National trends in population rates of opioid‐related mortality, hospitalization and emergency department visits in Canada between 2000 and 2017. A population‐based study
Bibliographic record
Abstract
BACKGROUND AND AIMS: Existing assessments of the time-trends of opioid-related mortality, hospitalization and emergency department visits in Canada have relied mainly on provincial databases, while national assessments generally do not provide information before 2016. We aimed to estimate Canadian national time trends in opioid-related mortality from 2000 to 2017 and opioid-related hospitalization and emergency department visits between 2000 and 2012. DESIGN: Retrospective cohort study. SETTING AND PARTICIPANTS: Residents of all Canadian provinces and territories for which comparable data were available from 2000 to 2017. MEASUREMENTS: We identified opioid-related mortality, hospitalization and emergency department visits using validated algorithms using ICD codes from administrative databases. We calculated crude rates and sex- and age-adjusted rates per million. For hospitalizations, we calculated case-fatality, 90-day and 365-day all-cause mortality and opioid-related re-hospitalization rates. We used Poisson regression to examine the significance of the time trend. FINDINGS: From 2000 to 2017, the adjusted opioid mortality rate in Canada (outside Quebec) increased significantly by 592.9% (from 20.0 opioid deaths per million in 2000 to 118.3 in 2017). The highest year-to-year increases were from 2015 to 2016 (31.8%) and from 2016 to 2017 (52.2%). The adjusted hospitalizations doubled significantly during the study period (an increase of 103.7%, from 159.7 opioid hospitalizations per million Canadians in 2000 to 325.3 in 2012). The adjusted rate of emergency department visits increased significantly by 188.7% (from 280.6 per million in 2000 to 810.1 in 2012). Case-fatality was 2.3% overall and was mainly constant during the study period. Both 90- and 365-day all-cause mortality increased significantly between 2000 and 2011 (from 1.7 to 3.1% and 3.9 to 7.4%, respectively), while re-hospitalization for opioid-related diagnoses was reduced (from 7.8 to 6.4% and 14.2 to 12.9%, respectively). CONCLUSIONS: Opioid-related mortality, hospitalization and emergency department visits in Canada have been increasing gradually since 2000.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".