IDENTIFICATION OF FACTORS IMPACTING THE DETERMINATION OF HIV INCIDENCE IN

1. IDENTIFICATION OF FACTORS IMPACTING THE DETERMINATION OF HIV INCIDENCE IN NON-RESEARCH-BASED, CLINICAL POPULATIONS Sill, A.M.1; Constantine, N.T. 2; Charurat, M. 1; Blattner W.A. 1; Jack, N.J. 3; Figueroa, J.P. 4; Donastorg, Y. 5; Fitzgerald, D. 6; Pape, J.W. 7; Cleghorn, F.R.1 1 Institute of Human Virology, Baltimore, MD; 2 University of Maryland School of Medicine, Baltimore, MD, 3Ministry of Health, Port of Spain, Trinidad; 4 Ministry of Health, Kingston, Jamaica; 5Cornell University, New York, NY; 6GHESKIO, Port au Prince, Haiti

2. Institute of Human Virology Division of Epidemiology and Prevention Objectives • To generate HIV-1 incidence estimates on archived, seropositive samples using the sensitive/less sensitive assay (S/LS assay). • To adjust incidence estimates to correct for missing or imperfect data and to correct for seropositive samples that are unavailable for S/LS testing.

3. Institute of Human Virology Division of Epidemiology and Prevention Sources of error in incidence calculations in settings that have not been guided by a research protocol • Anonymous and unlinked cross-sectional surveys • that do not uniquely identify each individual; • Surveys that include HIV patients on ARV therapy; • Year to year comparisons of incidence rates in • populations with varying mixtures of risk factors; • Seasonal HIV incidence trends that may fluctuate • significantly; • Retrospectively banked specimens and data that are • incomplete in the absence of a structured • incidence surveillance protocol; • Poor sensitivity of HIV antibody tests or an • inconclusive testing algorithm.

4. Institute of Human Virology Division of Epidemiology and Prevention Calculating Incidence N recents 365 x 100 I= Nscreen - Nestabl w Where w = window period, Nscreened = total number screened Nestabl = number established HIV+ x OR 365 w x Ninc Nneg + 365 x Ninc w 2 Where w = window period, Ninc = number recent HIV infection, Nneg = number HIV seronegative. I=

5. Institute of Human Virology Division of Epidemiology and Prevention Potentially Affected Parameters of the Incidence Calculation All seropositive specimens not available for S/LS testing N recents 365 100 I= x Nnegs + Nrecents w x • May not be fully accounted for or is an inaccurate count • interrupted sampling frame • incomplete or truncated screening data • The degree of risk in populations included in summary incidence • estimates may vary from year to year, and from site to site, making • comparisons of incidence difficult.

6. Institute of Human Virology Division of Epidemiology and Prevention Adjustments to Incidence Estimates(Example 1) • Site must categorize all institutions into risk categories • (examples Blood Bank, STD, ANC, Hospital outpatient department) • Stratify estimates by risk category; • Examine % prevalent infections by risk category and question site about • its accuracy N recents 365 I= 100 x Nnegs + Nrecents w x • Found that a few facilities provided screening data only seropositive clients • Data from these sources were excluded from the analysis file when pooling • multi-source incidence estimates

7. N recents 365 100 I= Nneg + Nrecents w x x Institute of Human Virology Division of Epidemiology and Prevention Adjustments to Incidence Estimates(Example 2) • Assume that just 55% of all seropositive specimens are available for • S/LS testing • Total N screened = 1,000 clients = N recents detected by the S/LS I= = 1,000 x .55 (truncate to represent 55% random sampling of all negatives)

8. N recents 365 100 I= Nneg + Nrecents w x x Institute of Human Virology Division of Epidemiology and Prevention Adjustments to Incidence Estimates(Example 3) • Assume 15% of all seropositive specimens are Ab indeterminate • or inconclusive • Total N screened = 1,000 clients = N recents detected by the S/LS = N screened minus HIV+ minus Ab indeterminates or inconclusives

9. Institute of Human Virology Division of Epidemiology and Prevention Adjusting for Missing Specimens: (Assumptions) • That the proportion of the received seropositive are • representative of all seropositive samples that existed; • 2. Risk and timing since seroconversion is uniform across  • all seropositove samples hence the "corrected" number  • of recents can be extrapolated. ; • 3. That the prevalence rate does not differ significantly • from site to site and from year to year.

10. Corrected N recents 365 x 100 x I= Nneg + Nrecents 170 N recents 365 x x 100 95% CI= Nneg + Nrecents 183,162 Uncorrected (AS= % Available Specimens) N recents/AS 365 x 100 x I= Nneg + Nrecents/AS 170 95% CI= N recents/AS 365 x x 100 Nneg + Nrecents/AS 183,162 Institute of Human Virology Division of Epidemiology and Prevention Effect on % Missing Samples on Incidence Estimates

11. Institute of Human Virology Division of Epidemiology and Prevention Effect on % Missing Samples on Incidence Estimates

12. Institute of Human Virology Division of Epidemiology and Prevention Recommendations/Conclusions • Incidence surveys that include less than 50% of HIV+ • samples should present estimates with this caveat. • Adjustments can be made to the incidence estimates • that include S/LS testing on 50% or more of the • seropositive specimens, but not less. • Investigators could consider performing WB to help • resolve whether inconclusives are seroconverting or are • negative. This determination could have a profound • impact on incidence estimates.

13. Institute of Human Virology Division of Epidemiology and Prevention Recommendations/Conclusions • Such adjustments may provide a more • realistic incidence estimate in the absence • of complete sample sets; • Adjustments must be verified by statistically testing the likelihood that the adjustments do not confound the incidence estimate.