What you want can be provided by adding the LSMEANS statement in your GEE step. Since your model involves the interaction of the two predictors, the effect of one of the predictors requires more than just exponentiating that predictor's main effect parameter. It must also involve parameters of the interaction. Also, since each predictor is categorical, there isn't a single estimate of its effect. A predictor's effect is assessed by a set of comparisons among its levels. This is automatically done for you by the LSMEANS statement. The following statement provides, in the Differences tables, the exponentiated comparisons among the levels of one predictor, averaged over the levels of the interacting predictor. The exponentiated estimates are ratios of mean counts. The E option shows the combination of parameters that are involved in the computation that is exponentiated. The manner in which that averaging is done can be altered by additional options available in the LSMEANS statement - see the documentation of the LSMEANS statement.
lsmeans PREPG_CAN_NEW trimester / e diff exp cl;
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