HIV Incidence Determination from
Cross-Sectional Data:
New Laboratory Methodologies
Timothy Mastro, MD, FACP, DTM&H
Global Health, Population & Nutrition, FHI 360
IAS 2013 - Kuala Lumpur, Malaysia
2 July 2013
Why determine HIV incidence?
• Characterize the epidemic in a population
– Monitor changes over time
– Identify important sub-populations for interventions
•
•
•
•
Assess the impact of programs
Identify populations for HIV intervention trials
Endpoint in community-level intervention trials
Identify individuals for interventions
– Prioritization
– Interrupt transmission
Standard Methods for Incidence
Determination are Unsatisfactory
• Indirect methods; repeat cross-sectional
measurements; modeling
• Back calculation methods not timely or
reliable
• Prospective follow-up of cohorts is expensive
and unrepresentative of general population
– Enrollment into a study leads to behavior
change
– Study interventions change incidence
Advantages of an Accurate Cross-Sectional
HIV Incidence Testing Algorithm
• Cost: can be done from a cross-sectional
survey
• Scale: can be done on a national level; added
on to other surveys with biologic specimens
• Time: no need for long-term follow-up;
relatively easy to repeat
• Inclusion: relatively easy to include
marginalized populations
What is Cross-Sectional HIV Incidence Testing?
Laboratory method that can reliably discriminate between
recent and non-recent infection
RITA = Recent Infection Testing Algorithm
MAA = Multi Assay Algorithm
Methods used for Cross-Sectional
Incidence Testing
• Serologic
o
o
o
o
o
o
o
o
BED-CEIA (Parekh ARHR 2002)
BioRad 1 / 2 + O Avidity (Masciotra CROI 2013 #1055)
Vironostika LS (Young ARHR 2003)
LAg (Duong PLoS One 2012)
V3 IDE (Barin JCM 2005)
Vitros LS (Keating JCM 2012)
Abbott AxSYM HIV 1 / 2 g Avidity (Suligoi JAIDS 2003)
Bio-Plex Multi-analyte (Curtis ARHR 2012)
• Nucleic Acid
o HRM (Towler ARHR 2010)
o Sequence based
• Base ambiguity (Kouyos CID 2011)
• Hamming distance - Q10 (Park AIDS 2011)
• Algorithm
o Multi Assay Algorithm (Laeyendecker JID 2013)
Fundamental Concepts
# Positive
by incidence algorithm
Incidence
estimate =
Prevalence = Incidence x Mean Duration
# HIV
Uninfected
X mean window period
• Mean Window Period: the average duration of time that a person
is classified recent (positive) by an incidence testing algorithm
• Mean Window Period: Bigger = Better
o identify more people = lower variance of the incidence estimate
• Mean Window Period: Too Big = Not Good
o Want to measure infections that occurred in the past year
o If too many samples from individuals infected >1 year test positive by your
incidence algorithm, you will bias your incidence estimate
Brookmeyer AIDS 2009
Brookmeyer JAIDS 2010
How do you Measure HIV Incidence
in a Cross-Sectional Cohort?
