You should read Milliken and Johnson's Analysis of Messy Data.
Probably the best approach is to fit a means model, and address the tests of effects through the use of contrast and estimate statements.
Another approach, find out what combinations of the independent variables are missing. If you can combine levels so that there are no empty cells in the highest level interaction in your model, and it still addresses your objectives, then you are home free.
Also, check that the covariates age and wbvcalc are not completely confounded with region.
Good luck,
Steve Denham
Added thought:
Last, you say that you are fitting a "reduced model". I would guess that you have dropped some terms that were "non-significant." Is that really a good idea? Were those terms part of the study design, so that they have meaning, even if not significant? And most importantly, are the least squares means estimable in the "full model"?
Message was edited by: SteveDenham