The coefficients associated with number of predictor disorders in the best-fitting model represent predictive associations of comorbidity expressed as deviations from the pure-disorder coefficients. For example, absent effects of comorbidity, respondents with three predictor disorders having pure-disorder ORs of, say, 1.3, 1.5, and 1.7 would have an expected OR of 3.3 (i.e., 1.3 × 1.5 × 1.7). If the actual OR for these respondents is 4.0, then the number-of-disorders OR would be 1.2 (i.e., 4.0/3.3), indicating that the predictive effect of comorbidity is 20% higher than expected from the pure-disorder coefficients. The best-fitting model assumes that these number-of-disorders coefficients are a function of each respondent’s weighted number of predictor disorders, with weights defined by pure-disorder coefficients.
