Share this article on:

The missing 27%

Akullian, Adama; Bershteyn, Annaa; Jewell, Brittab; Camlin, Carol S.c

doi: 10.1097/QAD.0000000000001638

aInstitute for Disease Modeling, Bellevue, Washington

bDivision of Biostatistics, School of Public Health, University of California, Berkeley

cDepartment of Obstetrics, Gynecology, and Reproductive Sciences, Department of Medicine, University of California, San Francisco, California, USA.

Correspondence to Adam Akullian, Institute for Disease Modeling, 3150 139th Ave SE, Bellevue, WA 98005, USA. Tel: +1 425 495 8662; e-mail:

Received 3 July, 2017

Revised 21 July, 2017

Accepted 21 August, 2017

This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.

Though a wide body of observational and model-based evidence underscores the promise of Universal Test and Treat (UTT) to reduce population-level HIV incidence in high-burden areas of Sub-Saharan Africa (SSA) [1,2], the only cluster-randomized trial of UTT completed to date, ANRS 12249, did not show a significant reduction in incidence [3]. More UTT trials are currently underway, and some have already exceeded the Joint United Nations Programme on HIV/AIDS (UNAIDS) 90–90–90 targets [4,5]. Still, even with high test and treat coverage, it is unknown whether ongoing trials will engage populations with the greatest potential for onward transmission to achieve the ambitious goal of reducing new HIV infections by 90% between 2010 and 2013 [6]. Ultimately, even strategies that successfully meet or exceed the 90–90–90 targets will leave up to 27% of people living with HIV/AIDS virally nonsuppressed. The epidemiological profile of the ‘missing 27%’ – including their risk behavior, mobility, and network connectedness – is not well understood and must be better characterized to fully evaluate the effectiveness of UTT.

Part of the uncertainty in UTT's effectiveness rests in the risk profile of people living with HIV/AIDS (PLWHA) who fail to achieve viral suppression. Mathematical modeling has provided optimistic projections for the population-level effect of UTT on the course of the HIV epidemic [7], with the size of the effect depending on epidemiologic context [8]. These models, however, are subject to varying degrees of parametric uncertainty and often rely on simplistic assumptions about transmission heterogeneity across the HIV cascade of care [7,9–11]. In contrast to common model-based assumptions, engagement in the cascade of care is not independent of transmission potential [12,13]. In the cluster-randomized ANRS 12249 and HIV Prevention Trials Network (HVTN) 071 Population Effects of Antiretroviral Therapy to reduce HIV Transmission (PopART) trials, for example, those unlinked to care tended to be younger [14–16] and in less-stable relationships [14,15]. In the Sustainable East Africa Research in Community Health (SEARCH) cluster-randomized test and treat study, viral suppression at 2-years post intervention was two-fold lower among 15–24-year-old HIV-positive individuals compared with those over 44 years [5]. Age disparities in viral suppression within UTT is concerning given that younger populations may play a larger role in transmission than previously thought [17]. Model-based estimates of UTT effectiveness have also yet to consider the effect of mobile populations – who are at high risk of HIV acquisition and transmission [18], and are among the most difficult to engage in the cascade of care [19] – on UTT. Mobile populations tend to be younger, more likely to be living with HIV, and more likely to engage in higher-risk sexual behavior [20–22]. Given the unique risk profile and lower propensity to engage in the cascade of care among mobile populations, there is a need to incorporate more complex dimensions of population mobility into existing models of population-level UTT effectiveness. Novel approaches that adapt prevention strategies and care programs specifically for mobile populations may be crucial for achieving the ambitious goal of UNAIDS to end the epidemic by 2030.

Finally, considerable debate exists as to the frequency of HIV testing needed for a UTT scenario to dramatically reduce the magnitude of the epidemic. This debate is centered primarily around the contribution of early and acute HIV (EHI) infection to onward transmission [23,24]. Although some argue that EHI threatens the population-level effectiveness of UTT [10,25], others assert that, despite elevated infectiousness of EHI [26], yearly UTT can theoretically lead to elimination [23,27]. Mathematical models of UTT on HIV transmission dynamics, however, have often relied on simplifying assumptions about sexual risk behavior in the period immediately following HIV infection; assuming, for example, that sexual contact rates remain constant from initial infection through the early infectious period [10]. In fact, the risk profile of newly infected individuals – most of whom are unaware of their HIV status – differs substantially from those who have been infected for longer periods of time [28], and theoretical simulation studies demonstrate that heterogeneity in sexual contact rates over time can dramatically increase the fraction of secondary infections that occur during EHI [29,30]. In this way, epidemics with similar basic reproductive numbers (R0) can theoretically exhibit considerable variability in the proportion of secondary transmissions that occur during EHI. Settings where EHIs account for a large fraction of secondary infections may present a serious challenge to the promise of UTT [31].

