The relationship between COVID-19 and opioid-related emergency department visits in Alberta, Canada: an interrupted time series analysis
Bibliographic record
Abstract
INTRODUCTION: Emergency departments (EDs) are important health care access points for people who use drugs (PWUD), but little is known about whether the onset of the COVID-19 pandemic was associated with changes in opioid-related emergency presentations. We investigated whether (1) the onset of the COVID-19 pandemic was associated with any change in average rates of opioid-related ED visits in Alberta; and (2) this varied across regions with different COVID-19 case rates. METHODS: We conducted maximum-likelihood interrupted time series analyses to compare opioid-related ED visits during the "prepandemic period" (3 March 2019-1 March 2020) and the "pandemic period" (2 March 2020-14 March 2021). RESULTS: There were 8883 and 11 657 opioid-related ED visits during the prepandemic and pandemic periods, respectively. The onset of the COVID-19 pandemic was associated with an increase in opioid-related ED visits (Edmonton: IRR = 1.37, 95% CI: 1.30- 1.44, p < 0.05; Calgary: IRR = 1.14, 95% CI: 1.07-1.20, p < 0.05; Other health zones: IRR = 1.14, 95% CI: 1.07-1.21, p < 0.05). Changing COVID-19 case counts did not correspond with changing rates of opioid-related ED visits across regions. CONCLUSION: The increase in opioid-related ED visits associated with the onset of the COVID-19 pandemic was unrelated to COVID-19 case prevalence in Alberta.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".