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Table 1.  Baseline Characteristics of Patients Hospitalized for Heart Failure at GWTG-HF Sites by Dual-Eligibility Quintilesa
Baseline Characteristics of Patients Hospitalized for Heart Failure at GWTG-HF Sites by Dual-Eligibility Quintilesa
Table 2.  Adherence to Quality Metrics, In-Hospital Outcomes, and 30-Day Outcomes by Dual-Eligibility Quintilesa
Adherence to Quality Metrics, In-Hospital Outcomes, and 30-Day Outcomes by Dual-Eligibility Quintilesa
Table 3.  Associations of Guideline-Based Care and In-Hospital or 30-Day Outcomes With Dual-Eligible Quintiles
Associations of Guideline-Based Care and In-Hospital or 30-Day Outcomes With Dual-Eligible Quintiles
1.
Centers of Medicare & Medicaid Services. Data analysis brief: Medicare-Medicaid dual enrollment 2006 through 2018. Published September 2019. Accessed May 10, 2020. https://www.cms.gov/Medicare-Medicaid-Coordination/Medicare-and-Medicaid-Coordination/Medicare-Medicaid-Coordination-Office/DataStatisticalResources/Downloads/MedicareMedicaidDualEnrollmentEverEnrolledTrendsDataBrief2006-2018.pdf
2.
Congressional Budget Office. Dual-eligible beneficiaries of Medicare and Medicaid: characteristics, health care spending, and evolving policies. Published June 6, 2013. Accessed May 7, 2020. https://www.cbo.gov/publication/44308
3.
Jacobson  G, Newman  T, Damico  A. Medicare’s role for dual eligible beneficiaries. 2012. Kaiser Family Foundation. Published April 4, 2012. Accessed May 7, 2020. https://www.kff.org/medicare/issue-brief/medicares-role-for-dual-eligible-beneficiaries/
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Lloren  A, Liu  S, Herrin  J,  et al. Measuring hospital-specific disparities by dual eligibility and race to reduce health inequities. Health Serv Res. 2019;54(suppl 1)(24):243-254. doi:10.1111/1475-6773.13108
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Centers for Medicare & Medicaid. Hospital Readmissions Reduction Program. Accessed May 7, 2020. https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/AcuteInpatientPPS/Readmissions-Reduction-Program
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Gu  Q, Koenig  L, Faerberg  J, Steinberg  CR, Vaz  C, Wheatley  MP.  The Medicare Hospital Readmissions Reduction Program: potential unintended consequences for hospitals serving vulnerable populations.   Health Serv Res. 2014;49(3):818-837. doi:10.1111/1475-6773.12150 PubMedGoogle ScholarCrossref
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Joynt Maddox  KE, Reidhead  M, Qi  AC, Nerenz  DR.  Association of stratification by dual enrollment status with financial penalties in the hospital readmissions reduction program.   JAMA Intern Med. 2019;179(6):769-776. doi:10.1001/jamainternmed.2019.0117 PubMedGoogle ScholarCrossref
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Joynt  KE, Jha  AK.  Characteristics of hospitals receiving penalties under the Hospital Readmissions Reduction Program.   JAMA. 2013;309(4):342-343. doi:10.1001/jama.2012.94856 PubMedGoogle ScholarCrossref
9.
Wadhera  RK, Joynt Maddox  KE, Wasfy  JH, Haneuse  S, Shen  C, Yeh  RW.  Association of the Hospital Readmissions Reduction Program with mortality among Medicare beneficiaries hospitalized for heart failure, acute myocardial infarction, and pneumonia.   JAMA. 2018;320(24):2542-2552. doi:10.1001/jama.2018.19232 PubMedGoogle ScholarCrossref
10.
Fonarow  GC, Ziaeian  B.  Hospital Readmission Reduction Program for heart failure: the spread of intended and unintended consequences.   J Am Coll Cardiol. 2019;73(9):1013-1015. doi:10.1016/j.jacc.2018.12.043 PubMedGoogle ScholarCrossref
11.
Struthers  AD, Anderson  G, Donnan  PT, MacDonald  T. Social deprivation increases cardiac hospitalisations in chronic heart failure independent of disease severity and diuretic non-adherence. Heart. 2000;83(1):12-16. doi:10.1136/heart.83.1.12
12.
American Heart Association. Get With The Guidelines–Heart Failure overview: heart failure facts. Accessed May 7, 2020. https://www.heart.org/en/professional/quality-improvement/get-with-the-guidelines/get-with-the-guidelines-heart-failure/get-with-the-guidelines-heart-failure-overview
13.
Duke Clinical Research Institute. Disease registries. Accessed May 7, 2020. https://dcri.org/our-work/health-services-research/disease-registries/
14.
Segal  M, Rollins  E, Hodges  K, Roozeboom  M.  Medicare-Medicaid eligible beneficiaries and potentially avoidable hospitalizations.   Medicare Medicaid Res Rev. 2014;4(1):E1-E13. doi:10.5600/mmrr.004.01.b01 PubMedGoogle ScholarCrossref
15.
Doll  JA, Hellkamp  AS, Goyal  A, Sutton  NR, Peterson  ED, Wang  TY.  Treatment, outcomes, and adherence to medication regimens among dual Medicare-Medicaid–eligible adults with myocardial infarction.   JAMA Cardiol. 2016;1(7):787-794. doi:10.1001/jamacardio.2016.2724 PubMedGoogle ScholarCrossref
16.
Downing  NS, Wang  C, Gupta  A,  et al.  Association of racial and socioeconomic disparities with outcomes among patients hospitalized with acute myocardial infarction, heart failure, and pneumonia: an analysis of within- and between-hospital variation.   JAMA Netw Open. 2018;1(5):e182044-e182044. doi:10.1001/jamanetworkopen.2018.2044 PubMedGoogle ScholarCrossref
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Eapen  ZJ, McCoy  LA, Fonarow  GC,  et al.  Utility of socioeconomic status in predicting 30-day outcomes after heart failure hospitalization.   Circ Heart Fail. 2015;8(3):473-480. doi:10.1161/CIRCHEARTFAILURE.114.001879 PubMedGoogle ScholarCrossref
Original Investigation
April 7, 2021

