Evidence map›Paper›PMID 40415419›Full record

Observational studyAddiction (Abingdon, England)2025

Identifying key risk factors for intentional self-harm, including suicide, among a cohort of people prescribed opioid agonist treatment: A predictive modelling study.

Nicola R Jones, Matthew Hickman, Chrianna Bharat, Suzanne Nielsen, Sarah Larney, Nimnaz Fathima Ghouse, Julia Lappin, Louisa Degenhardt

Abstract readObservational Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in Addiction (Abingdon, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Observational
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Nicola R JonesNational Drug and Alcohol Research Centre, University of New South Wales, Sydney, Australia.ORCID https://orcid.org/0000-0002-5742-4598
Matthew HickmanNational Drug and Alcohol Research Centre, University of New South Wales, Sydney, Australia.ORCID https://orcid.org/0000-0001-9864-459X
Chrianna BharatNational Drug and Alcohol Research Centre, University of New South Wales, Sydney, Australia.ORCID https://orcid.org/0000-0002-9778-0386
Suzanne NielsenMonash Addiction Research Centre, Eastern Health Clinical School, Monash University, Melbourne, Australia.ORCID https://orcid.org/0000-0001-5341-1055
Sarah LarneyNational Drug and Alcohol Research Centre, University of New South Wales, Sydney, Australia.ORCID https://orcid.org/0000-0002-5602-4963
Nimnaz Fathima GhouseNational Drug and Alcohol Research Centre, University of New South Wales, Sydney, Australia.
Julia LappinNational Drug and Alcohol Research Centre, University of New South Wales, Sydney, Australia.ORCID https://orcid.org/0000-0001-5946-2144
Louisa DegenhardtNational Drug and Alcohol Research Centre, University of New South Wales, Sydney, Australia.ORCID https://orcid.org/0000-0002-8513-2218

Funding

TRANSFORM - Transforming the evidence base on estimating prevalence of opioid use disorder and expanded access to interventions to prevent drug related deaths: an international data linkage studyR01DA059822 · NIDA · UNIVERSITY OF NEW SOUTH WALES · PI Joshua Adam Barocas, Louisa Degenhardt · 2024 to 2026
$2.3M
Australian Government Department of HealthFIC NIH HHS R01 DA144740Fonds de Recherche du Québec - Santé 296569Health ResearchNational Health and Medical Research Council 1135991National Health and Medical Research Council 2016825National Health and Medical Research Council 2025894National Institute for Health ResearchNIDA NIH HHS R01 DA059822NIH HHSNIHR Bristol Biomedical Research CentreNSW Ministry of HealthUniversity of Bristol
6 · The paper itself

Abstract

BACKGROUND AND

aimsPeople 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).

designRetrospective observational cohort study using linked administrative data.

settingNew South Wales, Australia.

participants46 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.

findingsGradient 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.

conclusionsAmong 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.

Indexed as

Analgesics, OpioidOpiate Substitution TreatmentOpioid-Related DisordersSelf-Injurious BehaviorSuicideAdultFemaleHospitalizationHumansMachine LearningMaleMental DisordersMiddle AgedNew South WalesRetrospective StudiesRisk AssessmentAnalgesics, Opioiddata linkagemachine learningopioid‐related disordersself‐harmsuicide & feature analysis

Identifiers

PMID40415419
PMCPMC12426353

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.