OBJECTIVE: The authors compared the predictive accuracy of two risk assessment methods that are feasible to use in routine clinical settings: brief risk assessment tools and patients' self-perceptions of risk. METHODS: In 2002-2003, clinical interviewers met with 86 high-risk inpatients with co-occurring mental and substance use disorders (excluding schizophrenia) to carefully elicit the patients' global rating of their risk of behaving violently and to complete two brief risk assessment tools-the Clinically Feasible Iterative Classification Tree (ICT-CF) and the Modified Screening Tool (MST). Two months after discharge, patients were reinterviewed in the community to assess their involvement in violence. RESULTS: Patients' self-perceptions of risk performed quite well in predicting serious violence (area under the curve [AUC]=.74, sensitivity=50%), particularly compared with the ICT-CF (AUC=.59, sensitivity=40%) and the MST (AUC=.66, sensitivity=30%). Self-perceived risk also added significant incremental utility to these tools in predicting violence. CONCLUSIONS: Patients' self-perceptions hold promise as a method for improving risk assessment in routine clinical settings. Assuming it replicates and generalizes beyond the research context, this finding encourages a shift away from unaided clinical judgment toward a feasible method of risk assessment built on patient collaboration.

OBJECTIVE: Psychiatric symptoms play a crucial role in psychology and psychiatry. However, little is known about how dimensions of symptoms--other than symptom level--relate to psychiatric outcomes. Until recently, methods for measuring dynamic aspects of symptoms have not been available to clinicians or researchers. The authors sought to test whether systematic patterns of change in psychiatric symptoms can be recovered across weekly assessments of individuals at high risk for violence. A secondary objective was to explore whether dynamic features of symptoms (specifically, oscillation speed and dysregulation) are concurrently associated with violence, an important indicator of functional impairment for these individuals. METHOD: Participants (N=132) were drawn from a sample of patients evaluated at the emergency room of an urban psychiatric hospital. Patients actuarially classified as being at high risk for violence were eligible for participation in the study. Participants and collateral informants were interviewed weekly for 26 weeks following an acute psychiatric evaluation. Psychiatric symptoms were assessed using the Brief Symptom Inventory. Measures of symptom fluctuation and regulation were derived using dynamical systems models. Involvement in violence was assessed using self, informant, and official reports. RESULTS: Individuals' symptom dynamics were recovered by a linear oscillator model that described how quickly symptoms oscillated and whether symptoms were amplifying or moving back toward equilibrium across time. Patterns of rapid symptom fluctuation and symptom amplification were concurrently associated with violence. CONCLUSIONS: Psychiatric researchers and clinicians have long been interested in adopting more dynamic approaches to understanding symptom change. This study is the first to demonstrate that systematic fluctuations in symptom patterns may be captured by dynamic models. Moreover, the concurrent association between symptom dynamics and violence suggests avenues for future research to test how features of symptom fluctuation could affect behavior.

Antisocial personality disorder (ASPD) is associated with suicide, violence, and risk-taking behavior and can slow response to first-line treatment for Axis I disorders. ASPD may be assessed infrequently because few efficient diagnostic tools are available. This study evaluated 2 promising self-report measures for assessing ASPD--the ASPD scale of the Personality Diagnostic Questionnaire-4 (PDQ-4; S. E. Hyler, 1994) and the Personality Assessment Inventory (PAI; L. Morey, 1991, 2007)--as well as the ASPD module of the Structured Clinical Interview for DSM-IV Axis II (SCID-II; M. B. First, R. L. Spitzer, M. Gibbon, J. B. W. Williams, and L. S. Benjamin, 1997). The measures were administered to 1,345 offenders in court-mandated residential substance abuse treatment programs and prisons. PDQ-4 and PAI scores related strongly to SCID-II symptom counts (rs = .67 and .51, respectively), indicating these measures convey useful clinical information about the severity of offenders' ASPD pathology. The dimensional association between the measures was relatively invariant across gender, race, and site, although differences in mean scores were observed. Levels of agreement of the SCID-II with the PDQ-4 (kappa = .31) and PAI (kappa = .32) in classifying participants as ASPD was limited. Alternative thresholds for both self-report measures were identified and cross-validated.

Given the availability of violence risk assessment tools, clinicians are now better able to identify high-risk patients. Once these patients have been identified, clinicians must monitor risk state and intervene when necessary to prevent harm. Clinical practice is dominated by the assumption that increases in psychiatric symptoms elevate risk of imminent violence. This intensive study of patients (N = 132) at high risk for community violence is the first to evaluate prospectively the temporal relation between symptoms and violence. Symptoms were assessed with the Brief Symptom Inventory and threat/control override (TCO) scales. Results indicate that a high-risk patient with increased anger in 1 week is significantly more likely to be involved in serious violence in the following week. This was not true of other symptom constellations (anxiety, depression, TCO) or general psychological distress. The authors found no evidence that increases in the latter symptoms during 1 week provide an independent foundation for expecting violence during the following week.