Association of Dual Eligibility for Medicare and Medicaid With Heart Failure Quality and Outcomes Among Get With The Guidelines–Heart Failure Hospitals

Author Affiliations
  • 1Division of Cardiology, David Geffen School of Medicine, University of California, Los Angeles
  • 2Division of Cardiology, Veteran Affairs Greater Los Angeles Healthcare System, Los Angeles, California
  • 3Fielding School of Public Health, University of California, Los Angeles
  • 4Division of Cardiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
  • 5Duke Clinical Research Institute, Duke University School of Medicine, Durham, North Carolina
  • 6Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina
  • 7Department of Medicine, Stanford School of Medicine, Palo Alto, California
  • 8Division of Cardiology, University of Colorado School of Medicine, Aurora
  • 9Deputy Editor, JAMA Cardiology
  • 10Ahmanson Cardiomyopathy Center, David Geffen School of Medicine, University of California, Los Angeles
  • 11Associate Editor for Health Care Quality and Guidelines, JAMA Cardiology
JAMA Cardiol. 2021;6(7):791-800. doi:10.1001/jamacardio.2021.0611
Key Points

Question  What is the association between the proportion of dual-eligible Medicare and Medicaid beneficiaries at the hospital level with quality of care and outcomes for heart failure (HF)?

Findings  In this cohort study, patients treated at hospitals with the highest proportion of dual-eligible beneficiaries were less likely to receive important HF process measures, including evidence-based β-blocker prescription on discharge, measurement of left ventricular function, and anticoagulation for atrial fibrillation or atrial flutter, and had significantly higher 30-day all-cause or HF readmissions.

Meaning  Targeting disparities in HF in hospitals that have a higher share of dual-eligible beneficiaries could help bridge the gap in HF quality of care and outcomes demonstrated across socioeconomic statuses.

Abstract

Importance  The Centers for Medicare & Medicaid Services uses a new peer group–based payment system to compare hospital performance as part of its Hospital Readmissions Reduction Program, which classifies hospitals into quintiles based on their share of dual-eligible beneficiaries for Medicare and Medicaid. However, little is known about the association of a hospital’s share of dual-eligible beneficiaries with the quality of care and outcomes for patients with heart failure (HF).

