Variations in Health Care Measures by Insurance Status for

Patients with a Respiratory System Diagnosis Requiring

Intubation and Continuous Ventilator Support

Mark A. Schnitzler MS, Dennis L. Lambert PhD,

Linda M. Mundy MD, Robert S. Woodward PhD

The Health Administration Program, and the Department of Internal Medicine

at the Washington University School of Medicine

St. Louis Missouri 63110.

Address Correspondence:

Mark A. Schnitzler

The Health Administration Program

Washington University School of Medicine

4547 Clayton Avenue

St. Louis, Missouri 63110

Ph	(314) 362-4277

Fax	(314) 362-3265

e-mail	schnitz@wuecona.wustl.edu

Abstract

A number of articles have reported that the uninsured and Medicaid populations have higher rates of mortality and morbidity than the privately insured.. This study adds to the literature by applying Braveman's distinction between traditional commercial and PPO plans and private HMOs and PHPs to patients in the particularly expensive DRG 475, respiratory system diagnoses requiring intubation and continuous ventilator support, as recorded in a HCUP-3, a large interstate database. The principal finding is that patients covered by the more traditional commercial and PPO insurance plans have the highest inpatient mortality rate and the fewest procedures performed as compared with all other insurance categories. These and other significant differences within the two privately insured categories and between Medicaid and the uninsured populations emphasize the importance of more careful analyses of insurance categories as a determinant of health care access and outcomes.

Key words: quality of care, Healthcare Cost and Utilization Project, HCUP-3, insurance status, continuous ventilator support, DRG 475.

Introduction

Insurance category clearly correlates with mortality, morbidity, and utilization. The adult Medicaid and uninsured populations experience higher death rates than the privately insured.1,2 The uninsured and Medicaid covered face restricted access to medical care and receive less care than the privately insured.3,4 Among the privately insured capitated plans provide different levels of services than traditional fee-for-service plans. For example, Health Maintenance Organizations HMO's generally have lower rates of hospitalization and higher rates of preventive services.5-8 It has also been reported that uninsured and Medicaid insured individuals have shorter hospitalizations and are charged less than those with private insurance.4-6, 8-13

To expand the understanding of the relationship between insurance status and health care delivery, this study examined patients hospitalized with for a respiratory system diagnosis who were intubated and received continuous ventilator support, DRG 475.14 While DRG 475 is uncommon, representing less than 0.5% of all hospital discharges,15 it has both high costs and high risks of death. As a consequence, it represents an observation where the relationship between in-hospital care and outcomes may be more direct than for lower risk discharges. The goal of this study is to determine if there are statistically significant differences by insurance status for DRG 475 patients in four health care measures related to hospital care: inpatient mortality, the number of procedures performed, the length of hospitalization, and total hospital charges.

Methods

Data Source and Sample

Data were abstracted from the Nationwide Inpatient Sample (NIS), Release 1 of the Healthcare Cost and Utilization Project (HCUP-3), sponsored by the Agency for Health Care Policy and Research. The NIS is drawn from a sample of hospitals in 11 states (Arizona, California, Colorado, Florida, Illinois, Iowa, Massachusetts, New Jersey, Pennsylvania, Washington, and Wisconsin) and contains approximately a 20% sample of discharges from community hospitals in the United States between 1989 and 1992. A smaller initial sample was collected in 1988 but is excluded from this study because of differing data collection methods and data limitations. All patients between the ages of 18 and 64, discharged in DRG 475, respiratory system diagnosis with ventilator support, were included in the study. Records from Arizona and Iowa for all four years, and Pennsylvania for 1989 did not include insurance status and were excluded. This initial sample consisted of 24,663 hospital discharges. After additional exclusions for insurance status, described below, the sample consisted of 21,149 hospital discharges.

Variables

Insurance Status

Insurance status was determined by the reported expected primary payor at admission. While the actual primary payor after discharge was unavailable in this data set, five types of insurance coverage were included and analyzed: uninsured (uninsured, indigent, or free care); Medicare; Medicaid; traditional private commercial and PPO plans; and private HMOs and PHPs.9 We excluded 76 Workers Compensation, 84 CHAMPUS or CHAMPVA, 588 other government, 2118 other, and 648 missing insurance status records.

