Research Article
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Published Online: 1 October 2019

Field Validation of Limiting-Antigen Avidity Enzyme Immunoassay to Estimate HIV-1 Incidence in Cross-Sectional Survey in Swaziland

Publication: AIDS Research and Human Retroviruses
Volume 35, Issue Number 10

Abstract

Reliable and accurate laboratory assays to detect recent HIV-1 infection have potential as simple and practical methods of estimating HIV-1 incidence in cross-sectional surveys. This study describes validation of the limiting-antigen (LAg) avidity enzyme immunoassay (EIA) in a cross-sectional national survey, conducted in Swaziland, comparing it to prospective follow-up incidence. As part of the Swaziland HIV-1 Incidence Measurement Survey (SHIMS), 18,172 individuals underwent counseling and HIV rapid testing in a household-based, population survey conducted from December 2010 to June 2011. Plasma samples from HIV-positive persons were classified as recent infections using an incidence testing algorithm with LAg-Avidity EIA (normalized optical density ≤1.5) followed by viral load (VL ≥1,000 copies/mL). All HIV-seronegative samples were tested for acute HIV-1 infection by nucleic acid amplification test (NAAT) pooling. HIV-seronegative individuals who consented to follow-up were retested ∼6 months later to detect observed HIV-1 seroconversion. HIV-1 incidence estimates based on LAg+VL and NAAT were calculated using assay-specific parameters and were compared with prospective incidence estimate. A total of 5,803 (31.9%) of 18,172 survey participants tested HIV seropositive; of these 5,683 (97.9%) were further tested with LAg+VL algorithm. The weighted annualized incidence from the longitudinal cohort study was 2.4% (95% confidence interval 2.0–2.7). Based on cross-sectional testing of HIV positives with LAg+VL algorithm, overall weighted annualized HIV-1 incidence was 2.5% (2.0–3.0), whereas NAAT-based incidence was of 2.6%. In addition, LAg-based incidence in men (1.8%; 1.2–2.5) and women (3.2%; 2.4–3.9) were similar to estimates based on observed incidence (men = 1.7%, women = 3.1%). Changes in HIV-1 incidence with age in men and women further validate plausibility of the algorithm. These results demonstrate that the LAg EIA, in a serial algorithm with VL, is a cost-effective tool to estimate HIV-1 incidence in cross-sectional surveys.

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References

1. Brown JL, Sales JM, DiClemente RJ. Combination HIV prevention interventions: the potential of integrated behavioral and biomedical approaches. Curr HIV/AIDS Rep 2014;11:363–375.
2. Anderson SJ, Cherutich P, Kilonzo N, et al.: Maximising the effect of combination HIV prevention through prioritisation of the people and places in greatest need: A modelling study. Lancet 2014;384:249–256.
3. Parekh BS, Hu DJ, Vanichseni S, et al.: Evaluation of a sensitive/less-sensitive testing algorithm using the 3A11-LS assay for detecting recent HIV seroconversion, among individuals with HIV-1 subtype B or E infection in Thailand. AIDS Res Hum Retroviruses 2001;17:453–458.
4. McDougal JS, Pilcher CD, Parekh BS, et al.: Surveillance for HIV-1 incidence using tests for recent infection in resource-constrained countries. AIDS 2005;19(Suppl 2):S25–S30.
5. UNAIDS: UNAIDS Reference Group on estimates, modelling and projections—Statement on the use of the BED assay for the estimation of HIV-1 incidence for surveillance or epidemic monitoring. Wkly Epidemiol Rec 2006;81:40.
6. UNAIDS: WHO/UNAIDS technical update on HIV incidence assays for surveillance and epidemic monitoring. Available at www.who.int/diagnostics_laboratory/links/hiv_incidence_assay/en (2013), accessed May 30, 2014.
7. Young CL, Hu DJ, Byers R, et al.: Evaluation of a sensitive/less sensitive testing algorithm using the bioMerieux Vironostika-LS assay for detecting recent HIV-1 subtype B’ or E infection in Thailand. AIDS Res Hum Retroviruses 2003;19:481–486.
8. Parekh B, Wei X, Dobbs T, Kuehl D, Hu D, Nkengasong J: Development of a limiting-antigen avidity-based enzyme immunoassay using a chimeric recombinant gp41 protein to detect recent HIV-1 infection. In: 14th Conference on Retroviruses and Opportunistic Infections, Los Angeles, CA, February 25–28, 2007.
9. Parekh BS, McDougal JS: Application of laboratory methods for estimation of HIV-1 incidence. Indian J Med Res 2005;121:510–518.
10. Rawal BD, Degula A, Lebedeva L, et al.: Development of a new less-sensitive enzyme immunoassay for detection of early HIV-1 infection. J Acquir Immune Defic Syndr 2003;33:349–355.
11. Parekh BS, Kennedy MS, Dobbs T, et al.: Quantitative detection of increasing HIV type 1 antibodies after seroconversion: A simple assay for detecting recent HIV infection and estimating incidence. AIDS Res Hum Retroviruses 2002;18:295–307.
12. Parekh BS, Pau CP, Kennedy MS, Dobbs TL, McDougal JS: Assessment of antibody assays for identifying and distinguishing recent from long-term HIV type 1 infection. AIDS Res Hum Retroviruses 2001;17:137–146.
13. Janssen RS, Satten GA, Stramer SL, et al.: New testing strategy to detect early HIV-1 infection for use in incidence estimates and for clinical and prevention purposes. [Erratum appears in JAMA 1999 May 26;281(20):1893.] JAMA 1998;280:42–48.
14. 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:e0114947.
15. Duong YT, Qiu M, De AK, et al.: 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 2012;7:e33328.
16. Wei X, Liu X, Dobbs T, et al.: Development of two avidity based assays to detect recent HIV-1 seroconversion using a multi-subtype gp41 recombinant protein. AIDS Res Hum Retroviruses 2010;26:61–71.
17. Guy R, Gold J, Calleja JMG, et al.: Accuracy of serological assays for detection of recent infection with HIV and estimation of population incidence: A systematic review. Lancet Infect Dis 2009;9:747–759.
18. Hargrove JW, Humphrey JH, Mutasa K, et al.: Improved HIV-1 incidence estimates using the BED capture enzyme immunoassay. AIDS 2008;22:511–518.
19. http://aidsinfo.unaids.org. AIDSInfo. accessed July 1, 2013.
20. Justman J, Reed JB, Bicego G, et al.: Swaziland HIV Incidence Measurement Survey (SHIMS): A prospective national cohort study. Lancet HIV 2017;4:e83–e92.
21. Bicego GT, Nkambule R, Peterson I, et al.: Recent patterns in population-based HIV prevalence in Swaziland. PLoS One 2013;8:e77101.
22. Duong YT, Mavengere Y, Patel H, et al.: Poor performance of the determine HIV-1/2 Ag/Ab combo fourth-generation rapid test for detection of acute infections in a National Household Survey in Swaziland. J Clin Microbiol 2014;52:3743–3748.
23. UNAIDS/WHO Working Group on Global HIV/AIDS and STI Surveillance: When and how to use assays for recent infection to estimate HIV incidence at a population level. France: WHO Press; 2011.
24. Welte A, McWalter TA, Barnighausen T: A simplified formula for inferring HIV incidence from cross-sectional surveys using a test for recent infection [comment]. AIDS Res Hum Retroviruses 2009;25:125–126.
25. Swaziland Country Report on Monitoring the Political Declaration on HIV and AIDS. Swaziland: Ministry of Health; 2014.
26. Cohen MS, Gay CL, Busch MP, Hecht FM; The detection of acute HIV infection. J Infect Dis 2010;202(Suppl 2):S270–S277.
27. Hallett TB, Ghys P, Barnighausen T, Yan P, Garnett GP: Errors in “BED”-derived estimates of HIV incidence will vary by place, time and age. PLoS One 2009;4:e5720.
28. Kassanjee R, Pilcher CD, Keating SM, et al.: Independent assessment of candidate HIV incidence assays on specimens in the CEPHIA repository. AIDS 2014;28:2439–2449.
29. (Kenya) Ministry of Health: Kenya AIDS Indicator Survey 2012 final report. Ministry of Health, National AIDS Control Program, Nairobi, 2014. pp. 43–46.
30. Shisana O, Rehle T, Simbayi LC, et al.: South African National HIV Prevalence, Incidence and Behaviour Survey, 2012. Cape Town 2014.
31. Kassanjee R, Pilcher CD, Busch MP, et al.: Viral load criteria and threshold optimization to improve HIV incidence assay characteristics. AIDS 2016;30:2361–2371.
32. 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:e82772.

