Neighborhood Socioeconomic Disadvantage and Hospitalized Heart Failure Outcomes in the American Heart Association Get With The Guidelines-Heart Failure Registry

Heart failure (HF) affects over 6.5 million adults in the United States^1,2 and carries a survival comparable to many cancers with ≈50% mortality at 5 years after diagnosis.³ HF remains the most common reason for hospitalization among patients age ≥65 years,² and patients hospitalized for HF carry exceedingly high risk for 30-day mortality and rehospitalization.⁴ Despite the availability of multiple approved oral medications proven to reduce risk of mortality and HF hospitalization,^5–9 HF contributes to high burden of cardiovascular disease, reduced patient-reported quality of life, and increased health care expenditure.^1,2

Substantial research has examined how socioeconomic environments may play an important role in premature cardiovascular mortality¹⁰ and risk for HF hospitalization,¹¹ yet few studies have examined relationships between socioeconomic characteristics and HF outcomes. In-hospital HF mortality has been shown to vary by race and ethnicity¹² and has been associated with lower area-level median household income.¹³ However, associations between the broader socioeconomic environment and HF hospitalization characteristics and clinical outcomes have not been analyzed. A deeper understanding of neighborhood socioeconomic status (SES) disadvantage and HF admission outcomes may better direct initiatives towards reducing inequities in HF care at the national level.

Accordingly, the purpose of this study was to examine the association between neighborhood socioeconomic disadvantage with HF hospitalization outcomes, including length of stay (LOS), HF quality metrics, and all-cause mortality, across a large and representative national cohort in the American Heart Association Get With The Guidelines-Heart Failure (GWTG-HF) registry. In addition, we examine how characteristics, such as sex, race, and HF subtypes, may modify the associations of neighborhood-level SES disadvantage and outcomes.

Methods

Because of the sensitive nature of the data collected for this study, requests to access the data set from qualified researchers trained in human subject confidentiality protocols may be sent to the American Heart Association GWTG Quality Programs Research Committee.

Data Source

The GWTG-HF registry data are owned by the American Heart Association.¹⁴ The GWTG-HF registry is an in-hospital quality improvement registry which includes patient-level data as part of a standardized clinical reporting system.^14,15 GWTG-HF uses a web-based patient management tool (Outcome Sciences Inc, Cambridge, MA) to collect clinical data through manual entry, provide decision support, and provide real-time online reports.^14,15 Trained personnel from each participating center reviewed and collected prespecified data on patients admitted with either a new diagnosis or exacerbation of chronic HF during each hospitalization.¹⁴ All participating institutions were required to comply with local regulatory and privacy guidelines and, if required, to secure Institutional Review Board approval. Since this database is predominantly used at the local site for quality improvement purposes, sites were granted a waiver of informed consent under the common rule. The data were made available to contestants of the Heart Failure Data Challenge, which was hosted by the American Heart Association and the Association of Black Cardiologists. All data management and statistical analyses were performed and documented using the secure, cloud-based Precision Medicine Platform.

Study Population

For the present analysis, we included patients enrolled into the GWTG-HF registry who were hospitalized for acute HF between 2017 and 2020. These deidentified data were accessed as part of the American Heart Association and Association of Black Cardiologists GWTG-HF Data Challenge. A total of 593 053 hospitalizations for acute HF between 2017 and 2020 were included in the GWTG-HF registry. Our study population was limited to 321 314 (54%) hospitalizations with available 5- or 9-digit residential ZIP Codes and complete ascertainment of in-hospital outcomes.

Neighborhood Socioeconomic Disadvantage

Area-level SES scores were calculated for patient residential neighborhoods, with neighborhood defined by a ZIP Code tabulation area (ZCTA).¹⁶ ZIP Codes were converted to ZCTAs using a publicly available conversion file. A summary SES measure was calculated for each ZTCA using data from the 2019 American Communities Survey 5-year estimates, which is derived from the US census. Neighborhood SES was based on a validated algorithm¹⁶ that incorporated average household income, home value, percentage of households receiving interest, dividends, or net rental income, percentage of adults over the age of 25 who had completed high school or college, and percentage of working adults who were employed in executive, managerial, or professional specialties. Design, methods, and individual variables selected for calculating neighborhood SES have been previously described elsewhere.^16–18 In brief, Z scores were calculated for each of the 6 SES variables within categories of wealth, income, education, and employment. The 6 Z scores were then summed for each ZCTA, with larger scores representing worse neighborhood SES disadvantage. This linkage between ZIP Code and US census data provided the opportunity to examine in-hospital outcomes among patients in the GWTG-HF registry residing across a diverse group of neighborhoods nationwide.

