title: Decision making for HIV prevention and treatment scale up: bridging the gap between theory and practice.
abstract: BACKGROUND
  Effectively controlling the HIV epidemic will require efficient use of limited resources. Despite ambitious global goals for HIV prevention and treatment scale up, few comprehensive practical tools exist to inform such decisions.
  
  
  METHODS
  We briefly summarize modeling approaches for resource allocation for epidemic control, and discuss the practical limitations of these models. We describe typical challenges of HIV resource allocation in practice and some of the tools used by decision makers. We identify the characteristics needed in a model that can effectively support planners in decision making about HIV prevention and treatment scale up.
  
  
  RESULTS
  An effective model to support HIV scale-up decisions will be flexible, with capability for parameter customization and incorporation of uncertainty. Such a model needs certain key technical features: it must capture epidemic effects; account for how intervention effectiveness depends on the target population and the level of scale up; capture benefit and cost differentials for packages of interventions versus single interventions, including both treatment and prevention interventions; incorporate key constraints on potential funding allocations; identify optimal or near-optimal solutions; and estimate the impact of HIV interventions on the health care system and the resulting resource needs. Additionally, an effective model needs a user-friendly design and structure, ease of calibration and validation, and accessibility to decision makers in all settings.
  
  
  CONCLUSIONS
  Resource allocation theory can make a significant contribution to decision making about HIV prevention and treatment scale up. What remains now is to develop models that can bridge the gap between theory and practice.
authors.affiliation: Department of Management Science and Engineering, Stanford University, Stanford, California 94305, USA. ssabina@stanford.edu
authors.forename: Sabina S
authors.initials: SS
authors.lastname: Alistar
authors.forename: Margaret L
authors.initials: ML
authors.lastname: Brandeau
mesh_terms: D003657:Decision Making; D005260:Female; D015658:HIV Infections / Q000188:drug therapy* / Q000517:prevention & control*; D006801:Humans; D008297:Male; D008962:Models, Theoretical; D040841:Resource Allocation / Q000458:organization & administration*