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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?
Uninfected Recently Infected Long-term Infected # Recently Infected Incidence estimate = # HIV Uninfected Average time of recent infection (window period) x Brookmeyer & Quinn AJE 1995

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

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 1st 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 % characterized as “recent” 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 % characterized as “recent” BED-CEIA: Does not converge to zero Cannot determine window period (average time classified as recently infected) 20% % % % % Duration of infection (years)

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

10 Comparison of HIV incidence Estimates
Study Analysis Estimated annual incidence (95% CI) HIVNET 001/001.1 Longitudinal 12-18 months 1.04% 0.70 – 1.55% MAA 18 months 0.97% 0.51 – 1.71% HPTN 064 Longitudinal months 0.24% % 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 Study Teams and Participants Quinn Laboratory
Thomas Quinn Jordyn Gamiel Amy Oliver Caroline Mullis Kevin Eaton Amy Mueller 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 HPTN Network Lab Susan Eshleman Matthew Cousins UCLA Ron Brookmeyer Jacob Konikoff SCHARP Deborah Donnell Jim Hughes HIVNET 001/1.1 Connie Celum Susan Buchbinder George Seage Haynes Sheppard CDC Michele Owen Bernard Branson Bharat Parekh Andrea Kim Connie Sexton U01/UM1-AI068613 1R01-AI095068 Study Teams and Participants

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

15 Comparison of cross-sectional incidence testing to known incidence
Longitudinal cohort Perform cross-sectional incidence testing Survey rounds 1 2 3 4 Compare the incidence estimate based on HIV seroconversion to the estimate based on cross-sectional testing using the multi-assay algorithm HIV incidence between survey rounds (HIV seroconversion) HIV- HIV+

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


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