Patients were stratified into 5 groups according to their neighborhood socioeconomic index score that corresponded to national quintiles of neighborhood socioeconomic score, as performed in prior analyses.¹⁷ While no consensus exists for SES index cutoffs, the distribution of SES indices was divided into the following neighborhood-level SES-disadvantage quintiles based on national socioeconomic data¹⁶: Q1 (lowest amount of socioeconomic disadvantage); Q2, low; Q3, middle; Q4, high; and Q5 (highest amount of socioeconomic disadvantage).

In-Hospital Outcomes

The primary outcome was in-hospital all-cause mortality. As secondary outcomes, we also examined LOS and guideline-recommended quality measures among patients who survived to discharge. Quality measures for all HF hospitalizations included (1) scheduled follow-up appointment within 7 days of discharge, (2) prescribed anticoagulation for atrial arrhythmias among eligible patients (without documented contraindications), and (3) composite referral to HF disease management, a 60 minutes’ patient education, or a HF interactive workbook provided at discharge. Quality measures for HF with reduced ejection fraction (HFrEF) were examined among eligible patients alive at discharge with no documented contraindications for (1) β-blocker, (2) ACE (angiotensin-converting enzyme) inhibitor, ARB (angiotensin receptor blocker), or ARNI (angiotensin receptor-neprilysin inhibitor), (3) mineralocorticoid receptor antagonist, and (4) composite prescription or counseling for implantable cardioverter defibrillator (ICD) or cardiac resynchronization therapy defibrillator (CRT-D).

Covariates

Covariates were obtained from the publicly available case report form in the American Heart Association GWTG-HF registry and included demographics (age, sex, race and ethnicity, geographic region, and hospital) and the following comorbidities as have been included in recent contemporary analyses^19,20: HFrEF (defined by an ejection fraction ≤40%), atrial fibrillation, atrial flutter, chronic obstructive pulmonary disease, diabetes, smoking within the last year, hyperlipidemia, hypertension, chronic kidney disease, prior coronary revascularization (including percutaneous or surgical intervention), valvular heart disease, and presence of ICD/CRT-D.

Statistical Analysis

Descriptive analyses of demographics, clinical characteristics, and therapies were analyzed across the 5 SES quintiles using the Cochran-Armitage test for trend and linear regression, respectively, for categorical and continuous variables.

Associations between neighborhood disadvantage quintile with in-hospital death was analyzed using multivariable logistic regression with a generalized estimator equation to account for nonindependence of the observations by treating the hospital as a statistical cluster. Covariates were added to the prespecified models in sequential order to observe the impact of covariate adjustment. Model 1 (primary model) adjusted only for demographics (age, sex, race and ethnicity, geographic region, and hospital). Model 2 (data-driven model) additionally adjusted for comorbidities which differed in prevalence by 5% or more across the 5 SES quintiles. These included HFrEF, atrial fibrillation, chronic obstructive pulmonary disease, diabetes, and smoking within the last year. Model 3 (final model) additionally adjusted for atrial flutter, hyperlipidemia, body mass index, hypertension, chronic kidney disease, prior coronary revascularization (including percutaneous or surgical intervention), valvular heart disease, and presence of ICD/CRT-D. Prespecified subgroup analyses were performed for sex, race, and HF subtype (HFrEF, ejection fraction ≤40%; HF with preserved ejection fraction [HFpEF], ejection fraction >40%), with potential heterogeneity tested by the multiplicative interaction. As exploratory analyses, we also examined relationships between neighborhood SES quintiles with LOS and HF hospitalization quality measure achievement rates. For LOS outcomes, we excluded patients who transferred in or out of the hospital visit under consideration (9%). For hospital quality metrics, patients with missing values (≤10% of total) were excluded. The relationship between LOS and SES deprivation quintile was modeled by linear regression, with adjustment for demographic factors. Model fit was ascertained by assessing heteroskedasticity of model residuals. All statistical analyses were performed using SAS Studio version 3.8 (SAS Institute; Cary, NC) using the Precision Medicine Platform provided by the GWTG-HF Data Challenge.

