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Why determine HIV incidence?

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

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Why determine HIV incidence?

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  1. HIV Incidence Determination from Cross-Sectional Data: New Laboratory MethodologiesTimothy Mastro, MD, FACP, DTM&HGlobal Health, Population & Nutrition, FHI 360IAS 2013 - Kuala Lumpur, Malaysia2 July 2013

  2. 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

  3. 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

  4. 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

  5. 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

  6. Methods used for Cross-Sectional Incidence Testing • Serologic • 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 • HRM (Towler ARHR 2010) • Sequence based • Base ambiguity (Kouyos CID 2011) • Hamming distance - Q10 (Park AIDS 2011) • Algorithm • Multi Assay Algorithm (Laeyendecker JID 2013)

  7. Fundamental Concepts Prevalence = Incidence x Mean Duration # Positive by incidence algorithm Incidence estimate • 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 • identify more people = lower variance of the incidence estimate • Mean Window Period: Too Big = Not Good • Want to measure infections that occurred in the past year • If too many samples from individuals infected >1 year test positive by your incidence algorithm, you will bias your incidence estimate = # HIV Uninfected X mean window period Brookmeyer AIDS 2009 Brookmeyer JAIDS 2010

  8. How do you Measure HIV Incidence in a Cross-Sectional Cohort? HIV Uninfected MAA or RITA positive MAA or RITA negative reversion 100 Determine mean window period using numeric integration # MAA Positive ProbabilityRecent Incidence estimate = # HIV Uninfected Mean window period x 0 Duration of Infection Brookmeyer & Quinn AJE 1995

  9. Theoretical Framework for Cross-Sectional Incidence Testing Individual Time Varying AIDS (Hayashida ARHR 2008) HAART (Marinda JAIDS 2010) Viral breakthrough (Wendel PLoS One 2013) 100 Population Duration of epidemic (Hallett PloS One 2009) Access to HAART Current state of epidemic (Kulich 2013 submitted) ProbabilityRecent 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)

  10. Groups Working on Cross-Sectional Incidence Assays • Centers for Disease Control and Prevention • Global AIDS Program • Division of HIV/AIDS Prevention • Consortium for the Evaluation and Performance of HIV Incidence Assays (CEPHIA) • 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 • http://www.who.int/diagnostics_laboratory/links/hiv_incidence_assay/en/index.html • HPTN Network Laboratory

  11. Development of a Multi-Assay Algorithm for Subtype B ≤200 cells/ul CD4 cell count MAA Negative >200 cells / ul • 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) ≥1.0 OD-n BED CEIA MAA Negative <1.0 OD-n ≥80% Avidity MAA Negative <80% ≤400 copies/ml HIV viral load MAA Negative >400 copies/ml MAA Positive • 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

  12. Comparison of Cross-Sectional Incidence Testing to Observed Incidence Longitudinal cohort Enrollment 12 months Perform cross-sectional incidence testing 6 months Compare the estimate using cross-sectional incidence testing to that observed longitudinally HIV- HIV+ HIV incidence between survey rounds (HIV seroconversion) • Longitudinal cohorts • HIVNET 001, HPTN 061, HPTN 064

  13. Comparison of Observed Longitudinal Incidence to Incidence Estimated Using the MAA Brookmeyer JAIDS 2013

  14. Switching the Focus to Africa Subtype A & D endemic Subtype C endemic Piot and Quinn NEJM in Press

  15. Problems with Subtype D Samples from Individuals Infected 2+ Years 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 assays • FHI-HC Uganda Trial • Longosz CROI 2013 #1057 % Positive by Assay % Positive 330 199 138 902 628 Mullis ARHR 2013 in press Subtype C Subtype A & D Laeyendecker ARHR 2012

  16. Subtype A & C Classification by Time from Seroconversion Cohorts tested: CAPRISA, FHI-HC, HPTN 039, Partners, PEPI, RHSP Duration of Infection # Samples 0-6 months 419 6-12 months 387 12-24 months 321 > 24 months 3039 Percent of S Positive by Assay or Algorithm +CD4>200 + VL>200 +CD4>200 + VL>200 Mean Window Period (days) 259 205 595 245

  17. 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

  18. CDC Laboratory Limiting Antigen (LAg) Avidity Assay

  19. From CDC, Bharat Parekh Laboratory 2010 >> • 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) 2012 >>

  20. 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

  21. 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 • with AIDS • with low viral loads • False recent rate = 1.6% • Ongoing discussions on use

  22. 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

  23. 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

  24. Acknowledgments - I • Oliver Laeyendecker • Sue Eshleman • Bharat Parekh • Yen Duong • Andrea Kim • Joyce Neal • Buzz Prejean • Irene Hall • Charles Morrison • Paul Feldblum • KarineDube • Mike Busch • Alex Welte • Gary Murphy • Christine Rousseau • Txema Garcia-Calleja

  25. Acknowledgements HPTN Network Lab Susan Eshleman Matt Cousins Estelle Piwowar-Manning JHU HIV Specialty Lab Quinn Laboratory, NIAID Oliver Laeyendecker Jordyn Gamiel Andrew Longosz Amy Oliver Caroline Mullis Kevin Eaton Amy Mueller Johns Hopkins University MACS, ALIVE, Moore Clinic & Elite Suppressor Cohort Lisa Jacobson Joseph Margolick Greg Kirk Shruti Mehta Jacquie Astemborski Richard Moore Joel Blankson CEPHIA Gary Murphy Michal Busch Alex Welte Chris Pilcher UCLA Ron Brookmeyer Jacob Korikoff Thomas Coates Agnes Fammia HPTN 061 Kenneth Mayer Beryl Koblin HIVNET 001/1.1 Connie Celum Susan Buchbinder George Seage Haynes Sheppard HPTN 064 Sally Hodder Jessica Justman SCHARP Deborah Donnell Jim Hughes RHSP Ronald Gray Maria Wawer Tomas Lutalo Fred Nalagola PEPI TahaTaha Charles University, Prague Michal Kulich Arnošt Komárek MarekOmelka EXPLORE Beryl Koblin Margaret Chesney • CDC • Michele Owen • Bernard Branson • Bharat Parekh • Yen Duong • Andrea Kim • Connie Sexton FHI Charles Morrison Imperial College in London Tim Hallett Partner in Prevention Connie Celum University of Witwatersrand & SACEMA, South Africa Thomas McWalter Reshma Kassanjee U01/UM1-AI068613 1R01-AI095068 Study Teams and Participants

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