Blog
About

13
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Seroconverting Blood Donors as a Resource for Characterising and Optimising Recent Infection Testing Algorithms for Incidence Estimation

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Introduction

          Biomarker-based cross-sectional incidence estimation requires a Recent Infection Testing Algorithm (RITA) with an adequately large mean recency duration, to achieve reasonable survey counts, and a low false-recent rate, to minimise exposure to further bias and imprecision. Estimating these characteristics requires specimens from individuals with well-known seroconversion dates or confirmed long-standing infection. Specimens with well-known seroconversion dates are typically rare and precious, presenting a bottleneck in the development of RITAs.

          Methods

          The mean recency duration and a ‘false-recent rate’ are estimated from data on seroconverting blood donors. Within an idealised model for the dynamics of false-recent results, blood donor specimens were used to characterise RITAs by a new method that maximises the likelihood of cohort-level recency classifications, rather than modelling individual sojourn times in recency.

          Results

          For a range of assumptions about the false-recent results (0% to 20% of biomarker response curves failing to reach the threshold distinguishing test-recent and test-non-recent infection), the mean recency duration of the Vironostika-LS ranged from 154 (95% CI: 96–231) to 274 (95% CI: 234–313) days in the South African donor population (n = 282), and from 145 (95% CI: 67–226) to 252 (95% CI: 194–308) days in the American donor population (n = 106). The significance of gender and clade on performance was rejected (p−value = 10%), and utility in incidence estimation appeared comparable to that of a BED-like RITA. Assessment of the Vitros-LS (n = 108) suggested potentially high false-recent rates.

          Discussion

          The new method facilitates RITA characterisation using widely available specimens that were previously overlooked, at the cost of possible artefacts. While accuracy and precision are insufficient to provide estimates suitable for incidence surveillance, a low-cost approach for preliminary assessments of new RITAs has been demonstrated. The Vironostika-LS and Vitros-LS warrant further analysis to provide greater precision of estimates.

          Related collections

          Most cited references 40

          • Record: found
          • Abstract: found
          • Article: not found

          Quantitative detection of increasing HIV type 1 antibodies after seroconversion: a simple assay for detecting recent HIV infection and estimating incidence.

          We have devised a simple enzyme immunoassay (EIA) that detects increasing levels of anti-HIV IgG after seroconversion and can be used for detecting recent HIV-1 infection. Use of a branched peptide that included gp41 immunodominant sequences from HIV-1 subtypes B, E, and D allowed similar detection of HIV-specific antibodies among various subtypes. Because of the competitive nature of the capture EIA, a gradual increase in the proportion of HIV-1-specific IgG in total IgG was observed for 2 years after seroconversion. This was in contrast to results obtained with the conventional EIA using the same antigen in solid phase, which plateaus soon after seroconversion. The assay was used to test 622 longitudinal specimens from 139 incident infections in the United States (subtype B) and in Thailand (subtypes B and E). The assay was also performed with an additional 8 M urea incubation step to assess the contribution of high-avidity antibodies. Normalized optical density (OD-n) was calculated (ODspecimen/ODcalibrator), using a calibrator specimen. An incremental analysis indicated that a cutoff of 1.0 OD-n and a seroconversion period of 160 days offered the best combination of sensitivity and specificity for classifying incident or long-term infections. The urea step increased the seroconversion period to 180 days with similar sensitivity and specificity. Separate analysis of B and E subtype specimens yielded the same optimal OD-n threshold and similar seroconversion periods. The assay was further validated in African specimens (subtypes A, C, and D) where the observed incidence was within 10% of the expected incidence. This assay should be useful for detecting recent HIV-1 infection and for estimating incidence among diverse HIV-1 subtypes worldwide.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            New testing strategy to detect early HIV-1 infection for use in incidence estimates and for clinical and prevention purposes.

