HIV treatment as prevention: models, data, and questions--towards evidence-based decision-making.

Antiretroviral therapy (ART) for those infected with HIV can prevent onward transmission of infection, but biological efficacy alone is not enough to guide policy decisions about the role of ART in reducing HIV incidence. Epidemiology, economics, demography, statistics, biology, and mathematical modelling will be central in framing key decisions in the optimal use of ART. PLoS Medicine, with the HIV Modelling Consortium, has commissioned a set of articles that examine different aspects of HIV treatment as prevention with a forward-looking research agenda. Interlocking themes across these articles are discussed in this introduction. We hope that this article, and others in the collection, will provide a foundation upon which greater collaborations between disciplines will be formed, and will afford deeper insights into the key factors involved, to help strengthen the support for evidence-based decision-making in HIV prevention

HIV treatment as prevention : models, data, and questions-towards evidence-based decision-making

Antiretroviral therapy (ART) for those infected with HIV can prevent onward transmission of infection, but biological efficacy alone is not enough to guide policy decisions about the role of ART in reducing HIV incidence. Epidemiology, economics, demography, statistics, biology, and mathematical modelling will be central in framing key decisions in the optimal use of ART. PLoS Medicine, with the HIV Modelling Consortium, has commissioned a set of articles that examine different aspects of HIV treatment as prevention with a forward-looking research agenda. Interlocking themes across these articles are discussed in this introduction. We hope that this article, and others in the collection, will provide a foundation upon which greater collaborations between disciplines will be formed, and will afford deeper insights into the key factors involved, to help strengthen the support for evidence-based decision-making in HIV prevention

A framework for understanding the epidemiological impact of HIV treatment.

<p>The published results of models <a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001259#pmed.1001259-Montaner1" target="_blank">[38]</a>,<a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001259#pmed.1001259-AbuRaddad1" target="_blank">[53]</a>–<a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001259#pmed.1001259-Goodreau1" target="_blank">[55]</a> that have estimated the contribution of different stages of HIV infection to onward transmission are compiled in a median cumulative distribution of infections generated by one infected person over the course of his/her infection in the absence of treatment (red line). The horizontal axis shows time from the time of infection to 12 years, which is the mean survival time for those with untreated HIV infection <a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001259#pmed.1001259-Todd1" target="_blank">[56]</a>. The vertical axis shows the cumulative transmission, from 0% (no new infections generated yet) to 100% (all onward transmission completed). (Note that the uncertainty in this distribution is not indicated.) The shading indicates the approximate CD4 cell count category at each time point <a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001259#pmed.1001259-Eligibility1" target="_blank">[25]</a>,<a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001259#pmed.1001259-Lodi1" target="_blank">[26]</a>. Currently, treatment tends to be initiated well below a CD4 cell count of 200 cells/μl <a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001259#pmed.1001259-Cornell1" target="_blank">[32]</a>, meaning that the contribution of treatment to prevention is minimal because most of the transmission from that person has already occurred before treatment starts. If increased testing and improved linkages to care enabled individuals to start treatment at a CD4 cell count very close to 200 cells/μl, this could result in a substantial reduction in HIV incidence, because ∼25%–30% of transmission normally arises from individuals after that point. The prevention impact would be expected to be even greater with initiation close to a CD4 cell count of 350 cells/μl. If the average number of new infections arising from an infected person in a susceptible population exceeds one before treatment could be feasibility initiated, then treatment could not eliminate the HIV epidemic. In this framework, the influence of other forms of prevention will be to change the shape of the graph. For instance, if many men are circumcised or individuals have fewer new sexual partners per time unit, then new infections arising from an infected person will grow more slowly over time, so that on average one new infection might be generated only after the point at which a feasible programme could have initiated treatment.</p

Modeling Scenarios for the End of AIDS

At the end of 2012, 3 decades after the human immunodeficiency virus (HIV) was first identified, neither a cure nor a fully preventive vaccine was available. Despite multiple efforts, the epidemic remains an exceptional public health challenge. At the end of 2012, it was estimated that, globally, 35 million people were living with HIV, 2.3 million had become newly infected, and 1.6 million had died from AIDS-related causes. Despite substantial prevention efforts and increases in the number of individuals on highly active antiretroviral therapy (HAART), the epidemic burden continues to be high. Here, we provide a brief overview of the epidemiology of HIV transmission, the work that has been done to date regarding HIV modeling in different settings around the world, and how to finance the response to the HIV epidemic. In addition, we suggest discussion topics on how to move forward with the prevention agenda and highlight the role of treatment as prevention (TasP) in curbing the epidemic

Comparative assessment of five trials of universal HIV testing and treatment in sub‐Saharan Africa

Abstract Design: Universal voluntary HIV counselling and testing followed by prompt initiation of antiretroviral therapy (ART) for all those diagnosed HIV‐infected (universal test and treat, UTT) is now a global health standard. However, its population‐level impact, feasibility and cost remain unknown. Five community‐based trials have been implemented in sub‐Saharan Africa to measure the effects of various UTT strategies at population level: BCPP/YaTsie in Botswana, Max ART in Swaziland, HPTN 071 (PopART) in South Africa and Zambia, SEARCH in Uganda and Kenya and ANRS 12249 TasP in South Africa. This report describes and contrasts the contexts, research methodologies, intervention packages, themes explored, evolution of study designs and interventions related to each of these five UTT trials. Methods: We conducted a comparative assessment of the five trials using data extracted from study protocols and collected during baseline studies, with additional input from study investigators. We organized differences and commonalities across the trials in five categories: trial contexts, research designs, intervention packages, trial themes and adaptations. Results: All performed in the context of generalized HIV epidemics, the trials highly differ in their social, demographic, economic, political and health systems settings. They share the common aim of assessing the impact of UTT on the HIV epidemic but differ in methodological aspects such as study design and eligibility criteria for trial populations. In addition to universal ART initiation, the trials deliver a wide range of biomedical, behavioural and structural interventions as part of their UTT strategies. The five studies explore common issues, including the uptake rates of the trial services and individual health outcomes. All trials have adapted since their initiation to the evolving political, economic and public health contexts, including adopting the successive national recommendations for ART initiation. Conclusions: We found substantial commonalities but also differences between the five UTT trials in their design, conduct and multidisciplinary outputs. As empirical literature on how UTT may improve efficiency and quality of HIV care at population level is still scarce, this article provides a foundation for more collaborative research on UTT and supports evidence‐based decision making for HIV care in country and internationally