Declining hepatitis C rates in first‐time blood donors: insight from surveillance and case‐control risk factor studies
Bibliographic record
Abstract
BACKGROUND: Hepatitis C virus (HCV) rates have decreased steadily in first-time donors in Canada since testing was implemented but reasons are unclear. A description of factors that may have played a role in this decline is reported. STUDY DESIGN AND METHODS: Descriptive analysis of first-time blood donors by HCV positivity status and year (1993--2006), sex, and age was carried out. HCV-positive first-time donors and matched controls participated in a confidential scripted telephone interview about risk factors in 1993 through 1994 and in 2005 through 2006, and risk factors independently predicting HCV positivity were determined with multiple logistic regression. RESULTS: HCV-positive donations occurred most frequently in donors born between 1945 and 1964 and decreased in this birth cohort over time (p < 0.01). At present, most first-time donors (74%) are born after 1964. History of intravenous drug use, sex with an intravenous drug user, blood transfusion, and tattoo independently predicted (p < 0.01) HCV positivity in both periods (1993--1994 and 2005--2006). CONCLUSION: Most HCV-positive donors were born between 1945 and 1964, and the decline in HCV rates is associated primarily with this birth cohort. The key risk factors predicting HCV positivity did not change over the 13 years of the study. With approximately two-thirds of HCV-positive Canadians in the general population having been tested for HCV, potential donors may be aware of their HCV status and be likely to self-defer. This, and an increasing proportion of first-time donors born after 1964, may contribute to declining HCV rates in first-time 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 | 0.005 | 0.014 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".