Regional differences in access to direct-acting antiviral treatments for hepatitis C across Ontario: A cross-sectional study
Bibliographic record
Abstract
Background: Direct-acting antivirals (DAAs) are curative treatments for hepatitis C virus (HCV) infection, a condition affecting over 100,000 Ontarians. Although DAAs are covered under the public drug programs in Ontario, receiving prescriptions depends on access to healthcare. The aim of this study is to understand the relationship between DAA treatment rates and distance to prescriber in Ontario, Canada. Methods: We conducted a cross-sectional study and identified patients who filled a DAA prescription through the Ontario Drug Benefit (ODB) in 2019. We calculated crude (per 100,000 ODB recipients) and adjusted (by a regional HCV infection rate) DAA treatment rates by public health unit (PHU). We reported median distances to provider for all visit types, in-person visits, virtual visits, and proportions of visits that were virtual. Results: In 2019, the crude DAA treatment rate for Ontario is 83.0 patients per 100,000 ODB recipients. The HCV-adjusted DAA treatment rate ranges from 28.2 (Northwestern Ontario) to 188.5 (Eastern Ontario) per 100,000. In our primary analysis, patients in rural PHUs, including Northwestern and Porcupine, were among the highest median distances to prescriber for all visit types (1,195 km and 556 km, respectively). These PHUs also had the highest proportions of virtual visits (greater than 60%). Urban PHUs, such as Toronto and Ottawa, had smaller median distances for all visit types, with smaller proportions of virtual visits (10.8% and 12.4%, respectively). Conclusion: We observed heterogeneity in treatment rates, distance to DAA prescribers and use of virtual care in the management of HCV. Increasing use of telemedicine in regions with limited utilization of DAAs may improve access.
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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".