The risk of repeated self-harm and suicide after emergency department presentation with self-harm in mental health presenters: a retrospective cohort study with data linkage in Queensland, Australia
Bibliographic record
Abstract
Background: Presentation to the emergency department (ED) with an index episode of self-harm is recognised as a risk factor for subsequent repeated self-harm and suicide. We describe demographic and clinical characteristics of adults (>18 years) presenting with mental health problems and self-harm to EDs in Queensland, Australia, and identify risk factors associated with repeated self-harm and suicide. Methods: This was a state-wide retrospective cohort study of adults presenting with an index self-harm presentation to any of the 27 public EDs in Queensland, Australia, over six years (1st January 2012 to 31st December 2017). We linked ED records with a state-wide death register. Primary outcomes were re-presentation with self-harm, or death by suicide. We constructed a multivariable Cox regression model to identify independent risk factors for re-presentation with self-harm, or death by suicide. We calculated the risk of repeated ED presentation for self-harm and suicide at 12- and 24-months. Findings: During the study period, 43,797 individuals presented to Queensland EDs with a self-harm related diagnosis. Half of the cohort were female (n = 20,980, 47.9%) and under age 35 (n = 23,871, 54.5%). A quarter (n = 10,991; 25.1%) had a repeated episode of self-harm and 515 (1.2%) died by suicide. Socioeconomic disadvantage, arrival by ambulance, self-presentation, small/medium hospital size, less-urgent triage category, not admitted status and previous mental health or physical health visits were associated with a re-presentation with self-harm. Suicide was associated with male sex, older age, and hospital admission. The repeated self-harm risk was 18.9% (95%CI, 18.5%-19.3%) at 12-months and 24.3% (95%CI, 23.9%-24.7%) at 24-months. The suicide risk was 0.7% (95%CI, 0.6%-0.7%) at 12-months and 1.0% (95%CI, 0.9%-1.1%) at 24-months. Interpretation: One in four people re-presented to ED with self-harm. Suicide was particularly associated with older males. Implementing evidence-based interventions to support people presenting to ED with self-harm should be a public health priority. Funding: This study was funded by an Emergency Medicine Foundation (EMF) grant (EMJS-382-R35-2021) and a National Health and Research Council Grant (1121898).
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".