Finally, I would recommend identifying variables that are significantly different across drinkers/non-drinkers, which are not really of interest to you, but nevertheless are different across the groups. In disease research, these are commonly the comorbidities that patients have, since patients have multitudes of problems at older ages (depression, electrolyte imbalnce, hypertension, etc.). Once you identify these variables, run a logistic regression (same dependent variable on only these variables). Before run-time, specify in SPSS you want the "logit". The logit is called the "propensity score." Do the run, and at the far rightmost column of the data set you will see "logit_1". Next, in your risk prediction models, use your primary risk and adjustment factors, and the logit to represent all the junk variables (which were different across drinker.non-drinker but not really of primary interest to you -- these are also called confounders). This latter model with the propesnity score representing nuisance factors may be better than models including all the nuisance variables.