Accessible Requires Authentication Published by De Gruyter March 14, 2017

Cross-Sectional HIV Incidence Surveillance: A Benchmarking of Approaches for Estimating the ‘Mean Duration of Recent Infection’

Reshma Kassanjee, Daniela De Angelis, Marian Farah, Debra Hanson, Jan Phillipus Lourens Labuschagne, Oliver Laeyendecker, Stéphane Le Vu, Brian Tom, Rui Wang and Alex Welte


The application of biomarkers for ‘recent’ infection in cross-sectional HIV incidence surveillance requires the estimation of critical biomarker characteristics. Various approaches have been employed for using longitudinal data to estimate the Mean Duration of Recent Infection (MDRI) – the average time in the ‘recent’ state. In this systematic benchmarking of MDRI estimation approaches, a simulation platform was used to measure accuracy and precision of over twenty approaches, in thirty scenarios capturing various study designs, subject behaviors and test dynamics that may be encountered in practice. Results highlight that assuming a single continuous sojourn in the ‘recent’ state can produce substantial bias. Simple interpolation provides useful MDRI estimates provided subjects are tested at regular intervals. Regression performs the best – while ‘random effects’ describe the subject-clustering in the data, regression models without random effects proved easy to implement, stable, and of similar accuracy in scenarios considered; robustness to parametric assumptions was improved by regressing ‘recent’/‘non-recent’ classifications rather than continuous biomarker readings. All approaches were vulnerable to incorrect assumptions about subjects’ (unobserved) infection times. Results provided show the relationships between MDRI estimation performance and the number of subjects, inter-visit intervals, missed visits, loss to follow-up, and aspects of biomarker signal and noise.

Funding statement: South African National Research Foundation, (Grant /Award Number: ‘UID 44895’); Bill and Melinda Gates Foundation, (Grant /Award Number: ‘OPP1022972’); Medical Research Council, (Grant /Award Number: ‘Unit Programme number MC_UP_1302/3’, ‘Unit Programme number U105260566’); National Institutes of Health, (Grant /Award Number: ‘R01 AI095068’, ‘R37 AI51164’); Division of Intramural Research, National Institute of Allergy and Infectious Diseases.


Braunstein, S.L., D. Nash, A.A. Kim, et al. 2011. “Dual Testing Algorithm of BED-CEIA and Axsym Avidity Index Assays Performs Best in Identifying Recent HIV Infection in a Sample of Rwandan Sex Workers.” PLoS One 6(4): e18402. Search in Google Scholar

Brookmeyer, R., J. Konikoff, O. Laeyendecker, et al. 2013. “Estimation of HIV Incidence Using Multiple Biomarkers.” American Journal of Epidemiology 177(3): 264–272. Search in Google Scholar

Brookmeyer, R., and T.C. Quinn. 1995. “Estimation of Current Human Immunodeficiency Virus Incidence Rates from a Cross-Sectional Survey Using Early Diagnostic Tests.” American Journal of Epidemiology 141(2): 166–172. Search in Google Scholar

Burchell, A.N., L. Calzavara, N. Ramuscak, et al. 2003. “Symptomatic Primary HIV Infection or Risk Experiences? Circumstances Surrounding HIV Testing and Diagnosis among Recent Seroconverters.” International Journal of STD & AIDS 14(9): 601–608. Search in Google Scholar

Busch, M.P., C.D. Pilcher, T.D. Mastro, et al. 2010. “Beyond Detuning: 10 Years of Progress and New Challenges in the Development and Application of Assays for HIV Incidence Estimation.” AIDS 24(18): 2763–2771. Search in Google Scholar

Centers for Disease Control and Prevention. 2004. HIV Testing Survey, 2002. Atlanta, GA: U.S. Department of Health and Human Services. Search in Google Scholar

Centers for Disease Control and Prevention, United States Department of Health and Human Services. Grant opportunity: Population-based HIV Impact Assessments in Resource-Constrained Settings under the President’s Emergency Plan for AIDS Relief (PEPFAR). 2015. Accessed November 27, 2015. Search in Google Scholar

