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Table 3 The binary logistic regression analysis results of the associated factors of patient medication choice before and after September 2017

From: How government health insurance coverage of novel anti-cancer medicines benefited patients in China – a retrospective analysis of hospital clinical data

Variables

Before September 2017

After September 2017

OR (95%CI:

lower, upper)

p-value

OR (95%CI: lower, upper)

p-value

Age

(Ref: <40 years old)

40-49 years old

0.73 (0.20,2.62)

0.73

1.93 (0.57,5.02)

0.20

50-59 years old

0.59 (0.17,0.91)

0.01

1.54 (0.36,6.57)

0.56

>60 years old

0.34 (0.09,0.87)

0.01

0.62 (0.15,0.80)

0.02

Household registration

(Ref: Urban)

Rural

0.52 (0.29,0.74)

<0.01

0.98 (0.47,1.04)

0.99

Level of disposable income of patient residential area

(Ref: Low)

Middle

3.47 (1.40,8.59)

0.01

1.06 (0.68,3.83)

0.76

High

5.76 (1.48,12.17)

0.01

1.98 (1.42,17.27)

0.02

Type of health insurance coverage

(Ref: Urban employee)

Urban/rural resident

0.50 (0.35,0.79)

0.03

1.08 (0.29,4.00)

0.91

Total OOP

0.47 (0.29,0.68)

0.01

0.47 (0.14,0.96)

0.03

Local patients

(Ref: Yes)

No

0.84 (0.38,1.85)

0.66

0.28 (0.10,0.83)

0.02

Tumor progression stage (Ref: I)

II

0.820 (0.32,1.77)

0.52

0.89 (0.30,2.58)

0.83

III

1.012 (0.39,2.64)

0.98

0.77 (0.17,0.96)

0.04

IV

0.751 (0.18,0.86)

0.02

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  1. Note: Bold means statistically significant (p<0.05).