HIV incidence determination in the United States: a multiassay approach

Abstract

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.

Figure 1.

Multiassay algorithm for cross-sectional human immunodeficiency virus (HIV) incidence determination. Individuals with all of the following results are classified as recently infected: CD4⁺ T-cell count of >200 cells/mm³, a BED capture enzyme immunoassay (BED-CEIA) finding of <1.0 optical density units (OD-n), an avidity index of <80%, and a viral load of >400 copies/mL (see Methods). The term “stop“ indicates nonrecent HIV infection.

Figure 2.

Modeled probabilities of an individual being classified as recently infected as a function of duration of infection. Figure shows modeled probability curves of samples being classified as recent using the BED capture enzyme immunoassay (BED-CEIA) alone and the multiassay algorithm from fitting a cubic spline to the logit of the probability. Circles and squares show observed proportions by the BED-CEIA alone and the multiassay algorithm, respectively, when samples are stratified into intervals (Table 1).