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. Author manuscript; available in PMC: 2023 Feb 1.
Published in final edited form as: Am Heart J. 2021 Nov 20;244:135–148. doi: 10.1016/j.ahj.2021.11.011

Is the Affordable Care Act Medicaid Expansion Associated with Receipt of Heart Failure Guideline-Directed Medical Therapy By Race and Ethnicity?

Khadijah K Breathett 1, Haolin Xu 2, Nancy K Sweitzer 3, Elizabeth Calhoun 4, Roland A Matsouaka 5, Clyde W Yancy 6, Gregg C Fonarow 7, Adam D DeVore 8, Deepak L Bhatt 9, Pamela N Peterson 10
PMCID: PMC8727506  NIHMSID: NIHMS1758640  PMID: 34813771

Abstract

Background:

Uninsurance is a known contributor to racial/ethnic health inequities. Insurance is often needed for prescriptions and follow-up appointments. Therefore, we determined whether the Affordable Care Act(ACA) Medicaid Expansion was associated with increased receipt of guideline-directed medical treatment(GDMT) at discharge among patients hospitalized with heart failure(HF) by race/ethnicity.

Methods:

Using Get With The Guidelines-HF registry, logistic regression was used to assess odds of receiving GDMT(HF medications; education; follow-up appointment) in early versus non-adopter states before(2012-2013) and after ACA Medicaid Expansion(2014-2019) within each race/ethnicity, accounting for patient-level covariates and within-hospital clustering. We tested for an interaction(p-int) between GDMT and pre/post Medicaid Expansion time periods.

Results:

Among 271,606 patients(57.5% early adopter, 42.5% non-adopter), 65.5% were White, 22.8% African American, 8.9% Hispanic, and 2.9% Asian race/ethnicity. Independent of ACA timing, Hispanic patients were more likely to receive all GDMT for residing in early adopter states compared to non-adopter states (p<0.0001). In fully-adjusted analyses, ACA Medicaid Expansion was associated with higher odds of receipt of ACEI/ARB/ARNI in Hispanic patients [before ACA:OR 0.40(95%CI:0.13,1.23); after ACA:OR 2.46(1.10,5.51); p-int=0.0002], but this occurred in the setting of an immediate decline in prescribing patterns, particularly among non-adopter states, followed by an increase that remained lowest in non-adopter states. The ACA was not associated with receipt of GDMT for other racial/ethnic groups.

Conclusions:

Among GWTG-HF hospitals, Hispanic patients were more likely to receive all GDMT if they resided in early adopter states rather than non-adopter states, independent of ACA Medicaid Expansion timing. ACA implementation was only associated with higher odds of receipt of ACEI/ARB/ARNI in Hispanic patients. Additional steps are needed for improved GDMT delivery for all.

Keywords: health policy, heart failure, evidence-based medicine, hospitalization

INTRODUCTION

Heart failure (HF) disproportionately affects racial and ethnic groups of color.1 African American and Hispanic patients have higher prevalence of HF than White patients.1 African American patients have the highest mortality from HF compared to all racial and ethnic groups.1 In addition, African American and Hispanic patients are less likely to receive guideline-directed medical therapy (GDMT) 2,3 and appropriate subspecialist care4–6 than White patients. It is of prime importance to consider how to improve access to life-saving GDMT to racial and ethnic groups of color.

Uninsurance is a known contributor to racial and ethnic health inequities.7 Insurance is needed for visits with healthcare professionals and for many prescription medications. Although some HF GDMT medications are available at low-cost, the total cost of all GDMT medication is too expensive for many uninsured patients.8 In addition, physicians believe that uninsurance contributes to inequities in healthcare delivery among racial and ethnic groups of color.9 Since physician prescribing is influenced by patient insurance status,9 improved patient access to healthcare insurance may lessen racial and ethnic disparities in prescribing of HF GDMT.

Implementation of the 2014 Affordable Care Act (ACA) Medicaid Expansion has been associated with significant increases in access to care, particularly among African American and Hispanic patients.7,10 More than 20 million Americans became insured within the first two years of the ACA implementation.11 The ACA has been positively associated with improvements among patients with HF. A 30% increase in heart transplant listings was observed in African American patients in association with the ACA Medicaid Expansion.10 HF hospitalizations among uninsured populations also declined.12 In the first couple years following the ACA Medicaid Expansion, hospitals that adopted the ACA Medicaid Expansion saved approximately $6.2 billion from the reduction in uncompensated care.13 Therefore, inpatient education for patients hospitalized with HF may be more accessible in hospitals that receive additional funding through the ACA, which subsequently may lead to better delivery of HF GDMT. Among patients hospitalized with HF, it is unknown whether the ACA Medicaid Expansion was associated with increased receipt of guideline-based care on discharge from hospitalization for HF within racial and ethnic groups.

Using a national registry of hospitals committed to improving care quality, the Get With The Guidelines (GWTG)-HF Registry, we examined the association between the ACA Medicaid Expansion and receipt of GDMT on discharge from hospitalization for HF within African American, Hispanic, Asian, and White patients. Specifically, we evaluated for initiation of evidence-based medication, 60 minutes of HF education, and a scheduled post discharge appointment among racial and ethnic groups according to whether the patient was hospitalized in a state that approved the ACA Medicaid Expansion (adopter state group status) and January 2014 timing of ACA Medicaid Expansion implementation.

METHODS

Data Source

GWTG-HF is an established registry of over 600 voluntary hospitals across the U.S. that are dedicated to improving quality of patient care and performance of hospitals. Participating hospitals receive support to improve healthcare delivery through education, access to the latest clinical data, and ongoing performance feedback.14 The database includes all inpatient hospitalizations for HF among adults aged 18 years and older.15 Demographics including race and ethnicity as well as clinical information are uploaded into the registry by respective hospital sites. Race and ethnicity were categorized by self-report on case forms. Race options included: American Indian or Alaska Native, Asian, Black or African American, Native Hawaiian or Pacific Islander, White, and unable to determine. Ethnicity options included Hispanic ethnicity. As a quality improvement program, participating hospitals had waivers for informed consent under the common rule and were required to obtain institutional review board approval.16 Analytic method are available upon request from the study team. Data is not publicly available, and requests for data should go to American Heart Association GWTG-HF. This study was directly funded by Dr. Breathett’s American Heart Association (AHA) Get With the Guidelines Young Investigator Database Seed Grant Award; and National Heart, Lung, and Blood Institute (NHLBI) K01HL142848, R56HL159216, R25HL126146 subaward 11692sc, and L30HL148881. The authors are solely responsible for the design and conduct of this study, all study analyses, the drafting and editing of the paper and its final contents.

