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Updated: Jun 24, 2026

Rapid Screening of HIV Reverse Transcriptase and Integrase Inhibitors
Published on: April 9, 2014
Targeted testing for acute HIV infection in North Carolina
William C Miller1, Peter A Leone, Sandra McCoy
1Division of Infectious Diseases, Department of Medicine, School of Medicine, University of North Carolina at Chapel Hill, CB #7030, 130 Manning Drive, Chapel Hill, North Carolina 27599 7435, USA. bill_miller@unc.edu
Background:
Persons with acute HIV infection contribute disproportionately to HIV transmission. The identification of these persons is a critical public health challenge. We developed targeted approaches for detecting HIV RNA in persons with negative serological tests.
Methods:
Persons undergoing publicly funded HIV testing in North Carolina between October 2002 and April 2005 were included in this cross-sectional study. We used logistic regression to develop targeted testing approaches. We also assessed simple approaches based on clinic type and geography. Algorithm development used persons with recent HIV infection, determined by a detuned enzyme-linked immunosorbent assay. Validation used persons with acute HIV infection, identified with an HIV RNA pooling procedure.
Results:
Among 215 528 eligible persons, 232 persons had recent HIV infection and 44 had acute HIV infection. A combination of five indicators (testing site, sexual preference, sex with a person with HIV infection, county HIV incidence, and race) identified 92% of recent infections when testing 50% of the population. In validation among persons with acute HIV infection, this indicator combination had sensitivities of 98% in years 1 and 2 and 88% in year 3. A simple combination of testing site and county performed nearly as well [development (recent infections): sensitivity = 95%; validation (acute infections): sensitivity = 86% in years 1 and 2; 81% in year 3; cut-off established for testing 50% of population.]
Conclusion:
Acute HIV infection can be identified accurately using targeted testing. Simple approaches for identifying the types of clinics and geographical areas where infections are concentrated may be logistically feasible and cost-efficient.
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