Essays on the Health Economics of Pharmaceuticals

• Main Author: Chorniy, Anna
• Format: Dissertation
• Language: English
• Published: ProQuest Dissertations & Theses 01-01-2015
• Subjects: Economics; Pharmaceutical sciences
• ISBN: 9781321775570; 1321775571

The effects of pharmaceutical treatment on patient health, pricing of pharmaceuticals and their regulation are the backbone of my research. My work reflects two current trends used to advance our knowledge in the field: the use of dynamic structural models that is supplemented by detailed administrative individual-level data. This thesis consists of three chapters that address a number of policy-relevant questions in health economics using both individual- and market-level outcomes. In the first chapter I take a market-level approach to look at the effect of mergers between insurance companies on Medicare Part D plan premiums and generosity of coverage. In the following two chapters I study the effects of ADHD treatment on children's health and behavioral outcomes. The first chapter focuses on the insurance design and pricing of insurance plans that cover prescription drugs. We examine horizontal mergers amongst Medicare Part D insurers with the aim of decomposing market power, cost efficiency, and bargaining power merger effects. We apply a differences-in-differences identification strategy to panel data on plans offered between 2006 and 2012 to document the effects of mergers on plan premiums and drug coverage characteristics. The results indicate substantial market power as plans affected by a merger increase premiums on average. But, premiums fall and drug coverage improves for merging insurers that restructure plans and renegotiate contracts with drug suppliers by consolidating existing plans. We attribute these effects to improved cost efficiencies and increased bargaining power. In the second and third chapters I look at the individual-level outcomes following medical treatment of Attention-deficit/hyperactivity disorder (ADHD). In the U.S. the incidence of ADHD diagnosis among children increased significantly over the past decade. The most recent National Survey of Children's Health 2011/12 reports that over 5 million children aged 2–17 (7.9%) have been diagnosed with ADHD. Over 68% of these children are taking medications for the disorder. However, little is known about the existing prescribing practices, physician learning process, and relative efficacies of various ADHD treatment strategies. In Chapter 2, I build on the literature on investment in human capital (see for example Heckman et al. (2006)) to model the timing of the first diagnosis, treatment, and adverse outcomes over time. ADHD is a common chronic mental condition that impairs noncognitive skills. A child who has ADHD has a relatively low stock of abilities at birth. Once a child is diagnosed, her family can invest in medical treatment to reduce the gap in abilities of a child with ADHD compared to her non-ADHD peers. In the model, ADHD treatments are the only available type of investment. While on treatment, the child is able to improve her outcomes in the short-run, accumulate cognitive and noncognitive skills and possibly improve her long-run outcomes. Using a 10-year panel of South Carolina Medicaid claims, I model the probability of the initial diagnosis of ADHD, dynamic treatment choice decisions and subsequent adverse events later in life. Controlling for endogeneity, Using a 10-year panel of South Carolina Medicaid claims, I model the probability of the initial diagnosis of ADHD, dynamic treatment choice decisions and subsequent adverse events later in life. Controlling for endogeneity, I find that there is a strong persistence in treatment choices across time periods. The results also suggest that pharmacological treatment has only short-term positive impact on the probability of such adverse events as injuries, teenage pregnancy, and STDs, and no impact on substance abuse disorders. Behavioral therapy alone is not as effective as it is in combination with ADHD drugs, but for STDs and substance abuse disorders it seems to show relatively long-lasting effects in contrast to drugs alone. In Chapter 3, I extend Crawford and Shum's (2005) model to explore the effect of treatment interruptions (drug holidays) in addition to the effects of various drug therapies. The evidence suggests that children diagnosed with ADHD face significant uncertainty regarding efficacy and severity of adverse effects of ADHD medications. Almost half of these children switch therapies during the first six months of treatment. This suggests a considerable amount of experimentation by doctors. Using South Carolina Medicaid claims data for 2003–2012, I estimate a dynamic model of demand for ADHD drugs under uncertainty. In the model, highly heterogeneous patients learn about the efficacy of available treatments through experimenting. I find that patients are heterogeneous in the underlying illness severity. The probability that the patient will be able to function successfully in their everyday life without ADHD treatment varies from 1.8% to 76.7% in the baseline model specification. Although merely suggestive, it might point at the presence of overdiagnosis and overprescription practices. I also find that there is a lot of uncertainty regarding patient-drug match by both symptomatic and curative properties. Although some drugs are better than others for each of the patient types by severity of their condition, their match value distributions overlap significantly. In other words, knowing patient type, does not resolve patient-drug match uncertainty. Although the model with drug holidays yields overall similar results to the baseline model, in their current formulation they cannot be directly compared because of the differences in the choice set. Notably, drug holidays rank first for the healthy type by symptomatic relief properties. Both dynamic models allow for policy-relevant simulations, for example one could evaluate the effect of interruptions in treatment on the overall treatment cost and disease duration, accounting for patient heterogeneity in response to treatment for ADHD and potentially develop better guidelines that can improve the quality of drug-patient matches and patients outcomes. This is left for future work.