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Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease logoLink to Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease
. 2021 Jan 21;10(3):e016601. doi: 10.1161/JAHA.120.016601

Contemporary Reevaluation of Race and Ethnicity With Outcomes in Heart Failure

Samuel T Savitz 1,2,3,✉, Thomas Leong 1, Sue Hee Sung 1, Keane Lee 1,4, Jamal S Rana 1,5,6, Grace Tabada 1, Alan S Go 1,6,7,8,9
PMCID: PMC7955425  PMID: 33474975

Abstract

Background

Variation in outcomes by race/ethnicity in adults with heart failure (HF) has been previously observed. Identifying factors contributing to these variations could help target interventions. We evaluated the association of race/ethnicity with HF outcomes and potentially contributing factors within a contemporary HF cohort.

Methods and Results

We identified members of Kaiser Permanente Northern California, a large integrated healthcare delivery system, who were diagnosed with HF between 2012 and 2016 and had at least 1 year of prior continuous membership and left ventricular ejection fraction data. We used Cox regression with time‐dependent covariates to evaluate the association of self‐identified race/ethnicity with HF or all‐cause hospitalization and all‐cause death, with backward selection for potential explanatory variables. Among 34 621 patients with HF, compared with White patients, Black patients had a higher rate of HF hospitalization (adjusted hazard ratio [HR], 1.28; 95% CI, 1.18–1.38) but a lower rate of death (adjusted HR, 0.78; 95% CI, 0.72–0.85). In contrast, Asian/Pacific Islander patients had similar rates of HF hospitalization, but lower rates of all‐cause hospitalization (adjusted HR, 0.89; 95% CI, 0.85–0.93) and death (adjusted HR, 0.75; 95% CI, 0.69–0.80). Hispanic patients also had a lower rate of death (adjusted HR, 0.85; 95% CI, 0.80–0.91). Sensitivity analyses showed that effect sizes for Black patients were larger among patients with reduced ejection fraction.

Conclusions

In a contemporary and diverse population with HF, Black patients experienced a higher rate of HF hospitalization and a lower rate of death compared with White patients. In contrast, selected outcomes for Asian/Pacific Islander and Hispanic patients were more favorable compared with White patients. The observed differences were not explained by measured potentially modifiable factors, including pharmacological treatment. Future research is needed to identify explanatory mechanisms underlying ongoing racial/ethnic variation to target potential interventions.

Keywords: health disparities, hospitalization, mortality, race and ethnicity

Subject Categories: Heart Failure, Health Services, Mortality/Survival, Race and Ethnicity

Nonstandard Abbreviations and Acronyms

HFpEF

heart failure with preserved ejection fraction

HFrEF

heart failure with reduced ejection fraction

KPNC

Kaiser Permanente Northern California

PI

Pacific Islander

Clinical Perspective

What Is New?

  • In a large, diverse, and contemporary cohort of adult patients with heart failure, Black patients had a higher rate of heart failure hospitalization and a lower rate of all‐cause death compared with White patients.

  • In contrast, Asian/Pacific Islander and Hispanic patients had similar rates of heart failure hospitalization but lower rates of death.

  • The variation in outcomes was not explained by potentially modifiable factors, including medication use, cardiovascular procedures, and area‐based socioeconomic status.

What Are the Clinical Implications?

  • Efforts are needed to delineate mechanisms that drive the observed racial/ethnic variation in different types of clinical outcomes.

  • Interventions should address racial/ethnic variation while focusing on improving outcomes for all patients with heart failure.

The public health burden of chronic heart failure (HF) is large and growing because of the aging of the population and improvements in prevention and treatment of atherosclerotic cardiovascular disease and its risk factors. 1 , 2 Between 2011 and 2017, HF as the listed cause of death has increased 38% and age‐adjusted mortality attributed to HF has increased by 21%. 2 Given the large and increasing burden of HF, additional interventions are needed to improve outcomes among patients with HF, especially in those with HF with preserved ejection fraction (HFpEF). A better understanding of modifiable factors that may influence HF outcomes among at‐risk patient subgroups could help inform such interventions.

Parallel significant demographic changes nationally further highlight the importance of evaluating whether racial/ethnic variation persists in HF outcomes. 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 Earlier studies reported that compared with White patients, Asian, and Pacific Islander (PI) patients had a lower rate of HF hospitalization, 5 whereas Black and Hispanic patients had higher rates. 3 , 5 , 7 , 8 , 9 , 10 Other studies have reported similar rates of hospitalizations between racial/ethnic groups. 3 , 7 , 8 , 10 With regard to all‐cause mortality, compared with White patients, Asian/PI, Black, and Hispanic patients had a lower rate in selected studies. 3 , 7 , 8 , 9 , 10 In contrast, some studies have found no difference in all‐cause mortality for White patients compared with Asian/PI or Hispanic patients. 7 , 10 However, variation in populations, time period studied, and ability to control for confounders may have contributed to these conflicting findings, and it is unclear to what extent observed variation in outcomes is attributable to potentially modifiable factors. Furthermore, given HF hospitalization has often been used as a proxy for quality of care, 11 , 12 , 13 it is important to examine concordance between hospitalization rates and death by race/ethnicity.

To address these knowledge gaps, we evaluated outcomes in major racial/ethnic groups within a more contemporary, diverse community‐based cohort of adults with HF who had equal access to care in a large, integrated delivery system. We further examined potentially contributing factors, including cardiovascular and noncardiovascular health status, treatments received, area‐based access to care measures, and area‐based socioeconomic status (SES) measures.

Methods

The data, analytic methods, and study materials will not be made available to other researchers for the purposes of reproducing the results or replicating the procedure because of human subject restrictions.

Source Population

The source population was from Kaiser Permanente Northern California (KPNC), an integrated healthcare delivery system currently caring for >4.5 million sociodemographically diverse members across 21 hospitals and >250 offices in northern California. KPNC includes members with coverage through employers, the individual market, Medicaid (Medi‐Cal–California's Medicaid program), and Medicare Advantage. More than 35% to 65% of individuals in northern California counties receive care through KPNC, and its membership is highly representative of the local surrounding and statewide population. 14 The primary data source was the KPNC electronic health record system, which contains comprehensive data on demographic characteristics, diagnoses, procedures, echocardiogram results, laboratory results, and medication dispensing and use. The KPNC institutional review board approved this study. Waiver of informed consent was granted because of the nature of the study.

We also used access to care measures from an area deprivation index, 15 a rural‐urban county classification scheme, 16 and the Area Health Resource File. 17 The Area Health Resource File is a resource with county‐level data on access to care and healthcare utilization that is administered by the Health Resources and Service Administration. 17

Study Sample

We first identified all adult members with diagnosed HF between 2012 and 2016 with follow‐up data through 2017. HF was defined as having ≥1 hospitalization with a primary discharge code for HF and/or ≥3 outpatient visits with a diagnosis code for HF during the study period (2012–2016) (see Table S1 for International Classification of Diseases, Ninth Revision, Clinical Modification [ICD‐9‐CM], and International Classification of Diseases, Tenth Revision, Clinical Modification [ICD‐10‐CM], codes). We defined the index date as the date of the first qualifying diagnosis. This approach has been previously shown to have a high positive predictive value compared against chart review 18 , 19 using Framingham criteria. 20 We further categorized HF on the basis of left ventricular ejection fraction (EF) status from echocardiogram reports: preserved (HFpEF: ≥50%), midrange (HF with midrange EF: 41%–49%), and reduced (HF with reduced EF [HFrEF]: <40%). If a patient had multiple echocardiogram reports, we used the one closest to the date of the index diagnosis. We excluded patients aged <21 years, who had <12 months of prior continuous health plan membership or drug benefit coverage before entry, who had no valid echocardiography data, and who had an organ transplant or who died on the index date (see Figure 1 for sample flow diagram).

Figure 1. Heart failure cohort assembly.

Figure 1

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AHRF indicates Area Health Resource File; EF, ejection fraction; ER, emergency room; and PI, Pacific Islander.

