|Title||Voluntary universal testing and treatment is unlikely to lead to HIV elimination: a modeling analysis|
|Publication Type||Journal Article|
|Year of Publication||2009|
|Authors||Wagner, BG , Blower SM |
Recently Granich et al. at the World Health Organization (WHO) concluded, using mathematical modeling, that HIV epidemics could be eliminated within a decade. They assumed all individuals would be tested annually and every infected individual (regardless of stage of infection) would be put on treatment. Based on this modeling study the WHO is considering using universal testing and treatment as an HIV elimination strategy. Here we examine the study by Granich et al. and assess its validity. We present new analyses of their model by varying assumptions and parameter values. We find that under certain very optimistic assumptions HIV elimination would be (theoretically) possible, but it would take at least 70 years. To obtain this result we assumed ∼65% of symptomatic and ∼20% of asymptomatic individuals would be treated per year; ARVs would reduce infectivity of treated individuals a hundred fold, and only 5% of symptomatic individuals would give up treatment per year. Even under optimistic assumptions we find elimination to be unlikely. For example, we show if ∼65% of symptomatic individuals are treated per year and treated individuals are completely noninfectious, HIV will remain endemic with a prevalence of 34% and an incidence of 2% per year. We conclude that the model developed by Granich et al., when used with realistic parameter values, does not show HIV elimination is possible. However our modeling results show treatment could act as an effective prevention tool and significantly reduce transmission, even if only symptomatic individuals receive ARVs. Treatment should first, and foremost, be used for therapeutic purposes. Hence, we recommend when resources are limited - targeting those in need of treatment. Such a strategy would be ethical, feasible and epidemiologically sound. We advise that models used as health policy tools should be carefully evaluated and their results interpreted with caution.