This presentation is part of: Poster Session - CEA: Methods and Applications; Health Services Research
PRACTICAL USE OF COST-EFFECTIVENESS FOR COMMUNITY PLANNING OF HIV PREVENTION
Shin-Yi Wu, Ph.D.1, Deborah Cohen, MD, MPH1, and Thomas A. Farley, MD, MPH2. (1) RAND, Health, Santa Monica, CA, (2) Tulane University School of Public Health and Tropical Medicine, Community Health Sciences, New Orleans, LA
Purpose: To develop a practical tool to guide local communities to correctly use cost-effectiveness (CE) information in the literature to set priorities and allocate resources to prevent as many HIV infections as possible. Methods: We developed “Maximizing the Benefit”, a free tool containing a CE calculator, a multi-attribute utility model with optimization capability, and a user manual. The CE calculator is a pre-programmed Excel spreadsheet tool that compares the cost-effectiveness of 25 HIV prevention strategies in the literature, including individual behavior-change, biomedical, and structural interventions. It also allows users to specify their own interventions. Users can input local data on the size and HIV prevalence of the target group, intervention effectiveness, and local costs. The tool then uses Bernoulli models and proportionate-change models to estimate the number of HIV infections prevented and the cost per infection prevented. The multi-attribute utility model then takes the CE information along with other contextual factors that influence the decisions of prevention planning in its priority-setting analysis. Users will specify the contextual factors, such as the local acceptability of an intervention, and determine their relative weights of importance. An optimization program can be built in the tool for decision support of resource allocation. The user manual includes detailed guidance about how to use the tool, the methods for cost-effectiveness estimation, and how to accurately interpret the CE estimation and include it in HIV prevention planning. Results: The tool shows that the most important factors in determining the cost-effectiveness of the interventions are the local HIV prevalence and the cost per person reached. For low-prevalence populations (e.g. heterosexuals) the only cost-effective interventions were structural interventions (e.g. mass media, condom distribution), whereas for high-prevalence populations such as men who have sex with men (MSM) and injecting drug users (IDUs), individual and small-group interventions were still relatively cost-effective. Among the most cost-effective strategies overall were showing videos in STD clinics and raising alcohol taxes. When other contextual factors are considered, local communities may generate different portfolios for their HIV prevention programs. Conclusions: Comparing the CE of HIV prevention interventions provides insight that can help local communities optimize their HIV prevention strategies. Our tool can facilitate the practical use of CE in community planning to maximize the number of infections prevented.