Objective  To evaluate the association between a hospital’s proportion of patients with dual eligibility for Medicare and Medicaid and HF quality of care and outcomes.

Design, Setting, and Participants  This retrospective cohort study evaluated 436 196 patients hospitalized for HF using the Get With The Guidelines–Heart Failure registry from January 1, 2010, to December 31, 2017. The analysis included patients 65 years or older with available data on dual-eligibility status. Hospitals were divided into quintiles based on their share of dual-eligible patients. Quality and outcomes were analyzed using unadjusted and adjusted multivariable logistic regression models. Data analysis was performed from April 1, 2020, to January 1, 2021.

Main Outcomes and Measures  The primary outcome was 30-day all-cause readmission. The secondary outcomes included in-hospital mortality, 30-day HF readmissions, 30-day all-cause mortality, and HF process of care measures.

Results  A total of 436 196 hospitalized HF patients 65 years or older from 535 hospital sites were identified, with 258 995 hospitalized patients (median age, 81 years; interquartile range, 74-87 years) at 455 sites meeting the study criteria and included in the primary analysis. A total of 258 995 HF hospitalizations from 455 sites were included in the primary analysis of the study. Hospitals in the highest dual-eligibility quintile (quintile 5) tended to care for patients who were younger, were more likely to be female, belonged to racial minority groups, or were located in rural areas compared with quintile 1 sites. After multivariable adjustment, hospitals with the highest quintile of dual eligibility were associated with lower rates of key process measures, including evidence-based β-blocker prescription, measure of left ventricular function, and anticoagulation for atrial fibrillation or atrial flutter. Differences in clinical outcomes were seen with higher 30-day all-cause (adjusted odds ratio, 1.24; 95% CI, 1.14-1.35) and HF (adjusted odds ratio, 1.14; 95% CI, 1.03-1.27) readmissions in higher dual-eligible quintile 5 sites compared with quintile 1 sites. Risk-adjusted in-hospital and 30-day mortality did not significantly differ in quintile 1 vs quintile 5 hospitals.

Conclusions and Relevance  In this cohort study, hospitals with a higher share of dual-eligible patients provided care with lower rates of some of the key HF quality of care process measures and with higher 30-day all-cause or HF readmissions compared with lower dual-eligibility quintile hospitals.

Introduction

More than 12 million dual-eligible beneficiaries are currently enrolled in Medicare and Medicaid.1 These individuals qualify for Medicare based on age (≥65 years) or disability.1 They qualify for Medicaid based on lower income, with 86% having income below the federal poverty level. Dual-eligible individuals are more likely than their single-eligible counterparts to have multiple chronic conditions, cognitive and functional limitations, and institutional residence, including nursing homes.2,3 Dual-eligible patients also have high health care use and potentially avoidable hospitalizations.2,3 A 2019 study4 found that, among dual-eligible beneficiaries, heart failure (HF) contributed to the highest share of preventable hospitalizations at 21.1%.

The Hospital Readmissions Reduction Program (HRRP) is one of several value-based programs started by the US Centers for Medicare & Medicaid Services (CMS) after the passage of the Patient Protection and Affordable Care Act in 2010.5 The HRRP was implemented to reduce preventable 30-day hospital readmission rates and improve postdischarge quality of care primarily through financial penalties on hospitals with higher than expected 30-day readmission rates.5 However, studies6-10 have identified unintended effects of the HRRP, particularly disproportionate penalization of institutions that serve socioeconomically disadvantaged populations, such as dual-eligible beneficiaries, which adds financial burden to already underresourced institutions. A prior study11 found socioeconomic and demographic disparities, with low socioeconomic status associated with worse HF quality of care and outcomes, such as high hospital readmission rates. Therefore, starting in 2019, the CMS instituted a new peer group–based payment system to classify hospitals that uses the proportion of patients qualifying for dual eligibility as a proxy for socioeconomic profiles of hospitals; the goal of this change was to allow comparisons with hospitals that serve patient populations within similar socioeconomic profiles.5 This change has significantly reduced the penalty rates for hospitals with a higher share of dual-eligible beneficiaries,7 but how HF quality of care and outcomes differ by a hospital’s share of dual-eligible beneficiaries is not well understood. Significant differences may exist in the quality of hospital care between tiers of hospitals defined by the proportion of dual-eligible individuals served, and poor outcomes will persist without adequate appreciation from the HRRP.