Race/Ethnicity Measure

We categorized patients into the following racial/ethnic groups: non‐Hispanic White, non‐Hispanic Black, Asian/PI, and Hispanic patients. These designations were based on self‐identified race and ethnicity. We conceptualized race/ethnicity as a social construct rather than a biological one because race/ethnicity are not meaningful biological categories. 21

Outcomes

Outcomes included hospitalization for HF, which we defined as hospitalizations with a primary discharge diagnosis code for HF (Table S1), hospitalization for any cause, and all‐cause death. Hospitalizations were captured through the KPNC comprehensive electronic health record (which includes out‐of‐network admissions), and deaths were identified from the electronic health record (including proxy reporting), regional cancer registry, and California state death certificate information. We did not attempt to classify potential cause of death because of known substantial misclassification in listed cause(s) of death on death certificates. 22

Explanatory Factors

We categorized potential explanatory factors into 7 distinct domains (Table 1). The first domain was HF characteristics, including EF category (HFpEF, HF with midrange EF, or HFrEF) and presumed incident or prevalent HF. Patients were categorized as having presumed incident HF if they did not have a prior HF diagnosis in the 5 years before the index date; and as having prevalent HF if they did have a prior HF diagnosis in the 5 years before the index date. The second domain included other demographic characteristics, including sex and age (continuous and a quadratic term). The third domain was area‐based access‐to‐care measures (ie, rural status and area health resource file regional medical supply measures). The fourth domain included time‐dependent cardiovascular and noncardiovascular comorbidities that may be associated with the outcomes of death and/or hospitalization. The fifth domain was time‐dependent cardiovascular procedures (ie, coronary revascularization, pacemaker, implantable cardioverter‐defibrillator, and cardiac resynchronization therapy). The sixth domain included area‐based SES measures at the census block‐group level (ie, area deprivation index, low area education, and low area income). The seventh and final domain included time‐dependent receipt of relevant medications and a proxy for high medication adherence (ie, proportion of days covered >80%). We included time‐dependent covariates because it is possible that differences in treatment (cardiovascular procedures and relevant medications) or the management of comorbidities over time could vary across racial/ethnic groups and explain differences in outcomes.

Table 1.

Models and Domains of Potential Explanatory Factors

Model Domain Covariates
Model 1 Race/ethnicity Race/ethnicity
HF characteristics Presumed incident vs prevalent HF, HF setting, index year
Other demographic characteristics Age, sex
Access to care PCP shortage area, county bed supply, county cardiologist supply, county PCP supply
Time‐dependent comorbidities Comorbidity point score, acute myocardial infarction, unstable angina, stroke/transient ischemic attack, atrial fibrillation or flutter, ventricular tachycardia or fibrillation, mitral and/or aortic valvular disease, peripheral artery disease, smoking status, dyslipidemia, hypertension, diabetes mellitus and insulin use, hospitalized bleeding, hyperthyroidism, hypothyroidism, diagnosed dementia, diagnosed depression, chronic lung disease, chronic liver disease, systemic cancer, body mass index (kg/m2), estimated glomerular filtration rate (mL/min per 1.73 m2), urine dipstick protein excretion, anemia (hemoglobin <13 g/dL in men, <12 g/dL in women), systolic blood pressure (mm Hg), diastolic blood pressure (mm Hg), high‐density lipoprotein (mg/dL), low‐density lipoprotein (mg/dL), B‐type natriuretic peptide (pg/mL)
Time‐dependent cardiovascular procedures Coronary artery bypass surgery, percutaneous coronary intervention, pacemaker, ICD, CRT
Area‐based SES ADI quintiles, low education, low income
Model 2: model 1+time updated measures of medication use and adherence Time‐dependent medication and adherence ACE inhibitor/angiotensin II receptor blocker, aldosterone, β blocker, calcium channel blocker, diuretic, statins, other lipid‐lowering drugs, anticoagulant, medication adherence (time independent)
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ACE indicates angiotensin‐converting enzyme; ADI, area deprivation index; CRT, cardiac resynchronization therapy; HF, heart failure; ICD, implantable cardioverter‐defibrillator; PCP, primary care physician; and SES, socioeconomic status.

Statistical Analysis

Analyses were performed using SAS 9.4 (SAS Institute Inc, Cary, NC). Baseline characteristics were compared between racial/ethnic groups using standard descriptive statistics. For variables with missing data (ie, selected laboratory results, vital signs, and medication adherence), a “missing” category was included. Cox regression with time‐dependent covariates was performed to evaluate the association between race/ethnicity and each outcome of interest, with backward selection of potential explanatory factors. We retained the race/ethnicity variable in all models despite statistical significance because it is the primary predictor variable. Standard errors (SEs) were estimated using a robust sandwich estimator, with the clusters defined as the primary treating facility for each patient. We analyzed 2 models: the first model included all explanatory factors except medication and adherence, and the second model included all explanatory factors as well as medications and adherence. We separately evaluated the impact of adjusting for medications and adherence to evaluate an a priori hypothesis that medication use and adherence are modifiable factors that may explain differences between the racial/ethnic groups. The modeling approach is described below:

  1. Model 1: racial/ethnic group+HF characteristics+other demographic characteristics+access‐to‐care measures+comorbidities+cardiovascular procedures+area‐based SES measures.

  2. Model 2: model 1+medication use and adherence.

Finally, we performed 5 sensitivity analyses. First, we conducted analyses stratified by presumed incident versus prevalent HF status. Second, we examined associations stratified by HF type (HFpEF, HF with midrange EF, or HFrEF). Third, we examined associations stratified by age at index date (<70, 70–80, and >80 years). Fourth, we examined associations stratified by sex (women and men). Fifth, we reevaluated the models using recurrent events using the Andersen‐Gill model 23 instead of time‐to‐first event for HF and all‐cause hospitalization.

RESULTS

Sample Characteristics

We identified 34 621 eligible adults with HF (59.3% HFpEF), of whom 3978 (11.5%) were Asian/PI individuals, 3641 (10.5%) were Black individuals, 4120 (11.9%) were Hispanic individuals, and 22 882 (66.1%) were White individuals (Table 2). White patients were older than Asian/PI, Black, and Hispanic patients, but there were no material differences across racial/ethnic groups in baseline comorbidities, pharmacological treatment, and receipt of cardiovascular procedures. However, White patients were less likely to live in the highest deprivation quintile (12.5%) compared with Black (22.8%) and Hispanic (19.5%) patients, but not Asian/PI patients (9.0%). Follow‐up occurred until censoring or end of study follow‐up on December 31, 2017, with mean (SD) follow‐up of 1080 (638) days.

Table 2.

