UNLABELLED: Introduction The treatment as prevention strategy has gained popularity as a way to reduce the incidence of HIV by suppressing viral load such that transmission risk is decreased. The effectiveness of the strategy also requires early diagnosis. METHODS: Informed by data on the influence of diagnosis and treatment on reducing transmission risk, a model simulated the impact of increasing testing and treatment rates on the expected incidence of HIV in Australia under varying assumptions of treatment efficacy and risk compensation. The model utilises Australia's National HIV Registry data, and simulates disease progression, testing, treatment, transmission and mortality. RESULTS: Decreasing the average time between infection and diagnosis by 30% is expected to reduce population incidence by 12% (~126 cases per year, 95% confidence interval (CI): 82-198). Treatment of all people living with HIV with CD4 counts <500cellsμL(-1) is expected to reduce new infections by 30.9% (95% CI: 15.9-37.6%) at 96% efficacy if no risk compensation occurs. The number of infections could increase up to 12.9% (95% CI: 20.1-7.4%) at 26% efficacy if a return to prediagnosis risk levels occur. CONCLUSION: Treatment as prevention has the potential to prevent HIV infections but its effectiveness depends on the efficacy outside trial settings among men who have sex with men and the level of risk compensation. If antiretroviral therapy has high efficacy, risk compensation will not greatly change the number of infections. If the efficacy of antiretroviral therapy is low, risk compensation could lead to increased infections.
UNLABELLED: Introduction The treatment as prevention strategy has gained popularity as a way to reduce the incidence of HIV by suppressing viral load such that transmission risk is decreased. The effectiveness of the strategy also requires early diagnosis. METHODS: Informed by data on the influence of diagnosis and treatment on reducing transmission risk, a model simulated the impact of increasing testing and treatment rates on the expected incidence of HIV in Australia under varying assumptions of treatment efficacy and risk compensation. The model utilises Australia's National HIV Registry data, and simulates disease progression, testing, treatment, transmission and mortality. RESULTS: Decreasing the average time between infection and diagnosis by 30% is expected to reduce population incidence by 12% (~126 cases per year, 95% confidence interval (CI): 82-198). Treatment of all people living with HIV with CD4 counts <500cellsμL(-1) is expected to reduce new infections by 30.9% (95% CI: 15.9-37.6%) at 96% efficacy if no risk compensation occurs. The number of infections could increase up to 12.9% (95% CI: 20.1-7.4%) at 26% efficacy if a return to prediagnosis risk levels occur. CONCLUSION: Treatment as prevention has the potential to prevent HIV infections but its effectiveness depends on the efficacy outside trial settings among men who have sex with men and the level of risk compensation. If antiretroviral therapy has high efficacy, risk compensation will not greatly change the number of infections. If the efficacy of antiretroviral therapy is low, risk compensation could lead to increased infections.
Authors: Nicholas A Medland; James H McMahon; Eric P F Chow; Julian H Elliott; Jennifer F Hoy; Christopher K Fairley Journal: J Int AIDS Soc Date: 2015-11-30 Impact factor: 5.396
Authors: Muhammad S Jamil; Garrett Prestage; Christopher K Fairley; Kirsty S Smith; John M Kaldor; Andrew E Grulich; Anna M McNulty; Marcus Chen; Martin Holt; Damian P Conway; Handan Wand; Phillip Keen; Colin Batrouney; Jack Bradley; Benjamin R Bavinton; Dermot Ryan; Darren Russell; Rebecca J Guy Journal: BMC Infect Dis Date: 2015-12-10 Impact factor: 3.090
Authors: Martin Holt; Toby Lea; Jason Asselin; Margaret Hellard; Garrett Prestage; David Wilson; John de Wit; Mark Stoové Journal: J Int AIDS Soc Date: 2015-11-11 Impact factor: 5.396
Authors: D Callander; M Stoové; A Carr; J F Hoy; K Petoumenos; M Hellard; J Elliot; D J Templeton; S Liaw; D P Wilson; A Grulich; D A Cooper; A Pedrana; B Donovan; J McMahon; G Prestage; M Holt; C K Fairley; N McKellar-Stewart; S Ruth; J Asselin; P Keen; C Cooper; B Allan; J M Kaldor; R Guy Journal: BMC Infect Dis Date: 2016-12-12 Impact factor: 3.090