Methods

We conducted a cross-sectional study using the hospital as the unit of analysis. The sample included all short-term acute and critical access nonfederal hospitals that submitted >15 inpatient Medicare claims to the Centers for Medicare and Medicaid Services for discharges from July 1, 2005 to June 30, 2008 for Medicare fee-for-service beneficiaries with a principal discharge diagnosis of AMI. We used the 2006 American Hospital Association Survey of Hospitals for data on hospital characteristics and data from 2009 Population Facts (Nielsen Claritas, Inc., Los Angeles, California), previously used in cardiology outcomes research, to characterize the SES profile of hospitals’ patient population.

Outcome was hospital 30-day RSMR for July 2005 through June 2008, calculated with the model used by the Centers for Medicare and Medicaid Services for public reporting of 30-day RSMRs. The 30-day RSMR is calculated for each hospital using a hierarchical generalized linear model. For each hospital, the RSMR is estimated by dividing the predicted number of deaths within 30 days of admission by the expected number of deaths within 30 days of admission and then multiplying this ratio by the national unadjusted 30-day mortality rate. The national rate is obtained using data on deaths from the Medicare beneficiary denominator file. The approach accounts for sampling variability due to differences in hospital AMI volume and for lack of statistical independence in patients treated in the same hospital. Specifications for RSMR, including patient-level variables (e.g., medical history, clinical co-morbidities, age, and gender) used for standardization, have been previously described.

Independent variables, selected based on previous literature and our hypotheses, included hospital teaching status. We categorized teaching status in 3 levels: (1) membership in the Council of Teaching Hospitals (COTH), (2) having residency programs accredited by the Accrediting Commission for General Medical Education but not being a member of COTH, and (3) being nonteaching. Other variables included number of staffed beds (≤50, 51 to 100, 101 to 200, 201 to 300, >300 beds), AMI volume per year (16 to 50, 51 to 180, 180 to 500, and >500 Medicare discharges with AMI), cardiac facilities (no catheterization laboratory, catheterization laboratory but no open heart surgery, open heart surgery), urban/rural level (division, metropolitan, micropolitan, and rural as defined by the United States Census), ownership type (government-owned, nonprofit, and for-profit), and census region of hospital. We also examined the reported presence of cardiac rehabilitation programs, tobacco treatment programs, and hospice beds. We included hospice beds because some have argued that hospitals have higher mortality rates if hospice care is available as part of the hospital. We measured the SES profile of patients with AMI as an additional hospital characteristic using the SES scale developed by Nielsen Claritas, Inc., in 2009 Population Facts and used in previous studies, which we linked to the Medicare claims data. The SES scale is based on a zip-code algorithm in which each zip code is given a score for 2009 using the most recent United States Census data and derived from a weighting of household income, educational level, occupation, and home value, projected forward to reflect economic and population growth for each region. We classified each patient discharge into a quintile according to the SES score assigned to the patient’s zip code. A patient discharge was classified as “low SES” if the patient lived in a zip code that had a lowest quintile SES score of all Medicare patients hospitalized with AMI. For each hospital, we then calculated the percentage of Medicare AMI discharges that were categorized as low SES. We categorized this variable in quintiles, which corresponded to hospitals with ≤2%, 3% to 10%, 11% to 30%, 31% to 65%, and 66% to 100% of patients with AMI who were from low SES zip codes.

We summarized all independent variables and estimated a weighted analysis of variance model for each using RSMR as the dependent variable, weighted for the number of AMI admissions included in the RSMR calculation. For each bivariate model, we reported the p values for each level of categorical variables, the overall p value based on the F test, and the proportion of variance in RSMR explained by each independent variable, as measured by R ² statistics. Because the 30-day RSMR incorporates patient-level factors related to medical history, clinical co-morbidities, age, and gender, the R ² statistics refer to the proportion of the variation remaining after accounting for these patient-level factors that is explained by the independent variables.

To assess the association of each independent variable with RSMR, we estimated a multivariable weighted least squares model using RSMR as the dependent variable, weighted for number of AMI admissions included in RSMR calculations. We calculated Wald p values for each categorical independent variable. All analyses were performed using SAS 9.1 (SAS Institute, Cary, North Carolina) and STATA 11 (STATA Corp., College Station, Texas).

Results

From the initial sample of 4,601 hospitals and 587,779 AMI discharges, 143 hospitals were excluded because they did not have a match in the American Hospital Association Survey of Hospitals, resulting in 4,458 hospitals with 582,949 AMI discharges. We then excluded 1,094 hospitals because they had <15 AMI discharges during the study period. We eliminated 456 hospitals with 62,773 AMI discharges due to missing data, resulting in a final sample of 2,908 hospitals, representing 513,202 discharges. Hospitals excluded due to missing data had fewer beds, lower AMI volume, and more patients with from low SES zip codes than included hospitals but did not differ significantly (p >0.05) in RSMRs.

