ArticleHealth care management science2005
Improved allocation of HIV prevention resources: using information about prevention program production functions.
Article in Health care management science, 2005. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
11 citing papers in PubMed.
- Quantifying optimal resource allocation strategies for controlling epidemics.Journal of the Royal Society, Interface · 2023Article
- Optimal resource allocation in HIV self-testing secondary distribution among Chinese MSM: data-driven integer programming models.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2022Article
- Cost-Effectiveness Analysis in Implementation Science: a Research Agenda and Call for Wider Application.Current HIV/AIDS reports · 2021Review
- The impact of localized implementation: determining the cost-effectiveness of HIV prevention and care interventions across six United States cities.AIDS (London, England) · 2020Article
- Creating impact with operations research in health: making room for practice in academia.Health care management science · 2016Article
- HIV epidemic control-a model for optimal allocation of prevention and treatment resources.Health care management science · 2014Article
- Optimal allocation of resources in female sex worker targeted HIV prevention interventions: model insights from Avahan in South India.PloS one · 2014Article
- Assessing the efficiency of mother-to-child HIV prevention in low- and middle-income countries using data envelopment analysis.Health care management science · 2012Article
- Optimal investment in HIV prevention programs: more is not always better.Health care management science · 2009Article
- Predicting the effectiveness of prevention: a role for epidemiological modeling.The journal of primary prevention · 2008Article
- Decision making for HIV prevention and treatment scale up: bridging the gap between theory and practice.Medical decision making : an international journal of the Society for Medical Decision MakingArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
To allocate HIV prevention resources effectively, it is important to have information about the effectiveness of alternative prevention programs as a function of expenditure. We refer to this relationship as the "production function" for a prevention program. Few studies of HIV prevention programs have reported this relationship. This paper demonstrates the value of such information. We present a simple model for allocating HIV prevention resources, and apply the model to an illustrative HIV prevention resource allocation problem. We show that, without sufficient information about prevention program production functions, suboptimal decisions may be made. We show that epidemiologic data, such as estimates of HIV prevalence or incidence, may not provide enough information to support optimal allocation of HIV prevention resources. Our results suggest that good allocations can be obtained based on fairly basic information about prevention program production functions: an estimate of fixed cost plus a single estimate of cost and resulting risk reduction. We find that knowledge of production functions is most important when fixed cost is high and/or when the budget is a significantly constraining factor. We suggest that, at the minimum, future data collection on prevention program effectiveness should include fixed and variable cost estimates for the intervention when implemented at a "typical" level, along with a detailed description of the intervention and detailed description of costs by category.
Indexed as
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.