Seroconverting blood donors as a resource for characterising and optimising recent infection testing algorithms for incidence estimation

Simple item page
dc.contributor.authorKassanjee, Reshmaen_ZA
dc.contributor.authorWelte, Alexen_ZA
dc.contributor.authorMcWalter, Thomas A.en_ZA
dc.contributor.authorKeating, Sheila M.en_ZA
dc.contributor.authorVermeulen, Marionen_ZA
dc.contributor.authorStramer, Susan L.en_ZA
dc.contributor.authorBusch, Michael P.en_ZA
dc.date.accessioned2011-10-13T16:59:25Z
dc.date.available2011-10-13T16:59:25Z
dc.date.issued2011-06-09
dc.descriptionCITATION: Kassanjee, R. et al. 2011. Seroconverting blood donors as a resource for characterising and optimising recent infection testing algorithms for incidence estimation. PLoS ONE, 6(6): e20027, doi:10.1371/journal.pone.0020027.
dc.descriptionThe original publication is available at http://journals.plos.org/plosone
dc.description.abstractIntroduction: 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. © 2011 Kassanjee et al.
dc.description.versionPublisher's version
dc.format.extent8 pages
dc.identifier.citationKassanjee, R. et al. 2011. Seroconverting blood donors as a resource for characterising and optimising recent infection testing algorithms for incidence estimation. PLoS ONE, 6(6): e20027, doi:10.1371/journal.pone.0020027.
dc.identifier.issn1932-6203 (online)
dc.identifier.otherdoi:10.1371/journal.pone.0020027
dc.identifier.urihttp://hdl.handle.net/10019.1/17100
dc.language.isoen
dc.publisherPublic Library of Science
dc.rights.holderAuthors retain copyright
dc.subjectBlood donorsen_ZA
dc.subjectRecent Infection Testing Algorithmen_ZA
dc.subjectAlgorithmsen_ZA
dc.titleSeroconverting blood donors as a resource for characterising and optimising recent infection testing algorithms for incidence estimationen_ZA
dc.typeArticle
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
kassanjee_seroconverting_2011.PDF
Size:
613.81 KB
Format:
Adobe Portable Document Format
Description:
Download article
Download
Collections
Research Articles ((SACEMA) South African Centre for Epidemiological Modelling and Analysis )