Results

Patient Characteristics

Patients hospitalized for HF in the GWTG-HF registry between 2017 and 2020 presented from 15 388 unique ZCTAs (number of hospitalizations/ZCTA range: 1–793) and 456 unique hospitals. Of 321 314 hospitalizations for HF, 61 778 (19%) were from the highest disadvantaged neighborhoods (mean 6-item SES score −4.2 [−11.5 to −2.6]) and 67 881 (21%) were from the lowest disadvantaged neighborhoods (mean 6-item SES score 4.8 [2.6–14.4]). Patient demographic and clinical characteristics varied by socioeconomic environments (Table 1). Compared with patients from least disadvantaged neighborhoods, those from the most disadvantaged neighborhoods were nearly a decade younger in age (67±15 versus 76±14 years), more often Black (42% versus 9%) or Hispanic (14% versus 5%), and more often had HFrEF (51% versus 40%), chronic pulmonary disease (39% versus 31%), diabetes (52% versus 40%), and recent tobacco use (25% versus 11%); P_trend across SES quintiles <0.0001 for all (Table 1).

Hospitalization Course and LOS

Hospitalization characteristics are shown in Table 2. A higher proportion of patients from most disadvantaged neighborhoods were discharged to home (78% from 68%), and fewer were discharged to other health care facilities (13% versus 21%) or home hospice (2% versus 3%) when compared with those from least disadvantaged neighborhoods. Mean LOS was ≈5 days for all SES categories except for the most disadvantaged, which had a mean stay of ≈6 days (P_trend<0.0001 for all; Table 2). The overall demographic-adjusted LOS increased by ≈1.5 hours (β=0.06 days [95% CI, 0.04–0.08 days]) per SES-disadvantage quintile.

In-Hospital Mortality

A total of 8238 in-hospital deaths were recorded in our GWTG-HF study population. Crude mortality rates declined with worsening SES-disadvantage categories. Without any adjustments, in-hospital mortality was highest for patients from the least socioeconomic disadvantaged neighborhoods (3%), and lowest for those from the most disadvantaged neighborhoods (2%). However, these observations were related to patient age, and after adjustments for demographics, the mortality risk sequentially increased with each increment in worsening neighborhood disadvantage groups. With the least disadvantaged neighborhoods as the reference, the demographic-adjusted odds of death sequentially increased across worsening SES deprivation categories (P_trend=0.001) and were ≈20% greater for those from high (1.18 [1.06–1.31]) and the highest (1.20 [1.07–1.36]) disadvantaged neighborhoods. After full adjustment, the odds of death were 28% greater for those from highest disadvantaged neighborhood (1.28 [1.12–1.48], P_trend across worsening SES deprivation quintiles =0.0003 (Figure 1, Table 3).

In-hospital mortality by SES-disadvantage group among prespecified subgroups by sex, race, or HF subtype are shown in Table 3. An increasing trend in mortality odds ratios was observed with worsening neighborhood SES quintile for all categories, but there was no evidence of statistical interaction by EF type, race, or sex.

Guideline-Directed Quality Measures

As SES deprivation worsened, the proportion of patients with follow-up scheduled within 7 days of hospital discharge declined (82%–74%, P_trend across quintiles <0.0001) as did anticoagulation for atrial arrhythmias (89%–87%, P_trend across quintiles <0.0001; Figure 2). However, patients from most disadvantaged compared with least disadvantaged neighborhoods received higher referrals for HF disease management programs, although the difference was small (74% versus 73%; Figure 2). Target achievement rates of guideline-directed medical therapy (GDMT) prescriptions at discharge for eligible patients with HFrEF were in general higher for patients from most compared with least disadvantaged neighborhoods (Figure 3), although the overall proportion of patients receiving prescriptions for mineralocorticoid receptor antagonist was <50%, and the proportions receiving counseling or prescription for ICD/CRT-D were less than two-thirds across all neighborhood disadvantaged groups. Prescription for β-blockers did not differ by neighborhood deprivation (96% for each neighborhood SES category).

