There are two different effect scales in this zero-inflated Poisson regression. The count component describes the mean conditional on belonging to the susceptible component, including its possible zero outcomes. Its logarithmic link function coefficients exponentiate to ratios of those conditional means. The zero component describes the structural-zero probability ; its logit coefficients exponentiate to odds ratios, not probability ratios.
Holding the other covariates fixed, Centre B versus Centre A changes the susceptible count mean by the factor
an increase of about . It simultaneously multiplies the odds of being a structural zero by
Thus Centre B is associated both with higher count intensity among susceptible patients and with greater odds of belonging to the never-at-risk class.
Use no personality disorder as the reference. For women, borderline disorder multiplies the susceptible count mean by , and other disorder multiplies it by . Because the count model has sex-by-disorder interaction terms, the corresponding male comparisons must add the interactions before exponentiating:
These compare men with the stated disorder to otherwise comparable men without a disorder; the female comparisons use female reference patients. The interaction multipliers and alone are ratios of these sex-specific disorder ratios, not the overall male disorder effects.
In the zero component there is no sex-by-disorder interaction. Borderline and other disorders multiply the structural-zero odds relative to no disorder by and , respectively, holding centre fixed; these comparisons are the same for both sexes in the fitted zero model.
The marginal mean is . Therefore none of these count multipliers alone is the population-average change in expected episodes when the same covariate also changes . For a count coefficient change and zero-logit change from baseline zero predictor , the marginal mean ratio is
For centre use ; for disorder use its sex-specific count contrast and its zero contrast. This is the component and marginal effects in zero-inflated regression distinction needed to interpret these results carefully.

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