not the more accurate adjudication procedures used in clinical trials, which are not feasible in a study of this size. However, these limitations do not apply to all-cause mortality. We examined all-cause mortality because we had no information about causes of death. We adjusted for a variety of demographic, social, economic, and healthcare-quality indexes that could affect race-based differences in care, but we cannot exclude the possibility that unmeasured confounders may also play a role in the observed differences. We defined our cohort on the basis of an eGFR ≥60 mL·min−1·1.73 m−2, but we did not have markers of earlier stages of CKD (eg, proteinuria). We imply that the described associations are present in the overall VA population regardless of level of kidney function using separate analyses in patients with eGFR ≥60 and <60 mL·min−1·1.73 m−2 but without analyzing all VA-enrolled patients as a single cohort.
Conclusions
There are significant differences in major clinical outcomes experienced by black patients enrolled in an open-access healthcare system (US VHA) compared with their white counterparts. Black veterans experienced a lower incidence of CHD, higher incidence of stroke, and lower all-cause mortality compared with white veterans. Differences in mortality and in incident CHD could not be explained by differences in demographic, comorbidity, and socioeconomic characteristics, suggesting that there may be important sociocultural or evolutionary transmitted biological differences (eg, neurohormonal, epigenetic, gene variants) explaining the development of cardiovascular or other diseases in individuals of different races. Future studies will need to elucidate the nature of such putative differences to determine whether race-specific measures are needed for the prevention and treatment of cardiovascular disease.
Acknowledgments
We thank Dulcie Kermah, MPH, for help with NHANES analyses and Praveen Potukuchi, B Pharm, MSc, MS, for help with preparing tables and figures. Drs Kovesdy and Kalantar-Zadeh are employees of the US Department of Veterans Affairs. Opinions expressed in this paper are those of the authors and do not necessarily represent the opinion of the Department of Veterans Affairs.
CLINICAL PERSPECTIVE
Supplemental Material
File (015124_supplemental_material.pdf)
- Download
- 915.04 KB
References
1.
US Census Bureau. State and County QuickFacts. http://quickfacts.census.gov/qfd/states/00000.html. Accessed December 15, 2014.
2.
Infant mortality and low birth weight among black and white infants—United States, 1980–2000. MMWR Morb Mortal Wkly Rep. 2002;51:589–592.
3.
Tucker MJ, Berg CJ, Callaghan WM, Hsia J. The black-white disparity in pregnancy-related mortality from 5 conditions: differences in prevalence and case-fatality rates. Am J Public Health. 2007;97:247–251. doi: 10.2105/AJPH.2005.072975.
4.
Gold DR, Wright R. Population disparities in asthma. Annu Rev Public Health. 2005;26:89–113. doi: 10.1146/annurev.publhealth.26.021304.144528.
5.
Hyman DJ, Pavlik VN. Characteristics of patients with uncontrolled hypertension in the United States. N Engl J Med. 2001;345:479–486. doi: 10.1056/NEJMoa010273.
6.
Clark LT, Ferdinand KC, Flack JM, Gavin JR, Hall WD, Kumanyika SK, Reed JW, Saunders E, Valantine HA, Watson K, Wenger NK, Wright JT. Coronary heart disease in African Americans. Heart Dis. 2001;3:97–108.
7.
Mays VM, Cochran SD, Barnes NW. Race, race-based discrimination, and health outcomes among African Americans. Annu Rev Psychol. 2007;58:201–225. doi: 10.1146/annurev.psych.57.102904.190212.
8.
Freedman BI, Register TC. Effect of race and genetics on vitamin D metabolism, bone and vascular health. Nat Rev Nephrol. 2012;8:459–466. doi: 10.1038/nrneph.2012.112.
9.
Foster MC, Coresh J, Fornage M, Astor BC, Grams M, Franceschini N, Boerwinkle E, Parekh RS, Kao WH. APOL1 variants associate with increased risk of CKD among African Americans. J Am Soc Nephrol. 2013;24:1484–1491. doi: 10.1681/ASN.2013010113.
10.
