The assessment of dynamic risk among forensic psychiatric patients transitioning to the community.

Law and Human Behavior 2016, Vol. 40, No. 4, 374 –386

© 2016 American Psychological Association 0147-7307/16/$12.00 http://dx.doi.org/10.1037/lhb0000183

The Assessment of Dynamic Risk Among Forensic Psychiatric Patients Transitioning to the Community Stephanie R. Penney

Lisa A. Marshall

Centre for Addiction and Mental Health, Toronto, Ontario, Canada, and University of Toronto

Ontario Shores Centre for Mental Health Sciences, Whitby, Ontario, Canada, and University of Toronto

Alexander I. F. Simpson This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.

Centre for Addiction and Mental Health, Toronto, Ontario, Canada, and University of Toronto Individuals with serious mental illness (SMI; i.e., psychotic or major mood disorders) are vulnerable to experiencing multiple forms of adverse safety events in community settings, including violence perpetration and victimization. This study investigates the predictive validity and clinical utility of modifiable risk factors for violence in a sample of 87 forensic psychiatric patients found Not Criminally Responsible on Account of Mental Disorder (NCRMD) transitioning to the community. Using a repeated-measures prospective design, we assessed theoretically based dynamic risk factors (e.g., insight, psychiatric symptoms, negative affect, treatment compliance) before hospital discharge, and at 1 and 6 months postdischarge. Adverse outcomes relevant to this population (e.g., violence, victimization, hospital readmission) were measured at each community follow-up, and at 12 months postdischarge. The base rate of violence (23%) was similar to prior studies of discharged psychiatric patients, but results also highlighted elevated rates of victimization (29%) and hospital readmission (28%) characterizing this sample. Many of the dynamic risk indicators exhibited significant change across time and this change was related to clinically relevant outcomes. Specifically, while controlling for baseline level of risk, fluctuations in dynamic risk factors predicted the likelihood of violence and hospital readmission most consistently (hazard ratios [HR] ⫽ 1.35–1.84). Results provide direct support for the utility of dynamic factors in the assessment of violence risk and other adverse community outcomes, and emphasize the importance of incorporating time-sensitive methodologies into predictive models examining dynamic risk. Keywords: community violence, dynamic risk, mental illness, risk assessment, victimization

& Kramp, 2009) and in population-based birth cohorts (Arseneault, Moffitt, Caspi, Taylor, & Silva, 2000; Brennan et al., 2000; Swanson et al., 2006). A smaller number of prospective studies have assessed community violence among psychiatric patients following hospital discharge, reporting base rates between 17% and 38% occurring within the first two years of community tenure (Douglas, Ogloff, Nicholls, & Grant, 1999; Doyle & Dolan, 2006; Michel et al., 2013; Nicholls, Ogloff, & Douglas, 2004; Steadman et al., 1998; Wallace, Mullen, & Burgess, 2004), and lower rates for more serious forms of violence (e.g., those resulting in a formal conviction; 3% to 10%; Douglas et al., 1999; Soyka, MorhartKlute, & Schoech, 2004; Wallace et al., 2004). Studies of the prevalence of violent reoffending among forensic patients transitioning to the community report comparable base rates, although the range is somewhat wider (3% to 36%; Alcock & White, 2009; de Vogel, de Ruiter, Hildebrand, Bos, & van de Ven, 2004; Doyle & Dolan, 2006; Gray, Taylor, & Snowden, 2008; Hayes, Kemp, Large, & Nielssen, 2014; Michel et al., 2013; Miraglia & Hall, 2011; Philipse, Koeter, van der Staak, van den Brink, 2006; Skipworth, Brinded, Chaplow, & Frampton, 2006), likely owing to the greater variability in the measurement of violence (e.g., self-report vs. official records), sample type (e.g., patients released from maximum vs. minimum secure hospitals), and lengths of community follow-ups (6 months to 15 years) that

Several large-scale studies have investigated whether individuals with serious mental illness (SMI; i.e., psychotic or major mood disorders) are at greater risk for violence as compared with members of the general population. Much of this research suggests that persons with SMI, particularly those experiencing psychotic disorders, are at elevated risk for violence toward others (Brennan, Mednick, & Hodgins, 2000; Douglas, Guy, & Hart, 2009; Swanson et al., 2006; Van Dorn, Volavka, & Johnson, 2012), a finding that has emerged across diverse samples including incarcerated offenders (Eronen, Hakola, & Tiihonen, 1996; Wallace et al., 1998), civil psychiatric patients (Munkner, Haastrup, Joergensen,

This article was published Online First February 25, 2016. Stephanie R. Penney, Centre for Addiction and Mental Health, Toronto, Ontario, Canada, and Department of Psychiatry, University of Toronto; Lisa A. Marshall, Ontario Shores Centre for Mental Health Sciences, Whitby, Ontario, Canada, and Department of Psychiatry, University of Toronto; Alexander I. F. Simpson, Centre for Addiction and Mental Health, and Department of Psychiatry, University of Toronto. Correspondence concerning this article should be addressed to Stephanie R. Penney, Complex Mental Illness Program, Centre for Addiction and Mental Health, 1001 Queen Street West, Toronto, Ontario, Canada M6J 1H4. E-mail: [email protected] 374

This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.

DYNAMIC FACTORS AND VIOLENCE RISK ASSESSMENT

characterize these studies. Most recently, among a large sample of Canadian forensic patients transitioning into the community, Charette and colleagues (2015) found a rate of serious violent reoffending of just 0.6% at a 3-year follow-up mark, and 8.8% including all types of violent reoffenses. In a meta-analysis including all forensic samples in the literature (i.e., both inpatient and outpatient), there was no significant association between psychosis and violence (Douglas et al., 2009). Nevertheless, establishing reliable prevalence rates for community violence is a critical area of study within forensic psychiatric samples given that the perception of threat to public safety posed by such patients in the community is often higher. Furthermore, it is not often recognized that individuals with SMI, including forensic service users, are at greater risk of being victimized by crime and violence than they are of perpetrating violence (Silver, Arseneault, Langley, Caspi, & Moffitt, 2005; Teplin, McClelland, Abram, & Weiner, 2005). In addition to reporting on rates of community violence, many of the above cited studies have investigated the variables that appear to predict such incidents. Significant advances have been made in identifying robust and reliable risk factors for violence, and these advances have been translated into empirically validated instruments for assessing violence risk (Douglas, Hart, Webster, & Belfrage, 2013; Webster, Martin, Brink, Nicholls, & Desmarais, 2009). Risk factors for violence that have received consistent empirical support across diverse samples include static, or relatively stable historical variables (e.g., male gender, young age at first offense, history of substance use or relationship problems), as well as dynamic, or theoretically modifiable indicators (e.g., active psychiatric symptoms, current substance use, insight, mood and affect, treatment or supervision compliance). Despite this progress, there has been little research aimed at identifying risk mechanisms, that is, the process or processes through which a risk factor increases the probability of an outcome such as violence. For example, although several studies investigate the relation or cooccurrence between SMI and violence, few have theorized on what may be the underlying mechanisms responsible for this association. It is possible that specific symptoms of SMI such as psychosis, when active or intensifying, may cause violence directly by focusing, destabilizing, or disinhibiting behavior (Douglas et al., 2009; Link & Stueve, 1994); however, such symptoms can also motivate violence indirectly by increasing stress or exposure to provocation and conflict (Hiday, 1997). Furthermore, other symptoms of SMI (e.g., negative symptoms of psychosis) may have a nonsignificant or inverse relationship with violence (Swanson et al., 2006; Witt, Van Dorn, & Fazel, 2013; cf. Douglas et al., 2009). Recent literature points to the necessity of assessing changes in risk status over time to accurately conceptualize an individual’s risk and focus treatment more effectively (Douglas et al., 2013). The measurement of change in dynamic risk indicators can also facilitate better-timed interventions, as well as help evaluate the effectiveness of already implemented risk management strategies (Douglas & Skeem, 2005). It may also be an important first step in advancing the study of risk factors to the identification of risk mechanisms, or even causal risk mechanisms. As in the example above, psychiatric symptoms may only be a relevant risk factor for violence when they are active or increasing over a short period of time. Kraemer and colleagues (1997) and others (e.g., Kazdin, 1996) have outlined conditions required to infer that an observed change linked to an outcome is reflective of a risk mechanism:

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first, the measurement of change needs to have a gradient relationship with the outcome. Thus, the more change occurs (e.g., as a result of an intervention, or simply the passage of time) the better the relationship between change and the outcome. Second, the risk factor(s) must be measured prior to the outcome, and third, the proposed mechanism of change (e.g., an increase in disinhibition as a result of active psychosis) should be theoretically relevant to the outcome (violence). The fourth condition necessary for the occurrence of a risk mechanism is for interventions to have the ability to influence the mechanism (e.g., medications can stabilize symptoms and correspond to a reduction in violence). As applied to the field of violence risk assessment, the study of dynamic risk factors for violence can help move the field closer to the study of risk mechanisms by investigating the temporal sequencing of risk factors and outcomes, and studying how fluctuations in risk factors may temporally precede and predict increases or decreases in outcomes. Unfortunately, few studies have investigated dynamic risk indicators in this manner. There is little research describing the trajectory of change among empirically based dynamic risk factors, the feasibility of trying to capture such change in clinical populations, and whether such change relates to relevant outcomes such as violence or offending. More commonly, studies have used either single time-point estimates of dynamic risk (e.g., Desmarais, Nicholls, Wilson, & Brink, 2012; McDermott, Edens, Quanbeck, Busse, & Scott, 2008), or have looked at change over time but without linking this change to prospectively measured outcomes (e.g., Belfrage & Douglas, 2002; Viljoen et al., 2012). One study examined changes in risk factors such as employment and relationship problems, negative affect, substance use and levels of social support, showing that changes in these factors were significantly associated with violent recidivism in a sample of Canadian offenders (Brown, St. Amand, & Zamble, 2009). Also in a correctional setting, Olver and colleagues (Lewis, Olver, & Wong, 2013; Olver, Nicholaichuk, Kingston, & Wong, 2014; Olver, Wong, Nicholaichuk, & Gordon, 2007) demonstrated that positive changes in dynamic risk factors such as cognitive distortions, emotional control, substance abuse, insight and treatment compliance, measured at pre- and posttreatment, predicted reductions in the rate of general and violent recidivism up to a follow-up of 10 years. In a forensic psychiatric sample, Wilson, Desmarais, Nicholls, Hart, and Brink (2013) demonstrated that dynamic risk factors from the Historical, Clinical, Risk Management-20 (HCR-20; Webster, Douglas, Eaves, & Hart, 1997) and the Short-Term Assessment of Risk and Treatability (START; Webster et al., 2009) fluctuated over time and improved the prediction of institutional violence over and above static factors. To our knowledge, just one prior study has investigated change in dynamic risk indicators over a critical transition point in patients’ lives such as hospital discharge. Michel and colleagues (2013), using a prospective, repeated-measures design, found that several of the dynamic risk factors appearing on the HCR-20 showed significant change over five assessment periods in a mixed sample of civil and forensic patients, and that the change was predictive of aggressive behavior measured at six months prospectively. Many studies have also focused on single criterion measures (e.g., recidivism), whereas fewer have examined multiple violence-related outcomes (e.g., both perpetrating and experiencing violence) to better contextualize the adverse safety outcomes that impede suc-

