When a predictor is involved in interaction, PROC LOGISTIC does not provide an odds ratio estimate for it in the default table that it presents. I suspect that you are using the EXPB option which adds a column in the parameter estimates table containing exponentiated values of the estimates, but it is important to understand that these values are not always odds ratio estimates. Also, it makes little sense to talk about the overall odds ratio for a predictor involved in interaction just as one doesn't talk of the main effect of a predictor in an ordinary regression model when it is involved in interaction. When the model allows for interaction, the predictor has a different effect at each level of the interacting predictor(s). These separate estimates are provided by the ODDSRATIO statement and this statement computes those estimates appropriately regardless of the coding (parameterization) used for CLASS predictors. Rather than the EXPB option, the ODDSRATIO statement is the recommended way to obtain odds ratio estimates.
That said, a single odds ratio estimate can be provided for a predictor in interaction by using the LSMEANS statement, but note the coefficients used (as shown by the E option) for the two individual log odds. As they show, each log odds is essentially averaged over the levels of the interacting predictor which may not be very meaningful (and possibly misleading) particularly when the predictor of interest has very different effect in each level of the interacting predictor. Note also that the LSMEANS statement requires use of PARAM=GLM in the CLASS statement.
proc logistic;
class a b/param=glm;
model y(event="1")=a|b;
lsmeans a/diff e cl oddsratio;
run;
