I don't know why you are saying that these are "no longer survey data". Jeremy Miles is right that you have probability weights, and need to account for them differently. (Why glm refers to the number of trials as weight is a trick question to the developers' conscience.) See also Jeremy's earlier answer on a somewhat related topic.

You should still stick to the survey package, and at the very least try the svyglm function of it. However, since you have longitudinal data, correlations within "subjects" (unique_pid) will not be properly accounted for in the modeling part with neither glm nor svyglm. You would need some version of gee to do that properly, but I don't think you can easily marry gee and survey packages, although conceptually is quite possible (GEE generates estimating equations, and if a statistical estimand is produced by estimating equations, it can wrapped into a survey-compatible estimator).

I don't know why you are saying that these are "no longer survey data". Jeremy Miles is right that you have probability weights, and need to account for them differently. (Why glm refers to the number of trials as weight is a trick question to the developers' conscience.) See also Jeremy's earlier answer on a somewhat related topic.

You should still stick to the survey package, and at the very least try the svyglm function of it. However, since you have longitudinal data, correlations within "subjects" (unique_pid) will not be properly accounted for in the modeling part with neither glm nor svyglm. You would need some version of gee to do that properly, but I don't think you can easily marry gee and survey packages, although conceptually is quite possible (GEE generates estimating equations, and if a statistical estimand is produced by estimating equations, it can wrapped into a survey-compatible estimator).