I am trying to run a model to estimate how well catastrophic illnesses such as TB, AIDS etc affect spending on hospitalization. Now I have "per hospitalization cost" as the dependent variable and various individual markers as independent variables, almost all of which are dummy such as gender, head of household status, poverty status and of course a dummy for whether you have the illness (plus age and age squared) and a bunch of interaction terms.
As is to be expected, there is a significant--and I mean a lot, of data piled up at zero (i.e., no expenditure on hosptalization in the 12 month reference period). What would be the best way to deal with a model such as this?
As of now I decided to convert the cost into ln(1+cost) so as to include all observations and then run a GLM model.
Am I on the right track?
Sorry for not posting about the cross-posting. I wasn't aware that was required.
A small clarification- I am not treating cost as a count variable- I transform the cost variable as ln(1+cost) so that the observations where cost=0 do not drop out.