BACKGROUND: The US National HIV/AIDS Strategy calls for 90% HIV diagnosis and 80% viral suppression among the diagnosed by 2020. Tools using local HIV data are needed to help jurisdictions create tailored HIV prevalence projections and estimate future demand for medical and support services in order to meet NHAS goals.
METHODS: Using viral load data from Georgia’s enhanced HIV/AIDS Reporting System, state level death rates for people living with HIV and the general population, and published estimates for HIV transmission rates, we developed a model for projecting future HIV prevalence. Keeping death rates and HIV transmission rates for undiagnosed, diagnosed/no viral suppression, and diagnosed/viral suppression constant, we describe results from three simulations with varying inputs projecting HIV incidence and prevalence from 2012 to 2020.
RESULTS: Maintaining 2012 Georgia rates through 2020 (status quo scenario) resulted in a decreased percent undiagnosed (from 18.7% to 12.2%), increased viral suppression (VS)(from 38.5% to 43.4%.), stable incidence (~2100/year) and increased prevalence (from 57,189 to 65,383). A 50% increase in the rate of transition from diagnosed/not VS to VS resulted in a decreased percent undiagnosed (11.4%), increased VS (53.5%), decreased incidence (1838/year), and increased prevalence (64,517) by 2020. Tripling the rate by which those diagnosed/not VS become VS, and reducing return of VS to not VS to 0.1/year resulted in a further decreased percent undiagnosed of 9%, increased VS (77.5%), and decreased incidence (1202/year), but prevalence continued to increase to 62,229 by 2020 (see Figure). In this scenario, the number of persons receiving HIV care, antiretroviral therapy (ART), and achieving VS would increase almost 2.5 times from 17,913 in 2012 to 43,845 in 2020.
CONCLUSIONS: Even with achievement of ~90% diagnosis, ~80% VS among the diagnosed and ~44% incidence decrease, this model projects increased HIV prevalence in Georgia through 2020. Caution is advisable in using the rhetoric of “a world free from AIDS” as this is not the same as the end of HIV. Dramatic improvements in the HIV care infrastructure are imperative to prepare for more than doubling the number of people living with HIV accessing services and medications. A strength of this modeling tool is that a user-friendly interface has been created online with adjustable slide ranges for jurisdictions to enter local data, and modify rates based on changing epidemiology or new transmission estimates.