Table 8

Optimal logistic regression models to predict vaccination outcomes

Model1: Vaccination status2: Willingness to be vaccinated
PredictorEstimate±SEP valueOR (95% CI)Estimate±SEP valueOR (95% CI)
Orthodoxy Score0.33±0.03<2×10−16*1.39 (1.30 to 1.47)0.41±0.04<2×10-16 *1.50 (1.40 to 1.62)
Income0.236±0.0640.00025*1,27 (1.12 to 1.44)
Alternative media use−0.81±0.270.0024*0.44 (0.26 to 0.75)
Scientific original publications use−0.88±0.350.0110.42 (0.21 to 0.82)−0.60±0.370.1040.55 (0.27 to 1.13)
Size of household−0.193±0.0850.0240.82 (0.70 to 0.97)
Age (10 years)0.094±0.0590.1141.01 (1.0 to 1.02)
Public TV and radio use0.15±0.230.5131.16 (0.74 to 1.84)0.51±0.260.0461.67 (1.01 to 2.75)
AICc756.4588.7
Adj. KL-R20.2120.251
Sensitivity0.6840.669
Specificity0.8190.899
Accuracy0.7520.784
AUC0.8180.844
  • Intercept calculated but omitted. Sensitivity and specificity are those that maximise the overall accuracy of classification.

  • *significant predictors.

  • Adj. KL-R2, adjusted Kullback-Leibler-R2; AICc, bias-corrected Akaike information criterion; AUC, area under the curve.