Identifying the impact of health insurance on subgroups with changing rates of diagnosis.

Abstract

Expanded access to health care often leads to new diagnoses for previously undetected conditions. New diagnoses make it difficult to identify the causal effect of expanding health insurance on individuals with particular diagnoses: the newly diagnosed in the treatment group are likely to differ in unobserved ways from the control group. This paper provides two methods for dealing with this problem depending on the data available to the researcher and diagnosis-specific knowledge. If there is no panel dimension to the data, then the causal effect for the subgroup of interest can be bounded from either above or below depending on the condition in question. If panel data are available, then the newly diagnosed can be identified, and their treated outcomes subtracted from the overall effect of interest. I apply these methods to find that the difference-in-discontinuities estimator underestimates the effect of Medicare prescription drug coverage on the uptake of insulin by first-time users by 20%.