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IDENTIFICATION OF FACTORS IMPACTING THE DETERMINATION OF HIV INCIDENCE IN NON-RESEARCH-BASED, CLINICAL POPULATIONS Sill, A.M. 1 ; Constantine, N.T. 2 ;

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Presentation on theme: "IDENTIFICATION OF FACTORS IMPACTING THE DETERMINATION OF HIV INCIDENCE IN NON-RESEARCH-BASED, CLINICAL POPULATIONS Sill, A.M. 1 ; Constantine, N.T. 2 ;"— Presentation transcript:

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, 3 Ministry of Health, Port of Spain, Trinidad; 4 Ministry of Health, Kingston, Jamaica; 5 Cornell University, New York, NY; 6 GHESKIO, Port au Prince, Haiti

2 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. Institute of Human Virology Division of Epidemiology and Prevention

3 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. Institute of Human Virology Division of Epidemiology and Prevention

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

5 Potentially Affected Parameters of the Incidence Calculation I= N recents 365 x x 100 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. All seropositive specimens not available for S/LS testing Nnegs + Nrecents w Institute of Human Virology Division of Epidemiology and Prevention

6 I= N recents 365 x x 100 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 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 Nnegs + Nrecents w Institute of Human Virology Division of Epidemiology and Prevention

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

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

9 Adjusting for Missing Specimens: (Assumptions) 1.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. Institute of Human Virology Division of Epidemiology and Prevention

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

11 Effect on % Missing Samples on Incidence Estimates % Available Specimens Uncorrected Incidence “Corrected” Incidence 10% % % % % % % % % %0.87 Institute of Human Virology Division of Epidemiology and Prevention

12 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. Institute of Human Virology Division of Epidemiology and Prevention

13 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. Recommendations/Conclusions Institute of Human Virology Division of Epidemiology and Prevention


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