Literature DB >> 24590937

Prevention and treatment produced large decreases in HIV incidence in a model of people who inject drugs.

Brandon D L Marshall, Samuel R Friedman, João F G Monteiro, Magdalena Paczkowski, Barbara Tempalski, Enrique R Pouget, Mark N Lurie, Sandro Galea.   

Abstract

In the United States, people who inject drugs continue to be at greatly increased risk of HIV infection. To estimate the effectiveness of various prevention scenarios, we modeled HIV transmission in a dynamic network of drug users and people who did not use drugs that was based on the New York Metropolitan Statistical Area population. We compared the projected HIV incidence in 2020 and 2040 if current approaches continue to be used to the incidence if one or more of the following hypothetical interventions were applied: increased HIV testing, improved access to substance abuse treatment, increased use of needle and syringe programs, scaled-up treatment as prevention, and a "high impact" combination scenario, consisting of all of the strategies listed above. No strategy completely eliminated HIV transmission. The high-impact combination strategy produced the largest decrease in HIV incidence-a 62 percent reduction compared to the status quo. Our results suggest that increased resources for and investments in multiple HIV prevention approaches will be required to eliminate HIV transmission among people who inject drugs.

Entities:  

Keywords:  AIDS/HIV; Epidemiology; Mental Health/Substance Abuse; Public Health; Special Populations

Mesh:

Year:  2014        PMID: 24590937      PMCID: PMC4469974          DOI: 10.1377/hlthaff.2013.0824

Source DB:  PubMed          Journal:  Health Aff (Millwood)        ISSN: 0278-2715            Impact factor:   6.301


  35 in total

1.  HIV incidence among injection drug users in New York City, 1990 to 2002: use of serologic test algorithm to assess expansion of HIV prevention services.

Authors:  Don C Des Jarlais; Theresa Perlis; Kamyar Arasteh; Lucia V Torian; Sara Beatrice; Judith Milliken; Donna Mildvan; Stanley Yancovitz; Samuel R Friedman
Journal:  Am J Public Health       Date:  2005-06-28       Impact factor: 9.308

2.  Estimating the prevalence of injection drug users in the U.S. and in large U.S. metropolitan areas from 1992 to 2002.

Authors:  Joanne E Brady; Samuel R Friedman; Hannah L F Cooper; Peter L Flom; Barbara Tempalski; Karla Gostnell
Journal:  J Urban Health       Date:  2008-03-15       Impact factor: 3.671

3.  HIV prevalence rates among injection drug users in 96 large US metropolitan areas, 1992-2002.

Authors:  Barbara Tempalski; Spencer Lieb; Charles M Cleland; Hannah Cooper; Joanne E Brady; Samuel R Friedman
Journal:  J Urban Health       Date:  2008-11-18       Impact factor: 3.671

4.  Rates of HIV-1 transmission per coital act, by stage of HIV-1 infection, in Rakai, Uganda.

Authors:  Maria J Wawer; Ronald H Gray; Nelson K Sewankambo; David Serwadda; Xianbin Li; Oliver Laeyendecker; Noah Kiwanuka; Godfrey Kigozi; Mohammed Kiddugavu; Thomas Lutalo; Fred Nalugoda; Fred Wabwire-Mangen; Mary P Meehan; Thomas C Quinn
Journal:  J Infect Dis       Date:  2005-03-30       Impact factor: 5.226

5.  HIV incidence among injection drug users in Baltimore, Maryland (1988-2004).

Authors:  Shruti H Mehta; Noya Galai; Jacquie Astemborski; David D Celentano; Steffanie A Strathdee; David Vlahov; Kenrad E Nelson
Journal:  J Acquir Immune Defic Syndr       Date:  2006-11-01       Impact factor: 3.731

6.  Behavioral surveillance among people at risk for HIV infection in the U.S.: the National HIV Behavioral Surveillance System.

Authors:  Kathleen M Gallagher; Patrick S Sullivan; Amy Lansky; Ida M Onorato
Journal:  Public Health Rep       Date:  2007       Impact factor: 2.792

7.  Variation in HIV-1 set-point viral load: epidemiological analysis and an evolutionary hypothesis.

Authors:  Christophe Fraser; T Déirdre Hollingsworth; Ruth Chapman; Frank de Wolf; William P Hanage
Journal:  Proc Natl Acad Sci U S A       Date:  2007-10-22       Impact factor: 11.205

8.  Theorizing "Big Events" as a potential risk environment for drug use, drug-related harm and HIV epidemic outbreaks.

Authors:  Samuel R Friedman; Diana Rossi; Naomi Braine
Journal:  Int J Drug Policy       Date:  2008-12-19

9.  Relationship between adherence level, type of the antiretroviral regimen, and plasma HIV type 1 RNA viral load: a prospective cohort study.

Authors:  M Martin; E Del Cacho; C Codina; M Tuset; E De Lazzari; J Mallolas; J-M Miró; J M Gatell; J Ribas
Journal:  AIDS Res Hum Retroviruses       Date:  2008-10       Impact factor: 2.205

10.  Full participation in harm reduction programmes is associated with decreased risk for human immunodeficiency virus and hepatitis C virus: evidence from the Amsterdam Cohort Studies among drug users.

Authors:  Charlotte Van Den Berg; Colette Smit; Giel Van Brussel; Roel Coutinho; Maria Prins
Journal:  Addiction       Date:  2007-09       Impact factor: 6.526

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  34 in total

1.  Evaluating HIV prevention strategies for populations in key affected groups: the example of Cabo Verde.

Authors:  João Filipe G Monteiro; Sandro Galea; Timothy Flanigan; Maria de Lourdes Monteiro; Samuel R Friedman; Brandon D L Marshall
Journal:  Int J Public Health       Date:  2015-04-03       Impact factor: 3.380

2.  Modeling a Theory-Based Approach to Examine the Influence of Neurocognitive Impairment on HIV Risk Reduction Behaviors Among Drug Users in Treatment.

Authors:  Tania B Huedo-Medina; Roman Shrestha; Michael Copenhaver
Journal:  AIDS Behav       Date:  2016-08

3.  Marshall and Galea respond to "data theory in epidemiology".

Authors:  Brandon D L Marshall; Sandro Galea
Journal:  Am J Epidemiol       Date:  2014-12-05       Impact factor: 4.897

4.  Systems Modeling to Advance the Promise of Data Science in Epidemiology.

Authors:  Magdalena Cerdá; Katherine M Keyes
Journal:  Am J Epidemiol       Date:  2019-05-01       Impact factor: 4.897

5.  HIV/AIDS and the African-American Community 2018: a Decade Call to Action.

Authors:  Cato T Laurencin; Christopher J Murdock; Lynne Laurencin; Donna M Christensen
Journal:  J Racial Ethn Health Disparities       Date:  2018-06-04

6.  Understanding the effects of different HIV transmission models in individual-based microsimulation of HIV epidemic dynamics in people who inject drugs.

Authors:  J F G Monteiro; D J Escudero; C Weinreb; T Flanigan; S Galea; S R Friedman; B D L Marshall
Journal:  Epidemiol Infect       Date:  2016-01-12       Impact factor: 2.451

7.  Acute HIV infection transmission among people who inject drugs in a mature epidemic setting.

Authors:  Daniel J Escudero; Mark N Lurie; Kenneth H Mayer; Caleb Weinreb; Maximilian King; Sandro Galea; Samuel R Friedman; Brandon D L Marshall
Journal:  AIDS       Date:  2016-10-23       Impact factor: 4.177

8.  The Interaction of Risk Network Structures and Virus Natural History in the Non-spreading of HIV Among People Who Inject Drugs in the Early Stages of the Epidemic.

Authors:  Kirk Dombrowski; Bilal Khan; Patrick Habecker; Holly Hagan; Samuel R Friedman; Mohamed Saad
Journal:  AIDS Behav       Date:  2017-04

9.  Network Viral Load: A Critical Metric for HIV Elimination.

Authors:  Britt Skaathun; Aditya S Khanna; Ethan Morgan; Samuel R Friedman; John A Schneider
Journal:  J Acquir Immune Defic Syndr       Date:  2018-02-01       Impact factor: 3.731

10.  A non-inferiority trial of an evidence-based secondary HIV prevention behavioral intervention compared to an adapted, abbreviated version: Rationale and intervention description.

Authors:  Roman Shrestha; Archana Krishnan; Frederick L Altice; Michael Copenhaver
Journal:  Contemp Clin Trials       Date:  2015-08-05       Impact factor: 2.226

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