Characteristics of Opioid-Related Deaths in Ontario, Canada: Leveraging the Drug and Drug/Alcohol Related Death (DDARD) Database
Bibliographic record
Abstract
IntroductionReview of post-mortem toxicological results is the gold standard for identifying whether a death is opioid-related. The Drug and Drug/Alcohol Related Death (DDARD) database contains abstracted information from the Office of the Chief Coroner of Ontario, for all opioid-related deaths that occurred in Ontario, Canada between 1991 and 2016. Objectives and ApproachThe DDARD, which contains manner of death and drug concentrations from post-mortem toxicology results for opioids-related deaths in Ontario, was linked to the data repository housed at ICES. The objective of this project was to examine demographic characteristics and the type of opioid contributing to opioid-related deaths in FY2015/16. Individuals identified within DDARD who died from an opioid-related cause were linked to demographic, hospitalization and prescription drug databases to report on age, gender, neighbourhood income quintile, past health services utilization for opioid-toxicity, alcohol use disorders (AUD), mental health emergency department (ED) visits, and opioid(s) present at time of death. ResultsWe identified 737 opioid-related deaths in FY2015/16, the majority of which involved men (n=497; 67.4%), those living in lower socioeconomic status areas (n=395; 53.6%), and those residing in urban regions (n=655; 88.9%). Nearly half (n=325; 44.1%) of opioid-related deaths occurred among those aged 45 to 65 years. We found 9.5% (n=70) of individuals had a previous hospital visit for opioid toxicity, 25.4% (n=187) had previously diagnosed AUD, and 42.5% (n=313) had a previous mental health ED visit. Overall, 250 (33.9%) individuals had an active opioid prescription at time of death with oxycodone (n=92; 36.8%) the most commonly dispensed. Among those who didn’t have an active opioid prescription at time of death (n=484; 66%), fentanyl (n=184; 37.8%) was the most commonly found opioid on post-mortem toxicology. Conclusion/ImplicationsThis project demonstrates how data obtained through chart abstractions can be used to enhance existing administrative health datasets. Given the concern around the safety of opioids, it is important to examine the characteristics and type of opioid(s) involved at time of opioid-related death in order to develop targeted preventative strategies.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".