Freedman BI, Kopp JB, Langefeld CD, Genovese G, Friedman DJ, Nelson GW, Winkler CA, Bowden DW, Pollak MR. The apolipoprotein L1 (APOL1) gene and nondiabetic nephropathy in African Americans. J Am Soc Nephrol. 2010;21:1422–1426. doi: 10.1681/ASN.2010070730.
11.
Friedman DJ, Kozlitina J, Genovese G, Jog P, Pollak MR. Population-based risk assessment of APOL1 on renal disease. J Am Soc Nephrol. 2011;22:2098–2105. doi: 10.1681/ASN.2011050519.
12.
Genovese G, Friedman DJ, Ross MD, Lecordier L, Uzureau P, Freedman BI, Bowden DW, Langefeld CD, Oleksyk TK, Uscinski Knob AL, Bernhardy AJ, Hicks PJ, Nelson GW, Vanhollebeke B, Winkler CA, Kopp JB, Pays E, Pollak MR. Association of trypanolytic ApoL1 variants with kidney disease in African Americans. Science. 2010;329:841–845. doi: 10.1126/science.1193032.
13.
Tzur S, Rosset S, Shemer R, Yudkovsky G, Selig S, Tarekegn A, Bekele E, Bradman N, Wasser WG, Behar DM, Skorecki K. Missense mutations in the APOL1 gene are highly associated with end stage kidney disease risk previously attributed to the MYH9 gene. Hum Genet. 2010;128:345–350. doi: 10.1007/s00439-010-0861-0.
14.
Kalantar-Zadeh K, Kovesdy CP, Derose SF, Horwich TB, Fonarow GC. Racial and survival paradoxes in chronic kidney disease. Nat Clin Pract Nephrol. 2007;3:493–506. doi: 10.1038/ncpneph0570.
15.
Kovesdy CP, Quarles LD, Lott EH, Lu JL, Ma JZ, Molnar MZ, Kalantar-Zadeh K. Survival advantage in black versus white men with CKD: effect of estimated GFR and case mix. Am J Kidney Dis. 2013;62:228–235. doi: 10.1053/j.ajkd.2012.12.012.
16.
Bild DE, Detrano R, Peterson D, Guerci A, Liu K, Shahar E, Ouyang P, Jackson S, Saad MF. Ethnic differences in coronary calcification: the Multi-Ethnic Study of Atherosclerosis (MESA). Circulation. 2005;111:1313–1320. doi: 10.1161/01.CIR.0000157730.94423.4B.
17.
Lee TC, O’Malley PG, Feuerstein I, Taylor AJ. The prevalence and severity of coronary artery calcification on coronary artery computed tomography in black and white subjects. J Am Coll Cardiol. 2003;41:39–44.
18.
Tang W, Arnett DK, Province MA, Lewis CE, North K, Carr JJ, Pankow JS, Hopkins PN, Devereux RB, Wilk JB, Wagenknecht L; Investigators of the FHS and HyperGEN. Racial differences in the association of coronary calcified plaque with left ventricular hypertrophy: the National Heart, Lung, and Blood Institute Family Heart Study and Hypertension Genetic Epidemiology Network. Am J Cardiol. 2006;97:1441–1448. doi: 10.1016/j.amjcard.2005.11.076.
19.
Freedman BI, Langefeld CD, Lu L, Palmer ND, Carrie SS, Bagwell BM, Hicks PJ, Xu J, Wagenknecht LE, Raffield LM, Register TC, Jeffrey CJ, Bowden DW, Divers J. APOL1 associations with nephropathy, atherosclerosis, and all-cause mortality in African Americans with type 2 diabetes. Kidney Int. 2015;87:176–181. doi: 10.1038/ki.2014.255.
20.
Powe CE, Evans MK, Wenger J, Zonderman AB, Berg AH, Nalls M, Tamez H, Zhang D, Bhan I, Karumanchi SA, Powe NR, Thadhani R. Vitamin D-binding protein and vitamin D status of black Americans and white Americans. N Engl J Med. 2013;369:1991–2000. doi: 10.1056/NEJMoa1306357.
21.
