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?
HIV
Uninfected
Incidence
=
estimate
Brookmeyer & Quinn AJE 1995
Recently
Infected
Long-term
Infected
# Recently
Infected
# HIV
Uninfected
Average time of
x recent infection
(window period)
Problem: Infinite time ‘recently infected’
and regression to ‘recently infected’
HIV
Uninfected
Incidence
=
estimate
Recently
Infected
Long-term
Infected
# Recently
Infected
?
# HIV
Uninfected
Average time of
x recent infection
?
How to find the recently infected people
Development of a
multi-assay algorithm
CD4 cell count
≤ 200 cells / ul
Stop
> 200 cells / ul
≥ 1.0 OD-n
Stop
BED CEIA
< 1.0 OD-n
≥ 80%
Avidity
Stop
< 80%
HIV viral load
≤ 400 copies/ ml
Stop
> 400 copies / ml
Classified as recently
infected
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
0 – 6 months
6 months – 1 year
1-2 years
2-3 years
3-4 years
4-5 years
≥ 5 years
# samples
142
166
263
301
440
125
345
BED-CEIA
Multi-assay
algorithm
56.3
36.7
24.7
20.6
14.5
12.0
13.6
47.9
9.0
0.8
0.7
0.5
0.0
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: Does not converge to zero
Cannot determine window period
(average time classified as recently infected)
80%
40%
60%
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
20%
% characterized as “recent”
100%
BED-CEIA
0
2
4
6
Duration of infection (years)
8
80%
60%
BED-CEIA: Does not converge to zero
Cannot determine window period
(average time classified as recently infected)
40%
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
Multi-assay algorithm : Does converge to zero
Window period: 141 days (95% CI: 94-150 days)
BED
20%
% characterized as “recent”
100%
BED-CEIA vs. Multi Assay Algorithm
MAA
0
2
4
6
Duration of infection (years)
8
Comparison of HIV incidence Estimates
Study
HIVNET
001/001.1
HPTN 064
Analysis
Longitudinal
12-18 months
MAA
18 months
Longitudinal
6-12 months
MAA
12 months
Estimated annual incidence
(95% CI)
1.04%
0.70 – 1.55%
0.97%
0.51 – 1.71%
0.24%
0.07 - 0.62%
0.13%
0.01 – 0.76%
Eshleman (2012) In Press JID
Laeyendecker (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 multiassay 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
HPTN Network Lab
Susan Eshleman
Matthew Cousins
UCLA
Ron Brookmeyer
Jacob Konikoff
SCHARP
Deborah Donnell
Jim Hughes
CDC
Michele Owen
Bernard Branson
Bharat Parekh
Andrea Kim
Connie Sexton
Quinn Laboratory
Thomas Quinn
Jordyn Gamiel
Amy Oliver
Caroline Mullis
Kevin Eaton
Amy Mueller
HIVNET 001/1.1
Connie Celum
Susan Buchbinder
George Seage
Haynes Sheppard
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
U01/UM1-AI068613
1R01-AI095068
Study Teams and Participants
Theoretical framework for cross sectional
incidence testing
Individual Time Varying
AIDS
Antiviral Treatment
Population
Stage of the epidemic
Access to ARVs
Time Infected
Individual Fixed
Age, Race, Gender
Route of infection
Geography
Infecting subtype
Viral load set-point
Assay Outcome
Comparison of cross-sectional
incidence testing to known incidence
Longitudinal cohort
1
2
Survey rounds
3
4
Perform cross-sectional
incidence testing
Compare the incidence
estimate based on HIV
seroconversion to the
estimate based on
cross-sectional testing
using the multi-assay
algorithm
HIVHIV+
HIV incidence between survey
rounds (HIV seroconversion)
Why a Bigger Window is Better
10,000,000
Window period
21 days
45 days
141 days
365 days
1,000,000
Population
100,000
needed to
screen to find
ten recently 10,000
infected
1,000
individuals
100
0
0.5
1
1.5
2
2.5
Incidence (percent/ year)
3