Continued low uptake of treatment for hepatitis C virus infection in a large community‐based cohort of inner city residents
Bibliographic record
Abstract
BACKGROUND & AIMS: Despite advances in HCV treatment, recent data on treatment uptake is sparse. HCV treatment uptake and associated factors were evaluated in a community-based cohort in Vancouver, Canada. METHODS: The CHASE study is a cohort of inner city residents recruited from January 2003-June 2004. HCV status and treatment were retrospectively and prospectively determined through data linkages with provincial virology and pharmacy databases. Logistic regression analyses were used to identify factors associated with HCV treatment uptake. RESULTS: Among 2913, HCV antibody testing was performed in 2405, 64% were HCV antibody-positive (n = 1533). Individuals with spontaneous clearance (18%, n = 276) were excluded. Among the remaining 1257 HCV antibody-positive participants (mean age 42, 71% male), 29% were Aboriginal. At enrolment, the majority reported recent injecting (60%) and non-injecting drug use (87%). Between January 1998 and March 2010, 6% (77 of 1257) initiated HCV treatment. In adjusted analyses, Aboriginal ethnicity [adjusted odds ratio (AOR) 0.23; 95% CI 0.10, 0.51] and crack cocaine use (AOR 0.61; 95% CI 0.37, 0.99) were associated with a decreased odds of receiving HCV treatment, while methamphetamine injecting (AOR 0.16; 95% CI 0.02, 1.18) trended towards a lower odds of receiving treatment. HCV treatment uptake ranged from 0.2 (95% CI 0.0, 0.7) per 100 person-years (PYs) in 2003 to 1.6 (95% CI 0.9, 2.6) per 100 PYs in 2009. CONCLUSION: HCV treatment uptake remains low in this large community-based cohort of inner city residents with a high HCV prevalence and access to universal healthcare.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".