REACH: A practical HIV resource allocation tool for decision makers

Abstract

With more than 34 million people currently living with HIV and 1.8 million dying from HIV annually, there is a great need for continued HIV control efforts. However, funds for HIV prevention and treatment continue to fall short of estimated need and are further jeopardized by the current global economic down-turn. Thus, efficient allocation of resources among interventions for preventing and treating HIV is crucial. Decision makers, who face budget constraints and other practical considerations, need tools to help them identify sets of interventions that will yield optimal results for their specific settings in terms of their demographic, epidemic, cultural, and economic contexts and resources available to them. Existing theoretical models are often too complex for practical use by decision makers, whereas the practical tools that have been developed are often too simple. As a result, decisions are often made based on historical patterns, political interests, and decision maker heuristics, and may not make the most effective use of limited HIV control resources. To address this gap between theory and practice, we developed a planning tool for use by regional and country-level decision makers in evaluating potential resource allocations. The Resource Allocation for Control of HIV (REACH) model, implemented in Microsoft Excel, has a user-friendly design and allows users to customize key parameters to their own setting, such as demographics, epidemic characteristics and transmission modes, and economic setting. In addition, the model incorporates epidemic dynamics; accounts for how intervention effectiveness depends on the target population and the level of scale up; captures benefit and cost differentials for combinations of interventions versus single interventions, including both treatment and prevention interventions; incorporates key constraints on potential funding allocations; identifies optimal or near-optimal solutions based on epidemic characteristics, local realities, and available level of investment; and estimates the impact of HIV interventions on the health care system and resulting resource needs. In this chapter we describe the model and then present example analyses for three different settings, Uganda, Ukraine, and Saint Petersburg, Russia. We conclude with a discussion of insights gained from application of the model thus far, and we describe our ongoing work in further developing and applying the model.