I’m modelling tuberculosis (TB) case rates at the neighborhood level and trying to identify risk factors that are associated with higher rates. I would like to identify communities with below average case rates given their risk factor profile with the idea that these would be the best places for new case-finding. It’s a bit circular I realize to use the same units I’m identifying risk factors with to then identify areas with expected higher rates, but my thinking is that I could identify the communities which are at the lower end of the distribution for each risk factor.
My plan then would be to construct a Poisson model with significant risk factors, plug in each neighborhood and calculate the ‘expected rate’. Then calculate the difference by subtracting ‘observed’ from ‘expected’ rates, and then identify the communities with the highest difference.
Is this approach legitimate? Or is this using the model in a way it isn’t meant to be used?