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Table 2 Crude and adjusted odds ratios for factors associated with later stage at diagnosis derived from ordinal logistic regression (unknown stage excluded) N = 4362

From: Sociodemographic disparities in survival from colorectal cancer in South Australia: a population-wide data linkage study

    Crude    Adjusted#  
Variables   OR 95 % CI p-value OR 95 % CI p-value
Site Colon 1.00    1.00   
  Rectum 0.82 0.73-0.91 <0.001 0.81 0.72-0.91 <0.001
Age group 50-59 yrs 1.00    1.00   
  60-69 yrs 0.82 0.70-0.95 <0.008 0.76 0.66-0.89 <0.001
  70-79 yrs 0.88 0.77-1.02 0.087 0.77 0.67-0.89 0.001
Sex Females 1.00    1.00   
  Males 0.97 0.87-1.08 0.562 0.96 0.86-1.07 0.436
SES Lowest (quintile) 1.00    1.00   
  Low 0.89 0.75-1.06 0.208 0.90 0.75-1.08 0.248
  Mid 0.99 0.83-1.18 0.909 0.96 0.79-1.15 0.636
  Low-high 0.93 0.78-1.10 0.395 0.90 0.75-1.08 0.268
  Highest 1.08 0.91-1.26 0.438 1.03 0.86-1.23 0.756
Residence Inner urban 1.00    1.00   
  Outer urban 0.85 0.70-1.05 0.080 0.89 0.74-1.07 0.248
  Rural 1.02 0.88-1.20 0.717 0.99 0.83-1.16 0.812
  Remote 0.92 0.73-1.16 0.473 0.99 0.77-1.26 0.912
Private Insurance No 1.00    1.00   
  Yes 0.88 0.76-0.94 0.002 0.84 0.75-0.95 0.003
Co-morbidities None 1.00    1.00   
  One (not severe) 1.25 1.09-1.44 0.002 1.25 1.09-1.44 0.002
  Multiple or severe 1.48 1.25-1.76 <0.001 1.50 1.26-1.79 <0.001
Diagnosis year 2003-2008 (cont) 0.97 0.94-1.00 0.024 0.97 0.94-1.00 0.027
  1. #Ordinal logistic regression model adjusted for all factors simultaneously – excludes missing stage (n = 328)
  2. Ordinal logistic regression is an extension of logistic regression that incorporates the ordinal nature of the dependent variable (in this case stage at diagnosis). It can be used for modelling a dependant variable that has more than two ordered categories. The method is analogous to a series of binary models predicting the following combinations of binary stage groupings (e.g. stage A v stage B + C + D, A + B v C + D and A + B + C v D)
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