convert odds ratio to logistic coefficient

Anyone remember how to convert odds ratio to logistic coefficient?
I am trying to use this in my interpretation of the results of an interaction variable and need to calculate the OR and 95% CI for the difference in difference if this makes sense.
Yes, the model outputs log odds then you exponentiate to get odds ratios. So, natural log transform them to get coefficients.
I was wondering if you have any contribution on interpreting the interaction effect of a categorical (binary) and continuous variable in a logistic regression. The information i have come by so far seem unclear


Omega Contributor
Well, it is pretty straightforward - there is a bigger impact on the odds of the outcome for the continuous variable given which category you are in for the categorical variable. So if you have a gene and an environmental exposure, people with the gene have a greater odds of the outcome when also exposed to the same continuous variable as individuals without the gene. So the gene has an impact and the exposure has an impact, but when you have both there is a multiplicative impact (given you are using a product term to examine for interaction).
Many thanks for your response, that was helpful.
This is what my output looks like with the outcome as ‘ever used drug Avg’ yes(1) / no(0).
B S.E. Sig. Exp(B) Lower Upper

Policy implementation(1) -1.624 .103 .000 .197 .161 .241

years since policy implementation -.153 .036 .000 .858 .858 .921

Ever arrested(1) .346 .057 .000 1.414 1.263 1.583

Ever arrested (1) by years since policy .072 .014 .000 1.075 1.046 1.105

I'm interpreting the interaction as 'Participants who have been arrested before have greater odds of using Avg over the same period following the policy implementation compared to those who have never been arrested (OR 1.08; 1.05-1.11).’
I know that presenting the OR for the interaction of two categorical variables usually does not make sense, but I’m thinking using the OR for the interaction as in this example should be alright.



Omega Contributor
It is traditional to present stratified estimates for the two groups.

You should also plug numbers into the model and plot the slopes of years by category, you are looking for non-parallel lines (disordinal).