In binary logistic regression, how do you interpret a categorical-by-categorical interaction term?

Question

I am working with multiply imputed data and I have run a logistic regression model with 6 predictors (3 dichotomous and 3 categorical) and their interaction terms, controlling for a number relevant covariates in SAS.

The predictor variables are as follow:

• CVD (0 = No; 1 =Yes)
• HCA (0 = Low; 1= High)
• EDU (1=High School Dropout; 2=Graduated High School; 3=Some College; 4=Graduated College)
• POV (1=Low; 2=Medium; 3=High)
• LAN (1=Spanish; 2=English)
• STA (0=Old, 1=New, 2=None)

My outcome variable is diabetes medication use and is coded as 0 = no use and 1 = current use

I found that for the following:

1. One of the levels of the interaction of EDU*STA is marginally significant.
2. Only one of the levels of the interaction of POV*STA is marginally significant.

My questions are:

1. How do you decipher a categorical-by-categorical interaction when only one level of the interaction is marginally significant?
2. Would it be appropriate to calculate the adjusted odds, as a way to interpret the interaction?
3. If 95% CI do not include 0 but the CI overlap, does it means that the results are not statistically significant?
4. How do you visualize these interactions?

Thank you