Discussion

Among a large, contemporary quality improvement registry of patients hospitalized for HF across the United States, patients from socioeconomically disadvantaged neighborhoods were on average a decade younger in age, more commonly Black or Hispanic, had higher comorbidity burden at the time of admission compared with those from lower disadvantaged neighborhoods, and longer LOS. Discharges to long-term, acute care, and hospice facilities were lower among those with most compared with least disadvantaged neighborhood groups. Compared with those from the least disadvantaged neighborhoods, the odds of in-hospital all-cause mortality were 28% greater among those from most disadvantaged neighborhoods, after adjusting for demographics, comorbidities, and hospital characteristics. Guideline-recommended prescribing rates for HF therapies also varied by neighborhood SES, albeit by small differences.

Patient Characteristics of Neighborhood Socioeconomic Disadvantage Groups

Similar to these observations from the GWTG-HF registry, Black patients presenting to emergency departments or hospitals for worsening HF are reported to be ≈10 years younger than White patients but were more likely to be discharged home without admission.²¹ Our analysis suggests that despite being younger, there exists a disproportionately higher comorbidity burden alongside worse clinical parameters in patients with highest neighborhood disadvantage, portraying possibly greater clinical risk at time of admission. While some racial and ethnicity minority groups may develop nonischemic HF earlier in life,²² middle-aged non-Hispanic Black adults develop a greater burden of chronic disease and multimorbidity at an earlier age, on average, than their non-Hispanic White counterparts.²³ The same pattern has been observed by the average age of a patient at the time of a hospital visit, demonstrating that Hispanics have the lowest average age followed by Native Americans, Blacks, Asians, and Whites.²⁴ Socially vulnerable patients may not receive chronic ambulatory management of their comorbidities²⁵ or may die at home without acute life-prolonging therapies. While we could not compare out-of-hospital characteristics and adverse events in our present analysis, disparities in clinical risk among those from higher disadvantaged neighborhoods at the time of worsening HF and subsequent outcomes may exhibit a multifactorial relationship between socioeconomic environments.¹⁰ These data highlight a need for targeted public health interventions, including system-level interventions that improve health care services access in those with SES deprivation,²⁶ and provider-patient interventions to improve monitoring of progressive HF following diagnosis, particularly in higher risk subgroups.

Neighborhood Socioeconomic Disadvantage and HF Hospitalization Outcomes

Our present analysis of contemporary HF hospitalizations confirms earlier observations of higher in-hospital mortality risk among patients with lower median household income in a 2-year National Inpatient Sample from 2015 to 2017.¹³ Averbuch et al¹³ described a marginally higher risk of in-hospital death, increasing by 2% to 3% for patients with low or medium SES relative to those with high SES. In our analysis from the GWTG-HF registry, we observed a higher in-hospital mortality risk for patients from the most compared with least deprivation categories, a pattern which was observed irrespective of race or sex. Our study relied upon a ZCTA-derived neighborhood SES score, which provided a broad estimation of social vulnerability beyond a focus on economic deprivation¹³ and included wealth, income, education, and employment. Our findings confirm observations that non-Hispanic Black residents from most vulnerable US regions have higher risk for HF mortality, as recently reported from the Underlying Cause of Death files from the Center for Disease Control.¹⁰

The adjusted in-hospital mortality risks associated with worsening neighborhood socioeconomic disadvantage groups tended to be higher for patients with HFpEF than HFrEF. Temporal surveillance trends from the Atherosclerosis Risk in Communities Study revealed that 28-day mortality is higher in HFpEF than HFrEF when adjusted for markers of congestion.²⁷ Yet, assessment of acute presentation of worsening HF in HFpEF is often challenging due to comorbidities that contribute to overlapping symptoms, including chronic pulmonary and renal disease.²⁸ While there has been recent focus on the potential to reduce worsening HF events in HFpEF with novel agents, including ARNI^28,29 and sodium-glucose cotransporter-1/2 or -2 inhibitors,^30,31 there continue to exist limitations in effective therapies for mortality reduction in this population. Further research is needed to better understand the mortality risk associated with neighborhood-level socioeconomic characteristics among HF subtypes.

