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. 2019 Jan 18;19:47. doi: 10.1186/s12913-019-3892-9

Table 7.

Generalized linear regression analysis of potential factors influence medical costs

Variables	Total costs			Total OOP costs
Variables	β	SE	P-value	β	SE	P-value
Intercept	8.944	0.286	0.000	7.359	0.411	0.000
UEBMI	0.398	0.238	0.094	0.771	0.341	0.024
URBMI	0.350	0.241	0.146	0.924	0.347	0.008
Student	0^a			0^a
Male	0.003	0.072	0.972	−0.041	0.103	0.691
Female	0^a			0^a
Mental labourer	0.281	0.229	0.220	0.473	0.330	0.151
Manual labourer	0.247	0.125	0.048	0.390	0.180	0.030
Retire	0.219	0.125	0.079	0.069	0.180	0.699
Unknown	0^a			0^a
New cases	0.164	0.091	0.069	0.138	0.130	0.288
Previously treated cases	0^a			0^a
Native	0.038	0.061	0.531	0.064	0.088	0.468
Migration in China	0^a			0^a
Negative cases	−0.200	0.061	0.001	−0.064	0.088	0.465
Positive cases	0^a			0^a
Age	− 0.003	0.002	0.107	−0.008	0.003	0.005
Scale	0.111^b	0.013		0.229^b	0.027

Dependent variable:total costs/total OOP costs

Model (intercept): health insurance, gender, occupation, patient category, residence, sputum smear test and age

a Set to zero because this parameter is redundant

b Maximum likelihood estimate