Baseline Characteristics of Adults With HF by Race and Ethnicity

Characteristics Overall (N=34 621) Asian/Pacific Islander Patients (N=3978) Black Patients (N=3641) Hispanic Patients (N=4120) White Patients (N=22 882) P Value Time Dependent
HF characteristics
EF by categories <0.001 No
Preserved EF (HFpEF) 20 527 (59.3) 2375 (59.7) 1988 (54.6) 2451 (59.5) 13 713 (59.9)
Midrange EF (HFmrEF) 6069 (17.5) 653 (16.4) 635 (17.4) 722 (17.5) 4059 (17.7)
Reduced EF (HFrEF) 8025 (23.2) 950 (23.9) 1018 (28.0) 947 (23.0) 5110 (22.3)
Prevalent HF 12 285 (35.5) 1324 (33.3) 1534 (42.1) 1502 (36.5) 7925 (34.6) <0.001 No
HF setting <0.01 No
Outpatient 28 654 (82.8) 3254 (81.8) 2954 (81.1) 3397 (82.5) 19 049 (83.2)
Inpatient 5967 (17.2) 724 (18.2) 687 (18.9) 723 (17.5) 3833 (16.8)
Index year <0.001 No
2012 13 206 (38.1) 1410 (35.4) 1546 (42.5) 1558 (37.8) 8692 (38.0)
2013 5351 (15.5) 626 (15.7) 571 (15.7) 621 (15.1) 3533 (15.4)
2014 4990 (14.4) 602 (15.1) 497 (13.7) 573 (13.9) 3318 (14.5)
2015 5366 (15.5) 660 (16.6) 519 (14.3) 662 (16.1) 3525 (15.4)
2016 5708 (16.5) 680 (17.1) 508 (14.0) 706 (17.1) 3814 (16.7)
Other demographic characteristics
Age, mean (SD), y 74.3 (12.3) 71.2 (13.7) 69.1 (13.3) 72.3 (12.9) 76.1 (11.3) <0.001 Yes
Sex <0.001 No
Women 15 906 (45.9) 1653 (41.6) 1913 (52.5) 1880 (45.6) 10 460 (45.7)
Men 18 715 (54.1) 2325 (58.4) 1728 (47.5) 2240 (54.4) 12 422 (54.3)
Access‐to‐care measure
PCP shortage area 20 526 (59.3) 2109 (53.0) 1461 (40.1) 2596 (63.0) 14 360 (62.8) <0.001 No
County bed supply, mean (SD) 24.0 (15.3) 26.4 (13.3) 23.0 (10.4) 23.8 (13.3) 23.8 (16.5) <0.001 No
County cardiologist supply, mean (SD) 0.7 (0.3) 0.8 (0.4) 0.6 (0.3) 0.7 (0.3) 0.7 (0.3) <0.001 No
County PCP supply, mean (SD) 9.1 (2.2) 9.8 (2.4) 9.1 (2.1) 8.9 (2.1) 9.0 (2.1) <0.001 No
Comorbidities
Comorbidity point score, mean (SD) 74.6 (39.0) 70.4 (38.2) 72.0 (40.1) 76.5 (40.2) 75.4 (38.7) <0.001 Yes
Acute myocardial infarction 4211 (12.2) 591 (14.9) 406 (11.2) 537 (13.0) 2677 (11.7) <0.001 Yes
Unstable angina 1296 (3.7) 151 (3.8) 124 (3.4) 179 (4.3) 842 (3.7) 0.13 Yes
Stroke/transient ischemic attack 2460 (7.1) 235 (5.9) 299 (8.2) 305 (7.4) 1621 (7.1) <0.01 Yes
Atrial fibrillation or flutter 14 379 (41.5) 1430 (35.9) 877 (24.1) 1319 (32.0) 10 753 (47.0) <0.001 Yes
Ventricular tachycardia or fibrillation 876 (2.5) 78 (2.0) 114 (3.1) 84 (2.0) 600 (2.6) <0.01 Yes
Mitral and/or aortic valvular disease 7081 (20.5) 777 (19.5) 511 (14.0) 769 (18.7) 5024 (22.0) <0.001 Yes
Peripheral artery disease 2851 (8.2) 316 (7.9) 310 (8.5) 365 (8.9) 1860 (8.1) 0.35 Yes
Smoking status <0.001 Yes
Smoker 2027 (5.9) 191 (4.8) 349 (9.6) 176 (4.3) 1311 (5.7)
Passive smoker 133 (0.4) 9 (0.2) 23 (0.6) 13 (0.3) 88 (0.4)
Former smoker 16 987 (49.1) 1404 (35.3) 1658 (45.5) 1847 (44.8) 12 078 (52.8)
Never smoker 15 474 (44.7) 2374 (59.7) 1611 (44.2) 2084 (50.6) 9405 (41.1)
Dyslipidemia 29 569 (85.4) 3423 (86.0) 3062 (84.1) 3611 (87.6) 19 473 (85.1) <0.001 Yes
Hypertension 29 821 (86.1) 3391 (85.2) 3349 (92.0) 3655 (88.7) 19 426 (84.9) <0.001 Yes
Diabetes mellitus and insulin use <0.001 Yes
No diabetes mellitus 18 974 (54.8) 1807 (45.4) 1716 (47.1) 1642 (39.9) 13 809 (60.3)
Diabetes mellitus without insulin use 10 040 (29.0) 1443 (36.3) 1210 (33.2) 1445 (35.1) 5942 (26.0)
Diabetes mellitus with insulin use 5607 (16.2) 728 (18.3) 715 (19.6) 1033 (25.1) 3131 (13.7)
Hospitalized bleeding 2127 (6.1) 267 (6.7) 206 (5.7) 280 (6.8) 1374 (6.0) 0.06 Yes
Hyperthyroidism 1680 (4.9) 171 (4.3) 165 (4.5) 219 (5.3) 1125 (4.9) 0.13 Yes
Hypothyroidism 7001 (20.2) 592 (14.9) 377 (10.4) 813 (19.7) 5219 (22.8) <0.001 Yes
Diagnosed dementia 2481 (7.2) 235 (5.9) 241 (6.6) 310 (7.5) 1695 (7.4) <0.01 Yes
Diagnosed depression 7251 (20.9) 432 (10.9) 649 (17.8) 918 (22.3) 5252 (23.0) <0.001 Yes
Chronic lung disease 14 342 (41.4) 1381 (34.7) 1544 (42.4) 1634 (39.7) 9783 (42.8) <0.001 Yes
Chronic liver disease 1549 (4.5) 199 (5.0) 185 (5.1) 256 (6.2) 909 (4.0) <0.001 Yes
Systemic cancer 6824 (19.7) 587 (14.8) 729 (20.0) 613 (14.9) 4895 (21.4) <0.001 Yes
Body mass index, kg/m2 <0.001 Yes
>40.0 3107 (9.0) 147 (3.7) 601 (16.5) 429 (10.4) 1930 (8.4)
30.0–39.9 11 346 (32.8) 849 (21.3) 1412 (38.8) 1526 (37.0) 7559 (33.0)
25.0–29.9 10 771 (31.1) 1337 (33.6) 881 (24.2) 1304 (31.7) 7249 (31.7)
18.5–24.9 8556 (24.7) 1488 (37.4) 657 (18.0) 790 (19.2) 5621 (24.6)
<18.5 657 (1.9) 120 (3.0) 65 (1.8) 48 (1.2) 424 (1.9)
Missing 184 (0.5) 37 (0.9) 25 (0.7) 23 (0.6) 99 (0.4)
Estimated glomerular filtration rate, mL/min per 1.73 m2 <0.001 Yes
90–150 5065 (14.6) 655 (16.5) 556 (15.3) 759 (18.4) 3095 (13.5)
60–89 15 193 (43.9) 1458 (36.7) 1331 (36.6) 1567 (38.0) 10 837 (47.4)
45–59 6417 (18.5) 655 (16.5) 699 (19.2) 655 (15.9) 4408 (19.3)
30–44 4152 (12.0) 471 (11.8) 449 (12.3) 498 (12.1) 2734 (11.9)
15–29 1710 (4.9) 271 (6.8) 230 (6.3) 245 (5.9) 964 (4.2)
<15 371 (1.1) 94 (2.4) 79 (2.2) 63 (1.5) 135 (0.6)
Dialysis 1122 (3.2) 256 (6.4) 226 (6.2) 269 (6.5) 371 (1.6)
Missing 591 (1.7) 118 (3.0) 71 (2.0) 64 (1.6) 338 (1.5)
