Male Circumcision for HIV Prevention in High HIV Prevalence Settings: What Can Mathematical Modelling Contribute to Informed Decision Making? UNAIDS/WHO/SACEMA Expert Group on Modelling the Impact and Cost of Male Circumcision for HIV

Three recent randomised controlled trials [1–3] in Kenya, South Africa, and Uganda have confirmed previous observational studies [4] and ecological experience [5] and demonstrated beyond reasonable doubt that male circumcision performed by well-trained medical professionals reduces the risk of men acquiring HIV through female-to-male transmission by approximately 60% [5,6]. Furthermore, results from the Kenyan trial indicate that the protective effects of circumcision are sustained for at least 42 mo [7], which suggests that circumcision is likely to provide life-long partial protection. Although the evidence from the randomised trials is compelling, the longer-term population-level impact of introducing or expanding safe male circumcision services within comprehensive HIV prevention programmes remains unknown. Consequently, although some countries with a high prevalence of HIV have held stakeholder meetings and are developing policies on male circumcision for HIV prevention, many have not done so. In addition, the introduction and/or expansion of male circumcision programmes for HIV prevention raises a host of ethical, legal, and human rights issues [8–10]. Furthermore, the introduction/expansion of these programmes could be hindered by weak health infrastructures, scarce human resources for health [11], cultural concerns, political barriers, and financial constraints. In the face of these challenges, some decision-makers in sub-Saharan Africa are asking whether the introduction or expansion of male circumcision services for the reduction of HIV incidence will be cost-effective over the short, medium, and long term. Estimating the long-term population impact and cost-effectiveness of male circumcision programmes requires mathematical modelling approaches. However, when different modelling approaches use different baseline assumptions and input variables, they sometimes produce conflicting results. The Joint United Nations Programme on HIV/AIDS (UNAIDS), the World Health Organization (WHO), and the South African Centre for Epidemiological Modelling and Analysis (SACEMA) recently convened three expert group meetings in Geneva (2005), Stellenbosch (2007), and London (2008) to review published and unpublished modelling