Drug‐related harm coinciding with income assistance payments: results from a community‐based cohort of people who use drugs
Bibliographic record
Abstract
BACKGROUND AND AIMS: Income assistance is critical to the health and wellbeing of socio-economically marginalized people who use illicit drugs (PWUD). However, past literature paradoxically identifies unintended increases in drug-related harm coinciding with synchronized payments that may magnify signals for drug use. The scope of such harm has not been fully characterized among non-institutionalized populations. This study examined socio-demographic, health and drug use-related correlates of payment-coincident drug-related harm. DESIGN: This observational study uses data from prospective community-based longitudinal cohorts of PWUD between December 2013 and May 2018. SETTING: Vancouver, British Columbia, Canada. PARTICIPANTS: A total of 1604 PWUD receiving monthly income assistance. Our sample included 586 (36.5%) women, 861 (53.7%) non-white participants and 685 (42.7%) people living with HIV. MEASUREMENTS: The primary outcome was a self-reported composite measure of drug-related harm in the past 6 months coinciding with income assistance, including higher-frequency substance use, non-fatal overdose and service barriers or interruptions. Subanalyses disaggregated this outcome. FINDINGS: Payment-coincident drug-related harm was reported among 77.7% of participants during the study period. In multivariable models, key correlates positively and significantly associated with payment-coincident harm included: street-based income generation [adjusted odds ratio (aOR) = 1.48, 95% confidence interval (CI) = 1.26-1.74, P < 0.001], sex work (aOR = 1.66, 95% CI = 1.35-2.04, P < 0.001), illegal income generation (aOR = 1.57, 95% CI = 1.35-1.83 P < 0.001), homelessness (aOR = 1.34, 95% CI = 1.13-1.58, P < 0.001), exposure to violence (aOR = 1.31, 95% CI = 1.03-1.66, P = 0.032), daily crack cocaine use (aOR = 1.99, 95% CI = 1.59-2.50, P < 0.001), heavy alcohol use (aOR = 1.64, 95% CI = 1.37-1.97, P < 0.001) and injection drug use (aOR = 2.55, 95% CI = 2.01-3.23, P < 0.001). In subanalyses, specific harms were more likely among individuals reporting social, structural and health vulnerabilities. CONCLUSIONS: In Vancouver, Canada, people who use illicit drugs who receive income assistance report high prevalence of payment-coincident drug-related harm, particularly people experiencing socioeconomic and structural marginalization or engaging in high-intensity drug use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".