Hepatitis B Blood Donor Screening Data: An Under-Recognized Resource for Canadian Public Health Surveillance
Bibliographic record
Abstract
Hepatitis B surveillance is essential to achieving Canada's goal of eliminating hepatitis B by 2030. Hepatitis B rates, association of infection with vaccine age-eligibility, and risk factors were analyzed among 1,401,603 first-time Canadian blood donors from 2005 to 2020. Donors were classified as having likely chronic or likely resolved/occult infections based on hepatitis B surface antigen, anti-hepatitis B core antigen, and hepatitis B nucleic acid test results. Likely chronically infected and control donors (ratio 1:4) participated in risk-factor interviews. The 2019 rate of likely chronic infection was 61.9 per 100,000 (95% CI 46.5-80.86) and 1449.5 per 100,000 for likely resolved/occult infections (95% CI 1370.7-1531.7). Likely chronic infections were higher in males (OR 3.2; 95% CI 2.7-3.7) and the vaccine-ineligible birth cohort (OR 1.9; 95% CI 1.6-2.2). The main risk factors were living with someone who had hepatitis (OR 12.5; 95% CI 5.2-30.0) and ethnic origin from a high-prevalence country (OR 8.4; 95% CI 5.9-11.9). Undiagnosed chronic hepatitis B may be more prevalent in Canada than currently determined by traditional passive hepatitis B reporting. Blood donor data can be useful in informing hepatitis B rates and evaluating vaccination programs in Canada.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.015 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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; 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".