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cessful community reintegration among psychiatric patients. This is in spite of the finding that individuals with SMI, including forensic patients, are at greater risk of being victims of violence and crime relative to those who are not disordered (Silver et al., 2005; Teplin et al., 2005). Furthermore, other relevant outcomes such as hospital readmission are rarely examined, despite the clinically intuitive suggestion that readmission may well represent a proxy for violence risk to the extent that higher risk patients are rehospitalized before an act of violence materializes in the community. Consequently, outcomes of victimization experiences and rehospitalization remain understudied, particularly in terms of how they may be impacted by changes in known risk factors for violence.

The Current Study The study of how dynamic risk factors operate in real time has the potential to advance the field of violence risk assessment and illuminate processes through which risk factors increase the probability of clinically relevant outcomes such as violence or victimization. Few studies have examined dynamic variables in this manner, and essentially none have assessed changes in risk over key transition points such as discharge from hospital and the commencement of community living. The paucity of prospective, multiwave designs impedes the study of dynamic risk since single time point estimates cannot capture change over time, and so do not inform what the magnitude of change may be across different risk factors and how this change relates to outcomes. In this context, the present study examines how modifiable risk factors for violence fluctuate over time, and how they impact an individual’s likelihood of engaging in or experiencing violence, as well as being readmitted to hospital. We further examine how dynamic risk indicators function in relation to static risk, to assess whether dynamic factors offer incremental utility over static factors, as well as examine whether the magnitude of change observed in dynamic factors varies as a function of static risk.

Method Study Design We employed a prospective, repeated-measures design to assess change over time and follow-up patients as they transition from a secure hospital setting to the community. Our data collection points were at baseline (within the month before discharge), one month (follow-up 1), and six months (follow-up 2) postdischarge. We inquired about outcomes at each follow-up, and collected recidivism data through the Canadian Police Information Centre (CPIC) at an average of 34 months postdischarge (SD ⫽ 12.51; Min ⫽ 11.40, Max ⫽ 57.03). In addition, health record information from the hospitals at which data were collected was reviewed at 12 months postdischarge to code for the presence of rehospitalization, as well as any other incident that could potentially be dangerous to the participant or others (e.g., acts of violence, illegal behaviors, or victimization experiences that may not have been self-reported or picked up in official police records; incidents of self-harm, absconding and psychiatric decompensation). Our follow-up time intervals are similar to prior studies in the area

(e.g., Brown et al., 2009; Michel et al., 2013; Skipworth et al., 2006).

Participants The sample comprised 87 forensic patients across two psychiatric hospitals in the province of Ontario. Each hospital’s respective forensic programs serve a highly similar patient population, and discharge approximately 30 patients per year. The majority of participants were male (83.9%), with a mean age of 36.44 (SD ⫽ 9.82). All had been found Not Criminally Responsible on Account of Mental Disorder (NCRMD) under the Criminal Code of Canada (equivalent to adjudication as Not Guilty by Reason of Insanity in the United States), with an average length of hospital stay of 45 months prior to the beginning of the study period (range ⫽ 4 –152 months). In Canada, both summary and indictable offenses are eligible for a NCRMD defense, resulting in a broad range of offenses ultimately receiving this designation. The most common index offense in the current sample was assault (23.0%) and assault causing bodily harm (28.7%), but ranged from technical breaches of probation (2.3%) to murder (9.2%). Most (82.8%) participants had been charged with one or more violent offenses as part of the predicate offense. The most frequent primary diagnosis was schizophrenia (81.6%), with more than half (57.5%) of the sample diagnosed with a comorbid substance use disorder. Mood disorders were infrequently represented in this sample, with 5.7% of the sample diagnosed with major depression and 5.7% with bipolar disorder. Breakdown in terms of ethnicity was as follows: 42.5% Caucasian, 20.7% Black, 14.9% Asian, and 11.5% “other.” Exclusion criteria for the study included significant psychiatric symptoms or impaired cognitive functioning that would interfere with meaningful participation or providing informed consent. Participants had to be able to speak English, although not necessarily as a first language. Of the 87 participants enrolled at baseline, 66 (75.9%) completed one community follow-up, and 59 (67.8%) completed two follow-up interviews. The primary reasons for attrition were an inability to contact the participant in time or the participants’ refusal to participate. There were no differences with respect to sex, age, ethnicity, index offense, psychiatric diagnosis or risk (as measured by HCR-20V3 total score or the PCL-R) between those participants who completed all three interviews and those who completed the baseline interview only. The study was approved by the ethics review boards of each hospital before the commencement of data collection.

Measures Dynamic risk indicators. Dynamic risk factors in the current study included those appearing on the Historical, Clinical, and Risk Management-20, Version 3 (HCR-20V3; Douglas et al., 2013). The HCR-20V3 represents the most recent revision of the HCR-20 (Webster et al., 1997), an extensively validated structured professional tool that has been used to assess violence risk in diverse samples (e.g., civil, forensic, correctional). It consists of 10 items relating to historical factors (e.g., previous violence, past problems with substance use or employment, trauma history), 5 items describing current clinical concerns (e.g., insight, active symptoms of SMI, current treatment compliance), and 5 items describing areas for future risk management (e.g., future plans for housing or employment, presence of social



supports). Each item may be scored on a 3-point scale as 0 (not present), 1 (possibly or partially present), or 2 (definitely present). A final summary risk rating is then presented as low, moderate, or high. Investigations into the newly revised tool have been positive to date, demonstrating good to excellent interrater reliability (Douglas & Belfrage, 2014) and predictive validity for violence (de Vogel, van den Broek, & de Vries Robbé, 2014; Doyle et al., 2014; Strub, Douglas, & Nicholls, 2014) in civil and forensic samples. Dynamic risk factors are those appearing on the Clinical (C) and Risk Management (R) subscales of the HCR-20V3. The entire HCR-20V3 was rated at baseline, while the dynamic indicators were assessed at baseline and then again at the last community follow-up. Because of finite resources the HCR-20V3 was only able to be scored for participants at one of the research sites. Consequently, n ⫽ 63 for analyses involving the HCR-20V3. Observations regarding the presence and severity of psychiatric symptoms were made using the 24-item Brief Psychiatric Rating Scale (BPRS; Overall & Gortham, 1988), a structured interview designed to assess multiple domains of symptomatology (e.g., psychosis, depression, mania, suicidality). Although the BPRS has primarily been investigated in nonforensic mental health contexts, Greenwood and Burt (2001) showed good concordance to ‘gold standard’ ratings when raters were forensic mental health professionals working in an inpatient setting. Recently, van Beek et al. (2015) demonstrated that the factor structure of the BPRS in forensic patients showed large overlap with prior research conducted in both in- and outpatient populations with schizophrenia and mixed diagnoses. The BPRS has also been shown to predict assaultive behaviors in forensic samples (Hoptman, Yates, Patalinjug, Wack, & Convit, 1999; McNeil & Binder, 1994) and to be sensitive to changes in symptoms following treatment initiation (Ebrahim, Gibler, Hayes, & Gacono, 1994). In the current study, participants were asked to consider only the past 30 days when responding to the interview questions for the BPRS so as to enable the assessment of change over repeated measurement intervals. We also included four measures of negative affect amenable to assessing participants’ emotional experiences in time-bound intervals (e.g., within the last month, within the last week), again to permit the assessment of change over time. The State–Trait Anxiety Inventory (STAI; Spielberger, Gorsuch, Lushene, Vagg, & Jacobs, 1983) is comprised of two, 20-item subscales designed to measure the intensity of anxiety a person feels presently, in the moment (STAI-State subscale) and the frequency of anxiety in terms of how a person “generally” feels (STAI-Trait subscale). The State subscale has been found to be a sensitive indicator of changes in transitory anxiety experienced by patients in psychotherapy, while the Trait subscale has been widely used in assessing clinical anxiety in medical and psychiatric patients. Both subscales demonstrate high internal consistency in samples of working adults, and are found to discriminate between normals and psychiatric patients for whom anxiety is a major concern (Spielberger et al., 1983). The State-Trait Anger Expression Inventory (STAXI-2; Spielberger, 1999) adopts a similar format to the STAI and assesses state anger, or the angry feelings experienced by an individual at a given time, and trait anger, or the overall tendency for an individual to experience angry feelings. The STAXI-2 is widely used in forensic settings. A recent review of the STAXI’s psychometric properties in forensic samples provided satisfactory support of the measure’s reliability and validity (Schamborg, Tully, & Browne, 2015), although it has been noted to be vulnerable to social desirability response biases

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in certain groups (e.g., clients undergoing court-ordered assessments; McEwan, Davis, MacKenzie, & Mullen, 2009). The Perceived Stress Scale (PSS; Cohen, Kamarck, & Mermelstein, 1983) is a 14-item measure of the degree to which situations in one’s life are appraised as stressful. It asks respondents about their current levels of experienced stress, and related feelings and thoughts over the past month. The psychometric properties of the scale were recently reviewed across 19 studies (Lee, 2012), including a large stratified population sample (N ⫽ 2387) and a sample of psychiatric patients (N ⫽ 96). The internal consistency of the scale was good (␣ ⬎ .70) across studies, as were convergent and criterion-related validity (validity coefficients: .61–.86). Finally, the Positive and Negative Affect Schedule (PANAS; Watson, Clark, & Tellegen, 1988) is a widely used measure of emotional experience, consisting of 20 adjectives to reflect positive (e.g., interested, excited, proud) and negative (e.g., upset, nervous, irritable) affects. The scales are internally consistent (␣ ⫽ .86 –.90; Watson et al., 1988), largely uncorrelated, and demonstrate excellent levels of convergent (validity coefficient: r ⫽ .67) and discriminant (validity coefficient: r ⫽ ⫺.31) validity (Crawford & Henry, 2004), including within offender samples (Brown et al., 2009). Participants were asked to rate the degree to which they have felt each of these emotions over the past week. Each of these four measures was administered at baseline and both community follow-ups (just the ‘state’ items of the STAI and STAXI-2 were administered at follow-up so as not to overlap with the baseline measurement of trait anxiety and anger). Static risk indicators. Static risk factors were measured at baseline via the Historical subscale of the HCR-20V3 as well as the Psychopathy Checklist, Revised (PCL-R; Hare, 2003). The PCL-R is a 20-item symptom construct rating scale designed to measure the interpersonal, affective, and behavioral features of psychopathic personality disorder. Psychopathy, when measured by the PCL-R, is a significant predictor of violent and nonviolent criminality (Hemphill, Hare, & Wong, 1998; Salekin, Rogers, & Sewell, 1996; Walters, 2003), with average weighted effect sizes (d) ranging from .47 to .58 for general recidivism and .43 to .79 for violent recidivism (Leistico, Salekin, DeCoster, & Rogers, 2008). Violence and victimization outcomes. The occurrence of a new violent offense over the follow-up period was tracked via CPIC records. Because official records tend to underrepresent rates of recidivism, we supplemented them with two self-report measures (i.e., the MacArthur Violence Risk Assessment Instrument [MAC-VI; Monahan et al., 2001] and Self-Report of Delinquency [SRD; Elliott, Dunford, & Huizinga, 1987]) at each follow-up. The MAC-VI is an expanded version of the Conflict Tactics Scale (CTS; Straus, Hamby, Boney-McCoy, & Sugarman, 1996), and evaluates whether the respondent has engaged in or been the victim of eight categories of aggression: (a) pushing, grabbing, or shoving; (b) kicking, biting, or choking; (c) slapping; (d) throwing an object; (e) hitting with a fist or object; (f) sexual assault; (g) threatening with a weapon; and (h) using a weapon. The SRD includes 15 items assessing lifetime and current involvement in violent (e.g., assault and weapons charges) and nonviolent (e.g., narcotics and property crimes) offenses. The scale produces results concordant with official measures of delinquency (Elliott et al., 1987), and demonstrates functional invariance across sex and ethnicity (Knight, Little, Losoya, & Mulvey, 2004). Instructions


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were modified for both measures to include only the period of time since the last interview. Rehospitalization and other adverse outcomes. Health record information was reviewed at 12 months postdischarge to code for the presence of rehospitalization, as well as the reasons for such if it occurred. In addition, we coded any other incident that could potentially be dangerous to the participant or others, including acts of violence (e.g., assaulting or threatening another person) or victimization experiences (e.g., being assaulted or threatened, having property stolen) that may not have been reported by the participant or picked up in police records.

Procedure All eligible patients within one month of hospital discharge were approached by the study’s principal investigator or a research assistant. Once interested patients provided consent, a review of the health record (containing psychosocial histories, psychiatric reports, and daily nursing reports) was conducted to gather demographic information and information required to rate the HCR-20V3. PCL-R scores were also taken from the file; these scores were generated by trained assessors with a Master’s or Doctoral degree in psychology. A researcher then scheduled an interview with the participant, at which time the self-report questionnaires were also administered. Research staff then scored the static and dynamic risk factors appearing on the HCR-20V3 based on all available file information, plus interview data gathered at baseline. The dynamic risk factors on the HCR-20V3 were scored based on the last six months of clinical information in the file, plus interview data. Follow-up data collection took place in-person at one month (follow-up 1) and six months (follow-up 2) postdischarge, and involved a review of file information that had accumulated since the

previous assessment, an interview, and a rerating of the dynamic risk items on the HCR-20V3 (the latter at follow-up 2 only, based on the past six months and as per guidelines described in the HCR-20V3 user manual). In addition, the participant completed several of the same self-report questionnaires administered at baseline. The primary outcomes for the study (i.e., a new incident of violence, victimization, or rehospitalization) were assessed via self-report questionnaires, interview data, as well as file information. Lastly, official recidivism data (CPIC) were collected at a minimum of 12 months postdischarge. Participants were paid $20 per interview. This was deemed low enough to be noncoercive, yet acknowledged the participant’s time and effort. The study’s P.I. and two research assistants scored the HCR-20V3. All raters received training in the administration and scoring of the HCR-20V3 from a 2-day workshop led by the tool’s primary author (K. Douglas). Interrater reliability was assessed by having two raters independently score 12 cases. Intraclass correlation coefficients (ICCA,1) revealed good to excellent reliability for the subscale and total scores on the HCR-20V3 (range: .85 [95% C.I. ⫽ .57–.96] to .94 [95% C.I. ⫽ .82–.98] for baseline ratings; .72 [95% C.I. ⫽ .27–.96] to .93 [95% C.I. ⫽ .79 –.98] for follow-up 2 ratings), as well as the summary risk rating (ICCA,1 ⫽ 1.0 [baseline], .72 [follow-up 2; 95% C.I. ⫽ .27–.96]). Internal consistency and descriptive statistics for the HCR-20V3 and self-report questionnaires employed in the study are reported in Table 1.

Data Analysis We first calculated descriptive statistics to examine the prevalence of community violence, victimization, and rehospitalization during the follow-up period, and to assess the direction and magnitude of change in our dynamic risk indicators across the three

Table 1 Internal Consistency and Descriptive Statistics Coefficient alpha (␣)

M (SD)

Measure/subscale

Baseline

Follow-up 1

Follow-up 2

Baseline

Follow-up 1

Follow-up 2

Diff baseline-F2 paired t (p, Cohen’s d [95% C.I.])

BPRS total score STAI Trait anxiety State anxiety STAXI-2 Trait anger State anger PSS total score PANAS Positive affect Negative affect HCR-20V3 H C R Total score PCL-R

.77

.71

.78

35.06 (8.29)

33.15 (6.80)

33.78 (7.88)

1.98 (.05, .26 [.00–.52])

.91 .93

— .90

— .94

35.76 (11.56) 33.33 (11.63)

— 32.21 (9.86)

— 32.17 (11.18)

— 1.22 (.23, .16 [⫺.10–.42])

.90 .96 .75

— .96 .70

— .97 .78

14.69 (5.18) 18.18 (7.48) 29.01 (6.55)

— 17.30 (6.63) 28.46 (5.91)

— 17.58 (6.84) 29.07 (6.46)

— 1.14 (.26, .15 [⫺.11–.40]) .50 (.62, .07 [⫺.19–.32])

.87 .92

.90 .88

.90 .81

34.21 (8.15) 16.00 (7.38)

32.39 (9.11) 14.27 (5.61)

32.24 (8.75) 14.24 (4.85)

2.19 (.03, .29 [.02–.55]) 2.16 (.04, .28 [.02–.55])

.57 .68 .33 .71 —

— — — — —

— .73 .60 .80 —

12.52 (2.55) 3.33 (1.99) 3.92 (1.40) 19.78 (4.40) 15.26 (6.42)

— — — — —

— 3.40 (2.34) 3.68 (1.79) 19.60 (5.45) —

— ⫺.27 (.79, .03 [.00–.22]) 1.15 (.26, .15 [⫺.10–.39]) .47 (.64, .06 [⫺.19–.31]) —

Note. Bolded text in Table 1 refers to a significant difference at the p ⬍. 05 level. BPRS ⫽ Brief Psychiatric Rating Scale. Minimum/maximum values ⫽ 24 –168. STAI ⫽ State-Trait Anxiety Inventory. Minimum/maximum values ⫽ 20 – 80 (Trait and State subscales). STAXI-2 ⫽ State-Trait Anger Expression Inventory. Minimum/maximum values ⫽ 10 – 40 (Trait subscale), 10 – 60 (State subscale). PANAS ⫽ Positive and Negative Affect Schedule. Minimum/maximum values ⫽ 10 –50 (Positive and Negative affect subscales). PSS ⫽ Perceived stress scale. Minimum/maximum values ⫽ 14 –56. HCR-20V3 ⫽ Historical, Clinical, Risk Management-20 (Version 3). Maximum/minimum values ⫽ 0 –20 (H), 0 –10 (C & R). PCL-R ⫽ Psychopathy Checklist, Revised. Maximum/minimum values ⫽ 0 – 40.



data collection points through group-based (dependent t test) and person-based (cluster analysis) approaches. The latter approach was used to investigate whether subgroups of individuals could be identified that exhibited varying amounts and trajectories of change on the specified risk indicators, and whether these groups differed on the likelihood of adverse community outcomes. Specifically, a two-step cluster analysis was used to classify participants on measures of psychiatric symptoms and affect (i.e., BPRS, STAI, STAXI-2, PSS, and PANAS) as well as the dynamic risk scales of the HCR-20V3. A hierarchical cluster analysis using Ward’s method with squared Euclidean distances was conducted to establish the optimal number of clusters in the baseline data. Examination of the agglomeration schedule, dendrogram, and percentages of individuals in each cluster was performed to determine the optimal solution. Using the cluster solution and initial cluster centers obtained from the hierarchical analysis, a k-means analysis was computed to reassign observations on the basis of the minimization of distances between each observation and cluster centers. Group membership obtained from the cluster analysis of baseline data was then used as a grouping factor to compute analyses of variance using follow-up 1 and 2 data. A multinomial ordinal logistic regression (LR) model was next estimated to test the predictive efficacy of the static and dynamic risk indicators measured at baseline and three dichotomized outcomes (violence perpetration, victimization, rehospitalization), either self-reported or gathered from the health record. To compare the relative predictive power of static versus dynamic variables, we computed hierarchical LR analyses entering static variables in the first block of the regression followed by dynamic predictors in the second block. Incremental validity of dynamic risk factors is demonstrated if these factors account for significantly more variance compared to the static risk factors on their own. Lastly, we conducted Cox regression survival analyses with time-dependent covariates to investigate the utility of change in dynamic risk indicators in the context of predicting community outcomes. The Cox model is ideal as it can account for individual differences in length of follow-up time and allows for the inclusion of individuals who have not yet experienced the outcome of interest by the completion of the study. The Cox model also readily incorporates time-dependent covariates (variables whose values change over time) by using the measurement score that was taken in closest temporal proximity prior to the event of interest (i.e., violence, victimization, or rehospitalization) in the analysis. For individuals not experiencing an outcome, the last measurement score (i.e., follow-up 2) is used. In this way, interpersonal variations in the timing of outcomes vis-a`-vis the measurement of risk are accounted for, and it is ensured that the measurement of a risk factor always precedes the outcome it is asked to predict. Consistent with prior research (Brown et al., 2009; Wilson et al., 2013) and theory (Douglas & Skeem, 2005; Kraemer et al., 1997), a risk factor was interpreted as dynamic if it changed significantly over time as evidenced by the group- and person-based change approaches, and, if incorporating information about how the variable changed over time (i.e., within the Cox regression) increased predictive accuracy with respect to community outcomes. All analyses were conducted using IBM SPSS Statistics 20 for Windows.

379 Results

Descriptives Information pertaining to the prevalence of community violence, victimization, and rehospitalization during the follow-up period is shown in Table 2. As shown, acts of violence were relatively infrequent in this sample when assessed via self-report during the earlier months post hospital discharge. In contrast, when outcomes were assessed via health records review at 12 months postdischarge, incidents of violence and victimization were more often noted. Indicators of treatment noncompliance (e.g., missed outpatient appointments with a psychiatrist, medication noncompliance) were prevalent during the follow-up period, occurring for more than half (61.2%) of the sample and including nine patients being absent without official leave (AWOL; patients who missed one or more outpatient appointments and could not be located, or patients who absconded from hospital after being readmitted). More than one-quarter (27.6%) of the sample had one or more incidents of readmission to hospital within the year after discharge. CPIC data revealed no new criminal charges or convictions among any participants. Results from dependent t tests conducted to examine change in the mean levels of the dynamic risk indicators over time (see Table 1) demonstrated declines in the total score of the BPRS from baseline to the second community follow-up, as well as in indicators of both positive and negative affect as measured by the PANAS. When the sample was split according to a measure of static risk (HCR-20V3 Historical subscale median [⫽ 12]), there were significant declines observed in the C and R subscales of the HCR-20V3, but only within the low static risk group, t [26] ⫽ 2.43, 2.57, p ⫽ .02, d ⫽ .47, .50 [95% C.I. ⫽ .09 –.89] for the C and R subscales, respectively. No other differences emerged on the remaining dynamic risk indicators when examined according to static risk status. With respect to the summary risk rating of the HCR-20V3, there was a significant increase in the proportion of participants rated as low risk from baseline to follow-up (18% vs. 38%, z ⫽ ⫺2.59, p ⫽ .01), and a significant decline in the proportion of participants rated as moderate risk (76% vs. 54%,

Table 2 Prevalence of Adverse Outcomes Postdischarge 1 month

6 months

12 months

Outcome

n

%

n

%

n

%

Violence Victimization Rehospitalization Self-harm/injury Psychiatric decompensation AWOL Official recidivism (CPIC)

1 5 — — — — 0

1.5 7.6 — — — — .0

3 12 18 — — — 0

5.1 20.3 20.7 — — — .0

20 25 24 4 12 9 0

23.0 28.7 27.6 4.6 13.8 10.4 .0

Note. Figures are cumulative. Violence and victimization outcomes are taken from the participant’s self-report at 1 (n ⫽ 66) and 6 (n ⫽ 59) months, and from the combined self-report and health record data at 12 months (n ⫽ 87) postdischarge. All other outcomes are taken from the health record at 12 months. AWOL ⫽ absent without official leave; CPIC ⫽ Canadian Police Information Centre.


380

Cluster Analysis to Identify Subgroups Based on Dynamic Risk Indicators Based on the procedure outlined above, a two-cluster solution was found to provide the most interpretable pattern to the data, and one that both maximized the homogeneity of individuals within clusters and the heterogeneity of individuals between clusters. The two-cluster solutions are graphically presented in Figure 1 (for the BPRS, STAI, STAXI-2, PSS, and PANAS measures) and Figure 2 (for the HCR-20V3 C and R subscales). The corresponding ANOVA results are presented in Table 3 (for ease of reading, data from just the baseline and final follow-up are presented). Results revealed that participants in Cluster 1 had significantly lower levels of psychiatric symptoms, state anxiety and anger, negative affect and stress. This was true at all three time points with the exception of state anger at the second follow-up. Participants in Cluster 1 also had significantly higher levels of positive affect, but only at baseline. In addition to the mean-level differences witnessed between the two clusters, there were differences with respect to the trajectory of change in these risk indicators over time. Specifically, the trajectory of Cluster 1 was one of relative stability, while the BPRS, STAI and PANAS (negative) showed significant decline over time for Cluster 2 (F2,61 ⫽ 3.06, p ⫽ .05, ␩2 ⫽ .09 [95% C.I. ⫽ .00 –.23] [BPRS]; F2,61 ⫽ 5.43, p ⫽ .01, ␩2 ⫽ .15 [95% C.I. ⫽ .01–.30] [STAI]; F2,61 ⫽ 3.44, p ⫽ .04, ␩2 ⫽ .10 [95% C.I. ⫽ .00 –.24] [PANAS]). Post hoc analyses

revealed significant change from baseline to follow-up 1 for the BPRS, from baseline to both follow-up 1 and 2 for the STAI, and from baseline to follow-up 2 for the PANAS. There were no differences among the two clusters in terms of the prevalence of community outcomes. On the HCR-20V3, participants in Cluster 1 were rated as significantly lower on the C and R subscales, as well as the total score, as compared to those in Cluster 2. This was true at both baseline and follow-up 2. In contrast to the clusters defined by the affective and symptom-based measures above, the solution defined by the HCR-20V3 subscales did not exhibit different trajectories of change between the two clusters; that is, the two clusters varied only by mean levels of the risk scores, and not by the pattern of change over time. Significantly more participants in Cluster 2 were found to have engaged in an act of violence (␹2(1, N ⫽ 63) ⫽ 5.11, p ⫽ .02, Cramer’s V ⫽ .29), experienced victimization (␹2(1, N ⫽ 63) ⫽ 4.23, p ⫽ .04, Cramer’s V ⫽ .26), as well as be readmitted to hospital (␹2(1, N ⫽ 63) ⫽ 7.61, p ⫽ .01, Cramer’s V ⫽ .35) within the 12 months postdischarge, as compared to participants in Cluster 1.

Prediction of Adverse Community Outcomes With Dynamic and Static Risk Indicators To assess the incremental validity of dynamic risk indicators over measures of static risk, we conducted two sets of direct entry hierarchical logistic regression analyses. We first examined whether the symptom- and affect-based dynamic risk factors added to the capacity of the Historical (H) subscale on HCR-20V3 to predict outcomes. The H subscale scores were added in Step 1 of each of three models predicting violence perpetration, victimization, and rehospitalization, respectively. Results (see Table 4) show that only the H subscale consistently predicted each of the outcomes, while the symptom- and affect-based dynamic risk indicators failed to account for significant additional variance in the model when included. We next examined the predictive capacity of the C and R subscales on HCR-20V3, again controlling for scores on the H subscale (see Table 5). Results demonstrated that the H subscale was again predictive of all three outcomes. When using the PCL-R as an

60 50 40 Mean


z ⫽ 2.62, p ⫽ .01). The proportion of individuals rated as high risk remained similar at baseline (6%) and follow-up (8%). A portion of our sample showed increases (i.e., worsening) in HCR-20V3 dynamic risk scores over time, whereas some showed decreases or no change, resulting in a range of negatively to positively valenced difference scores. Similar patterns were observed for the remaining dynamic risk measures. Consequently, the use of mean-level comparisons can mask important heterogeneity at the level of the individual case, and so a person-based approach was used next to examine whether subgroups of individuals could be identified based on different trajectories of change of dynamic risk indicators.

Cluster 1

30

Cluster 2

20 10 0

Figure 1.

Changes in symptoms and affect over three time points.


381

25

Mean

20 15

Cluster 1 Cluster 2

10 5


0 C scale

R scale

Figure 2.

Total score

HCR-20V3 dynamic risk scores over three time points.

indicator of static risk, this measure failed to predict any of the three outcomes, whether examined alone or in conjunction with dynamic risk indicators. With respect to the summary risk rating of the HCR-20V3, no one rated as low risk at baseline went on to perpetrate an act of violence in the community, whereas one-quarter of individuals rated as either moderate (12/48) or high (1/4) risk went on to perpetrate violence, ␹2(2, N ⫽ 63) ⫽ 3.47, p ⫽ .18, Cramer’s V ⫽ .24. Of the individuals who went on to experience an act of victimization in the community, 18%, 33%, and 75% of these individuals were rated as low, moderate, and high risk on the HCR-20V3 at baseline, respectively, ␹2(2, N ⫽ 63) ⫽ 4.26, p ⫽ .12, Cramer’s V ⫽ .26. The corresponding percentages when using rehospitalization as the outcome were 9%, 29%, and 75%, ␹2(2, N ⫽ 63) ⫽ 6.28, p ⫽ .04, Cramer’s V ⫽ .32. Lastly, the ability of change in dynamic risk indicators to predict adverse community outcomes was investigated using Cox regression survival analyses with time-dependent covariates. Controlling for static risk as measured by the H scale of the HCR-20V3, time-dependent scores on the dynamic subscales of the HCR-20V3 were significantly associated with an increased odds of engaging in violence (hazard ratio [HR] ⫽ 1.35 [95% C.I. ⫽ 1.03–1.77] and

1.62 [95% C.I. ⫽ 1.15–2.27], p ⫽ .03, 01 for the C and R scales, respectively) and being rehospitalized (HR ⫽ 1.43 [95% C.I. ⫽ 1.15–1.78] and 1.84 [95% C.I. ⫽ 1.38 –2.45], p ⫽ .001 for the C and R scales, respectively). Of the symptom- and affect-based scales, only time-dependent scores on the BPRS were shown to have a significant association with victimization outcomes after controlling for the effect of static risk (HR ⫽ 1.09 [95% C.I. ⫽ 1.03–1.15], p ⫽ .003).

