Classification Of Violence Risk Research Articles

Clinical (In)Efficiency in the Prediction of Dangerous Behavior

The prediction of dangerous and/or violent behavior is particularly important to the conduct of the U.S. criminal justice system when it makes decisions about restrictions of personal freedom, such as preventive detention, forensic commitment, parole, and in some states such as Texas, when to permit an execution to proceed of an individual found guilty of a capital crime. This article discusses the prediction of dangerous behavior both through clinical judgment and actuarial assessment. The general conclusion drawn is that for both clinical and actuarial prediction of dangerous behavior, we are far from a level of accuracy that could justify routine use. To support this later negative assessment, two topic areas are emphasized: (1) the MacArthur Study of Mental Disorder and Violence, including the actuarial instrument developed as part of this project (the Classification of Violence Risk), along with all the data collected that helped develop the instrument; and (2) the U.S. Supreme Court case of Barefoot v. Estelle (1983) and the American Psychiatric Association “friend of the court” brief on the (in)accuracy of clinical prediction for the commission of future violence. Although now three decades old, Barefoot v. Estelle is still the controlling Supreme Court opinion regarding the prediction of future dangerous behavior and the imposition of the death penalty in states, such as Texas; for example, see Coble v. Texas (2011) and the Supreme Court denial of certiorari in that case.

The Classification of Violence Risk.

(Reprinted with permission from Behav. Sci. Law 24: 721-730, 2006).

Violent Behavior and Gender of Swedish Psychiatric Patients: A Prospective Clinical Study

This study was designed to investigate the base rate of violent behavior, the predictive validity of the Classification of Violence Risk (COVR) software, and specific risk factors for violence among nonforensically involved psychiatric patients in Sweden. On discharge from two psychiatric hospitals in Stockholm, 331 patients were interviewed. Telephone interviews with the patients and supportive others, as well as data from a national criminal register, were used to measure violent behavior 20 weeks after discharge. After the baseline interview, patients were assigned to different risk groups by the COVR software. Predicted risk was compared with the occurrence of actual acts of violence during the follow-up. Gender differences in base rates of violent behavior among the general psychiatric population were not found during the 20 weeks of follow-up after discharge. Violent behavior was significantly predicted by young age of males and by level of anger, violent thoughts, and victimization of females. The predictive validity of the COVR software was comparable between females (area under the curve [AUC]=.78) and males (AUC=.76). Violent behavior was uncommon for all patients. Although several risk factors were significantly associated with violence by each gender, the COVR software could predict violence equally well for both genders.

The Predictive Ability of the Classification of Violence Risk (COVR) in a Forensic Psychiatric Hospital

Objective: Aggression is a common problem in psychiatric and correctional facilities. An actuarial instrument, the Classification of Violence Risk (COVR), has demonstrated utility in predicting community aggression among civilly committed psychiatric patients and, more recently, an ability to predict institutional aggression in a forensic facility. This prospective study aimed to extend validation of the use of the COVR in a forensic facility. Methods: The study, conducted at a large U.S. forensic facility, used nursing records to document incidents of physical aggression by 146 patients for 20 weeks after COVR administration. Correlational and chi square analyses were conducted to evaluate the efficacy of the COVR in predicting aggression. Results: The COVR showed modest predictive ability for institutional aggression, with a correlation of .331 and an area under the curve of .725. Conclusions: Results support use of the COVR in a forensic facility; its ability to predict institutional aggression is similar to that found for community aggression. (Psychiatric Services 62:430–433, 2011)

The Predictive Ability of the Classification of Violence Risk (COVR) in a Forensic Psychiatric Hospital

Aggression is a common problem in psychiatric and correctional facilities. An actuarial instrument, the Classification of Violence Risk (COVR), has demonstrated utility in predicting community aggression among civilly committed psychiatric patients and, more recently, an ability to predict institutional aggression in a forensic facility. This prospective study aimed to extend validation of the use of the COVR in a forensic facility. The study, conducted at a large U.S. forensic facility, used nursing records to document incidents of physical aggression by 146 patients for 20 weeks after COVR administration. Correlational and chi square analyses were conducted to evaluate the efficacy of the COVR in predicting aggression. The COVR showed modest predictive ability for institutional aggression, with a correlation of .331 and an area under the curve of .725. Results support use of the COVR in a forensic facility; its ability to predict institutional aggression is similar to that found for community aggression.

