AIDS and Behavior

, Volume 22, Issue 7, pp 2277–2283 | Cite as

Comparison of Self-report to Biomarkers of Recent HIV Infection: Findings from the START Trial

  • Katherine E. Schlusser
  • Shweta Sharma
  • Pola de la Torre
  • Giuseppe Tambussi
  • Rika Draenert
  • Angie N. Pinto
  • Julia A. Metcalf
  • Danielle German
  • James D. Neaton
  • Oliver Laeyendecker
  • for the INSIGHT START Study Group
Original Paper


Identifying individuals with recent HIV infection is critical to research related to viral reservoirs, outbreak investigations and intervention applications. A multi-assay algorithm (MAA) for recency of infection was used in conjunction with self-reported date of infection and documented date of diagnosis to estimate the number of participants recently infected in the Strategic Timing of AntiRetroviral Treatment (START) trial. We tested samples for three groups of participants from START using a MAA: (1) 167 individuals who reported being infected ≤ 6 months before randomization; (2) 771 individuals who did not know their date of infection but were diagnosed within 6 months before randomization; and (3) as controls for the MAA, 199 individuals diagnosed with HIV ≥ 2 years before randomization. Participants with low titer and avidity and a baseline viral load > 400 copies/mL were classified as recently infected. A significantly higher percentage of participants who self-reported being infected ≤ 6 months were classified as recently infected compared to participants diagnosed ≥ 2 years (65% [109/167] vs. 2.5% [5/199], p < 0.001). Among the 771 individuals who did not know their duration of infection at randomization, 206 (26.7%) were classified as recently infected. Among those diagnosed with HIV in the 6 months prior to enrollment, the 373 participants who reported recent infection (n = 167) or who had confirmed recent infection by the MAA (n = 206) differed significantly on a number of baseline characteristics from those who had an unknown date of infection and were not confirmed by the MAA (n = 565). Participants recently infected by self-report and/or MAA were younger, more likely to be Asian, less likely to be black, less likely to be heterosexual, more likely to be enrolled at sites in the U.S., Europe or Australia, and have higher HIV RNA levels. There was good agreement between self-report of recency of infection and the MAA. We estimate that 373 participants enrolled in START were infected within 6 months of randomization. Compared to those not recently infected, these participants were younger, had higher HIV RNA levels and were more likely to come from high income countries and from populations such as MSM with more regular HIV testing.


Self-report Biomarkers Recent HIV infection START trial 



We wish to thank the participants and clinical staff of the study. Additionally, see Initiation of Antiretroviral Therapy in Early Asymptomatic HIV Infection. N Engl J Med. Aug 27 2015;373(9):795-807 for the complete list of START investigators. This study was supported in part by the Division of Intramural Research, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH). Support was provided by the NIH Clinical Center, National Cancer Institute, National Heart, Lung, and Blood Institute, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke, National Institute of Arthritis and Musculoskeletal and Skin Diseases, Agence Nationale de Recherches sur le SIDA et les Hépatites Virales (France), National Health and Medical Research Council (Australia), National Research Foundation (Denmark), Bundes ministerium für Bildung und Forschung (Germany), European AIDS Treatment Network, Medical Research Council (United Kingdom), National Institute for Health Research, National Health Service (United Kingdom), and University of Minnesota. Antiretroviral drugs were donated to the central drug repository by AbbVie, Bristol-Myers Squibb, Gilead Sciences, GlaxoSmithKline/ViiV Healthcare, Janssen Scientific Affairs, and Merck (UM1-AI068641 and UM1-AI120197). Additional support was provided by the HIV Prevention Trials Network sponsored by NIAID, National Institute of Child Health and Human Development, National Institute of Drug Abuse, National Institute of Mental Health, and the Office of AIDS Research, of the NIH DHHS (UM1 AI068613), and NIAID (R01 AI095068).

Compliance with Ethical Standards

Conflict of interest

All the authors declare that they have no conflict of interest.

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

Research Involving human and Animal Participants

This article does not contain any studies with animals performed by any of the authors.

