Impact of the COVID-19 Pandemic on Patterns of Prescription Opioid Use: A Retrospective Cohort Study of Adults Without a Cancer Diagnosis Initiating Opioids Using Administrative Claims Data
Bibliographic record
Abstract
BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has led to disruptions of healthcare delivery and may thus have impacted patterns of prescription opioid use, including risk factors for long-term use. OBJECTIVE: We aimed to describe changes in patterns of prescription opioid use due to the COVID-19 pandemic in community-dwelling adults without a cancer diagnosis. METHODS: Using administrative claims data of the province of Quebec, Canada, a random sample of adults (aged ≥18 years) was selected. These were members of the public drug plan without a cancer diagnosis who initiated a prescription opioid in the outpatient setting between 1 January, 2018 and 28 December, 2020. We assessed the daily dose of initial prescription opioids, the number of days' supply of initial dispensing, and the total duration of opioid use over the first 6 months following initiation. We applied interrupted autoregressive integrated moving average models to examine weekly patterns of prescription opioids before and during the pandemic (starting at the lockdown). Our models included a step intervention function (immediate change) and a ramp intervention function (slope change). RESULTS: There were 112,650 and 34,261 patients who initiated opioid therapy, respectively, in the 115-week pre-pandemic period and in the 41-week pandemic period. At the start of the lockdown, there was a significant immediate decrease in opioid treatment initiation (-326; 95% confidence interval [CI] -419 to -234) and initial daily dose (-1.7 morphine milligram equivalents; 95% CI -2.7 to -0.7). Conversely, there was a significant immediate increase in the number of days' supply of initial dispensing (1.4 days; 95% CI 1.0 to 1.8) and the total duration of opioid use over 6 months (5.7 days; 95% CI 4.6 to 6.8). All these weekly measures returned to values close to those of the pre-pandemic period 10 weeks after the start of lockdown. CONCLUSIONS: Our findings showed that the COVID-19 lockdown had an impact on initial number of days' supply, which is a risk factor for long-term use and ultimately opioid-related harm. However, over time, prescription practices and use reverted to those observed in the pre-pandemic period.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".