Urine dipstick protein excretion <0.001 Yes
None or trace 15 938 (46.0) 1408 (35.4) 1448 (39.8) 1779 (43.2) 11 303 (49.4)
≥1 3764 (10.9) 414 (10.4) 478 (13.1) 471 (11.4) 2401 (10.5)
≥2 2108 (6.1) 306 (7.7) 325 (8.9) 319 (7.7) 1158 (5.1)
≥3 1328 (3.8) 316 (7.9) 215 (5.9) 283 (6.9) 514 (2.2)
Missing 11 483 (33.2) 1534 (38.6) 1175 (32.3) 1268 (30.8) 7506 (32.8)
Anemia (last hemoglobin <13 g/dL in men, <12 g/dL in women) 15 249 (44.0) 1872 (47.1) 1939 (53.3) 2064 (50.1) 9374 (41.0) <0.001 Yes
Systolic blood pressure, mm Hg <0.001 Yes
≥180 937 (2.7) 131 (3.3) 198 (5.4) 128 (3.1) 480 (2.1)
160–179 2311 (6.7) 283 (7.1) 380 (10.4) 315 (7.6) 1333 (5.8)
140–159 6348 (18.3) 741 (18.6) 816 (22.4) 784 (19.0) 4007 (17.5)
130–139 7312 (21.1) 820 (20.6) 764 (21.0) 858 (20.8) 4870 (21.3)
121–129 5712 (16.5) 630 (15.8) 581 (16.0) 722 (17.5) 3779 (16.5)
<120 11 873 (34.3) 1345 (33.8) 881 (24.2) 1299 (31.5) 8348 (36.5)
Missing 128 (0.4) 28 (0.7) 21 (0.6) 14 (0.3) 65 (0.3)
Diastolic blood pressure, mm Hg <0.001 Yes
≥110 387 (1.1) 54 (1.4) 104 (2.9) 34 (0.8) 195 (0.9)
100–109 672 (1.9) 71 (1.8) 150 (4.1) 59 (1.4) 392 (1.7)
90–99 1920 (5.5) 228 (5.7) 345 (9.5) 197 (4.8) 1150 (5.0)
85–89 2008 (5.8) 242 (6.1) 290 (8.0) 243 (5.9) 1233 (5.4)
81–84 2424 (7.0) 266 (6.7) 302 (8.3) 253 (6.1) 1603 (7.0)
≤80 27 082 (78.2) 3089 (77.7) 2429 (66.7) 3320 (80.6) 18 244 (79.7)
Missing 128 (0.4) 28 (0.7) 21 (0.6) 14 (0.3) 65 (0.3)
High‐density lipoprotein, mg/dL <0.001 Yes
≥60 6828 (19.7) 777 (19.5) 853 (23.4) 586 (14.2) 4612 (20.2)
50–59 6823 (19.7) 814 (20.5) 731 (20.1) 765 (18.6) 4513 (19.7)
40–49 9786 (28.3) 1179 (29.6) 1020 (28.0) 1240 (30.1) 6347 (27.7)
35–39 4598 (13.3) 482 (12.1) 409 (11.2) 628 (15.2) 3079 (13.5)
<35 4621 (13.3) 475 (11.9) 396 (10.9) 674 (16.4) 3076 (13.4)
Missing 1965 (5.7) 251 (6.3) 232 (6.4) 227 (5.5) 1255 (5.5)
Low‐density lipoprotein, mg/dL <0.001 Yes
≥200 251 (0.7) 37 (0.9) 34 (0.9) 37 (0.9) 143 (0.6)
160–199 903 (2.6) 116 (2.9) 132 (3.6) 99 (2.4) 556 (2.4)
130–159 2577 (7.4) 287 (7.2) 307 (8.4) 297 (7.2) 1686 (7.4)
100–129 6277 (18.1) 651 (16.4) 702 (19.3) 679 (16.5) 4245 (18.6)
70–99 12 797 (37.0) 1377 (34.6) 1415 (38.9) 1432 (34.8) 8573 (37.5)
<70 10 383 (30.0) 1316 (33.1) 898 (24.7) 1414 (34.3) 6755 (29.5)
Missing 1433 (4.1) 194 (4.9) 153 (4.2) 162 (3.9) 924 (4.0)
B‐type natriuretic peptide, pg/mL <0.001 Yes
>500 4559 (13.2) 547 (13.8) 498 (13.7) 528 (12.8) 2986 (13.1)
100–500 9121 (26.3) 909 (22.9) 812 (22.3) 1006 (24.4) 6394 (27.9)
<100 3304 (9.5) 355 (8.9) 442 (12.1) 392 (9.5) 2115 (9.2)
Missing 17 636 (50.9) 2167 (54.5) 1889 (51.9) 2194 (53.3) 11 386 (49.8)
Cardiovascular procedures
Coronary artery bypass surgery 1542 (4.5) 224 (5.6) 100 (2.7) 198 (4.8) 1020 (4.5) <0.001 Yes
Percutaneous coronary intervention 4911 (14.2) 673 (16.9) 443 (12.2) 658 (16.0) 3137 (13.7) <0.001 Yes
ICD 1126 (3.3) 96 (2.4) 115 (3.2) 132 (3.2) 783 (3.4) <0.05 Yes
Pacemaker or CRT 2854 (8.2) 283 (7.1) 193 (5.3) 338 (8.2) 2040 (8.9) <0.001 Yes
Area‐based SES measures
ADI quintiles <0.001 No
Quintile 1 8787 (25.4) 1258 (31.6) 332 (9.1) 688 (16.7) 6509 (28.4)
Quintile 2 8366 (24.2) 1270 (31.9) 773 (21.2) 994 (24.1) 5329 (23.3)
Quintile 3 6279 (18.1) 584 (14.7) 734 (20.2) 794 (19.3) 4167 (18.2)
Quintile 4 6008 (17.4) 498 (12.5) 947 (26.0) 818 (19.9) 3745 (16.4)
Quintile 5 4845 (14.0) 357 (9.0) 829 (22.8) 802 (19.5) 2857 (12.5)
Missing 336 (1.0) 11 (0.3) 26 (0.7) 24 (0.6) 275 (1.2)
Low education 4493 (13.0) 467 (11.7) 983 (27.0) 1077 (26.1) 1966 (8.6) <0.001 No
Low income 1695 (5.0) 136 (3.5) 384 (10.7) 276 (6.8) 899 (4.0) <0.001 No
Medication and adherence
ACE inhibitor/angiotensin II receptor blocker 22 320 (64.5) 2566 (64.5) 2359 (64.8) 2723 (66.1) 14 672 (64.1) 0.11 Yes
Aldosterone 2133 (6.2) 190 (4.8) 301 (8.3) 237 (5.8) 1405 (6.1) <0.001 Yes
β Blocker 25 208 (72.8) 2894 (72.8) 2566 (70.5) 2960 (71.8) 16 788 (73.4) <0.01 Yes
Calcium channel blocker 11 436 (33.0) 1438 (36.1) 1495 (41.1) 1461 (35.5) 7042 (30.8) <0.001 Yes
Diuretic 23 041 (66.6) 2379 (59.8) 2508 (68.9) 2700 (65.5) 15 454 (67.5) <0.001 Yes
Statins 23 823 (68.8) 2811 (70.7) 2355 (64.7) 2911 (70.7) 15 746 (68.8) <0.001 Yes
Other lipid‐lowering drugs 1636 (4.7) 186 (4.7) 82 (2.3) 205 (5.0) 1163 (5.1) <0.001 Yes
Anticoagulant 10 108 (29.2) 969 (24.4) 708 (19.4) 918 (22.3) 7513 (32.8) <0.001 Yes
Medication adherence <0.001 No
Adherent 22 596 (65.3) 2453 (61.7) 2003 (55.0) 2515 (61.0) 15 625 (68.3)
Nonadherent 10 674 (30.8) 1314 (33.0) 1482 (40.7) 1447 (35.1) 6431 (28.1)
Missing 1351 (3.9) 211 (5.3) 156 (4.3) 158 (3.8) 826 (3.6)
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All results present frequency and overall percentage in parentheses unless otherwise specified. ACE indicates angiotensin‐converting enzyme; ADI, area deprivation index; CRT, cardiac resynchronization therapy; EF, ejection fraction; HF, heart failure; HFpEF, HF with preserved EF; HFmrEF, HF with midrange EF; HFrEF, HF with reduced EF; ICD, implantable cardioverter‐defibrillator; PCP, primary care physician; and SES, socioeconomic status.

