TABLE 4.

Key Odds Ratios from Multivariate Models Predicting Urban Physicians' Participation in Medicaid

	Percentage of the Poor That Are White		Racial Segregation		Poverty Segregation		Full Model
	(1) Primary Care Physicians (n= 5,551)	(2) Specialist Physicians (n= 3,031)	(3) Primary Care Physicians (n= 5,551)	(4) Specialist Physicians (n= 3,031)	(5) Primary Care Physicians (n= 5,551)	(6) Specialist Physicians (n= 3,031)	(7) Primary Care Physicians (n= 5,551)	(8) Specialist Physicians (n= 3,031)
Percentage of the Poor That Are White
<25%	(1.00)	(1.00)					(1.00)	(1.00)
25%–<45%	1.42a	1.26					1.21	1.01
45%–<65%	1.46a	1.35					1.32b	1.20
65%+	2.06c	4.87c					1.83a	4.36c
Racial Segregationd
<.35			(1.00)	(1.00)			(1.00)	(1.00)
.35–<.50			0.66a	0.99			0.65a	0.86
.50–<.60			0.63a	0.63e			0.87	0.83
.60+			0.64a	0.80			0.79	0.51e
Poverty Segregationd
<.25					(1.00)	(1.00)	(1.00)	(1.00)
.25–<.33					0.90	0.61e	1.13	0.83
.33–<.37					0.52c	0.52a	0.73e	0.96
.37+					0.78	0.96	1.12	2.13e

All models control for physician and practice characteristics (age, gender, race, type of physician, board certification, place of medical school graduation, type of practice), Medicaid characteristics (Medicaid/Medicare reimbursement ratio, Medicaid hassle factor, percentage of county population receiving Medicaid, Medicaid managed care penetration), and county characteristics (per capita income, primary care or specialty physician/population ratio).

^ap < .01.

^bp < .10.

^cp < .001.

^dSegregation is measured using the dissimilarity index, which can be interpreted as the percentage of the subgroup (nonwhites or poor population in racial and poverty segregation, respectively) that would need to move in order to be evenly spread across the zip codes in the county.

^ep < .05.