Interpreting HIV diagnostic histories into infection time estimates : analytical framework and online tool

Simple item page
dc.contributor.authorGrebe, Eduarden_ZA
dc.contributor.authorFacente, Shelley N.en_ZA
dc.contributor.authorBingham, Jeremyen_ZA
dc.contributor.authorPilcher, Christopher D.en_ZA
dc.contributor.authorPowrie, Andrewen_ZA
dc.contributor.authorGerber, Jarryden_ZA
dc.contributor.authorPriede, Garethen_ZA
dc.contributor.authorChibawara, Trusten_ZA
dc.contributor.authorBusch, Michael P.en_ZA
dc.contributor.authorMurphy, Garyen_ZA
dc.contributor.authorKassanjee, Reshmaen_ZA
dc.contributor.authorWelte, Alexen_ZA
dc.date.accessioned2019-10-28T05:24:59Z
dc.date.available2019-10-28T05:24:59Z
dc.date.issued2019-10-26
dc.date.updated2019-10-27T06:28:41Z
dc.descriptionCITATION: Grebe, E., et al. 2019. Interpreting HIV diagnostic histories into infection time estimates : analytical framework and online tool. BMC Infectious Diseases, 19:894, doi:10.1186/s12879-019-4543-9.
dc.descriptionThe original publication is available at https://bmcinfectdis.biomedcentral.com
dc.description.abstractBackground: It is frequently of epidemiological and/or clinical interest to estimate the date of HIV infection or time-since-infection of individuals. Yet, for over 15 years, the only widely-referenced infection dating algorithm that utilises diagnostic testing data to estimate time-since-infection has been the ‘Fiebig staging’ system. This defines a number of stages of early HIV infection through various standard combinations of contemporaneous discordant diagnostic results using tests of different sensitivity. To develop a new, more nuanced infection dating algorithm, we generalised the Fiebig approach to accommodate positive and negative diagnostic results generated on the same or different dates, and arbitrary current or future tests – as long as the test sensitivity is known. For this purpose, test sensitivity is the probability of a positive result as a function of time since infection. Methods: The present work outlines the analytical framework for infection date estimation using subject-level diagnostic testing histories, and data on test sensitivity. We introduce a publicly-available online HIV infection dating tool that implements this estimation method, bringing together 1) curatorship of HIV test performance data, and 2) infection date estimation functionality, to calculate plausible intervals within which infection likely became detectable for each individual. The midpoints of these intervals are interpreted as infection time ‘point estimates’ and referred to as Estimated Dates of Detectable Infection (EDDIs). The tool is designed for easy bulk processing of information (as may be appropriate for research studies) but can also be used for individual patients (such as in clinical practice). Results: In many settings, including most research studies, detailed diagnostic testing data are routinely recorded, and can provide reasonably precise estimates of the timing of HIV infection. We present a simple logic to the interpretation of diagnostic testing histories into infection time estimates, either as a point estimate (EDDI) or an interval (earliest plausible to latest plausible dates of detectable infection), along with a publicly-accessible online tool that supports wide application of this logic. Conclusions: This tool, available at https://tools.incidence-estimation.org/idt/, is readily updatable as test technology evolves, given the simple architecture of the system and its nature as an open source project.en_ZA
dc.description.urihttps://bmcinfectdis.biomedcentral.com/articles/10.1186/s12879-019-4543-9
dc.description.versionPublisher's version
dc.format.extent10 pages
dc.identifier.citationGrebe, E., et al. 2019. Interpreting HIV diagnostic histories into infection time estimates : analytical framework and online tool. BMC Infectious Diseases, 19:894, doi:10.1186/s12879-019-4543-9
dc.identifier.issn1471-2334 (online)
dc.identifier.otherdoi:10.1186/s12879-019-4543-9
dc.identifier.urihttp://hdl.handle.net/10019.1/106734
dc.language.isoen_ZAen_ZA
dc.publisherBMC (part of Springer Nature)
dc.rights.holderAuthors retain copyright
dc.subjectHIV-positive personsen_ZA
dc.subjectHIV infections -- Diagnosis -- Datingen_ZA
dc.subjectHIV infections -- History -- Mathematical modelsen_ZA
dc.titleInterpreting HIV diagnostic histories into infection time estimates : analytical framework and online toolen_ZA
dc.typeArticle
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
grebe_interpreting_2019.pdf
Size:
592.32 KB
Format:
Adobe Portable Document Format
Description:
Download article
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
0 B
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Research Articles ((SACEMA) South African Centre for Epidemiological Modelling and Analysis )