Understanding real-world adherence in the directly acting antiviral era: A prospective evaluation of adherence among people with a history of drug use at a community-based program in Toronto, Canada
Bibliographic record
Abstract
BACKGROUND: Direct acting antiviral (DAA) treatments for Hepatitis C (HCV) are now widely available with sustained virologic response (SVR) rates of >90%. A major predictor of response to DAAs is adherence, yet few real-world studies evaluating adherence among marginalized people who use drugs and/or alcohol exist. This study evaluates patterns and factors associated with non-adherence among marginalized people with a history of drug use who were receiving care through a primary care, community-based HCV treatment program where opiate substitution is not offered on-site. METHODS: Prospective evaluation of chronic HCV patients initiating DAA treatment. Self-report medication adherence questionnaires were completed weekly. Pre/post treatment questionnaires examined socio-demographics, program engagement and substance use. Missing adherence data was counted as a missed dose. RESULTS: Of the 74 participants, who initiated treatment, 76% were male, the average age was 54 years, 69% reported income from disability benefits, 30% did not have stable housing and only 24% received opiate substitution therapy. Substance use was common in the month prior to treatment initiation with, 11% reported injection drug use, 30% reported non-injection drug use and 18% moderate to heavy alcohol use. The majority (85%) were treatment naïve, with 76% receiving sofosbuvir/ledipasvir (8-24 weeks) and 22% Sofosbuvir/Ribarvin (12-24 weeks). The intention to treat proportion with SVR12 was 87% (60/69). In a modified ITT analysis (excluding those with undetectable RNA at end of treatment), 91% (60/66) achieved SVR12. Overall, 89% of treatment weeks had no missed doses. 41% of participants had at least one missed dose. In multivariate analysis the only factor independently associated with weeks with missed doses was moderate to heavy alcohol use (p=0.05). CONCLUSION: This study demonstrates that strong adherence and SVR with DAAs is achievable, with appropriate supports, even in the context of substance use, and complex health/social issues.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 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".