Opioid use in the era of COVID-19: a multifaceted study of the opioid epidemic in Canada
Bibliographic record
Abstract
Introduction: The COVID-19 pandemic has had wide economic, social, and health impacts, and has disproportionately affected individuals who were already vulnerable. Individuals who use opioids have dealt with evolving public health measures and disruptions while also dealing with the ongoing opioid epidemic. Opioid-related mortalities in Canada increased throughout the COVID-19 pandemic, but it is unclear to what extent public health measures and the progression of the pandemic contributed to opioid-related harms. Methods: To address this gap, we used emergency room (ER) visits recorded in the National Ambulatory Care Reporting System (NACRS) between 1 April 2017, and 31 December 2021, to investigate trends of opioid-related harms throughout the pandemic. This study also included semi-structured interviews with service providers in the field of opioid use treatment, to help contextualize the trends seen in ER visits and offer perspectives on how opioid use and services have changed throughout the COVID-19 pandemic. Results: Overall, the number of hospitalizations related to an opioid use disorder (OUD) decreased with progressing waves of the pandemic and with increasing severity of public health measures in Ontario. The rate of hospitalizations related to opioid poisonings (e.g., central nervous system and respiratory system depression caused by opioids) significantly increased with the progressing waves of the pandemic, as well as with increasing severity of public health measures in Ontario. Discussion: The increase in opioid-related poisonings is reflected in the existing literature whereas the decrease in OUDs is not. Moreover, the increase in opioid-related poisonings aligns with the observations of service providers, whereas the decrease in OUD contradicts the trends that service providers described. This discrepancy could be explained by factors identified by service providers, including the pressures on ERs during the pandemic, hesitancy to seek treatment, and drug toxicity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".