Use of a Multifaceted Approach to Analyze HIV Incidence in a Cohort Study of Women in the United States: HIV Prevention Trials Network 064 Study

Eshleman, Susan H.; Hughes, James P.; Laeyendecker, Oliver; Wang, Jing; Brookmeyer, Ron; Johnson-Lewis, LeTanya; Mullis, Caroline E.; Hackett, John; Vallari, Ana; Justman, Jessica; Hodder, Sally

doi:10.1093/infdis/jis658

Cited by 44 publications

(50 citation statements)

References 18 publications

(29 reference statements)

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“…1, MAA no. 3) was used to estimate annual incidence in three clinical cohorts (HPTN 064, HIVNET 001, and HPTN 064; see Materials and Methods) (8,28,32). Those HIV incidence estimates were compared to the incidence estimates obtained using the optimized MAAs that did not include the HRM diversity assay (Fig.…”

Section: Resultsmentioning

confidence: 99%

“…a Shown are longitudinal and cross-sectional incidence estimates for three cohort studies. b Longitudinal HIV incidence estimates were calculated as the number of seroconversion events divided by the number of person-years of follow-up (8,28,32). CI, confidence interval.…”

Section: Discussionmentioning

confidence: 99%

“…The overall HIV incidence in the HPTN 064 study was 0.32% (95% CI, 0.14 to 0.74%) (27). In that study, cross-sectional incidence was assessed using a MAA that included the BED-CEIA, the Bio-Rad Avidity assay, CD4 cell count, and HIV viral load (8). A total of 38 women were identified with HIV-infection in that study.…”

Section: Discussionmentioning

confidence: 99%

“…The shadow measures the time period prior to sample collection that is being assessed by a given MAA (6). A 4-assay MAA was developed that provides accurate HIV incidence estimates in cohorts from the United States (4,7,8), where most infections are caused by subtype B HIV (9). This MAA includes CD4 cell count, the BED capture enzyme immunoassay (BED-CEIA) (10), the Bio-Rad Avidity assay (11), and viral load.…”

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confidence: 99%

“…(Some HRM diversity data have been presented previously in abstract form [20]; the serologic assay, viral load, and CD4 cell count data that were used to develop MAAs are described in previous reports [4,7], and cross-sectional incidence estimates were generated previously using different methods [8,21]. )…”

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confidence: 99%

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HIV Diversity as a Biomarker for HIV Incidence Estimation: Including a High-Resolution Melting Diversity Assay in a Multiassay Algorithm

et al. 2014

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pMultiassay algorithms (MAAs) can be used to estimate cross-sectional HIV incidence. We previously identified a robust MAA that includes the BED capture enzyme immunoassay (BED-CEIA), the Bio-Rad Avidity assay, viral load, and CD4 cell count. In this report, we evaluated MAAs that include a high-resolution melting (HRM) diversity assay that does not require sequencing. HRM scores were determined for eight regions of the HIV genome (2 in gag, 1 in pol, and 5 in env). The MAAs that were evaluated included the BED-CEIA, the Bio-Rad Avidity assay, viral load, and the HRM diversity assay, using HRM scores from different regions and a range of region-specific HRM diversity assay cutoffs. The performance characteristics based on the proportion of samples that were classified as MAA positive by duration of infection were determined for each MAA, including the mean window period. The cross-sectional incidence estimates obtained using optimized MAAs were compared to longitudinal incidence estimates for three cohorts in the United States. The performance of the HRM-based MAA was nearly identical to that of the MAA that included CD4 cell count. The HRM-based MAA had a mean window period of 154 days and provided cross-sectional incidence estimates that were similar to those based on cohort follow-up. HIV diversity is a useful biomarker for estimating HIV incidence. MAAs that include the HRM diversity assay can provide accurate HIV incidence estimates using stored blood plasma or serum samples without a requirement for CD4 cell count data.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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HIV Diversity as a Biomarker for HIV Incidence Estimation: Including a High-Resolution Melting Diversity Assay in a Multiassay Algorithm

