Laeyendecker O, Brookmeyer R, Cousins MM, Mullis CE, Konikoff J, Donnell D, Celum C, Buchbinder SP, Seage GR 3rd, Kirk GD, Mehta SH, Astemborski J, Jacobson LP, Margolick JB, Brown J, Quinn TC, Eshleman SH. HIV incidence determination in the United States: A multi-assay approach. J Infect Dis. 2013, 207: 232-9. PMC3532826
BACKGROUND: Accurate testing algorithms are needed for estimating human immunodeficiency virus (HIV) incidence from cross-sectional surveys. METHODS: We developed a multiassay algorithm (MAA) for HIV incidence that includes the BED capture enzyme immunoassay (BED-CEIA), an antibody avidity assay, HIV load, and CD4(+) T-cell count. We analyzed 1782 samples from 709 individuals in the United States who had a known duration of HIV infection (range, 0 to >8 years). Logistic regression with cubic splines was used to compare the performance of the MAA to the BED-CEIA and to determine the window period of the MAA. We compared the annual incidence estimated with the MAA to the annual incidence based on HIV seroconversion in a longitudinal cohort. RESULTS: The MAA had a window period of 141 days (95% confidence interval [CI], 94-150) and a very low false-recent misclassification rate (only 0.4% of 1474 samples from subjects infected for >1 year were misclassified as indicative of recent infection). In a cohort study, annual incidence based on HIV seroconversion was 1.04% (95% CI, .70%-1.55%). The incidence estimate obtained using the MAA was essentially identical: 0.97% (95% CI, .51%-1.71%). CONCLUSIONS: The MAA is as sensitive for detecting recent HIV infection as the BED-CEIA and has a very low rate of false-recent misclassification. It provides a powerful tool for cross-sectional HIV incidence determination.