Income level and drug related harm among people who use injection drugs in a Canadian setting
Bibliographic record
Abstract
Background—Higher income is generally associated with better health outcomes; however, among people who inject drugs (IDU) income generation frequently involves activities, such as sex work and drug dealing, which pose significant health risks. Therefore, we sought to examine the relationship between level of income and specific drug use patterns and related health risks. Methods—This study involved IDU participating in a prospective cohort study in Vancouver, Canada. Monthly income was categorized based on non-fixed quartiles at each follow-up with the lowest level serving as the reference category in generalized linear mixed-effects regression. Results—Among our sample of 1,032 IDU, the median average monthly income over the study follow-up was $1050 [Interquartile range=785–2000]. In multivariate analysis, the highest income category was significantly associated with sex work (Adjusted Odds Ratio [AOR]=7.65), drug dealing (AOR=5.06), daily heroin injection (AOR=2.97), daily cocaine injection (AOR=1.65), daily crack smoking (AOR=2.48), binge drug use (AOR=1.57) and unstable housing (AOR=1.67). The high income category was negatively associated with being female (AOR=0.61) andaccessing addiction treatment (AOR=0.64), (all p < 0.05). In addition, higher income was strongly associated with higher monthly expenditure on drugs (>$400) (OR=97.8). Conclusion—Among IDU in Vancouver, average monthly income levels were low and higher total monthly income was linked to high-risk income generation strategies as well as a range of drug use patterns characteristic of higher intensity addiction and HIV risk. These findings underscore the need for interventions that provide economic empowerment and address high intensity addiction, especially for female IDU.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| 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".