In this study, we evaluate the differences in HF care and outcomes and hospitals’ share of dual-eligible beneficiaries, using the Get With The Guidelines–Heart Failure (GWTG-HF) registry. The study’s primary objective was to evaluate associations between dual-eligibility quintiles with HF quality of care and outcomes. We hypothesize that there may be significant differences in HF quality of care among hospitals, with patients treated in hospitals in the highest dual-eligible tiers, which tend to be underresourced and predominantly take care of socioeconomically disadvantaged patients, having poorer HF quality of care and outcomes.

Methods
Study Design and Data Collection

This retrospective cohort analysis evaluated patients hospitalized for HF using the GWTG-HF registry.12 The GTWG-HF registry is a quality improvement initiative implemented by the American Heart Association with an ongoing prospective database on hospitalized patients for HF through voluntary participation of hospitals.12 Because data were used primarily at the local site for quality improvement, sites were granted a waiver of informed patient consent under the Common Rule. The institutional review boards at each participating site have approved GWTG-HF participation. Analyses of deidentified data from GWTG-HF for research purposes were approved by the Duke Clinical Research Institute Institutional Review Board.13 This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.

Study Population

We identified 436 196 hospitalized HF patients 65 years or older from 535 hospital sites from January 1, 2010, to December 31, 2017, with available CMS claims data and information on dual-eligibility status. A total of 258 995 hospitalizations at 455 sites met the study criteria and were included in the primary analysis. We include race/ethnicity data, which is an important factor associated with HF care and outcomes, as self-reported by patients and recorded by hospital staff at GWTG-HF participating hospitals as part of the registry. For continuous variables, we used multiple imputation for fully conditional specification methods with 10 imputed data sets. Missing medical history was imputed to no. Laboratory variables had the highest percentage of missing data. Additional detail on variable missingness is detailed in eTable 1 in the Supplement. Hospitals were stratified into quintiles based on the proportion of fee-for-service Medicare and Medicaid Advantage hospital stays for which the patient is dually eligible according to the new HRRP peer group–based assessment of hospital performance. We used the 258 995 patients in the GWTG-HF registry who were linked to CMS claims to calculate the site dual-eligibility rates. An exploratory analysis with the excluded hospitalizations was used as a sensitivity cohort to assess for any significant differences with the primary analysis cohort. Additional sensitivity analyses were performed with missing medical history treated as an integer variable. Data analysis was performed from April 1, 2020, to January 1, 2021.

Outcomes

The primary quality measures of the study include individual and composite key HF process measures.12 The individual HF process measures include the following: (1) angiotensin-converting enzyme inhibitor, angiotensin receptor blocker, or angiotensin receptor neprilysin inhibitor prescription rates at discharge for patients with reduced left ventricular ejection fraction (LVEF); (2) evidence-based β-blocker (bisoprolol, carvedilol, or metoprolol succinate) prescription rates; (3) documented evaluation of the LVEF assessment; (4) documented postdischarge clinic or home health follow-up appointment for HF; and (5) 100% adherence with all 4 process measures for eligible patients (HF defect-free care). We also looked at additional HF process measures as secondary end points: aldosterone antagonist prescription rates at discharge with eligible patients with LVEFs less than 35%, hydralazine and nitrate prescription rates at discharge for eligible Black patients with HF, anticoagulation for atrial fibrillation or atrial flutter at discharge, deep vein thrombosis prophylaxis during hospitalization, influenza vaccination before discharge during influenza season, pneumococcal vaccination before discharge, complete discharge instructions, smoking cessation counseling during hospital stay, percentage of HF patients taking any β-blocker at discharge, and implantable cardioverter defibrillator (ICD) placement or prescription at discharge for eligible patients with LVEFs of 35% or less.

The clinical outcome measures include in-hospital mortality, in-hospital length of stay, 30-day all-cause mortality, 30-day all-cause readmissions, and 30-day HF readmissions.

