Decreasing Hepatitis C Incidence Among a Population With Repeated Tests: British Columbia, Canada, 1993–2011
Bibliographic record
Abstract
OBJECTIVES: We estimated HCV incidence among individuals who repeatedly underwent anti-HCV testing. METHODS: We studied HCV-negative individuals who had at least 2 tests between April 1992 and September 2012 in British Columbia, Canada. We calculated incidence as the number of new infections per 100 person-years at risk. RESULTS: From 1992 to 2012, 323 598 individuals who persistently tested negative and 7490 HCV seroconverters contributed 1 774 262 person-years of observation time. Incidence rates ranged from 2.66 infections per 100 person-years (95% confidence interval [CI] = 2.07, 3.35) in 1993 to 0.25 infections per 100 person-years (95% CI = 0.21, 0.29) in 2011. Rates declined sharply in the 1990s and declined more gradually in the 2000s. Incidence declined with age; highest incidence rates were among those aged 15 to 24 years. Incidence among male repeat testers exceeded that of female repeat testers across all years, although the gap narrowed over time. CONCLUSIONS: Addictions treatment, harm reduction, prevention education, and novel initiatives to remove barriers in health infrastructure need to be intensified for those who inject drugs, particularly men and younger persons.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".