Study Cohort

Adults 18 years of age and older with an inpatient hospitalization with primary discharge diagnosis of HF at a participating U.S. GWTG-HF center from January 2012 to September 2019 were included (Supplemental Table 1). Hospitals were included if they enrolled at least 50 patients with HF in the year prior to 2012 (n= 274 hospitals) in order to reduce random variation. Hospitals were stratified into early adopter (implemented ACA Medicaid Expansion by January 2014, n=23 states and Washington D.C., n=112 hospitals Figure 1) and non-adopter state groups (did not adopt ACA Medicaid Expansion through September 2019, n=13 states, n=89 hospitals). Hospitals were excluded for adoption of ACA Medicaid Expansion after initial January 2014 implementation or for reducing basic coverage in 2014 (n=14 states, n=73 hospitals).17,18 Patients were excluded for reasons that would limit ability to examine eligibility for discharge GDMT [n= 66,074 patients; missing ejection fraction, missing/unable to determine race/ethnicity or race/ethnicity too small to study (American Indian or Alaska Native, Native Hawaiian or Pacific Islander), comfort measures/terminal illness, discharge to hospice, transfer out of participating hospital, in-hospital death, left against medical advice, or missing discharge disposition; Supplemental Table 1]. Other missing values for vitals, laboratory measures, and hospital characteristics were imputed (Supplemental Table 2). The final study cohort included 201 hospitals with 271,606 patients.

Figure 1. State Groups.

Figure 1.

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Blue indicates early adopters of the ACA Medicaid Expansion (adoption by January 2014); red, non-adopters of the ACA Medicaid Expansion through September 2019; grey, exclusion for adoption of ACA Medicaid Expansion after 2014 or reduction in coverage. This figure was created with presentation magazine.

Outcomes of Interest

GDMT was assessed as six individual outcomes which are representative of pertinent American Heart Association HF discharge achievement, quality, and reporting metrics.19,20 Achievement metrics included receipt of (1) angiotensin converting enzyme inhibitor (ACEI)/ angiotensin receptor blocker (ARB)/angiotensin receptor-neprilysin inhibitors (ARNI), (2) evidence-based beta blocker (carvedilol, metoprolol succinate, or bisoprolol), and (3) post discharge appointment with specialist or primary care. Quality metrics included receipt of (4) aldosterone receptor antagonist and (5) hydralazine/nitrate. Reporting metric included (6) receipt of 60 minutes of HF education.

Patient eligibility for all medications required a diagnosis of HF with reduced ejection fraction (HFrEF) and no documented contraindications or intolerance to therapy (i.e. aldosterone receptor antagonists: potassium > 5mEq/L, creatinine >2.5 mg/dL in men or > 2mg/dL in women or glomerular filtration rate >30 mL/min/1.73m2). Specifically, ACEI/ARB/ARNI, beta blockers, and hydralazine/nitrate included left ventricular ejection fraction (LVEF) <40% or narrative description of moderate to severe left ventricular systolic dysfunction. Patient eligibility for hydralazine/nitrate also included African American patient race. Patient eligibility for aldosterone receptor antagonist included LVEF ≤35%. Patient eligibility for remaining measures, education and post discharge appointment, had no LVEF requirements.

Statistical Analyses

Baseline characteristics were evaluated between early adopter and non-adopter state groups for the overall population and among the subgroup of patients with only HFrEF. Receipt of each of the six types of GDMT were evaluated and described for all eligible patients and within racial and ethnic groups (Non-Hispanic White, Non-Hispanic African American, Hispanic, and non-Hispanic Asian). Results were stratified by early adopter and non-adopter state groups. Differences in GDMT between patients receiving care in early adopter and non-adopter states were compared using Chi-square tests.

Logistic regression models were used to examine the associations of adopter state status and ACA Medicaid Expansion within each race and ethnic group. Models were adjusted a-priori for patient-level variables [age, sex, clinical history (Supplement), vital signs at admission (systolic blood pressure, heart rate), labs at admission [serum creatinine, serum sodium], and hospital variables [hospital size {number of beds), teaching status, rural location, and geographic region]. Generalized estimating equations were used to account for within-hospital clustering. The odds ratio of receiving each GDMT in early adopter versus non-adopter states were reported for the periods before ACA Medicaid Expansion and after ACA Medicaid Expansion. P-values are 2-sided tests and were considered statistically significant at p<0.008 in order to account for multiple comparison for 6 GDMT outcomes using Bonferroni correction. Interactions were tested between GDMT and pre/post ACA Medicaid Expansion implementation time periods. In order to demonstrate trends, piecewise linear models were fit to the estimated eligible percentage of patients receiving each form of GDMT with cutoff of January 2014 (time of ACA Medicaid Expansion) for each racial and ethnic group and adopter state group.

As a sensitivity analysis, we examined whether the associations between GDMT, adopter state group, and timing of hospitalization admission (before versus after ACA implementation) differed by race/ethnicity. We tested the three-way interaction term of race/ethnicity by adopter state group by timing of hospitalization admission. The unadjusted models include the first order, second order, and the three-way interaction term of the three variables. The adjusted models include all variables in the unadjusted models and adjustment variables described in the prior paragraph. Interactions were not tested for receipt of hydralazine/nitrate since this is only GDMT for patients of Black race, not other racial and ethnic groups. Thus, using Bonferroni correction for 5 GDMT outcomes, a three-way interaction p-value <0.01 was considered statistically significant for difference of associations across race/ethnicity. All analyses were executed using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA).

