Resource allocation for infectious disease control : informing theory and practice
- Sabina Stefania Alistar.
- May 2012.
- Physical description
- online resource (xiv, 182 pages) : illustrations (some color)
- Alistar, Sabina Stefania.
- Bendavid, Eran. thesis advisor.
- Brandeau, Margaret L. thesis advisor (primary).
- Owens, Douglas K. thesis advisor.
- Stanford University. Department of Management Science and Engineering.
- Stanford University. Committee on Graduate Studies. degree grantor.
- Includes bibliographical references.
- Operations research and mathematical modeling can play a key role in informing high-cost, high-impact health policy decisions. This dissertation describes my research on resource allocation for infectious disease control. Resource allocation is especially difficult in this context because epidemics are nonlinear (preventing one infection now may prevent scores of future infections); interventions usually do not have additive effects; the relationship between investment and outcomes is generally nonlinear and epidemics vary across settings in terms of transmission modes and key risk groups. Hence, the problem of resource allocation for epidemic control is complex and cannot be solved by intuition alone. Decision makers have little guidance in choosing investment portfolios that will provide the maximum health benefits for their particular setting. In Chapter 1, I discuss the gap between the theory and the practice of resource allocation for epidemic control, and identify the key features necessary for a model to be useful in practice. In theoretical work presented in Chapter 2, I address the gap between epidemiological measures and the resource allocation decision. I present a new theoretical framework that quantifies the effects of investment in treatment and prevention interventions on a key epidemiological parameter, the reproductive rate of infection, which measures an outbreak's potential for becoming an epidemic. The approach uses production functions to account for nonlinearities of intervention scale-up effects. I develop analytical results characterizing the optimal solution and present illustrative examples with data for Uganda and Russia. I also present a simple structural estimation technique for evaluating the shape of production functions based on real-world data. In Chapter 3 I develop a dynamic compartmental model of interventions for HIV control in mixed HIV epidemics. I apply the model to analyze the tradeoffs between scaling up methadone substitution therapy for injection drug users and antiretroviral treatment for HIV-infected individuals in Ukraine, a representative case. I show that methadone is an economically attractive option for HIV control, and that excluding drug users from HIV programs significantly limits the benefits that can be obtained by scaling up these interventions. In Chapter 4 I describe a spreadsheet-based planning tool I have created in collaboration with the Joint United Nations Programme on HIV/AIDS (UNAIDS). The REACH (Resource Allocation for Control of HIV) model is designed for use by planners around the world in evaluating investment portfolios for HIV control. The tool is easy to use, includes optimization capability, and accounts for non-additive and nonlinear effects of interventions. I present illustrative implementation of the model for three settings (Uganda, Ukraine and Saint Petersburg, Russia). I also describe my ongoing work with UNAIDS decision makers to test and implement the model for regional and country-level HIV resource allocation. I conclude in Chapter 5 with remarks on the significance of the work and directions for future research.
- Communicable Disease Control > economics
- HIV Infections > prevention & control
- Resource Allocation > organization & administration
- Acquired Immunodeficiency Syndrome > epidemiology
- Decision Making
- Health Policy > economics
- Publication date
- Submitted to the Department of Management Science and Engineering and the Committee on Graduate Studies of Stanford University.
- Thesis (Ph.D.)--Stanford University, 2012.