Discussion Violence risk assessment has traditionally focused on static risk factors with few studies examining changes in dynamic risk factors over time or the temporal sequencing of risk factors and outcome (cf. Brown et al., 2009; Michel et al., 2013; Wilson et al., 2013). This is despite the consensus that dynamic indicators hold utility in clinical and correctional settings as they may offer greater precision in risk assessment and provide targets for intervention (Douglas & Skeem, 2005; Dvoskin & Heilbrun, 2001; Olver et al., 2014). This prospective study utilized a repeated-measures design to investigate fluctuations in dynamic risk factors at three discrete time points. Although few episodes of serious violence occurred

Table 3 Analysis of Variance Results for the Cluster Solutions Baseline

Follow-up 2 ␩ (95% C.I.)

F(1,57)

␩2 (95% C.I.)

12.12ⴱ 34.17ⴱⴱ .60 10.29ⴱ 1.46 20.23ⴱⴱ 41.30ⴱⴱ 19.11ⴱⴱ 51.63ⴱⴱ

.18 (.03–.34) .38 (.18–.52) .01 (.00–.11) .16 (.02–.32) .03 (.00–.15) .27 (.09–.43) .40 (.21–.54) .24 (.07–.40) .46 (.27–.59)

2

Variable

Cluster 1

Cluster 2

Group size (n) Measure/subscale (M, SD) BPRS total score STAI (state) STAXI-2 (state) PSS total score PANAS positive PANAS negative HCR-20V3 C HCR-20V3 R HCR-20V3 total

62

25

31.52 (4.25) 27.21 (5.71) 15.76 (2.27) 26.97 (5.58) 35.65 (7.83) 13.08 (3.55) 2.29 (1.35) 3.34 (1.18) 17.20 (2.69)

43.84 (9.36) 48.52 (8.02) 24.20 (11.59) 34.08 (6.08 30.64 (7.98) 23.24 (9.28) 5.27 (1.49) 5.00 (1.11) 24.59 (2.48)

F(1,85)

71.77ⴱⴱ 194.79ⴱⴱ 30.49ⴱⴱ 27.46ⴱⴱ 7.20ⴱ 55.05ⴱⴱ 65.26ⴱⴱ 29.58ⴱⴱ 114.35ⴱⴱ

.46 (.30–.57) .70 (.59–.77) .26 (.12–.40) .24 (.10–.38) .08 (.01–.20) .39 (.23–.52) .52 (.33–.63) .33 (.14–.48) .65 (.50–.74)

Note. Group sizes for the HCR-20V3 were 41 and 22 for Cluster 1 and 2, respectively. ⴱ p ⬍ .01. ⴱⴱ p ⬍ .001.

Cluster 1

Cluster 2

40

19

31.53 (6.31) 27.41 (8.70) 17.10 (7.51) 27.31 (6.09) 33.21 (9.38) 12.51 (3.76) 2.32 (1.89) 3.05 (1.61) 16.93 (4.25)

38.53 (8.87) 41.95 (9.27) 18.58 (5.20) 32.68 (5.77) 30.26 (7.10) 17.79 (5.00) 5.41 (1.68) 4.86 (1.49) 24.59 (3.63)


382

Table 4 Logistic Regression Testing the Incremental Validity of Dynamic Risk Indicators Over Static Risk Scores Violence


Variable Step 1 HCR-20V3 H Step 2 HCR-20V3 H BPRS total score STAI (state) STAXI-2 (state) PSS total score PANAS positive PANAS negative

␤

Wald

OR (95% C.I.)

.41ⴱⴱ

7.18

ⴱⴱ

.46 ⫺.04 .05 ⫺.02 .04 .08 ⫺.05

7.24 .48 .50 .05 .31 2.28 .34

Victimization ␤

Wald

OR (95% C.I.)

1.50 (1.12–2.02)

.23ⴱ

3.82

1.25 (1.01–1.57)

1.58 (1.13–2.20) .96 (.85–1.08) 1.05 (.92–1.19) .98 (.81–1.18) 1.04 (.90–1.21) 1.09 (.98–1.22) .95 (.80–1.13)

ⴱ

1.26 (1.01–1.61) 1.03 (.93–1.14) 1.05 (.93–1.18) 1.01 (.86–1.18) .96 (.86–1.08) 1.08 (.99–1.19) .93 (.80–1.09)

.23 .03 .05 .01 ⫺.04 .08 ⫺.07

3.33 .28 .61 .01 .49 2.80 .79

Rehospitalization ␤

Wald

OR (95% C.I.)

.26ⴱ

4.38

1.29 (1.02–1.64)

ⴱ

5.26 1.19 .48 .86 .91 .15 .10

1.39 (1.05–1.84) .94 (.83–1.06) 1.04 (.93–1.18) .91 (.74–1.11) .94 (.84–1.06) 1.02 (.93–1.11) .97 (.83–1.14)

.33 ⫺.07 .04 ⫺.10 ⫺.06 .02 ⫺.03

Note. OR ⫽ Odds ratio. Model results: Violence: ␹2(1, N ⫽ 63) ⫽ 8.74, p ⫽ .003. ⌬␹2(6, N ⫽ 63) ⫽ 3.41, p ⫽ .76. Harrell’s c ⫽ .80. Victimization: ␹2(1, N ⫽ 63) ⫽ 4.15, p ⫽ .04. ⌬␹2(6, N ⫽ 63) ⫽ 6.74, p ⫽ .35. Harrell’s c ⫽ .71. Rehospitalization: ␹2(1, N ⫽ 63) ⫽ 4.82, p ⫽ .03. ⌬␹2(6, N ⫽ 63) ⫽ 4.67, p ⫽ .59. Harrell’s c ⫽ .75. ⴱ p ⱕ .05. ⴱⴱ p ⬍ .01.

during the study period, dynamic risk factors were found to vary over time and be related to a number of relevant clinical outcomes, some of which may serve as proxies for violence risk and recidivism. Although no official reports of violence were recorded, selfreport and health record information indicated rates of violence similar to previous studies of discharged civil and forensic patients (e.g., Alcock & White, 2009; Doyle & Dolan, 2006; Skipworth et al., 2006; Steadman et al., 1998). From a public safety perspective, it is relevant that no patient in the community engaged in serious violence resulting in serious injury or life-threatening harm to another person. Turning to victimization experiences, findings from this sample are consistent with prior studies indicating that individuals with SMI are more vulnerable to being victims of violence and crime than members of the general population (Silver et al., 2005; Teplin et al., 2005), and that the experience of recent victimization may increase one’s risk of perpetrating violence in the future (Sadeh, Binder, & McNeil, 2014). Over a quarter of participants were rehospitalized within 12 months of discharge. Rehospitalization is a clinically significant outcome for patients and an indicator of overall service effectiveness. The readmission rate reported here may be indicative of timely intervention by outpatient teams to manage a perceived

increased risk for violence or some other adverse outcome. At the same time, hospital readmission is not typically viewed as a positive outcome indicator, and may signal challenges that forensic service users often face upon transitioning to the community (e.g., unstable housing, a lack of social or family support, increased stress). Furthermore, high rates of readmission to inpatient units translate into increased costs and may discourage the patient and his or her caregivers if readmission is equated with treatment failure. Although prior studies have examined risk factors for rehospitalization in general medical or psychiatric samples (Lin et al., 2010; Rosca et al., 2006; Vigod et al., 2015; Weiden, Kozma, Grogg, & Locklear, 2004), there have been few extensions into forensic populations where the legal ramifications associated with a readmission are often more consequential. Results presented here suggest that risk factors originally intended to predict violence and offending have utility in predicting the likelihood of hospital readmission as well. A variable-based approach to measuring change in dynamic risk factors over time showed significant declines in psychiatric symptoms as well as both positive and negative forms of affect occurring in the first six months of community tenure. Similarly, results from a person-based approach suggested that patients who entered the study with relatively higher levels of psychiatric symptoms,

Table 5 Logistic Regression Testing the Incremental Validity of HCR-20V3 Dynamic Risk Indicators Over Static Risk Scores Violence ␤

Wald

OR (95% C.I.)

H

.41ⴱⴱ

7.18

H C R

.44ⴱⴱ ⫺.24 .52

6.55 1.44 2.69

Variable Step 1 HCR-20V3 Step 2 HCR-20V3 HCR-20V3 HCR-20V3

Victimization ␤

Wald

OR (95% C.I.)

1.50 (1.12–2.02)

.23ⴱ

3.82

1.55 (1.11–2.16) .78 (.53–1.17) 1.68 (.90–3.14)

.22ⴱ ⫺.14 .33

3.23 .76 1.77

Rehospitalization ␤

Wald

OR (95% C.I.)

