Payment Source, Quality of Care, and Outcomes in Patients Hospitalized with Heart Failure

Abstract

Objectives

This study analyzed the relationship between payment source and quality of care and outcomes in heart failure (HF).

Background

HF is a major cause of morbidity and mortality. There is a lack of studies assessing the association of payment source on HF quality of care and outcomes.

Methods

We analyzed 99,508 HF admissions from 244 sites between January 2005 and September 2009. Patients were grouped based on payer status (private/HMO; no insurance; Medicare; Medicaid) with the private/HMO group as reference.

Results

The no insurance group was less likely to receive evidence-based beta-blockers (EBBB; adjusted odds ratio [OR] 0.73, 95% confidence intervals [CI] 0.62 to 0.86), an implantable cardioverter defibrillator (ICD; OR 0.59, 95% CI 0.50 to 0.70) or anticoagulation for atrial fibrillation (OR 0.73, 95% CI 0.61 to 0.87). Similarly, the Medicaid group was less likely to receive EBBB (OR 0.86, 95% CI 0.78 to 0.95) or ICD (OR 0.86, 95% CI 0.78 to 0.96). ACEI/ARB and beta-blockers were prescribed less frequently in the Medicare group (OR 0.89, 95% CI 0.81 to 0.98). Medicare, Medicaid, and no insurance groups had longer hospital stays. Higher adjusted rates of in-hospital mortality were seen in patients with Medicaid (OR 1.22, 95% CI 1.06 to 1.41) and in reduced systolic function patients with no insurance.

Conclusion

Decreased quality of care and outcomes for patients with HF was observed in no insurance, Medicaid and Medicare groups compared to the private/HMO group.

Introduction

Heart failure (HF) is a major cause of morbidity and mortality and places a significant economic drain on the health care system (1). Mortality from HF is higher in patients with a reported lower socioeconomic status (2). However, there is a lack of evidence to explain this observation. Potential disparities in health care quality of care might account for these findings but data from the few small studies that exist are inconsistent, with some studies showing a possible association with poorer quality of care and adverse outcomes (3) and other studies failing to replicate these findings (4). Prior studies also suggest that HF patients with different types of health insurance may receive different treatment which may result in differences in short-term and long-term outcomes (2, 3). Currently, there are no consistent data that differences in quality of care based on a patient’s health insurance exist in the realm of inpatient management and follow-up care in patients hospitalized with HF. Determining possible differences in quality of care and outcomes in patients by insurance type is warranted in order to both develop interventions aimed at improving adherence to HF quality of care measures and outcomes.

Get with the Guidelines Heart Failure (GWTG-HF) prospectively tracks several performance measures and other quality of care indicators for patients hospitalized with HF (5, 6). In this study, we sought to investigate HF quality of care measures, hospital length of stay, and in-hospital mortality stratified by type of health insurance. The goal was to determine the association of payment status on healthcare quality and in-hospital outcomes in a contemporary database in order to identify potential targets to achieve reductions in health disparities and improve outcomes.

Methods

Data Collection

As previously described (7, 8), the GWTG-HF program is a national, prospective, observational and ongoing voluntary clinical registry and continuous quality-improvement initiative. The registry enrolls adults hospitalized with an episode of new or worsening HF as the primary reason for admission or with significant HF symptoms that developed during hospitalization in which HF was the primary discharge diagnosis. Participating hospitals are from all census regions of the United States and include teaching and nonteaching, rural and urban, and large and small hospitals.

Clinical information about consecutive eligible patients is submitted by participating institutions online using an interactive case report form in compliance with Joint Commission and Centers for Medicare and Medicaid standards. Outcome Sciences, Inc. serves as the data collection and coordination center for GWTG. Clinical data are abstracted using standardized definitions; variables collected include demographic and clinical characteristics, medical history, previous treatments, contraindications to therapies, and outcomes. Checks are performed to ensure that the reported data are complete and accuracy of data quality is monitored. Participants’ institutional review boards review and approve the GWTG protocol. Sites were granted a waiver of informed consent under the common rule because data were used primarily at the local site for quality improvement. The Duke Clinical Research Institute serves as the data analysis center.