Statistical Analysis

We described baseline characteristics of the study population and assessed adherence rates with GWTG-HF measures and postdischarge 30-day outcomes across hospital dual-eligibility quintiles. We report numbers (percentages) for categorical variables and medians (interquartile ranges [IQRs]) for continuous variables. We used χ2 tests and standardized differences to assess differences in baseline characteristics, adherence rates with HF process measures, and postdischarge 30-day outcomes for categorical variables and the Kruskal-Wallis test for continuous variables. We used unadjusted and adjusted multivariable logistic for patient characteristics (age, sex, race/ethnicity, vital signs, serum creatinine level, sodium level, LVEF, and medical history) and hospital characteristics (region, teaching status, rural location, and hospital size). We used logistic regression models to assess associations of hospital dual-eligibility quintiles with HF guideline–directed care and postdischarge 30-day outcomes along with generalized estimating equations to adjust for in-hospital clustering and calculate robust variance estimates. We performed similar statistical analyses using the exploratory study population with all HF admissions without applying the study inclusion criteria (n = 430 015 HF hospitalizations and 455 hospitals) because the sensitivity cohort also stratified by dual-eligibility quintiles. All analyses were executed using SAS software, version 9.4 (SAS Institute Inc). Two-sided P < .05 was considered to be statistically significant based on the primary outcome of 30-day all-cause readmissions.

Results
Hospital and Population Baseline Characteristics

A total of 258 995 hospitalizations with a discharge diagnosis of HF from 455 sites were included in the primary analysis of the study. Quintile 1 has the lowest share of dual-eligible beneficiaries (range, 0% to <13.3%), and quintile 5 has the highest share (range, 31.9%-83.3%). Hospitals in the highest dual-eligibility quintile were more likely to be rural or have teaching status. The median age of the overall population was 81 years (interquartile range, 74-87 years). Compared with quintile 1 sites, quintile 5 sites cared for populations of patients who tended to be younger (median age, 80 vs 82 years); more likely female (56.2% vs 52.8%); and predominantly from racial/ethnic minority groups (47.6% vs 11.3%), with Black patients making up 22.7% and Hispanic patients making up 16.9% of the population other than White in quintile 5 sites compared with 5.3% of Black patients and 2.6% of Hispanic patients in quintile 1 sites. Patients from quintile 5 sites also had higher rates of diabetes, long-term dialysis, and history of smoking but lower rates of atrial fibrillation, valvular heart disease, and hyperlipidemia. Quantitatively documented LVEF assessment was available for more than 95% of patients within each quintile subgroup, with the median ejection fraction ranging from 45% to 50%, which was similar across the quintiles. A detailed comparison of patient and hospital characteristics by dual-eligibility quintiles is provided in Table 1 and eTables 2 to 4 in the Supplement.

HF Process Measures

We observed lower rates of the following HF process measures: LVEF assessment, postdischarge appointment for HF, and HF defect-free care, anticoagulation for atrial fibrillation, and ICD placement or prescription at discharge, with hospitals in higher quintiles having lower rates compared with hospitals in lower quintiles. Table 2 and eTable 5 in the Supplement detail adherence to process of care measures and outcomes using absolute percentage differences with 95% CIs. The largest differences between quintile 1 and quintile 5 hospitals in achievement measures were seen in postdischarge appointment (6.3%; 95% CI, 5.7%-6.9%) and HF defect-free care (6.4%; 95% CI, 5.9%-6.9%). Anticoagulation for atrial fibrillation was also lower in quintile 5 compared with quintile 1 (7.5%; 95% CI, 6.8%-8.2%). For outcomes, comparing quintile 5 with quintile 1 hospitals, the largest difference was seen in 30-day all-cause readmission, with higher readmission rates in quintile 5 hospitals (4.4%; 95% CI, 3.8%-5.0%), whereas the absolute differences for 30-day all-cause mortality and 30-day HF readmission were less than 2%. No difference was found in in-hospital mortality between quintile 5 and quintile 1 hospitals. We also present differences by dual-eligibility quintiles in Table 2.

HF In-Hospital and Postdischarge Outcomes

The overall in-hospital mortality rate was 3.4%, with quintile 3 sites having the highest rate at 3.9%. Higher-quintile sites (quintiles 4 and 5) had lower mortality rates compared with lower-quintile sites, with quintile 5 sites having the lowest 30-day mortality rate at 8.0%. In contrast, higher-quintile sites had higher 30-day all-cause (range, 21.3%-25.6%) and HF (range, 8.2%-10.0%) readmission rates, with quintile 5 sites having the highest rates of readmission compared with the lower-tier quintiles (10.0% vs 9.7% in quintile 4, 8.8% in quintile 3, 8.4% in quintile 2, and 8.2% in quintile 1) (Table 2).

