number of influencers and biases, including natural and HIV/ART related fertility changes within a population and the varying composition of the surveillance sample as new sites are included in surveys. Data from DHS also carry their own unique biases such as test refusal; therefore, the sources of data available and used in the Spectrum model, along with their inherent biases, will greatly influence UNAIDS estimates, potentially in a way that may not accurately reflect incidence within the general population. It is difficult to predict in what direction these biases may affect the resulting modeled incidence trends, as the overall effect will likely be multi-factorial and change over time. However, preliminary work is being conducted to specifically investigate the effects of biases on HIV prevalence data, including underestimation of HIV prevalence due to testing refusal of survey respondents knowing their positive status.

In general, most nationally reported HIV prevalence trends within females 15-24 are in agreement with the _direction_ of modeled incidence trends from Spectrum (e.g. Central African Republic, Congo, Ethiopia, Ghana, Kenya, Tanzania, Lesotho, South Africa, Zimbabwe, Botswana, and Namibia), though the _relative amount_ of change may be quite different in these two data sources (e.g. Tanzania, Swaziland, Zambia), or the _timing_ of changes may (e.g. Burundi and Kenya) may not match. Since ANC and DHS/AIS prevalence data inform the Spectrum model, it is not surprising that most of these trends reflect each other.

Few research trials or studies exist in which measured HIV incidence trends have been assessed over long –periods of time; most incidence rates were measured over a few years to assess the impact of an intervention or suitability of a site for an intervention trial. A consistent finding in countries with directly measured HIV incidence from trials or studies was that these empirical incidence rates were higher than the modeled UNAIDS estimates. This is likely due to such studies focusing on higher-risk demographics (ex. high-risk young women, discordant couples), or geographic areas with higher HIV transmission intensity than the national average situation. Most studies included in this review are not representative of national incidence, as very few have included more than one trial site or region within a country<sup>5</sup> . Trial and study sites/populations are also atypical, as they have more resources with regard to service provision and ability to implement ART scale-up and other interventions. Altogether, these findings suggest that modeled national incidence estimates are not indicative of incidence within specific demographics and/or regions. The wide range of HIV incidence levels in different demographics is well illustrated by the South African 2012 national HIV incidence estimates (Shisana et al., 2014): At 1.72% for adults aged 15.49 years, but 0.55% in young men, 2.54% in young women, 3.08% in adults cohabiting, and 4.54% in

> 5 One important exception is the SHIMS study, whose directly measured incidence value of 2.4% in 2011 is in close accordance with the Spectrum incidence estimate of 2.47%. The SHIMS incidence survey was designed to be nationally representative.

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