Curtis, K.A., D.L. Hanson, et al. 2013. “Evaluation of a Multiplex Assay for Estimation of HIV-1 Incidence.” PLoS One 8(5): e64201. Search in Google Scholar

Duong, Y.T., R. Kassanjee, and A. Welte. 2015. “Recalibration of the Limiting Antigen Avidity EIA to Determine Mean Duration of Recent Infection in Divergent HIV-1 Subtypes.” PLoS One 10(2): e0114947. Search in Google Scholar

Duong, Y.T., M. Qiu, A.K. De, et al. 2012. “Detection of Recent HIV-1 Infection Using a New Limiting-Antigen Avidity Assay: Potential for HIV-1 Incidence Estimates and Avidity Maturation Studies.” PLoS One 7(3): e33328. Search in Google Scholar

Fiebig, E.W., D.J. Wright, B.D. Rawal, et al. 2003. “Dynamics of HIV Viremia and Antibody Seroconversion in Plasma Donors: Implications for Diagnosis and Staging of Primary HIV Infection.” AIDS 17(13): 1871–1879. Search in Google Scholar

Hallett, T.B., P. Ghys, T. Barnighausen, et al. 2009. “Errors in ‘BED’-Derived Estimates of HIV Incidence Will Vary by Place, Time and Age.” PLoS One 4(5): e5720. Search in Google Scholar

Hargrove, J., H. Eastwood, G. Mahiane, et al. 2012. “How Should We Best Estimate the Mean Recency Duration for the BED Method?” PLoS One 7(11): e49661. Search in Google Scholar

Hargrove, J.W., J.H. Humphrey, K. Mutasa, et al. 2008. “Improved HIV-1 Incidence Estimates Using the BED Capture Enzyme Immunoassay.” AIDS 22(4): 511–518. Search in Google Scholar

HIV Modelling Consortium Work Package on Characterisation of Tests for Recent Infection. 2015. Accessed November 27, 2015. Search in Google Scholar

Incidence Assay Critical Path Working Group. 2011. “More and Better Information to Tackle HIV Epidemics: Towards Improved HIV Incidence Assays.” PLoS Medicine 8(6): e1001045. Search in Google Scholar

Janssen, R.S., G.A. Satten, S.L. Stramer, et al. 1998. “New Testing Strategy to Detect Early HIV-1 Infection for Use in Incidence Estimates and for Clinical and Prevention Purposes.” Jama 280(1): 42–48. Search in Google Scholar

Kaplan, E.L., and P. Meier. 1958. “Nonparametric Estimation from Incomplete Observations.” Journal of the American Statistical Association 53(282): 457–481. Search in Google Scholar

Kassanjee, R., T.A. McWalter, T. Barnighausen, et al. 2012. “A New General Biomarker-Based Incidence Estimator.” Epidemiology 23(5): 721–728. Search in Google Scholar

Kassanjee, R., C.D. Pilcher, S.M. Keating, et al. 2014. “Independent Assessment of Candidate HIV Incidence Assays on Specimens in the CEPHIA Repository.” AIDS 28(16): 2439–2449. Search in Google Scholar

Keating, S.M., D. Hanson, M. Lebedeva, et al. 2012. “Lower-Sensitivity and Avidity Modifications of the Vitros Anti-HIV 1+2 Assay for Detection of Recent HIV Infections and Incidence Estimation.” Journal of Clinical Microbiology 50(12): 3968–3976. Search in Google Scholar

Laeyendecker, O., R. Brookmeyer, M.M. Cousins, et al. 2013. “HIV Incidence Determination in the United States: A Multiassay Approach.” The Journal of Infectious Diseases 207(2): 232–239. Search in Google Scholar

Le Vu, S., J. Pillonel, C. Semaille, et al. 2008. “Principles and Uses of HIV Incidence Estimation from Recent Infection Testing – a Review.” Euro Surveillance 13(36): 11–16. Search in Google Scholar

Lee, H.Y., E.E. Giorgi, B.F. Keele, et al. 2009. “Modeling Sequence Evolution in Acute HIV-1 Infection.” Journal of Theoretical Biology 261(2): 341–360. Search in Google Scholar

