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Cross-sectional HIV incidence estimation in an evolving epidemic.

ABSTRACT: The cross-sectional approach to HIV incidence estimation overcomes some of the challenges with longitudinal cohort studies and has been successfully applied in many settings around the world. However, the cross-sectional approach does rely on an initial training data set to develop and calibrate the statistical methods to be used in cross-sectional surveys. The problem addressed in this paper is that the initial training data set may, over time, not reflect the current target population of interest because of evolution of the epidemic. For example, the mismatch between the target population and the initial data set could occur because of increasing use of anti-retroviral therapy among HIV-infected persons throughout the world. We developed methods to adjust the initial training data set with the goal that the adjusted data sets better reflect the target population. These adjustment procedures could help avoid the time and expense of collecting a completely new training data set from the current target population. We report the results of a simulation study to evaluate the procedures. We applied the methods to a dataset of HIV subtype B infection. The adjustment procedures could be applicable in situations other than cross-sectional incidence estimation where complex statistical analyses are to be conducted using an initial data set but those results may not be directly transportable to a new target population of interest. The approach we have proposed could offer a practical and cost-effective way to apply cross-sectional incidence methods to new target populations as the epidemic evolves.

SUBMITTER: Morrison D

PROVIDER: S-EPMC6743486 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Cross-sectional HIV incidence estimation in an evolving epidemic.

Morrison Doug D Laeyendecker Oliver O Brookmeyer Ron R

Statistics in medicine 20190521 19

The cross-sectional approach to HIV incidence estimation overcomes some of the challenges with longitudinal cohort studies and has been successfully applied in many settings around the world. However, the cross-sectional approach does rely on an initial training data set to develop and calibrate the statistical methods to be used in cross-sectional surveys. The problem addressed in this paper is that the initial training data set may, over time, not reflect the current target population of inter ...[more]

PMID: 31115081

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Project description:IntroductionCross-sectional incidence testing is used to estimate population-level HIV incidence and measure the impact of prevention interventions. There are limited data evaluating the accuracy of estimates in settings where antiretroviral therapy coverage and levels of viral suppression are high. Understanding cross-sectional incidence estimates in these settings is important as viral suppression can lead to false recent test results. We compared the accuracy of multi-assay algorithms (MAA) for incidence estimation to that observed in the community-randomized HPTN 071 (PopART) trial, where the majority of participants with HIV infection were virally suppressed.MethodsHIV incidence was assessed during the second year of the study, and included only individuals who were tested for HIV at visits 1 and 2 years after the start of the study (2016-2017). Incidence estimates from three MAAs were compared to the observed incidence between years 1 and 2 (MAA-C: LAg-Avidity <2.8 ODn + BioRad Avidity Index <95% + VL >400 copies/ml; LAg+VL MAA: LAg-Avidity <1.5 ODn + VL >1000 copies/ml; Rapid+VL MAA: Asanté recent rapid result + VL >1000 copies/ml). The mean duration of recent infection (MDRI) used for the three MAAs was 248, 130 and 180 days, respectively.Results and discussionThe study consisted of: 15,845 HIV-negative individuals; 4406 HIV positive at both visits; and 221 who seroconverted between visits. Viral load (VL) data were available for all HIV-positive participants at the 2-year visit. Sixty four (29%) of the seroconverters and 3227 (72%) prevelant positive participants were virally supressed (<400 copies/ml). Observed HIV incidence was 1.34% (95% CI: 1.17-1.53). Estimates of incidence were similar to observed incidence for MAA-C, 1.26% (95% CI: 1.02-1.51) and the LAg+VL MAA, 1.29 (95% CI: 0.97-1.62). Incidence estimated by the Rapid+VL MAA was significantly lower than observed incidence (0.92%, 95% CI: 0.69-1.15, p<0.01).ConclusionsMAA-C and the LAg+VL MAA provided accurate point estimates of incidence in this cohort with high levels of viral suppression. The Rapid+VL significantly underestimated incidence, suggesting that the MDRI recommended by the manufacturer is too long or the assay is not accurately detecting enough recent infections.

Project description:BackgroundMulti-assay algorithms (MAAs) are used to estimate population-level HIV incidence and identify individuals with recent infection. Many MAAs use low viral load (VL) as a biomarker for long-term infection. This could impact incidence estimates in settings with high rates of early HIV treatment initiation. We evaluated the performance of two MAAs that do not include VL.MethodsSamples were collected from 219 seroconverters (infected < 1 year) and 4376 non-seroconverters (infected > 1 year) in the HPTN 071 (PopART) trial; 28.8% of seroconverter samples and 73.2% of non-seroconverter samples had VLs ≤ 400 copies/mL. Samples were tested with the Limiting Antigen Avidity assay (LAg) and JHU BioRad-Avidity assays. Antibody reactivity to two HIV peptides was measured using the MSD U-PLEX assay. Two MAAs were evaluated that do not include VL: a MAA that includes the LAg-Avidity assay and BioRad-Avidity assay (LAg + BR) and a MAA that includes the LAg-Avidity assay and two peptide biomarkers (LAg + PepPair). Performance of these MAAs was compared to a widely used MAA that includes LAg and VL (LAg + VL).ResultsThe incidence estimate for LAg + VL (1.29%, 95% CI: 0.97-1.62) was close to the observed longitudinal incidence (1.34% 95% CI: 1.17-1.53). The incidence estimates for the other two MAAs were higher (LAg + BR: 2.56%, 95% CI 2.01-3.11; LAg + PepPair: 2.84%, 95% CI: 1.36-4.32). LAg + BR and LAg + PepPair also misclassified more individuals infected > 2 years as recently infected than LAg + VL (1.2% [42/3483 and 1.5% [51/3483], respectively, vs. 0.2% [6/3483]). LAg + BR classified more seroconverters as recently infected than LAg + VL or LAg + PepPair (80 vs. 58 and 50, respectively) and identified ~ 25% of virally suppressed seroconverters as recently infected.ConclusionsThe LAg + VL MAA produced a cross-sectional incidence estimate that was closer to the longitudinal estimate than two MAAs that did not include VL. The LAg + BR MAA classified the greatest number of individual seroconverters as recently infected but had a higher false recent rate.

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Cross-sectional HIV incidence estimation in an evolving epidemic.

Publications

Cross-sectional HIV incidence estimation in an evolving epidemic.

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