Prevalence of Injecting Drug Use and Coverage of Interventions to Prevent HIV and Hepatitis C Virus Infection Among People Who Inject Drugs in Canada
Bibliographic record
Abstract
Objectives. To determine the number of people who inject drugs (PWID) in Canada and the annual coverage of opioid agonist treatment (OAT) and needle-and-syringe provision for PWID. Methods. We estimated the number of PWID in 11 of 13 Canadian provinces and territories in 2011 by using indirect multiplier methods based on provincial and territorial methadone recipient totals and proportion of surveyed PWID receiving methadone. We modeled annual increases for 2011 to 2016 on Quebec and British Columbia longitudinal data. We calculated needle-and-syringe coverage (World Health Organization [WHO] recommendation: ≥ 200 per PWID) and OAT coverage (WHO recommendation: ≥ 40 per 100 PWID) per province and territory annually. Results. An estimated 130 000 individuals in Canada (0.55%) injected drugs in 2011, increasing to 171 900 individuals (0.70%) in 2016. Needle-and-syringe coverage increased from 193 to 291 per PWID, and OAT coverage increased from 55 to 66 per 100 PWID over the study period. Conclusions. While the number of PWID increased between 2011 and 2016, OAT coverage remained high, and needle-and-syringe coverage generally improved over time. Public Health Implications. These data will inform public health surveillance, service planning, and resource allocation, and assist monitoring of treatment and harm-reduction coverage outcomes.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".