Estimated prevalence of Hepatitis C Virus infection in Canada, 2011
Bibliographic record
Abstract
BACKGROUND: Prevalence estimates contribute to our understanding of the magnitude of a particular health condition and in planning appropriate public health interventions. OBJECTIVE: To estimate the prevalence of chronic Hepatitis C virus (HCV) infection, anti-HCV-positive status (anti-HCV) and the proportion of undiagnosed HCV infections in Canada. METHODS: A combination of back-calculation and workbook methods was used. The back-calculation method estimated prevalent chronic HCV infection and the proportion undiagnosed using the Canadian Cancer Registry's data on hepatocellular carcinoma reported between 1992 and 2008 and the Canadian Notifiable Disease Surveillance System's data on Hepatitis C virus (HCV) cases reported between 1991 and 2009 in a Markov multi-state disease progression model with parameters adjusted to Canada. The workbook method divided the total population of Canada into population subsets and developed estimates of population size and anti-HCV prevalence for each. Sub-population size estimates were multiplied by anti-HCV prevalence measures to calculate the prevalence of anti-HCV by sub-population. A measure of spontaneous clearance was used to estimate the number of persons with chronic HCV from estimates of the number of anti-HCV-positive persons. RESULTS: The back-calculation method estimated the prevalence of chronic HCV infection at 0.64% and the proportion of undiagnosed chronic HCV infection at 44% in 2011. The workbook method estimated the anti-HCV prevalence at 0.96% (plausibility range: 0.61% to 1.34%) and chronic HCV infection at 0.71% (0.45 - 0.99%). INTERPRETATION: By combining mid-point estimates from both methods, it is estimated that between 0.64% to 0.71% of the overall Canadian population was living with chronic HCV infection in 2011 and 44% of these individuals were undiagnosed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".