Evaluation of a pre‐exposure prophylaxis (PrEP)/post‐exposure prophylaxis (PEP) deferral policy among blood donors
Bibliographic record
Abstract
BACKGROUND: The use of pre-/post-exposure prophylaxis (PrEP/PEP) may interfere with routine HIV screening. As a result, blood services worldwide have adopted a variety of deferral policies to mitigate increased residual risk. In this study, we evaluated the operational impact of modifying the donor health questionnaire (DHQ) to include explicit questions to assess PrEP/PEP exposure. STUDY DESIGN AND METHODS: We conducted a retrospective study between June 2019 and October 2020 of all blood donors attempting to donate at Canadian Blood Services. Sixteen-months post-implementation, we summarized self-reported PrEP/PEP rates and their indications for use. We also assessed deferral rates and the sensitivity of using existing risk questions to defer people exposed to PrEP/PEP. RESULTS: Of 1,122,075 donations, 89 people (eight per 100,000 donations) reported PrEP (64%)/PEP (34%) use in the last 4 months. People exposed to PrEP were more likely to be men (94%) and taking PrEP for lifestyle reasons (87%). In contrast, indications for PEP use included occupational exposure (50%) and sexual assault (27%). Most donors who answered affirmatively to PrEP/PEP exposure were deferred (96%). If potential donors were not directly asked about their PrEP/PEP use, the majority would not have been deferred for any other reasons (PrEP 32/57 (56%) and PEP 15/30 (50%)). CONCLUSION: Not all blood services have adopted direct questions to identify PrEP/PEP exposure; some rely on existing DHQ questions. In Canada, despite DHQ questions such as medication use in the last 3-days, more than half of people exposed to PrEP/PEP would not have been identified and deferred.
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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 | 0.021 | 0.032 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".