Outcome and Resource Allocation

The outcome measure was inpatient mortality as defined by a discharge disposition of "died". Measures of resource allocation included the number of procedures performed, the length of hospitalization, and total hospital charges. The number of procedures performed was the number of procedures listed on the patient's administrative record at discharge. Length of hospitalization was specified as the number of days from admission to discharge. Length of hospitalization was defined as one for an admission and discharge on the same day. Total hospital charges included charges for all hospital services, procedures, and supplies. Physician fees are excluded from the records and were, therefore, not included in total hospital charges.

Covariate Factors

Demographic patient characteristics included: age in years, and binary variables indicating major ethnic groups, gender, and low average income in the patient's zip code. Race was not a required record in all states; consequently there were 10,264 (49%) observations with race missing. Separate Black and Hispanic race variables were entered when recorded, with all missing observations included in the Other reference group which included whites, Asians, Native Americans, and other races. The NIS included an eight category indicator of the median income of the patient's zip code. The lowest category was included in analyses to indicate a high level of poverty. There were 956 missing values for this record which were included in the reference group.

Differences in admission diagnosis were accounted for by including a binary variable for each of the reported principal diagnoses. There were 188 separate International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) codes16 indicated as the principal diagnosis (Table 2). Results are not reported for individual principal diagnoses, but the reported estimates have been adjusted for them.

Hospital characteristics were included as additional possible determinants of mortality and resource allocation. These included: teaching hospital, for profit ownership structure, and total annual discharges. Teaching hospital and for profit ownership structure were included as binary indicator variables. Total annual discharges were divided by 10,000 and entered as a continuous variable.

Statistical Analyses

Statistical analyses were performed with PC SAS (version 6.11, SAS Institute, Carey, North Carolina). Multivariate regressions were used to estimate coefficients adjusted for covariate factors for the number of procedures performed, LOS, and total hospital charges. Skewness in the equation residuals was accounted for by taking the natural log of the dependent variables, producing coefficients that are interpreted approximately as percentages.17 Multivariate logistic regression analysis was used to estimate odds ratios adjusted for covariate factors for inpatient mortality. Principal diagnoses in which each patient either survived or died were set in the reference group in the logistic regression because of exact sample separation. Estimated coefficients and odds ratios are reported with 95 percent confidence intervals. The overall significance of the principal diagnoses is reported from a type III sum of squares F-test for multivariate regression analyses and a likelihood ratio test for multivariate logistic regression analysis.

Results

Patient Outcomes: Inpatient Mortality

The sample average inpatient mortality rate was 28.1% Table 1. Higher than average unadjusted inpatient mortality was observed for commercial/PPO (29.9%) and Medicaid (28.3%) patients, while lower than average inpatient mortality was observed for uninsured (26.8%), Medicare (26.3%), and HMO/PHP (25.5%) patients. Multivariate analysis controlling for covariate factors (Table 3) confirmed the observed inpatient mortality pattern. Adjusted odds ratios relative to commercial/PPO insurance were less than one for each of the alternative insurance categories and the HMO/PHP, Medicare, and uninsured odds ratios were significantly less than one. Odds ratios were 0.84 (95% CI: 0.73 to 0.96) for HMO/PHP, 0.85 (95% CI: 0.78 to 0.93) for Medicare, 0.87 (95% CI: 0.77 to 0.99) for uninsured, and 0.96 (0.88 to 1.05) for Medicaid patients. Age was associated with significantly greater odds of inpatient mortality with an odds ratio of 1.012 per year (95% CI: 1.009 to 1.016). Men were at greater risk of inpatient mortality than women with an odds ratio of 1.10 (95% CI: 1.03 to 1.18). Black patients had a greater risk of inpatient mortality with an odds ratio of 1.16 (95% CI: 1.04 to 1.31), while Hispanic patients were not found to be at significantly greater risk. For profit hospitals experienced significantly higher inpatient mortality rates than not for profit hospitals with an odds ratio of 1.12 (95% CI: 1.01 to 1.25), and larger hospitals, as measured by annual hospital discharges, experienced significantly higher inpatient mortality rates than smaller hospitals with an odds ratio of 1.07 per 10,000 discharges (95% CI: 1.04 to 1.11). The overall effect of principal diagnosis stratifiers on inpatient mortality was significant with a p-value less than 0.0001.

