An international comparison of HIV prevalence and incidence in blood donors and general population: a BEST Collaborative study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Efficiency in mitigating HIV transmission risk by transfusion may vary internationally. We compared HIV prevalence and incidence in blood donors across different jurisdictions in relation to those rates in the general population and differences in deferral practices. MATERIALS AND METHODS: Data from 2007 to 2016 were collected in Australia, Brazil (São Paulo), Canada, England, France, Italy, Ireland, Japan, the Netherlands, New Zealand, Norway, Spain (Basque Country), USA (Vitalant) and Wales. For each country/region, the number of HIV antibody-positive donations and nucleic acid testing (NAT)-only-positive donations was broken down according to first-time or repeat donor status, along with the relevant denominators. RESULTS: There is a modest correlation between HIV prevalence among first-time donors and HIV prevalence in the general population. However, rates of HIV-positive donations in repeat donors, a proxy for incidence, do not correlate with incidence rates in the general population. Rates in donors from Italy and Basque Country, where deferral criteria for men having sex with men are less stringent, are higher compared with most other jurisdictions. Rates of NAT-only-positive donations are extremely low and do not differ significantly after adjustment for multiple comparisons. CONCLUSION: Donor HIV rates are only weakly associated with those observed in the general population. Countries with less stringent deferral criteria have higher HIV rates in their donor population, but the rates remain very low.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".