期刊
AMERICAN JOURNAL OF EPIDEMIOLOGY
卷 177, 期 3, 页码 264-272出版社
OXFORD UNIV PRESS INC
DOI: 10.1093/aje/kws436
关键词
acquired immunodeficiency syndrome; algorithms; cross-sectional studies; HIV; incidence; models; statistical
资金
- National Institutes of Health [R01-AI095068]
- Division of Intramural Research, National Institute of Allergy and Infectious Diseases (NIAID)
- NIAID [1UM1-AI068613]
- National Institute on Drug Abuse (NIDA)
- National Institute of Mental Health
- Office of AIDS Research, National Institutes of Health
- HIVNET
- NIAID
- NIDA
- National Cancer Institute
- National Heart, Lung, and Blood Institute
- National Institute of Alcohol Abuse and Alcoholism
The incidence of human immunodeficiency virus (HIV) is the rate at which new HIV infections occur in populations. The development of accurate, practical, and cost-effective approaches to estimation of HIV incidence is a priority among researchers in HIV surveillance because of limitations with existing methods. In this paper, we develop methods for estimating HIV incidence rates using multiple biomarkers in biological samples collected from a cross-sectional survey. An advantage of the method is that it does not require longitudinal follow-up of individuals. We use assays for BED, avidity, viral load, and CD4 cell count data from clade B samples collected in several US epidemiologic cohorts between 1987 and 2010. Considering issues of accuracy, cost, and implementation, we identify optimal multiassay algorithms for estimating incidence. We find that the multiple-biomarker approach to cross-sectional HIV incidence estimation corrects the significant deficiencies of currently available approaches and is a potentially powerful and practical tool for HIV surveillance.
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