Hi everyone.

For a problem I am trying to tackle, I have created a negative binomial mixed model. The model contains several fixed effects, and only contains a random intercept for each cluster within the data.
Code:
use http://www.stata-press.com/data/r13/drvisits

meglm numvisit reform age educ married badh loginc || id:, family(nbinomial) link(log) intpoints(30) irr
predict remeans, remeans
In order to ease the interpretation of the regression coefficients, I'm creating a table (see example below) that describes multiple scenarios in which the fixed effects take different values, and therefore the total estimate of the dependent variable changes. The random intercept is of course a cluster-level effect, so that it does not change at the individual level, and shall be reported as a range.
Scenario 1 Scenario 2 Scenario 3
Fixed effect 1 Value = 1 Value = 2 Value = 3
Fixed effect 2 Value = 3 Value = 2 Value = 1
Fixed effect 3 Value = 2 Value = 3 Value = 2
Random intercept [-X, +Y] [-X, +Y] [-X, +Y]
Total estimate sum of above rows
Considering that my model reports incidence rate ratios, I can manually calculate the fixed effects part, but I am unsure how to interpret the reported values of the random intercept.
Can I just add them to the estimate from the fixed effects, or do they need to be rescaled?