hiv incidence determination from cross-sectional data: new laboratory methodologies timothy mastro,...
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HIV Incidence Determination from Cross-Sectional Data: New Laboratory Methodologies
Timothy Mastro, MD, FACP, DTM&HGlobal Health, Population & Nutrition, FHI 360
IAS 2013 - Kuala Lumpur, Malaysia2 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 AlgorithmMAA = Multi Assay Algorithm
Methods used for Cross-Sectional Incidence Testing
• Serologico BED-CEIA (Parekh ARHR 2002)o BioRad 1 / 2 + O Avidity (Masciotra CROI 2013 #1055)o Vironostika LS (Young ARHR 2003)o LAg (Duong PLoS One 2012)o V3 IDE (Barin JCM 2005)o Vitros LS (Keating JCM 2012)o Abbott AxSYM HIV 1 / 2 g Avidity (Suligoi JAIDS 2003)o Bio-Plex Multi-analyte (Curtis ARHR 2012)
• Nucleic Acido HRM (Towler ARHR 2010)o Sequence based
• Base ambiguity (Kouyos CID 2011)• Hamming distance - Q10 (Park AIDS 2011)
• Algorithmo Multi Assay Algorithm (Laeyendecker JID 2013)
Fundamental Concepts
• Mean Window Period: the average duration of time that a person is classified recent (positive) by an incidence testing algorithm
• Mean Window Period: Bigger = Bettero identify more people = lower variance of the incidence estimate
• Mean Window Period: Too Big = Not Goodo Want to measure infections that occurred in the past yearo If too many samples from individuals infected >1 year test positive by your incidence
algorithm, you will bias your incidence estimate
Incidence estimate
# Positiveby incidence algorithm
X mean window period
=
# HIV Uninfected
Brookmeyer AIDS 2009 Brookmeyer JAIDS 2010
Prevalence = Incidence x Mean Duration
How do you Measure HIV Incidence in a Cross-Sectional Cohort?
HIVUninfected
MAA orRITA
positive
MAA or RITA
negative
Incidence estimate
# MAA Positive
Mean window period
=
x# HIV
Uninfected
Brookmeyer & Quinn AJE 1995
Duration of Infection
Prob
abili
ty R
ecen
t 100
0
Determine mean window period using numeric integration
reversion
Theoretical Framework for Cross-Sectional Incidence Testing
Individual Time Varying AIDS (Hayashida ARHR 2008)HAART (Marinda JAIDS 2010)Viral breakthrough (Wendel PLoS One 2013)
PopulationDuration of epidemic (Hallett PloS One 2009)Access to HAARTCurrent state of epidemic (Kulich 2013 submitted)
Duration of InfectionPr
obab
ility
Rec
ent 100
0
Individual FixedRace (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 Preventiono Global AIDS Programo Division of HIV/AIDS Prevention
• Consortium for the Evaluation and Performance of HIV Incidence Assays (CEPHIA)o Develop a specimen repository and evaluate assayso Evaluate assays, alone or in combinationo Laboratory-to-laboratory comparison of assay performanceo http://www.incidence-estimation.com/page/homepage
• WHO HIV Incidence Assay Working Groupo 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
>200 cells / ul
<1.0 OD-n
>400 copies/ml
<80%
MAA Positive
CD4 cell count
BED CEIA
Avidity
HIV viral load
≤200 cells/ul
≥1.0 OD-n
≥80%
≤400 copies/ml
MAANegative
MAANegative
MAANegative
MAANegative
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)
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
Comparison of Cross-Sectional Incidence Testing to Observed Incidence
Longitudinal cohort
Enrollment 6 months 12 months
HIV incidence between survey rounds (HIV seroconversion)
HIV-HIV+