Study population

The population for this study consisted of 106,351 HF admissions from 249 fully participating sites between January 2005 and September 2009. Patients were excluded if they were missing data on payment source (N=5,916) or mortality (N=927). This left a study cohort of 99,508 at 244 sites. Data were stratified into groups by payment source (Medicare, Medicaid, none, and Private/HMO). Patients with Medicare along with private/HMO insurance were classified as private/HMO. Patients with Medicaid and Medicare were classified as Medicaid.

Outcome measures

The main pre-specified quality of care outcomes that were measured include the core measures used by the Centers for Medicare and Medicaid and The Joint Commission (8, 10), as follows: (1) complete discharge instructions; (2) documented evaluation of left ventricular function before arrival, during hospitalization, or planned after discharge; (3) angiotensin-converting enzyme inhibitor (ACEI) or angiotensin receptor blocker (ARB) for HF patients with left ventricular systolic dysfunction (LVSD) without contraindications or intolerance; and (4) adult smoking cessation advice/counseling for patients with a history of smoking cigarettes. An additional GWTG-HF measure of any beta-blocker use at discharge for patients with LVSD without contraindications or intolerance was also included. Patients with a documented contraindication were excluded from quality measures. Two composite measures established by the GWTG-HF program were also assessed. One was a composite quality measure-an opportunity quality of care index-that was based on the number of therapeutic interventions in relation to the circumstances when those interventions were indicated for the 5 measures (# successes with quality measure/#eligible for quality measure). The other composite measure was an all or none measure for 100% compliance with all 5 quality measures (whether eligible patients received 100% of guideline-based therapy, up to a maximum of all 5 measures). Additional quality measures of interest included (1) anticoagulant at discharge for patients with atrial fibrillation, (2) aldosterone antagonists prescribed at discharge for LVSD, (3) hydralazine-nitrates in African-American patients with LVSD, (4) evidence-based specific beta-blocker (carvedilol, metoprolol succinate, bisoprolol fumarate) prescribed at discharge for patients with LVSD, (5) implantable cardioverter defibrillator (ICD) placed or prescribed at discharge for patients with LVEF ≤30%, and (6) ACEI/ARB and beta-blocker for LVSD at discharge. In the subgroup from year 2009, the use of deep venous thrombosis (DVT) prophylaxis and administration of the influenza and pneumococcal vaccinations were also assessed. Finally, hospital length of stay (number of days from admission to discharge) and in-hospital deaths were also assessed.

Statistical analysis

Baseline characteristics were compared across payment source groups using the Pearson chi-square test for categorical variables and the Kruskal Wallis test for continuous variables. Categorical variables were reported as percentages and continuous variables were reported as median (interquartile range). Multivariable logistic regression was performed using the generalized estimating equations methods to adjust for clustering within hospitals to determine whether payment source independently influenced each outcome and quality of care measure. Private/HMO was the reference group. Log-transformation was used for the length of stay analysis to achieve an approximately normal distribution. Models were adjusted for patient characteristics and medical history and hospital characteristics. A P value of <0.05 was considered significant for all tests. All analyses were performed using SAS software (version 9.2, SAS Institute, Inc, Cary, NC). The authors are solely responsible for the design and conduct of this study, including all analyses, drafting and editing, and its final contents.

