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

Matthew M. Cousins; Jacob Konikoff; Devin Sabin; Leila Khaki; Andrew F. Longosz; Oliver Laeyendecker; Connie Celum; Susan P. Buchbinder; George R. Seage; Gregory D. Kirk; Richard D. Moore; Shruti H. Mehta; Joseph B. Margolick; Joelle Brown; Kenneth H. Mayer; Beryl A. Kobin; Darrell Wheeler; Jessica E. Justman; Sally L. Hodder; Thomas C. Quinn; Ron Brookmeyer; Susan H. Eshleman

doi:10.1371/journal.pone.0101043

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

Matthew M. Cousins, Jacob Konikoff, Devin Sabin, Leila Khaki, Andrew F. Longosz, Oliver Laeyendecker, Connie Celum, Susan P. Buchbinder, George R. Seage, Gregory D. Kirk, Richard D. Moore, Shruti H. Mehta, Joseph B. Margolick, Joelle Brown, Kenneth H. Mayer, Beryl A. Kobin, Darrell Wheeler, Jessica E. Justman, Sally L. Hodder, Thomas C. QuinnRon Brookmeyer, Susan H. Eshleman

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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 crosssectional 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 ambiguitybased 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.

Original language	English (US)
Article number	e101043
Journal	PloS one
Volume	9
Issue number	6
DOIs	https://doi.org/10.1371/journal.pone.0101043
State	Published - Jun 26 2014

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Cousins, M. M., Konikoff, J., Sabin, D., Khaki, L., Longosz, A. F., Laeyendecker, O., Celum, C., Buchbinder, S. P., Seage, G. R., Kirk, G. D., Moore, R. D., Mehta, S. H., Margolick, J. B., Brown, J., Mayer, K. H., Kobin, B. A., Wheeler, D., Justman, J. E., Hodder, S. L., ... Eshleman, S. H. (2014). A comparison of two measures of HIV diversity in multi-assay algorithms for HIV incidence estimation. PloS one, 9(6), Article e101043. https://doi.org/10.1371/journal.pone.0101043

Cousins, MM, Konikoff, J, Sabin, D, Khaki, L, Longosz, AF, Laeyendecker, O, Celum, C, Buchbinder, SP, Seage, GR, Kirk, GD , Moore, RD , Mehta, SH , Margolick, JB, Brown, J, Mayer, KH, Kobin, BA, Wheeler, D, Justman, JE, Hodder, SL, Quinn, TC, Brookmeyer, R & Eshleman, SH 2014, 'A comparison of two measures of HIV diversity in multi-assay algorithms for HIV incidence estimation', PloS one, vol. 9, no. 6, e101043. https://doi.org/10.1371/journal.pone.0101043

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title = "A comparison of two measures of HIV diversity in multi-assay algorithms for HIV incidence estimation",

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 crosssectional 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 ambiguitybased 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.",

author = "Cousins, {Matthew M.} and Jacob Konikoff and Devin Sabin and Leila Khaki and Longosz, {Andrew F.} and Oliver Laeyendecker and Connie Celum and Buchbinder, {Susan P.} and Seage, {George R.} and Kirk, {Gregory D.} and Moore, {Richard D.} and Mehta, {Shruti H.} and Margolick, {Joseph B.} and Joelle Brown and Mayer, {Kenneth H.} and Kobin, {Beryl A.} and Darrell Wheeler and Justman, {Jessica E.} and Hodder, {Sally L.} and Quinn, {Thomas C.} and Ron Brookmeyer and Eshleman, {Susan H.}",

year = "2014",

month = jun,

day = "26",

doi = "10.1371/journal.pone.0101043",

language = "English (US)",

volume = "9",

journal = "PloS one",

issn = "1932-6203",

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T1 - A comparison of two measures of HIV diversity in multi-assay algorithms for HIV incidence estimation

AU - Cousins, Matthew M.

AU - Konikoff, Jacob

AU - Sabin, Devin

AU - Khaki, Leila

AU - Longosz, Andrew F.

AU - Laeyendecker, Oliver

AU - Celum, Connie

AU - Buchbinder, Susan P.

AU - Seage, George R.

AU - Kirk, Gregory D.

AU - Moore, Richard D.

AU - Mehta, Shruti H.

AU - Margolick, Joseph B.

AU - Brown, Joelle

AU - Mayer, Kenneth H.

AU - Kobin, Beryl A.

AU - Wheeler, Darrell

AU - Justman, Jessica E.

AU - Hodder, Sally L.

AU - Quinn, Thomas C.

AU - Brookmeyer, Ron

AU - Eshleman, Susan H.

PY - 2014/6/26

Y1 - 2014/6/26

N2 - 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 crosssectional 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 ambiguitybased 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.

AB - 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 crosssectional 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 ambiguitybased 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.

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JO - PloS one

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

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