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HIV TREATMENT AS PREVENTION

Sunday, 29th of July 2012

• HIV TREATMENT AS PREVENTION

       This introductory article forms part of a collection on this complex subject. Readers with further interests are advised to consult the homepage of www.plosmedicine.org

       Full text is at http://www.plosmedicine.org/article/info%3Adoi%2F10.1371%2Fjournal.pmed.1001259

       Key Points

       It has been established that ART for those infected with HIV can prevent onward transmission of infection, but biological efficacy alone is not enough to confirm the impact that ART could have on the HIV epidemic, or to show how best to use ART to reduce incidence of HIV. This will be among the most important issues in the field of HIV prevention for the foreseeable future.

       Epidemiology, economics, demography, statistics, biology, and mathematical modelling will be central in framing key decisions in the optimal use of ART.

       The HIV Modelling Consortium aims to coordinate and promote research across these disciplines, and facilitate communication between researchers and policy-makers. At a collaborative meeting of this consortium in November 2011, several interlocking themes emerged that are discussed in this article and covered in more depth by other articles in this collection.

       Mathematical modelling is used to investigate the potential impact of treatment on HIV incidence. However, because of incomplete information on all the factors that could influence impact, substantial uncertainties will remain. Models should acknowledge those uncertainties and help prioritise data collection where this could strengthen model conclusions.

       The current economic constraints on HIV prevention bring to the fore the role of modelling to help assess the value and cost-effectiveness of ART. Understanding costs and integrating costing and epidemiological models will be key areas of ongoing and future research to help inform decision-making processes. Models are also being used to help design and interpret trials that test hypotheses about the impact of expanded access to treatment on the spread of HIV in communities.

       We hope that this article and others in the collection will provide a solid foundation upon which greater collaborations between disciplines will be formed, so as to better integrate the role of modelling into the wider scientific process and to more clearly articulate the strengths and weaknesses of particular modelling analyses. This approach will ultimately strengthen the support for evidence-based decision-making in HIV programmes.