Trends in accidental drug overdose mortality in Canada: An analysis from 1974 to 2023
Bibliographic record
Abstract
• Overdose mortality rates in Canada were relatively stable between 1974 and 1991, accelerated in 1991, and again in 2013, with the latter increase linked to illicit fentanyl. • Crude mortality rates increased tenfold from 1974 to 2023, spanning all ages 15–74 years. • Pronounced provincial disparities reflect differences in healthcare practices and drug market dynamics. • Overdose mortality rates were similar between sexes in the early years, but male rates soon surpassed and remained higher, with both sexes experiencing steep increases after 2014. Overdose deaths in Canada have been rising since 2016, but long-term trends remain poorly characterized. We examined national overdose mortality trends from 1974 to 2023 and explored differences by sex, age, and province. We conducted a retrospective analysis of accidental and undetermined‐intent poisoning deaths in the Canadian Vital Statistics Death Database, calculating crude mortality rates (CMR) using Statistics Canada population estimates. We used segmented regression to model temporal trends and calculated average annual percentage change (AAPC) for each resulting segment. Analyses were stratified by sex, age (<25, 25–44, 45–64, and ≥65), and province. Between 1974 and 2023, 80,944 overdose deaths were recorded. Segmented regression of CMR revealed three phases: a period of relative stability (AAPC: -0.28 %; 1974–1991), followed by two accelerations (AAPC: 5.46 %; 1991–2013 and AAPC: 12 %; 2013–2023) CMRs were similar by sex until 2013–15, then surged in both males (AAPC: 13.81 %; 2012–2023) and females (AAPC: 9.32 %; 2015–2023). Rates in youth (<25) were stable until the early 2000s, then rose sharply (AAPC: 30.62 %; 2014–2017) before slowing, while rates among adults aged 25–44 (AAPC: 13.59 %; 2012–2023), 45–64 (AAPC: 11.56 %; 2014–2023), and ≥65 (AAPC: 18.48 %; 2020–2023) increased in recent years. Rates increased the most in Western provinces compared to Quebec and the Atlantic provinces. Canada’s overdose epidemic reflects a segmented trajectory, with marked accelerations in 1996 and 2013, driven by healthcare practices, evolving drug markets, and social vulnerabilities. Regional and demographic disparities underscore the need for targeted, historically informed public health 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".