期刊
JOURNAL OF INFECTIOUS DISEASES
卷 207, 期 2, 页码 232-239出版社
OXFORD UNIV PRESS INC
DOI: 10.1093/infdis/jis659
关键词
HIV; incidence testing; United States; epidemiology
资金
- HIV Prevention Trials Network
- National Institute of Allergy and Infectious Diseases (NIAID)
- National Institute of Child Health and Human Development
- National Institute on Drug Abuse (NIDA)
- National Institute of Mental Health
- Office of AIDS Research, NIH, DHHS [U01-AI46745, U01-AI48054, UM1-AI068613]
- NIAID [R01-AI095068, N01-AI35176, N01-AI-45200, AI-45202]
- Division of Intramural Research, NIAID
- HIVNET
- NIDA [R01-DA-04334, R01-DA12568]
- National Cancer Institute
- National Heart, Lung, and Blood Institute [U01-AI35042, U01-AI35043, U01-AI35039, U01-AI35040, U01-AI35041, UL1-RR025005]
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.
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