title: HIV incidence determination in the United States: a multiassay approach.
abstract: 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.
authors.affiliation: National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA. olaeyen1@jhmi.edu
authors.forename: Oliver
authors.initials: O
authors.lastname: Laeyendecker
authors.forename: Ron
authors.initials: R
authors.lastname: Brookmeyer
authors.forename: Matthew M
authors.initials: MM
authors.lastname: Cousins
authors.forename: Caroline E
authors.initials: CE
authors.lastname: Mullis
authors.forename: Jacob
authors.initials: J
authors.lastname: Konikoff
authors.forename: Deborah
authors.initials: D
authors.lastname: Donnell
authors.forename: Connie
authors.initials: C
authors.lastname: Celum
authors.forename: Susan P
authors.initials: SP
authors.lastname: Buchbinder
authors.forename: George R
authors.initials: GR
authors.lastname: Seage
authors.forename: Gregory D
authors.initials: GD
authors.lastname: Kirk
authors.forename: Shruti H
authors.initials: SH
authors.lastname: Mehta
authors.forename: Jacquie
authors.initials: J
authors.lastname: Astemborski
authors.forename: Lisa P
authors.initials: LP
authors.lastname: Jacobson
authors.forename: Joseph B
authors.initials: JB
authors.lastname: Margolick
authors.forename: Joelle
authors.initials: J
authors.lastname: Brown
authors.forename: Thomas C
authors.initials: TC
authors.lastname: Quinn
authors.forename: Susan H
authors.initials: SH
authors.lastname: Eshleman
mesh_terms: D000465:Algorithms; D018791:CD4 Lymphocyte Count; D015331:Cohort Studies; D003430:Cross-Sectional Studies; D005260:Female; D015483:HIV Antibodies / Q000097:blood / Q000502:physiology; D015658:HIV Infections / Q000175:diagnosis* / Q000453:epidemiology* / Q000276:immunology; D006679:HIV Seropositivity / Q000453:epidemiology; D015497:HIV-1 / Q000276:immunology* / Q000302:isolation & purification; D006801:Humans; D007124:Immunoenzyme Techniques; D015994:Incidence; D008297:Male; D019562:Viral Load
chemical_list: D015483:HIV Antibodies