Rates of Hospitalization and Death by Race/Ethnicity

For the outcome of HF hospitalization, the crude annual incidence was significantly higher for Black patients (17.8 per 100 person‐years; 95% CI, 17.0–18.6 per 100 person‐years) compared with other racial/ethnic groups (Figure 2A and Table 3). In contrast, for the outcome of hospitalization for any cause, Asian/PI patients experienced a lower crude annual incidence (53.0 per 100 person‐years; 95% CI, 51.7–54.3 per 100 person‐years) than the other racial/ethnic groups (Figure 2B and Table 3). Finally, a higher crude annual incidence of all‐cause death was observed for White patients (12.4 per 100 person‐years; 95% CI, 12.1–12.7 per 100 person‐years) compared with Asian/PI, Black, and Hispanic patients (Figure 2C and Table 3).

Figure 2. Clinical outcomes by racial/ethnic group among adults with heart failure (HF).

Figure 2

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A, Hospitalization for HF. B, Hospitalization for any cause. C, All‐cause death. The P value for the log‐rank statistic is <0.01 for all 3 outcomes. PI indicates Pacific Islander.

Table 3.

Crude Rates for Outcomes of Death, Hospitalization for HF, and Hospitalization for Any Cause by Race/Ethnicity

Race/Ethnicity Rate Per 100 PY (95% CI)
Hospitalization for HF
Asians/PIs 10.4 (9.8–11.0)
Black patients 17.8 (17.0–18.6)
Hispanic patients 13.0 (12.4–13.6)
White patients 10.9 (10.6–11.1)
Overall 11.8 (11.6–12.0)
Hospitalization for any cause
Asians/PIs 53.0 (51.7–54.3)
Black patients 71.4 (69.9–73.0)
Hispanic patients 64.8 (63.4–66.3)
White patients 58.2 (57.6–58.8)
Overall 59.8 (59.4–60.3)
All‐cause death
Asians/PIs 8.7 (8.2–9.3)
Black patients 9.6 (9.1–10.2)
Hispanic patients 10.5 (9.9–11.1)
White patients 12.4 (12.1–12.7)
Overall 11.4 (11.2–11.6)
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HF indicates heart failure; PI, Pacific Islander; and PY, person‐years.

Multivariable Association of Race/Ethnicity and Outcomes

In a fully adjusted model for HF hospitalization that accounted for any differences in patient characteristics, HF characteristics, access‐to‐care measures, therapies received, and area‐based SES measures, Black patients had a higher adjusted rate than White patients (adjusted hazard ratio [HR], 1.28; 95% CI, 1.18–1.38) (Table 4, model 2; and Figure 3). Compared with White race, Hispanic ethnicity and Asian/PI race were not independently associated with HF hospitalization.

Table 4.

Multivariable Association of Race/Ethnicity and Outcomes in Adults With HF

Model Asian/Pacific Islander vs White Patients Black vs White Patients Hispanic vs White Patients
Hospitalization for HF Hospitalization for Any Cause Death Hospitalization for HF Hospitalization for Any Cause Death Hospitalization for HF Hospitalization for Any Cause Death
Model 1: race/ethnicity, HF characteristics, demographics, access to care, time‐dependent comorbidities and cardiovascular procedures, and area‐based SES 0.99 (0.91–1.08) 0.88 (0.84–0.93) ‡ 0.74 (0.69–0.80) ‡ 1.30 (1.20–1.41) ‡ 1.00 (0.95–1.05) 0.82 (0.76–0.89) ‡ 1.06 (0.98–1.15) 0.97 (0.93–1.02) 0.86 (0.80–0.91) ‡
Model 2: model 1+time‐dependent medication use and adherence 1.00 (0.92–1.08) 0.89 (0.85–0.93) ‡ 0.75 (0.69–0.80) ‡ 1.28 (1.18–1.38) ‡ 0.99 (0.94–1.03) 0.78 (0.72–0.85) ‡ 1.06 (0.99–1.15) 0.97 (0.93–1.02) 0.85 (0.80–0.91) ‡
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Data are given as hazard ratio (95% CI). HF indicates heart failure; and SES, socioeconomic status.

‡

P<0.001.

Figure 3. Multivariable association of race and ethnicity with hospitalization for heart failure, hospitalization for any cause, and all‐cause mortality in adults with heart failure.

Figure 3

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The hazard ratios come from model 2 in Table 3. PI indicates Pacific Islander.

In the fully adjusted model for the outcome of hospitalization for any cause, compared with White patients, Asian/PI patients had a lower adjusted rate (HR, 0.89; 95% CI, 0.85–0.93) (Table 4, model 2; and Figure 3). However, Black and Hispanic patients did not have significantly different adjusted rates of hospitalization for any cause compared with White patients.

For all‐cause death, compared with White patients, Asian/PI (HR, 0.75; 95% CI, 0.69–0.80), Black (HR, 0.78; 95% CI, 0.72–0.85), and Hispanic (HR, 0.85; 95% CI, 0.80–0.91) patients had lower adjusted rates of death in fully adjusted models (Table 4, model 2; and Figure 3).

For all 3 outcomes, results from models that did not include time‐dependent medication and adherence (Table 4, model 1) were similar in magnitude and the same in terms of statistical significance compared with results of models that included these covariates (Table 4, model 2).

Sensitivity Analyses

Findings were generally similar across the sensitivity analyses examining patients with presumed incident HF; patients with HFpEF only; patients with HFrEF only; age‐stratified models (<70, 70–80, and >80 years); and sex‐stratified models (Table 5). For HF hospitalization, Black patients had a higher rate compared with White patients, although the differences were not statistically significant for all models, with no significant differences for Asian/PI and Hispanic patients in nearly all models. For all‐cause hospitalization, Asian/PI patients had a significantly lower rate than White patients in 6 of the 9 models; and there were no significant differences for Black or Hispanic patients. For all‐cause death, Asian/PI, Black, and Hispanic patients had significantly lower rates compared with White patients in nearly all models. In addition, the magnitude of the difference for Black patients compared with White patients who had HFrEF was greater for rate of HF hospitalization (HR, 1.35; 95% CI, 1.18–1.55) and for death (HR, 0.70; 95% CI, 0.60–0.80) (Table 5, sensitivity analysis 3). In contrast, the respective differences were attenuated for Black patients compared with White patients who had HFpEF for HF hospitalization (HR, 1.19; 95% CI, 1.07–1.33) and for death (HR, 0.82; 95% CI, 0.74–0.91).

Table 5.