RESULTS

Baseline Characteristics

Among 271,606 patients, 57.5% were from early adopter states and 42.5% from non-adopter states (Table 1). The majority of patients were White race in both early adopter (65.7%) and non-adopter (65.1%) states. A lower proportion of Africans-American patients were in early adopter (19.2%) than non-adopter (27.6%) states. In contrast, a higher proportion of Hispanic and Asian patients were in early adopter (10.5% Hispanic, 4.6% Asian) than non-adopter (6.6% Hispanic, 0.6% Asian) states. Across both adopter and non-adopter states, patients’ median age was 73 years, 46.7% were women, and median ejection fraction was 43%. The majority of patients from both state groups had public payors with higher proportion of Medicaid payors among early adopter (18.3%) than non-adopter (14.0%) state groups. Compared to patients from non-adopter states, patients from early adopter states had modestly lower proportion of most medical comorbidities. A higher proportion of hospitals in early adopter states had teaching status, heart transplant availability, and were not located in a rural setting compared to hospitals in non-adopter states. The majority of hospitals in early adopter states were in the Northeast and West regions, and the majority of hospitals in non-adopter states were in the South region of the U.S.

Table 1.

Baseline Characteristics of Early Adopter and Non-Adopter States

Variable Overall
N=271,606		 Early Adopter States
N=156,167		 Non-Adopter States
N=115,439
Demographics
Age# 73 (61 - 83) 73 (61 - 83) 72 (60 - 82)
Women 126,896 (46.7) 72,081 (46.2) 54,815 (47.5)
Race/Ethnicity
 White 177,815 (65.5) 102,618 (65.7) 75,197 (65.1)
 African American 61,856 (22.8) 29,960 (19.2) 31,896 (27.6)
 Hispanic (any race) 24,055 (8.9) 16,420 (10.5) 7,635 (6.6)
 Asian 7,880 (2.9) 7,169 (4.6) 711 (0.6)
EF, %# 43 (26 - 57) 43 (27 - 58) 43 (25 - 55)
EF groups#
 HFrEF, EF ≤40 130,274 (48.0) 73,433 (47.0) 56,841 (49.2)
 HFmEF, EF 41-49 25,983 (9.6) 15,209 (9.7) 10,774 (9.3)
 HFpEF, EF ≥50 115,349 (42.5) 67,525 (43.2) 47,824 (41.4)
Insurance Status
 Medicaid 38,822 (16.4) 24,176 (18.3) 14,646 (14.0)
 Medicare 114,429 (48.3) 60,010 (45.4) 54,419 (52.1)
 Private/Other 73,697 (31.1) 44,386 (33.5) 29,311 (28.1)
 No Insurance/Unknown 9,842 (4.2) 3,733 (2.8) 6,109 (5.8)
Medical History
Atrial Flutter/Fibrillation 104,494 (38.9) 61,276 (39.9) 43,218 (37.7)
COPD or Asthma 91,285 (34.0) 49,868 (32.5) 41,417 (36.1)
Diabetes 123,338 (46.0) 67,759 (44.1) 55,579 (48.5)
Heart failure (chart diagnosis) 195,222 (72.8) 111,028 (72.3) 84,194 (73.4)
Hyperlipidemia 147,783 (55.1) 81,917 (53.3) 65,866 (57.4)
Hypertension 222,121 (82.8) 124,937 (81.3) 97,184 (84.8)
PVD 31,026 (11.6) 15,502 (10.1) 15,524 (13.5)
CAD 129,092 (48.1) 71,027 (46.2) 58,065 (50.6)
Prior MI 54,923 (20.5) 31,227 (20.3) 23,696 (20.7)
CVA/TIA 43,029 (16.0) 23,422 (15.2) 19,607 (17.1)
ICD only 26,847 (10.0) 14,730 (9.6) 12,117 (10.6)
Anemia 56,175 (20.9) 31,551 (20.5) 24,624 (21.5)
Pacemaker 35,126 (13.1) 19,566 (12.7) 15,560 (13.6)
Dialysis, chronic 10,894 (4.1) 6,105 (4.0) 4,789 (4.2)
Renal insufficiency, chronic 65,378 (24.4) 34,239 (22.3) 31,139 (27.2)
Depression 37,680 (14.0) 20,592 (13.4) 17,088 (14.9)
Valvular Heart Disease 48,405 (18.0) 27,427 (17.8) 20,978 (18.3)
CRT-P 2,449 (0.9) 1,083 (0.7) 1,366 (1.2)
CRT-D 17,579 (6.6) 9,094 (5.9) 8,485 (7.4)
Ischemic Etiology 141,412 (52.7) 78,149 (50.9) 63,263 (55.2)
Med History Panel Missing 3,288 (1.2) 2,511 (1.6) 777 (0.7)
Smoking 47,244 (17.4) 25,270 (16.2) 21,974 (19.1)
Vitals on Admission
Heart Rate, bpm# 84 (72 - 98) 84 (71 - 98) 84 (72 - 99)
SBP, mmHg# 140 (121 - 160) 138 (120 - 159) 141 (122 - 163)
DBP, mmHg# 77 (66 - 90) 76 (65 - 89) 78 (67 - 92)
Body mass index# 28.6 (24.0 - 35.0) 28.3 (23.8 - 34.6) 29.0 (24.2 - 35.6)
Lipids
Total Cholesterol, mg/dL# 135 (110 - 165) 133 (108 - 163) 136 (112 - 167)
HDL, mg/dL# 39 (30 - 49) 38 (30 - 48) 39 (31 - 50)
LDL, mg/dL# 74 (56 - 98) 74 (55 - 97) 75 (57 - 99)
Triglycerides, mg/dL# 91 (68 - 127) 91 (68 - 127) 91 (68 - 127)
Other Lab Measures
Serum sodium, mEq/L# 138 (136 - 141) 139 (136 - 141) 138 (136 - 141)
Hemoglobin, g/dL# 11.8 (10.2 - 13.4) 11.9 (10.3 - 13.5) 11.7 (10.2 - 13.2)
Albumin, g/dL# 3.5 (3.1 - 3.8) 3.4 (3.1 - 3.8) 3.5 (3.1 - 3.8)
BNP, pg/mL# 824 (395 - 1,657) 782 (383 - 1,523) 871 (408 - 1,812)
nBNP, pg/mL# 5,063 (2,240 - 11,293) 5,040 (2,208 - 11,001) 5,103 (2,281 - 11,603)
Serum creatinine, mg/dL# 1.3 (1.0 - 1.8) 1.3 (1.0 - 1.8) 1.3 (1.0 - 1.9)
BUN, mg/dL# 24 (17 - 37) 25 (17 - 38) 24 (17 - 36)
Troponin, ng/dL# 0.04 (0.02 - 0.10) 0.04 (0.02 - 0.10) 0.04 (0.02 - 0.10)
Potassium, mEq/L# 4.2 (3.8 - 4.6) 4.2 (3.8 - 4.6) 4.1 (3.8 - 4.5)
HbA1C, %# 6.6 (5.9 - 7.8) 6.6 (5.9 - 7.8) 6.6 (5.9 - 7.8)
Blood Glucose, mg/dL# 114 (96 - 149) 115 (97 - 152) 112 (95 - 146)
EKG QRS Duration, ms# 106 (90 - 138) 106 (90 - 138) 106 (90 - 138)
Year of index admission
 2012 38,235 (14.1) 23,057 (14.8) 15,178 (13.1)
 2013 35,962 (13.2) 21,356 (13.7) 14,606 (12.7)
 2014 33,691 (12.4) 19,371 (12.4) 14,320 (12.4)
 2015 34,508 (12.7) 19,931 (12.8) 14,577 (12.6)
 2016 35,974 (13.2) 20,091 (12.9) 15,883 (13.8)
 2017 36,519 (13.4) 21,230 (13.6) 15,289 (13.2)
 2018 35,023 (12.9) 19,334 (12.4) 15,689 (13.6)
 2019 21,694 (8.0) 11,797 (7.6) 9,897 (8.6)
Hospital Characteristics
Number of Beds# 395 (261 - 581) 415 (264 - 581) 394 (238 - 508)
Geographic Region
 Northeast 54,557 (20.1) 54,557 (34.9) 0 (0.0)
 Midwest 59,964 (22.1) 36,210 (23.2) 23,754 (20.6)
 South 103,447 (38.1) 12,921 (8.3) 90,526 (78.4)
 West 53,638 (19.7) 52,479 (33.6) 1,159 (1.0)
Rural Location 9,886 (3.7) 4,244 (2.8) 5,642 (4.9)
Teaching Status 207,153 (76.6) 121,046 (78.0) 86,107 (74.9)
Heart Transplant Hospital 13,763 (6.8) 9,942 (8.4) 3,821 (4.5)
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#

