Shift in disparities in hepatitis C treatment from interferon to <scp>DAA</scp> era: A population‐based cohort study
Bibliographic record
Abstract
We evaluated the shift in the characteristics of people who received interferon-based hepatitis C virus (HCV) treatments and those who received recently introduced direct-acting antivirals (DAAs) in British Columbia (BC), Canada. The BC Hepatitis Testers Cohort includes 1.5 million individuals tested for HCV or HIV, or reported cases of hepatitis B and active tuberculosis in BC from 1990 to 2013 linked to medical visits, hospitalization, cancer, prescription drugs and mortality data. This analysis included all patients who filled at least one prescription for HCV treatment until 31 July 2015. HCV treatments were classified as older interferon-based treatments including pegylated interferon/ribavirin (PegIFN/RBV) with/without boceprevir or telaprevir, DAAs with RBV or PegIFN/RBV, and newer interferon-free DAAs. Of 11 886 people treated for HCV between 2000 and 2015, 1164 (9.8%) received interferon-free DAAs (ledipasvir/sofosbuvir: n=1075; 92.4%), while 452 (3.8%) received a combination of DAAs and RBV or PegIFN/RBV. Compared to those receiving interferon-based treatment, people with HIV co-infection (adjusted odds ratio [aOR]: 2.96, 95% CI: 2.31-3.81), cirrhosis (aOR: 1.77, 95% CI: 1.45-2.15), decompensated cirrhosis (aOR: 1.72, 95% CI: 1.31-2.28), diabetes (aOR: 1.30, 95% CI: 1.10-1.54), a history of injection drug use (aOR: 1.34, 95% CI: 1.09-1.65) and opioid substitution therapy (aOR: 1.30, 95% CI: 1.01-1.67) were more likely to receive interferon-free DAAs. Socio-economically marginalized individuals were significantly less likely (most deprived vs most privileged: aOR: 0.71, 95% CI: 0.58-0.87) to receive DAAs. In conclusion, there is a shift in prescription of new HCV treatments to previously excluded groups (eg HIV-co-infected), although gaps remain for the socio-economically marginalized populations.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".