1.25 (1.01–1.57)

.26ⴱ

4.38

1.29 (1.02–1.64)

1.25 (1.01–1.59) .87 (.63–1.20) 1.39 (.86–2.25)

.21† .10 .33

2.70 .34 1.57

1.23 (.98–1.58) 1.11 (.79–1.55) 1.39 (.83–2.32)

Note. OR ⫽ Odds ratio. Model results: Violence: ␹2(1, N ⫽ 63) ⫽ 8.74, p ⫽ .003. ⌬␹2(2, N ⫽ 63) ⫽ 3.25, p ⫽ .20. Harrell’s c ⫽ .78. Victimization: ␹2(1, N ⫽ 63) ⫽ 4.15, p ⫽ .04. ⌬␹2(2, N ⫽ 63) ⫽ 1.94, p ⫽ .38. Harrell’s c ⫽ .70. Rehospitalization: ␹2(1, N ⫽ 63) ⫽ 4.82, p ⫽ .03. ⌬␹2(2, N ⫽ 63) ⫽ 3.39, p ⫽ .18. Harrell’s c ⫽ .73. † p ⫽ .07. ⴱ p ⱕ .05. ⴱⴱ p ⬍ .01.



anxiety, negative affect, and stress showed a decline in these symptoms and emotions over time. Decreases in dynamic risk as measured by the HCR-20V3 were only apparent in patients with an already low level of static risk. This finding contrasts somewhat to the work done by Olver and colleagues in incarcerated sexual offenders, who found that higher-risk offenders exhibited greater change as compared to lower risk offenders. These investigators suggested that higher risk offenders with many needs areas have more room for improvement (e.g., to reduce their risk scores), in contrast to lower risk offenders (Lewis et al., 2013; Olver et al., 2007). In the current sample of forensic patients, it may be that among those with an already low level of static risk, it is comparatively easier to effect change and improvements in dynamic risk indicators surrounding acute mental health concerns and ongoing treatment compliance. In contrast, for those individuals with a higher baseline level of static risk, effecting change may be more difficult as the dynamic risk factors are underpinned by both illness-related factors as well as more enduring criminogenic ones (e.g., a lengthier history of employment instability and substance misuse, personality disorder). Examination of the relative contribution of static and dynamic risk factors to outcomes indicated that static factors fared better with respect to predicting violence, victimization, and rehospitalization. An exception to this pertained to the PCL-R, as it failed to predict outcomes in any of the models tested. Although the predictive validity of the PCL-R in relation to violence and recidivism has been demonstrated in both civil and forensic samples (Leistico et al., 2008), the prevalence of psychopathy is observed to be low in forensic populations (Hare, Clark, Grann, & Thornton, 2000). This was true of the current sample, as indicated by a relatively low mean total score, as well as the fact that no participant scored above 30 (and just 3 scored above 25). It is thus possible that the restricted range of scores precluded a significant relationship from emerging between the PCL-R and our outcomes. Also of relevance are recent meta-analytic findings to suggest that comprehensive risk assessment instruments such as the HCR-20 fare better in terms of predicting violence as compared to the PCL-R (Singh, Grann, & Fazel, 2011). In the context of a logistic regression model, dynamic risk indicators failed to incrementally enhance the variance accounted for by static variables, a finding that differs from recent studies in forensic samples (Michel et al., 2013; Wilson et al., 2013). However, when the trajectory of dynamic variables was accounted for, time-dependent scores on the dynamic subscales of the HCR-20V3 were significantly related to violence and rehospitalization after controlling for static risk. Furthermore, when fluctuations in psychiatric symptoms were examined, they predicted increased victimization rates. These findings align not only with the large body of literature documenting the predictive validity of the HCR-20V3 in relation to violence (Douglas et al., 2014), but further suggest that risk factors for rehospitalization within forensic samples may share significant overlap with risk factors for violence. This may especially be the case if those patients being readmitted are also those being judged at elevated risk for community violence, thus prompting the readmission. With respect to victimization, several hypotheses have been offered to explain why those with SMI experience heightened rates of victimization, including the idea that individuals with SMI are more vulnerable owing to their mental impairment (e.g., poor reality testing, judgment, and prob-

383

lem solving) and the social context of their lives (e.g., poverty, unemployment and homelessness; Lehman & Linn, 1984; Maniglio, 2009). Current results highlight the importance of changes in affect and psychiatric symptoms that may occur most proximally to an experience of victimization. From a methods perspective, these findings, and particularly the difference between the logistic regression and time-dependent models, underscore that the measurement of variation in dynamic variables is fundamental to understanding their impact and predictive utility. In particular, results suggest that it is primarily the change/increase in dynamic variables, rather than their presence or absence, that confers increasing risk. Results also highlight the importance of employing measurement frameworks that can capture fluctuations in dynamic risk factors over time, and which are sensitive to the timing of their measurement vis-a`-vis outcomes. In the context of the current study, if results from the (non–timedependent) regressions were taken in isolation, they would suggest little added value of dynamic risk factors. To date, just one other study (Brown et al., 2009) has employed time-dependent analyses in this manner. Dynamic risk factors in the domains of psychiatric symptoms and negative and positive affect were assessed via self-report during the first six months after discharge from hospital. In contrast, dynamic variables appearing on the HCR-20V3 were assessed by researchers, who relied on the participants’ self-report as well as clinical documentation in the health record. Ratings on the HCR-20V3 appeared to have stronger predictive ability, particularly in terms of forecasting outcomes of violence perpetration and rehospitalization. In contrast, all self-report measures used in the current study failed to predict any of the adverse outcomes, with the exception of the BPRS (for which final ratings are similarly generated by researchers and based on interview data plus file information). Thus, results point to the relatively higher utility of expert rater data as compared with data from the self-report questionnaires in terms of predicting community outcomes. Concerns surrounding the reliability and veracity of self-report data have been articulated, particularly in justice-involved samples (JungerTas & Marshall, 1999; Krohn, Thornberry, Gibson, & Baldwin, 2010). In the context of this study, despite reassurances of confidentiality and anonymity, participants may have been reluctant to report increased symptomatology, negative affect, or episodes of violence in the community. Perhaps fearing readmission to hospital, participants appeared to emphasize their mental wellness and may not have disclosed challenges they may have been experiencing during community interviews. Interpretation of findings is tempered by certain limitations of the study design. First, the relatively small sample size and limited follow-up period likely impacted the prevalence of outcomes detected and impacts the overall statistical power to detect effects. Second, as reported above, we experienced some attrition over the two community follow-ups. Although we were still able to track the occurrence of any outcomes in these individuals as well as rate the HCR-20V3, the sample size for the self-report data was affected. Third, the follow-up period may have limited the assessment of change in our dynamic risk factors, particularly for those variables whose trajectory of change is either more acute in nature (e.g., fluctuating on a daily or weekly basis), or very gradual and prolonged. There is a dearth of theoretical knowledge on optimal timelines for assessing change among different classes of dynamic



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risk factors (Douglas & Skeem, 2005). Consequently, future studies should consider designing more sophisticated measurement timeframes that incorporate hypotheses regarding the anticipated nature of change for the specific variables in question. Notwithstanding these limitations, this is one of the first studies to provide direct support for the utility of dynamic factors in the assessment of violence risk and other adverse community outcomes, and highlights the need to incorporate time-sensitive methodologies into predictive models. From a clinical practice perspective, results emphasize the importance of conducting repeated risk assessments on patients at specified intervals, and to recognize the importance of changes in risk factors, rather than their simple presence or absence, in terms of prognosis and outcome. Future studies may consider increasing the frequency of data collection intervals over a longer period of time to better map the trajectory of change among dynamic risk indicators and more accurately connect changes in dynamic variables to violent incidents or other proxies of risk and recidivism. Finally, to move closer to causal models of violence perpetration and other outcomes, studies must also seek to document that targeted interventions have the ability to influence dynamic risk factors, and that this influence ultimately reduces the likelihood of an adverse outcome.

References Alcock, D., & White, T. (2009). Study of the clinical and forensic outcome of admission to a forensic psychiatry day hospital at one, two, and three years. Journal of Forensic Psychiatry & Psychology, 20, 107–119. http://dx.doi.org/10.1080/14789940802279963 Arseneault, L., Moffitt, T. E., Caspi, A., Taylor, P. J., & Silva, P. A. (2000). Mental disorders and violence in a total birth cohort: Results from the Dunedin Study. Archives of General Psychiatry, 57, 979 –986. http://dx.doi.org/10.1001/archpsyc.57.10.979 Belfrage, H., & Douglas, K. S. (2002). Treatment effects on forensic psychiatric patients measured with the HCR-20 violence risk assessment scheme. The International Journal of Forensic Mental Health, 1, 25–36. http://dx.doi.org/10.1080/14999013.2002.10471158 Brennan, P. A., Mednick, S. A., & Hodgins, S. (2000). Major mental disorders and criminal violence in a Danish birth cohort. Archives of General Psychiatry, 57, 494 –500. http://dx.doi.org/10.1001/archpsyc.57 .5.494 Brown, S. L., St. Amand, M. D., & Zamble, E. (2009). The dynamic prediction of criminal recidivism: A three-wave prospective study. Law and Human Behavior, 33, 25– 45. http://dx.doi.org/10.1007/s10979-0089139-7 Charette, Y., Crocker, A. G., Seto, M. C., Salem, L., Nicholls, T. L., & Caulet, M. (2015). The national trajectory project of individuals found not criminally responsible on account of mental disorder in Canada. Pt. 4: Criminal recidivism. The Canadian Journal of Psychiatry / La Revue canadienne de psychiatrie, 60, 127–134. Retrieved from http://www.ncbi.nlm.nih.gov/pmc/ articles/PMC4394712/ Cohen, S., Kamarck, T., & Mermelstein, R. (1983). A global measure of perceived stress. Journal of Health and Social Behavior, 24, 385–396. http://dx.doi.org/10.2307/2136404 Crawford, J. R., & Henry, J. D. (2004). The positive and negative affect schedule (PANAS): Construct validity, measurement properties and normative data in a large non-clinical sample. British Journal of Clinical Psychology, 43, 245–265. http://dx.doi.org/10.1348/0144665031752934 Desmarais, S. L., Nicholls, T. L., Wilson, C. M., & Brink, J. (2012). Using dynamic risk and protective factors to predict inpatient aggression: Reliability and validity of START assessments. Psychological Assessment, 24, 685–700. http://dx.doi.org/10.1037/a0026668

