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