An interrupted time series analysis of trends in opioid-related emergency department visits from pre-COVID-19 pandemic to pandemic, from the Canadian Hospitals Injury Reporting and Prevention Program
Bibliographic record
Abstract
BACKGROUND: Opioid-related emergency department (ED) visits in Canada increased during the COVID-19 pandemic, but how trends in volume and case severity changed from pre-pandemic times through the pandemic is not known. Trends in ED visits related to specific types of opioids also remain unclear. Our objective was to describe pre-pandemic trends and how they changed with the onset of COVID-19 and thereafter. METHODS: Based on data from the Canadian Hospitals Injury Reporting and Prevention Program, we identified opioid-related ED visits and constructed a time series from March 12, 2018 through March 7, 2021-two pre-COVID periods and one COVID period. We used an interrupted time series (ITS) analysis to examine trends in volume and case severity. We compared medians and means of monthly counts and percentages of severe cases between the periods, by sex, age, and opioid type. RESULTS: Before the pandemic, there was an increasing trend in fentanyl-related visits for males, females and 25- to 64-year-olds, and a decreasing trend in heroin-related visits for males and 18- to 64-year-olds. Fentanyl-related visits for 18- to 24-year-olds showed an immediate increase at the start of the pandemic and a decreasing trend during the pandemic. Heroin-related visits for 12- to 17-year-olds had an immediate increase at the start of the pandemic; for 18- to 24-year-olds and 45- to 64-year-olds, the prior decreasing pre-pandemic trend ceased. For pooled opioid-related visits, no significant trend in the percentage of severe cases was observed throughout the entire study period. CONCLUSION: This study shows that an ITS approach in trend analysis is a valuable supplement to comparisons of before and after measures (with or without controlling seasonal effects). The findings provide evidence on how ED presentations for opioid use evolved in Canada from 2018 to 2021. The results can inform policies designed to reduce opioid-related harm in the context of a public health emergency.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".