Skip to main content

Table 5 Probit results and marginal effects for Model 3

From: Targeting services to reduce social inequalities in utilisation: an analysis of breast cancer screening in New South Wales

  High Risk (age 50 – 69) Non High Risk (age 40 – 49) Non High Risk (age 70 – 79)
  Coefficient Marginal effect Coefficient Marginal effect Coefficient Marginal effect
Income ($'00/wk) 0.344 * 0.015 # 0.404 * 0.012 1.466 0.104 #
Income squared -0.050 *   -0.041 *   -0.282 -0.007
Non Australian born -0.268 * -0.069 # -0.045 -0.018 -0.019 -0.087
Aboriginal -0.200 -0.131 # -0.152 -0.076 -0.204  
Central Sydney -0.233 -0.059 # -0.004 -0.001 -0.253 -0.099
South Eastern Sydney -0.271 * -0.070 # 0.026 0.010 0.081 0.030
South Western Sydney -0.503 * -0.143 # -0.170 -0.066 -0.208 -0.081
Wentworth -0.412 * -0.113 # -0.027 -0.011 0.066 0.025
Western Sydney -0.291 * -0.076 # -0.179 -0.069 -0.237 -0.092
Central Coast -0.183 -0.045 -0.079 -0.031 0.029 0.011
Far West -0.305 * -0.080 # 0.074 0.029 -0.159 -0.061
Greater Murray -0.234 * -0.059 # -0.048 -0.019 -0.297 -0.116 #
Macquarie -0.543 * -0.157 # -0.194 -0.075 -0.239 -0.093
Mid North Coast -0.227 * -0.057 # 0.170 0.067 -0.147 -0.057
Mid Western -0.658 * -0.197 # -0.108 -0.042 -0.423 * -0.166 #
New England -0.239 * -0.057 # -0.299 * -0.012 -0.058 -0.101
Northern Rivers -0.225 * -0.061 # -0.030 -0.113 # -0.259 -0.022
Southern -0.254 * -0.065 # -0.269 * -0.103 # -0.349 * -0.137 #
Hunter -0.086 -0.020 0.354 * 0.140 # 0.061 0.023
Illawarra -0.334 * -0.088 # -0.173 -0.067 -0.424 * -0.167 #
Constant 0.500 *   -1.148 *   -1.510  
Log L -3022.030 -2484.780 -1372.700
Observations 5482 3785 2041
Pseudo R2 0.026 0.017 0.019