How universal does universal test and treat have to be?
Authors: Rebecca F Baggaley, T Déirdre Hollingsworth
Journal: The Lancet HIV (2020)

HIV treatment as prevention, not only of HIV-related disease but also of transmission, was proposed in 2006 as a means to end the epidemic.1Montaner JS Hogg R Wood E et al.The case for expanding access to highly active antiretroviral therapy to curb the growth of the HIV epidemic.Lancet. 2006; 368: 531-536Summary Full Text Full Text PDF PubMed Scopus (411) Google Scholar Several influential studies and models predicted that early treatment of most people who live with HIV (PLWH) could reduce HIV transmission rates to near zero, at the population level.1Montaner JS Hogg R Wood E et al.The case for expanding access to highly active antiretroviral therapy to curb the growth of the HIV epidemic.Lancet. 2006; 368: 531-536Summary Full Text Full Text PDF PubMed Scopus (411) Google Scholar, 2Granich RM Gilks CF Dye C De Cock KM Williams BG Universal voluntary HIV testing with immediate antiretroviral therapy as a strategy for elimination of HIV transmission: a mathematical model.Lancet. 2009; 373: 48-57Summary Full Text Full Text PDF PubMed Scopus (1519) Google Scholar, 3Tanser F Bärnighausen T Grapsa E Zaidi J Newell ML High coverage of ART associated with decline in risk of HIV acquisition in rural KwaZulu-Natal, South Africa.Science. 2013; 339: 966-971Crossref PubMed Scopus (609) Google Scholar As a result, treatment as prevention evolved into the universal HIV test-and-treat (UTT) approach, which entails offering HIV counselling and testing to an entire population and ART to all PLWH. In 2014, UNAIDS announced ambitious new goals to “end AIDS by 2030” by reaching 90-90-90 targets by 2020: 90% of PLWH knowing their status, 90% ART coverage among those knowing their status, and 90% viral suppression among people on ART. Great progress has been made, with an estimated 79% of people having been diagnosed, 62% of individuals aware of their status being on ART, and 53% of those on ART being virally suppressed globally, although heterogeneity exists between countries.4UNAIDSCommunities at the Centre.https://www.unaids.org/sites/default/files/media_asset/2019-global-AIDS-update_en.pdfDate: 2019Date accessed: January 16, 2020Google Scholar However, until UTT community-based trial results began to be reported 4 years ago, it remained unclear to what extent this remarkable increase in coverage translated into HIV prevention. In The Lancet HIV, Adam Akullian and colleagues5Akullian A Morrison M Garnett GP et al.The effect of 90–90–90 on HIV-1 incidence and mortality in eSwatini: a mathematical modelling study.Lancet HIV. 2020; (published online Feb 13. https://doi.org/10.1016/S2352-3018(19)30436-9.)Summary Full Text Full Text PDF PubMed Scopus (15) Google Scholar report that ART scale-up in eSwatini, which has the world's highest HIV prevalence, has largely been a success story, dramatically reducing adult HIV incidence and mortality. Using a mathematical model calibrated with demographic, HIV prevalence, and ART coverage data, they estimate that adult mortality decreased by more than 50% and HIV incidence by more than 40%, between 2010 and 2016. However, Akullian and colleagues also predict that with ART coverage maintained at current levels, HIV incidence will remain above 1 per 100 person-years—far above the target of 1 per 1000 person-years defined as representing epidemic control. Importantly, even 100% ART coverage within an average of 6 months since infection acquisition (implying annual universal HIV testing with 100% coverage), would reduce adult HIV incidence to 0·73 (95% CI 0·55–0·92) per 100 person-years by 2030 and 0·46 (0·33–0·59) per 100 person-years by 2050—still far off the epidemic control target. These findings concur with results of the UTT trials. Substantial reductions in HIV incidence were observed in study communities because ART access improved in both intervention and control groups (where standard of care was offered), but no significant differences in population-level HIV incidence reductions were found by study group, with HIV incidence reductions due to ART falling far short of epidemic control.6Baral S Rao A Sullivan P et al.The disconnect between individual-level and population-level HIV prevention benefits of antiretroviral treatment.Lancet HIV. 2019; 6: e632-e638Summary Full Text Full Text PDF PubMed Scopus (43) Google Scholar It seems logical that 90-90-90 levels would be insufficient to end the epidemic, as such coverage implies that 27% of PLWH would remain virally unsuppressed and capable of HIV transmission. But why have Akullian and colleagues predicted that UTT at even higher levels of ART coverage, at 95-95-95 and beyond, will not end the HIV epidemic, when previous models came to much more positive conclusions in similar settings (figure)? Reasons the authors posit are HIV transmissions during the elevated infectiousness of early HIV infection and demographic gaps in testing, treatment, and viral suppression coverage. Treatment coverage is lower in younger age groups in eSwatini, particularly among young men. If these and other demographic groups that UTT misses are disproportionate transmitters of HIV infection, the population-level effectiveness is compromised. Early models of UTT were criticised for not adequately addressing behavioural heterogeneity or the natural history of HIV infection.2Granich RM Gilks CF Dye C De Cock KM Williams BG Universal voluntary HIV testing with immediate antiretroviral therapy as a strategy for elimination of HIV transmission: a mathematical model.Lancet. 