Michael Tang, MD Infectious Disease Fellow Division of Infectious Diseases and Global Public Health Department of Medicine University of California, San Diego

1. HIV & Global Health Rounds
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presentations by infectious disease and global public health clinicians,
physicians, and researchers. The goal of these presentations is to
provide the most current research, clinical practices, and trends in HIV,
HBV, HCV, TB, and other infectious diseases of global significance.
The slides from the HIV & Global Health Rounds presentation that you
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presenter’s express permission.

2. HIGH Rounds:
Incidence Testing in HIV
Michael Tang, MD
February 14, 2020

3. Disclosures
• None

4. Overview
• Why is incidence important?
• Methods for incidence testing
• Repeated testing & Serial Prevalence
• CD4 Depletion Model
• Avidity Testing
• Viral Diversity
• Comparisons within the Primary Infection Resource Consortium
(PIRC) group
• Future directions

5. Definitions
• Acute HIV infection: earliest phase of HIV infection
• Prior to seroconversion generally depending on positive p24 antigen and
positive NAT test
• Duration depends on the diagnostic assay used
• Early HIV infection: ≤6 months from infection
• Recent HIV infection ≤12 months from infection
• Prevalent HIV infection: >12 months in duration
• Incidence: number of new infections (both diagnosed and
undiagnosed) in a given period of time

6. Why is incidence important?

7. Why is incidence important?
https://www.cdc.gov/hiv/pdf/library/reports/surveillance/cdc-hiv-surveillance-supplemental-report-vol-24-1-infographic.pdf

8. Ending the HIV Epidemic

9. Ending the HIV Epidemic
• More than 50% of new HIV
diagnoses occurred in 48
counties, DC and San Juan,
Puerto Rico.
• These high burden jurisdictions
are charged with leading the
charge in decreasing HIV
incidence.

10. Why is Incidence important?
• Generally it is our outcome measure of choice for interventional
studies
• Both the 5 year and 10 year goal target new infections or incidence as the
outcome
• Identifying infections as incident or prevalent allows appropriate monitoring
HIV prevention efforts and allocation of resources.
• Clinical management includes rapid implementation of ART,
independent of CD4 or stage of HIV
• Diagnosis numbers can lag behind incidence and are influenced by
testing rates and diagnosis delays

11. Methods of measuring incidence
• Cohort studies
• Generally considered the gold standard
• Serial Prevalence
• CD4 Depletion Model
• Currently used by the CDC
• Avidity Testing
• Viral Diversity

12. Cohort study
• Longitudinal follow up of cohorts without HIV infection and frequent
retesting to determine when people acquire a new infection.
• Expensive, logistically difficult and time consuming
• Monitoring migration in and out of populations can be difficult
• These populations are not always representative of the overall
population we want to monitor
• Participants often receive risk reduction counseling, etc.

13. Example of Cohort study
• The Africa Health Research
Institute performs
surveillance 2x/year on 2
sites within the rural
KwaZulu-Natal region
• About 65-75% of eligible HIV
negative persons
participated in surveillance.
Vandormael et al. The state of the HIV epidemic in rural KwaZulu-Natal, South Africa: a novel application of disease metrics to assess
trajectories and highlight areas for intervention. International Journal of Epidemiology. January 2020:dyz269

14. Serial Prevalence
• Involves measuring prevalence of diagnosis at 2 time points, and
estimating incidence
• Uses the formula to calculate incidence
• Requires knowledge of mortality rates, migration. Also noted to be
expensive to perform, and unlikely to provide good information about
current trends in HIV incidence
• Used in the late 1980s when estimating incidence in the active duty
military and blood donors to set minimum estimates of HIV incidence
in general population
Karon et al. HIV prevalence estimates and AIDS Case Projections for the US. MMWR Nov 1990
Brookmeyer R. Measuring the HIV/AIDS Epidemic: Approaches and Challenges. Epidemiologic Reviews. April 2010

15. CD4 Depletion Model
• In 2011, Dr. Lodi and the CASCADE collaboration developed a CD4
depletion model to estimate duration from seroconversion to various
CD4 cut offs
• Utilized 25 cohorts of individuals with well-estimated dates of
seroconversion from Europe, Australia, Canada and sub-Saharan
Africa, with ~18,500 individuals included in the analysis
• Used a linear mixed model effect to estimate time to different CD4
cut offs from infection
• Rick Song, and the CDC used this depletion model to extrapolate
duration of infection from the initial CD4 count

16. Satcher Johnson, Anna & Ruiguang Song. (2017,
October 3). Estimating HIV incidence, prevalence,
and undiagnosed infection in the United States
[Webinar]. In Prevention Science and Methodology
Group Virtual Grand Rounds.

