Candidate for the Lee B. Lusted Student Prize Competition
Purpose: Although significant funds are expended on HIV prevention and treatment worldwide, it is estimated that for every person newly enrolled in antiretroviral therapy (ART), two new infections occur. Public health decision makers must determine how to best balance investment of limited resources in treatment scale up and prevention programs, but do not have good tools to support such decision making. We develop a simple model for determining the optimal mix of HIV treatment and prevention programs, given a fixed budget.
Method: We use a cost-effectiveness framework that considers net present costs and two objectives, maximizing quality-adjusted life years gained or HIV infections averted. We develop methods of linearly estimating health benefits and costs that account for epidemic effects of reducing disease transmission. We allow for subadditive benefits from concurrent program implementation and diseconomies of scale in program costs. We illustrate our model with the examples of preexposure prophylaxis (PrEP) and community-based education (CBE) compared with ART for men who have sex with men (MSM) in the US.
Result: Health benefits estimated using our model are similar to those estimated from a dynamic model, indicating that the simple model can accurately inform resource allocation decisions. We find that for MSM in the US it is always better to invest in ART scale up over PrEP, because PrEP is a much less efficient use of resources than is treatment scale up. CBE is much less costly: with linear costs, it is best to invest as much as possible in CBE before investing in ART scale up; if CBE has increasing marginal costs, a mix of CBE and ART scale up is optimal. In sensitivity analysis we find that greater efficacy of the prevention intervention or a more rapidly growing epidemic favors increased investment in prevention, while a shorter time horizon favors increased investment in ART scale up.
Conclusion: Our model provides a simple yet accurate framework for determining the optimal mix of HIV prevention and treatment programs. Our examples demonstrate that HIV budgets are often best spent on the program that offers the greatest “bang for the buck.” This is an intuitive finding, which is supplemented by the simple methods we outline for estimating the health benefits and costs of treatment and prevention programs.
See more of: The 34th Annual Meeting of the Society for Medical Decision Making