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?

HIVUninfected

Recently Infected

Long-term Infected

Incidence estimate

# Recently Infected

Average time of recent infection(window period)

=

x# HIV

UninfectedBrookmeyer & Quinn AJE 1995

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

HIVUninfected

Recently Infected

Long-term Infected

Incidence estimate

# Recently Infected

Average time of recent infection

=

x# HIV

Uninfected

?

?

How to find the recently infected people

Development of a multi-assay algorithm

> 200 cells / ul

< 1.0 OD-n

> 400 copies / ml

< 80%

Classified as recently infected

CD4 cell count

BED CEIA

Avidity

HIV viral load

≤ 200 cells / ul

≥ 1.0 OD-n

≥ 80%

≤ 400 copies/ ml

Stop

Stop

Stop

Stop

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

% classified as recent Duration of HIV

Infection

# samples BED-CEIA Multi-assay algorithm

0 – 6 months 142 56.3 47.9 6 months – 1 year 166 36.7 9.0 1-2 years 263 24.7 0.8 2-3 years 301 20.6 0.7 3-4 years 440 14.5 0.5 4-5 years 125 12.0 0.0 ≥ 5 years 345 13.6 0.0

None of 500 samples from individuals infected 8+ years (Johns Hopkins HIV Clinical Practice Cohort) were misclassified as recent

using the multi-assay algorithm

BED-CEIA

Duration of infection (years)

0 2 4 6 8

The probability of testing recently infected by time from seroconversion is fitted with a cubic splineThe area under the modeled probability curve using numerical integration provided the window period

BED-CEIA: Does not converge to zeroCannot determine window period (average time classified as recently infected)

20%

4

0%

60%

8

0%

1

00%

% c

hara

cter

ized

as

“rec

ent”

Duration of infection (years)

0 2 4 6 8

BED-CEIA vs. Multi Assay Algorithm

Multi-assay algorithm : Does converge to zeroWindow period: 141 days (95% CI: 94-150 days)

20%

4

0%

60%

8

0%

1

00%

% c

hara

cter

ized

as

“rec

ent”

BED-CEIA: Does not converge to zeroCannot determine window period (average time classified as recently infected)

The probability of testing recently infected by time from seroconversion is fitted with a quadratic splineThe area under the modeled probability curve using numerical integration provided the window period

MAA

BED

Comparison of HIV incidence Estimates

Study Analysis Estimated annual incidence (95% CI)

HIVNET 001/001.1

Longitudinal12-18 months 1.04% 0.70 – 1.55%

MAA 18 months 0.97% 0.51 – 1.71%

HPTN 064

Longitudinal 6-12 months 0.24% 0.07 - 0.62%

MAA12 months 0.13% 0.01 – 0.76%

Eshleman (2012) In Press JIDLaeyendecker (2012) Submitted

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

AcknowledgementsHPTN Network Lab Susan Eshleman Matthew Cousins

CDC Michele Owen Bernard Branson Bharat Parekh Andrea Kim Connie Sexton

UCLA Ron Brookmeyer Jacob Konikoff

Quinn Laboratory Thomas Quinn Jordyn Gamiel Amy Oliver Caroline Mullis Kevin Eaton Amy Mueller

SCHARP Deborah Donnell Jim Hughes

Johns Hopkins UniversityMACS, ALIVE, Moore Clinic Lisa Jacobson Joseph Margolick Greg Kirk Shruti Mehta Jacquie Astemborski Richard Moore Jeanne Keruly

HPTN 064 Sally Hodder Jessica Justman

Study Teams and Participants

HIVNET 001/1.1 Connie Celum Susan Buchbinder George Seage Haynes Sheppard

U01/UM1-AI0686131R01-AI095068

Theoretical framework for cross sectional incidence testing

Assay OutcomeTime Infected

Individual Time Varying AIDSAntiviral Treatment

PopulationStage of the epidemicAccess to ARVs

Individual FixedAge, Race, GenderRoute of infectionGeographyInfecting subtypeViral load set-point

Perform cross-sectional incidence testing

Comparison of cross-sectional incidence testing to known incidence

Longitudinal cohort

Survey rounds1 2 3 4

HIV incidence between survey rounds (HIV seroconversion)

HIV-HIV+

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

0 0.5 1 1.5 2 2.5 3100

1,000

10,000

100,000

1,000,000

10,000,000

Why a Bigger Window is Better

Incidence (percent/ year)

Population needed to screen to find ten recently infected individuals

Window period21 days45 days141 days365 days