Resource Allocation Measures: Procedures

The average number of reported performed procedures was 3.2. Fewer than the average number of unadjusted procedures were performed for commercial/PPO (3.1) and Medicare (3.1) patients, while more than the average number of procedures were performed for Medicaid (3.3), uninsured (3.6), and HMO/PHP (3.8) patients. Multivariate analysis controlling for covariate factors (Table 3) substantiated these findings. Medicare patients had 1.9% more (95% CI: 0.1 to 3.7), Medicaid patients had 3.5% more (95% CI: 1.6 to 5.4), uninsured patients had 8.5% more (95% CI: 6.0 to 11.1), and HMO/PHP patients had 14.3% more (95% CI: 11.5 to 17.2) procedures performed than commercial/PPO patients. Age was associated with significantly more procedures, 0.17% per year (95% CI: 0.11 to 0.23). Race was a significant determinant of the number of procedures performed. Black and Hispanic patients were associated with more procedures, 18.2% (95% CI: 15.8 to 20.6), and 28.3% (95% CI: 24.9 to 31.8), respectively. Patients from zip codes with the highest level of poverty had significantly fewer procedures performed, -6.0% (95% CI: -9.2 to - 2.8). Significantly fewer procedures were performed at for profit hospitals, -2.6% (95% CI: -4.9 to -0.3), and larger hospitals, -1.3% per 10,000 discharges (95% CI: -2.0 to -0.6). Principal diagnosis stratifiers as a group were a significant determinant of the number of procedures performed with a p-value less than 0.0001.

Length of Hospitalization

The average length of hospitalization was 12.5 days. Shorter than average unadjusted stays were observed for uninsured (10.4), HMO/PHP (10.6), and commercial/PPO (11.7) patients, while longer than average stays were observed for Medicaid (13.5), and Medicare (14.0) patients. Multivariate analysis substantiated this pattern (Table 3). HMO/PHP patients had 7.0% shorter (95% CI: -12.5 to -1.6), and uninsured patients had 16.5% shorter (95% CI: -21.5 to -11.6) stays than commercial/PPO patients, and Medicaid patients had 10.4% longer (95% CI: 6.7 to 14.0), and Medicare patients had 16.3% longer (95% CI: 12.8 to 19.7) stays than commercial/PPO patients. Age was associated with significantly longer stays, 0.83% per year (95% CI: 0.71 to 0.95). Significantly shorter stays were found for men, -8.0% (95% CI: -10.8 to -5.3), blacks, - 6.1% (95% CI: -10.8 to -1.4), Hispanics, -7.1% (95% CI: -13.7 to -0.5), and patients from the zip codes with the most poverty, -10.0% (-16.1 to -3.9). Longer stays were found at teaching hospitals, 3.9% (95% CI: 0.8 to 7.1), and larger hospitals, 3.0% per 10,000 discharges (95% CI: 1.6 to 4.3). The principal diagnoses were significant as a group with a p-value less than 0.0001.

Total Hospital Charges

Average total hospital charges were $31,127. Lower than average unadjusted hospital charges were observed for uninsured ($26,394), and commercial/PPO ($29,543) patients, and higher than average charges were observed for Medicare ($31,617), HMO/PHP ($31,626), and Medicaid ($35,065) patients. A similar pattern was observed in multivariate analysis controlling for covariate factors (Table 3). The adjusted percentage difference between uninsured and commercial/PPO patients was significant at -13.4% (95% CI: -17.8 to -9.0). The other insurance categories displayed significantly higher adjusted charges than commercial/PPO patients with adjusted percentage differences of 5.2% (95% CI: 0.4 to 10.0) for HMO/PHP, 9.0% (95% CI: 6.0 to 12.1) for Medicare, and 13.8% (95% CI: 10.6 to 17.0) for Medicaid patients. Age was associated with significantly higher charges, 0.51% per year (95% CI: 0.39 to 0.62). Men had significantly lower charges than women, -4.2% (95% CI: -6.6 to -1.8), and blacks had significantly lower charges, -4.6% (95% CI: -8.8 to -0.4). Significantly higher adjusted charges were noted for teaching hospitals, 7.6% (95% CI: 4.8 to 10.4), for profit hospitals, 18.5% (95% CI: 14.6 to 22.4), and larger hospitals, 4.1% per 10,000 discharges (95% CI: 2.9 to 5.4). The principal diagnosis stratifiers were significant as a group with a p-value less than 0.0001.

