Table 2.

Multivariable models for length of stay after total knee arthroplasty in California, 2007-2010 (N = 133,603).

Characteristics	Negative binomial model on LOS, indicating relative LOS			Logistic regression model for probability of having outlier LOS (>9 d)
	aRatio	[95% CI]		Odds ratio	[95% CI]
Sex
Male#	1.00			1.000
Female	1.031d	1.025	1.037	0.635d	0.561	0.718
Age (y)
50-54#	1.000			1.000
55-59	1.004	0.989	1.019	0.889	0.629	1.258
60-64	1.006	0.992	1.021	1.040	0.753	1.437
65-69	1.028d	1.014	1.042	1.217	0.889	1.666
70-74	1.051d	1.037	1.066	1.557c	1.145	2.117
75-79	1.093d	1.078	1.108	1.973d	1.454	2.677
Over 80	1.154d	1.138	1.171	2.932d	2.176	3.951
Ethnic group
White#	1.000			1.000
Hispanic	1.063d	1.054	1.072	1.214b	1.019	1.447
Black	1.111d	1.096	1.126	1.917d	1.503	2.445
Asian	1.118d	1.102	1.135	2.045d	1.615	2.590
All others	1.045d	1.026	1.064	1.096	0.760	1.581
Insurance type
All other insurance#	1.000			1.000
Medicaid	1.216d	1.195	1.238	3.721d	2.932	4.724
Depression comorbidity
No#	1.000			1.000
Yes	1.052d	1.041	1.064	1.826d	1.496	2.229
Year of admission
2007#	1.000			1.050	0.906	1.218
2008	0.958d	0.950	0.966	0.800c	0.681	0.940
2009	0.918d	0.910	0.925	0.634d	0.535	0.751
2010	0.888d	0.881	0.895	0.607d	0.513	0.718

^aExponent of the coefficient, which is the expected change in log count for a one-unit increase in a “Characteristic,” interpreted as a ratio to the ^#Reference category.

^bP < .05.

^cP < .01.

^dP < .001.