Evidence of increased Fentanyl use during the COVID-19 pandemic among opioid agonist treatment patients in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Amid the opioid crisis, the health care system is restructuring to prevent and treat COVID-19. Individuals in opioid agonist treatment (OAT) are uniquely challenged because of disruption to treatment, medication diversion, and isolation during the pandemic. METHODS: Between January and September 2020, we utilized the electronic medical record from a chain of 67 opioid agonist treatment clinics in Ontario, Canada, to examine routinely collected urine drug screen results of patients in opioid agonist treatment by Public Health Units. RESULTS: We present evidence of a 108% increase in the percentage of fentanyl positive urine drug screens from April to September (p< 0.001). During the same period, health regions in northern and southwestern Ontario, areas with a high concentration of rural communities, have seen the most notable increase in the percent of fentanyl positive urine drug screen results. CONCLUSION: The use of fentanyl increased by 108% among OAT patients in Ontario during the COVID 19 pandemic. We argue that the persistent increase of fentanyl exposure over time, specifically in the OAT population, suggests that reduced monitoring may decrease OAT's effectiveness and negatively impact patient outcomes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".