Bias in estimates of HIV incidence based on the detuned assay: A proposed solution Robert S Remis, Robert WH Palmer, Janet M Raboud Department of Public Health Sciences, University of Toronto Mount Sinai Hospital, Toronto, Ontario STARHS satellite meeting Bangkok, Thailand, July 11, 2004

MOHLTC, Laboratories Branch, IMC – 2001 Background STARHS assay of HIV-positive specimens identifies recent infectionsSTARHS assay of HIV-positive specimens identifies recent infections Used to calculate HIV incidence density, a critical indicator usually difficult to measureUsed to calculate HIV incidence density, a critical indicator usually difficult to measure Numerator is discordant specimens; denominator is person-time from window periodNumerator is discordant specimens; denominator is person-time from window period But analysis using diagnostic specimens may be subject to strong testing biasBut analysis using diagnostic specimens may be subject to strong testing bias

MOHLTC, Laboratories Branch, IMC – 2001 Problem of bias In 2002, assessed sources, direction and strength of bias with diagnostic specimens (Remis et al, XIV ICA)In 2002, assessed sources, direction and strength of bias with diagnostic specimens (Remis et al, XIV ICA) For MSM, bias up to 7.3 fold, with plausible parameter values yielding bias of 2-3 foldFor MSM, bias up to 7.3 fold, with plausible parameter values yielding bias of 2-3 fold Principal source of bias “seroconversion effect” i.e. increased likelihood of HIV testing following infection due to seroconversion illness or to high risk exposurePrincipal source of bias “seroconversion effect” i.e. increased likelihood of HIV testing following infection due to seroconversion illness or to high risk exposure Quantified as proportion of subjects who test within 90 days after HIV infection (Psce)Quantified as proportion of subjects who test within 90 days after HIV infection (Psce)

MOHLTC, Laboratories Branch, IMC – 2001 Proposed solution #1 Incidence density calculated using STARHS assay with diagnostic specimens must be interpreted with great cautionIncidence density calculated using STARHS assay with diagnostic specimens must be interpreted with great caution Need to adjust calculated HIV incidence taking into account bias due to P sceNeed to adjust calculated HIV incidence taking into account bias due to P sce Originally proposed studies to measure knowledge of seroconversion illness and assess likelihood of immediate HIV testing under various scenariosOriginally proposed studies to measure knowledge of seroconversion illness and assess likelihood of immediate HIV testing under various scenarios

MOHLTC, Laboratories Branch, IMC – 2001 Not so fast Studies take time and cost moneyStudies take time and cost money Population studied may not be representative (may need many surveys to include different populations)Population studied may not be representative (may need many surveys to include different populations) Questions about likely HIV testing hypothetical; answers may not be validQuestions about likely HIV testing hypothetical; answers may not be valid No help with historical specimensNo help with historical specimens

MOHLTC, Laboratories Branch, IMC – 2001 Eureka! HIV incidence calculated from STARHS assay at different window periods provides empirical evidence of PsceHIV incidence calculated from STARHS assay at different window periods provides empirical evidence of Psce Slope of HIV incidence at different window periods is direct and quantitative indicator of strength of PsceSlope of HIV incidence at different window periods is direct and quantitative indicator of strength of Psce

MOHLTC, Laboratories Branch, IMC – 2001 Incidence calculated using different window periods with Vironostika assay, 2001

MOHLTC, Laboratories Branch, IMC – 2001 Determination of Psce and true incidence using empirical data Algebraic formula developed in 2002 expressed measured incidence density as a function of true incidence density, P sce and HIV testing parametersAlgebraic formula developed in 2002 expressed measured incidence density as a function of true incidence density, P sce and HIV testing parameters

MOHLTC, Laboratories Branch, IMC – 2001 Measured incidence as function of P sce and true incidence

MOHLTC, Laboratories Branch, IMC – 2001 Measured incidence as function of P sce and true incidence Where: I’ est = measured incidence density N = study population T obs = study period T win = detuned window period I true = true incidence densit I true = true incidence densit T test = mean inter-test interval P sce = proportion seroconverting <90 days after infection

MOHLTC, Laboratories Branch, IMC – 2001 Determination of Psce and true incidence using empirical data True incidence density is unknownTrue incidence density is unknown Can determine value of Psce and true incidence density by varying values through range to fit to measured incidence densityCan determine value of Psce and true incidence density by varying values through range to fit to measured incidence density Repeated at discrete values of window period and modeled incidence is fit to observed incidence by selecting values of true incidence density and Psce that minimize the differenceRepeated at discrete values of window period and modeled incidence is fit to observed incidence by selecting values of true incidence density and Psce that minimize the difference Minimize sum of squares of difference (goodness-of-fit)Minimize sum of squares of difference (goodness-of-fit)

MOHLTC, Laboratories Branch, IMC – 2001 Determination of Psce and true incidence using empirical data Programmed software in APLProgrammed software in APL Vary Psce from 0% to 50% in increments of 0.1%Vary Psce from 0% to 50% in increments of 0.1% Vary true HIV incidence from 0 to 20 per 100 person-years in increments of 0.01Vary true HIV incidence from 0 to 20 per 100 person-years in increments of 0.01 Program selects values of Psce and incidence for which sum of squares of difference between observed and modeled incidence is lowestProgram selects values of Psce and incidence for which sum of squares of difference between observed and modeled incidence is lowest

MOHLTC, Laboratories Branch, IMC – 2001 Psce, measured and true HIV incidence by year and health region among MSM, %4.6%10.5%21.1%0.4%6.1%3.9%0.0%14.7%TORONTO OTTAWA OTTAWA OTHER OTHER TrueincidenceMeasuredincidencePsce

MOHLTC, Laboratories Branch, IMC – 2001 Crude and adjusted HIV incidence among MSM and IDU, Toronto,

MOHLTC, Laboratories Branch, IMC – 2001 Crude and adjusted HIV incidence among MSM and IDU, Ottawa,

MOHLTC, Laboratories Branch, IMC – 2001 Summary of findings Adjustment of HIV incidence Goodness-of-fit approach allowed adjustment to remove testing biasGoodness-of-fit approach allowed adjustment to remove testing bias Modelled HIV incidence fit very well to observed HIV incidenceModelled HIV incidence fit very well to observed HIV incidence Data using specimens from diagnostic HIV testing should be presented with both crude and adjusted values of HIV incidenceData using specimens from diagnostic HIV testing should be presented with both crude and adjusted values of HIV incidence

MOHLTC, Laboratories Branch, IMC – 2001 Acknowledgements Ontario Laboratory Enhancement Study fundingOntario Laboratory Enhancement Study funding Ontario HIV Treatment NetworkOntario HIV Treatment Network Centre for Infectious Disease Prevention and Control, Health CanadaCentre for Infectious Disease Prevention and Control, Health Canada Neil Hershfield developed custom software to adjust HIV incidenceNeil Hershfield developed custom software to adjust HIV incidence