Copyright © Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited. Using modeling to inform international guidelines for antiretroviral treatment Timothy B. Hallett a , Nicolas A. Menzies b , Paul Revill c , Daniel Keebler d,e , Annick Bo ´ rquez a , Ellen McRobie a and Jeffrey W. Eaton a AIDS 2014, 28 (Suppl 1):S1–S4 International guidelines for interventions and medical care promote health by enabling populations to benefit from the best scientific evidence and accumulated experience of the global community. However, setting guidelines is difficult, especially when the best clinical practice has to be balanced with practical constraints and concern for overall population health outcomes. The 2013 consolidated guidelines for the use of antiretro- virals to treat HIV, promulgated by the World Health Organization (WHO), replace several distinct guideline documents about the provision of antiretrovirals in different circumstances [1]. In providing such guidance, the consequences of decisions must simultaneously be considered in many dimensions (morbidity, mortality, new infections, resistance, resource needs) and over a range of timescales, whilst also weighing the strength of various forms of evidence and accounting for the attendant uncertainties. These questions lend themselves to mathematical modeling and economic evaluation as a means to synthesize data in a transparent and precise way and to anticipate the implications of competing approaches for population health [2]. For the last several years, the HIV Modelling Consortium (www.hivmodelling.org) has worked with agencies including the WHO, Joint United Nations Programme on HIV/AIDS (UNAIDS), National Institutes of Health, the World Bank and the Bill & Melinda Gates Foundation, and with almost 100 different mathematical modelers, to provide robust analysis informing con- temporary policy questions. In advance of the anti- retroviral guidelines revision, the WHO invited the HIV Modelling Consortium to prepare analyses for the panels tasked with developing the 2013 consolidated anti- retroviral guidelines, so that the insights about the health and cost consequences gained from the modelled analyses could inform the guideline development process. Crucially, the modeling was not done post hoc to justify decisions that had already been made, but rather to provide useful analysis for the questions that remained open at that time. The first step of developing modeling analyses for the guidelines panels involved collaborative discussions between the WHO and HIV Modelling Consortium to define specific policy questions which modeling could usefully contribute. We focused on two broad issues: who should be initiated on antiretroviral therapy (ART), and how should patients on ART be monitored. We decided to approach these questions within the framework of cost-effectiveness analysis (CEA), as this explicitly compares the health benefits produced by a particular policy with the resources required to achieve those benefits and allows for a set of policies to be chosen that will generate the greatest health gains from available resources. The final step was to agree on how to model these processes. Given that our previous work has demonstrated a large influence of model choice on results [3,4], it was decided to base the analysis not on a single model but instead on a diverse set of existing epidemiological models. We believed that consensus findings across the models would increase confidence in this evidence to support policy, and that the discrepancies between the model analyses would signal important uncertainties that might have gone undetected in a single model. Summary results have been presented elsewhere by Eaton and Menzies et al. [5] and Keebler and Revill et al. [6]. In brief, we estimated that, in programmes with high ART coverage, expanding treatment eligibility to HIV-positive adults with CD4 þ cell counts up to 500 cells/ml or even higher appears to be cost-effective, but this will need to be accompanied by large expansions in HIV testing and a Department of Infectious Disease Epidemiology, Imperial College London, London, UK, b Center for Health Decision Science, Harvard School of Public Health, Boston, Massachusetts, USA, c Centre for Health Economics, University of York, York, UK, d South African Department of Science and Technology/National Research Foundation Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, South Africa, and e Department of Epidemiology, University of California, Los Angeles, CA, USA. Correspondence to Timothy B. Hallett, Department of Infectious Disease Epidemiology, Imperial College London, St Mary’s Campus, Norfolk Place, London W2 1PG, UK. E-mail: timothy.hallett@imperial.ac.uk DOI:10.1097/QAD.0000000000000115 ISSN 0269-9370 Q 2014 Wolters Kluwer Health | Lippincott Williams & Wilkins S1