The demographic-adjusted mean LOS increased with worsening neighborhood-level SES disadvantage. Patients from the most disadvantaged neighborhoods also had a lower proportion of referrals to acute care facilities or hospice. Other posthospitalization care patterns have been reported following acute trauma³² or traumatic brain injury, particularly among Hispanic and Black patients,³³ suggesting that patients from disadvantaged groups may not receive ongoing acute care or rehabilitation following HF hospitalization. However, this may be confounded by increased age and frailty in those hospitalized for HF from less disadvantaged neighborhoods. Nonetheless, there may be a role for improvement in post-HF hospitalization care for at-risk underprivileged patients.

HF Hospitalization Quality Measures Vary Among Neighborhood Socioeconomic Disadvantage Groups

There were gaps in HF hospitalization quality metrics by neighborhood socioeconomic disadvantage groups, as over a quarter of patients from high and highest disadvantaged neighborhoods did not have posthospitalization follow-up arranged within 7 days. Approximately one-quarter of the overall study population lacked referrals for HF disease management, and while between-group differences were small, the referral rate declined with worsening neighborhood deprivation. Disparate quality metrics in HF care were apparent across the entire population, although with inconsistent direction in relationship by socioeconomic disadvantage groups. A recent randomized trial using hospital-based quality improvement interventions showed no significant difference of postdischarge care patterns.³⁴ Understanding which socioeconomic barriers play a role in contemporary HF populations may allow for a more targeted approach to address this gap in achievement of quality metrics.

In US clinical practice between 2007 and 2018, nearly one-third of patients hospitalized with acute HFrEF were not prescribed target doses of β-blocker and nearly half or more were not prescribed target doses of ACE inhibitor/ARB/ARNI or mineralocorticoid receptor antagonist at the time of discharge.³⁵ More recently, >90% of eligible hospitalized patients with HFrEF were not prescribed ARNI at the time of discharge and very few actually receive ARNI during follow-up.¹⁹ Our study results demonstrate that prescribing rates for β-blocker and ACE inhibitor/ARB/ARNI are high at discharge (not representative of target dose), with similar rates for β-blockers and small differences for ACE inhibitor/ARB/ARNI across SES groups. Yet, less than one-half were prescribed mineralocorticoid receptor antagonist and less than two-thirds provided ICD/CRT-D. Data regarding social vulnerability and GDMT use is limited. Higher (albeit smaller) observed differences in achieved GDMT prescribing among those from disadvantaged neighborhoods might be due to a lower age and less frail cohort, despite their higher comorbidity burden. Additional work is needed to understand care patterns in in-hospital GDMT initiation across SES groups, and initiatives are needed to shift clinical inertia towards in-hospital GDMT prescribing to improve long-term clinical outcomes across socially vulnerable groups.³⁶

Limitations

This study has several imitations. This was an observational study and as such, we are unable to rule out residual confounding. The cross-sectional design did not capture any dynamic characteristics of neighborhoods related to population migration nor were we able to consider historical economic practices which may have ongoing repercussions, even when area-level neighborhood deprivation status changes over time. The GWTG-HF registry only included hospitals enrolled in the American Heart Association quality reporting program and may not be generalizable to all hospitals or ambulatory patients with chronic HF at risk for worsening HF events. Only 54% of the population had residential address data available for inclusion, potentially limiting generalizability due to the large number excluded hospitalized patients. However, demographic characteristics were similar between hospitalized patients with and without available ZIP Codes (Table S1). While the neighborhood deprivation score relied on ZTCA characteristics beyond median household income, we were unable to describe patient-level socioeconomic deprivation. Data abstraction was deidentified and limited to the in-hospital visit, as such, we were unable to identify individual patients who may have had repeat hospitalization encounters during the study period. Since these data were not linked to the Centers for Medicare and Medicaid Services database, we were also unable to analyze out-of-hospital and downstream clinical outcomes following the HF hospitalization.