Ferdinand KC, Townsend RR. Hypertension in the US black population: risk factors, complications, and potential impact of central aortic pressure on effective treatment. Cardiovasc Drugs Ther. 2012;26:157–165. doi: 10.1007/s10557-011-6367-8.
22.
Gosmanova EO, Lu JL, Streja E, Cushman WC, Kalantar-Zadeh K, Kovesdy CP. Association of medical treatment nonadherence with all-cause mortality in newly treated hypertensive US veterans. Hypertension. 2014;64:951–957. doi: 10.1161/HYPERTENSIONAHA.114.03805.
23.
VA Information Resource Center. VIReC Resource Guide: VA Corporate Data Warehouse. Hines, IL: US Department of Veterans Affairs, Health Services Research and Development Service, VA Information Resource Center; 2012.
24.
US Department of Veterans Affairs. Veteran population. http://www.va.gov/vetdata/Veteran_Population.asp. Accessed December 15, 2014.
25.
Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF, Feldman HI, Kusek JW, Eggers P, Van Lente F, Greene T, Coresh J; CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration). A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150:604–612.
26.
Stroupe KT, Tarlov E, Zhang Q, Haywood T, Owens A, Hynes DM. Use of Medicare and DOD data for improving VA race data quality. J Rehabil Res Dev. 2010;47:781–796.
27.
Sohn MW, Zhang H, Arnold N, Stroupe K, Taylor BC, Wilt TJ, Hynes DM. Transition to the new race/ethnicity data collection standards in the Department of Veterans Affairs. Popul Health Metr. 2006;4:7. doi: 10.1186/1478-7954-4-7.
28.
Kovesdy CP, Bleyer AJ, Molnar MZ, Ma JZ, Sim JJ, Cushman WC, Quarles LD, Kalantar-Zadeh K. Blood pressure and mortality in U.S. veterans with chronic kidney disease: a cohort study. Ann Intern Med. 2013;159:233–242. doi: 10.7326/0003-4819-159-4-201308200-00004.
29.
Kovesdy CP, Lu JL, Molnar MZ, Ma JZ, Canada RB, Streja E, Kalantar-Zadeh K, Bleyer AJ. Observational modeling of strict vs conventional blood pressure control in patients with chronic kidney disease. JAMA Intern Med. 2014;174:1442–1449. doi: 10.1001/jamainternmed.2014.3279.
30.
US Department of Veterans Affairs. VA Information Resource Center. http://www.virec.research.va.gov/Resources/Info-About-VA-Data.asp. Accessed December 15, 2014.
31.
VIReC Research User Guide; VHA Medical SAS Inpatient Datasets FY2006. Hines, IL: US Department of Veterans Affairs, VA Information Resource Center; September 2007.
32.
VIReC Research User Guide; VHA Medical SAS Outpatient Datasets FY2006. Hines, IL: US Department of Veterans Affairs, VA Information Resource Center; September 2007.
33.
Sohn MW, Arnold N, Maynard C, Hynes DM. Accuracy and Completeness of Mortality Data in the Department of Veterans Affairs. Popul Health Metr. 2006;4:2.
34.
KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int Suppl. 2013;3:1–150.
35.
Fine JP, Gray RJ. A proportional hazards model for the subdistribution of competing risk. J Am Stat Assoc. 1999;94:496–509.
36.
Go AS, Mozaffarian D, Roger VL, Benjamin EJ, Berry JD, Blaha MJ, Dai S, Ford ES, Fox CS, Franco S, Fullerton HJ, Gillespie C, Hailpern SM, Heit JA, Howard VJ, Huffman MD, Judd SE, Kissela BM, Kittner SJ, Lackland DT, Lichtman JH, Lisabeth LD, Mackey RH, Magid DJ, Marcus GM, Marelli A, Matchar DB, McGuire DK, Mohler ER, Moy CS, Mussolino ME, Neumar RW, Nichol G, Pandey DK, Paynter NP, Reeves MJ, Sorlie PD, Stein J, Towfighi A, Turan TN, Virani SS, Wong ND, Woo D, Turner MB; American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Executive summary: heart disease and stroke statistics—2014 update: a report from the American Heart Association. Circulation. 2014;129:399–410. doi: 10.1161/01.cir.0000442015.53336.12.
