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Table 5 Predictors of health post-injury

From: Differences in perceived fairness and health outcomes in two injury compensation systems: a comparative study

  Health post-injury
Model 1 Model 2
Independent variable OR CI   p AOR CI   p
Fairness claims process 2.78 1.45, 5.33 .002 2.83 1.40, 5.71 .004
Age      1.01 0.99, 1.04 .23
Gender      0.91 0.45, 1.83 .78
Country of birth      1.00 0.48, 2.12 .99
Socio-economic status      1.12 0.57, 2.20 .74
Education      0.88 0.42, 1.88 .75
Marital status      0.90 0.46, 1.79 .77
Injury      1.35 0.57, 3.16 .49
Hospital admission      2.04 0.97, 4.26 .06
Time after injury      0.62 0.32, 1.20 .16
Health pre-injury      6.15 1.09, 34.61 .04
  1. Note: Model 1 Nagelkerke R2 = 0.08; Model 2 Nagelkerke R2 = 0.16
  2. Multiple logistic regression analysis, modelling the probability of good or excellent health (versus fair or poor health). Model 1 explores the unadjusted association between the overall fairness perception and health. Model 2 adjusts for demographic, injury variables, and pre-injury health. There was no multicollinearity
  3. Coding: Overall fairness claims process (0 = disagree/neutral; 1 = agree); Gender (0 = Male; 1 = Female); Country of birth (0 = Other; 1 = Australia); Socio-economic status (0 = Lower; 1 = Higher); Education (0 = Low/Medium; 1 = High); Marital status (0 = Single/Divorced; 1 = Married); Injury (0 = Other; 1 = Whiplash/soft tissue injury); Hospital admission (0 = No; 1 = Yes); Time after injury (0 = 12 months; 1 = 24 months); Health pre-injury (0 = Poor; 1 = Good); Health post-injury (0 = poor/fair, 1 = good/excellent). Reference category = 0