An actuarial model of violence risk assessment for persons with mental disorders
OBJECTIVES: An actuarial model was developed in the MacArthur Violence Risk Assessment Study to predict violence in the community among patients who have recently been discharged from psychiatric facilities. This model, called the multiple iterative classification tree (ICT) model, showed considerable accuracy in predicting violence in the construction sample. The purpose of the study reported here was to determine the validity of the multiple ICT model in distinguishing between patients with high and low risk of violence in the community when applied to a new sample of individuals.
METHODS: Software incorporating the multiple ICT model was administered with independent samples of acutely hospitalized civil patients. Patients who were classified as having a high or a low risk of violence were followed in the community for 20 weeks after discharge. Violence included any battery with physical injury, use of a weapon, threats made with a weapon in hand, and sexual assault.
RESULTS: Expected rates of violence in the low- and high-risk groups were 1 percent and 64 percent, respectively. Observed rates of violence in the low- and high-risk groups were 9 percent and 35 percent, respectively, when a strict definition of violence was used, and 9 percent and 49 percent, respectively, when a slightly more inclusive definition of violence was used. These findings may reflect the "shrinkage" expected in moving from construction to validation samples.
CONCLUSIONS: The multiple ICT model may be helpful to clinicians who are faced with making decisions about discharge planning for acutely hospitalized civil patients.
John Monahan, Henry J. Steadman, Pamela Clark Robbins, Paul S. Appelbaum, Steven M. Banks, Thomas Grisso, Kirk Heilburn, Edward P. Mulvey, Loren H. Roth, and Eric Silver. "An actuarial model of violence risk assessment for persons with mental disorders" Psychiatric services (Washington, D.C.) 56.7 (2005).
Available at: http://works.bepress.com/thomas_grisso/87