HIV Incidence Determination in the United States: A Multi-Assay Approach.

Publication Type:

Journal Article


The Journal of infectious diseases, Volume 207, Issue 2, p.232-9 (2013)


2013, Center-Authored Paper, November 2012, Vaccine and Infectious Disease Division


Background. Accurate testing algorithms are needed for estimating HIV incidence from cross-sectional surveys.Methods. We developed a multi-assay algorithm (MAA) for HIV incidence that includes the BED capture immunoassay (BED-CEIA), an antibody avidity assay, HIV viral load, and CD4 cell count. We analyzed 1,782 samples from 709 individuals in the United States with known duration of HIV infection (0-8+ 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 annual incidence estimated with the MAA to annual incidence based on HIV seroconversion in a longitudinal cohort.Results. The MAA had a window period of 141 days (95% CI: 94-150) and a very low false-recent misclassification rate (only 0.4% of 1,474 samples infected >1 year were misclassified as recently infected). In a cohort study, annual incidence based on HIV seroconversion was 1.04% (95% CI: 0.70%-1.55%). The incidence estimate obtained using the MAA was essentially identical: 0.97% (95% CI: 0.51%-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.