17. 𝐶𝐷4 𝑇 = 𝐼𝑛𝑡𝑒𝑟𝑐𝑒𝑝𝑡 + 𝑆𝑙𝑜𝑝𝑒 𝑥 𝑇 + 𝐸𝑟𝑟𝑜𝑟

18. CD4 Depletion Model
• Using the calculated diagnosis date from above, able to determine a
diagnosis delay distribution and estimate % of infections from each
year that are still undiagnosed
• Diagnosis delay distribution is used to calculate population incidence
• Also able to calculate prevalence from cumulative incidence and
cumulative mortality rates

19. CD4 model – Advantages and Disadvantages
• Convenient way to estimate
incidence, prevalence, %
undiagnosed
• Inexpensive
• Relies on accuracy of CD4
depletion model (developed in
2011)
• Known issues with acute/early
infections, but felt that since these
are a small proportion
• Given changes in our treatment of
CD4, unlikely to be able to
improve accuracy of CD4
depletion model

20. Biomarker/Avidity testing
• Multiple types of assays, but the general principle is the same:
proportion of HIV specific antibody binding increases as time from
infection increases
• Values below a cutoff indicate a recent infection
• Important test characteristics include Mean Duration of Recent
Infection (MDRI) and False Recency Rate (FRR).
• Goal for MDRI is 1 year, although the longer that is possible, the more useful
the assay is.
• Goal for FRR is 0%, but generally shoot for <2%.

21. WHO Working Group on HIV Incidence Measurement Data Use Group. Boston, MA, USA: World Health Organization; 2018

22. Biomarker/Avidity testing - Limitations
• Misclassification: elite controllers, suppressed on ART, low CD4
(although less of an issue with newer assays)
• MDRI and FRR have some variability depending on the population
• Each of the recency tests were created with subtype B in mind (other
than BED), and characteristics of test changes with different subtypes

23. Effect of HIV-1 Subtype on Biomarker Assays
• Both MDRI and FRR will change as a result of different biomarker assays.
Kassanjee R, et al. Independent assessment of candidate HIV incidence assays on specimens in the CEPHIA repository: AIDS. 2014;28(16):2439-2449.

24. Viral diversity
• HIV infection occurs secondary to a single virus with viral diversity
increasing during infection
• Able to use genotypic resistance tests to evaluate viral diversity
• Generally, these are done via bulk sequencing. Nucleotides are
reported out at each position when >80% consensus is present. If not,
position is labeled ambiguous.
• The percentage of these ambiguous positions increases as HIV
infection progresses
• Based on Swiss cohort, cut off of 0.5% ambiguity was determined
with 87% sensitivity, and 70% specificity for <1 year of infection
Kouyos RD et al. Ambiguous Nucleotide Calls From Population-based Sequencing of HIV-1 are a Marker for Viral Diversity and the Age of Infection. Clinical
Infectious Diseases. 2011;52(4):532-539. doi:10.1093/cid/ciq164

25. Viral Diversity – Limitations
• Potential confounding with super infection
• Unable to use for people on or previously on ART, or those who a
genotype cannot be successfully performed.
• Costly to perform unless genotyping is needed

26. SD Primary Infection Resource Consortium
• 834 ART naïve, newly diagnosed HIV-1 individuals enrolled between
June 1996-July 2019
• Clinical data, including CD4+ counts were collected at baseline (Day
0), weeks 4, 12, 24, and every 24 weeks thereafter
• Persons with acute infection had additional CD4+ and VL measures at
weeks 2 and 8

27. https://www.hiv.uw.edu/go/screening-diagnosis/acute-recent-early-hiv/core-concept/all

28. PIRC Algorithm
• Acute – NAT positive, WB neg/indeterminate
• Early infection – stratified based on recency assay level
• Early infection – stratified based on last negative HIV test
• Prevalent infections – Positive WB, no previous test within 365 days