indicates continuous variables with interquartile range in parentheses; ischemic etiology includes medical history of coronary artery disease (CAD), myocardial infarction (MI), prior percutaneous coronary intervention (PCI), prior coronary artery bypass grafting (CABG), or prior PCI/CABG; EF, ejection fraction; HFrEF, heart failure with reduced ejection fraction; HFmEF, heart failure with midrange ejection fraction; HFpEF, heart failure with preserved ejection fraction; COPD, chronic obstructive pulmonary disease; ICD, implantable cardioverter defibrillator; CRT-P, cardiac resynchronization therapy- pacemaker; CRT-D, CRT defibrillator; SBP, systolic blood pressure; DBP, diastolic BP; HDL, high-density lipoprotein; LDL, low-density lipoprotein; BNP, brain natriuretic peptide; nBNP, N-terminal pro BNP; BUN, blood urea nitrogen; HbA1c, hemoglobin A1c.

Among 130,274 patients with HFrEF, 56% were from early adopter states, and 44% were from non-adopter states (Supplemental Table 3). The median ejection fraction was 25%. Median age was 69 years. Patients with HFrEF had patterns similar to the entire cohort for other variables including racial and ethnic proportions, Medicaid payors, patient comorbidities, and hospital characteristics.

Outcomes

Receipt of GDMT by State Group

Compared to patients from non-adopter states, patients from early adopter states had modestly higher receipt of ACEI/ARB/ARNI, beta blockers, and post discharge appointment without accounting for ACA Medicaid Expansion timing (Table 2). Receipt of mineralocorticoid receptor antagonists and hydralazine/nitrate were similar for both state groups. Receipt of 60 minutes of HF education was modestly lower among early adopter states than non-adopter states. Receipt of GDMT reached 90% or higher only with ACEI/ARB/ARNI and beta blockers metrics in both state groups. All other forms of GDMT in this study had receipt ranging from 29% (hydralazine/nitrates) to 76.9% (post discharge appointment) for eligible patients.

Table 2.

Achievement of Heart Failure Metrics in Early Adopter Versus Non-Adopter States

Variable Overall
N=261,190		 Early Adopter States
N=149,292		 Non-Adopter States
N=111,898		 P Value
Achievement Measures
ACEI/ARB/ARNI 78,680 (95.7) 44,981 (96.2) 33,699 (95.1) <.0001 *
Evidence-Based Specific Beta Blockers 99,310 (93.0) 56,469 (93.2) 42,841 (92.6) 0.0003 *
Post Discharge Appointment for HF Patients 162,195 (76.0) 93,298 (76.9) 68,897 (74.7) <.0001 *
Quality Measures
Mineralocorticoid receptor antagonist 38,240 (51.2) 21,507 (51.2) 16,733 (51.1) 0.7289
Hydralazine/Nitrate 3,171 (30.4) 2,143 (31.2) 1,028 (29.0) 0.0246
Reporting Measures
60 Minutes of HF Education 112,433 (41.4) 62,125 (39.8) 50,308 (43.6) <.0001 *
White
ACEI/ARB/ARNI 45,673 (95.5) 26,074 (95.9) 19,599 (94.9) <.0001 *
Evidence-Based Specific Beta Blockers 57,887 (92.0) 32,962 (92.1) 24,925 (91.8) 0.1804
Post Discharge Appointment for HF Patients 100,695 (74.8) 57,681 (75.2) 43,014 (74.3) 0.0004 *
Mineralocorticoid receptor antagonist 22,053 (48.5) 12,485 (48.8) 9,568 (48.2) 0.2335
60 Minutes of HF Education 69,028 (38.8) 39,325 (38.3) 29,703 (39.5) <.0001 *
African American
ACEI/ARB/ARNI 23,134 (96.5) 11,484 (96.7) 11,650 (96.3) 0.1066
Evidence-Based Specific Beta Blockers 29,152 (94.8) 14,120 (95.0) 15,032 (94.6) 0.1487
Post Discharge Appointment for HF Patients 41,366 (79.3) 20,179 (81.6) 21,187 (77.3) <.0001 *
Mineralocorticoid receptor antagonist 11,818 (56.2) 5,584 (53.9) 6,234 (58.4) <.0001 *
Hydralazine/Nitrate 3,144 (30.4) 2,122 (31.2) 1,022 (29.0) 0.0245
60 Minutes of HF Education 29,737 (48.1) 12,575 (42.0) 17,162 (53.8) <.0001 *
Hispanic
ACEI/ARB/ARNI 7,851 (94.9) 5,599 (96.6) 2,252 (90.8) <.0001 *
Evidence-Based Specific Beta Blockers 9,519 (92.9) 6,899 (94.3) 2,620 (89.2) <.0001 *
Post Discharge Appointment for HF Patients 14,893 (73.8) 10,634 (77.0) 4,259 (66.9) <.0001 *
Mineralocorticoid receptor antagonist 3,570 (54.5) 2,722 (60.1) 848 (41.9) <.0001 *
60 Minutes of HF Education 11,156 (46.4) 8,045 (49.0) 3,111 (40.7) <.0001 *
Asian
ACEI/ARB/ARNI 2,022 (97.2) 1,824 (97.6) 198 (93.8) 0.0017 *
Evidence-Based Specific Beta Blockers 2,752 (94.5) 2,488 (94.6) 264 (93.3) 0.3579
Post Discharge Appointment for HF Patients 5,241 (78.6) 4,804 (79.2) 437 (73.0) 0.0004 *
Mineralocorticoid receptor antagonist 799 (48.3) 716 (48.4) 83 (47.4) 0.7994
60 Minutes of HF Education 2,512 (31.9) 2,180 (30.4) 332 (46.7) <.0001 *
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ACEI indicates angiotensin converting enzyme inhibitor; ARB, angiotensin receptor blocker; ARNI, angiotensin receptor-neprilysin inhibitor.

