Trends in Suicide Mortality in Canada by Sex and Age Group, 1981 to 2017: A Population-Based Time Series Analysis: Tendances de la mortalité par suicide au Canada selon le sexe et le groupe d’âge, 1981 – 2017 : Une analyse de séries chronologiques dans la population
Bibliographic record
Abstract
Objectives:Suicide is a complex global public health issue. The objective of this study was to assess time trends in suicide mortality in Canada by sex and age group.Methods:We extracted data from the Canadian Vital Statistics Death Database for all suicide deaths among individuals aged 10 years and older based on International Statistical Classification of Diseases and Related Health Problems, Ninth Revision (E950-959; 1981 to 1999) and International Statistical Classification of Diseases and Related Health Problems, 10th Revision (X60-X84, Y87·0; 2000 to 2017) for a 37-year period, from 1981 to 2017. We calculated annual age-standardized, sex-specific, and age group-specific suicide mortality rates, and used Joinpoint Regression for time trend analysis.Results:The age-standardized suicide mortality rate in Canada decreased by 24.0% from 1981 to 2017. From 1981 to 2007, there was a significant annual average decrease in the suicide rate by 1.1% (95% confidence interval, −1.3 to −0.9), followed by no significant change between 2007 and 2017. From 1981 to 2017 and from 1990 to 2017, females aged 10 to 24 and 45 to 64 years old, respectively, had a significant increase in suicide mortality rates. However, males had the highest suicide mortality rates in all years in the study; the average male-to-female ratio was 3.4:1.Conclusion:The 3-decade decline in suicide mortality rates in Canada paralleled the global trend in rate reductions. However, since 2008, the suicide rate in Canada was relatively unchanged. Although rates were consistently higher among males, we found significant rate increases among females in specific age groups. Suicide prevention efforts tailored for adult males and young and middle-aged females could help reduce the suicide mortality rate in Canada.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".