Trends in Prescription Chronic Pain Medication Use before and during the First Wave of the COVID-19 Pandemic in Québec, Canada: An Interrupted Time Series Analysis
Bibliographic record
Abstract
Background: In Canada, a state of health emergency was declared in May 2020 as a result of the COVID-19 pandemic. This study aimed to assess trends in the use of prescription medication for pain management by people living with chronic pain before and during the first wave of the pandemic. Methods: Participants (n = 177) were adults reporting chronic pain who had completed a web-based questionnaire in 2019 and for whom complete longitudinal private and public insurance prescription claims were available. The monthly prevalence of medication use for nonsteroidal anti-inflammatory drugs (NSAIDs), opioids, and prescribed cannabinoids was assessed. An interrupted time series analysis was then performed to evaluate if the COVID-19 pandemic had had an impact on trends in pain medication use. Results: The beginning of the first wave of the pandemic was associated with the onset of a downward trend in opioid use (p < 0.05); no such association was found regarding NSAIDs. However, point prevalence of opioid use at the beginning (Nov. 2019) and at the end (Mai 2020) of the study period remained somewhat stable (17.0% vs. 16.4%). Regarding prescribed cannabinoids, a gradual increase in use was observed over the entire study period independently from the impact of the first wave of the pandemic (15.3% vs. 22.6%, p < 0.05). Conclusion: While the occurrence of the first wave did have an impact on opioid use among people living with chronic pain, access to and use of opioids appear to have returned to normal before the end of the first wave of COVID-19.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".