Violent behaviour by general psychiatric patients in Sweden – validation of Classification of Violence Risk (COVR) software

The objectives of the study are to report the 20-week base rate of violent behaviour in the community among a general psychiatric patient population from Stockholm and to establish the validity of a violence risk assessment software program, Classification of Violence Risk (COVR), in a European setting. Three hundred and thirty one patients at two psychiatric hospitals in Stockholm were interviewed upon discharge. Telephone interviews with the patients and collaterals were conducted 10 and 20weeks later. The violent behaviour was also measured through a national criminal register. The allocation of patients into different risk groups according to COVR software was compared with the occurrence of actual acts of violence during the follow-up. The base rate of violent behaviour was 5.7% and a ROC-analysis showed that the AUC for COVR was 0.77. Since there were few patients in the high risk groups, the 95% confidence interval for the proportion of violent patients was wide. The base rate of violent behaviour is relatively low in Sweden and prediction is therefore difficult. The predictive validity of COVR software is comparable to other risk assessment tools.

Assessing Risk of Future Violence Among Forensic Psychiatric Inpatients With the Classification of Violence Risk (COVR)

Assessing Risk of Future Violence Among Forensic Psychiatric Inpatients With the Classification of Violence Risk (COVR)

Investigating the Validity of the Classification of Violence Risk in a UK Sample

We attempted to cross-validate the Classification of Violence Risk, or COVR (Monahan, Steadman, Appelbaum et al., 2005), in a sample of acute psychiatric patients in the North West of England. The study used a prospective design to follow-up 93 patients assessed using the COVR for 20 weeks post-discharge. Overall, the COVR did not demonstrate significant predictive validity with respect to violence. More than half of patients classified as average risk by the COVR were violent in the follow-up compared to less than 20% of those in the very low risk group, but none of the high-risk participants were violent. In contrast, violence was significantly predicted by key variables, including female gender, violence preceding admission, and personality disorder. There is a need for more cross-validation research of the COVR.

Neuroprediction, Violence, and the Law: Setting the Stage.

In this paper, our goal is to (a) survey some of the legal contexts within which violence risk assessment already plays a prominent role, (b) explore whether developments in neuroscience could potentially be used to improve our ability to predict violence, and (c) discuss whether neuropredictive models of violence create any unique legal or moral problems above and beyond the well worn problems already associated with prediction more generally. In "Violence Risk Assessment and the Law", we briefly examine the role currently played by predictions of violence in three high stakes legal contexts: capital sentencing ("Violence Risk Assessment and Capital Sentencing"), civil commitment hearings ("Violence Risk Assessment and Civil Commitment"), and "sexual predator" statutes ("Violence Risk Assessment and Sexual Predator Statutes"). In "Clinical vs. Actuarial Violence Risk Assessment", we briefly examine the distinction between traditional clinical methods of predicting violence and more recently developed actuarial methods, exemplified by the Classification of Violence Risk (COVR) software created by John Monahan and colleagues as part of the MacArthur Study of Mental Disorder and Violence [1]. In "The Neural Correlates of Psychopathy", we explore what neuroscience currently tells us about the neural correlates of violence, using the recent neuroscientific research on psychopathy as our focus. We also discuss some recent advances in both data collection ("Cutting-Edge Data Collection: Genetically Informed Neuroimaging") and data analysis ("Cutting-Edge Data Analysis: Pattern Classification") that we believe will play an important role when it comes to future neuroscientific research on violence. In "The Potential Promise of Neuroprediction", we discuss whether neuroscience could potentially be used to improve our ability to predict future violence. Finally, in "The Potential Perils of Neuroprediction", we explore some potential evidentiary ("Evidentiary Issues"), constitutional ("Constitutional Issues"), and moral ("Moral Issues") issues that may arise in the context of the neuroprediction of violence.

Assessing Risk of Future Violence Among Forensic Psychiatric Inpatients With the Classification of Violence Risk (COVR)

Instruments are needed to help clinicians make decisions about a patient's risk of future violence in order to manage this risk, protect others, and allocate resources. One such actuarial instrument-the Classification of Violence Risk (COVR)-was developed from the MacArthur Violence Risk Assessment Study. The COVR has not been validated in a sample other than the one with which it was constructed or outside of the United States. The purpose of this study was to provide an independent validation of the COVR in a sample of forensic psychiatric inpatients in the United Kingdom. The prospective study was conducted at four medium-security forensic psychiatric units over six months. Two risk assessment instruments were completed for 52 patients: the COVR and the Violence Risk Appraisal Guide (VRAG), a well-established actuarial instrument. Incidents of verbal aggression, physical aggression toward others, and aggression against property were documented for the next six months from nursing records. Predictive accuracy of the instruments was analyzed using both correlational techniques and signal detection theory. COVR was a good predictor of both verbal and physical aggression. Its predictive ability was similar to that of the VRAG, although the VRAG was a better predictor of violence to property. The study provides the first independent validation of the COVR and evidence of the usefulness of the COVR in predicting harmful behavior in forensic inpatient settings in the United Kingdom.

