The Association between COVID-19 and Changes in Opioid Prescribing Patterns and Opioid-Related Overdoses: A Retrospective Cohort Study
Bibliographic record
Abstract
Recent data suggest that restrictions related to COVID-19 resulted in changes in the prescribing patterns of opioids. We sought to analyze Ontario health data for changes in frequencies among new and continuing users for the following opioid prescription characteristics: the type of opioid, the average daily dose, and the prescriber’s specialty. Utilizing data on the Ontario Health Data Platform, we defined two 149-day windows as “before” and “after” based on the initial COVID-19 provincial lockdown. A total of 882,268 individuals met our inclusion criteria and were classified as either “new” or “continuing” users. Chi-square tests and Fisher’s exact tests were applied for each level of our primary outcomes to determine whether there were significant changes in prescription proportions before and after the lockdown. A decline of 28% was observed for the number of new users after the lockdown. Statistically significant changes were observed for new users across almost all opioid prescription characteristics between the before and after windows. The proportion of new users who received at least one dispensing event from a pharmacist increased by 26.32%, whereas continuing users increased by 378.61%. There were no statistically significant shifts in opioid prescriptions among individuals with a reported toxicity event during the study period. In terms of opioid prescribing patterns, new users experienced greater change following the onset of the pandemic lockdown than continuing users. Our findings potentially showcase the unintended impacts that COVID-19-related restrictions had on non-COVID-19-related health services, which can inform future policy decisions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".