            Differentiating individuals with early human immunodeficiency virus 1 (HIV-1) infection from those infected for longer periods is difficult but important for estimating HIV incidence and for purposes of clinical care and prevention. To develop and validate a serologic testing algorithm in which HIV-1-positive persons with reactive test results on a sensitive HIV-1 enzyme immunoassay (EIA) but nonreactive results on a less sensitive (LS) EIA are identified as having early infection. Diagnostic test and testing strategy development, validation, and application. Specimens were tested with both a sensitive HIV-1 EIA (3A11 assay) and a less sensitive modification of the same EIA (3A11-LS assay). For assay development: 104 persons seroconverting to HIV-1 comprising 38 plasma donors, 18 patients of a sexually transmitted disease clinic in Trinidad, and 48 participants in the San Francisco Men's Health Study (SFMHS); 268 men without the acquired immunodeficiency syndrome (AIDS) in the SFMHS who had been infected for at least 2.5 years; and 207 persons with clinical AIDS; for testing strategy validation: 488 men in the SFMHS from 1985 through 1990 and 1275449 repeat blood donors at 3 American Red Cross blood centers from 1993 through 1995; and for HIV-1 incidence estimates: 2717910 first-time blood donors. We retrospectively identified persons eligible for a study of early infection. Ability to identify early HIV infection. Estimated mean time from being 3A11 reactive/3A11-LS nonreactive to being 3A11 reactive/3A11-LS reactive was 129 days (95% confidence interval [CI], 109-149 days) [corrected]. Our testing strategy accurately diagnosed 95% of persons with early infection; however, 0.4% (1/268) of men with established infection and 2% (5/207) of persons with late-stage AIDS were misdiagnosed as having early HIV-1 infection. Average yearly incidence estimates in SFMHS subjects were 1.5% per year vs observed average incidence of 1.4 per 100 person-years. Incidence in repeat blood donors using the sensitive/less sensitive assay testing strategy was 2.95 per 100000 per year (95% CI, 1.14-6.53/100000) vs observed incidence of 2.60 per 100000 person-years (95% CI, 1.49-4.21/100000). Overall incidence in first-time blood donors was 7.18 per 100000 per year (95% CI, 4.51-11.20/100000) and did not change statistically significantly between 1993 and 1996. Use of the sensitive/less sensitive testing strategy alone would have identified all 17 persons with antibodies to HIV-1 eligible for a study of early HIV-1 infection and would have increased enrollment. The sensitive/less sensitive testing strategy provides accurate diagnosis of early HIV-1 infection, provides accurate estimates of HIV-1 incidence, can facilitate clinical studies of early HIV-1 infection, and provides information on HIV-1 infection duration for care planning.
              Bookmark
              • Record: found
              • Abstract: not found
              • Book: not found

              Principles of Statistical Inference

               D R Cox (2006)
                Bookmark

                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2011
                9 June 2011
                : 6
                : 6
                Affiliations
                [1 ]South African DST/NRF Centre for Epidemiological Modelling and Analysis (SACEMA), University of Stellenbosch, Stellenbosch, South Africa
                [2 ]School of Computational and Applied Mathematics, University of the Witwatersrand, Johannesburg, South Africa
                [3 ]Blood Systems Research Institute, San Francisco, California, United States of America
                [4 ]South African National Blood Service, Johannesburg, South Africa
                Scientific Support Office, American Red Cross, Gaithersburg, Maryland, United States of America
                University of Cape Town, South Africa
                Author notes

                Conceived and designed the experiments: MPB. Performed the experiments: MPB SMK MV SLS. Analyzed the data: RK AW TAM. Contributed reagents/materials/analysis tools: MPB SMK MV SLS AW TAM RK. Wrote the paper: RK AW TM.

                PONE-D-11-04352
                10.1371/journal.pone.0020027
                3111407
                21694760
                Kassanjee et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                Counts
                Pages: 8
                Categories
                Research Article
                Biology
                Computational Biology
                Population Modeling
                Infectious Disease Modeling
                Population Biology
                Epidemiology
                Infectious Disease Epidemiology
                Mathematics
                Statistics
                Biostatistics
                Statistical Methods
                Medicine
                Epidemiology
                Epidemiological Methods
                Infectious Disease Epidemiology
                Infectious Diseases
                Viral Diseases
                HIV
                HIV epidemiology
                Infectious Disease Modeling
                Non-Clinical Medicine
                Health Care Policy
                Screening Guidelines
                Public Health
                Health Screening

                Uncategorized

                Comments

                Comment on this article