Supplementary material

10461_2018_2048_MOESM1_ESM.docx (29 kb)
Supplementary material 1 (DOCX 28 kb)


  1. 1.
    Ostrowski M, Benko E, Yue FY, et al. Intensifying antiretroviral therapy with raltegravir and maraviroc during early human immunodeficiency virus (HIV) infection does not accelerate HIV reservoir reduction. Open Forum Infect Dis. 2015;2(4):ofv138.CrossRefPubMedCentralPubMedGoogle Scholar
  2. 2.
    Le T, Wright EJ, Smith DM, et al. Enhanced CD4 + T-cell recovery with earlier HIV-1 antiretroviral therapy. N Engl J Med. 2013;368(3):218–30.CrossRefPubMedCentralPubMedGoogle Scholar
  3. 3.
    Okulicz JF, Le TD, Agan BK, et al. Influence of the timing of antiretroviral therapy on the potential for normalization of immune status in human immunodeficiency virus 1-infected individuals. JAMA Intern Med. 2015;175(1):88–99.CrossRefPubMedCentralPubMedGoogle Scholar
  4. 4.
    Friedman SR, Downing MJ Jr, Smyrnov P, et al. Socially-integrated transdisciplinary HIV prevention. AIDS Behav. 2014;18(10):1821–34.CrossRefPubMedCentralPubMedGoogle Scholar
  5. 5.
    Mastro TD, Kim AA, Hallett T, et al. Estimating HIV incidence in populations using tests for recent infection: issues, challenges and the way forward. J HIV AIDS Surveill Epidemiol. 2010;2(1):1–14.PubMedCentralPubMedGoogle Scholar
  6. 6.
    Dennis AM, Murillo W, de Maria Hernandez F, et al. Social network-based recruitment successfully reveals HIV-1 transmission networks among high-risk individuals in El Salvador. J Acquir Immune Defic Syndr. 1999;63(1):135–41.CrossRefGoogle Scholar
  7. 7.
    Laeyendecker O, Brookmeyer R, Oliver AE, et al. Factors associated with incorrect identification of recent HIV infection using the BED capture immunoassay. AIDS Res Hum Retrovir. 2012;28(8):816–22.CrossRefPubMedCentralPubMedGoogle Scholar
  8. 8.
    Mullis CE, Munshaw S, Grabowski MK, et al. Differential specificity of HIV incidence assays in HIV subtypes A and D-infected individuals from Rakai, Uganda. AIDS Res Hum Retrovir. 2013;29(8):1146–50.CrossRefPubMedCentralPubMedGoogle Scholar
  9. 9.
    Parekh BS, Hanson DL, Hargrove J, et al. Determination of mean recency period for estimation of HIV type 1 incidence with the BED-capture EIA in persons infected with diverse subtypes. AIDS Res Hum Retrovir. 2011;27(3):265–73.CrossRefPubMedGoogle Scholar
  10. 10.
    Longosz AF, Serwadda D, Nalugoda F, et al. Impact of HIV subtype on performance of the limiting antigen-avidity enzyme immunoassay, the bio-rad avidity assay, and the BED capture immunoassay in Rakai, Uganda. AIDS Res Hum Retrovir. 2014;30(4):339–44.CrossRefPubMedCentralPubMedGoogle Scholar
  11. 11.
    Kassanjee R, Pilcher CD, Keating SM, et al. Independent assessment of candidate HIV incidence assays on specimens in the CEPHIA repository. AIDS. 2014;28(16):2439–49.CrossRefPubMedCentralPubMedGoogle Scholar
  12. 12.
    Laeyendecker O, Rothman RE, Henson C, et al. The effect of viral suppression on cross-sectional incidence testing in the johns hopkins hospital emergency department. J Acquir Immune Defic Syndr. 1999;48(2):211–5.CrossRefGoogle Scholar
  13. 13.
    Kassanjee R, Pilcher CD, Busch MP, et al. Viral load criteria and threshold optimization to improve HIV incidence assay characteristics. AIDS. 2016;30(15):2361–71.CrossRefPubMedCentralPubMedGoogle Scholar
  14. 14.
    Hayashida T, Gatanaga H, Tanuma J, Oka S. Effects of low HIV type 1 load and antiretroviral treatment on IgG-capture BED-enzyme immunoassay. AIDS Res Hum Retrovir. 2008;24(3):495–8.CrossRefPubMedGoogle Scholar
  15. 15.
    Longosz AF, Mehta SH, Kirk GD, et al. Incorrect identification of recent HIV infection in adults in the United States using a limiting-antigen avidity assay. AIDS. 2014;28(8):1227–32.CrossRefPubMedCentralPubMedGoogle Scholar
  16. 16.
    Marinda ET, Hargrove J, Preiser W, et al. Significantly diminished long-term specificity of the BED capture enzyme immunoassay among patients with HIV-1 with very low CD4 counts and those on antiretroviral therapy. J Acquir Immune Defic Syndr. 1999;53(4):496–9.CrossRefGoogle Scholar
  17. 17.
    Wendel SK, Mullis CE, Eshleman SH, et al. Effect of natural and ARV-induced viral suppression and viral breakthrough on anti-HIV antibody proportion and avidity in patients with HIV-1 subtype B infection. PLoS ONE. 2013;8(2):e55525.CrossRefPubMedCentralPubMedGoogle Scholar
  18. 18.
    Laeyendecker O, Kulich M, Donnell D, et al. Development of methods for cross-sectional HIV incidence estimation in a large, community randomized trial. PLoS ONE. 2013;8(11):e78818.CrossRefPubMedCentralPubMedGoogle Scholar
  19. 19.
    Konikoff J, Brookmeyer R, Longosz AF, et al. Performance of a limiting-antigen avidity enzyme immunoassay for cross-sectional estimation of HIV incidence in the United States. PLoS ONE. 2013;8(12):e82772.CrossRefPubMedCentralPubMedGoogle Scholar
  20. 20.
    Huynh D, Laeyendecker O, Brookmeyer R. A serial risk score approach to disease classification that accounts for accuracy and cost. Biometrics. 2014;70(4):1042–51.CrossRefPubMedCentralPubMedGoogle Scholar
  21. 21.
    Lundgren JD, Babiker AG, Gordin F, et al. Initiation of antiretroviral therapy in early asymptomatic HIV infection. N Engl J Med. 2015;373(9):795–807.CrossRefPubMedGoogle Scholar
  22. 22.
    Duong YT, Kassanjee R, Welte A, et al. Recalibration of the limiting antigen avidity EIA to determine mean duration of recent infection in divergent HIV-1 subtypes. PLoS ONE. 2015;10(2):e0114947.CrossRefPubMedCentralPubMedGoogle Scholar
  23. 23.
    Longosz AF, Morrison CS, Chen PL, et al. Immune responses in Ugandan women infected with subtypes A and D HIV using the BED capture immunoassay and an antibody avidity assay. J Acquir Immune Defic Syndr. 1999;65(4):390–6.CrossRefGoogle Scholar
  24. 24.
    Hemelaar J, Gouws E, Ghys PD, Osmanov S. Global trends in molecular epidemiology of HIV-1 during 2000–2007. Aids. 2011;25(5):679–89.CrossRefPubMedCentralPubMedGoogle Scholar
  25. 25.
    Hensen B, Lewis JJ, Schaap A, et al. Frequency of HIV-testing and factors associated with multiple lifetime HIV-testing among a rural population of Zambian men. BMC Public Health. 2015;15:960.CrossRefPubMedCentralPubMedGoogle Scholar
  26. 26.
    Fisher DG, Reynolds GL, Jaffe A, Johnson ME. Reliability, sensitivity and specificity of self-report of HIV test results. AIDS Care. 2007;19(5):692–6.CrossRefPubMedGoogle Scholar
  27. 27.
    Ross MW, Loxley W, Wodak A, Buzolic A, Monheit B, Stowe A. Drug users’ self-reported false-positive HIV status. Am J Public Health. 1993;83(9):1349–51.CrossRefPubMedCentralPubMedGoogle Scholar

