How do we decide how representative our donors are for public health surveillance?
Bibliographic record
Abstract
BACKGROUND: Surveillance of blood donors is fundamental to safety of the blood supply. Such data can also be useful for public health policy but tend to be underutilized. When the COVID-19 pandemic arrived, blood centers around the world measured blood donor SARS-CoV-2 seroprevalence to inform public health policy. There is now a movement toward blood centers becoming more involved in public health research and surveillance post-pandemic. However, blood donors are a healthy population and not representative of all segments of the general population. In this article, we explain how blood centers can evaluate their donor base to understand which part of the general population they are representative of. STUDY DESIGN AND METHODS: Methodologic approaches for evaluating samples relative to the target population were reviewed. Blood donor data that are available to most blood centers were identified and application to assess representativeness of blood donors was evaluated. RESULTS: Key aspects of blood donor data to compare with general population data include donor selection criteria, health indicators, geography, and demographics. In some cases, statistical adjustment can improve representativeness. DISCUSSION: Comparing key blood donor data with corresponding general population data can define the subset of the general population for which a particular blood center's donors may be representative of. We suggest that donors are an ideal convenience population for surveillance of infectious agents which are frequently asymptomatic and main routes of transmission are not deferrable, for studying the natural history of disease in an initially well population, and for vaccination serology surveillance.
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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 | 0.280 | 0.482 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".