Estimation of delay to diagnosis and incidence in HIV using indirect evidence of infection dates.

ABSTRACT:

Background

Minimisation of the delay to diagnosis is critical to achieving optimal outcomes for HIV patients and to limiting the potential for further onward infections. However, investigation of diagnosis delay is hampered by the fact that in most newly diagnosed patients the exact timing of infection cannot be determined and so inferences must be drawn from biomarker data.

Methods

We develop a Bayesian statistical model to evaluate delay-to-diagnosis distributions in HIV patients without known infection date, based on viral sequence genetic diversity and longitudinal viral load and CD4 count data. The delay to diagnosis is treated as a random variable for each patient and their biomarker data are modelled relative to the true time elapsed since infection, with this depende

SUBMITTER: Stirrup OT 

PROVIDER: S-EPMC6020319 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature