Is there a simple rule for interpretation of Interactions (and their directions) in binary logistic regression? I have a binary logistic regression with Y (a disease) and 5 independent variables (and some of their 2-sided interactions which did not cause multicollinearity). All of my single IVs significantly predict Y:


*

*A: positive beta for males (males are more likely to get affected)

*B: a positive beta (older people are more likely to be affected)

*C (yes/no): a positive beta for smoking (smokers are more likely to be affected)

*D (continuous): a positive beta (more traumatic patients are more likely to have disease)

*E (yes/no): a Negative beta for treatment (treated cases were less likely to have diseases).


Now 4 interactions are significant and I want to interpret them. I know I should state that in a significant interaction, I should say that the effect of variable A on Y differed for B(1) and B(2). For example the effect of age on disease differed in males and females. But I don't know in which class (males or females), it was greater, and I don't know how to determine it.
The significant interactions and their direction of betas are as follows:


*

*4 by 3:   positive beta

*4 by 5: positive beta

*1 by 2:       Negative beta

*3 by 2:      Negative beta


I would appreciate if you could kindly guide me. I searched for this issue but the discussions on the web are all sophisticated (e.g. this one) and beyond me. I just want to know is there a simple rule to determine the direction of interaction [i.e., "is A's effect on Y greater in B(1) or B(2)?"], given the directions of the coefficients of the involved variables (A and B) and the coefficient of the interaction itself (A*B)? (B(1) and B(2) are the levels of binary variables (man or woman) or extreme ends in continuous variables ([young and old], [easy or difficult])
Many thanks in advance.
 A: A positive interaction effect between A and B means that when A increases, the effect (in this case log odds ratio) of B increases. A negative interaction effect means that when A increases, the effect of B decreases. 
When interpreting the results, I often find it easiest to work in the odds metric rather than the log(odds) metric. I tend to start with the baseline odds just to refresh my (and my audience's) memory on what odds are, then continue to interpret the odds ratios of the main effects (odds ratios are literary that: ratios of odds), and then go on to the interaction effects, which in logistic regression are ratios of odds ratios. A complete example is given in my answer to Interpreting interaction terms in logit regression with categorical variables
A: I'm not familiar with binary logistic regression, but in interaction effects in general, the way you understand them is by plotting (usually means, perhaps different in this case?).  That will allow you to see the relationship between different levels and state the interaction effect specifically.
