Violence Risk Assessment: Using the Oxford Mental Illness and Violence Tool – Psychiatric Times

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SPECIAL REPORT: FORENSIC PSYCHIATRY

Sustainable Development Goals (SDGs):

1. Goal 3: Good Health and Well-being
2. Goal 16: Peace, Justice, and Strong Institutions

Clinical Psychiatry and Precision Medicine

Clinical psychiatry is shifting toward precision medicine. Until now, one-size-fits-all strategies have had a patchy success rate, leaving many patients with delayed diagnoses, uncertainty about their care, and suboptimal treatment. By harnessing the opportunities provided by big data, new research is investigating how diagnosis, prognosis, and treatment can be personalized.

Prognosis and Risk Assessment

In terms of prognosis, clinical prediction models can provide individual-level risk estimates for important future outcomes.¹ These models statistically combine data on risk factors from large data sets to assess the risks of serious adverse outcomes for mental health, such as suicidal behaviors, violence toward others, and severe relapses in mental state. These can be translated into risk assessment tools, which allow clinicians to input information on individual factors, which can be used to calculate a risk for that individual based on the underlying statistical model. Such tools based on high-quality prognostic models can complement clinical assessment and assist clinicians in making more evidence-based decisions, particularly early on in the patient pathway. This is especially important for estimating the risk of perpetration of violence and criminality, which is increased in those with severe mental illness and affects a minority of patients.

For example, individuals with schizophrenia spectrum disorders have a risk that is 2 to 5 times higher than the general population (including in studies with careful adjustment for confounders, in individuals without diagnosed comorbidities, and in sibling comparisons that inherently account for shared background familial factors such as early environment).² This risk increases further with comorbid substance misuse,² and absolute rates vary according to illness stage and setting.³ Moreover, a review of the prevalence of violent outcomes in psychiatric patients found some patient groups with elevated rates. This included around a fifth of individuals who present in psychiatric emergency settings in the next 12 months and more than one-third in involuntarily committed patients and patients with first-episode psychosis, also over the subsequent 12 months (Figure 1).³ More recent work in first-episode psychosis has also found that around 1 in 10 individuals have violent outcomes that lead to police contact in the year after referral to mental health services.⁴

FIGURE 1. Estimated Mean Prevalence of Violence Perpetration in Psychiatric Patients by Study Setting Over 6 to 12 Months: Pooled Estimate³

These observational studies’ findings highlight the need for accurate and validated violence prediction tools in this population, which, if linked to effective interventions, can assist in reducing future risks. Although some risk assessment approaches and instruments exist, such as the Historical Clinical Risk Management-20, they are mainly used in forensic mental health and may have limitations if used in general psychiatry. One significant limitation is that they are often overly time-consuming and costly. For example, some of these older risk assessment approaches, particularly those described as structured clinical judgment tools, take up to 16 hours to complete,⁵ making them impractical for clinical use. Another shortcoming is that they do not provide probability scores for individuals but only categorize them into broad groups of low, medium, and high risk. However, classification (using cutoffs) is not a good aim when models are relatively accurate—here, you will want to know probabilities. This is exactly how the Framingham Risk Score (or, for that matter, the weather) is communicated—not in a categorical way of yes/no but in relation to probabilities, which allows individuals to decide how to act depending on the decision to be made and its implications (eg, changing diet or adding a statin or not). Additionally, structured clinical judgment tools typically show poor predictive accuracy in real-world clinical settings compared with research settings, raising concerns about their everyday clinical use. This is likely because these tools were developed using small sample sizes, focused on a specific patient population (eg, forensic patients), and were not externally tested in real-world settings different from the one in which they were developed.^6,7

Novel Tools for Risk Assessment

Therefore, novel tools are required to assist clinicians in making well-informed decisions, facilitating early interventions, and ensuring consistency in risk assessment within and across clinical teams. A new generation of tools that provide probability scores have been developed using population-based registers that cover a wide range of patient populations and provide extensive information about them over time.

One of these tools, using data from more than 75,000 individuals with severe mental illness in Sweden, is the Oxford Mental Illness and Violence (OxMIV) tool, which aims to assist clinicians in assessing the risk of violence within 12 months of assessment among individuals with schizophrenia spectrum and bipolar disorders. The tool was built with 16 risk factors, including demographic, criminal history, and clinical variables, such as age, sex at birth, previous violent crime, past drug or alcohol misuse, and recent antipsychotic treatment.⁸ The OxMIV tool has been validated and updated across diverse patient populations in England, Germany, and the Netherlands,^9-11 demonstrating applicability in different clinical settings. Clinicians can easily access its user-friendly interface online, making it simple to integrate into practice (Figure 2).

FIGURE 2. OxMIV’s Web-Based Interface

For example, when a clinician assesses the risk of violence in an individual with schizophrenia spectrum disorders or bipolar disorder, they can quickly enter all necessary data into the OxMIV calculator and estimate the individual’s risk level in percentages. Risk estimates become less accurate and clinically useful at very high levels; therefore, they are presented at a maximum of 20%. Importantly, OxMIV allows unknown values for some risk factors (providing a range of predicted risks).

