UCLA

Two key issues faced by any policy maker in healthcare are providing effective treatments for ailments and ensuring that these treatments are available to patients. In this dissertation, we use contract theory, epidemic modeling, and queueing theory to study the effectiveness and availability of treatment in the context of medicines and vaccines.

In the first essay, ``Flexible FDA Approval Policies", we analyze the problem faced by the Food and Drug Administration (FDA) of deciding whether to approve or reject novel drugs based on evidence of their safety and efficacy. Traditionally, the FDA requires clinical trial evidence that is statistically significant at the 2.5% level, but the agency often uses regulatory discretion when making approval decisions. Factors including disease severity, prevalence, and availability of existing therapies are qualitatively considered, but transparent, quantitative guidelines that systematically assess these characteristics are lacking. We develop a novel queueing model of the drug approval process which explicitly incorporates these factors, as well as obsolescence, or when newer drugs replace older formulas. We show that the optimal significance level is higher for diseases with lengthy clinical trials, greater attrition rates in the development stage, low intensity of research and development, or low levels of obsolescence among drugs on the market.

Using publicly available data, we estimate model parameters and calculate the optimal significance levels for drugs targeting three diseases: breast cancer, HIV, and hypertension. Our results indicate that the current 2.5% significance level is too stringent for some diseases yet too lenient for others. A counterfactual analysis of the FDA's Fast Track program demonstrates that, by bringing drugs to patients more quickly, this program achieves a level of societal benefit that cannot be attained by solely changing approval standards.

The second essay, ``Contracts to Increase the Effectiveness and Availability of Vaccines", studies contractual issues between global health organizations (GHOs) and pharmaceutical companies in the vaccine supply chain for neglected tropical diseases (NTDs). NTDs are a diverse group of conditions that affect over 1 billion individuals worldwide but which have historically received inadequate funding. Current funding mechanisms, such as the Advanced Market Commitment, do not incentivize pharmaceutical companies to exert costly research and development (R&D) effort to develop highly efficacious vaccines. We develop a joint game-theoretic and epidemic model that allows us to study different payment contracts and their impact on the spread of the disease. We show that traditional wholesale price contracts perform poorly and at best mitigate -- diminish the number of cases -- the spread of the disease, while performance-based contracts that directly link payment to vaccine efficacy have the potential to eliminate -- reduce the number of cases to zero -- the disease.

We formulate epidemic models for two NTDs: Chagas, a vector-borne disease most commonly found in Central and South America, and Ebola. We estimate model parameters and conduct a numerical analysis in which we explore the performance of each contract under a variety of cost scenarios. Our results indicate that, when the cost of treating the disease with no vaccine is sufficiently high, performance-based contracts have the potential to facilitate disease eradication, but when treatment costs are low, alternate disease containment methods such as vector control or mass drug administration may be more cost-effective.