What impact did the COVID-19 pandemic have on the variability of fentanyl concentrations in the Vancouver, Canada illicit drug supply? An interrupted time-series analysis
Bibliographic record
Abstract
Background: Increases in fatal overdoses were observed coinciding with the COVID-19 pandemic across the USA and Canada. Hypothesised explanations include pandemic-attributable healthcare service disruption, social isolation and illicit drug market disruption. Using data from a community drug checking service, this study sought to evaluate how COVID-19 pandemic measures affected the variability in fentanyl concentrations within the local illicit drug market. Methods: Using a validated quantification model for fentanyl, Fourier-transform infrared spectra from fentanyl-positive drug checking samples in Vancouver, Canada were analysed to determine fentanyl concentration. An interrupted time-series analysis using an ordinary least squares model with autoregressive adjusted SEs was conducted to measure how the variance in monthly fentanyl concentrations changed following the declaration of the COVID-19 public health emergency in March 2020. Results: Over the study period, 4713 fentanyl-positive samples were available for analysis. Monthly variance of fentanyl concentrations ranged from 7.9% in December 2017 to 159.2% in September 2020. An interrupted time-series analysis of variance in fentanyl concentrations increased significantly following the declaration of the COVID-19 public health emergency, with an immediate level change of 26.1 (95% CI 7.2 to 45.0, p=0.011) and a slope change of 15.8 (95% CI 10.2 to 21.4, p<0.001). Conclusion: Though community drug checking samples may not be generalisable to the wider illicit drug market, our study found that variance in fentanyl concentrations increased significantly following the declaration of the COVID-19 public health emergency. While it remains unclear whether the observed increase in the variability of fentanyl concentration in illicit opioids was a direct result of COVID-19 and related measures, the volatility of fentanyl concentrations is likely to have posed significant risk to people who used drugs in this setting.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".