37.
Geronimus AT, Bound J, Waidmann TA, Hillemeier MM, Burns PB. Excess mortality among blacks and whites in the United States. N Engl J Med. 1996;335:1552–1558. doi: 10.1056/NEJM199611213352102.
38.
Karter AJ, Ferrara A, Liu JY, Moffet HH, Ackerson LM, Selby JV. Ethnic disparities in diabetic complications in an insured population. JAMA. 2002;287:2519–2527.
39.
Gao SW, Oliver DK, Das N, Hurst FP, Lentine KL, Agodoa LY, Sawyers ES, Abbott KC. Assessment of racial disparities in chronic kidney disease stage 3 and 4 care in the department of defense health system. Clin J Am Soc Nephrol. 2008;3:442–449. doi: 10.2215/CJN.03940907.
40.
Jha AK, Shlipak MG, Hosmer W, Frances CD, Browner WS. Racial differences in mortality among men hospitalized in the Veterans Affairs health care system. JAMA. 2001;285:297–303.
41.
Deswal A, Petersen NJ, Souchek J, Ashton CM, Wray NP. Impact of race on health care utilization and outcomes in veterans with congestive heart failure. J Am Coll Cardiol. 2004;43:778–784. doi: 10.1016/j.jacc.2003.10.033.
42.
Bennett CL, Horner RD, Weinstein RA, Dickinson GM, DeHovitz JA, Cohn SE, Kessler HA, Jacobson J, Goetz MB, Simberkoff M. Racial differences in care among hospitalized patients with Pneumocystis carinii pneumonia in Chicago, New York, Los Angeles, Miami, and Raleigh-Durham. Arch Intern Med. 1995;155:1586–1592.
43.
Akerley WL, Moritz TE, Ryan LS, Henderson WG, Zacharski LR. Racial comparison of outcomes of male Department of Veterans Affairs patients with lung and colon cancer. Arch Intern Med. 1993;153:1681–1688.
44.
Dominitz JA, Samsa GP, Landsman P, Provenzale D. Race, treatment, and survival among colorectal carcinoma patients in an equal-access medical system. Cancer. 1998;82:2312–2320.
45.
Washington DL, Harada ND, Villa VM, Damron-Rodriguez J, Dhanani S, Shon H, Makinodan T. Racial variations in Department of Veterans Affairs ambulatory care use and unmet health care needs. Mil Med. 2002;167:235–241.
46.
Davis TM. Ethnic diversity in type 2 diabetes. Diabet Med. 2008;25(suppl 2):52–56. doi: 10.1111/j.1464-5491.2008.02499.x.
47.
Davis TM, Coleman RL, Holman RR; UKPDS Group. Ethnicity and long-term vascular outcomes in type 2 diabetes: a prospective observational study (UKPDS 83). Diabet Med. 2014;31:200–207. doi: 10.1111/dme.12353.
48.
Chinevere TD, Murray CK, Grant E, Johnson GA, Duelm F, Hospenthal DR. Prevalence of glucose-6-phosphate dehydrogenase deficiency in U.S. Army personnel. Mil Med. 2006;171:905–907.
49.
Hecker PA, Leopold JA, Gupte SA, Recchia FA, Stanley WC. Impact of glucose-6-phosphate dehydrogenase deficiency on the pathophysiology of cardiovascular disease. Am J Physiol Heart Circ Physiol. 2013;304:H491–H500. doi: 10.1152/ajpheart.00721.2012.
50.
Mozos I. Mechanisms linking red blood cell disorders and cardiovascular diseases. Biomed Res Int. 2015;2015:682054. doi: 10.1155/2015/682054.
51.
Gillum RF, Mussolino ME, Madans JH. Coronary heart disease incidence and survival in African-American women and men: the NHANES I Epidemiologic Follow-up Study. Ann Intern Med. 1997;127:111–118.
52.
