Evaluation of the false recent classification rates of multiassay algorithms in estimating HIV type 1 subtype C incidence

Sikhulile Moyo, Tessa LeCuyer, Rui Wang, Simani Gaseitsiwe, Jia Weng, Rosemary Musonda, Hermann Bussmann, Madisa Mine, Susan Engelbrecht, Joseph Makhema, Richard Marlink, Marianna K. Baum, Vladimir Novitsky, M. Essex

Publication Date: 01/01/2014

Laboratory cross-sectional assays are useful for the estimation of HIV incidence, but are known to misclassify individuals with long-standing infection as recently infected. The false recent rate (FRR) varies widely across geographic areas; therefore, accurate estimates of HIV incidence require a locally defined FRR. We determined FRR for Botswana, where HIV-1 subtype C infection is predominant, using the BED capture enzyme immunoassay (BED), a Bio-Rad Avidity Index (BAI) assay (a modification of the Bio-Rad HIV1/2+O EIA), and two multiassay algorithms (MAA) that included clinical data. To estimate FRR, stored blood samples from 512 antiretroviral (ARV)-naive HIV-1 subtype C-infected individuals from a prospective cohort in Botswana were tested at 18-24 months postenrollment. The following FRR mean (95% CI) values were obtained: BED 6.05% (4.15-8.48), BAI 5.57% (3.70-8.0), BED-BAI 2.25% (1.13-4.0), and a combination of BED-BAI with CD4 (>200) and viral load (>400) threshold 1.43% (0.58-2.93). The interassay agreement between BED and BAI was 92.8% (95% CI, 90.1-94.5) for recent/long-term classification. Misclassification was associated with viral suppression for BED [adjusted OR (aOR) 10.31; p=0.008], BAI [aOR 9.72; p=0.019], and MAA1 [aOR 16.6; p=0.006]. Employing MAA can reduce FRR to <2%. A local FRR can improve cross-sectional HIV incidence estimates. Publisher: