Hi gvandekaa,

It seems to me that you would interpret poisson regression coefficients exactly as they are. I obtained the following from: http://www.oxfordjournals.org/tropej/online/ma_chap13.pdf

Poisson regression distribution
The assumptions include:
1. Logarithm of the disease rate changes linearly with equal increment increases in the
exposure variable.
2. Changes in the rate from combined effects of different exposures or risk factors are
multiplicative.
3. At each level of the covariates the number of cases has variance equal to the mean.
4. Observations are independent.
Methods to identify violations of assumption (3) i.e. to determine whether variances are too
large or too small include plots of residuals versus the mean at different levels of the predictor
variable. Recall that in the case of normal linear regression, diagnostics of the model used
plots of residuals against fits (fitted values). This means that the same diagnostics can be used
in the case of Poisson Regression.

I was wondering whether there is an option available to recode poisson regression coefficients into effect size (r)?