Compare the estimate using cross-sectional incidence testing to that observed longitudinally
Longitudinal cohorts• HIVNET 001, HPTN 061, HPTN 064
Perform cross-sectional incidence testing
Comparison of Observed Longitudinal Incidence to Incidence Estimated Using the MAA
Brookmeyer JAIDS 2013
Switching the Focus to Africa
Piot and Quinn NEJM in Press
Subtype C endemic
Subtype A & D endemic
Problems• Among people infected 2+ years, observed a
greater frequency of recent (positive) results in east Africa vs. southern Africa
• Rakai Health Sciences Program – 506 Individuals infected 2+ years
• Subtype D infected people fail to elicit a mature antibody response, on assayso FHI-HC Uganda Trial o Longosz CROI 2013 #1057
South
Africa
Botswan
a
Tanzan
iaKenya
Uganda
0%1%2%3%4%5%6%7%8%9%
10%
BED < 0.8 Avidity < 40%MAA
Samples from Individuals Infected 2+ Years
Subtype C Subtype A & D
330 199 138 902 628
Laeyendecker ARHR 2012
Problems with Subtype D
% P
ositi
ve b
y As
say
Subtype A Subtype D0
5
10
15
% P
ositi
ve
Mullis ARHR 2013 in press
Subtype A & C Classification by Time from Seroconversion
BED <0.8 AI <40% BED <1.0 + AI <80% BED <1.2 + AI <90%0%
20%
40%
60%
80%
100%
Cohorts tested:CAPRISA, FHI-HC, HPTN 039,Partners, PEPI, RHSP
Perc
ent o
f S P
ositi
ve b
y As
say
or A
lgor
ithm
Mean Window Period (days) 595 245 205 259
+CD4>200 + VL>200 +CD4>200 + VL>200
Duration of Infection # Samples0-6 months 4196-12 months 38712-24 months 321> 24 months 3039
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
Use of a chimeric multi-subtype gp41 protein for worldwide application
Two avidity assays including a new concept of LAg-Avidity EIA
AIDS Research and Human Retroviruses, (2010)
2010 >>
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)
Mean recent period (in days) for LAg-Avidity EIA by cohort/subtypes (cutoff 1.0), 2012
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 AIDSo 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
• Oliver Laeyendecker• Sue Eshleman• Bharat Parekh• Yen Duong• Andrea Kim• Joyce Neal• Buzz Prejean• Irene Hall
• Charles Morrison• Paul Feldblum• Karine Dube• Mike Busch• Alex Welte• Gary Murphy• Christine Rousseau• Txema Garcia-Calleja
AcknowledgementsHPTN Network Lab Susan Eshleman Matt Cousins Estelle Piwowar-Manning JHU HIV Specialty Lab
University of Witwatersrand & SACEMA, South Africa Thomas McWalter Reshma Kassanjee
CDC Michele Owen Bernard Branson Bharat Parekh Yen Duong Andrea Kim Connie Sexton
UCLA Ron Brookmeyer Jacob Korikoff Thomas Coates Agnes Fammia
Imperial College in London Tim Hallett
Quinn Laboratory, NIAID Oliver Laeyendecker Jordyn Gamiel Andrew Longosz Amy Oliver Caroline Mullis Kevin Eaton Amy Mueller
SCHARP Deborah Donnell Jim Hughes
Charles University, Prague Michal Kulich Arnošt Komárek Marek Omelka
Johns Hopkins UniversityMACS, ALIVE, Moore Clinic & Elite Suppressor Cohort Lisa Jacobson Joseph Margolick Greg Kirk Shruti Mehta Jacquie Astemborski Richard Moore Joel Blankson
Study Teams and Participants
HIVNET 001/1.1 Connie Celum Susan Buchbinder George Seage Haynes Sheppard
EXPLORE Beryl Koblin Margaret ChesneyFHI Charles Morrison
RHSP Ronald Gray Maria Wawer Tomas Lutalo Fred Nalagola
Partner in Prevention Connie Celum
PEPI Taha Taha
HPTN 061 Kenneth Mayer Beryl KoblinHPTN 064 Sally Hodder Jessica Justman
U01/UM1-AI0686131R01-AI095068
CEPHIA Gary Murphy Michal Busch Alex Welte Chris Pilcher