et al. 2014

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Identification and validation of a multi‐assay algorithm for cross‐sectional HIV incidence estimation in populations with subtype C infection

et al. 2018

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IntroductionCross‐sectional methods can be used to estimate HIV incidence for surveillance and prevention studies. We evaluated assays and multi‐assay algorithms (MAAs) for incidence estimation in subtype C settings.MethodsWe analysed samples from individuals with subtype C infection with known duration of infection (2442 samples from 278 adults; 0.1 to 9.9 years after seroconversion). MAAs included 1‐4 of the following assays: Limiting Antigen Avidity assay (LAg‐Avidity), BioRad‐Avidity assay, CD4 cell count and viral load (VL). We evaluated 23,400 MAAs with different assays and assay cutoffs. We identified the MAA with the largest mean window period, where the upper 95% confidence interval (CI) of the shadow was <1 year. This MAA was compared to the LAg‐Avidity and BioRad‐Avidity assays alone, a widely used LAg algorithm (LAg‐Avidity <1.5 OD‐n + VL >1000 copies/mL), and two MAAs previously optimized for subtype B settings. We compared these cross‐sectional incidence estimates to observed incidence in an independent longitudinal cohort.ResultsThe optimal MAA was LAg‐Avidity <2.8 OD‐n + BioRad‐Avidity <95% + VL >400 copies/mL. This MAA had a mean window period of 248 days (95% CI: 218, 284), a shadow of 306 days (95% CI: 255, 359), and provided the most accurate and precise incidence estimate for the independent cohort. The widely used LAg algorithm had a shorter mean window period (142 days, 95% CI: 118, 167), a longer shadow (410 days, 95% CI; 318, 491), and a less accurate and precise incidence estimate for the independent cohort.ConclusionsAn optimal MAA was identified for cross‐sectional HIV incidence in subtype C settings. The performance of this MAA is superior to a testing algorithm currently used for global HIV surveillance.

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Statistical considerations for cross‐sectional HIV incidence estimation based on recency test

Gao

Bannick

2022

Statistics in Medicine

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Longitudinal cohorts to determine the incidence of HIV infection are logistically challenging, so researchers have sought alternative strategies. Recency test methods use biomarker profiles of HIV-infected subjects in a cross-sectional sample to infer whether they are "recently" infected and to estimate incidence in the population. Two main estimators have been used in practice: one that assumes a recency test is perfectly specific, and another that allows for false-recent results. To date, these commonly used estimators have not been rigorously studied with respect to their assumptions and statistical properties. In this article, we present a theoretical framework with which to understand these estimators and interrogate their assumptions, and perform a simulation study and data analysis to assess the performance of these estimators under realistic HIV epidemiological dynamics. We find that the snapshot estimator and the adjusted estimator perform well when their corresponding assumptions hold. When assumptions on constant incidence and recency test characteristics fail to hold, the adjusted estimator is more robust than the snapshot estimator. We conclude with recommendations for the use of these estimators in practice and a discussion of future methodological developments to improve HIV incidence estimation via recency test.

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Use of a Multifaceted Approach to Analyze HIV Incidence in a Cohort Study of Women in the United States: HIV Prevention Trials Network 064 Study

Abstract: NCT00995176.

Cited by 44 publications

References 18 publications

HIV Diversity as a Biomarker for HIV Incidence Estimation: Including a High-Resolution Melting Diversity Assay in a Multiassay Algorithm

HIV Diversity as a Biomarker for HIV Incidence Estimation: Including a High-Resolution Melting Diversity Assay in a Multiassay Algorithm

Identification and validation of a multi‐assay algorithm for cross‐sectional HIV incidence estimation in populations with subtype C infection

Statistical considerations for cross‐sectional HIV incidence estimation based on recency test

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