Discussion

In this study the effects of insurance status at admission on inpatient mortality, the number of procedures performed, length of hospitalization, and total hospital charges were examined for a national sample of DRG 475 patients, individuals hospitalized with a respiratory system diagnosis requiring endotracheal intubation and continuous mechanical ventilation. The major finding is, after controlling for patient, hospital, and disease characteristics, patients covered by private commercial/PPO insurance had the highest inpatient mortality rate and the fewest number of procedures performed as compared to the other insurance categories. Private commercial/PPO insurance status was also associated with lower total hospital charges than all but the uninsured and shorter lengths of hospitalization than all but the uninsured and those with private HMO/PHP insurance. Furthermore, these results are largely repeated when the data is restricted to patients in either of the large diagnostic groups pneumonia or respiratory failure and in a separate analysis of patients who survived.

On the surface, these results appear to contradict published studies in which the uninsured and Medicaid populations experienced higher rates of mortality and morbidity than the privately insured.1,2 The literature has also reported that, relative to privately insured patients, uninsured and Medicaid patients experience shorter hospitalizations and are charged less.4-6,8-13 The current study provides evidence that Medicaid and uninsured DRG 475 patients receive more care than the individuals covered by the traditional types of private insurance, commercial/PPO plans. Medicaid patients receive a significantly greater number of procedures, have significantly longer lengths of stay, and have significantly higher charges. While the uninsured have significantly shorter stays and lower charges, they still receive a significantly larger number of procedures and have a significantly lower mortality rate.

More importantly, the results highlight the importance of distinguishing between traditional commercial/PPO insurance plans and the private HMO/PHP plan counterparts While the patients with private commercial insurance have the highest mortality rates, patients with private HMO/PHP coverage have the lowest mortality. The two groups also have significantly different numbers of procedures, length of hospitalization, and hospital charges. The results also suggest that policy makers should not presume similarities between Medicaid and the uninsured. In this study, the mortality rate of, and the number of procedures performed on, patients with Medicaid more closely approximated that of the population insured by commercial/PPO plans while the uninsured more closely approximated that of individuals with HMO/PHP coverage.

Of course, data from administrative data bases such as HCUP-3 do not have sufficient information to compare either the appropriateness or quality of care delivered by different insurance categories. For example, analyses of these data can not determine whether the higher mortality rate of the commercial/PPO and Medicaid insured individuals occurred because of lower quality care, because physicians in these plans had financial incentives to take greater risks and provide more extraordinary care, or because these individuals had lower initial health status. Furthermore, any measure of the quality of care should account for patient perceptions and preferences. Since these perceptions and preferences are complex and difficult to measure,18 it is unlikely that the simple measures of patient outcomes and resource allocation that are often reported will provide clear indications of the quality of care. Nevertheless, this study does cast some doubt on previous publications suggesting Medicaid and uninsured patients receive lower quality health care.

Other limitations of this study included missing data and the determination of insurance status. Forty-nine percent of the observations on the patient's race and five percent of the observations on the patient's median zip code income group were missing. While the small amount of missing income group data is of little concern, the absence of nearly half of the information on patient's race limits the ability to control for race. Insurance status was the expected principal payor at admission, which may have changed between admission and discharge and the actual principal payor after discharge was unavailable. This type of data has been used previously in this literature,9,10 and expected principal payor may be more relevant than ultimate payor after discharge. Expected payor status represents the perceived insurance status of the population, and during treatment the patient, the patient's family, and the health care providers do not know with certainty the ultimate payor, while they do know the expected payor. Therefore, we believe the expected principal payor is more relevant than ultimate payor for an analysis of how the population's insurance status affects the delivery of health care.

Our society is considering a variety of health care reforms that will impact the health insurance industry. The eventual impact may be small resulting in little change in the status quo or large with a dramatic shift to a single public payor. While much of the focus of this debate has revolved around cost containment, there has been a clear interest in quality of care differences between existing types of insurance plans. Questions have been raised about medical care quality and accessibility by the publicly insured, and uninsured relative to the privately insured. There is additional debate over the quantity and quality of care provided by private commercial/PPO plans as compared to private HMO/PHP plans. Policy makers need to understand the details of both the cost and quality consequences of different types of health care insurance plans to make informed decisions about the future direction of the health care industry in this country. The results presented here suggest caution when generalizing conclusions in published studies as well as highlight the need for more extensive study of the impact of insurance status on the provision of health care. Areas for continued research include examinations of the impact of insurance status on specific areas of health care delivery and extensions of the measures of the quality of health care.

References

1. Franks P, Clancy CM, Gold MR. Health insurance and mortality evidence from a national	cohort. JAMA 1993;270:737.

2. Sorlic PD, Johnson NJ, Backlund E, Bradham DD. Mortality in the uninsured compared with	that in persons with public and private health insurance. Arch Intern Med 1994;154:2409. 3. Hafner-Eaton C. Physician utilization disparities between the uninsured and insured
comparisons of the chronically ill, acutely ill, and well nonelderly populations. JAMA	1993;269:787.

