Detection of recent HIV infections in African individuals infected by HIV-1 non-B subtypes using HIV antibody avidity

Abstract

Background To estimate HIV incidence several methods have been used to discriminate recent HIV infections from long-standing infections using a single serum sample. Objective To evaluate the performance of the anti-HIV avidity index (AI) for identifying recent HIV infections in individuals with a known date of seroconversion from Uganda, where the predominant HIV subtypes are A and D. Study design We selected 149 repository serum samples from Ugandan HIV-positive individuals and evaluated the AI. Specimens collected ≤6 months after seroconversion were considered as recent infections, and those collected >6 months as long-standing infections. All specimens were serotyped using a V3 peptide enzyme immunoassay. Results The mean AI was 0.55 ± 0.21 among the 108 patients with recent infections and 0.93 ± 0.14 among the 41 samples from long-standing infections ( p < 0.0001). The AI test showed a sensitivity of 85.2% and a specificity of 85.4% at a cutoff of 0.80. No significant association was observed between serotype and the misclassification of samples by AI. Conclusions The AI, which is inexpensive and easy-to-perform, can be useful in identifying recent HIV infections in countries where HIV-1 non-B subtypes are prevalent.