Age, period and cohort analysis of suicide trends in Australia, 1907–2020
Bibliographic record
Abstract
Background: Suicide rates have been increasing in Australia since the mid-2000s, especially for women aged ≤25 years. We conducted an age-period-cohort study to investigate these recent trends in the context of historical Australian suicide rates. Methods: Data on annual suicides in Australia from 1907 to 2020 were extracted from the General Record of Incidence of Mortality. We modelled age-specific effects for a reference cohort, after adjustment for period effects. Findings: We found evidence of age, cohort and period effects. For males, compared to the cohort born in 1946-1950, rates were higher for all cohorts born after this year. The period effect showed peaks in the risk of male suicide in the mid 1960s and the early 1990s, followed by a decline in risk until early 2010, after which the risk began to rise again. For females, compared to the cohort born in 1946-1950, the risk of suicide was higher for all cohorts born after this, with the highest risk for those born in 2006-2010. The period effect for females showed an elevated risk of suicide in the mid 1960s followed by a sharp decline, and an increase in risk after 2009. Interpretation: Suicide rates in Australia have fluctuated substantially over time and appear to be related to age trends as well as period and cohort trends. Advocacy and policy making tends to focus on contemporaneous changes in suicide rates. However, this study shows that focusing only on year-on-year changes in suicide rates ignores underlying trends for specific population birth-cohorts. Funding: None.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".