de Vogel, V., Ruiter, C. D., Hildebrand, M., Bos, B., & van de Ven, P. (2004). Type of discharge and risk of recidivism measured by the HCR-20: A retrospective study in a Dutch sample of treated forensic psychiatric patients. The International Journal of Forensic Mental Health, 3, 149 –165. http://dx.doi.org/10.1080/14999013.2004 .10471204 de Vogel, V., van den Broek, E., & de Vries Robbé, M. (2014). The value of the HCR-20 V3 in Dutch forensic psychiatric patients. The International Journal of Forensic Mental Health, 13, 109 –121. http://dx.doi .org/10.1080/14999013.2014.906518 Douglas, K. S., & Belfrage, H. (2014). Interrater reliability and concurrent validity of the HCR-20 Version 3. The International Journal of Forensic Mental Health, 13, 130 –139. http://dx.doi.org/10.1080/14999013.2014 .908429 Douglas, K. S., Guy, L. S., & Hart, S. D. (2009). Psychosis as a risk factor for violence to others: A meta-analysis. Psychological Bulletin, 135, 679 –706. http://dx.doi.org/10.1037/a0016311 Douglas, K. S., Hart, S. D., Webster, C. D., & Belfrage, H. (2013). HCR-20V3: Professional guidelines for evaluating risk of violence. Burnaby, Canada: Mental Health, Law, and Policy Institute, Simon Fraser University. Douglas, K. S., Ogloff, J. R., Nicholls, T. L., & Grant, I. (1999). Assessing risk for violence among psychiatric patients: The HCR-20 violence risk assessment scheme and the Psychopathy Checklist: Screening Version. Journal of Consulting and Clinical Psychology, 67, 917–930. http://dx .doi.org/10.1037/0022-006X.67.6.917 Douglas, K. S., Shaffer, C., Blanchard, A. J. E., Guy, L. S., Reeves, K. A., & Weir, J. (2014). HCR-20 violence risk assessment scheme: Overview and annotated bibliography. Burnaby, Canada: Mental Health, Law, and Policy Institute, Simon Fraser University. Douglas, K. S., & Skeem, J. L. (2005). Violence risk assessment: Getting specific about being dynamic. Psychology, Public Policy, and Law, 11, 347–383. http://dx.doi.org/10.1037/1076-8971.11.3.347 Doyle, M., Archer-Power, L., Coid, J., Kallis, C., Ullrich, S., & Shaw, J. (2014). Predicting post-discharge community violence in England and Wales using the HCR-20V3. The International Journal of Forensic Mental Health, 13, 140 –147. http://dx.doi.org/10.1080/14999013.2014 .906517 Doyle, M., & Dolan, M. (2006). Predicting community violence from patients discharged from mental health services. The British Journal of Psychiatry, 189, 520 –526. http://dx.doi.org/10.1192/bjp.bp.105.021204 Dvoskin, J. A., & Heilbrun, K. (2001). Risk assessment and release decision-making: Toward resolving the great debate. The Journal of the American Academy of Psychiatry and the Law, 29, 6 –10. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/11302389 Ebrahim, G. M., Gibler, B., Hayes, G., & Gacono, C. B. (1994). Patient response to clozapine in a forensic psychiatric hospital. Hospital & Community Psychiatry, 45, 271–273. http://dx.doi.org/10.1176/ps.45.3 .271 Elliott, D. S., Dunford, F. W., & Huizinga, D. (1987). The identification and prediction of career offenders utilizing self-reported and official data. In J. D. Burchard & S. Burchard (Eds.), Prevention of delinquent behavior (pp. 90 –121). Newbury Park, CA: Sage. Eronen, M., Hakola, P., & Tiihonen, J. (1996). Mental disorders and homicidal behavior in Finland. Archives of General Psychiatry, 53, 497–501. http://dx.doi.org/10.1001/archpsyc.1996.01830060039005 Gray, N. S., Taylor, J., & Snowden, R. J. (2008). Predicting violent reconvictions using the HCR-20. The British Journal of Psychiatry, 192, 384 –387. http://dx.doi.org/10.1192/bjp.bp.107.044065 Greenwood, A., & Burt, G. (2001). Validity of the Brief Psychiatric Rating Scale within a forensic inpatient hospital. Expert Evidence, 8, 15–30. http://dx.doi.org/10.1023/A:1012285717450 Hare, R. D. (2003). The Hare Psychopathy Checklist–Revised (2nd ed.). Toronto, Canada: Multi-Health Systems.


DYNAMIC FACTORS AND VIOLENCE RISK ASSESSMENT Hare, R. D., Clark, D., Grann, M., & Thornton, D. (2000). Psychopathy and the predictive validity of the PCL-R: An international perspective. Behavioral Sciences & the Law, 18, 623– 645. http://dx.doi.org/10.1002/ 1099-0798(200010)18:5⬍623::AID-BSL409⬎3.0.CO;2-W Hayes, H., Kemp, R. I., Large, M. M., & Nielssen, O. B. (2014). A 21-year retrospective outcome study of New South Wales forensic patients granted conditional and unconditional release. Australian and New Zealand Journal of Psychiatry, 48, 259 –282. http://dx.doi.org/10.1177/ 0004867413507610 Hemphill, J. F., Hare, R. D., & Wong, S. (1998). Psychopathy and recidivism: A review. Legal and Criminological Psychology, 3, 139 – 170. http://dx.doi.org/10.1111/j.2044-8333.1998.tb00355.x Hiday, V. A. (1997). Understanding the connection between mental illness and violence. International Journal of Law and Psychiatry, 20, 399 – 417. http://dx.doi.org/10.1016/S0160-2527(97)00028-9 Hoptman, M. J., Yates, K. F., Patalinjug, M. B., Wack, R. C., & Convit, A. (1999). Clinical prediction of assaultive behavior among male psychiatric patients at a maximum-security forensic facility. Psychiatric Services, 50, 1461–1466. http://dx.doi.org/10.1176/ps.50.11.1461 Junger-Tas, J., & Marshall, I. H. (1999). The self-report methodology in crime research. Crime and Justice, 25, 291–367. http://dx.doi.org/10 .1086/449291 Kazdin, A. E. (1996). Combined and multimodal treatments in child and adolescent psychotherapy: Issues, challenges, and research directions. Clinical Psychology: Science and Practice, 3, 69 –100. http://dx.doi.org/ 10.1111/j.1468-2850.1996.tb00059.x Knight, G. P., Little, M., Losoya, S. H., & Mulvey, E. P. (2004). The self-report of offending among serious juvenile offenders: Cross-gender, cross-ethnic/race measurement equivalence. Youth Violence and Juvenile Justice, 2, 273–295. http://dx.doi.org/10.1177/1541204004265878 Kraemer, H. C., Kazdin, A. E., Offord, D. R., Kessler, R. C., Jensen, P. S., & Kupfer, D. J. (1997). Coming to terms with the terms of risk. Archives of General Psychiatry, 54, 337–343. http://dx.doi.org/10.1001/archpsyc .1997.01830160065009 Krohn, M. D., Thornberry, T. P., Gibson, C. L., & Baldwin, J. M. (2010). The development and impact of self-report measures of crime and delinquency. Journal of Quantitative Criminology, 26, 509 –525. http:// dx.doi.org/10.1007/s10940-010-9119-1 Lee, E. H. (2012). Review of the psychometric evidence of the perceived stress scale. Asian Nursing Research, 6, 121–127. http://dx.doi.org/10 .1016/j.anr.2012.08.004 Lehman, A. F., & Linn, L. S. (1984). Crimes against discharged mental patients in board-and-care homes. The American Journal of Psychiatry, 141, 271–274. http://dx.doi.org/10.1176/ajp.141.2.271 Leistico, A. M. R., Salekin, R. T., DeCoster, J., & Rogers, R. (2008). A large-scale meta-analysis relating the hare measures of psychopathy to antisocial conduct. Law and Human Behavior, 32, 28 – 45. http://dx.doi .org/10.1007/s10979-007-9096-6 Lewis, K., Olver, M. E., & Wong, S. C. (2013). The Violence Risk Scale: Predictive validity and linking changes in risk with violent recidivism in a sample of high-risk offenders with psychopathic traits. Assessment, 20, 150 –164. http://dx.doi.org/10.1177/1073191112441242 Lin, C. H., Chen, M. C., Chou, L. S., Lin, C. H., Chen, C. C., & Lane, H. Y. (2010). Time to rehospitalization in patients with major depression vs. those with schizophrenia or bipolar I disorder in a public psychiatric hospital. Psychiatry Research, 180, 74 –79. http://dx.doi.org/10.1016/j .psychres.2009.12.003 Link, B. G., & Stueve, A. (1994). Psychotic symptoms and the violent/ illegal behavior of mental patients compared to community controls. In J. Monahan & H. J. Steadman (Eds.), Violence and mental disorder: Developments in risk assessment (pp. 137–159). Chicago, IL: University of Chicago Press.