Sensitivity Analyses of Race/Ethnicity and Outcomes in Adults With HF

Sensitivity Analysis Asian/Pacific Islander vs White Patients Black vs White Patients Hispanic vs White Patients
Hospitalization for HF Hospitalization for Any Cause Death Hospitalization for HF Hospitalization for Any Cause Death Hospitalization for HF Hospitalization for Any Cause Death
Sensitivity analysis 1: presumed incident HF only 0.99 (0.88–1.10) 0.88 (0.83–0.94)* 0.76 (0.69–0.84)* 1.30 (1.18–1.45)* 0.99 (0.93–1.05) 0.80 (0.71–0.89)* 1.05 (0.95–1.16) 0.96 (0.90–1.02) 0.86 (0.78–0.94) †
Sensitivity analysis 2: preserved EF only 0.95 (0.85–1.06) 0.86 (0.81–0.92)* 0.71 (0.64–0.78)* 1.19 (1.07–1.33) † 0.96 (0.90–1.02) 0.82 (0.74–0.91)* 1.10 (0.99–1.21) 0.98 (0.92–1.03) 0.86 (0.79–0.94)*
Sensitivity analysis 3: reduced EF only 0.93 (0.79–1.10) 0.94 (0.84–1.04) 0.77 (0.66–0.90)* 1.35 (1.18–1.55)* 1.03 (0.94–1.13) 0.70 (0.60–0.80)* 1.02 (0.87–1.19) 1.03 (0.93–1.14) 0.87 (0.76–1.00)
Sensitivity analysis 4: aged <70 y 0.97 (0.84–1.13) 0.86 (0.79–0.94)* 0.73 (0.63–0.85)* 1.39 (1.23–1.57)* 0.97 (0.90–1.05) 0.84 (0.73–0.98) ‡ 1.09 (0.96–1.06) 0.94 (0.87–1.02) 0.77 (0.66–0.89)*
Sensitivity analysis 5: aged 70–80 y 1.02 (0.88–1.17) 0.94 (0.87–1.02) 0.76 (0.67–0.87)* 1.08 (0.94–1.23) 0.96 (0.88–1.04) 0.68 (0.59–0.78)* 0.99 (0.87–1.13) 1.04 (0.97–1.12) 0.84 (0.75–0.94) †
Sensitivity analysis 6: aged >80 y 0.98 (0.85–1.13) 0.89 (0.82–0.97) † 0.76 (0.68–0.85)* 1.32 (1.15–1.54)* 1.09 (0.99–1.19) 0.88 (0.78–1.01) 1.12 (0.99–1.27) 0.94 (0.86–1.02) 0.91 (0.83–1.00)
Sensitivity analysis 7: women 1.10 (0.98–1.25) 0.94 (0.88–1.02) 0.76 (0.67–0.85)* 1.31 (1.18–1.46)* 1.00 (0.93–1.06) 0.82 (0.74–0.92)* 1.08 (0.97–1.21) 1.01 (0.95–1.08) 0.89 (0.81–0.98) ‡
Sensitivity analysis 8: men 0.93 (0.83–1.04) 0.86 (0.80–0.91)* 0.73 (0.66–0.80)* 1.24 (1.12–1.39)* 0.98 (0.92–1.05) 0.76 (0.68–0.85)* 1.07 (0.96–1.18) 0.95 (0.89–1.01) 0.82 (0.75–0.90)*
Sensitivity analysis 9: recurrent events 0.97 (0.89–1.06) 0.87 (0.83–0.91)* 1.41 (1.29–1.53)* 1.03 (0.98–1.08) 1.09 (1.00–1.18) ‡ 0.96 (0.93–1.00)
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Data are given as hazard ratio (95% CI). All models use backward selection and include all explanatory covariates, including medication and adherence. EF indicates ejection fraction; and HF, heart failure.

*

P<0.001.

†

P<0.01.

‡

P<0.05.

Discussion

Within a large, ethnically diverse, contemporary HF cohort with equal access to care within an integrated healthcare delivery system, we observed variation in selected outcomes by race/ethnicity even after accounting for differences in a wide range of individual‐ and area‐based potential confounders, including any differential receipt of pharmacological interventions. Compared with White patients, we found that Black patients experienced a higher adjusted rate of HF hospitalization but lower all‐cause mortality and no significant difference in overall hospitalization. In contrast, compared with White patients, Asian/PI patients had lower adjusted rates of hospitalization for any cause and death but no significant difference in HF hospitalization. Hispanic patients had lower adjusted rates of all‐cause hospitalization and death but no significant difference in the adjusted rate of HF hospitalization. Sensitivity analyses were largely consistent with the primary results, with similar or stronger associations when analyzing presumed incident HF cases only and modest variation in the strength of associations by HF characteristics and by age groups.

Our findings support and clarify prior studies suggesting that in the setting of HF, Black patients experience higher rates of hospitalization 3 , 7 , 8 , 9 , 10 but lower rates of death 3 , 7 , 8 , 9 , 10 than White patients. Furthermore, our finding that Asian/PI patients had a lower rate of death compared with White patients is consistent with certain previous studies. 3 , 7 , 8 In contrast, we observed that Asian/PI patients had lower rates of all‐cause hospitalization compared with White patients, which is consistent with one prior study, 5 whereas others did not report significant differences. 3 , 7 , 8 , 10 We found that there was no significant difference between Hispanic ethnicity and the adjusted rate of HF hospitalization and a lower rate of all‐cause death compared with White patients, which is in contrast to 2 studies that observed higher rates of HF hospitalization and similar rates of death. 7 , 10 Of note, both studies 7 , 10 used a hospital‐based cohort rather than the more representative cohort we used in our study, which comprehensively included patients with HF identified from both ambulatory and inpatient settings.

Of note, California state‐level data in patients with HF from 2014 to 2016 noted that Black patients had a higher, whereas Hispanic and Asian/PI patients had a lower, age‐adjusted HF‐related death rate compared with White patients (HF‐related death defined as having HF being listed on the death certificate). 24 With regard to HF hospitalization, Black patients had a higher and Hispanic patients had a similar age‐adjusted rate compared with White patients, whereas data were unavailable for Asian/PI patients. 24 Several important differences exist between our study and reported statewide data. First, the statewide data do not adjust for known confounders across racial/ethnic groups other than age or address potentially modifiable factors. Second, our study focused on patients with HF receiving comprehensive care in an integrated delivery system with equal access to care, whereas significant variation in access to care and quality of care exists by race/ethnicity statewide. 25 Third, we focused on all‐cause mortality compared with deaths attributed to HF based on death certificate data, which have known misclassification. 26 Fourth, the statewide findings may be driven by a higher prevalence of HF in Black patients than White patients 27 and do not directly compare to a cohort of patients with diagnosed HF, such as in our study.

Limited data exist about whether the association between race/ethnicity and clinical outcomes varies by HF characteristics. One study found that the difference in hospitalization for Black patients compared with White patients was greater in patients with HF who had EF >40% compared with EF <40%, 9 whereas HF hospitalization and death rates were higher for Black patients in another HFpEF population. 8 In contrast, we found that the associations between Black race and outcomes were stronger in patients with HFrEF and attenuated in patients with HFpEF.

Our discordant finding that Black patients had higher rates of HF hospitalizations and lower rates of death than White patients, despite extensive adjustment for differences in patient characteristics, treatments received, and area‐based measures of access to care and SES, is intriguing. The generally consistent and favorable or neutral findings among Asian/PI patients and Hispanic patients are also of interest. More important, we were unable to identify a clearly modifiable factor (eg, pharmacological treatment) or nonmodifiable factor to fully explain our observed racial/ethnic variation in outcomes. A prior analysis of racial/ethnic variation in coronary disease outcomes among KPNC patients found similar results, in which Black, Asian, and Hispanic patients had lower or similar risk of coronary events compared with White patients, and the variation was not explained by modifiable or nonmodifiable factors. 28 Although we were able to control for many potentially modifiable factors, we were unable to account for certain self‐management practices, such as dietary patterns, 29 exercise, 30 actual medication adherence, 31 or individual‐level SES. 32 In addition, hospitalizing a patient for HF exacerbation may also play a different management role in various racial/ethnic groups, and may not reflect variable quality of care. We did not find material differences across race/ethnicity in the use of proven HF therapies in our population, and differential patterns of inpatient versus outpatient health care 33 , 34 utilization were not associated with survival. It is also possible that there are causative differences across race/ethnicity that impact the development and complications associated with HF. Black patients have a higher incidence of HF than White patients, 27 which occurs at an earlier age and is more likely to be attributed to hypertension rather than ischemic heart disease. 35 , 36 , 37 Prior studies have also found structural, functional, and vascular differences between Black and White patients that may contribute to a higher incidence of HF and worse outcomes for patients who have HF for Black patients compared with White patients. These differences include worse arterial stiffness and microcirculatory function, 38 lower levels of natriuretic peptides, 39 , 40 differences in the left ventricular structural and functional changes in response to arterial afterload, 41 and a higher prevalence of malignant left ventricular hypertrophy. 42 Structural or functional cardiac or vascular differences, or responses to therapies, may also potentially explain the differences we found in HF‐related outcomes across racial/ethnic groups. It is possible that the clinical factors associated with acute HF symptoms that lead to HF hospitalization may differ from the clinical factors associated with death among patients with HF. If so, then these differences between Black and White patients may contribute to higher HF hospitalization rates in Black patients while not negatively impacting survival. Further studies are needed to determine if specific mechanisms can be identified for the purpose of developing future interventions.

