Yale University

Stochastic models to demonstrate the effect of motivated testing on HIV incidence estimates using the serological testing algorithm for recent HIV seroconversion (STARHS).

TitleStochastic models to demonstrate the effect of motivated testing on HIV incidence estimates using the serological testing algorithm for recent HIV seroconversion (STARHS).
Publication TypeJournal Article
Year of Publication2010
AuthorsWhite, Edward W., Thomas Lumley, Steven M. Goodreau, Gary Goldbaum, and Stephen E. Hawes
JournalSexually transmitted infections
Volume86
Issue7
Pagination506-11
Date Published2010 Dec
ISSN1472-3263
KeywordsAIDS Serodiagnosis, Algorithms, Bias (Epidemiology), Enzyme-Linked Immunosorbent Assay, HIV Seropositivity, Homosexuality, Male, Humans, Incidence, Male, Monte Carlo Method, Motivation, Patient Acceptance of Health Care, Random Allocation, Sexual Partners, Stochastic Processes, Time Factors
AbstractTo produce valid seroincidence estimates, the serological testing algorithm for recent HIV seroconversion (STARHS) assumes independence between infection and testing, which may be absent in clinical data. STARHS estimates are generally greater than cohort-based estimates of incidence from observable person-time and diagnosis dates. The authors constructed a series of partial stochastic models to examine whether testing motivated by suspicion of infection could bias STARHS.
DOI10.1136/sti.2009.037481
Alternate JournalSex Transm Infect

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