Differences among diagnostic testing algorithms in the time from HIV diagnosis to care.
Authors of this article are:
Peruski AH, Wu B, Selik RM.
A summary of the article is shown below:
BACKGROUND: The association between the type of diagnostic testing algorithm for HIV infection and the time from diagnosis to care has not been fully evaluated. Here we extend an earlier analysis of this association by controlling for patient and diagnosing facility characteristics.STUDY DESIGN: Descriptive analysis of HIV infection diagnoses during 2016 reported to the National HIV Surveillance System through December 2017. Algorithm type: traditional = initial HIV antibody immunoassay followed by a Western blot or immunofluorescence antibody test; recommended = initial HIV antigen/antibody immunoassay followed by HIV-1/2 type-differentiating antibody test; rapid = two CLIA-waived rapid tests on the same date.RESULTS: In multivariate analyses controlling for patient and diagnosing facility characteristics, persons whose infection was diagnosed using the rapid algorithm were more likely to be linked to care within 30 days than those whose infection was diagnosed using the other testing algorithms (p < 0.01). The median time to link to care during a 30-day follow-up was 9.0 days (95% CI 8.0-12.0) after the rapid algorithm, 17.0 days (95% CI 17.0-18.0) after the recommended algorithm, and 23.0 days (95% CI 22.0-25.0) after the traditional algorithm.CONCLUSIONS: The time from HIV diagnosis to care varied with the type of testing algorithm. The median time to care was shortest for the rapid algorithm, longest for the traditional algorithm, and intermediate for the recommended algorithm. These results demonstrate the importance of choosing an algorithm with a short time between initial specimen collection and report of the final result to the patient.Copyright © 2019 Elsevier B.V. All rights reserved.
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This article is a good source of information and a good way to become familiar with topics such as: Diagnostic tests;HIV infections;Testing algorithms.
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