Trends in drug overdose deaths among adults 65 years of age and older in Canada (2000–2022)
Bibliographic record
Abstract
Background: Although young adults and middle-aged adults have borne the brunt of the drug overdose crisis in Canada, older adults are also at an increased risk of harms. We examined trends in drug overdose deaths and opioid overdose deaths among adults 65 years of age and older. Methods: Age-standardized rates of drug overdose deaths in Canada (2000-2022) and of opioid overdose deaths in Ontario (2003-2021) were computed. Drug overdose deaths were based on vital statistics registries, while opioid overdose deaths were based on toxicologic testing. Trends were characterized using joinpoint regression. Results: Drug overdose deaths among adults 65 years of age and older in Canada rose from 4.3 to 9.9 deaths per million in the entire population between 2000 and 2022 (Average Annual Percentage Change [AAPC; 95 % CI]: 3.1 % [2.6 %-3.6 %]). Increases were observed in males (AAPC [95 % CI]: 4.0 % [3.1 %-4.9 %]), females (2.1 % [1.0 %-3.2 %]) and unintentional deaths (6.0 % [1.0 %-11.3 %]) after stratification by sex and manner of death. Opioid overdose deaths among adults 65 years of age and older in Ontario increased from 1.5 to 5.2 deaths per million in the entire population between 2003 and 2021 (AAPC [95 % CI]: 7.5 % [4.5 %-10.5 %]). Conclusions: Drug overdose deaths more than doubled in Canada and opioid overdose deaths more than tripled in Ontario among adults 65 years of age and older during the past two decades. These findings indicate a need for education of patients, prioritization of harm reduction interventions, screening, intervention and treatment and adherence to prescribing guidelines.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".