*

indicates, p<0.008.

When stratified by race and ethnicity, there was state group variability in receipt of GDMT with the exception of Hispanic patients (Table 2). Hispanic patients from early adopter states had significantly higher receipt of each of the six GDMT compared to non-adopter states (p<0.0001), with proportions 5.1% to 18.2% higher among early adopter residents. In other races and ethnicities, ACEI/ARB/ARNI was modestly higher among White and Asian patients from early adopter states compared to non-adopter states. Post discharge appointments were higher among White, African American, and Asian patients from early adopter than non-adopter states. Mineralocorticoid receptor antagonist prescribing was lower among African American patients from early adopter than non-adopter states. HF education of 60 minutes was lower among White, African American, and Asian patients from early adopter states than non-adopter states. No significant differences were observed in other GDMT among racial and ethnic groups.

Receipt of GDMT in Relation to ACA Medicaid Expansion Timing

In analyses fully adjusted for patient-level covariates and within-hospital clustering, the association between receipt of ACEI/ARB/ARNI among Hispanic patients within early versus non-adopter states differed pre/post ACA. The odds of Hispanic patients receiving ACEI/ARB/ANI were lower before ACA: OR 0.40 (95% CI: 0.13, 1.23) and increased after ACA: OR 2.46 (95% CI 1.10, 5.51); p-interaction (p-int) = 0.0002; Table 3. However, the change reflects an immediate decline in the proportion of Hispanic patients from both early adopter and non-adopter states who were prescribed ACEI/ARB/ARNI after 2014 (Figure 2a). The decline was greatest within non-adopter states. Prescribing patterns increased in both groups over the six years following the ACA but remained the lowest among patients in non-adopter states. Changes were not significant for prescribing of ACEI/ARB/ARNI among other racial/ethnic groups (Table 3, Figure 2b). The odds of receiving an mineralocorticoid receptor antagonist was significantly lower for African American patients from early adopter versus non-adopter states independent of timing of the ACA Medicaid Expansion [before ACA: OR 0.33 (95% CI: 0.17, 0.62); after ACA: OR 0.37 (95% CI: 0.21, 0.64); p-int = 0.6406; Supplemental Figure 1]. No significant differences were observed in receipt of other GDMT by race and ethnicity (Supplemental Figures 2–5).

Table 3.

Odds of Receiving GDMT in Early Adopter States Compared to Non-Adopters States Before and After the ACA Medicaid Expansion