Howard VJ, Kleindorfer DO, Judd SE, McClure LA, Safford MM, Rhodes JD, Cushman M, Moy CS, Soliman EZ, Kissela BM, Howard G. Disparities in stroke incidence contributing to disparities in stroke mortality. Ann Neurol. 2011;69:619–627. doi: 10.1002/ana.22385.
Information & Authors
Information
Published In
Copyright
© 2015 American Heart Association, Inc.
Versions
You are viewing the most recent version of this article.
History
Received: 25 December 2014
Accepted: 10 August 2015
Published online: 18 September 2015
Published in print: 20 October 2015
Keywords
Subjects
Authors
Disclosures
None.
Sources of Funding
This study was supported by grant R01DK096920 to Drs Kovesdy and Kalantar-Zadeh and is the result of work supported with resources and the use of facilities at the Memphis VA Medical Center and the Long Beach VA Medical Center. Support for VA/CMS data is provided by the Department of Veterans Affairs, Veterans Health Administration, Office of Research and Development, Health Services Research and Development, VA Information Resource Center (project numbers SDR 02-237 and 98-004). Dr Norris is supported by National Institutes of Health grants TR000124, MD000182 and AG021684.
Metrics & Citations
Metrics
Citations
Download Citations
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Select your manager software from the list below and click Download.
- Women Veterans’ Stroke Risk Factors: Identified Prevention Methods and Knowledge Gaps, Military Medicine, 190, 3-4, (530-540), (2024).https://doi.org/10.1093/milmed/usae445
- If We Measure, We Can Improve, Journal of the American College of Cardiology, 84, 13, (1276-1278), (2024).https://doi.org/10.1016/j.jacc.2024.07.031
- Care Fragmentation, Social Determinants of Health, and Postoperative Mortality in Older Veterans, Journal of Surgical Research, 300, (514-525), (2024).https://doi.org/10.1016/j.jss.2024.04.082
- Relationship between health system quality and racial and ethnic equity in diabetes care, Health Affairs Scholar, 2, 7, (2024).https://doi.org/10.1093/haschl/qxae073
- Homelessness and Race are Mortality Predictors in US Veterans Undergoing CABG, Seminars in Thoracic and Cardiovascular Surgery, 36, 3, (323-332), (2024).https://doi.org/10.1053/j.semtcvs.2022.10.001
- A MUC 5 B Gene Polymorphism, rs35705950-T, Confers Protective Effects Against COVID-19 Hospitalization but Not Severe Disease or Mortality , American Journal of Respiratory and Critical Care Medicine, 206, 10, (1220-1229), (2022).https://doi.org/10.1164/rccm.202109-2166OC
- Uptake and Equity in Influenza Vaccination Among Veterans with VA Coverage, Veterans Without VA Coverage, and Non-Veterans in the USA, 2019–2020, Journal of General Internal Medicine, 38, 5, (1152-1159), (2022).https://doi.org/10.1007/s11606-022-07797-7
- Grand challenges in oral health and nutrition: We are what we eat, Frontiers in Oral Health, 3, (2022).https://doi.org/10.3389/froh.2022.999817
- Implantable cardioverter-defibrillator placement among patients with left ventricular ejection fraction ≤35 % at least 40 days after acute myocardial infarction, American Heart Journal Plus: Cardiology Research and Practice, 19, (100186), (2022).https://doi.org/10.1016/j.ahjo.2022.100186
- European Society of Cardiology: cardiovascular disease statistics 2021, European Heart Journal, 43, 8, (716-799), (2022).https://doi.org/10.1093/eurheartj/ehab892
- See more
Loading...
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Personal login Institutional LoginPurchase Options
Purchase this article to access the full text.
eLetters(0)
eLetters should relate to an article recently published in the journal and are not a forum for providing unpublished data. Comments are reviewed for appropriate use of tone and language. Comments are not peer-reviewed. Acceptable comments are posted to the journal website only. Comments are not published in an issue and are not indexed in PubMed. Comments should be no longer than 500 words and will only be posted online. References are limited to 10. Authors of the article cited in the comment will be invited to reply, as appropriate.
Comments and feedback on AHA/ASA Scientific Statements and Guidelines should be directed to the AHA/ASA Manuscript Oversight Committee via its Correspondence page.