4. Horner, RD, Bennett CL, Rodriguez D, et al. Relationship between procedures and health	insurance for critically ill patients with pneumocystis carinii pneumonia. Am J Resp Crit	Care Med 1995;152:1435.

5. Weissman J, Epstein AM. Case mix and resource utilization by uninsured hospital patients in	the Boston metropolitan area. JAMA 1989;261:3572.

6. Becker ER, Sloan FA. Utilization of hospital services: the roles of teaching, case mix, and	reimbursement. Inquiry. 1983;20:248.

7. Gordon T, DeAngelis C, Peterson R. Capitation reimbursement for pediatric primary care.	Pediatrics 1986;77:29.

8. Wenneker MB, Weissman JS, Epstein AM. The association of payor with utilization of	cardiac procedures in Massachusetts. JAMA 1990;264:1255.

9. Braveman P, Schaaf VM, Egerter S, Bennett T, Schecter W. Insurance-related differences in	the risk of ruptured appendix. N Engl J Med 1994;331:444.

10. Braveman P, Egerter S, Bennett T, Showstack J. Differences in hospital resource allocation	among sick newborns according to insurance coverage. JAMA 1991;266:3300.

11. Haas JS, Goldman L. Acutely injured patients with trauma in Massachusetts: differences in	care and mortality, by insurance status. Am J Public Health 1994;84:1605.

12. Hadley J, Steinberg EP, Feder J. Comparison of uninsured and privately insured hospital	patients. JAMA 1991;265:374.

13. Ayanian JZ, Kohler BA, Abe T, Epstein AM. The relation between health insurance	coverage and clinical outcomes among women with breast cancer. N Engl J Med	1993;329:326.

14. Health Care Financing Agency. Fed. Reg. 52FR 33143, Sept. 1, 1987.

15. Ernst & Young LLP. The DRG handbook: comparative clinical and financial standards.	Cleveland, Ohio: HCIA Inc., 1995.

16. The international classification of diseases, 9th rev., clinical modification: ICD-9-CM. 4th	ed. Salt lake City: Med-Index, 1991.

17. Berndt ER. The practice of econometrics classic and contemporary. Reading,
Massachusetts: Addison-Wesley, 1991.

18. Testa MA, Simonson DC. Assessment of quality-of-life outcomes. N Engl J Med
1996;334:835.

Table 1. Descriptive Statistics of the Patient Sample.

Variable:	No. (%)	Mean (range)
Insurance
Medicare	5,224 (25)
Medicaid	4,651 (22)
Uninsured	1,919 (9)
Private HMO/PHP	1,461 (7)
Private commercial/PPO	7,894 (37)
Patient Demographics
Age in Years	50.4 (18 - 64)
Gender
Male	11,311 (53)
Female	9,838 (47)
Race
Black	1,857 (9)
Hispanic	887 (4)
Other	8,141 (38)
Missing Data	10,264 (49)
Zip code median income group
Lowest 12.5%	1,046 (5)
Highest 87.5%	19,147 (91)
Missing Data	956 (5)
Outcome and Resource Allocation
Inpatient mortality	5,933 (28)
Number of procedures performed	3.2 (1 - 21)
Length of hospitalization	12.5 (1 - 776)
Total hospital charges	$31,127 (350 - 681,505)
Hospital Characteristics
Hospital Teaching Status
Teaching	7,358 (35)
Nonteaching	13,791 (65)
Hospital Ownership Structure
For profit	2,332 (11)
Not for profit	18,817 (89)
Annual hospital discharges	15,684 (63 - 91,530)

Table 2. Principal Diagnoses.

