HPTN 071 (PopART): A Cluster-Randomized Trial of the Population Impact of an HIV Combination Prevention Intervention Including Universal Testing and Treatment: Mathematical Model

National Institute of Allergy and Infectious Diseases, United States of America
PLoS ONE (Impact Factor: 3.23). 01/2014; 9. DOI: 10.1371/journal.pone.0084511


The HPTN 052 trial confirmed that antiretroviral therapy (ART) can nearly eliminate HIV transmission from successfully treated HIV-infected individuals within couples. Here, we present the mathematical modeling used to inform the design and monitoring of a new trial aiming to test whether widespread provision of ART is feasible and can substantially reduce population-level HIV incidence.

Methods and Findings
The HPTN 071 (PopART) trial is a three-arm cluster-randomized trial of 21 large population clusters in Zambia and South Africa, starting in 2013. A combination prevention package including home-based voluntary testing and counseling, and ART for HIV positive individuals, will be delivered in arms A and B, with ART offered universally in arm A and according to national guidelines in arm B. Arm C will be the control arm. The primary endpoint is the cumulative three-year HIV incidence.
We developed a mathematical model of heterosexual HIV transmission, informed by recent data on HIV-1 natural history. We focused on realistically modeling the intervention package. Parameters were calibrated to data previously collected in these communities and national surveillance data.
We predict that, if targets are reached, HIV incidence over three years will drop by >60% in arm A and >25% in arm B, relative to arm C. The considerable uncertainty in the predicted reduction in incidence justifies the need for a trial. The main drivers of this uncertainty are possible community-level behavioral changes associated with the intervention, uptake of testing and treatment, as well as ART retention and adherence.

The HPTN 071 (PopART) trial intervention could reduce HIV population-level incidence by >60% over three years. This intervention could serve as a paradigm for national or supra-national implementation. Our analysis highlights the role mathematical modeling can play in trial development and monitoring, and more widely in evaluating the impact of treatment as prevention.

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Available from: Anne Cori, Mar 21, 2014
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    • "Since it is usually impossible to measure the full state of the system, successful model driven data collection must not only measure state variables (e.g., the number susceptible or infectious), but also attempt to determine the dynamic regime in which those variables were collected. Simulation of trial design is a growing area of research, with numerous applications to vaccine trials (e.g., Van de Velde et al., 2007; Yang et al., 2006) and growing use in other settings (e.g., PopART Cori et al., 2014). The development of standard tools similar to those available for standard sample size calculations, or even a list of best practices, would go a long way to expanding the use of mechanistic models in study design. "
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    ABSTRACT: Infectious disease models are both concise statements of hypotheses and powerful techniques for creating tools from hypotheses and theories. As such, they have tremendous potential for guiding data collection in experimental and observational studies, leading to more efficient testing of hypotheses and more robust study designs. In numerous instances, infectious disease models have played a key role in informing data collection, including the Garki project studying malaria, the response to the 2009 pandemic of H1N1 influenza in the United Kingdom and studies of T-cell immunodynamics in mammals. However, such synergies remain the exception rather than the rule; and a close marriage of dynamic modeling and empirical data collection is far from the norm in infectious disease research. Overcoming the challenges to using models to inform data collection has the potential to accelerate innovation and to improve practice in how we deal with infectious disease threats. Copyright © 2014 The Authors. Published by Elsevier B.V. All rights reserved.
    Epidemics 12/2014; 5. DOI:10.1016/j.epidem.2014.12.002 · 1.87 Impact Factor
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    • "The trial design has been guided by the results of mathematical modelling. The preliminary PopART model is described in a related paper [64] and was fitted to routine HIV data from Zambia and South Africa, as well as data from the ZAMSTAR trial which was carried out in the same study areas. The model was used to explore the projected impact of the PopART interventions at different levels of coverage, and this informed the choice of intervention targets. "
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    ABSTRACT: Effective interventions to reduce HIV incidence in sub-Saharan Africa are urgently needed. Mathematical modelling and the HIV Prevention Trials Network (HPTN) 052 trial results suggest that universal HIV testing combined with immediate antiretroviral treatment (ART) should substantially reduce incidence and may eliminate HIV as a public health problem. We describe the rationale and design of a trial to evaluate this hypothesis. A rigorously-designed trial of universal testing and treatment (UTT) interventions is needed because: i) it is unknown whether these interventions can be delivered to scale with adequate uptake; ii) there are many uncertainties in the models such that the population-level impact of these interventions is unknown; and ii) there are potential adverse effects including sexual risk disinhibition, HIV-related stigma, over-burdening of health systems, poor adherence, toxicity, and drug resistance.In the HPTN 071 (PopART) trial, 21 communities in Zambia and South Africa (total population 1.2 m) will be randomly allocated to three arms. Arm A will receive the full PopART combination HIV prevention package including annual home-based HIV testing, promotion of medical male circumcision for HIV-negative men, and offer of immediate ART for those testing HIV-positive; Arm B will receive the full package except that ART initiation will follow current national guidelines; Arm C will receive standard of care. A Population Cohort of 2,500 adults will be randomly selected in each community and followed for 3 years to measure the primary outcome of HIV incidence. Based on model projections, the trial will be well-powered to detect predicted effects on HIV incidence and secondary outcomes. Trial results, combined with modelling and cost data, will provide short-term and long-term estimates of cost-effectiveness of UTT interventions. Importantly, the three-arm design will enable assessment of how much could be achieved by optimal delivery of current policies and the costs and benefits of extending this to UTT.Trial registration: NCT01900977.
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