Results

Baseline characteristics

The total study cohort consisted of 99,508 patients hospitalized with a diagnosis of HF of which 45,353 (45.6%) patients had documented reduced EF, 47,779 (48.0%) patients had preserved EF, and 6376 (6.4%) patients did not have EF documented. There were 28,702 (28.8%) patients documented to have Private/HMO insurance, 55,103 (55.4%) with Medicare, 10,684 (10.7%) with Medicaid, and 5019 (5.0%) who were uninsured or without insurance type documented. The baseline characteristics of the overall population stratified by insurance group are presented in Table 1. The median (IQR) age of the overall population was 75 (62, 83) years, with the oldest population seen in the Medicare group (78 [70, 85] years) and the youngest population seen in the group with no insurance (53 [46, 61] years). Overall, this was a predominantly white population (68%), with more black HF patients seen in the Medicaid and no insurance groups. There were also equal proportions of females and males in the overall population, but across groups there were differences with a higher relative proportion of females seen in the Medicaid and Medicare groups, while more males were seen in the Private/HMO and no insurance groups. While the Medicare group had more anemia, coronary artery disease (including an ischemic etiology), CVA/TIA, PVD, and renal insufficiency, those with Medicaid had more COPD/asthma, diabetes, and hypertension. Overall, the no-insurance groups had the lowest prevalence of comorbidities, except for hypertension. In addition, a greater proportion of Medicaid and no-insurance groups were treated at academic hospitals and at hospitals with intervention/PCI/surgery availability (Appendix A).

Table 1.

Baseline Characteristics in the Overall Population and by Payment Source

Characteristic	Total (N=99508)	Private/HMO/Other (N=28702)	Medicaid (N=10684)	Medicare (N=55103)	None/UTD (N=5019)	P
Age, yrs	75 (62–83)	71 (59–82)	62 (53–75)	78 (70–85)	53 (46–61)	<0.0001
Women	48.7	44.8	54.4	51.0	34.4	<0.0001
Race						<0.0001
White	67.7	69.4	37.7	75.5	34.8
Hispanic	6.2	5.0	11.8	5.2	12.7
Black/Other	23.1	22.2	47.2	16.5	48.6
History
Anemia	17.6	15.4	16.1	19.7	10.0	<0.0001
Atrial fibrillation/Atrial flutter	31.1	29.7	21.6	35.4	12.2	<0.0001
CAD	48.1	46.2	41.9	52.2	28.2	<0.0001
Ischemic etiology	54.7	52.8	48.0	58.9	32.8	<0.0001
COPD or asthma	29.1	26.0	34.0	30.5	21.7	<0.0001
CVA or TIA	13.9	11.8	14.7	15.4	7.1	<0.0001
Depression	9.5	8.7	10.6	10.1	5.5	<0.0001
Dialysis	10.8	10.6	11.7	11.0	8.3	<0.0001
Diabetes	41.8	41.2	47.3	41.7	33.9	<0.0001
PVD	11.6	10.5	9.4	13.3	3.7	<0.0001
Hypertension	74.9	73.1	78.0	75.1	75.2	<0.0001
Renal insufficiency	20.2	18.4	20.2	21.9	12.2	<0.0001

Values are presented as % or median (interquartile range).

Payment source and Quality of Care

Adherence to performance and quality measures varied by payment source (Appendix B). When the cohort with LVSD was considered, the group with no insurance had the highest proportions of prescriptions for many evidence-based therapies (e.g. ACEI/ARB at discharge and beta blockers) while the lowest proportions were seen in patients with Medicare. Similar high use of evidence-based management in patients with no insurance pertained to discharge instructions, smoking cessation, use of aldosterone antagonists, anticoagulation for atrial fibrillation, DVT prophylaxis and the 100% compliance measure when compared to all other groups. However, the lowest proportions of either ICD placement or a prescription for an ICD at discharge was detected in groups with no insurance or Medicaid (31% and 38% respectively) and the highest proportion was detected in the Medicare (42%) and Private/HMO (41%) insurance groups (P<0.0001 for all groups). Finally, the lowest rates of influenza or pneumococcal vaccinations were also seen in the Medicaid and no insurance groups.

Adjusted Quality of Care According to Payment Source

Differences in quality of care according to payment source persisted after GEE multivariable regression analyses accounting for common patient and hospital characteristics and clustering of patients within hospitals (Table 2). For example, when compared to the private insurance group, the no insurance group was less likely to receive anticoagulation for atrial fibrillation (OR 0.73, 95% CI 0.61 to 0.87), evidence-based specific beta-blockers for LVSD (OR 0.73, 95% CI 0.62 to 0.86) or an ICD implanted or plans to be implanted in appropriate candidates (OR 0.59, 95% CI 0.50 to 0.70). These no insurance patients were more likely to receive discharge instructions and smoking cessation counseling. The Medicaid group was also less likely to receive an ACEI/ARB and beta-blocker (OR 0.89, 95% CI 0.79 to 0.99) and similarly less likely to receive evidence-based specific beta-blockers (OR 0.86, 95% CI 0.78 to 0.95), or an ICD for LVSD (OR 0.86, 95% CI 0.78 to 0.96). Finally, the Medicare group was less likely to receive ACEI/ARB (OR 0.85, 95% CI 0.76 to 0.95) or the composite of an ACEI/ARB and beta-blocker (OR 0.89, 95% CI 0.81 to 0.98).

Table 2.

Univariable and Multivariable Models of Performance and Quality Measures by Payment Source

