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Table 2 Descriptive statistics for the sample patients by the UZMDP adoption

From: Did the universal zero-markup drug policy lower healthcare expenditures? Evidence from Changde, China

  Inpatient sample (n = 27,246) Outpatient sample (n = 48,282)
  Comparison group Treatment group P-value Comparison group Treatment group P-value
Number of patients 8,026 (29.46) 19,220 (70.54)   11,648 (24.12) 36,634 (75.88)  
Number of annual visits 2.50 (2.15) 1.76 (1.41) < 0.001 4.06 (5.13) 3.66 (3.69) < 0.001
Annual aggregate expenditures 18,677.56 (35,528.89) 15,279.62 (25,286.93) < 0.001 2,318.99 (7741.01) 1,009.73 (3,642.55) < 0.001
Annual drug expenditures 8,613.33 (20,868.09) 5,137.19 (8,553.18) < 0.001 932.81 (4176.39) 307.08 (484.50) < 0.001
Annual diagnosis expenditures 524.39 (3,160.27) 2,011.46 (4,924.52) < 0.001 7.86 (48.51) 66.28 (162.71) < 0.001
Annual laboratory expenditures 401.34 (892.66) 1,258.25 (1,447.37) < 0.001 4.25 (40.94) 26.22 (97.40) < 0.001
Annual medical consumables expenditures 537.15 (3,602.22) 986.74 (3,093.98) < 0.001 14.60 (175.57) 12.24 (96.35) 0.065
Age (year) 61.49 (16.00) 61.35 (20.91) 0.590 55.71 (16.84) 50.25 (16.04) < 0.001
Gender Female 4,012 (0.50) 8,780 (0.46) < 0.001 5,228 (0.45) 22,460 (0.61) < 0.001
Gender Male 4,014 (0.50) 10,440 (0.54) 6,420 (0.55) 14,174 (0.39)
Annual income 0 - 20,000 CNY 1,136 (0.14) 2,486 (0.13) < 0.001 1,592 (0.14) 1,938 ( 0.05) < 0.001
Annual income 20,000 - 40,000 CNY 5,182 (0.65) 13,149 (0.68) 7,126 (0.61) 17,042 (0.47)
Annual income 40,000 - 60,000 CNY 1,186 (0.15) 2,405 (0.13) 1,366 (0.12) 9,478 (0.26)
Annual income ≥ 60,000 CNY 522 (0.07) 1,180 (0.06) 1,564 (0.13) 8,176 (0.22)
Type of health insurance Residents 1,836 (0.23) 5,739 (0.30) < 0.001 50 (0.004) 1,803 (0.05) < 0.001
Type of health insurance Retired veteran cadres 71 (0.01) 186 (0.01) 740 (0.06) -
Type of health insurance Disabled soldiers 7 (0.001) 29 (0.002) - -
Type of health insurance Employees 6,112 (0.76) 13,266 (0.69) 10,858 (0.93) 34,831 (0.95)
  1. For continuous variables: mean (SD), while for categorical variables: mean (%)