Longosz, A.F., S.H. Mehta, G.D. Kirk, et al. 2014. “Incorrect Identification of Recent HIV Infection in Adults in the United States Using a Limiting-Antigen Avidity Assay.” AIDS 28(8): 1227–1232. Search in Google Scholar

Mahiane, S.G., A. Fiamma, and B. Auvert. 2014. “Mixture Models for Calibrating the BED for HIV Incidence Testing.” Statistics in Medicine 33(10): 1767–1783. Search in Google Scholar

Mastro, T.D., A.A. Kim, T. Hallett, et al. 2010. “Estimating HIV Incidence in Populations Using Tests for Recent Infection: Issues, Challenges and the Way Forward.” Journal of HIV AIDS Surveillance & Epidemiology 2(1): 1–14. Search in Google Scholar

McDougal, J.S., B.S. Parekh, M.L. Peterson, et al. 2006. “Comparison of HIV Type 1 Incidence Observed during Longitudinal Follow-Up with Incidence Estimated by Cross-Sectional Analysis Using the BED Capture Enzyme Immunoassay.” AIDS Research and Human Retroviruses 22(10): 945–952. Search in Google Scholar

Murphy, G., and J.V. Parry. 2008. “Assays for the Detection of Recent Infections with Human Immunodeficiency Virus Type 1.” Euro Surveillance 13(36): 4–10. Search in Google Scholar

Parekh, B.S., D.L. Hanson, and J. Hargrove. 2011. “Determination of Mean Recency Period for Estimation of HIV Type 1 Incidence with the BED-Capture EIA in Persons Infected with Diverse Subtypes.” AIDS Research and Human Retroviruses 27(3): 265–273. Search in Google Scholar

Parekh, B.S., M.S. Kennedy, T. Dobbs, et al. 2002. “Quantitative Detection of Increasing HIV Type 1 Antibodies after Seroconversion: A Simple Assay for Detecting Recent HIV Infection and Estimating Incidence.” AIDS Research and Human Retroviruses 18(4): 295–307. Search in Google Scholar

Schreiber, G.B., S.A. Glynn, G.A. Satten, et al. 2002. “HIV Seroconverting Donors Delay Their Return: Screening Test Implications.” Transfusion 42(4): 414–421. Search in Google Scholar

Sharma, U.K., M. Schito, A. Welte, et al. 2012. “Workshop Summary: Novel Biomarkers for HIV Incidence Assay Development.” AIDS Research and Human Retroviruses 28(6): 532–539. Search in Google Scholar

Sommen, C., D. Commenges, S. Le Vu, et al. 2011. “Estimation of the Distribution of Infection Times Using Longitudinal Serological Markers of HIV: Implications for the Estimation of HIV Incidence.” Biometrics 67(2): 467–475. Search in Google Scholar

Sweeting, M.J., D. De Angelis, J. Parry, et al. 2010. “Estimating the Distribution of the Window Period for Recent HIV Infections: A Comparison of Statistical Methods.” Statistics in Medicine 29(30): 3194–3202. Search in Google Scholar

The Consortium for the Evaluation and Performance of HIV Incidence Assays (CEPHIA). 2015. Accessed November 27, 2015. Search in Google Scholar

Turnbull, B.W. 1976. “The Empirical Distribution Function with Arbitrarily Grouped, Censored and Truncated Data.” Journal of the Royal Statistical Society, Series B (Statistical Methodology) 38(3): 290–295. Search in Google Scholar

Wang, R., and S.W. Lagakos. 2009. “Augmented Cross-Sectional Prevalence Testing for Estimating HIV Incidence.” Biometrics 66(3): 864–874. Search in Google Scholar

WHO Technical Working Group on HIV Incidence Assays. 2015. Accessed November 27, 2015. Search in Google Scholar

Supplemental Material

The online version of this article (DOI:scid-2016-0002) offers supplementary material, available to authorized users.

Received: 2016-5-16
Revised: 2016-9-21
Accepted: 2016-11-14
Published Online: 2017-3-14

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