A consecutive case series study of the association between psychiatric diagnoses and suicide methods using coronial data in Queensland, Australia, from 1989 to 2021
Bibliographic record
Abstract
OBJECTIVE: People with different psychiatric diagnoses use certain suicide methods more frequently. These findings could have implications for reducing suicide. Therefore, the aim of this study was to assess associations between three severe mental illnesses (SMIs) and suicide methods, while adjusting for other confounding factors that could influence choice of suicide methods. METHODS: We used information on all individuals dying by suicide in Queensland, Australia, from 1989 to 2021. Methods were compared to hanging, strangulation and suffocation for people with three different SMIs with adjusted risk ratios in multinomial logistic regression models. RESULTS: People with psychotic disorders were over three times more likely to jump from a high place, jump or lie before a moving object, or use a sharp object. They were also more than twice as likely to use explosive material or smoke, fire and flames; or drowning and submersion and 60% more likely to use crashing of a motor vehicle. Poisoning by drugs was around 50% higher in those with psychotic disorders or depression. No other methods were substantially elevated relative to hanging, strangulation and suffocation (HSS). People with bipolar and depression had a lower risk ratios (RR) for several suicide methods. CONCLUSION: People with psychotic disorders were the most likely to use diverse methods and would benefit the most from means restriction interventions. Trialling interventions to ensure that people with psychotic disorders can avoid situations where they could use these methods may reduce suicides in this group. Further analysis of what drugs people with depression and bipolar might use is needed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".