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Med Decis Making. 2012 Jan-Feb;32(1):105-17. doi: 10.1177/0272989X10391808. Epub 2010 Dec 29.

Decision making for HIV prevention and treatment scale up: bridging the gap between theory and practice.

Author information

  • 1Department of Management Science and Engineering, Stanford University, Stanford, California 94305, USA. ssabina@stanford.edu

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.

PMID:
21191118
[PubMed - indexed for MEDLINE]
PMCID:
PMC3271126
Free PMC Article
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