Unadjusted Analysis
 Adjusted Analysis
OR (95% CI) P-int OR (95% CI) P-int
ACEI/ARB/ARNI
White 0.1637 0.2270
Before Expansion 1.59 (1.00, 2.51) 1.31 (0.66, 2.61)
After Expansion 1.14 (0.83, 1.58) 0.97 (0.59, 1.60)
African American 0.6054 0.5725
Before Expansion 1.01 (0.53, 1.92) 0.47 (0.21, 1.04)
After Expansion 1.17 (0.73, 1.87) 0.56 (0.31, 1.02)
Hispanic 0.0007 * 0.0002 *
Before Expansion 0.67 (0.28, 1.64) 0.40 (0.13, 1.23)
After Expansion 3.61 (1.92, 6.78) * 2.46 (1.10, 5.51)
Asian 0.3270 0.3886
Before Expansion 1.33 (0.30, 5.90) 1.95 (0.19, 19.86)
After Expansion 2.95 (1.29, 6.73) 4.06 (0.77, 21.43)
Evidence-Based Specific Beta Blockers
White 0.5148 0.6529
Before Expansion 1.11 (0.89, 1.38) 0.90 (0.68, 1.19)
After Expansion 1.02 (0.80, 1.30) 0.85 (0.63, 1.16)
African American 0.7037 0.9608
Before Expansion 1.14 (0.82, 1.59) 0.79 (0.45, 1.38)
After Expansion 1.07 (0.77, 1.49) 0.78 (0.47, 1.30)
Hispanic 0.2757 0.1388
Before Expansion 1.20 (0.48, 2.99) 0.44 (0.16, 1.22)
After Expansion 2.61 (1.10, 6.19) 1.09 (0.52, 2.29)
Asian 0.6676 0.7704
Before Expansion 1.56 (0.71, 3.42) 1.07 (0.37, 3.08)
After Expansion 1.23 (0.58, 2.62) 0.91 (0.33, 2.47)
Post Discharge Appointment for Heart Failure Patients
White 0.2833 0.1468
Before Expansion 0.93 (0.61, 1.40) 0.86 (0.58, 1.28)
After Expansion 1.16 (0.79, 1.69) 1.16 (0.79, 1.71)
African American 0.9694 0.4709
Before Expansion 1.45 (0.89, 2.36) 0.68 (0.39, 1.19)
After Expansion 1.44 (0.91, 2.28) 0.78 (0.44, 1.39)
Hispanic 0.1642 0.1154
Before Expansion 0.90 (0.30, 2.63) 0.48 (0.16, 1.46)
After Expansion 2.27 (1.06, 4.86) 1.33 (0.69, 2.56)
Asian 0.0735 0.0307
Before Expansion 0.99 (0.46, 2.12) 0.64 (0.20, 2.06)
After Expansion 1.85 (0.94, 3.65) 1.44 (0.50, 4.15)
Mineralocorticoid receptor antagonist
White 0.9994 0.9273
Before Expansion 1.05 (0.81, 1.37) 0.74 (0.50, 1.10)
After Expansion 1.05 (0.73, 1.51) 0.75 (0.46, 1.23)
African American 0.7397 0.6406
Before Expansion 0.80 (0.50, 1.28) 0.33 (0.17, 0.62) *
After Expansion 0.87 (0.59, 1.29) 0.37 (0.21, 0.64) *
Hispanic 0.0830 0.1236
Before Expansion 1.54 (0.96, 2.49) 0.50 (0.19, 1.30)
After Expansion 2.53 (1.55, 4.15) * 0.76 (0.29, 2.04)
Asian 0.7685 0.5982
Before Expansion 0.99 (0.44, 2.26) 0.48 (0.14, 1.68)
After Expansion 1.14 (0.61, 2.13) 0.62 (0.24, 1.62)
Hydralazine/Nitrate
African American 0.5564 0.9352
Before Expansion 1.13 (0.75, 1.72) 1.18 (0.69, 2.00)
After Expansion 0.97 (0.64, 1.46) 1.20 (0.83, 1.75)
60 Minutes of Heart Failure Education
White 0.4304 0.5057
Before Expansion 0.85 (0.48, 1.50) 0.95 (0.42, 2.19)
After Expansion 1.05 (0.57, 1.91) 1.15 (0.49, 2.72)
African American 0.2662 0.2053
Before Expansion 0.52 (0.25, 1.09) 0.43 (0.12, 1.61)
After Expansion 0.71 (0.33, 1.52) 0.63 (0.16, 2.40)
Hispanic 0.0412 0.0934
Before Expansion 3.47 (1.26, 9.55) 2.59 (0.61, 10.95)
After Expansion 1.22 (0.55, 2.70) 1.09 (0.31, 3.90)
Asian 0.3725 0.4376
Before Expansion 0.37 (0.13, 1.05) 0.75 (0.15, 3.65)
After Expansion 0.57 (0.22, 1.46) 1.09 (0.24, 4.87)
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P-int indicates interaction between adopter state status and time before or after ACA Medicaid Expansion;

*

, p<0.008.

Models were adjusted for patient-level variables (age, gender, medical history, vital signs at admission, labs at admission), and hospital variables (region, number of beds, teaching status, rural location).

Figure 2. Percentage of Eligible Racial and Ethnic Groups Receiving ACEI/ARB/ARNI by State Group and Time.

Figure 2.

Figure 2.

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2a represents Hispanic patients, and 2b represents all racial and ethnic groups. Lines represent piecewise linear regression models of eligible percentage of patients receiving ACEI/ARB/ARNI over time. The vertical gray line indicates timing of ACA Medicaid Expansion; Solid lines, early adopter state group; dashed line, non-adopter state group; shaded color, 95% CI band; red, White patients; green, African American patients; aqua, Hispanic patients; purple, Asian patients.

Interactions of Patient Race/Ethnicity with Receipt of GDMT by State Group and Timing

Among GDMT that were assessed across all racial and ethnic groups, receipt of ACEI/ARB/ARNI was associated with significant differences by patient race/ethnicity, state group, and timing of admission hospitalization (before versus after ACA Medicaid Expansion) with p< 0.0074 in fully adjusted models (Supplemental Table 4). None of the other GDMT differed across combined interactions of race/ethnicity, state group, and timing.

DISCUSSION

Among hospitals voluntarily participating in quality initiatives through GWTG-HF, the ACA Medicaid Expansion was associated with receipt of one form of GDMT at HF hospitalization discharge within only Hispanic patients. There was an immediate decrease in prescribing of ACEI/ARB/ARNI in both early adopter and non-adopter state groups, but the decline was greatest among non-adopter states and subsequent increases in prescribing were lowest among non-adopter states. Thus, timing of the ACA Medicaid Expansion was associated with Hispanic patients from early adopter states having 2.46-fold greater odds of receiving ACEI/ARB/ARNI than Hispanic patients from non-adopter states. Interactions across race/ethnicity, state group, and timing revealed that only changes in ACEI/ARB/ARNI were statistically significant.

Under 90% of eligible patients received GDMT for HF with the exception of ACEI/ARB/ARNI and beta blockers. Independent of timing of the ACA Medicaid Expansion, Hispanic patients from early adopter states were more likely to receive all forms of GDMT than Hispanic patients from non-adopter states, and receipt of GDMT varied by state group for other races and ethnicities. This suggests that the ACA Medicaid Expansion had an inconsistent association with delivery of HF GDMT to patients of different racial and ethnic backgrounds within GWTG-HF hospitals, and additional changes are urgently needed.

In multiple studies, the ACA Medicaid Expansion has been associated with significant improvements in the delivery of healthcare, particularly among racial and ethnic patients of color. African American and Hispanic patients have had the greatest reductions in uninsurance7,21,22 due to their high proportion of uninsured. In cardiovascular care delivery, African American patients residing in early adopter states have had significant increases in receipt of heart transplants10 and ventricular assist devices,23 but only the former has been significantly associated with the ACA Medicaid Expansion. However, the association between the ACA Medicaid expansion and cardiovascular outcomes expands beyond race and ethnicity. The overall U.S. population of patients residing in early adopter states have experienced greater reductions in uninsured cardiovascular hospitalizations,24 and higher cardiovascular survival than patients residing in non-adopter states.25 Thus, this study hypothesized that GDMT for HF, of which insurance generally increases affordability, would be given at higher rates to patients residing in early adopter states than non-adopter states at the timing of ACA Medicaid Expansion.