Associations of Dual-Eligibility Quintiles With HF Quality of Care and Outcomes

Table 3 and eTable 6 in the Supplement summarize the results of the unadjusted and adjusted logistic regressions that evaluated associations of guideline-based care and outcomes with dual-eligibility quintiles. Comparing quintile 1 with quintile 5 sites, we found that higher dual-eligibility status was associated with lower rates of evidence-based specific β-blocker prescription (adjusted odds ratio [aOR], 0.70; 95% CI, 0.52-0.94; P = .02), measure of left ventricular function (aOR, 0.39; 95% CI, 0.21-0.72; P = .002), and anticoagulation for atrial fibrillation or atrial flutter (aOR, 0.68; 95% CI, 0.51-0.91; P = .01). Of interest, quintile 3 hospitals were associated with the lowest odds of postdischarge appointment (aOR, 0.53; 95% CI, 0.38-0.74; P < .001) and HF defect-free care (aOR, 0.60; 95% CI, 0.46-0.78; P < .001) compared with higher or lower dual-eligible quintiles. Quintile 3 hospitals had higher odds of in-hospital mortality compared with hospitals in all other quintiles (aOR, 1.26; 95% CI, 1.08-1.46; P = .003). For 30-day readmission rates, in both the unadjusted and adjusted regression models, higher dual-eligibility quintile hospitals had higher 30-day all-cause or HF readmissions. The highest associations were seen between quintile 1 and quintile 5 hospitals, with 24% higher odds of 30-day all-cause readmissions (aOR, 1.24; 95% CI, 1.14-1.35; P < .001) and 14% higher odds of 30-day HF readmissions (aOR, 1.14; 95% CI, 1.03-1.27; P = .01) associated with quintile 5 sites (Table 3). An alternative adjusted model adding an indicator variable for missing medical history showed similar results and is summarized in eTable 7 in the Supplement.

Sensitivity Analysis

An exploratory analysis of all registry patients (n = 430 015 HF hospitalizations and 455 hospitals) with all hospitalizations before applying the inclusion criteria yielded overall similar results as the primary analysis. Registry patients from the highest dual-eligible quintile sites (quintile 5) were younger, were more likely to be female, were more likely to belong to racial/ethnic minority groups, and had comorbidities similar to the primary analysis cohort. Patients at quintile 5 sites were also less likely to receive HF defect-free care compared with patients at quintile 1 sites. Further details of the exploratory analysis are summarized in eTable 2 in the Supplement. Similar findings were obtained when analyzing medical history as missing as in the integer variable (eTable 8 in the Supplement).

Discussion

This cohort study assessed associations of dual-eligibility status with HF quality of care and outcomes using the GWTG-HF registry. The study found that, among GWTG-HF hospitals, a higher share of dual-eligible beneficiaries of Medicare and Medicaid is associated with lower rates of key HF process measures and higher 30-day HF-related or all-cause readmissions.

Hospital Dual-Eligibility Quintile Characteristics and HF Process Measures

This study found that patients at GWTG-HF hospitals with higher proportions of dual-eligible beneficiaries were more likely to be of a race other than White, predominantly Black, female, younger, and rurally located. These findings are consistent with prior studies2,3,11,14,15 that found dual eligibility for Medicare and Medicaid, particularly full eligibility status, is an indicator of low socioeconomic status, higher rates of comorbidities, and high health care spending. This study found lower performance on HF process of care measures, particularly among patients from the highest quintile (quintile 5), who were less likely to have left ventricular function measured, to have a postdischarge HF appointment, to receive aldosterone antagonists, to receive defect-free HF care, to receive anticoagulation for atrial fibrillation, or to have an ICD placed or prescribed at discharge. Despite high health care use (up to 1.8 times higher) by dual-eligible beneficiaries for inpatient and outpatient services, previous studies6,15 have found lower key cardiac diagnostic and procedural rates among this patient population. For example, a 2016 study15 assessing dual eligibility and myocardial infarction care and outcomes using the Acute Coronary Treatment Intervention Outcomes Network Registry–Get With The Guidelines (ACTION Registry–GWTG) found that dual-eligible patients were less likely to receive primary percutaneous intervention for ST-segment elevation myocardial infarction and revascularization for non–ST-segment elevation myocardial infarction. In general, several patient-level and hospital-level determinants, including low socioeconomic status, Black and Hispanic races, female sex, and limited resources, have been variably indicated as independent factors associated with suboptimal quality of care for HF, consistent with the lower rates of some of the HF key measurements also seen in the higher dual-eligibility sites in this study.15-17

