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 MAALaeyendecker JID 2013

Comparison of Cross-Sectional Incidence Testing to Observed Incidence

Longitudinal cohortEnrollment 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 Afric

a

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