The results of this study differ from the many studies that have observed robust improvements in cardiovascular care delivery in association with the ACA Medicaid Expansion. Multiple reasons likely contribute to this study’s findings. First, this study was performed in GWTG-HF registry. GWTG-HF was designed to improve healthcare delivery through feedback, evidence based support, and commendation for improving metrics.14 Hospitals that participate in GWTG-HF registry have been successful in reducing variability in GDMT prescribing patterns, and reducing racial and ethnic disparities in cardiovascular outcomes as illustrated among overall prescription rates in this study.26,27 The margin for improvement may be lower in GWTG-HF hospitals than other hospitals since these hospitals are already using multiple modalities to increase access to GDMT. This supports findings from another GWTG-HF registry study. Among low income adults under age 65 years, a significant increase in follow-up visits and receipt of beta blockers was observed in early adopter patients compared to non-adopters in the first few years following the ACA Medicaid Expansion;12 however, no other significant changes were observed in delivery of GDMT. In addition, outcomes from the ACA may be secondary to the instability of ACA infrastructure in 2017, resulting in patients not seeking appropriate medical care since it was not clear whether the ACA benefits would endure.28

Second, additional patient factors may contribute to low receipt of HF GDMT. Patients may be unable to afford GDMT even if they are insured. In 2014, the ACA expanded access to Medicaid, prohibited insurance companies from limiting coverage due to pre-existing conditions, eliminated annual limits on healthcare coverage, provided tax benefits for having insurance (including private insurance), and made other forms of public insurance and employer-sponsored insurance more accessible through Health Insurance Marketplace.29 The ACA did not mandate equitable access to prescriptions, specialist care, and shared decision-making process. Most HF GDMT are available at low cost as generic medications, but the cost of the sum of medications needed for appropriate GDMT may be too high for some patients.30 In addition, Medicaid prescription coverage and pricing varies by state,31 which is particularly concerning for under-prescribed newer classes (i.e. ARNI) and older classes (i.e. hydralazine/isosorbide) which lack an affordable generic combination pill.32 This may be particularly complicating for prescribing of ARNI, which was FDA approved in 2015 for HFrEF and has had slow uptake.33,34 Barriers are compounded by specialists that do not universally accept Medicaid payors for outpatient clinical care,35 which impact discharge follow-up and has been related to delays in care.36 It is also unclear what role patient refusal, preferences, or misunderstanding of the importance of GDMT contributed to low receipt of GDMT upon discharge from a HF hospitalization.37 The shared decision-making process is not collected in GWTG-HF, with the exception of implantable cardioverter defibrillators in eligible patients.

Third, other hospital system factors may contribute to low patient receipt of GDMT. Independent of the timing of the ACA Medicaid Expansion, there were clear differences in prescribing patterns for GDMT by early adopter and non-adopter state group, particularly for Hispanic patients. GWTG-HF hospitals may have encountered system barriers that are present among other national hospital cohorts. Some of these racially disparate barriers have included inadequate receipt of care by cardiologist,4 inadequate resources to systematically provide care (i.e. lack of funding to support specialty nurses, ideal nurse-to-census ratio),9,38 and under-representation of health care professionals with shared backgrounds as patients.39,40 Systemic racial and ethnic bias may have also influenced delivery of GDMT as demonstrated in other studies.41–43 Patient choice is a also a factor. Hospitals residing in non-adopter states are projected to lose $167.8 billion in hospital reimbursement from 2013-2022.44 This may in part explain the drop in ACEI/ARB/ARNI prescriptions among Hispanic patients in non-adopter states given reduction in resources to ensure HF quality and performance measures are met upon discharge.20 It is unclear why this was only observed among one form of HF GDMT in non-adopter state groups and to a lesser extent in early adopter state groups.

This study highlights the urgency to disseminate successful methods for increasing HF GDMT delivery across all racial and ethnic groups. There is strong evidence that GWTG-HF participation increases delivery of HF GDMT45 and results in lower racial and ethnic disparities as observed here, but additional steps are needed. Current U.S. healthcare policies are insufficient. Changes in healthcare policy are warranted. Racial and ethnic disparities may be reduced by broadening ACA coverage: to eliminate gaps in prescription coverage,31 increase acceptance of Medicaid beneficiaries by providing equitable payments for service across beneficiaries,35 and reemphasis on quality of care and shared decision-making.37 There is a history of under prescribing ACEI/ARB and other HF therapies to Hispanic and African American patients across the U.S.2,3,46,47 A culture shift will be needed to remove hospital system barriers rooted in structural racism,9,48,49 increase diversity of healthcare professionals,39,40 and eliminate bias in medicine.41–43 Rapid assessment of methodologies for implementation can be performed with Consolidated Framework for Implementation Research, which includes evaluation of: (1) GDMT’s inherent characteristics that may impact implementation, (2) outer setting of patient barriers and hospital policies, (3) inner setting of hospital culture and engagement, (4) individuals beliefs that impact GDMT delivery, and (5) process of implementation.50

Limitations

The results cannot be generalized to all U.S. hospitals since this study was performed within hospitals known for providing high quality care through their involvement with the GWTG-HF registry.26 The association of the ACA Medicaid Expansion with delivery of GDMT may be underestimated for other hospitals. However, this study provides representation throughout the U.S. and has been observed to have similar patient and hospital characteristics to patients hospitalized with HF across the U.S.51 In addition, this study demonstrates key areas for improvement in delivering GDMT that impact the U.S. hospital care. Additional unmeasured patient and system factors may contribute to results as described earlier in the discussion. This study did not report data on post-discharge medication use, health status, or other clinical outcomes, and further studies are needed to assess the association of ACA Medicaid Expansion on these parameters. Finally, the ACA Medicaid Expansion policy has been in flux with multiple waiver changes of several adopter states throughout the study period, which were not excluded from the study. This may also underestimate the association of the ACA Medicaid Expansion with receipt of GDMT.

