Format

Send to

Choose Destination
PLoS One. 2014 Jun 26;9(6):e101043. doi: 10.1371/journal.pone.0101043. eCollection 2014.

A comparison of two measures of HIV diversity in multi-assay algorithms for HIV incidence estimation.

Author information

1
Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America.
2
Department of Biostatistics, School of Public Health, University of California Los Angeles, Los Angeles, California, United States of America.
3
National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, United States of America.
4
National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, United States of America; Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America.
5
Departments of Global Health and Medicine, University of Washington, Seattle, Washington, United States of America.
6
Bridge HIV, San Francisco Department of Health, San Francisco, California, United States of America; Departments of Epidemiology and Medicine, University of California San Francisco, San Francisco, California, United States of America.
7
Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, United States of America.
8
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America.
9
Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America.
10
Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America.
11
Department of Epidemiology, School of Public Health, University of California Los Angeles, Los Angeles, California, United States of America; Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California, United States of America.
12
The Fenway Institute/Beth Israel Deaconess Medical Center/Harvard Medical School, Boston, Massachusetts, United States of America.
13
Laboratory of Infectious Disease Prevention, New York Blood Center, New York, New York, United States of America.
14
Graduate School of Social Work, Loyola University Chicago, Chicago, Illinois, United States of America.
15
Departments of Epidemiology and Medicine, Columbia University, New York, New York, United States of America.
16
Department of Medicine, Division of Infectious Diseases, New Jersey Medical School, Newark, New Jersey, United States of America.

Abstract

BACKGROUND:

Multi-assay algorithms (MAAs) can be used to estimate HIV incidence in cross-sectional surveys. We compared the performance of two MAAs that use HIV diversity as one of four biomarkers for analysis of HIV incidence.

METHODS:

Both MAAs included two serologic assays (LAg-Avidity assay and BioRad-Avidity assay), HIV viral load, and an HIV diversity assay. HIV diversity was quantified using either a high resolution melting (HRM) diversity assay that does not require HIV sequencing (HRM score for a 239 base pair env region) or sequence ambiguity (the percentage of ambiguous bases in a 1,302 base pair pol region). Samples were classified as MAA positive (likely from individuals with recent HIV infection) if they met the criteria for all of the assays in the MAA. The following performance characteristics were assessed: (1) the proportion of samples classified as MAA positive as a function of duration of infection, (2) the mean window period, (3) the shadow (the time period before sample collection that is being assessed by the MAA), and (4) the accuracy of cross-sectional incidence estimates for three cohort studies.

RESULTS:

The proportion of samples classified as MAA positive as a function of duration of infection was nearly identical for the two MAAs. The mean window period was 141 days for the HRM-based MAA and 131 days for the sequence ambiguity-based MAA. The shadows for both MAAs were <1 year. Both MAAs provided cross-sectional HIV incidence estimates that were very similar to longitudinal incidence estimates based on HIV seroconversion.

CONCLUSIONS:

MAAs that include the LAg-Avidity assay, the BioRad-Avidity assay, HIV viral load, and HIV diversity can provide accurate HIV incidence estimates. Sequence ambiguity measures obtained using a commercially-available HIV genotyping system can be used as an alternative to HRM scores in MAAs for cross-sectional HIV incidence estimation.

PMID:
24968135
PMCID:
PMC4072769
DOI:
10.1371/journal.pone.0101043
[Indexed for MEDLINE]
Free PMC Article

Publication types, MeSH terms, Grant support

Publication types

MeSH terms

Grant support

Supplemental Content

Full text links

Icon for Public Library of Science Icon for PubMed Central
Loading ...
Support Center