Dual testing better for monitoring new cases of HIV

Johns Hopkins researchers will present results showing that tighter, dual testing standards work better for accurately distinguishing between new and old cases of HIV.

Current testing standards are based on a single test called the serological testing algorithm for recent HIV seroconversion, or STARHS for short. STARHS relies on differentiating newly infected from chronically infected individuals based on the quantity, or levels, of antibodies to HIV present in patients' blood.

Normally, the antibody concentration to HIV increases over time during the first six months of infection. However, effective use of anti-retroviral therapy can depress viral counts in patients to undetectably low levels, which also lower the antibody-to-HIV concentration in the blood. This creates confusion for those responsible for monitoring new infections and spread of HIV. According to the researchers, large numbers of artificially "new" cases also have the potential to hamper measurements of how successful are global treatment efforts in Africa, where aid from the United States is set to make antiretroviral therapy more widely available.

The Hopkins team successfully determined new cases from old by adding the Affinity/Avidity test to the current STARHS protocol, the test widely used by the United States Centers for Disease Control and Prevention. This second test measures the strength of antibody-antigen binding in the immune system's response to HIV infection. An immature response from a new infection produces weak binding, whereas a mature infection involves strong binding. In a cross-sectional study of more than 1,500 patients showing up in the Hopkins Emergency Department from June to August 2001, the testing of blood samples by STARHS showed 11 cases of new infection, but dual testing with Affinity/Avidity showed only six. Information gathered from interviews with two of the five discrepant patients confirmed that these two were taking antiretroviral therapy, masking their old infection as new.

The effect of ART on cross-sectional incidence testing: the 2001 Johns Hopkins Hospital sero-survey as an example. Oliver Laeyendecker, Charlamine Henson, Bobbi Jo Hone, Richard Rothman, KerrunneKetlogetwe, Judy Shahan, Gabor Kelen, and Thomas Quinn.