Measure	Insurance	Univariable OR(95% CI) vs. Private/HMO/Other	Univariable p-value	Multivariable OR(95% CI) vs. Private/HMO/Other1	Multivariable p-value
Performance
ACEI/ARB for LVSD at discharge	Medicaid	0.92 (0.82, 1.03)	0.13	0.88 (0.77, 1.00)	0.052
	Medicare	0.74 (0.68, 0.81)	<0.0001	0.85 (0.76, 0.95)	0.0032
	None/UTD	1.31 (1.18, 1.45)	<0.0001	1.09 (0.96, 1.24)	0.20
Beta blocker (any) for LVSD at discharge	Medicaid	1.00 (0.91, 1.10)	0.96	0.95 (0.84, 1.08)	0.42
	Medicare	0.88 (0.81, 0.96)	0.0037	0.98 (0.88, 1.10)	0.79
	None/UTD	1.10 (0.95, 1.26)	0.20	0.96 (0.79, 1.17)	0.70
Discharge instructions	Medicaid	0.93 (0.87, 0.99)	0.022	0.94 (0.87, 1.02)	0.12
	Medicare	0.97 (0.92, 1.01)	0.13	0.96 (0.90, 1.03)	0.25
	None/UTD	1.09 (1.01, 1.18)	0.025	1.13 (1.03, 1.25)	0.014
LV function assessed	Medicaid	0.91 (0.85, 0.96)	0.0009	0.83 (0.73, 0.93)	0.0016
	Medicare	0.84 (0.79, 0.89)	<0.0001	0.78 (0.69, 0.89)	0.0002
	None/UTD	1.08 (0.99, 1.17)	0.082	1.06 (0.87, 1.30)	0.57
Smoking Cessation	Medicaid	0.86 (0.77, 0.96)	0.0095	0.80 (0.67, 0.95)	0.012
	Medicare	0.84 (0.77, 0.92)	<0.0001	0.86 (0.74, 0.98)	0.029
	None/UTD	1.24 (1.12, 1.37)	<0.0001	1.28 (1.08, 1.53)	0.0057
Composite score (%)(#success/#eligible)	Medicaid	90.04 ± 21.39	0.0085		0.0050
[mean score ± SD]	Medicare	90.55 ± 21.88	<0.0001		0.0096
	None/UTD	93.87 ± 15.37	<0.0001		0.0020
	Private/HMO/Other	91.16 ± 20.29
100% compliance	Medicaid	0.93 (0.88, 0.99)	0.025	0.94 (0.88, 1.01)	0.11
	Medicare	0.96 (0.91, 1.01)	0.11	0.92 (0.86, 0.99)	0.020
	None/UTD	1.01 (0.95, 1.09)	0.68	1.09 (1.01, 1.19)	0.036
ACEI/ARB and BB for LVSD at discharge	Medicaid	0.94 (0.85, 1.03)	0.16	0.89 (0.79, 0.99)	0.049
	Medicare	0.78 (0.72, 0.85)	<0.0001	0.89 (0.81, 0.98)	0.022
	None/UTD	1.21 (1.09, 1.35)	0.0003	1.04 (0.90, 1.20)	0.58
Quality
Aldosterone Antagonist for LVSD at discharge	Medicaid	1.11 (1.01, 1.22)	0.025	0.99 (0.91, 1.08)	0.88
	Medicare	0.80 (0.75, 0.85)	<0.0001	0.97 (0.91, 1.03)	0.33
	None/UTD	1.13 (0.99, 1.29)	0.064	0.97 (0.86, 1.09)	0.62
Anticoagulation for atrial fibrillation	Medicaid	0.75 (0.67, 0.85)	<0.0001	0.74 (0.65, 0.83)	<0.0001
	Medicare	0.80 (0.74, 0.87)	<0.0001	0.94 (0.87, 1.01)	0.092
	None/UTD	0.96 (0.81, 1.13)	0.59	0.73 (0.61, 0.87)	0.0003
DVT prophylaxis (2009 only)	Medicaid	1.03 (0.90, 1.19)	0.64	1.00 (0.87, 1.15)	0.99
	Medicare	1.02 (0.91, 1.14)	0.76	1.04 (0.92, 1.17)	0.58
	None/UTD	1.00 (0.81, 1.23)	0.98	0.96 (0.78, 1.18)	0.71
Evidence-based specific Beta Blockers for LVSD	Medicaid	0.91 (0.83, 1.00)	0.042	0.86 (0.78, 0.95)	0.0040
	Medicare	0.88 (0.83, 0.93)	<0.0001	0.97 (0.91, 1.04)	0.42
	None/UTD	0.79 (0.67, 0.92)	0.0026	0.73 (0.62, 0.86)	0.0002
Hydralazine and Isosorbide dinitrate for AA at discharge	Medicaid	1.01 (0.87, 1.16)	0.93	1.01 (0.87, 1.18)	0.85
	Medicare	1.11 (0.97, 1.28)	0.12	1.04 (0.91, 1.20)	0.55
	None/UTD	0.97 (0.77, 1.22)	0.78	1.01 (0.82, 1.25)	0.90
ICD placed or prescribed at discharge	Medicaid	0.87 (0.78, 0.97)	0.013	0.86 (0.78, 0.96)	0.0075
	Medicare	0.92 (0.85, 0.99)	0.025	1.06 (0.98, 1.14)	0.17
	None/UTD	0.57 (0.47, 0.70)	<0.0001	0.59 (0.50, 0.70)	<0.0001
Influenza vaccination during flu season (2009 only)	Medicaid	0.99 (0.92, 1.05)	0.70	0.99 (0.92, 1.05)	0.69
	Medicare	1.02 (0.97, 1.07)	0.37	1.02 (0.96, 1.08)	0.49
	None/UTD	1.05 (0.93, 1.18)	0.45	1.07 (0.95, 1.20)	0.29
Pneumococcal vaccination (2009 only)	Medicaid	1.01 (0.94, 1.09)	0.81	1.01 (0.93, 1.09)	0.89
	Medicare	1.05 (1.00, 1.10)	0.036	1.04 (0.99, 1.10)	0.091
	None/UTD	1.04 (0.91, 1.20)	0.58	1.05 (0.92, 1.21)	0.45

¹All multivariable models adjusted for patient characteristics (age, race, sex, admission systolic blood pressure, heart rate, PMH of anemia, stroke history, diabetes, chronic obstructive pulmonary disease, hypertension, atrial fibrillation/flutter, peripheral vascular disease, renal failure, depression, smoking status, and etiology of HF) and hospital characteristics (region; number of beds; academic status; and heart transplant, surgical, and percutaneous coronary intervention capability).

Payment source and Hospital Length of Stay (LOS)

After multivariable adjustment, compared with the private insurance group, there were longer associated hospital stays in the Medicare, Medicaid and no insurance groups (Table 3). There were also longer associated hospital stays after adjustment in patients on Medicaid, Medicare and those with no insurance compared with the private insurance group in patients with preserved systolic function and in patients on Medicare and those with no insurance in patients with LVSD (Appendix D).