Mean of RSMR was 16.5% with an SD of 1.7 percentage points. The range was 10.9% to 24.9%. Unadjusted analysis ( Table 1 ) indicated that lower RSMRs (i.e., better hospital performance) were significantly (p <0.05) correlated with COTH teaching status (compared to having Accrediting Commission for General Medical Education residencies only or being a nonteaching hospital), more staffed beds, greater AMI volume, having open heart surgery facilities (vs having catheterization laboratories only or having neither open heart or catheterization laboratory facilities), more urban locations, for-profit or nonprofit ownership (compared to government-owned), New England region, presence of cardiac rehabilitation services, existence of tobacco treatment programs, presence of hospice beds, and having lower percent AMI discharges from lower SES areas. The SES profile of patients with AMI explained 3.8% of the variation in RSMR, and the variable with the highest R ² was AMI volume with an R ² of 8.8% ( Table 1 ).

Several hospital characteristics were significantly associated with lower 30-day RSMRs in the multivariable analysis ( Table 2 ) including COTH teaching hospitals (compared with non-COTH hospitals with residency programs and compared with nonteaching hospitals), greater AMI volume, larger number of beds, having open heart surgery capability (compared with having a catheterization laboratory only and compared with having no catheterization laboratory), more urban location, nonprofit versus government ownership, and New England region. In this multivariable model including AMI volume and number of staffed beds, AMI volume was negatively associated with 30-day RSMR, suggesting that for hospitals with a given number of beds, those with higher volume have lower RSMRs, and given a fixed volume, hospitals with a larger number of staffed beds had higher RSMRs. Percent AMI discharges from low SES zip codes was also significantly associated with RSMR. Having larger proportions of AMI discharges classified as low SES was generally associated with higher RSMR. Presence of cardiac rehabilitation, tobacco treatment services, and hospice beds was not significantly associated with RSMR. The proportion of variance in RSMR explained by multivariable regression was 17.1%.

Table 2

Multivariable weighted least squares regression (n = 2,908 hospitals)

	Coefficient ± SE	p Value	Wald p Value
Intercept	14.667 ± 0.324	<0.001
Type of hospital			<0.001
Council of Teaching Hospitals member	reference
Has residency programs	0.500 ± 0.104	<0.001
Nonteaching	0.628 ± 0.106	0.002
Number of staffed beds			0.001
≤50	reference
51–100	−0.059 ± 0.237 ⁎	0.804
101–200	0.367 ± 0.239 †	0.124
201–300	0.548 ± 0.248	0.027
>300	0.626 ± 0.254	0.014
Acute myocardial infarction annual discharges			<0.001
15–50	reference
51–180	−0.183 ± 0.189	0.335
181–500	−0.946 ± 0.204	<0.001
>500	−1.318 ± 0.219	<0.001
Cardiac facilities			<0.001
Open heart surgery capacity	reference
Catheterization laboratory only	0.417 ± 0.105	<0.001
Neither	0.286 ± 0.137	0.037
Urban/rural location			0.002
Metropolitan division ∥	reference
Metropolitan	0.305 ± 0.092	0.001
Micropolitan	0.466 ± 0.134	0.001
Rural	0.492 ± 0.234	0.035
Geographic region			<0.001
New England	reference
Mid Atlantic	0.674 ± 0.155	<0.001
South Atlantic	0.837 ± 0.151	<0.001
East north central	0.680 ± 0.147	<0.001
East south central	0.883 ± 0.188	<0.001
West north central	0.671 ± 0.175	0.001
West south central	1.304 ± 0.173	<0.001
Mountain	0.752 ± 0.206	<0.001
Pacific	1.036 ± 0.175	<0.001
United States Territories	2.961 ± 0.701	<0.001
Cardiac rehabilitation services			0.543
No	reference
Yes	−0.65 ± 0.106	0.543
Tobacco treatment services			0.241
No	reference
Yes	−0.102 ± 0.087	0.241
Hospice beds			0.641
No	reference
Yes	0.040 ± 0.079	0.641
Percent acute myocardial infarction—low socioeconomic status			<0.001
≤2%	reference
3%–10%	0.065 ± 0.112 ‡	0.562
11%–30%	0.274 ± 0.112 §	0.014
31%–65%	0.529 ± 0.127	<0.001
66%–100%	0.752 ± 0.183	<0.001
			R 2 17.1

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