Our study was strengthened by its large sample size, more contemporary study period, inclusion of all major racial/ethnic groups, use of EF data to categorize HF type, comprehensive longitudinal follow‐up on key explanatory variables (modifiable and nonmodifiable) and clinical outcomes, and targeted measures at the individual and area level. Our study also had several limitations. For example, despite the broad spectrum of available covariates, we were unable to completely rule out unmeasured confounders, including potential genetic and biologic differences across racial/ethnic groups as well as selected lifestyle factors, such as diet and exercise patterns, alcohol and illicit drug use, and detailed medication adherence, that may influence outcomes. We were also unable to measure potential differences in the clinical severity of HF beyond EF and receipt of different HF‐related therapies across racial/ethnic groups. However, we found similar results in the sensitivity analysis that was restricted to patients with presumed incident HF who are likely more comparable in terms of HF severity than patients with prevalent HF. We used all‐cause mortality as an outcome rather than death with HF as a contributing cause because there is substantial misclassification in listed cause(s) of death on death certificates. 22 Although it is possible that our findings of lower rates of death among the racial/ethnic minority groups compared with White patients were attributable to using all‐cause mortality rather than cause‐specific mortality, we were able to adjust for important noncardiovascular comorbidities that are associated with death. Although the findings were of interest, the sensitivity analyses should be considered exploratory given the reduced sample sizes in certain racial/ethnic subgroups and associated limited precision. All studied patients were receiving care within a large integrated healthcare delivery system in northern California, which has an emphasis on cardiovascular prevention and treatment, so the results may not be fully generalizable to all geographic areas and practice settings.

Conclusions

In summary, within a large, ethnically diverse population with HF, we observed that compared with White patients, Black patients experienced a higher rate of HF hospitalization but a lower rate of death; Asian/PI patients had lower rates of all‐cause hospitalization and death; and Hispanic patients had a lower rate of death. Future efforts are needed to better understand explanatory mechanisms for these observations and effective interventions to reduced adverse HF‐related outcomes across all racial/ethnic groups.

Sources of Funding

This work was supported by The Permanente Medical Group (TPMG) Delivery Science Research Program. Dr. Savitz received funding from the TPMG Delivery Science Fellowship Program that partially supported this work.

Disclosures

Dr Go has received funding through his institution from the National Heart, Lung, and Blood Institute; National Institute on Aging; National Institute of Diabetes and Digestive and Kidney Diseases; and Novartis. The remaining authors have no disclosures to report.

Supporting information

Table S1

Click here for additional data file. (10.4KB, pdf)

(J Am Heart Assoc. 2021;10:e016601. DOI: 10.1161/JAHA.120.016601.)

Supplementary Material for this article is available at https://www.ahajournals.org/doi/suppl/10.1161/JAHA.120.016601

For Sources of Funding and Disclosures, see page 13.

