We propose a dynamic framework to study the value of medical innovation in the context of infectious disease. We apply our framework to evaluate an HIV treatment breakthrough known as HAART. The model captures how, in lowering both the expected cost and likelihood of HIV infection, HAART reduced the implicit price of risky sex. Forward-looking agents responded by optimally shifting their behaviour. The model also imposes equilibrium constraints, explicitly capturing how optimal shifts in behaviour affect equilibrium choices by changing both infection probabilities and the ease of finding partners willing to engage in risky sex. Using the estimated model, we conduct counterfactual simulations to compute the value of HAART from the perspective of uninfected agents. This includes the option value of the innovation along with value accruing from changes in sex behaviour in response to HAART introduction. We also calculate the added value of a fully functional vaccine from the perspective of both infected and uninfected agents, where infected agents benefit from a vaccine due to resulting shifts in market equilibrium.

Evidence across disciplines suggests that talk therapy is more curative than antidepressants for mild-to-moderate depression and anxiety. Yet, few patients use it. We develop a dynamic choice model to analyse patient demand for the treatment of depression and anxiety. The model incorporates myriad potential impediments to therapy use along with links between mental health improvements and earnings. The estimated model reveals that mental health improvements are valuable, directly through utility and indirectly through earnings. However, patient reluctance to use therapy is nearly impervious to reasonable counterfactual policies (e.g. lowering prices or removing other costs). Patient behaviour might reflect stigma, biases in beliefs about the effectiveness of therapy, or a distaste for discussing personal or painful issues with a stranger. More broadly, the benefits of therapy estimated in randomized trials tell only half the story. If patients do not use treatments outside of an experimental setting—and we fail to understand why or how to get them to—estimated treatment effects cannot be leveraged.