Prior research has consistently demonstrated an association between substance use and involvement in violence among individuals with mental illness. Yet little is known about the temporal quality of this relationship, largely because longitudinal data required to address this issue are not readily available. This study examined the relationship between substance use (alcohol, marijuana, and other drug use) and violence at the daily level within a sample of mentally ill individuals at high risk for frequent involvement in violence (N = 132). Results support the serial nature of substance use and violence, with an increased likelihood of violence on days following the use of alcohol or multiple drugs, but not the inverse relationship. Implications for the utility of substance use as a risk marker for the assessment of future violence are discussed.

Mental health professionals' (MHPs') accuracy in assessing the risk of violence in female patients is particularly limited. Based on assessments made by 205 MHPs of 605 patients in an emergency room, this study explored potential causes of MHPs' poorer accuracy in assessing women's potential for violence. The dimensions that underlie MHPs' envisioned violence in patients were identified and were compared with those that characterized patients' reported violence during a 6-month follow-up period. There were three key findings from their study. First, violence envisioned by MHPs differed depending on their professional role and varied in its congruence with patients' reported violence. Second, patients' violence was organized by dimensions of domesticity and substance relatedness; women's violent incidents were more domestic than were men's. Third, when MHPs envisioned violence that was highly conditional on psychiatric deterioration and medication noncompliance, violence often did not occur.

To address several key questions in social science research, repeated interviews of individuals drawn from difficult populations are required. This article describes an approach for addressing the challenges associated with longitudinal interview studies, including locating research participants, obtaining reliable and valid interview data over time, and retaining participants across the course of the study. We applied this approach to conduct a study designed to identify changeable risk factors for violence among high-risk people with mental illness. To successfully conduct weekly interviews of these individuals in the community across a 6-month period, we developed a flexible and personalized interview format; carefully selected, trained, and supervised staff; and developed incentives to maximize participant retention. Each of these three steps is discussed as a guide for future longitudinal studies that involve interviewing difficult populations.

Advances in risk assessment have improved the ability to identify psychiatric patients at high risk for violence. Identifying these patients is necessary for developing treatment to address their needs. However, if violence is caused by risk factors that vary across patients, relatively homogeneous subgroups of high-risk patients must be identified and studied to develop effective risk management programs for each. This study was designed to identify and describe valid subtypes of patients reliably identified as at high risk by the multiple Iterative Classification Tree (ICT) risk assessment approach. After existing typologies of violent individuals were integrated to develop hypothesized subtypes of high-risk patients, data on 165 patients identified as at high risk by the multiple ICT were used to determine whether clinically meaningful subtypes could be identified and externally validated. Three groups (alpha, beta, and delta) largely consistent with the hypothesized subtypes and their correlates were identified. The implications of these findings for research and treatment development efforts are discussed.

Recently, psychopathy has become virtually synonymous with the Psychopathy Checklist (PCL) measures. However, the "gold standard" 2-factor model that underlines these measures has been questioned for its uncertain empirical support and emphasis on antisocial behavior that is not specific to psychopathic personality deviation. This study (N = 870 civil psychiatric patients) compares the fit of the traditional 2-factor model with that of a revised 3-factor model of psychopathy. The revised model better describes the structure of the Screening Version of the PCL (PCL:SV) than the traditional model. Although the revised model's exclusion of some items that assess antisocial behavior reduces the PCL:SV's power in predicting patient violence, this model arguably assesses psychopathy in a more specific, theoretically coherent fashion that may reduce misapplications of the construct. Implications for future research are discussed.

Recent studies indicate that a small, but critical subgroup of psychiatric patients is involved in a disproportionately large number of violent incidents among the mentally ill. This subgroup is an appropriate focus for intensive community-based treatment programs designed to reduce violence. However, little research has been conducted on methods for identifying patients who repeatedly become involved in violent incidents. This article describes a large follow-up study in which these patients were identified using a simple screening process that is feasible for routine use. This screening process efficiently and effectively identified a small minority of patients who were at risk for repeated involvement in violence. Patients deemed “at risk” by the screening process had an average of 7 violent incidents during a six-month follow-up period. The characteristics of these patients are described, and implications of the screening tool for conducting future research, targeting individuals for more intensive treatment services, and developing violence-focused treatment programs are discussed.