Literature DB >> 21184183

A model for allocating CDC's HIV prevention resources in the United States.

Arielle Lasry1, Stephanie L Sansom, Katherine A Hicks, Vladislav Uzunangelov.   

Abstract

The Division of HIV/AIDS Prevention (DHAP) at the Centers for Disease Control and Prevention has an annual budget of approximately $325 million for funding HIV prevention programs in the U.S. The purpose of this paper is to thoroughly describe the methods used to develop a national HIV resource allocation model intended to inform DHAP on allocation strategies that might improve the overall effectiveness of HIV prevention efforts. The HIV prevention resource allocation problem consists of choosing how to apportion prevention resources among interventions and populations so that HIV incidence is minimized, given a budget constraint. We developed an epidemic model that projects HIV infections over time given a specific allocation scenario. The epidemic model is then embedded in a nonlinear mathematical optimization program to determine the allocation scenario that minimizes HIV incidence over a 5-year horizon. In our model, we consider the general U.S. population and specific at-risk populations. The at-risk populations include 15 subgroups structured by gender, race/ethnicity and HIV transmission risk group. HIV transmission risk groups include high-risk heterosexuals, men who have sex with men and injection drug users. We consider HIV screening interventions and interventions to reduce HIV-related risk behaviors. The output of the model is the optimal funding scenario indicating the amounts to be allocated to all combinations of populations and interventions. For illustrative purposes only, we provide a sample application of the model. In this example, the optimal allocation scenario is compared to the current baseline funding scenario to highlight how the current allocation of funds could be improved. In the baseline allocation, 29% of the annual budget is aimed at the general population, while the model recommends targeting 100% of the budget to the at-risk populations with no allocation targeted to the general population. Within the allocation to behavioral interventions the model recommends an increase in targeting diagnosed positives. Also, the model allocation suggests a greater focus on MSM and IDUs with a 72% of the annual budget allocated to them, while the baseline allocation for MSM and IDUs totals 37%. Incorporating future epidemic trends in the decision-making process informs the selection of populations and interventions that should be targeted. Improving the use of funds by targeting the interventions and population subgroups at greatest risk may lead to improved HIV outcomes. These models can also direct research by pointing to areas where the development of cost-effective interventions can have the most impact on the epidemic.

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Year:  2010        PMID: 21184183     DOI: 10.1007/s10729-010-9147-2

Source DB:  PubMed          Journal:  Health Care Manag Sci        ISSN: 1386-9620


  14 in total

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3.  Public health consequences of screening patients for adherence to highly active antiretroviral therapy.

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4.  Effects of HIV counseling and testing on sexual risk behavior: a meta-analytic review of published research, 1985-1997.

Authors:  L S Weinhardt; M P Carey; B T Johnson; N L Bickham
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5.  Undiagnosed HIV prevalence among adults and adolescents in the United States at the end of 2006.

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7.  Subpopulation estimates from the HIV incidence surveillance system--United States, 2006.

Authors: 
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8.  Understanding differences in HIV sexual transmission among Latino and black men who have sex with men: The Brothers y Hermanos Study.

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Authors:  Ram K Shrestha; Hollie A Clark; Stephanie L Sansom; Binwei Song; Holly Buckendahl; Cindy B Calhoun; Angela B Hutchinson; James D Heffelfinger
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10.  Comparing the costs of HIV screening strategies and technologies in health-care settings.

Authors:  Paul G Farnham; Angela B Hutchinson; Stephanie L Sansom; Bernard M Branson
Journal:  Public Health Rep       Date:  2008 Nov-Dec       Impact factor: 2.792

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  17 in total

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Authors:  Jeffrey H Herbst; Marlene Glassman; James W Carey; Thomas M Painter; Deborah J Gelaude; Amy M Fasula; Jerris L Raiford; Arin E Freeman; Camilla Harshbarger; Abigail H Viall; David W Purcell
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Review 3.  Structural Design and Data Requirements for Simulation Modelling in HIV/AIDS: A Narrative Review.

Authors:  Xiao Zang; Emanuel Krebs; Linwei Wang; Brandon D L Marshall; Reuben Granich; Bruce R Schackman; Julio S G Montaner; Bohdan Nosyk
Journal:  Pharmacoeconomics       Date:  2019-10       Impact factor: 4.981

4.  From Theory to Practice: Implementation of a Resource Allocation Model in Health Departments.

Authors:  Emine Yaylali; Paul G Farnham; Karen L Schneider; Stewart J Landers; Oskian Kouzouian; Arielle Lasry; David W Purcell; Timothy A Green; Stephanie L Sansom
Journal:  J Public Health Manag Pract       Date:  2016 Nov-Dec

5.  Assessing the efficiency of mother-to-child HIV prevention in low- and middle-income countries using data envelopment analysis.

Authors:  Sérgio P Santos; Carla A E Amado; Mauro F Santos
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6.  Identifying and characterizing places for the targeted control of heterosexual HIV transmission in urban areas.

Authors:  Sarah Polk; Jonathan M Ellen; Caroline Fichtenberg; Steven Huettner; Meredith Reilly; Jenita Parekh; Jacky M Jennings
Journal:  AIDS Behav       Date:  2014-08

7.  Integrating Equity in a Public Health Funding Strategy.

Authors:  Kristy T Joseph; Ketra Rice; Chunyu Li
Journal:  J Public Health Manag Pract       Date:  2016 Jan-Feb

8.  Optimal Individualized Treatments in Resource-Limited Settings.

Authors:  Alexander R Luedtke; Mark J van der Laan
Journal:  Int J Biostat       Date:  2016-05-01       Impact factor: 0.968

9.  Research Synthesis, HIV Prevention Response, and Public Health: CDC's HIV/AIDS Prevention Research Synthesis Project.

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10.  Utility and delivery of behavioural interventions to prevent sexually transmitted infections.

Authors:  Sevgi O Aral
Journal:  Sex Transm Infect       Date:  2011-12       Impact factor: 3.519

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