ArticleHealth care management science2009
Optimal investment in HIV prevention programs: more is not always better.
Article in Health care management science, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Quantifying optimal resource allocation strategies for controlling epidemics.Journal of the Royal Society, Interface · 2023Article
- A reinforcement learning model to inform optimal decision paths for HIV elimination.Mathematical biosciences and engineering : MBE · 2021Article
- Article
- HIV Treatment and Prevention: A Simple Model to Determine Optimal Investment.Medical decision making : an international journal of the Society for Medical Decision Making · 2016Article
- Toward cost-effective staffing mixes for Veterans Affairs substance use disorder treatment programs.BMC health services research · 2015Article
- HIV epidemic control-a model for optimal allocation of prevention and treatment resources.Health care management science · 2014Article
- Optimal incentives for allocating HIV/AIDS prevention resources among multiple populations.Health care management science · 2012Article
- Assessing the efficiency of mother-to-child HIV prevention in low- and middle-income countries using data envelopment analysis.Health care management science · 2012Article
- Special issue on global health.Health care management science · 2012Article
- Allocating HIV prevention funds in the United States: recommendations from an optimization model.PloS one · 2012Article
- Modeling the impact of comorbidity on breast cancer patient outcomes.Health care management science · 2010Article
- From Theory to Practice: Implementation of a Resource Allocation Model in Health Departments.Journal of public health management and practice : JPHMPArticle
- 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
2 authors.
Funding
Abstract
This paper develops a mathematical/economic framework to address the following question: Given a particular population, a specific HIV prevention program, and a fixed amount of funds that could be invested in the program, how much money should be invested? We consider the impact of investment in a prevention program on the HIV sufficient contact rate (defined via production functions that describe the change in the sufficient contact rate as a function of expenditure on a prevention program), and the impact of changes in the sufficient contact rate on the spread of HIV (via an epidemic model). In general, the cost per HIV infection averted is not constant as the level of investment changes, so the fact that some investment in a program is cost effective does not mean that more investment in the program is cost effective. Our framework provides a formal means for determining how the cost per infection averted changes with the level of expenditure. We can use this information as follows: When the program has decreasing marginal cost per infection averted (which occurs, for example, with a growing epidemic and a prevention program with increasing returns to scale), it is optimal either to spend nothing on the program or to spend the entire budget. When the program has increasing marginal cost per infection averted (which occurs, for example, with a shrinking epidemic and a prevention program with decreasing returns to scale), it may be optimal to spend some but not all of the budget. The amount that should be spent depends on both the rate of disease spread and the production function for the prevention program. We illustrate our ideas with two examples: that of a needle exchange program, and that of a methadone maintenance program.
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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.