Estimating the population size of people who inject drugs in Canada, 2021
Bibliographic record
Abstract
Background: People who inject drugs are disproportionately affected by HIV and hepatitis C infections. Estimating the size and distribution of this population is essential in monitoring infectious diseases rates and progress towards elimination. Objective: This study aims to estimate the population sizes of people in Canada who have ever injected drugs, stratified by sex (assigned at birth), province/region and steroid injection, and those who have recently injected drugs (past 12 months), stratified by sex and steroid injection. While a previous national study reported estimates of recent injection by province, this study provides the first estimates of people who have ever injected drugs at both the national and provincial/regional levels. It is also the first to incorporate stratification by sex and steroid injection, using the most currently available data. Methods: Using combined cycles (2017-2021) of the Canadian Community Health Survey (CCHS), a nationally representative population-based survey, we applied the weighted prevalence of injection drug use to the 2021 Statistics Canada national population size estimate of individuals aged 15 years or more. To this, further adjustments were made using additional data to account for populations not sampled in the CCHS and under-reporting of injection drug use in surveys. Results: In 2021, an estimated 388,400 (95% CI: 338,900-436,500) people in Canada had ever injected drugs, representing 1.22% of the Canadian population 15 years of age and older. Among these, 75% were male and 25% were female. These estimates varied across regions, ranging from 0.92% to 2.47%. The estimated number of people who have recently injected drugs was 100,300 (95% CI: 82,300-119,200) or 0.31% of the population, of which 74% were male and 26% were female. Conclusion: Estimates of people who inject drugs at the national and provincial/regional levels can be used to track key epidemiological metrics that inform public health policy and programming.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".