Copyright information

© This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply  2018

Authors and Affiliations

  • Katherine E. Schlusser
    • 1
  • Shweta Sharma
    • 2
  • Pola de la Torre
    • 3
  • Giuseppe Tambussi
    • 4
  • Rika Draenert
    • 5
  • Angie N. Pinto
    • 6
  • Julia A. Metcalf
    • 7
  • Danielle German
    • 8
  • James D. Neaton
    • 2
  • Oliver Laeyendecker
    • 1
    • 9
  • for the INSIGHT START Study Group
  1. 1.Department of MedicineJohns Hopkins University School of MedicineBaltimoreUSA
  2. 2.Division of Biostatistics, School of Public HealthUniversity of MinnesotaMinneapolisUSA
  3. 3.Cooper University HospitalCamdenUSA
  4. 4.IRCCS-Ospedale San RaffaeleMilanItaly
  5. 5.Section Clinical Infectious DiseasesKlinikum der Universität Munich, Medizinische Klinik IVMunichGermany
  6. 6.The Kirby InstituteUNSW AustraliaSydneyAustralia
  7. 7.Division of Clinical Research, National Institute of Allergy and Infectious DiseasesNational Institutes of HealthBethesdaUSA
  8. 8.Department of Health, Behavior and Society, Bloomberg School of Public HealthJohns Hopkins UniversityBaltimoreUSA
  9. 9.Division of Intramural Research, National Institute of Allergy and Infectious DiseasesNational Institutes of HealthBethesdaUSA

Personalised recommendations