title: Cross-sectional HIV incidence estimation in HIV prevention research.
abstract: Accurate methods for estimating HIV incidence from cross-sectional samples would have great utility in prevention research. This report describes recent improvements in cross-sectional methods that significantly improve their accuracy. These improvements are based on the use of multiple biomarkers to identify recent HIV infections. These multiassay algorithms (MAAs) use assays in a hierarchical approach for testing that minimizes the effort and cost of incidence estimation. These MAAs do not require mathematical adjustments for accurate estimation of the incidence rates in study populations in the year before sample collection. MAAs provide a practical, accurate, and cost-effective approach for cross-sectional HIV incidence estimation that can be used for HIV prevention research and global epidemic monitoring.
authors.affiliation: Department of Biostatistics, School of Public Health, University of California Los Angeles, Los Angeles, CA, USA.
authors.forename: Ron
authors.initials: R
authors.lastname: Brookmeyer
authors.forename: Oliver
authors.initials: O
authors.lastname: Laeyendecker
authors.forename: Deborah
authors.initials: D
authors.lastname: Donnell
authors.forename: Susan H
authors.initials: SH
authors.lastname: Eshleman
mesh_terms: D000465:Algorithms; D015415:Biomarkers / Q000097:blood; D016022:Case-Control Studies; D003430:Cross-Sectional Studies; D015658:HIV Infections / Q000097:blood / Q000175:diagnosis / Q000453:epidemiology* / Q000517:prevention & control*; D006679:HIV Seropositivity; D006801:Humans; D007124:Immunoenzyme Techniques; D015994:Incidence; D057230:Limit of Detection; D015233:Models, Statistical; D015995:Prevalence; D016032:Randomized Controlled Trials as Topic / Q000379:methods; D015203:Reproducibility of Results; D012107:Research Design; D012307:Risk Factors; D014481:United States / Q000453:epidemiology; D019562:Viral Load
chemical_list: D015415:Biomarkers