Interpreting HIV diagnostic histories into infection time estimates : analytical framework and online tool
Date
2019-10-26
Journal Title
Journal ISSN
Volume Title
Publisher
BMC (part of Springer Nature)
Abstract
Background: 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.
Description
CITATION: 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.
The original publication is available at https://bmcinfectdis.biomedcentral.com
The original publication is available at https://bmcinfectdis.biomedcentral.com
Keywords
HIV-positive persons, HIV infections -- Diagnosis -- Dating, HIV infections -- History -- Mathematical models
Citation
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