Literature DB >> 15893552

A decrease in drug resistance levels of the HIV epidemic can be bad news.

María S Sánchez1, Robert M Grant, Travis C Porco, Kimber L Gross, Wayne M Getz.   

Abstract

Transient decreases in the proportion of individuals newly infected with an HIV-resistant virus (primary resistance) are documented for several cities of North America, including San Francisco. Using a staged SI deterministic model, we identified three potential causes consistent with the history of the epidemic: (1) increase in risky behaviour, (2) reduction in the proportion of HIV-acutely infected individuals undergoing treatment, and (3) replacement of mono- and dual-drug therapies with triple-drug therapies. Although observed patterns resemble scenario 1 most closely, these explanations are not mutually exclusive and may have contributed synergistically to the decline. Under scenario 1 the counterintuitive situation arises where, although the proportion of primary resistance cases decreases transiently, the epidemic worsens because the actual numbers of infected individuals and of drug resistance carriers increases. Our results call for improved efforts to control the epidemic in developed nations, and highlight the usefulness of drug resistant strains as epidemiological markers.

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Year:  2004        PMID: 15893552     DOI: 10.1016/j.bulm.2004.10.001

Source DB:  PubMed          Journal:  Bull Math Biol        ISSN: 0092-8240            Impact factor:   1.758


  5 in total

1.  Stochastic model of an influenza epidemic with drug resistance.

Authors:  Yaji Xu; Linda J S Allen; Alan S Perelson
Journal:  J Theor Biol       Date:  2007-05-17       Impact factor: 2.691

2.  Hidden drug resistant HIV to emerge in the era of universal treatment access in Southeast Asia.

Authors:  Alexander Hoare; Stephen J Kerr; Kiat Ruxrungtham; Jintanat Ananworanich; Matthew G Law; David A Cooper; Praphan Phanuphak; David P Wilson
Journal:  PLoS One       Date:  2010-06-08       Impact factor: 3.240

Review 3.  Modelling sexual transmission of HIV: testing the assumptions, validating the predictions.

Authors:  Rebecca F Baggaley; Christophe Fraser
Journal:  Curr Opin HIV AIDS       Date:  2010-07       Impact factor: 4.283

4.  Monitoring linked epidemics: the case of tuberculosis and HIV.

Authors:  María S Sánchez; James O Lloyd-Smith; Wayne M Getz
Journal:  PLoS One       Date:  2010-01-20       Impact factor: 3.240

5.  HIV drug-resistant strains as epidemiologic sentinels.

Authors:  María S Sánchez; Robert M Grant; Travis C Porco; Wayne M Getz
Journal:  Emerg Infect Dis       Date:  2006-02       Impact factor: 6.883

  5 in total

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