Table 2.

Hierarchical Logistic Regression Models for the Effect of Race and Gender on Post-operative Mortality and Morbidity

Variable	Mortality Model^a		Morbidity Model^b
	Odds Ratio	p	Odds Ratio	p
	[95% C.I.]	p	(95% C.I.)	p
Race
Black	1.74 [1.26,2.40]	0.02	1.24 [1.11,1.41]	0.01
Hispanic	1.03 [0.71,1.50]		1.12 [1.02,1.24]
Asian or Pacific Islander	0.90 [0.36,2.28]		1.14 [0.87,1.50]
Native American	0.66 [0.08,5.82]		1.10 [0.69,1.75]
Other	1.25 [0.74,2.10]		1.10 [0.93,1.30]
White (ref.)	1		1
Gender
Female	1.04 [0.83,1.31]	0.77	0.87 [0.81,0.94]	<0.001
Male (ref.)	1	0.77	1	<0.001

C.I. = Confidence Interval;

Model Performance: Area Under Receiver Operating Characteristics Curve (AUC) = 0.98, Nagelkerke PseudoR² = 0.50 with 64 degrees of freedom;

Model Performance: Area Under Receiver Operating Characteristics Curve (AUC) = 0.68, Nagelkerke PseudoR² = 0.07 with 52 degrees of freedom. Model adjustment for patient characteristics (age, sex, race, income, primary payer status, 29 co-morbid disease categories) operative characteristics (operation type, operative year, elective vs. non-elective status) and hospital characteristics (size, region, teaching status, rural location).