29. Goals
• Overall concerns – the CD4 depletion model may work well enough in
large population sizes (entire US population), concerned about its
ability to monitor changes at county level where there is only 200-
2000 new diagnoses each year
• Compare PIRC algorithm with the CD4 depletion model for
determining duration of infection
• Look at overall population as well as subpopulation of prevalent cases
• For prevalent cases, utilize patients who had longitudinal follow up >1 year
from their estimated date of infection prior to starting ART
• Determine if there is value in viral diversity as a marker for recency

30. Number of Participants 702
Gender Identity
Male sex - no. (%) 676 (96%)
Female sex - no. (%) 24 (3%)
Trans Male to Female - no. (%) 2 (0%)
Ethnicity - no. (%)
White 389 (57%)
Black 39 (6%)
Hispanic 205 (30%)
Other 23 (3%)
Age at presentation (years)
Median (IQR) 32.5 (26-40)
VL at entry
Median log10 copies/mL (IQR) 4.95 (4.11-5.69)
Pre-ART Peak VL
Median log10 copies/mL (IQR) 5.39 (4.74-5.94)
CD4 count at entry
Median cells/mm3 (IQR) 505 (378-652)
Duration of follow up without
treatment
Median days (IQR) 50 (12-232)
Transmission Risk Factor no. (%)
MSM 625 (89%)
MSM+IDU 27 (4%)
IDU 3 (0%)
Hetero Male 21 (3%)
Other/Unknown 26 (4%)

31. Estimated duration of infection at diagnosis
R2=0.017
N=702

32. PIRC DOI - CD4 DOI
Years
Mean -1.9 years (95% CI -2.1, -1.7),
median -0.54 years, p<0.0001

33. Estimated Duration of Infection in Prevalent Infections
R2 = 0.00058
N=159

34. Difference in duration of infection (PIRC-CD4)
Years
Mean difference 0.04, 95% CI -0.54-0.62, SD 3.7 years
P=0.89

35. PIRC vs CD4 Model 2x2 table
Count PIRC-Incident PIRC-Prevalent Total
CD4-Incident 270 113 383
CD4-Prevalent 262 185 447
Total 532 298 830

36. PIRC vs CD4 Model Sensitivity
Measure Estimate Lower 95% CI Upper 95% CI
Prevalence 0.641 0.608 0.673
Sensitivity 0.508 0.465 0.550
Specificity 0.621 0.565 0.674
PPV 0.705 0.657 0.748
NPV 0.414 0.369 0.460

37. PIRC vs Viral Diversity 2x2 table
Count PIRC-Incident PIRC-Prevalent Total
Viral Diversity-
Incident
531 53 584
Viral Diversity-
Prevalent
160 77 237
Total 691 130 821

38. Viral Diversity Sensitivity
Measure Estimate Lower 95% CI Upper 95% CI
Prevalence 0.842 0.815 0.865
Sensitivity 0.768 0.736 0.798
Specificity 0.592 0.506 0.673
PPV 0.909 0.883 0.930
NPV 0.325 0.268 0.387

39. Summary of findings
• CD4 depletion model performs poorly in estimating duration of
infection for both recent and prevalent cases
• When simplified to a binary evaluation (incident vs prevalent), sensitivity and
specificity were both poor
• Viral diversity has some value in identifying incident infections, but
has a high FRR (>40%), and relatively low concordance with the PIRC
algorithm (74%)

40. Limitations
• Cohort of primarily recent infections
• No method to validate the PIRC method of incidence (no gold
standard)
• None of the recency assays are FDA approved – cannot be used
outside of research studies
• Recency assays are strain specific
• Migration of people in and out of the population

41. Next steps
• Feasibility of implementation
• Low hanging fruit: Collecting data on acute infections and negative tests
• Use of recency testing on remnant samples
• Further development of recency tests to reach benchmarks
• Evaluation of multi-assay algorithms to decrease the FRR
• Look at CD4 response to ART as a potential predictor of incidence

42. Acknowledgements
• Susan Little
• Sanjay Mehta
• Christy Anderson
• Felix Torres
• Participants in research studies
• AVRC staff
• ID program support

43. CDC Testing Algorithm