Efforts are currently underway to better characterize the epidemiologic profile of populations that contribute the most to secondary infections in high-burden settings of SSA [32]. More studies from SSA, however, are needed to fill empirical gaps in our understanding of the heterogeneity in sexual risk behaviors and the propensity of HIV transmission across the HIV care cascade. Further modeling studies are also needed to assess whether projected long-term incidence reductions from UTT are sensitive to parametric uncertainties around both transmission heterogeneity across the cascade of care and the proportion of secondary cases linked to EHI. Until these efforts are undertaken, our ability to evaluate and learn from early failures of UTT – as well as the reasons for potential successes – will be limited.

Back to Top | Article Outline


Conflicts of interest

There are no conflicts of interest.

Back to Top | Article Outline


1. Eaton JW, Johnson LF, Salomon JA, Bärnighausen T, Bendavid E, Bershteyn A, et al. HIV treatment as prevention: systematic comparison of mathematical models of the potential impact of antiretroviral therapy on HIV incidence in South Africa. PLoS Med 2012; 9:e1001245.
2. Tanser 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; 339:966–971.
3. Iwuji C O-GJ, Balestre E, Larmarange J, Thiebaut R, Tanser F, et al. The impact of universal test and treat on HIV incidence in a rural South African population: ANRS 12249 TasP trial, 2012–2016. International AIDS Conference. Durban, South Africa; 2016
4. Gaolathe T, Wirth KE, Holme MP, Makhema J, Moyo S, Chakalisa U, et al. Botswana Combination Prevention Project study team. Botswana's progress toward achieving the 2020 UNAIDS 90-90-90 antiretroviral therapy and virological suppression goals: a population-based survey. Lancet HIV 2016; 3:e221–e230.
5. Petersen M, Balzer L, Kwarsiima D, Sang N, Chamie G, Ayieko J, et al. Association of implementation of a universal testing and treatment intervention with HIV diagnosis, receipt of antiretroviral therapy, and viral suppression in East Africa. JAMA 2017; 317:2196–2206.
6. Stover J, Bollinger L, Izazola JA, Loures L, DeLay P, Ghys PD, et al. What is required to end the AIDS epidemic as a public health threat by 2030? The cost and impact of the fast-track approach. PLOS ONE 2016; 11:e0154893.
7. Granich 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; 373:48–57.
8. Dodd PJ, Garnett GP, Hallett TB. Examining the promise of HIV elimination by ’test and treat’ in hyperendemic settings. AIDS 2010; 24:729–735.
9. Hontelez JA, Lurie MN, Bärnighausen T, Bakker R, Baltussen R, Tanser F, et al. Elimination of HIV in South Africa through expanded access to antiretroviral therapy: a model comparison study. PLoS Med 2013; 10:e1001534.
10. Kretzschmar ME, Schim van der Loeff MF, Birrell PJ, De Angelis D, Coutinho RA. Prospects of elimination of HIV with test-and-treat strategy. Proc Natl Acad Sci U S A 2013; 110:15538–15543.
11. Cori A, Ayles H, Beyers N, Schaap A, Floyd S, Sabapathy K, et al. HPTN 071 PopART Study Team. 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:e84511.
12. McGarrigle CA, Mercer CH, Fenton KA, Copas AJ, Wellings K, Erens B, Johnson AM. Investigating the relationship between HIV testing and risk behaviour in Britain: national survey of sexual attitudes and lifestyles. AIDS 2005; 19:77–84.
13. Rozhnova G, van der Loeff MF, Heijne JC, Kretzschmar ME. Impact of heterogeneity in sexual behavior on effectiveness in reducing HIV transmission with test-and-treat strategy. PLOS Comput Biol 2016; 12:e1005012.
14. Plazy M, Farouki KE, Iwuji C, Okesola N, Orne-Gliemann J, Larmarange J, et al. Anrs 12249 Tasp Study Group. Access to HIV care in the context of universal test and treat: challenges within the ANRS 12249 TasP cluster-randomized trial in rural South Africa. J Int AIDS Soc 2016; 19:20913.