Probability Recent
HIV
Uninfected
MAA or
RITA
positive
reversion
100
Determine mean
window period
using numeric
integration
0
MAA or
RITA
negative
Duration of Infection
Brookmeyer & Quinn AJE 1995
Incidence
estimate =
# MAA
Positive
# HIV
Mean window
Uninfected x
period
Theoretical Framework for
Cross-Sectional Incidence Testing
Population
Duration of epidemic
(Hallett PloS One 2009)
Access to HAART
Current state of epidemic
(Kulich 2013 submitted)
Probability Recent
Individual Time Varying
AIDS
(Hayashida ARHR 2008)
HAART (Marinda JAIDS 2010)
Viral breakthrough (Wendel PLoS One 2013)
100
0
Duration of Infection
Individual Fixed
Race (Laeyendecker ARHR 2012a)
Gender (Mullis ARHR in press)
Geography (Laeyendecker ARHR 2012b)
Infecting subtype (Parekh ARHR 2011)
Viral load set-point (Laeyendecker JAIDS 2008)
Groups Working on Cross-Sectional
Incidence Assays
• Centers for Disease Control and Prevention
o Global AIDS Program
o Division of HIV/AIDS Prevention
• Consortium for the Evaluation and Performance of HIV
Incidence Assays (CEPHIA)
o
o
o
o
Develop a specimen repository and evaluate assays
Evaluate assays, alone or in combination
Laboratory-to-laboratory comparison of assay performance
http://www.incidence-estimation.com/page/homepage
• WHO HIV Incidence Assay Working Group
o http://www.who.int/diagnostics_laboratory/links/hiv_incidence_assay
/en/index.html
• HPTN Network Laboratory
Development of a
Multi-Assay Algorithm for Subtype B
Performance Cohorts:
HIVNET 001, MACS, ALIVE
• MSM, IDU, women
• 1,782 samples from 709 individuals
• Duration of infection: 0.1 to 8+ years
• Mean Window Period:
141 days (95% CI: 94-150 days)
≤200 cells/ul
CD4 cell count
>200 cells / ul
≥1.0 OD-n
BED CEIA
MAA
Negative
<1.0 OD-n
≥80%
Avidity
Confirmation Data:
JHU HIV Clinical Practice Cohort
• 500 samples from 379 individuals
• Duration of infection: 8+ years
• No samples were recent by MAA
Laeyendecker JID 2013
MAA
Negative
<80%
MAA
Negative
≤400 copies/ml
HIV viral load
MAA
Negative
>400 copies/ml
MAA Positive
Comparison of Cross-Sectional Incidence
Testing to Observed Incidence
Longitudinal cohort
Enrollment
HIVHIV+
6 months
12 months
Perform cross-sectional
incidence testing
Compare the estimate
using cross-sectional
incidence testing to
that observed
longitudinally
HIV incidence between survey
rounds (HIV seroconversion)
Longitudinal cohorts
• HIVNET 001, HPTN 061, HPTN 064
Comparison of Observed Longitudinal Incidence to
Incidence Estimated Using the MAA
Brookmeyer JAIDS 2013
Switching the Focus to Africa
Subtype A & D endemic
Subtype C endemic
Piot and Quinn NEJM in Press
Problems with Subtype D
Samples from Individuals
Infected 2+ Years
Problems
•
Among people infected 2+ years, observed
BED < 0.8
a greater frequency of recent (positive)
10%
results in east Africa vs. southern Africa
9%
Rakai Health Sciences Program
– 506 Individuals infected 2+ years
% Positive
15
10
5
0
Subtype A
Subtype D
Mullis ARHR 2013 in press
•
% Positive by Assay
•
8%
7%
6%
5%
4%
3%
2%
1%
0%
330
Avidity < 40%
199
138
902
MAA
628
Subtype D infected people fail to elicit a
mature antibody response, on assays
o FHI-HC Uganda Trial
o Longosz CROI 2013 #1057
Subtype C
Subtype A & D
Laeyendecker ARHR 2012
Subtype A & C Classification by Time
from Seroconversion
Cohorts tested:
CAPRISA, FHI-HC, HPTN 039,
Partners, PEPI, RHSP
Percent of S Positive by Assay
or Algorithm
100%
80%
Duration of Infection
# Samples
0-6 months
419
6-12 months
387
12-24 months
321
> 24 months
3039
60%
40%
20%
0%
BED <0.8
Mean Window
Period (days) 595
AI <40%
245
BED <1.0 + AI <80% BED <1.2 + AI <90%
+CD4>200 + VL>200 +CD4>200 + VL>200
205
259
Project Accept (HPTN 043) Outcome
