Polysubstance toxicity deaths in Newfoundland and Labrador: a retrospective study
Bibliographic record
Abstract
OBJECTIVE: The present study examined the prevalence of polysubstance toxicity deaths in Newfoundland and Labrador between 2018 and 2023, describing sociodemographics of decedents and the most common substances contributing to death. METHODS: Death investigation data from the Office of the Chief Medical Examiner pertaining to polysubstance toxicity was obtained. Polysubstance toxicity deaths were defined as drug toxicity deaths in which substances from more than one drug class contributed to death. Trends in sociodemographics and substance combinations were quantitatively summarized. RESULTS: 313 individuals died from drug toxicity in the province between 2018 and 2023. Most deaths (n = 185, 59%) were determined to be caused by polysubstance toxicity. Polysubstance deaths increased 46% from 26 in 2018 to 38 in 2023. Most deaths were accidental in manner and the proportion of yearly accidental deaths increased sharply through the study period from 69% in 2018 to 89% in 2023. Male decedents outnumbered female decedents across the study period except for 1 year (2022), and young people (< 40 years old) had the highest death rate in the most recent study year. Cocaine was the most prevalent substance in toxicology reports and the combination of stimulants-opioids was the most prevalent drug class combination, followed by benzodiazepines-opioids and sedatives-opioids. CONCLUSION: Polysubstance toxicity is rising in Newfoundland and Labrador in recent years and our findings provide important information about sociodemographics and substance combinations to policymakers to aid in addressing this public health issue.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".