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Table 2 The generalised estimating equation analysis of medical expenses

From: Medical expenditure for patients with hemophilia in urban China: data from medical insurance information system from 2013 to 2015

Parameter B Std.Error 95% wald confidence Internal hypothesis test
Lower Upper wald chi-square df p-value
intercept 5169.088 2572.454 127.170 10,211.006 4.038 1 0.044
[region = 1] 1289.574 979.440 − 630.093 3209.241 1.734 1 0.188
[region = 2] 1700.035 1349.644 − 945.219 4345.288 1.587 1 0.208
[region = 3] 0b       
[gender = 1] − 544.542 1937.713 − 4342.389 3253.305 0.079 1 0.779
[gender = 2] 0b       
age −400.917651 707.808 − 1788.195 986.360 0.321 1 0.571
[types of BMI = 1] 360.977 1220.132 − 2030.437 2752.391 0.088 1 0.767
[types of BMII = 2] 0b       
[grades of medical institution = 0] 159.418 759.276 − 1328.734 1647.571 0.044 1 0.834
[grades of medica linstitution = 1] 9223.120 ####### −13,906.490 32,352.730 0.611 1 0.434
[grades of medica linstitution = 2] 1986.024 1177.259 −321.361 4293.410 2.846 1 0.092
[grades of medical institution = 3] 0b       
[types of medical service = 1] − 5085.887 772.286 − 6599.540 − 3572.233 43.369 1 0.000
[types of medical service = 2] 0b       
reimbursement ratio 27.518 10.756 6.437 48.600 6.546 1 0.011
scale 69,869,737.205