Population-based estimate of hepatitis C virus prevalence in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Hepatitis C virus (HCV) is the most burdensome infectious illness in Canada. Current screening strategies miss a significant proportion of cases, leaving many undiagnosed. Elevated HCV prevalence in those born between 1945 and 1965 has prompted calls for birth-cohort screening in this group. However, Canada lacks population-level data to support this recommendation. We performed a serosurvey to obtain population-based HCV prevalence estimates in Ontario residents born between 1945-1974, to generate evidence for birth-cohort screening recommendations. METHODS: We tested anonymized residual sera in five-year age-sex bands from Ontario for anti-HCV antibody. We performed descriptive epidemiological analysis and used a logistic regression model to determine HCV risk-factors. RESULTS: Of 10,006 sera analyzed, 155 (1.55%, 95% confidence interval (CI) 1.32, 1.81) were positive for HCV antibody. Individuals born between 1950-1964 had a significantly higher combined prevalence of 1.92% (95% CI 1.56, 2.34) compared to 1.14% (95% CI 0.69, 1.77) (p = 0.04) for those born between 1970-1974. For males, comprising 107/155 (69.03%) of positive samples, the highest prevalence was 3.00% (95% CI 1.95, 4.39) for the 1960-1964 birth-cohort. For females, the highest prevalence was 1.56% (95% CI 0.83, 2.65) for those born between 1955-1959. Male sex was significantly associated with positive HCV serostatus. INTERPRETATION: HCV prevalence in Ontario is highest among those in this birth cohort, and higher than previous estimates. The prevalence estimates presented in our study provide important data to underpin birth-cohort screening recommendations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".