Trends in testing algorithms used to diagnose HIV infection, 2011–2015, United States and 6 dependent areas
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Trends in testing algorithms used to diagnose HIV infection, 2011–2015, United States and 6 dependent areas

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  • English
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    Details:

    • Alternative Title:
      J Clin Virol
    • Personal Author:
    • Description:
      Background:

      In 2014 the Centers for Disease Control and Prevention (CDC) and the Association of Public Health Laboratories (APHL) issued updated laboratory testing recommendations for the diagnosis of HIV infection.

      Objectives:

      To examine trends in the use of HIV diagnostic testing algorithms, and determine whether the use of different algorithms is associated with selected patient characteristics and linkage to HIV medical care.

      Study design:

      Analysis of HIV infection diagnoses during 2011–2015 reported to the National HIV Surveillance System through December 2016. Algorithm classification: traditional = initial HIV antibody immunoassay followed by a Western blot or immunofluorescence antibody test; recommended = initial HIV antibody IA followed by HIV-1/2 type-differentiating antibody test; rapid = two CLIA-waived rapid tests on same date.

      Results:

      During 2011–2015, the percentage of HIV diagnoses made using the traditional algorithm decreased from 84% to 16%, the percentage using the recommended algorithm increased from 0.1% to 64%, and the percentage using the rapid testing algorithm increased from 0.1% to 2%. The percentage of persons linked to care within 30 days after HIV diagnosis in 2015 was higher for diagnoses using the recommended algorithm (59%) than for diagnoses using the traditional algorithm (55%) (p < 0.05).

      Conclusions:

      During 2011–2015, the percentage of HIV diagnoses reported using the recommended and rapid testing algorithms increased while the use of the traditional algorithm decreased. In 2015, persons with HIV diagnosed using the recommended algorithm were more promptly linked to care than those with diagnosis using the traditional algorithm.

    • Subjects:
    • Source:
      J Clin Virol. 103:19-24
    • Pubmed ID:
      29605799
    • Pubmed Central ID:
      PMC6360333
    • Document Type:
      Journal Article
    • Funding:
      CC999999/ImCDC/Intramural CDC HHS/United States
    • Collection(s):
    • Main Document Checksum:
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