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Table 3 Factors associated with IPs prevalence and amount paid (based on Probit and GLM)

From: Informal payments for inpatient health care in post-health transformation plan period: evidence from Iran

Characteristics

Part 1 Paying informally with money, yes/no Yes = 1 (Probit)

Part 2 Amount paid informally GLM

Variables

Coef (SE)

p-value

Marginal effects (SE)

Coef(SE)

p-value

Sex, female

−0.14(0.10)

0.185

−0.01(0.18)

− 0.57 (0.29)

0.052

Adult, yes

−0.88(0.15)

0.000

−0.14(0)

1.25 (1.05)

0.236

Residence (ref: country’s capital, Tehran)

 Other city

0.26(0.11)

0.015

0.03(0.01)

0.06 (0.29)

0.827

 Village

−0.29(0.25)

0.228

−0.03(0.19)

0.22(0.63)

0.722

 Insured, yes

0.75(0.25)

0.003

0.06(0)

4.76(0.40)

0.000

 Hospital stay, days

−0(0)

0.001

−0(0.86)

− 0(0)

0.000

Hospital type (ref: public)

 Private

0.38(0.13)

0.003

0.05(0.01)

−1.13(0.48)

0.018

 Social

−0.7(0.21)

0.001

−0.06(0)

−2.78(1.24)

0.025

Hospital service (ref: surgery)

 Medical treatment

0.87(0.15)

0.000

0.09(0)

1.11(1.02)

0.277

 Diagnostic measures

0.80(0.19)

0.000

0.08(0)

1.49(1.11)

0.182

 Caesarean Section

0.43(0.55)

0.434

0.04(0.5)

−5.290.98)

0.000

 Other

1.47(0.2)

0.000

0.20(0)

0.50(0.97)

0.603

 Household size

0.01(0.02)

0.638

0(0.64)

− 0.15(0.05)

0.002

 Household income, monthly

0(0)

0.000

0(0)

0(0)

0.183

 Household head, age

−0.03(0)

0.000

−0(0)

0(0)

0.735

Household head, an education level (ref: primary)

 High school

−0.60(0.14)

0.000

−0.06(0)

0.92

0.023

 College

−0.04(0.13)

0.740

−0(0.74)

0.11

0.698

N of respondents

2027

  

310

 
 

Prob>chi2 = 0.0000

  

AIC = 30.10

 
 

Pseudo R2 = 0.5318

  

BIC = -120.06

 
  1. Source: Authors’ analysis of data from Informal Patient Payments dataset
  2. Notes: Bolding used to reflect P values < 0.05. 0(0) values represent extremely low coefficient values