Probability of a false-negative HIV antibody test result during the window period: a tool for pre- and post-test counselling

Taylor, David; Durigon, M; Davis, Heather L.; Archibald, Chris; Konrad, B; Coombs, Daniel; Gilbert, Mark; Cook, Darrel; Krajden, Mel; Wong, Tom; Ogilvie, Gina

doi:10.1177/0956462414542987

Cited by 42 publications

(32 citation statements)

References 33 publications

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“…5 In addition, patients in the United States are not routinely screened for these viral infections and the surgeon's perception of "low risk" may be erroneous. Nearly, all (96%) of the surgeons were aware that the hospital authorities should be informed after any exposure, but only a minority (38%) of the surgeons had reported every exposure.…”

Section: Discussionmentioning

confidence: 99%

Percutaneous and Mucocutaneous Exposure Among Orthopaedic Surgeons

Maniar

Tawari

Suk

et al. 2015

Journal of Orthopaedic Trauma

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This study demonstrates that orthopaedic surgeons of all levels of training are at high risk of occupational exposure to blood-borne pathogens. Moreover, despite the level of training, the majority of surgeons do not follow the recommended steps, although we do not know the reasons for such behavior. Also, there is a low awareness of the significant risk of hepatitis transmission among orthopaedic surgeons treating a population with a high prevalence of undiagnosed hepatitis.

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Section: Discussionmentioning

confidence: 99%

Percutaneous and Mucocutaneous Exposure Among Orthopaedic Surgeons

Maniar

Tawari

Suk

et al. 2015

Journal of Orthopaedic Trauma

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“…We relied on two South African studies to assume that a mobile testing service could conduct m = 25 tests in a given zone on a given day; daily values ranging from 10 to 40 tests were considered in sensitivity analyses [11, 12]. We further assumed that individuals who are found to be uninfected return to the unobserved uninfected pool after 45 days, with values ranging from 10 to 90 days in the sensitivity analysis [13, 14]. Finally, we conducted the main analysis over 180 days (sensitivity analyses range, 90–365 days), reflecting our assumption that decision-makers might devote a half year to experimenting with new approaches to deploying HIV testing resources.…”

Section: Methodsmentioning

confidence: 99%

Bayesian adaptive algorithms for locating HIV mobile testing services

Gonsalves

Copple

Johnson

et al. 2018

BMC Med

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BackgroundWe have previously conducted computer-based tournaments to compare the yield of alternative approaches to deploying mobile HIV testing services in settings where the prevalence of undetected infection may be characterized by ‘hotspots’. We report here on three refinements to our prior assessments and their implications for decision-making. Specifically, (1) enlarging the number of geographic zones; (2) including spatial correlation in the prevalence of undetected infection; and (3) evaluating a prospective search algorithm that accounts for such correlation.MethodsBuilding on our prior work, we used a simulation model to create a hypothetical city consisting of up to 100 contiguous geographic zones. Each zone was randomly assigned a prevalence of undetected HIV infection. We employed a user-defined weighting scheme to correlate infection levels between adjacent zones. Over 180 days, search algorithms selected a zone in which to conduct a fixed number of HIV tests. Algorithms were permitted to observe the results of their own prior testing activities and to use that information in choosing where to test in subsequent rounds. The algorithms were (1) Thompson sampling (TS), an adaptive Bayesian search strategy; (2) Besag York Mollié (BYM), a Bayesian hierarchical model; and (3) Clairvoyance, a benchmarking strategy with access to perfect information.ResultsOver 250 tournament runs, BYM detected 65.3% (compared to 55.1% for TS) of the cases identified by Clairvoyance. BYM outperformed TS in all sensitivity analyses, except when there was a small number of zones (i.e., 16 zones in a 4 × 4 grid), wherein there was no significant difference in the yield of the two strategies. Though settings of no, low, medium, and high spatial correlation in the data were examined, differences in these levels did not have a significant effect on the relative performance of BYM versus TS.ConclusionsBYM narrowly outperformed TS in our simulation, suggesting that small improvements in yield can be achieved by accounting for spatial correlation. However, the comparative simplicity with which TS can be implemented makes a field evaluation critical to understanding the practical value of either of these algorithms as an alternative to existing approaches for deploying HIV testing resources.Electronic supplementary materialThe online version of this article (10.1186/s12916-018-1129-0) contains supplementary material, which is available to authorized users.

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“…In a recent study, both third and fourth generation HIV tests were reported to carry an error rate of 0Á01% (Taylor et al, 2015). There is little doubt that these groups do statistically have an increased chance to carry TTIs, a relationship proven with both correlation and causation [Centers for Disease Control and Prevention (CDC), 2015].…”

Section: Blood Donations: Justifying Blood Donor Restrictionsmentioning

confidence: 99%