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Curr Drug Targets Infect Disord. 2003 Dec;3(4):345-53.

Predicting the impact of antiretrovirals in resource-poor settings: preventing HIV infections whilst controlling drug resistance.

Author information

  • 1Department of Biomathematics & the UCLA AIDS Institute, David Geffen School of Medicine at UCLA, 10833 Le Conte Avenue, Los Angeles, CA 90095-1766, USA. sblower@mednet.ucla.edu

Abstract

There is currently an opportunity to carefully plan the implementation of antiretroviral (ARV) therapy in the developing world. Here, we use mathematical models to predict the potential impact that low to moderate usage rates of ARVs might have in developing countries. We use our models to predict the relationship between the specific usage rate of ARVs (in terms of the percentage of those infected with HIV who receive such treatment) and: (i) the prevalence of drug-resistant HIV that will arise, (ii) the future transmission rate of drug-resistant strains of HIV, and (iii) the cumulative number of HIV infections that will be prevented through more widespread use of ARVs. We also review the current state of HIV/AIDS treatment programs in resource-poor settings and identify the essential elements of a successful treatment project, noting that one key element is integration with a strong prevention program. We apply both program experience from Haiti and Brazil and the insights gleaned from our modeling to address the emerging debate regarding the increased availability of ARVs in developing countries. Finally, we show how mathematical models can be used as tools for designing robust health policies for implementing ARVs in developing countries. Our results demonstrate that designing optimal ARV-based strategies to control HIV epidemics is extremely complex, as increasing ARV usage has both beneficial and detrimental epidemic-level effects. Control strategies should be based upon the overall impact on the epidemic and not simply upon the impact ARVs will have on the transmission and/or prevalence of ARV-resistant strains.

PMID:
14754434
[PubMed - indexed for MEDLINE]
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