Physician distribution across China’s cities: regional variations

Table 4 Predicted probabilities for physician density after controlling for population and socioeconomic covariates, 2003–2013

Region	2003			2013
	Low density	Medium density	High density	Low density	Medium density	High density
	[0.00–1.48]	[1.49–2.46]	[2.47–9.68]	[0.00–1.48]	[1.49–2.46]	[2.47–9.68]
Model 1
Urban district	0.17	0.55	0.28	0.29	0.55	0.16
County-level city	0.34	0.53	0.13	0.52	0.42	0.07
Eastern China	0.17	0.55	0.28	0.29	0.55	0.16
Northeastern China	0.13	0.52	0.35	0.23	0.56	0.21
Central China	0.14	0.54	0.32	0.25	0.56	0.19
Western China	0.09	0.48	0.43	0.18	0.56	0.27
Non-U.A.	0.29	0.43	0.28	0.29	0.55	0.16
U.A.	0.32	0.43	0.25	0.32	0.54	0.14
Model 2
Urban district x Eastern	0.14	0.54	0.33	0.24	0.57	0.19
County-level city x Eastern	0.42	0.49	0.09	0.59	0.36	0.05
Urban district x Northeastern	0.18	0.56	0.26	0.30	0.55	0.15
County-level city x Northeastern	0.27	0.56	0.17	0.43	0.48	0.09
Urban district x Central	0.14	0.54	0.32	0.24	0.57	0.19
County-level city x Central	0.36	0.52	0.12	0.53	0.41	0.06
Urban district x Western	0.13	0.54	0.33	0.23	0.57	0.20
County-level city x Western	0.19	0.57	0.25	0.32	0.54	0.14

Note: Low, medium, and high density respectively represent physician density ranging from 0.00 to 1.48, 1.49 to 2.46, and 2.47 to 9.68 per 1000 population. Probabilities were derived on the basis of the models in Table 3 with covariates conditioned at the following values: High school education or above = 21.32%, GDP per capita = 2.89, population density = 779.80 (/km²), female-to-male ratio = 96.81%, migrant = 18.25%, minority = 7.88%, aged under 15 = 18.77%, aged over 65 = 7.94%. UA represents urban agglomeration. County-level city x Western represents county-level cities in Western China

ISSN: 1475-9276