Assessing Risk of Future Violence Among Forensic Psychiatric Inpatients With the Classification of Violence Risk (COVR)

Assessing Risk of Future Violence Among Forensic Psychiatric Inpatients With the Classification of Violence Risk (COVR)

Issues Regarding the Clinical Use of the Classification of Violence Risk (COVR) Assessment Instrument

The MacArthur Study of Mental Disorder and Violence produced an instrument for classifying hospitalized psychiatric patients according to their risk of behaving violently following discharge. The instrument, Classification of Violence Risk (COVR) has been computerized and is now commercially available to clinicians. A validation study performed by the original researchers showed that when the instrument was applied to a new sample of patients, it demonstrated a considerable reduction in positive predictive power. Potential factors affecting the instrument's accuracy in applied settings are reviewed. It is concluded that, until additional research clarifies uncertainty about the instrument, clinicians would do well to be very cautious in utilizing COVR results to make judgments as to violence risk, particularly when the test results suggest a high risk of future violence.

The classification of violence risk

The Classification of Violence Risk (COVR) is an interactive software program designed to estimate the risk that a person hospitalized for mental disorder will be violent to others. The software leads the evaluator through a chart review and a brief interview with the patient. At the end of this interview, the software generates a report that contains a statistically valid estimate of the patient's violence risk-ranging from a 1% to a 76% likelihood of violence-including the confidence interval for that estimate, and a list of the risk factors that the program took into account to produce the estimate. In this article, the development of the COVR software is described and several issues that arise in its administration are discussed.

Letters

Back to table of contents Previous article Next article LettersFull AccessJohn Monahan, Ph.D., Henry J. Steadman, Ph.D., and Pamela Clark Robbins, B.A.John MonahanSearch for more papers by this author, Ph.D., Henry J. SteadmanSearch for more papers by this author, Ph.D., and Pamela Clark RobbinsSearch for more papers by this author, B.A.Published Online:1 Jan 2006https://doi.org/10.1176/appi.ps.57.1.143AboutSectionsView EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InEmail In Reply: Dr. McCusker concludes that the authors "correctly state that the study affirmatively answered their research question." But regarding the COVR software that was being validated, he believes that "pending additional research findings, the instrument has, at best, questionable clinical usefulness." Although our article acknowledged that many questions "await studies using the software in actual clinical settings," we believe that at the present time the software "may be helpful to clinicians." The manual that accompanies the software makes clear that the software is a "tool" to inform clinical judgment and that "the application of clinical judgment represents the standard of care" in violence risk assessment.Dr. McCusker's coin-flipping analogy can be seriously misleading. Given its high true-negative rate, the COVR's overall accuracy makes it vastly better than a coin flip. Only after patients were classified by the software as high risk did they have a roughly 50 percent probability of being violent. The utility of the COVR seems most reasonably evaluated against alternative approaches to risk assessment rather than against absolutist expectations. In this regard, the findings reported in the COVR validation study represent a level of accuracy equal to the highest true-positive rate reported for unstructured clinical prediction, with a much lower false-negative rate (1). The clinical predictions reported by Lidz and colleagues (1) were achieved after detailed patient interviews by nurses, residents, and attending psychiatrists. In contrast, it takes seven minutes, on average, to administer the COVR.Reference1. Lidz C, Mulvey E, Gardner W: The accuracy of predictions of violence to others. JAMA 269:1007–1011,1993Crossref, Medline, Google Scholar FiguresReferencesCited byDetailsCited byIssues Regarding the Clinical Use of the Classification of Violence Risk (COVR) Assessment Instrument5 July 2007 | International Journal of Offender Therapy and Comparative Criminology, Vol. 51, No. 6Journal de Chirurgie, Vol. 144, No. 6Journal of Mental Health Counseling, Vol. 28, No. 4 Volume 57Issue 1 January 2006Pages 143-143 Metrics History Published online 1 January 2006 Published in print 1 January 2006