The hepatitis C epidemic in Canada: An overview of recent trends in surveillance, injection drug use, harm reduction and treatment
Bibliographic record
Abstract
Hepatitis C continues to be a significant public health concern in Canada, with the hepatitis C virus (HCV) responsible for more life-years lost than all other infectious diseases in Canada. An increase in reported hepatitis C infections was observed between 2014 and 2018. Here, we present changing epidemiological trends and discuss risk factors for hepatitis C acquisition in Canada that may have contributed to this increase in reported hepatitis C infections, focusing on injection drug use. We describe a decrease in the use of borrowed needles or syringes coupled with an increase in using other used injection drug use equipment. Also, an increased prevalence of injection drug use and use of prescription opioid and methamphetamine injection by people who inject drugs (PWID) may be increasing the risk of HCV acquisition. At the same time, while harm reduction coverage appears to have increased in Canada in recent years, gaps in access and coverage remain. We also consider how direct-acting antiviral (DAA) eligibility expansion may have affected hepatitis C rates from 2014 to 2018. Finally, we present new surveillance trends observed in 2019 and discuss how the coronavirus disease 2019 (COVID-19) pandemic may affect hepatitis C case counts from 2020 onwards. Continual efforts to i) enhance hepatitis C surveillance and ii) strengthen the reach, effectiveness, and adoption of hepatitis C prevention and treatment services across Canada are vital to reducing HCV transmission among PWID and achieving Canada's HCV elimination targets by 2030.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.020 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".