Hiv incidence determination in clade b epidemics a multi assay approach
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HIV incidence determination in clade B epidemics: A multi-assay approach. Oliver Laeyendecker, Brookmeyer R, Cousins MM, Mullis CE, Konikoff J, Donnell D, Celum C, Buchbinder SP, Seage GR, Kirk GD, Mehta SH, Astemborski J, Jacobson LP, Margolick JB, Brown J, Quinn TC, and Eshleman SH.

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HIV incidence determination in clade B epidemics: A multi-assay approach

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Hiv incidence determination in clade b epidemics a multi assay approach

HIV incidence determination in clade B epidemics: A multi-assay approach

Oliver Laeyendecker, Brookmeyer R, Cousins MM, Mullis CE, Konikoff J, Donnell D, Celum C, Buchbinder SP,

Seage GR, Kirk GD, Mehta SH, Astemborski J, Jacobson LP, Margolick JB, Brown J, Quinn TC, and Eshleman SH


How do you measure hiv incidence in a cross sectional cohort

How do you measure HIV incidence in a cross-sectional cohort?

HIV

Uninfected

Recently Infected

Long-term Infected

# Recently Infected

Incidence estimate

=

# HIV Uninfected

Average time of

recent infection

(window period)

x

Brookmeyer & Quinn AJE 1995


Problem infinite time recently infected and regression to recently infected

Problem: Infinite time ‘recently infected’ and regression to ‘recently infected’

HIV

Uninfected

Recently Infected

Long-term Infected

# Recently Infected

?

Incidence estimate

=

# HIV Uninfected

Average time of

recent infection

?

x


How to find the recently infected people

How to find the recently infected people


Development of a multi assay algorithm

Development of a multi-assay algorithm

≤ 200 cells / ul

CD4 cell count

Stop

> 200 cells / ul

≥ 1.0 OD-n

BED CEIA

Stop

< 1.0 OD-n

≥ 80%

Avidity

Stop

< 80%

≤ 400 copies/ ml

HIV viral load

Stop

> 400 copies / ml

Classified as recently infected


Samples to determine the performance of the maa

Samples to determine the performance of the MAA

  • Performance Cohorts: HIVNET 001, MACS, ALIVE

  • MSM, IDU, women

  • 1,782 samples from 709 individuals

  • Duration of HIV infection: 1 month to 8+ years

  • Includes individuals with AIDS, viral suppression, exposed to ARVs

  • Confirmation Data: Johns Hopkins HIV Clinical Practice Cohort

  • MSM, IDU, women

  • 500 samples from 379 individuals

  • Duration of HIV infection: 8+ years from 1st positive test

  • Includes individuals with AIDS, viral suppression, exposed to ARVs

  • Longitudinal cohorts

  • HIV001

  • HPTN 064


Proportion classified as recent

Proportion classified as recent

None of 500 samples from individuals infected 8+ years

(Johns Hopkins HIV Clinical Practice Cohort) were misclassified as recent using the multi-assay algorithm


Hiv incidence determination in clade b epidemics a multi assay approach

BED-CEIA

The probability of testing recently infected by time from seroconversion is fitted with a cubic spline

The area under the modeled probability curve using numerical integration provided the window period

% characterized as “recent”

BED-CEIA: Does not converge to zero

Cannot determine window period

(average time classified as recently infected)

20% 40% 60% 80% 100%

0 2 4 6 8

Duration of infection (years)


Hiv incidence determination in clade b epidemics a multi assay approach

BED-CEIA vs. Multi Assay Algorithm

The probability of testing recently infected by time from seroconversion is fitted with a quadratic spline

The area under the modeled probability curve using numerical integration provided the window period

% characterized as “recent”

BED-CEIA: Does not converge to zero

Cannot determine window period

(average time classified as recently infected)

20% 40% 60% 80% 100%

Multi-assay algorithm : Does converge to zero

Window period: 141 days (95% CI: 94-150 days)

BED

MAA

0 2 4 6 8

Duration of infection (years)


Hiv incidence determination in clade b epidemics a multi assay approach

Comparison of HIV incidence Estimates

Eshleman (2012) In Press JID

Laeyendecker (2012) Submitted


Summary

Summary

  • The multi-assay algorithm has a window period of 141 days with no misclassification of individuals infected 4+ years

  • Incidence estimates obtained using the multi-assay algorithm are nearly identical to estimates based on HIV seroconversion

  • We are now determining the optimal cut-off values for the multi-assay algorithm


Acknowledgements

Acknowledgements

Quinn Laboratory

Thomas Quinn

Jordyn Gamiel

Amy Oliver

Caroline Mullis

Kevin Eaton

Amy Mueller

Johns Hopkins University

MACS, ALIVE, Moore Clinic

Lisa Jacobson

Joseph Margolick

Greg Kirk

Shruti Mehta

Jacquie Astemborski

Richard Moore

Jeanne Keruly

HPTN 064

Sally Hodder

Jessica Justman

HPTN Network Lab

Susan Eshleman

Matthew Cousins

UCLA

Ron Brookmeyer

Jacob Konikoff

SCHARP

Deborah Donnell

Jim Hughes

HIVNET 001/1.1

Connie Celum

Susan Buchbinder

George Seage

Haynes Sheppard

  • CDC

  • Michele Owen

  • Bernard Branson

    • Bharat Parekh

    • Andrea Kim

    • Connie Sexton

U01/UM1-AI068613

1R01-AI095068

Study Teams and Participants


Theoretical framework for cross sectional incidence testing

Theoretical framework for cross sectional incidence testing

Individual Time Varying

AIDS

Antiviral Treatment

Population

Stage of the epidemic

Access to ARVs

Time Infected

Assay Outcome

Individual Fixed

Age, Race, Gender

Route of infection

Geography

Infecting subtype

Viral load set-point


Comparison of cross sectional incidence testing to known incidence

Comparison of cross-sectional incidence testing to known incidence

Longitudinal cohort

Perform cross-sectional

incidence testing

Survey rounds

1

2

3

4

Compare the incidence estimate based on HIV seroconversion to the estimate based on cross-sectional testing using the multi-assay algorithm

HIV-

HIV+

HIV incidence between survey rounds (HIV seroconversion)


Why a bigger window is better

Why a Bigger Window is Better

Window period

21 days

45 days

141 days

365 days

Population needed to screen to find ten recently infected individuals

Incidence (percent/ year)


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