Why the FUSS (Fentanyl Urine Screen Study)? A cross-sectional survey to characterize an emerging threat to people who use drugs in British Columbia, Canada
Bibliographic record
Abstract
BACKGROUND: Fentanyl-detected illicit drug overdose deaths in British Columbia (BC) recently increased dramatically from 13 deaths in 2012 to 90 deaths in 2014, signaling an emerging public health concern. Illicit fentanyl is sold as pills or powders, often mixed with other substances like heroin or oxycodone; reports from coroners suggested that fentanyl was frequently taken unknowingly by people who use drugs. This study aimed to assess the prevalence and characteristics of fentanyl use among clients accessing harm reduction (HR) services in BC. METHODS: Participants attending HR services at 17 sites across BC were invited to complete an anonymous questionnaire describing drugs they have used within the last 3 days and provide a urine sample to test for fentanyl. Data from eligible participants were analyzed using descriptive, bivariate, and multivariate statistical methods. RESULTS: Surveys from 17 HR sites were received, resulting in analysis of responses from 242 eligible participants. Most participants used multiple substances (median = 3), with crystal meth (59%) and heroin (52%) use most frequently reported. Seventy participants (29%) tested positive for fentanyl, 73% of whom did not report using fentanyl. Controlling for age, gender, and health authority, reported use of fentanyl (odds ratio (OR) = 6.13, 95% confidence interval (CI) = [2.52, 15.78], p < 0.001) and crystal methamphetamine (OR = 3.82, 95% CI = [1.79, 8.63], p < 0.001) use were significantly associated with fentanyl detection. CONCLUSIONS: The proportion of those testing positive who did not report knowingly using fentanyl represents a considerable public health concern. The risk of overdose among this vulnerable population highlights the need for targeted HR strategies, such as increased accessibility to naloxone, overdose education, and urine screens.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".