Improving risk management for violence in mental health services: a multimethods approach

Research output: Contribution to journalArticle

  • External authors:
  • Jeremy W Coid
  • Simone Ullrich
  • Constantinos Kallis
  • Mark Freestone
  • Rafael Gonzalez
  • Artemis Igoumenou
  • Anthony Constantinou
  • Norman Fenton
  • William Marsh
  • Min Yang
  • Bianca Destavola
  • Junmei Hu
  • Jenny Shaw
  • Mike Doyle
  • Laura Archer-power
  • Mary Davoren
  • Beatrice Osumili
  • Paul Mccrone
  • Katherine Barrett
  • David Hindle
  • Paul Bebbington


Background: Mental health professionals increasingly carry out risk assessments to prevent future violence by their patients. However, there are problems with accuracy and these assessments do not always translate into successful risk management.
Objectives: Our aim was to improve the accuracy of assessment and identify risk factors that are causal to be targeted by clinicians to ensure good risk management. Our objectives were to investigate key risks at the population level, construct new static and dynamic instruments, test validity and construct new models of risk management using Bayesian networks.
Methods and results: We utilised existing data sets from two national and commissioned a survey to identify risk factors at the population level. We confirmed that certain mental health factors previously thought to convey risk were important in future assessments and excluded others from subsequent parts of the study. Using a first-episode psychosis cohort, we constructed a risk assessment instrument for men and women and showed important sex differences in pathways to violence. We included a 1-year follow-up of patients discharged from medium secure services and validated a previously developed risk assessment guide, the Medium Security Recidivism Assessment Guide (MSRAG). We found that it is essential to combine ratings from static instruments such as the MSRAG with dynamic risk factors. Static levels of risk have important modifying effects on dynamic risk factors for their effects on violence and we further demonstrated this using a sample of released prisoners to construct risk assessment instruments for violence, robbery, drugs and acquisitive convictions. We constructed a preliminary instrument including dynamic risk measures and validated this in a second large data set of released prisoners. Finally, we incorporated findings from the follow-up of psychiatric patients discharged from medium secure services and two samples of released prisoners to construct Bayesian models to guide clinicians in risk management.
Conclusions: Risk factors for violence identified at the population level, including paranoid delusions and anxiety disorder, should be integrated in risk assessments together with established high-risk psychiatric morbidity such as substance misuse and antisocial personality disorder. The incorporation of dynamic factors resulted in improved accuracy, especially when combined in assessments using actuarial measures to obtain levels of risk using static factors. It is important to continue developing dynamic risk and protective measures with the aim of identifying factors that are causally related to violence. Only causal factors should be targeted in violence prevention interventions. Bayesian networks show considerable promise in developing software for clinicians to identify targets for intervention in the field. The Bayesian models developed in this programme are at the prototypical stage and require further programmer development into applications for use on tablets. These should be further tested in the field and then compared with structured professional judgement in a randomised controlled trial in terms of their effectiveness in preventing future violence.

Bibliographical metadata

Original languageEnglish
Pages (from-to)1-408
JournalProgramme Grants Appl Res
Issue number16
Early online dateNov 2016
Publication statusE-pub ahead of print - Nov 2016

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