# Zero inflated negative binomial in Stata

I'm trying to fit a negative binomial model on overdispersed count data with a large number of zeros. I have two questions:

1) How do I know that I have an 'excess' number of zeros, and that I should use zero inflated negative binomial? *note: my outcome variable is number of disease cases by week (total 520 weeks), and the outcome is stratified by sex(male, female), age(categorical variable, n=4) and district (n=3), such that I have 24 possible categories of counts by week (I did that to be able to include individual predictors in the count model)

2) How do I know which variables I should include in the 'inflation model'? Should I just include all predictors?

The idea of zero-inflated models in not that there are a lot of zeros in the dependent variable. Rather it is the idea that there are two separate processes in the data which can lead to an observation of zero. In one process, the observations do not participate in the count process - so could never have observed outcomes $Y_i \ne 0$ (call this the zero-inflation process). In the other, the observations do participate in the count process, but have a count of zero. This, clearly, could lead to an excess of zeroes, since there are two distinct processes for observing a zero.