15. Boyer S, Iwuji C, Gosset A, Protopopescu C, Okesola N, Plazy M, et al. ANRS 12249 TasP study group. Factors associated with antiretroviral treatment initiation amongst HIV-positive individuals linked to care within a universal test and treat programme: early findings of the ANRS 12249 TasP trial in rural South Africa. AIDS Care 2016; 28 (suppl 3):39–51.
16. Hayes R, Floyd S, Schaap A, Shanaube K, Bock P, Sabapathy K, et al. HPTN 071 (PopART) Study Team. A universal testing and treatment intervention to improve HIV control: one-year results from intervention communities in Zambia in the HPTN 071 (PopART) cluster-randomised trial. PLoS Med 2017; 14:e1002292.
17. Akullian A, Bershteyn A, Klein D, Vandormael A, Bärnighausen T, Tanser F. Sexual partnership age-pairings and risk of HIV acquisition in rural South Africa. AIDS 2017; 31:1755–1764.
18. Camlin CS, Kwena ZA, Dworkin SL, Cohen CR, Bukusi EA. ‘She mixes her business’: HIV transmission and acquisition risks among female migrants in western Kenya. Soc Sci Med 2014; 102:146–156.
19. Camlin CS, Ssemmondo E, Chamie G, El Ayadi AM, Kwarisiima D, Sang N, et al. SEARCH Collaboration. Men ‘missing’ from population-based HIV testing: insights from qualitative research. AIDS Care 2016; 28 (suppl 3):67–73.
20. Camlin CS, Hosegood V, Newell ML, McGrath N, Barnighausen T, Snow RC. Gender, migration and HIV in rural KwaZulu-Natal, South Africa. PLoS One 2010; 5:e11539.
21. Lurie MN, Williams BG, Zuma K, Mkaya-Mwamburi D, Garnett G, Sturm AW, et al. The impact of migration on HIV-1 transmission in South Africa: a study of migrant and nonmigrant men and their partners. Sex Transm Dis 2003; 30:149–156.
22. Andrews JR, Wood R, Bekker LG, Middelkoop K, Walensky RP. Projecting the benefits of antiretroviral therapy for HIV prevention: the impact of population mobility and linkage to care. J Infect Dis 2012; 206:543–551.
23. Cohen MS, Dye C, Fraser C, Miller WC, Powers KA, Williams BG. HIV treatment as prevention: debate and commentary: will early infection compromise treatment-as-prevention strategies?. PLoS Med 2012; 9:e1001232.
24. Cohen MS, Shaw GM, McMichael AJ, Haynes BF. Acute HIV-1 Infection. N Engl J Med 2011; 364:1943–1954.
25. Powers KA, Ghani AC, Miller WC, Hoffman IF, Pettifor AE, Kamanga G, et al. The role of acute and early HIV infection in the spread of HIV and implications for transmission prevention strategies in Lilongwe, Malawi: a modelling study. Lancet 2011; 378:256–268.
26. Attia S, Egger M, Muller M, Zwahlen M, Low N. Sexual transmission of HIV according to viral load and antiretroviral therapy: systematic review and meta-analysis. AIDS 2009; 23:1397–1404.
27. Eaton JW, Hallett TB. Why the proportion of transmission during early-stage HIV infection does not predict the long-term impact of treatment on HIV incidence. Proc Natl Acad Sci U S A 2014; 111:16202–16207.
28. Eaton LA, Kalichman SC. Changes in transmission risk behaviors across stages of HIV disease among people living with HIV/AIDS. J Assoc Nurses AIDS Care 2009; 20:39–49.
29. Romero-Severson EO, Alam SJ, Volz E, Koopman J. Acute-stage transmission of HIV: effect of volatile contact rates. Epidemiology 2013; 24:516–521.
30. Zhang X, Zhong L, Romero-Severson E, Alam SJ, Henry CJ, Volz EM, Koopman JS. Episodic HIV risk behavior can greatly amplify HIV prevalence and the fraction of transmissions from acute HIV infection. Stat Commun Infect Dis 2012; 4: pii:1041.
31. Powers KA, Kretzschmar ME, Miller WC, Cohen MS. Impact of early-stage HIV transmission on treatment as prevention. Proc Natl Acad Sci U S A 2014; 111:15867–15868.
32. de Oliveira T, Kharsany AB, Graf T, Cawood C, Khanyile D, Grobler A, et al. Transmission networks and risk of HIV infection in KwaZulu-Natal, South Africa: a community-wide phylogenetic study. Lancet HIV 2017; 4:e41–e50.

90–90–90; cascade of care; HIV transmission heterogeneity; migration; mobility; viral suppression

Copyright © 2017 Wolters Kluwer Health, Inc.