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HIV incidence determination in clade B epidemics: A multi-assay approach Oliver Laeyendecker, Brookmeyer R, Cousins MM, Mullis CE, Konikoff J, Donnell.

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Presentation on theme: "HIV incidence determination in clade B epidemics: A multi-assay approach Oliver Laeyendecker, Brookmeyer R, Cousins MM, Mullis CE, Konikoff J, Donnell."— Presentation transcript:

1 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

2 How do you measure HIV incidence in a cross-sectional cohort? HIV Uninfected Recently Infected Long-term Infected Incidence estimate # Recently Infected Average time of recent infection (window period) = x # HIV Uninfected Brookmeyer & Quinn AJE 1995

3 Problem: Infinite time recently infected and regression to recently infected HIV Uninfected Recently Infected Long-term Infected Incidence estimate # Recently Infected Average time of recent infection = x # HIV Uninfected ? ?

4 How to find the recently infected people

5 Development of a multi-assay algorithm > 200 cells / ul < 1.0 OD-n > 400 copies / ml < 80% Classified as recently infected CD4 cell count BED CEIA Avidity HIV viral load 200 cells / ul 1.0 OD-n 80% 400 copies/ ml Stop

6 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 1 st positive test Includes individuals with AIDS, viral suppression, exposed to ARVs Longitudinal cohorts HIV001 HPTN 064

7 Proportion classified as recent None of 500 samples from individuals infected 8+ years (Johns Hopkins HIV Clinical Practice Cohort) were misclassified as recent using the multi-assay algorithm

8 BED-CEIA Duration of infection (years) 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 BED-CEIA: Does not converge to zero Cannot determine window period (average time classified as recently infected) 20% 40% 60% 80% 100% % characterized as recent

9 Duration of infection (years) BED-CEIA vs. Multi Assay Algorithm Multi-assay algorithm : Does converge to zero Window period: 141 days (95% CI: days) 20% 40% 60% 80% 100% % characterized as recent BED-CEIA: Does not converge to zero Cannot determine window period (average time classified as recently infected) 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 MAA BED

10 Comparison of HIV incidence Estimates StudyAnalysisEstimated annual incidence (95% CI) HIVNET 001/001.1 Longitudinal months 1.04%0.70 – 1.55% MAA 18 months 0.97%0.51 – 1.71% HPTN 064 Longitudinal 6-12 months 0.24% % MAA 12 months 0.13%0.01 – 0.76% Eshleman (2012) In Press JID Laeyendecker (2012) Submitted

11 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 multi- assay algorithm are nearly identical to estimates based on HIV seroconversion We are now determining the optimal cut-off values for the multi-assay algorithm

12 Acknowledgements HPTN Network Lab Susan Eshleman Matthew Cousins CDC Michele Owen Bernard Branson Bharat Parekh Andrea Kim Connie Sexton UCLA Ron Brookmeyer Jacob Konikoff Quinn Laboratory Thomas Quinn Jordyn Gamiel Amy Oliver Caroline Mullis Kevin Eaton Amy Mueller SCHARP Deborah Donnell Jim Hughes 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 Study Teams and Participants HIVNET 001/1.1 Connie Celum Susan Buchbinder George Seage Haynes Sheppard U01/UM1-AI R01-AI095068

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14 Theoretical framework for cross sectional incidence testing Assay OutcomeTime Infected Individual Time Varying AIDS Antiviral Treatment Population Stage of the epidemic Access to ARVs Individual Fixed Age, Race, Gender Route of infection Geography Infecting subtype Viral load set-point

15 Perform cross-sectional incidence testing Comparison of cross-sectional incidence testing to known incidence Longitudinal cohort Survey rounds 1234 HIV incidence between survey rounds (HIV seroconversion) HIV- HIV+ Compare the incidence estimate based on HIV seroconversion to the estimate based on cross-sectional testing using the multi-assay algorithm

16 Why a Bigger Window is Better Incidence (percent/ year) Population needed to screen to find ten recently infected individuals Window period 21 days 45 days 141 days 365 days


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