Identifying key risk factors for intentional self‐harm, including suicide, among a cohort of people prescribed opioid agonist treatment: A predictive modelling study
Bibliographic record
Abstract
BACKGROUND AND AIMS: People with opioid use disorder are at increased risk of intentional self-harm and suicide. Although risk factors are well known, most tools for identifying individuals at highest risk of these behaviours have limited clinical value. We aimed to develop and internally validate models to predict intentional self-harm and suicide risk among people who have been in opioid agonist treatment (OAT). DESIGN: Retrospective observational cohort study using linked administrative data. SETTING: New South Wales, Australia. PARTICIPANTS: 46 330 people prescribed OAT between January 2005 and November 2017. MEASUREMENTS: Intentional self-harm and suicide prediction within a 30-day window using linked population datasets for OAT, hospitalisation, mental health care, incarceration and mortality. Machine learning algorithms, including neural networks and gradient boosting, assessed over 80 factors during the last 3, 6 and 12 months. Feature visualisation using SHapley Additive exPlanations. FINDINGS: Gradient boosting identified 30 important factors in predicting self-harm and/or suicide. These included the most recent frequency of emergency department presentations; hospital admissions involving mental disorders such as borderline personality, substance dependence, psychosis and depression/anxiety; and recent release from incarceration. The best fitting model had a Gini coefficient of 0.65 [area under the curve (AUC) = 0.82] and was applied to 2017 data to estimate the probability of self-harm and/or suicide. On average 46 people (0.16%) (from a total of 28 000 people in OAT) experienced intentional self-harm or suicide per month. Applying a 0.15% probability threshold, approximately 5167 people were classified as high risk, identifying 69% of all self-harm or suicide cases per month. This figure reduced to 450 per month after excluding people already identified in the previous month. CONCLUSIONS: Among people in opioid agonist treatment, administrative linked data can be used with advanced machine learning algorithms to predict self-harm and/or suicide in a 30-day prediction window.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".