HIV antiretroviral therapy and prevention use in US blood donors: a new blood safety concern
Bibliographic record
Abstract
Antiretroviral therapy (ART) to treat and pre-exposure prophylaxis (PrEP) to prevent HIV infection are effective tools to help end the HIV epidemic. However, their use could affect HIV transfusion-transmission risk. Three different ART/PrEP prevalence analyses in blood donors were conducted. First, blood samples from HIV-positive and a comparison group of infection-nonreactive donors were tested under blind using liquid chromatography-tandem mass spectrometry for ART. Second, blood donor samples from infection-nonreactive, 18- to 45-year-old, male, first-time blood donors in 6 US locations were tested for emtricitabine and tenofovir. Third, in men who have sex with men (MSM) participating in the 2017 Centers for Disease Control and Prevention National HIV Behavioral Surveillance (NHBS) from 5 US cities, self-reported PrEP use proximate to donation was assessed. In blind testing, no ART was detected in 300 infection-nonreactive donor samples, but in 299 HIV confirmed-infected donor samples, 46 (15.4%; 95% confidence interval [CI], 11.5% to 20.0%) had evidence of ART. Of the 1494 samples tested from first-time male donors, 9 (0.6%; 95% CI, 0.03% to 1.1%) had tenofovir and emtricitabine. In the NHBS MSM survey, 27 of 591 respondents (4.8%; 95% CI, 3.2% to 6.9%) reported donating blood in 2016 or 2017 and PrEP use within the same time frame as blood donation. Persons who are HIV positive and taking ART and persons taking PrEP to prevent HIV infection are donating blood. Both situations could lead to increased risk of HIV transfusion transmission if blood screening assays are unable to detect HIV in donations from infected donors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".