• Community randomized trial of community
mobilization and VCT in 34 communities in Africa
and 14 in Thailand; vs standard VCT
• HIV endpoint determined by cross-sectional
survey of n=1000 in each community and HIV
incidence estimate using the MAA optimized for
subtypes C, D, A (in Africa)
– BED <1.2, AI <90, CD4 >200, VL >400
• Overall, 14% reduction (0.08) in HIV incidence in
intervention communities
Coates, CROI, 2013
CDC Laboratory
Limiting Antigen (LAg) Avidity Assay
From CDC, Bharat Parekh Laboratory
2010 >>
AIDS Research and Human Retroviruses, (2010)
2012 >>


Use of a chimeric multi-subtype gp41 protein for
worldwide application
Two avidity assays including a new concept of LAgAvidity EIA
Mean recent period (in days) for LAg-Avidity EIA by cohort/subtypes
(cutoff 1.0), 2012
Cohort
No. of Subjects
(No. Spec)
HIV-1 Subtypes
Mean Recency
Period (95% CI)
Amsterdam &
Trinidad
32 (170)
B
132 (104-157)
Ethiopia
23 (143)
C
139 (106-178)
Kenya
34 (80)
A, D
143 (103-188)
ALL
89 (393)
A, B, C and D
141 (119-160)
Evaluated in multiple cohorts and compared to other
incidence estimates
LAg-Avidity Assay, Developments in 2013
• Available as commercial kit
• Evaluated by CEPHIA and
reviewed in by WHO WG &
external experts
• Improved performance
compared to BED assay
• New ODn = 1.5
• New window period = 130
(118-141) days
• Should exclude subjects
o with AIDS
o with low viral loads
• False recent rate = 1.6%
• Ongoing discussions on use
Summary - I
• Current work on new assays and multi-assay
algorithms is promising, but more work do
• We still need a simple, easy-to-use,
cross-sectional method for HIV incidence
determination in diverse global settings
– Persons on ART appear recently infected on
most assays
• Imperative to rigorously evaluate assays and
multi-assay algorithms before using as global
standard for surveys
Summary - II
• Differing precision required for various
applications; epidemiologic judgment required
• Currently, use of multiple methods, to allow
comparison, is recommended for estimating HIV
incidence in populations
• More work needed on guidance for users in
various global settings
Acknowledgments - I
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Oliver Laeyendecker
Sue Eshleman
Bharat Parekh
Yen Duong
Andrea Kim
Joyce Neal
Buzz Prejean
Irene Hall
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Charles Morrison
Paul Feldblum
Karine Dube
Mike Busch
Alex Welte
Gary Murphy
Christine Rousseau
Txema Garcia-Calleja
Acknowledgements
HPTN Network Lab
Susan Eshleman
Matt Cousins
Estelle Piwowar-Manning
JHU HIV Specialty Lab
UCLA
Ron Brookmeyer
Jacob Korikoff
Thomas Coates
Agnes Fammia
SCHARP
Deborah Donnell
Jim Hughes
Charles University, Prague
Michal Kulich
Arnošt Komárek
Marek Omelka
Imperial College in London
Tim Hallett
University of Witwatersrand
& SACEMA, South Africa
Thomas McWalter
Reshma Kassanjee
Quinn Laboratory, NIAID
Oliver Laeyendecker
Jordyn Gamiel
Andrew Longosz
Amy Oliver
Caroline Mullis
Kevin Eaton
Amy Mueller
HIVNET 001/1.1
Connie Celum
Susan Buchbinder
George Seage
Haynes Sheppard
EXPLORE
Beryl Koblin
Margaret Chesney
FHI
Charles Morrison
Partner in Prevention
Connie Celum
Johns Hopkins University
MACS, ALIVE, Moore Clinic
& Elite Suppressor Cohort
Lisa Jacobson
Joseph Margolick
Greg Kirk
Shruti Mehta
Jacquie Astemborski
Richard Moore
Joel Blankson
RHSP
Ronald Gray
Maria Wawer
Tomas Lutalo
Fred Nalagola
CEPHIA
Gary Murphy
Michal Busch
Alex Welte
Chris Pilcher
HPTN 061
Kenneth Mayer
Beryl Koblin
HPTN 064
Sally Hodder
Jessica Justman
PEPI
Taha Taha
CDC
Michele Owen
Bernard Branson
Bharat Parekh
Yen Duong
Andrea Kim
Connie Sexton
Study Teams and Participants
U01/UM1-AI068613
1R01-AI095068