Table 3.

Univariable and Multivariable Models of In-hospital Outcomes by Payment Source

Outcome	Insurance	Univariable OR(95% CI) vs. Private/HMO/Other	Univariable p-value	Multivariable OR(95% CI) vs. Private/HMO/Other1	Multivariable p-value
In-hospital death	Medicaid	0.99 (0.86, 1.13)	0.85	1.22 (1.06, 1.41)	0.0070
	Medicare	1.27 (1.11, 1.46)	0.0007	0.98 (0.86, 1.11)	0.74
	None/UTD	0.68 (0.51, 0.91)	0.010	1.32 (0.99, 1.76)	0.062
Length of stay [ratio of means]	Medicaid	1.05 (1.01, 1.09)	0.022	1.04 (1.01, 1.08)	0.020
	Medicare	1.09 (1.06, 1.11)	<0.0001	1.05 (1.02, 1.07)	0.0006
	None/UTD	1.01 (0.96, 1.05)	0.74	1.08 (1.04, 1.13)	0.0004

¹All multivariable models adjusted for patient characteristics as in table 2.

Log-transformation was used for the length of stay analysis to achieve an approximately normal distribution. The estimated mean difference of log(LOS) between groups, and the endpoints of the confidence interval for the difference, were transformed back by exponentiating. Mathematically, the reported numbers are equivalent to the ratio of geometric means, and its confidence interval, between the groups.

Payment Source and In-hospital Mortality

There were 2,944 in-hospital deaths (3.0%) during the study (Appendix B). After multivariable adjustment for patient characteristics and lab values, there was a higher overall mortality rate seen in the Medicaid group (OR 1.22, 95% CI 1.06 to 1.41) (Table 3). A higher rate of inhospital mortality was again seen in the Medicaid group in patients with preserved ejection fraction and in the no insurance group in patients with LVSD (Appendix D).

Discussion

Among hospitals participating in GWTG-HF, we found significant differences in the application of evidence based care and in-hospital outcomes by payment source in this large contemporary cohort of patients hospitalized with HF throughout the United States. Specifically, higher adjusted rates of in-hospital mortality were seen in patients with Medicaid and in patients with no insurance (in the group with LVSD). Similarly, when compared to patients with private/HMO insurance, longer HF hospitalization adjusted LOS was seen in patients receiving Medicaid, Medicare, and in patients with no insurance. In addition, when compared to patients receiving private/HMO insurance, we found that patients with no insurance, Medicaid, or Medicare less often received some of the guideline-recommended therapies that are currently included in the HF performance and quality measures. After adjustment for potential confounders, there was lower use of the composite of ACEI/ARB and beta-blockers in patients receiving Medicare and Medicaid. Patients with Medicaid were also less likely to receive smoking cessation instructions, evidence-based specific beta-blockers for LVSD, and an ICD in eligible patients. Having no insurance was similarly associated with lower use of anticoagulation for atrial fibrillation or evidence-based beta-blockers for LVSD and a decrease in the odds of receiving an ICD in eligible patients. Interestingly, patients without insurance were more likely to receive discharge instructions and smoking cessation counseling. Medicare patients were most likely to receive ICD placement which may reflect the financial impact ICD placements in patients without insurance or with Medicaid have on the provider. These findings suggest that even among hospitals participating in a national quality improvement program, HF patients’ insurance status is still associated with the care provided and clinical outcomes in the inpatient setting.

Despite major advances in the HF treatments, access to evidence-based care may be limited by patients’ insurance status. Limitations in or complete lack of health insurance may influence access to and delivery of care. However, there is a lack of studies assessing this in HF patients. One study demonstrated a significantly higher admission rate in a managed care cohort when compared to groups with other payment sources (10). However, there was no association between managed care and poor short-term outcomes of hospitalization. Our study adds significant insight demonstrating that insurance status is significantly associated with the use of guideline-recommended HF therapies and in-hospital clinical outcomes in a very large contemporary cohort of HF patients.

