Blood donor characteristics and blood safety before and during the <scp>COVID</scp>‐19 pandemic: A <scp>BEST</scp> Collaborative international survey
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Early in the COVID-19 pandemic, blood suppliers faced unique challenges meeting changing demand while maintaining safety for donors, recipients and staff. Actions taken may have altered the composition of the donor base and the frequency of confirmed-positive infectious disease marker (IDM) rates. No studies have evaluated the impact of the pandemic on donations, donor demographics and blood safety across several countries. MATERIALS AND METHODS: Whole blood/red blood cell (RBC) donors and donations and confirmed IDM reactivity recorded during from 11 March 2019 to 11 September 2019 ("pre-pandemic period") and from 11 March 2020 to 11 September 2020 ("pandemic period") were collected by 11 blood services participating in the Biomedical Excellence for Safer Transfusion (BEST) Collaborative. RESULTS: Eleven blood services from nine countries reported on over 4 million donations per period. On average, donations dropped by 4.0% between pre-pandemic and pandemic periods, driven by fewer donations from active repeat donors (-5.6%) and first-time [FT] donors (-14.0%) but partially offset by more donations from lapsed donors (+15.7%). The decline was also driven by fewer donations from male donors (-7.6%) and younger donors (i.e., 16-25 years: -19.0%). Overall, the rate of confirmed IDM positivity dropped from 100.0 to 88.6 per 100,000 donors (-11.4%) between pre-pandemic and pandemic periods. CONCLUSION: Early in the pandemic, blood donations, particularly from FT donors, decreased. In future respiratory virus pandemics, blood banks should anticipate changes in demand, collection site locations and capacity and donor behaviour. Unlike results in acute catastrophes, lower rates of confirmed IDM positivity were observed, in part related to lower numbers of FT, male and younger 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".