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REVIEW

Predicting the population impact of increased HIV testing and treatment in Australia

James Jansson A , Cliff C. Kerr A and David P. Wilson A B

A The Kirby Institute, University of New South Wales, Sydney, NSW 2010, Australia.

B Corresponding author. Email: dwilson@kirby.unsw.edu.au

Sexual Health 11(2) 146-154 https://doi.org/10.1071/SH13069
Submitted: 3 May 2013  Accepted: 30 October 2013   Published: 7 February 2014

Abstract

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 <500 cells μL–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.

Additional keywords: efficacy, mathematical model, prevention, risk compensation, treatment as prevention, viral load.


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