Smaller studies of other populations do report disparities in health care quality and outcomes based on socioeconomic status (3). Studies have demonstrated a link between socioeconomic inequalities and cardiovascular disease mortality (11) and a decreased likelihood of receiving regular medical care (12). While insurance status is not synonymous with socioeconomic status, they are interrelated. There is a lack of studies assessing the association of socioeconomic status and/or payer status with HF quality of care and outcomes and a few small studies have yielded opposing findings. There were higher associated readmission rates demonstrated among lower socioeconomic groups in other studies (13). However, findings from other studies on HF are inconsistent, with some failing to demonstrate an association between socioeconomic status and outcomes (4).

The prevalence of HF and mortality has been reported to be higher in those with lower socioeconomic status, but there is little evidence to explain this observation (2). It has been proposed that uncorrected risk factors such as smoking, hypertension, coronary artery disease, and diabetes mellitus in the lower socioeconomic groups may account for this finding (13). Indeed several of these factors are seen more often in lower socioeconomic groups, supporting this notion (14). However, differences among groups were seen after multivariable analyses in our study adjusting for many of these factors. Another possibility is that differences in implementation of guideline-endorsed HF therapy as we saw in this study may in part explain this observation. Our data demonstrate that significant differences exist in implementation of guideline-endorsed HF therapy and outcomes according to payer status, with decreased quality and outcomes in patients with no insurance, and Medicaid, and Medicare when compared to private/HMO groups. A bias not to prescribe drugs or therapies with a life-saving benefit to certain groups can certainly perpetuate the observed increases in HF prevalence and poor outcomes. The reasons for disparate prescribing behavior based on payer status are unknown. Access inequalities to specialist care during hospital admissions may explain some of the differences (3). One study demonstrated an increased rate of rehospitalization in HF patients with lower socioeconomic status that was independent of disease severity, suggesting that socioeconomic status may influence clinical management of HF (13). The same study also showed that the increased rate of rehospitalization was independent of noncompliance with diuretics arguing, at least in the case of diuretics, against medication noncompliance as a reason for increased morbidity in patients with lower socioeconomic status. The reasons behind the disparities warrant further investigation to help mitigate associated poorer outcomes among patients with lower socioeconomic status. Finally, it is unclear how health care quality of care and outcomes will be affected in the era of health care reform that is expected to expand insurance coverage to more Americans, including an expansion of Medicaid eligibility.

Limitations

There are limitations to this study. First, although insurance status was collected and analyzed there were no direct measures of socioeconomic status or mechanism to conclude why certain higher priced interventions were not advocated. For example, refusal due to inability to pay may have been present and might have impacted the results. Second, the lack of follow-up after discharge does not allow assessment of long term outcomes. Third, data were collected by medical chart review and depend on the accuracy and completeness of documentation and abstraction. Although contraindications and intolerance to medications were recorded as documented in the medical record, there may have been patients with contraindications or intolerances to treatments that were present but not documented, particularly for less well established quality measures. Given the observational nature of the study, residual measured and unobserved variables may have confounded the results. Although the GEE multivariable analyses adjusted for multiple baseline differences, selection bias influencing physician and patient decision-making may influence these findings. Furthermore, although this is a registry-based study with an opportunity to study patients in real-world setting, data collection is dependent on voluntary participation of hospitals such that findings may not be generalizable to hospitals that differ in care patterns or patient characteristics. Additionally, while it is likely that socioeconomic status correlates with insurance type, this study cannot distinguish whether these findings are influenced by payment model, socioeconomic status, or both. Finally, because of the large number of patients in this study, small differences might lead to statistical significance but lack clinical relevance.

Conclusions

This study using data from the GWTG-HF quality program suggests that implementation of guideline-endorsed HF therapy and in-hospital outcomes are associated with payer status, with decreased quality and outcomes in no insurance, Medicaid, and Medicare patients when compared to private/HMO patients. Addressing these differences in care and outcomes will require additional efforts.