Conclusions

Among patients enrolled in a large, diverse national quality reporting registry for HF hospitalization, those from disadvantaged neighborhoods exhibited higher associated in-hospital mortality despite being nearly a decade younger and more often Black or Hispanic than those with the least neighborhood disadvantage. This study also highlights gaps in quality metrics during hospitalization for HF across neighborhood socioeconomic disadvantage groups. The reason for these observations is complex and multifaceted, and further research in HF should incorporate detailed examination of socioeconomic and neighborhood characteristics to better describe this relationship. Additionally, these data suggest there are further opportunities to explore and improve care delivery patterns to address in-hospital outcomes among patients hospitalized for HF and presenting from disadvantaged neighborhoods.

Article Information

Acknowledgments

This project was developed through the Heart Failure Data Challenge, using the Get With The Guidelines-Heart Failure Registry data to target research related to heart failure and social/structural determinants of health. The data challenge was hosted by the American Heart Association and the Association of Black Cardiologists. The American Heart Association Precision Medicine Platform (https://precision.heart.org/) was used for data analysis.

Sources of Funding

The Get With The Guidelines-Heart Failure (GWTG-HF) Data Challenge was sponsored by the American Heart Association and the Association of Black Cardiologists. The GWTG-HF program is provided by the American Heart Association and sponsored, in part, by Novartis, Boehringer Ingelheim and Eli Lilly Diabetes Alliance, Novo Nordisk, Sanofi, AstraZeneca, and Bayer.

Supplemental Material

Table S1

Nonstandard Abbreviations and Acronyms

ACE	angiotensin-converting enzyme inhibitor
ARB	angiotensin receptor blocker
ARNI	angiotensin receptor-neprilysin inhibitor
CRT-D	Cardiac Resynchronization Therapy Defibrillator
GDMT	guideline-directed medical therapy
GWTG-HF	Get With The Guidelines-Heart Failure
HF	heart failure
HFpEF	heart failure with preserved ejection fraction
HFrEF	heart failure with reduced ejection fraction
ICD	implantable cardioverter defibrillator
LOS	length of stay
SES	socioeconomic status
ZCTA	ZIP Code tabulation area

Disclosures Drs Rao and Kelsey are supported by a National Institutes of Health (NIH) training grant (5T32HL069749-18). Dr Fudim was supported by the National Heart, Lung, and Blood Institute (NHLBI; K23HL151744), the American Heart Association (20IPA35310955), Mario Family Award, Duke Chair’s Award, Translating Duke Health Award, Bayer, Bodyport‚ and BTG Specialty Pharmaceuticals. He receives consulting fees from Audicor, AxonTherapies, Bodyguide, Bodyport, Boston Scientific, CVRx, Daxor, Edwards LifeSciences, Fire1, Inovise, NXT Biomedical, Vironix, Viscardia, and Zoll. Dr Fonarow reports research funding from the NIH and serving as a consultant for Abbott, Amgen, AstraZeneca, Bayer, Cytokinetics, Janssen, Medtronic, Merck, and Novartis. Dr Mentz received research support and honoraria from Abbott, American Regent, Amgen, AstraZeneca, Bayer, Boehringer Ingelheim/Eli Lilly, Boston Scientific, Cytokinetics, Fast BioMedical, Gilead, Innolife, Medtronic, Merck, Novartis, Relypsa, Respicardia, Roche, Sanofi, Vifor, Windtree Therapeutics, and Zoll. Dr DeVore reports research funding through his institution from the American Heart Association, Amgen, Biofourmis, Bodyport, Cytokinetics, American Regent Inc, the NHLBI, and Novartis. He also provides consulting services for and/or receives honoraria from Abiomed, Amgen, AstraZeneca, Cardionomic, InnaMed, LivaNova, Natera, Novartis, Procyrion, Story Health, Vifor, and Zoll. He has also received nonfinancial support from Abbott for educational and research activities. The other authors report no conflicts.

Footnotes

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