Objective: Contrast methods to assess the health effects of a treatment rate change when treatment benefits are heterogeneous across patients. Antibiotic prescribing for children with otitis media (OM) in Iowa Medicaid is the empirical example.
Methods: Instrumental variable (IV) and linear probability model (LPM) are used to estimate the effect of antibiotic treatments on cure probabilities for children with OM in Iowa Medicaid. Local area physician supply per capita is the instrument in the IV models. Estimates are contrasted in terms of their ability to make inferences for patients whose treatment choices may be affected by a change in population treatment rates.
Results: The instrument was positively related to the probability of being prescribed an antibiotic. LPM estimates showed a positive effect of antibiotics on OM patient cure probability while IV estimates showed no relationship between antibiotics and patient cure probability.
Conclusions: Linear probability model estimation yields the average effects of the treatment on patients that were treated. IV estimation yields the average effects for patients whose treatment choices were affected by the instrument. As antibiotic treatment effects are heterogeneous across OM patients, our estimates from these approaches are aligned with clinical evidence and theory. The average estimate for treated patients (higher severity) from the LPM model is greater than estimates for patients whose treatment choices are affected by the instrument (lower severity) from the IV models. Based on our IV estimates it appears that lowering antibiotic use in OM patients in Iowa Medicaid did not result in lost cures.