Hospital Dual-Eligibility Quintiles and In-Hospital and 30-Day Outcomes

This study found higher odds of 30-day all-cause or HF-related readmissions associated with higher dual-eligibility status in support of the study hypotheses. No significant difference in in-hospital mortality was found except between quintile 3 and quintile 5 hospitals. In contrast, higher quintiles (quintiles 4 and 5) were associated with lower 30-day all-cause mortality only in the unadjusted models. These findings are consistent with prior research3 that dual-eligible beneficiaries generally have 5% higher rates of multiple hospitalizations compared with other Medicare-only beneficiaries. A 2014 study6 of dual-eligibility status at the patient level and dual-eligibility share at the hospital level found that dual eligibility at both the patient and hospital levels is associated with higher readmission rates for HF. This finding is also supported by similar findings that indicated that dual-eligibility status is associated with higher 30-day readmission rates in patients treated for acute myocardial infarction using the ACTION-GWTG registry.15

The socioeconomic and demographic variables that have been identified as factors associated with suboptimal HF quality of care also contribute to the higher readmission rates in dual-eligible beneficiaries. In addition, poor access to primary care services to help coordinate and manage the complex comorbid conditions that dual-eligible patients tend to have has been identified as an important factor that contributes to higher readmission rates.4 Lack of adequate primary care services leads patients with multiple comorbidities to seek care through emergency department services, which leads to higher hospitalization rates.4 Higher in-hospital mortality was not consistently associated with higher dual-eligibility quintiles. However, a notable finding is that quintile 3 hospitals were associated with higher odds of in-hospital mortality compared with both higher and lower dual-eligible quintile hospitals. Quintile 3 hospitals also were associated with the lowest odds of defect-free HF care, which may be contributing to the higher in-hospital mortality in the group. No other significant notable hospital- or patient-level characteristic differences were found in this study to explain this finding. The trend was generally in the opposite direction for 30-day all-cause mortality, with quintile 4 and quintile 5 sites having lower estimates in the unadjusted model compared with quintile 1 sites. A 2018 study16 found that being Black or Hispanic was independently associated with lower odds of 30-day all-cause mortality and higher rates of 30-day all-cause readmissions in patients with HF after adjusting for socioeconomic status. The current study may have found similarities, with the highest dual-eligibility quintile (quintile 5) sites, where Black and Hispanic patients constitute close to 50% of the population, also associated with the lower trend of 30-day all-cause mortality in the unadjusted model.

Implications

The new stratified peer group system of the HRRP has significantly reduced penalty rates in the highest-tier groups, although it has not yet eliminated the penalty gap. However, concern remains regarding the notable quality-of-care differences between the peer groups as demonstrated by this study. In addition, although the comparison of hospitals within similar peer groups makes the comparison fairer because it accounts for socioeconomic factors, such a comparison could lead to different quality-of-care standards for each peer group. A key finding noted in this study is the higher 30-day all-cause readmission rate in quintile 5 hospitals. The differences in quality of care seen across the different peer groups do not appear to be wide enough to explain the difference in 30-day all-cause readmissions. This finding indicates that there are likely additional potential socioeconomic and postdischarge care delivery factors that will be important to identify if the goal of policy programs such as the HRRP is to ultimately reduce readmission rates in low-performing hospitals. Therefore, penalizing hospitals based on outcomes that may have additional factors intrinsic to the patient including social and economic determinants of health could be problematic.

