the need for treatment is predictable. The number of people on treatment over time is a function of the rates of diagnosis, treatment initiation, and survival on treatment. These rates are also predictable given assumptions about investment in treatment programs and the organization of those programs. The scale and success of treatment will determine the impact of treatment interventions. The social and economic burden of HIV/AIDS will depend upon the success of treatment programs, as well as the efforts undertaken to care for those affected. In contrast, the accumulation of new infections over time is more difficult to predict since it will depend on changing patterns of risk in the population, which in turn are influenced by demographic, cultural, and social phenomena, as well as the impact of preventive interventions.

Over the course of the epidemic, some patterns of risk behavior will likely change, making it difficult to link the current observed prevalence of HIV to measures of risk behavior with precision. Furthermore, the sensitive, private, and often stigmatized behaviors that place many at risk of acquiring HIV may not be accurately reported in surveys (Fenton et al., 2001; Slaymaker, 2004), making it difficult to relate incidence to behavior and predict how changes in behavior will influence incidence (Garnett et al., 2006). A straightforward assumption is that trends in HIV incidence will continue in the future. With incidence trends stable, future prevalence will be determined largely by the future expansion of ART, which has both direct and indirect effects on prevalence. The direct effect of ART expansion is to lengthen the lives of people with HIV, which consequently increases prevalence. ART also has several possible indirect effects on HIV prevalence, both beneficial and adverse, through its effects on transmission and therefore on incidence (see Table A-1).

In the aids20311 predictions, it is assumed that those on treatment have a reduced infectiousness (to 20 percent of the original transmission risk from an infected person to susceptible contacts) (aids2031 Consortium, 2011; Hecht et al., 2010). In formulating its baseline projections, the committee developed a model incorporating two of the biological effects from Table A-1—the reduction in transmission among those on effective ART and the increased exposure to risk caused by a longer HIV-infected lifespan. Since the degree to which the two behavioral effects in Table A-1 will manifest as treatment access increases is still unknown and will likely be responsive to national HIV/AIDS policies, the baseline model ignores these and the other indirect effects of ART on HIV transmission.2

Others have made projections of the impact of particular interventions over


aids2031 is a consortium of partners who came together to look at what has been learned about the HIV/AIDS response and consider the implications of the changing world around the HIV/AIDS pandemic (aids2031 Consortium, 2010).


The committee’s model is Version 4.05 of the open-source AIDSCost projection model, available for download on the Center for Global Development website (Over, 2009).

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