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Published In

cover image AIDS Research and Human Retroviruses
AIDS Research and Human Retroviruses
Volume 35Issue Number 10October 2019
Pages: 896 - 905
PubMed: 31204867

History

Published in print: October 2019
Published online: 1 October 2019
Published ahead of print: 15 August 2019
Published ahead of production: 17 June 2019

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Authors

Affiliations

Yen T. Duong*
Division of Global HIV & TB, Centers for Disease Control and Prevention, Atlanta, Georgia.
ICAP at Columbia University, Mailman School of Public Health, New York, New York.
Trudy Dobbs
Division of Global HIV & TB, Centers for Disease Control and Prevention, Atlanta, Georgia.
Yvonne Mavengere
ICAP at Columbia University, Mailman School of Public Health, New York, New York.
Julius Manjengwa
ICAP at Columbia University, Mailman School of Public Health, New York, New York.
Erin Rottinghaus
Division of Global HIV & TB, Centers for Disease Control and Prevention, Atlanta, Georgia.
Suzue Saito
ICAP at Columbia University, Mailman School of Public Health, New York, New York.
Naomi Bock
Division of Global HIV & TB, Centers for Disease Control and Prevention, Atlanta, Georgia.
Neena Philip
ICAP at Columbia University, Mailman School of Public Health, New York, New York.
Jessica Justman
ICAP at Columbia University, Mailman School of Public Health, New York, New York.
George Bicego
Division of Global HIV & TB, Centers for Disease Control and Prevention, Atlanta, Georgia.
John N. Nkengasong
Division of Global HIV & TB, Centers for Disease Control and Prevention, Atlanta, Georgia.
Bharat S. Parekh [email protected]
Division of Global HIV & TB, Centers for Disease Control and Prevention, Atlanta, Georgia.

Notes

*
Current affiliation: ICAP at Columbia University, New York, New York.
Address correspondence to: Bharat S. Parekh, Division of Global HIV & TB, Centers for Disease Control and Prevention, 1600 Clifton Road, Building 15/2611, Mailstop G19, Atlanta, Georgia 30329 [email protected]

Author Contributions

Conceived and designed the experiments: Y.T.D., J.J., J.N.N., G.B., and B.S.P. Performed the experiments: Y.T.D., T.D., Y.M., J.M., and E.R. Analyzed the results: Y.T.D., T.D., Y.M., and B.S.P. Contributed reagents/materials/analysis tools: Y.T.D., T.D., Y.M., J.M., E.R., and B.S.P. Wrote the article: Y.T.D. and B.S.P. Reviewed and edited the article: all authors.

Author Disclosure Statement

As an inventor of LAg-Avidity EIA, BSP receives a portion of royalties from sale of LAg-Avidity EIA as per policy of the U.S. government. No other competing financial interests exist.

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