Diagnosis	No. (%)
Respiratory Failure	8,177 (39)
Pneumonia	5,474 (26)
Asthma	1,956 (9)
Respiratory Neoplasm	715 (3)
Acute Respiratory Distress	575 (3)
Chronic Airway Obstruction	549 (3)
Acute Edema of Lungs	397 (2)
Respiratory Arrest	387 (2)
Acute Bronchitis	368 (2)
Chronic Bronchitis	309 (1)
Pulmonary Insufficiency Following Trauma	257 (1)
Traumatic Pneumothorax	216 (1)
Othera	1,769 (8)
a Principal diagnoses that accounted for less than 1% of the sample included: pulmonary tuberculosis, various infections, psychogenic respiratory failure, acute tracheitis, emphysema, bronchiectasis, extrinsic allergic alveolitis, respiratory conditions due to radiation, respiratory conditions due to other external agents, empyema, pleurisy, pneumothorax, abscess of lung, pulmonary congestion and hypostasis, postinflamitory pulmonary fibrosis, pulmonary alveolar proteinosis, idiopathic fibrosing alveolitis, alveolar pneumonopathies, pulmonary collapse, interstatial emphysema, pulmonary eosinophilia, other diseases of the lung, tracheostomy complication, unclassified diseases of the trachea, mediastinitis, unclassified respiratory disease, hyperventilation, apenea or tachypenea, hemoptysis, painful respiration, swelling in chest, asphyxia, fracture of ribs, fracture of sternum, flail chest, lung injury, injury, to other interthoratic organs, open trachea wound, foreign body in respiratory tract, burn of larynx, trachea, or lung, embolism as complication of trauma, traumatic subcutaneous emphysema, complications of lung transplant, respiratory complications of a procedure, and air embolism.

Table 3. Multivariate Analyses.a

Inpatient Mortality

Logistic Regression	Number of Procedures

Linear Regression	Length of Hospitalization

Linear Regression	Total Hospital Charges

Linear Regression
Adjusted Odds Ratiob

(95% CI)	Adjusted Percentb

Difference

(95% CI)	Adjusted Percentb

Difference

(95% CI)	Adjusted Percentb

Difference

(95% CI)
Insurance
Medicare	0.85 (0.78, 0.93)	1.9% (0.1, 3.7)	16.3% (12.8, 19.7)	9.0% (6.0, 12.1)
Medicaid	0.96 (0.88, 1.05)	3.5% (1.6, 5.4)	10.4% (6.7, 14.0)	13.8% (10.6, 17.0)
Uninsured	0.87 (0.77, 0.99)	8.5% (6.0, 11.1)	-16.5% (-21.5, -11.6)	-13.4% (-17.8, -9.0)
Private HMO/PHP	0.84 (0.73, 0.96)	14.3% (11.5, 17.2)	-7.0% (-12.5, -1.6)	5.2% (0.4, 10.0)
Private commercial/PPO (Ref.)	1.00	0.0%	0.0%	0.0%
Age in Years	1.012 (1.009, 1.016)	0.17% (0.11, 0.23)	0.83% (0.71, 0.95)	0.51% (0.39, 0.62)
Gender
Men	1.10 (1.03, 1.18)	-1.7% (-3.1, 0.3)	-8.0% (-10.8, -5.3)	-4.2% (-6.6, -1.8)
Women (Ref.)	1.00	0.0%	0.0%	0.0%
Race
Black	1.16 (1.04, 1.31)	18.2% (15.8, 20.6)	-6.1% (-10.8, -1.4)	-4.6% (-8.8, -0.4)
Hispanic	1.15 (0.98, 1.36)	28.3% (24.9, 31.8)	-7.1% (-13.7, -0.5)	-0.5% (-6.4, 5.4)
Other (Ref.)	1.00	0.0%	0.0%	0.0%
Zip code median income group
Lowest 12.5%	1.02 (0.87, 1.18)	-6.0% (-9.2, -2.8)	-10.0% (-16.1, -3.9)	1.5% (-3.9, 7.0)
Highest 87.5% (Ref.)	1.00	0.0%	0.0%	0.0%
Hospital Teaching Status
Teaching	1.00 (0.93, 1.08)	0.5% (-1.2, 2.2)	3.9% (0.8, 7.1)	7.6% (4.8, 10.4)
Nonteaching (Ref.)	1.00	0.0%	0.0%	0.0%
Hospital Ownership Structure
For profit	1.12 (1.01, 1.25)	-2.6% (-4.9, -0.3)	-3.7% (-8.0, 0.7)	18.5% (14.6, 22.4)
Not for profit (Ref.)	1.00	0.0%	0.0%	0.0%
Annual hospital discharges (10,000's)	1.07 (1.04, 1.11)	-1.3% (-2.0, -0.6)	3.0% (1.6, 4.3)	4.1% (2.9, 5.4)
Principal diagnoses (p-value)	<0.0001	<0.0001	<0.0001	<0.0001
a Results are adjusted for all variables listed.

b Reported adjusted risk ratios and percentage differences are relative to reference groups as indicated or, in the case of age and annual hospital discharges, for a unit change in the listed variable.