References

  • 1. Roth GA, Forouzanfar MH, Moran AE, Barber R, Nguyen G, Feigin VL, Naghavi M, Mensah GA, Murray CJ. Demographic and epidemiologic drivers of global cardiovascular mortality. N Engl J Med. 2015;372:1333–1341. DOI: 10.1056/NEJMoa1406656. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2. Sidney S, Go AS, Jaffe MG, Solomon MD, Ambrosy AP, Rana JS. Association between aging of the US population and heart disease mortality from 2011 to 2017. JAMA Cardiol. 2019;4:1280–1286. DOI: 10.1001/jamacardio.2019.4187. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3. Alexander M, Grumbach K, Remy L, Rowell R, Massie BM. Congestive heart failure hospitalizations and survival in California: patterns according to race/ethnicity. Am Heart J. 1999;137:919–927. DOI: 10.1016/S0002-8703(99)70417-5. [DOI] [PubMed] [Google Scholar]
  • 4. Bahrami H, Kronmal R, Bluemke DA, Olson J, Shea S, Liu K, Burke GL, Lima JA. Differences in the incidence of congestive heart failure by ethnicity: the Multi‐Ethnic Study of Atherosclerosis. Arch Intern Med. 2008;168:2138–2145. DOI: 10.1001/archinte.168.19.2138. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5. Brown DW, Haldeman GA, Croft JB, Giles WH, Mensah GA. Racial or ethnic differences in hospitalization for heart failure among elderly adults: Medicare, 1990 to 2000. Am Heart J. 2005;150:448–454. DOI: 10.1016/j.ahj.2004.11.010. [DOI] [PubMed] [Google Scholar]
  • 6. Chen J, Normand SL, Wang Y, Krumholz HM. National and regional trends in heart failure hospitalization and mortality rates for Medicare beneficiaries, 1998–2008. JAMA. 2011;306:1669–1678. DOI: 10.1001/jama.2011.1474. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7. Durstenfeld MS, Ogedegbe O, Katz SD, Park H, Blecker S. Racial and ethnic differences in heart failure readmissions and mortality in a large municipal healthcare system. JACC Heart Fail. 2016;4:885–893. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8. Gurwitz JH, Magid DJ, Smith DH, Hsu G, Sung SH, Allen LA, McManus DD, Goldberg RJ, Go AS; Cardiovascular Research Network PRESERVE Study . The complex relationship of race to outcomes in heart failure with preserved ejection fraction. Am J Med. 2015;128:591–600. DOI: 10.1016/j.amjmed.2014.11.034. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9. Rathore SS, Foody JM, Wang Y, Smith GL, Herrin J, Masoudi FA, Wolfe P, Havranek EP, Ordin DL, Krumholz HM. Race, quality of care, and outcomes of elderly patients hospitalized with heart failure. JAMA. 2003;289:2517–2524. DOI: 10.1001/jama.289.19.2517. [DOI] [PubMed] [Google Scholar]
  • 10. Vivo RP, Krim SR, Liang LI, Neely M, Hernandez AF, Eapen ZJ, Peterson ED, Bhatt DL, Heidenreich PA, Yancy CW, et al. Short‐ and long‐term rehospitalization and mortality for heart failure in 4 racial/ethnic populations. J Am Heart Assoc. 2014;3:e001134. DOI: 10.1161/JAHA.114.001134. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11. Billings J, Zeitel L, Lukomnik J, Carey TS, Blank AE, Newman L. Impact of socioeconomic status on hospital use in New York City. Health Aff (Millwood). 1993;12:162–173. DOI: 10.1377/hlthaff.12.1.162. [DOI] [PubMed] [Google Scholar]
  • 12. Oster A, Bindman AB. Emergency department visits for ambulatory care sensitive conditions: insights into preventable hospitalizations. Med Care. 2003;41:198–207. DOI: 10.1097/01.MLR.0000045021.70297.9F. [DOI] [PubMed] [Google Scholar]
  • 13. McIlvennan CK, Eapen ZJ, Allen LA. Hospital readmissions reduction program. Circulation. 2015;131:1796–1803. DOI: 10.1161/CIRCULATIONAHA.114.010270. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14. Gordon N, Lin T. The Kaiser Permanente Northern California adult member health survey. Perm J. 2016;20:15–225. DOI: 10.7812/TPP/15-225. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15. Kind AJ, Buckingham WR. Making neighborhood‐disadvantage metrics accessible—the neighborhood atlas. N Engl J Med. 2018;378:2456. DOI: 10.1056/NEJMp1802313. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16. Ingram DD, Franco SJ. 2013 NCHS urban‐rural classification scheme for counties. Vital Health Stat. 2012;2:1–65. [PubMed] [Google Scholar]
  • 17. Health Resources & Services Administration . Area health resources file. North Bethesda, MD: US Department of Health and Human Services; 2019. Available at: https://data.hrsa.gov/topics/health‐workforce/ahrf Accessed February 3, 2020. [Google Scholar]
  • 18. Go AS, Lee WY, Yang J, Lo JC, Gurwitz JH. Statin therapy and risks for death and hospitalization in chronic heart failure. JAMA. 2006;296:2105–2111. DOI: 10.1001/jama.296.17.2105. [DOI] [PubMed] [Google Scholar]
  • 19. Go AS, Yang J, Ackerson LM, Lepper K, Robbins S, Massie BM, Shlipak MG. Hemoglobin level, chronic kidney disease, and the risks of death and hospitalization in adults with chronic heart failure: the anemia in chronic heart failure: outcomes and resource utilization (ANCHOR) study. Circulation. 2006;113:2713–2723. DOI: 10.1161/CIRCULATIONAHA.105.577577. [DOI] [PubMed] [Google Scholar]
  • 20. McKee PA, Castelli WP, McNamara PM, Kannel WB. The natural history of congestive heart failure: the Framingham Study. N Engl J Med. 1971;285:1441–1446. DOI: 10.1056/NEJM197112232852601. [DOI] [PubMed] [Google Scholar]
  • 21. Witzig R. The medicalization of race: scientific legitimization of a flawed social construct. Ann Intern Med. 1996;125:675–679. [DOI] [PubMed] [Google Scholar]
  • 22. Snyder ML, Love SA, Sorlie PD, Rosamond WD, Antini C, Metcalf PA, Hardy S, Suchindran CM, Shahar E, Heiss G. Redistribution of heart failure as the cause of death: the Atherosclerosis Risk in Communities Study. Popul Health Metr. 2014;12:10. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23. Andersen PK, Gill RD. Cox's regression model for counting processes: a large sample study. Ann Stat. 1982;10:1100–1120. [Google Scholar]
  • 24. Centers for Disease Control and Prevention Division for Heart Disease and Stroke Prevention . Interactive atlas of heart disease and stroke. Atlanta, GA: Centers for Disease Control and Prevention; 2015;2020. Available at: https://nccd.cdc.gov/DHDSPAtlas/Reports.aspx Accessed January 30, 2020. [Google Scholar]
  • 25. Charles SA, McEligot AJ. Racial and ethnic disparities in access to care during the early years of affordable care act implementation in California. Calif J Health Promot. 2018;16:36. [PMC free article] [PubMed] [Google Scholar]
  • 26. Ziaeian B, Fonarow GC. Epidemiology and aetiology of heart failure. Nat Rev Cardiol. 2016;13:368–378. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27. Benjamin EJ, Muntner P, Alonso A, Bittencourt MS, Callaway CW, Carson AP, Chamberlain AM, Chang AR, Cheng S, Das SR, et al. Heart disease and stroke statistics‐2019 update: a report from the American Heart Association. Circulation. 2019;139:e56–e528. [DOI] [PubMed] [Google Scholar]
  • 28. Rana JS, Liu JY, Moffet HH, Jaffe MG, Sidney S, Karter AJ. Ethnic differences in risk of coronary heart disease in a large contemporary population. Am J Prev Med. 2016;50:637–641. [DOI] [PubMed] [Google Scholar]
  • 29. Yancy CW, Jessup M, Bozkurt B, Butler J, Casey DE Jr, Drazner MH, Fonarow GC, Geraci SA, Horwich T, Januzzi JL. 2013 ACCF/AHA guideline for the management of heart failure: executive summary: a report of the American College of Cardiology Foundation/American Heart Association Task Force on practice guidelines. Circulation. 2013;128:1810–1852. [DOI] [PubMed] [Google Scholar]
  • 30. Belardinelli R, Georgiou D, Cianci G, Purcaro A. Randomized, controlled trial of long‐term moderate exercise training in chronic heart failure: effects on functional capacity, quality of life, and clinical outcome. Circulation. 1999;99:1173–1182. [DOI] [PubMed] [Google Scholar]
  • 31. Wu J‐R, Moser DK, Chung ML, Lennie TA. Objectively measured, but not self‐reported, medication adherence independently predicts event‐free survival in patients with heart failure. J Card Fail. 2008;14:203–210. DOI: 10.1016/j.cardfail.2007.11.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32. Hawkins NM, Jhund PS, McMurray JJ, Capewell S. Heart failure and socioeconomic status: accumulating evidence of inequality. Eur J Heart Fail. 2012;14:138–146. DOI: 10.1093/eurjhf/hfr168. [DOI] [PubMed] [Google Scholar]
  • 33. Feltner C, Jones CD, Cene CW, Zheng ZJ, Sueta CA, Coker‐Schwimmer EJ, Arvanitis M, Lohr KN, Middleton JC, Jonas DE. Transitional care interventions to prevent readmissions for persons with heart failure: a systematic review and meta‐analysis. Ann Intern Med. 2014;160:774–784. DOI: 10.7326/M14-0083. [DOI] [PubMed] [Google Scholar]
  • 34. Hernandez AF, Greiner MA, Fonarow GC, Hammill BG, Heidenreich PA, Yancy CW, Peterson ED, Curtis LH. Relationship between early physician follow‐up and 30‐day readmission among Medicare beneficiaries hospitalized for heart failure. JAMA. 2010;303:1716–1722. DOI: 10.1001/jama.2010.533. [DOI] [PubMed] [Google Scholar]
  • 35. Bourassa MG, Gurne O, Bangdiwala SI, Ghali JK, Young JB, Rousseau M, Johnstone DE, Yusuf S; Studies of Left Ventricular Dysfunction (SOLVD) Investigators . Natural history and patterns of current practice in heart failure. J Am Coll Cardiol. 1993;22:14a–19a. DOI: 10.1016/0735-1097(93)90456-B. [DOI] [PubMed] [Google Scholar]
  • 36. Yancy CW, Fowler MB, Colucci WS, Gilbert EM, Bristow MR, Cohn JN, Lukas MA, Young ST, Packer M. Race and the response to adrenergic blockade with carvedilol in patients with chronic heart failure. N Engl J Med. 2001;344:1358–1365. DOI: 10.1056/NEJM200105033441803. [DOI] [PubMed] [Google Scholar]
  • 37. Bibbins‐Domingo K, Pletcher MJ, Lin F, Vittinghoff E, Gardin JM, Arynchyn A, Lewis CE, Williams OD, Hulley SB. Racial differences in incident heart failure among young adults. N Engl J Med. 2009;360:1179–1190. DOI: 10.1056/NEJMoa0807265. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38. Morris AA, Patel RS, Binongo JN, Poole J, Mheid IA, Ahmed Y, Stoyanova N, Vaccarino V, Din‐Dzietham R, Gibbons GH, et al. Racial differences in arterial stiffness and microcirculatory function between Black and White Americans. J Am Heart Assoc. 2013;2:e002154. DOI: 10.1161/JAHA.112.002154. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39. Gupta DK, de Lemos JA, Ayers CR, Berry JD, Wang TJ. Racial differences in natriuretic peptide levels: the Dallas Heart Study. JACC Heart Fail. 2015;3:513–519. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40. Gupta RM, Hadaya J, Trehan A, Zekavat SM, Roselli C, Klarin D, Emdin CA, Hilvering CRE, Bianchi V, Mueller C, et al. A genetic variant associated with five vascular diseases is a distal regulator of endothelin‐1 gene expression. Cell. 2017;170:522–533.e15. DOI: 10.1016/j.cell.2017.06.049. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41. Fernandes‐Silva MM, Shah AM, Hegde S, Goncalves A, Claggett B, Cheng S, Nadruz W, Kitzman DW, Konety SH, Matsushita K, et al. Race‐related differences in left ventricular structural and functional remodeling in response to increased afterload: the ARIC study. JACC Heart Fail. 2017;5:157–165. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42. Lewis AA, Ayers CR, Selvin E, Neeland I, Ballantyne CM, Nambi V, Pandey A, Powell‐Wiley TM, Drazner MH, Carnethon MR, et al. Racial differences in malignant left ventricular hypertrophy and incidence of heart failure: a multicohort study. Circulation. 2020;141:957–967. DOI: 10.1161/CIRCULATIONAHA.119.043628. [DOI] [PMC free article] [PubMed] [Google Scholar]

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