Limitations

This study has limitations. Hospitals volunteer to participate in the GWTG-HF program, which is a quality improvement initiative with a potential for selection bias and limitations in generalizability of the study findings to nonparticipating hospitals. A causal relationship between dual-eligibility status and HF quality of care and outcomes is also not possible to infer because of the observational nature of this study and potential for confounding, even though this may be limited by the hospital-level as opposed to patient-level analyses. The registry data are obtained through medical record review and are susceptible to reporting bias because of documentation or abstraction errors. This study calculated an HF composite measure and defect-free care relative to eligible patients to receive the specific quality measure, and these measurements rely on availability and documentation of eligibility status. The possibility of underdocumentation of eligibility status for each quality measure exists, particularly for those who may have not received the specific quality measure appropriately because of intolerance or other contraindications, and without proper documentation, these failures in documentation would be counted as failure of adherence to the specific quality measure.

Conclusions

This study indicates that hospitals with a high share of dual-eligible beneficiaries have lower rates of some of the key HF process measures, including rates of HF defect-free care and higher rates of 30-day readmissions. These results suggest that high-quintile dual-eligible hospitals are sharing a disproportionate burden of caring for patients with complex conditions and limited resources. Quality improvement initiatives and additional resources targeted toward hospitals that take care of a higher share of dual-eligible beneficiaries will be important to address disparities in HF care and outcomes.

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Article Information

Accepted for Publication: November 21, 2020.

Published Online: April 7, 2021. doi:10.1001/jamacardio.2021.0611

Corresponding Author: Gregg C. Fonarow, MD, Ahmanson Cardiomyopathy Center, David Geffen School of Medicine, University of California, Los Angeles, 10833 LeConte Ave, Room 47-123, Center for Health Sciences, Los Angeles, CA 90095 (gfonarow@mednet.ucla.edu).

Author Contributions: Ms Xu had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Bahiru, Ziaeian, Agarwal, Xu, DeVore, Allen, Yancy, Fonarow.

Acquisition, analysis, or interpretation of data: Ziaeian, Moucheraud, Xu, Matsouaka, Heidenreich, Fonarow.

Drafting of the manuscript: Bahiru, Fonarow.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Xu, Matsouaka, DeVore.

Obtained funding: Bahiru, Fonarow.

Administrative, technical, or material support: Agarwal, Allen, Fonarow.

Supervision: Ziaeian, Matsouaka, Yancy, Fonarow.

Conflict of Interest Disclosures: Dr Agarwal reported having a patent pending for HFrEF polypill. Dr DeVore reported receiving grants from the American Heart Association during the conduct of the study and grants from Amgen; AstraZeneca; Bayer; Intra-Cellular Therapies; American Regent; the National Heart, Lung, and Blood Institute; Novartis; and the Patient-Centered Outcomes Research Insitute; consulting fees from Amgen, AstraZeneca, Bayer, CareDx, InnaMed, LivaNova, Mardil Medical, Novartis, Procyrion, scPharmaceuticals, Story Health, and Zoll Consulting; and nonfinancial support from Abbott outside the submitted work. Dr Allen reported receiving personal fees from ACI Clinical, Novartis, Boston Scientific, Amgen, Cytokinetics, Medscape, UpToDate, and Circulation: Heart Failure; grants from the National Institutes of Health, the Patient-Centered Outcomes Research Insitute, and the American Heart Association during the conduct of the study. Dr Yancy reported that his spouse is employed by Abbott Labs. Dr Fonarow reported receiving personal fees from Abbott, Amgen, AstraZeneca, Bayer, Edwards, Janssen, Medtronic, Merck, and Novartis outside the submitted work. No other disclosures were reported.

Funding/Support: The Get With The Guidelines–Heart Failure (GWTG-HF) program is provided by the American Heart Association. The GWTG-HF program is sponsored in part by Novartis, Boehringer Ingelheim, Lilly, Novo Nordisk, Sanofi, AstraZeneca, and Bayer. This study was conducted through the Duke Clinical Research Institute (DCRI), which provided the data and full statistical support. The data for this study were provided by the DCRI.

Role of the Funder/Sponsor: The funding source 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 the decision to submit the manuscript for publication.

Disclaimer: Dr Yancy is a Deputy Editor and Dr Fonarow is the Associate Editor for Health Care Quality and Guidelines of JAMA Cardiology, but they were not involved in any of the decisions regarding review of the manuscript or its acceptance.

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