Benefits of Integrating Risk Prediction Tools

Integrating OxMIV and similar novel tools into clinical practice can complement clinical assessment and improve clinician confidence in making early personalized decisions to improve patient care, such as:

• Initiating early discussions on strategies to reduce medium- and longer-term violence risk with patients and their families.
• Collaborating with multidisciplinary teams to identify appropriate next steps for more detailed assessment and care.
• Targeting modifiable risk factors, such as substance misuse, nonadherence, and/or effectiveness of medication, impulsivity, unstable living conditions, and disengagement from services.
• Developing crisis plans with caregivers, family, and staff to efficiently manage emergencies.
• Collaborating with other services, including police, probation, substance misuse, housing, and social services.¹²

As psychiatry embraces precision medicine, the use of validated and scalable risk prediction tools can potentially improve mental health outcomes. Integrating these tools into clinical practice can assist violence risk assessment and allow for more personalized decisions early on in the treatment process, benefiting patients and their families while improving safety and well-being.

Dr Fazel is a professor of forensic psychiatry at the University of Oxford in England. Mr Scola is a postdoctoral research associate (pending PhD viva) in psychiatric epidemiology at the University of Oxford in England.

References

1. Oliver D, Arribas M, Perry BI, et al. Using electronic health records to facilitate precision psychiatry. Biol Psychiatry. 2024;96(7):532-542.
2. Whiting D, Gulati G, Geddes JR, Fazel S. Association of schizophrenia spectrum disorders and violence perpetration in adults and adolescents from 15 countries: a systematic review and meta-analysis. JAMA Psychiatry. 2022;79(2):120-132.
3. Swanson JW, McGinty EE, Fazel S, Mays VM. Mental illness and reduction of gun violence and suicide: bringing epidemiologic research to policy. Ann Epidemiol. 2015;25(5):366-376.
4. Whiting D, Lennox BR, Fazel S. Violent outcomes in first-episode psychosis: a clinical cohort study. Early Interv Psychiatry. 2020;14(3):379-382.
5. Viljoen JL, McLachlan K, Vincent GM. Assessing violence risk and psychopathy in juvenile and adult offenders: a survey of clinical practices. Assessment. 2010;17(3):377-395.
6. Fazel S, Wolf A, Vazquez-Montes MDLA, Fanshawe TR. Prediction of violent reoffending in prisoners and individuals on probation: a Dutch validation study (OxRec). Sci Rep. 2019;9(1):841.
7. Ogonah MGT, Seyedsalehi A, Whiting D, Fazel S. Violence risk assessment instruments in forensic psychiatric populations: a systematic review and meta-analysis. Lancet Psychiatry. 2023;10(10):780-789.
8. Fazel S, Wolf A, Larsson H, et al. Identification of low risk of violent crime in severe mental illness with a clinical prediction tool (Oxford Mental Illness and Violence tool [OxMIV]): a derivation and validation study. Lancet Psychiatry. 2017;4(6):461-468.
9. Whiting D, Mallett S, Lennox B, Fazel S. Assessing violence risk in first-episode psychosis: external validation, updating and net benefit of a prediction tool (OxMIV). BMJ Ment Health. 2023;26(1):e300634.
10. Negatsch V, Voulgaris A, Seidel P, et al. Identifying violent behavior using the Oxford mental illness and violence tool in a psychiatric ward of a German prison hospital. Front Psychiatry. 2019;10:264.
11. Lamsma J, Yu R, Fazel S; Genetic Risk and Outcome of Psychosis (GROUP) investigators. Validation and recalibration of OxMIV in predicting violent behaviour in patients with schizophrenia spectrum disorders. Sci Rep. 2022;12(1):461.
12. Whiting D. Improving Violence Risk Assessment and Intervention in First Episode Psychosis. Doctoral thesis. University of Oxford; 2021.

SDGs, Targets, and Indicators Analysis

1. Which SDGs are addressed or connected to the issues highlighted in the article?

• SDG 3: Good Health and Well-being
• SDG 16: Peace, Justice, and Strong Institutions

The article discusses the use of personalized risk assessment tools in psychiatry to improve mental health outcomes and reduce violence. This aligns with SDG 3, which aims to ensure healthy lives and promote well-being for all at all ages. It also relates to SDG 16, which focuses on promoting peaceful and inclusive societies, providing access to justice for all, and building effective, accountable, and inclusive institutions at all levels.

2. What specific targets under those SDGs can be identified based on the article’s content?

• SDG 3.4: By 2030, reduce by one-third premature mortality from non-communicable diseases through prevention and treatment and promote mental health and well-being.
• SDG 16.1: Significantly reduce all forms of violence and related death rates everywhere.

The article emphasizes the need for personalized risk assessment tools to improve mental health outcomes and reduce violence. Achieving these goals would contribute to SDG 3.4, which aims to reduce premature mortality from non-communicable diseases, including mental health conditions. It would also contribute to SDG 16.1, which focuses on reducing violence and related death rates.

3. Are there any indicators mentioned or implied in the article that can be used to measure progress towards the identified targets?

• Use of clinical prediction models and risk assessment tools to estimate individual-level risk for serious adverse outcomes in mental health.
• Development and validation of novel tools, such as the Oxford Mental Illness and Violence (OxMIV) tool, for assessing the risk of violence in individuals with severe mental illness.
• Integration of risk prediction tools into clinical practice to improve violence risk assessment and facilitate personalized decision-making.

The article highlights the use of clinical prediction models and risk assessment tools, such as the OxMIV tool, as indicators of progress towards personalized risk assessment in mental health. The development, validation, and integration of these tools into clinical practice can serve as indicators of progress in achieving the targets of SDG 3.4 and SDG 16.1.

Table: SDGs, Targets, and Indicators

Source: psychiatrictimes.com