Improving ART programme retention and viral suppression are key to maximising impact of treatment as prevention - a modelling study.

This is the final version of the article. Available from BioMed Central via the DOI in this record.BACKGROUND: UNAIDS calls for fewer than 500,000 new HIV infections/year by 2020, with treatment-as-prevention being a key part of their strategy for achieving the target. A better understanding of the contribution to transmission of people at different stages of the care pathway can help focus intervention services at populations where they may have the greatest effect. We investigate this using Uganda as a case study. METHODS: An individual-based HIV/ART model was fitted using history matching. 100 model fits were generated to account for uncertainties in sexual behaviour, HIV epidemiology, and ART coverage up to 2015 in Uganda. A number of different ART scale-up intervention scenarios were simulated between 2016 and 2030. The incidence and proportion of transmission over time from people with primary infection, post-primary ART-naïve infection, and people currently or previously on ART was calculated. RESULTS: In all scenarios, the proportion of transmission by ART-naïve people decreases, from 70% (61%-79%) in 2015 to between 23% (15%-40%) and 47% (35%-61%) in 2030. The proportion of transmission by people on ART increases from 7.8% (3.5%-13%) to between 14% (7.0%-24%) and 38% (21%-55%). The proportion of transmission by ART dropouts increases from 22% (15%-33%) to between 31% (23%-43%) and 56% (43%-70%). CONCLUSIONS: People who are currently or previously on ART are likely to play an increasingly large role in transmission as ART coverage increases in Uganda. Improving retention on ART, and ensuring that people on ART remain virally suppressed, will be key in reducing HIV incidence in Uganda.This work was supported by a Medical Research Council (UK) grant on Model Calibration (MR/J005088/1). RGW is additionally funded by the Medical Research Council (UK) (G0802414), the Bill and Melinda Gates Foundation (TB Modelling and Analysis Consortium: Grants 21,675/ OPP1084276 and Consortium to Respond Effectively to the AIDS/TB Epidemic 19,790.01), and CDC/PEPFAR via the Aurum Institute (U2GPS0008111). NM is supported by an MRC Skills Development Fellowship (MR/N014693/1). TJM is supported by Biotechnology and Biological Sciences Research Council grant number BB/I012192/1. RH receives support from the Medical Research Council (K012126/1). MS is supported by a National Institute for Health Research Post Doctoral Research Fellowship (PDF-2012- 05-258). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This article presents independent research part funded by the National Institute for Health Research (NIHR). The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. The authors declare no conflicts of interest