CONCLUSIONS

Among hospitals participating in the GWTG-HF registry, the ACA Medicaid Expansion was associated with increased receipt of only ACEI/ARB/ARNI among Hispanic patients from early adopter states compared to Hispanic patients from non-adopter states; however, this occurred with initial decline in prescribing of ACEI/ARB/ARNI among both state groups, particularly non-adopter states. The ACA Medicaid Expansion was not associated with receipt of HF GDMT for other racial or ethnic groups. Independent of the ACA Medicaid Expansion, Hispanic patients from early adopter states were significantly more likely to receive all forms of GDMT than Hispanic patients from non-adopter states. Overall, the ACA Medicaid Expansion had an inconsistent association with HF GDMT delivery within GWTG-HF hospitals. Investigation of additional implementation approaches are indicated for racial and ethnic equity in receipt of HF GDMT.

Supplementary Material

1

Sources of Funding and Conflicts of Interest:

Dr. Breathett has research funding from American Heart Association (AHA) Get With the Guidelines Young Investigator Database Seed Grant Award; National Heart, Lung, and Blood Institute (NHLBI) K01HL142848, R56HL159216, R25HL126146 subaward 11692sc, and L30HL148881; University of Arizona Health Sciences, Strategic Priorities Faculty Initiative Grant; University of Arizona, Sarver Heart Center, Novel Research Project Award in the Area of Cardiovascular Disease and Medicine, Anthony and Mary Zoia Research Award; and Women As One. Dr. Sweitzer has research grants from Novartis and Merck. Dr. Fonarow has consulting funding from Abbott, Amgen, Astra Zeneca, Bayer, CHF Solutions, Merck, Medtronic, Novartis. Dr. DeVore has research funding from AHA, Amgen, Bayer, Intra-Cellular Therapies, Luitpold Pharmaceuticals, NHLBI, Novartis and PCORI; consulting funding from Amgen, AstraZeneca, Bayer, InnaMed, LivaNova, Mardil Medical, Novartis, Procyrion, scPharmaceuticals, and Zoll. Dr. Bhatt discloses the following relationships - Advisory Board: Cardax, Cereno Scientific, Elsevier Practice Update Cardiology, Medscape Cardiology, PhaseBio, PLx Pharma, Regado Biosciences; Board of Directors: Boston VA Research Institute, Society of Cardiovascular Patient Care, TobeSoft; Chair: American Heart Association Quality Oversight Committee; Data Monitoring Committees: Baim Institute for Clinical Research (formerly Harvard Clinical Research Institute, for the PORTICO trial, funded by St. Jude Medical, now Abbott), Cleveland Clinic (including for the ExCEED trial, funded by Edwards), Duke Clinical Research Institute, Mayo Clinic, Mount Sinai School of Medicine (for the ENVISAGE trial, funded by Daiichi Sankyo), Population Health Research Institute; Honoraria: American College of Cardiology (Senior Associate Editor, Clinical Trials and News, ACC.org; Vice-Chair, ACC Accreditation Committee), Baim Institute for Clinical Research (formerly Harvard Clinical Research Institute; RE-DUAL PCI clinical trial steering committee funded by Boehringer Ingelheim; AEGIS-II executive committee funded by CSL Behring), Belvoir Publications (Editor in Chief, Harvard Heart Letter), Duke Clinical Research Institute (clinical trial steering committees, including for the PRONOUNCE trial, funded by Ferring Pharmaceuticals), HMP Global (Editor in Chief, Journal of Invasive Cardiology), Journal of the American College of Cardiology (Guest Editor; Associate Editor), Medtelligence/ReachMD (CME steering committees), MJH Life Sciences, Population Health Research Institute (for the COMPASS operations committee, publications committee, steering committee, and USA national co-leader, funded by Bayer), Slack Publications (Chief Medical Editor, Cardiology Today’s Intervention), Society of Cardiovascular Patient Care (Secretary/Treasurer), WebMD (CME steering committees); Other: Clinical Cardiology (Deputy Editor), NCDR-ACTION Registry Steering Committee (Chair), VA CART Research and Publications Committee (Chair); Research Funding: Abbott, Afimmune, Amarin, Amgen, AstraZeneca, Bayer, Boehringer Ingelheim, Bristol-Myers Squibb, Cardax, Chiesi, CSL Behring, Eisai, Ethicon, Ferring Pharmaceuticals, Forest Laboratories, Fractyl, Idorsia, Ironwood, Ischemix, Lexicon, Lilly, Medtronic, Pfizer, PhaseBio, PLx Pharma, Regeneron, Roche, Sanofi Aventis, Synaptic, The Medicines Company; Royalties: Elsevier (Editor, Cardiovascular Intervention: A Companion to Braunwald’s Heart Disease); Site Co-Investigator: Biotronik, Boston Scientific, CSI, St. Jude Medical (now Abbott), Svelte; Trustee: American College of Cardiology; Unfunded Research: FlowCo, Merck, Novo Nordisk, Takeda. Dr. Peterson has funding from NHLBI. The Get With The Guidelines–Heart Failure (GWTG-HF) program is provided by the American Heart Association. GWTG-HF is sponsored, in part, by Novartis, Boehringer Ingelheim, Lilly, Novo Nordisk, Sanofi, AstraZeneca and Bayer.

Role of Funders:

The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Contributor Information

Khadijah K. Breathett, Division of Cardiovascular Medicine, Sarver Heart Center, University of Arizona, Tucson, AZ.

Haolin Xu, Department of Biostatistics and Bioinformatics, Duke University, Durham, NC.

Nancy K. Sweitzer, Division of Cardiovascular Medicine, Sarver Heart Center, University of Arizona, Tucson, AZ.

Elizabeth Calhoun, Center for Population Science and Discovery, University of Arizona, Tucson, AZ.

Roland A. Matsouaka, Department of Biostatistics and Bioinformatics, Duke University, Durham, NC.

Clyde W. Yancy, Division of Cardiology, Northwestern University Feinberg School of Medicine, Chicago, IL.

Gregg C. Fonarow, Division of Cardiology, University of California Los Angeles, CA.

Adam D. DeVore, Division of Cardiology, Duke University, Durham, NC.

Deepak L. Bhatt, Division of Cardiovascular Medicine, Brigham and Women’s Hospital Heart & Vascular Center, Harvard Medical School, Boston, MA.

Pamela N. Peterson, Division of Cardiology, University of Colorado, Anschutz Medical Campus, Aurora, CO and Division of Cardiology, Denver Health Medical Center, Denver, CO.

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