2009; 373: 48-57Summary Full Text Full Text PDF PubMed Scopus (1519) Google Scholar Since then, the modelling community has attempted to explore reasons for differences in model predictions between research groups. More real-world data from treatment-as-prevention studies have reduced variability in predictions, but differences remain. Hontelez and colleagues7Hontelez JAC Lurie MN Bärnighausen T et al.Elimination of HIV in South Africa through expanded access to antiretroviral therapy: a model comparison study.PLoS Med. 2013; 10: e1001534Crossref PubMed Scopus (93) Google Scholar developed nine structurally different models of the South African epidemic, from the simplest (akin to early modelling), to models incorporating different HIV risk groups such as female sex workers, HIV natural history (including the high infectiousness accompanying early HIV infection), and cofactors such as sexually transmitted infections (STIs), male circumcision, and condom use. However, the more complex models still pointed in the same direction as the simple models, predicting HIV elimination with UTT at 90% coverage, albeit at later timepoints. We now know that, despite these attempts to reach consensus, the model failed to capture real-world epidemiology, one reason being that it assumed equal ART coverage across risk groups. Akullian and colleagues5Akullian A Morrison M Garnett GP et al.The effect of 90–90–90 on HIV-1 incidence and mortality in eSwatini: a mathematical modelling study.Lancet HIV. 2020; (published online Feb 13. https://doi.org/10.1016/S2352-3018(19)30436-9.)Summary Full Text Full Text PDF PubMed Scopus (15) Google Scholar found that closing age and sex gaps in ART coverage would reduce HIV incidence by a further 17% among young people (aged 15–24 years), but achieving this level of equity will be difficult. Similarly, although the preintervention modelling of the PopART (HPTN071) trial predicted a substantial effect of the intervention in models that incorporated extensive behavioural heterogeneity,8Cori A Ayles H Beyers N et al.HPTN 071 (PopART): a cluster-randomized trial of the population impact of an HIV combination prevention intervention including universal testing and treatment: mathematical model.PLoS One. 2014; 9: e84511Crossref PubMed Scopus (84) Google Scholar subsequent analyses suggest that a likely driver of what turned out to be optimistic model predictions was the grouping of small risk groups who have large numbers of partners (such as female sex workers and clients) within a much larger group of high-risk individuals—diluting their impact on transmission.6Baral S Rao A Sullivan P et al.The disconnect between individual-level and population-level HIV prevention benefits of antiretroviral treatment.Lancet HIV. 2019; 6: e632-e638Summary Full Text Full Text PDF PubMed Scopus (43) Google Scholar In the same way, post-hoc modelling of STI treatment trials provided insights on why the effect of those interventions was not as predicted.9Baggaley RF Griffin JT Chapman R et al.Estimating the public health impact of the effect of herpes simplex virus suppressive therapy on plasma HIV-1 viral load.AIDS. 2009; 23: 1005-1013Crossref PubMed Scopus (14) Google Scholar, 10White RG Orroth KK Korenromp EL et al.Can population differences explain the contrasting results of the Mwanza, Rakai, and Masaka HIV/sexually transmitted disease intervention trials? A modeling study.J Acquir Immune Defic Syndr. 2004; 37: 1500-1513Crossref PubMed Scopus (82) Google Scholar Models do not always get it right but they have been crucial in paving the way to empirical research that has given us a greater understanding of what is important for HIV prevention. Although Akullian and colleagues' findings indicate that UTT in isolation cannot control HIV in eSwatini, expanded access to ART has decreased HIV transmission.5Akullian A Morrison M Garnett GP et al.The effect of 90–90–90 on HIV-1 incidence and mortality in eSwatini: a mathematical modelling study.Lancet HIV. 2020; (published online Feb 13. https://doi.org/10.1016/S2352-3018(19)30436-9.)Summary Full Text Full Text PDF PubMed Scopus (15) Google Scholar As always with HIV, the details of the epidemic and the overlap between individuals who access interventions and those who are transmitting most efficiently is crucial, as Akullian and colleagues highlight. Access to timely health care is a human right and so ART should be available to all those who would benefit. There must be scale-up of the three pillars of UTT across all risk groups and this should be in combination with the other prevention methods at our disposal, including pre-exposure prophylaxis, behaviour change, syringe exchange, and condom use. Great strides have been achieved in UTT, but as Akullian and colleagues have shown, high-quality data combined with data analytics and modelling will be needed to help map the next steps toward elimination. We declare no competing interests. The effect of 90-90-90 on HIV-1 incidence and mortality in eSwatini: a mathematical modelling studyScale-up of ART over the last decade has already contributed to substantial reductions in HIV-1 incidence and mortality in eSwatini. Focused ART targeting would further reduce incidence, especially in younger individuals, but even the most aggressive treatment campaigns would be insufficient to end the epidemic in high-burden settings without a renewed focus on expanding preventive measures. Full-Text PDF Open Access