change in incidence, up or down), and incidence reduction greater than 25% and 50%. Additionally, variable HIV incidence data across years may over/underestimate those in need of treatment in the present and future. In some cases, recent modeled HIV incidence estimates and trends may be too optimistic. ANC data from Nigeria suggest that HIV incidence has stabilized, whereas Spectrum estimates continue to decline. Also, in cases where incidence has been directly measured in trials or studies, these values are often higher than modeled incidence (a large caveat being that many of these studies have been done in high-risk populations). Because of this potential uncertainty in Spectrum estimates and its recent trends, it will be very important in the future to continue to collect and analyze empirical incidence data, directly and indirectly measured.

Thus, it was chosen to group countries and assess overall progress in reducing incidence by the rate of change in modeled incidence between 2004 and 2012. By this measure, Botswana and Swaziland have made the most recent progress in reducing national incidence. In these cases, and the other countries with steep Spectrum modeled incidence declines (Malawi, Mozambique, Namibia, and Zambia) the downward trend in HIV incidence began prior to ART scale-up; thus, additional mechanisms are likely responsible for the reduction before and through this period. Investigation of what factors influenced the decline, including prevention programs and/or natural epidemic declines, in these countries is warranted (an analysis of epidemic, behavioral and program data to understand reasons for epidemic change in selected countries is being conducted by Imperial College London and funded by the World Bank).

Three countries in this analysis have shown very little change in, or stabilized, modeled incidence between 2004 and 2011. These countries include Burkina Faso, Burundi, and Uganda. Both Burkina Faso and Burundi support small HIV epidemics (no more than 4,502 estimated new infections in 2012) with recently low estimated incidence levels, so the stabilized trend is not as concerning compared to Uganda’s large epidemic. (122,806 new estimated infections in Uganda for 2012).

A focus of this review was to describe how incidence trends changed, if at all, during ART scale-up, with the acknowledged caveat that doing so is complicated by the inclusion of ART into the Spectrum estimation process, and the resulting incidence curves cannot be interpreted to assess ART effects on national-level HIV incidence. Additionally, the extent to which ART scale-up contributed to incidence trends cannot be determined without examining the effects that other HIV prevention programs (male circumcision and voluntary counseling and testing), HIV risk behavior changes in the population, or natural epidemic dynamics, could have also had (as previously mentioned, the Imperial College analysis addresses this issue). Despite these significant limitations to our analysis, it is evident that in all countries, modeled HIV incidence had started to decline prior to ART scale-up, and in most of these cases, the steepest declines happened well before 2004. In some cases, there was very little change or even an upward trend in modeled incidence during ART scale-up (2004-2012), but the design 49