385

Maniglio, R. (2009). Severe mental illness and criminal victimization: A systematic review. Acta Psychiatrica Scandinavica, 119, 180 –191. http://dx.doi.org/10.1111/j.1600-0447.2008.01300.x McDermott, B. E., Edens, J. F., Quanbeck, C. D., Busse, D., & Scott, C. L. (2008). Examining the role of static and dynamic risk factors in the prediction of inpatient violence: Variable- and person-focused analyses. Law and Human Behavior, 32, 325–338. http://dx.doi.org/10.1007/ s10979-007-9094-8 McEwan, T. E., Davis, M. R., MacKenzie, R., & Mullen, P. E. (2009). The effects of social desirability response bias on STAXI-2 profiles in a clinical forensic sample. British Journal of Clinical Psychology, 48, 431– 436. http://dx.doi.org/10.1348/014466509X454886 McNeil, D. E., & Binder, R. L. (1994). The relationship between acute psychiatric symptoms, diagnosis, and short-term risk of violence. Hospital & Community Psychiatry, 45, 133–137. http://dx.doi.org/10.1176/ ps.45.2.133 Michel, S. F., Riaz, M., Webster, C., Hart, S. D., Levander, S., MüllerIsberner, R., . . . Hodgins, S. (2013). Using the HCR-20 to predict aggressive behavior among men with schizophrenia living in the community. The International Journal of Forensic Mental Health, 12, 1–13. http://dx.doi.org/10.1080/14999013.2012.760182 Miraglia, R., & Hall, D. (2011). The effect of length of hospitalization on re-arrest among insanity plea acquittees. Journal of the American Academy of Psychiatry and the Law, 39, 524 –534. Retrieved from http:// www.jaapl.org/content/39/4/524.short Monahan, J., Steadman, H. J., Silver, E., Appelbaum, P. S., Robbins, P. C., Mulvey, E. P., . . . Banks, S. (2001). Rethinking risk assessment: The MacArthur study of mental disorder and violence. New York, NY: Oxford. Munkner, R., Haastrup, S., Joergensen, T., & Kramp, P. (2009). Registered criminality and sanctioning of schizophrenia patients. Nordic Journal of Psychiatry, 63, 485– 492. http://dx.doi.org/10.3109/08039480903118174 Nicholls, T. L., Ogloff, J. R., & Douglas, K. S. (2004). Assessing risk for violence among male and female civil psychiatric patients: The HCR-20, PCL:SV, and VSC. Behavioral Sciences & the Law, 22, 127–158. http://dx.doi.org/10.1002/bsl.579 Olver, M. E., Nicholaichuk, T. P., Kingston, D. A., & Wong, S. C. (2014). A multisite examination of sexual violence risk and therapeutic change. Journal of Consulting and Clinical Psychology, 82, 312–324. http://dx .doi.org/10.1037/a0035340 Olver, M. E., Wong, S. C., Nicholaichuk, T., & Gordon, A. (2007). The validity and reliability of the Violence Risk Scale-Sexual Offender version: Assessing sex offender risk and evaluating therapeutic change. Psychological Assessment, 19, 318 –329. http://dx.doi.org/10.1037/ 1040-3590.19.3.318 Overall, J. E., & Gortham, D. R. (1988). The Brief Psychiatric Rating Scale (BPRS): Recent developments in ascertainment and scaling. Psychopharmacology Bulletin, 24, 97–99. Philipse, M. W., Koeter, M. W., van der Staak, C. P., & van den Brink, W. (2006). Static and dynamic patient characteristics as predictors of criminal recidivism: A prospective study in a Dutch forensic psychiatric sample. Law and Human Behavior, 30, 309 –327. http://dx.doi.org/10 .1007/s10979-006-9013-4 Rosca, P., Bauer, A., Grinshpoon, A., Khawaled, R., Mester, R., & Ponizovsky, A. M. (2006). Rehospitalizations among psychiatric patients whose first admission was involuntary: A 10-year follow-up. Israel Journal of Psychiatry and Related Sciences, 43, 57– 64. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/16910387 Sadeh, N., Binder, R. L., & McNeil, D. E. (2014). Recent victimization increases risk for violence in justice-involved persons with mental illness. Law and Human Behavior, 38, 119 –125. http://dx.doi.org/10 .1037/lhb0000043 Salekin, R. T., Rogers, R., & Sewell, K. W. (1996). A review and meta-analysis of the Psychopathy Checklist and Psychopathy Checklist–


386


Revised: Predictive validity of dangerousness. Clinical Psychology: Science and Practice, 3, 203–215. http://dx.doi.org/10.1111/j.1468-2850 .1996.tb00071.x Schamborg, S., Tully, R. J., & Browne, K. D. (2015). The use of the State-Trait Anger Expression Inventory-II With forensic populations: A psychometric critique. International Journal of Offender Therapy and Comparative Criminology. Advance online publication. http://dx.doi .org/10.1177/0306624X15577932 Silver, E., Arseneault, L., Langley, J., Caspi, A., & Moffitt, T. E. (2005). Mental disorder and violent victimization in a total birth cohort. American Journal of Public Health, 95, 2015–2021. http://dx.doi.org/10 .2105/AJPH.2003.021436 Singh, J. P., Grann, M., & Fazel, S. (2011). A comparative study of violence risk assessment tools: A systematic review and metaregression analysis of 68 studies involving 25,980 participants. Clinical Psychology Review, 31, 499 –513. http://dx.doi.org/10.1016/j.cpr.2010.11.009 Skipworth, J., Brinded, P., Chaplow, D., & Frampton, C. (2006). Insanity acquittee outcomes in New Zealand. Australian and New Zealand Journal of Psychiatry, 40, 1003–1009. http://dx.doi.org/10.1080/j.1440-1614 .2006.01924.x Soyka, M., Morhart-Klute, V., & Schoech, H. (2004). Delinquency and criminal offenses in former schizophrenic inpatients 7–12 years following discharge. European Archives of Psychiatry and Clinical Neuroscience, 254, 289 –294. http://dx.doi.org/10.1007/s00406-004-0495-0 Spielberger, C. D. (1999). State-Trait Anger Expression Inventory-2. Port Huron, MI: Sigma Assessment Systems. Spielberger, C. D., Gorsuch, R. L., Lushene, R., Vagg, P. R., & Jacobs, G. A. (1983). Manual for the State-Trait Anxiety Inventory (Form Y). Palo Alto, CA: Consulting Psychologist Press. Steadman, H. J., Mulvey, E. P., Monahan, J., Robbins, P. C., Appelbaum, P. S., Grisso, T., . . . Silver, E. (1998). Violence by people discharged from acute psychiatric inpatient facilities and by others in the same neighborhoods. Archives of General Psychiatry, 55, 393– 401. http://dx .doi.org/10.1001/archpsyc.55.5.393 Straus, M. A., Hamby, S. L., Boney-McCoy, S., & Sugarman, D. B. (1996). The revised Conflict Tactics Scales (CTS2): Development and preliminary psychometric data. Journal of Family Issues, 17, 283–316. http:// dx.doi.org/10.1177/019251396017003001 Strub, D. S., Douglas, K. S., & Nicholls, T. L. (2014). The validity of version 3 of the HCR-20 violence risk assessment scheme amongst offenders and civil psychiatric patients. The International Journal of Forensic Mental Health, 13, 148 –159. http://dx.doi.org/10.1080/ 14999013.2014.911785 Swanson, J. W., Swartz, M. S., Van Dorn, R. A., Elbogen, E. B., Wagner, H. R., Rosenheck, R. A., . . . Lieberman, J. A. (2006). A national study of violent behavior in persons with schizophrenia. Archives of General Psychiatry, 63, 490 – 499. http://dx.doi.org/10.1001/archpsyc.63.5.490 Teplin, L. A., McClelland, G. M., Abram, K. M., & Weiner, D. A. (2005). Crime victimization in adults with severe mental illness: Comparison with the National Crime Victimization Survey. Archives of General Psychiatry, 62, 911–921. http://dx.doi.org/10.1001/archpsyc.62.8.911 van Beek, J., Vuijk, P. J., Harte, J. M., Smit, B. L., Nijman, H., & Scherder, E. J. (2015). The factor structure of the Brief Psychiatric Rating Scale (expanded version) in a sample of forensic psychiatric patients. Inter-

national Journal of Offender Therapy and Comparative Criminology, 59, 743–756. http://dx.doi.org/10.1177/0306624X14529077 Van Dorn, R., Volavka, J., & Johnson, N. (2012). Mental disorder and violence: Is there a relationship beyond substance use? Social Psychiatry and Psychiatric Epidemiology, 47, 487–503. http://dx.doi.org/10.1007/ s00127-011-0356-x Vigod, S. N., Kurdyak, P. A., Seitz, D., Herrmann, N., Fung, K., Lin, E., . . . Gruneir, A. (2015). READMIT: A clinical risk index to predict 30-day readmission after discharge from acute psychiatric units. Journal of Psychiatric Research, 61, 205–213. Viljoen, J. L., Beneteau, J. L., Gulbransen, E., Brodersen, E., Desmarais, S. L., Nicholls, T. L., & Cruise, K. R. (2012). Assessment of multiple risk outcomes, strengths, and change with the START:AV: A short-term prospective study with adolescent offenders. The International Journal of Forensic Mental Health, 11, 165–180. http://dx.doi.org/10.1080/ 14999013.2012.737407 Wallace, C., Mullen, P. E., & Burgess, P. (2004). Criminal offending in schizophrenia over a 25-year period marked by deinstitutionalization and increasing prevalence of comorbid substance use disorders. The American Journal of Psychiatry, 161, 716 –727. http://dx.doi.org/10 .1176/appi.ajp.161.4.716 Wallace, C., Mullen, P., Burgess, P., Palmer, S., Ruschena, D., & Browne, C. (1998). Serious criminal offending and mental disorder. Case linkage study. The British Journal of Psychiatry, 172, 477– 484. http://dx.doi .org/10.1192/bjp.172.6.477 Walters, G. D. (2003). Predicting institutional adjustment and recidivism with the psychopathy checklist factor scores: A meta-analysis. Law and Human Behavior, 27, 541–558. http://dx.doi.org/10.1023/A: 1025490207678 Watson, D., Clark, L. A., & Tellegen, A. (1988). Development and validation of brief measures of positive and negative affect: The PANAS scales. Journal of Personality and Social Psychology, 54, 1063–1070. http://dx.doi.org/10.1037/0022-3514.54.6.1063 Webster, C. D., Douglas, K. S., Eaves, D., & Hart, S. D. (1997). HCR-20: Assessing risk for violence (Version 2). Burnaby, Canada: Mental Health, Law, and Policy Institute, Simon Fraser University. Webster, C. D., Martin, M. L., Brink, J., Nicholls, T. L., & Desmarais, S. (2009). Manual for the Short-Term Assessment of Risk and Treatability (START) (Version 1.1). Port Coquitlam, BC: Forensic Psychiatric Services Commission and St. Joseph’s Healthcare. Weiden, P. J., Kozma, C., Grogg, A., & Locklear, J. (2004). Partial compliance and risk of rehospitalization among California Medicaid patients with schizophrenia. Psychiatric Services, 55, 886 – 891. Wilson, C. M., Desmarais, S. L., Nicholls, T. L., Hart, S. D., & Brink, J. (2013). Predictive validity of dynamic factors: Assessing violence risk in forensic psychiatric inpatients. Law and Human Behavior, 37, 377–388. http://dx.doi.org/10.1037/lhb0000025 Witt, K., van Dorn, R., & Fazel, S. (2013). Risk factors for violence in psychosis: Systematic review and meta-regression analysis of 110 studies. PLoS ONE, 8, e55942. http://dx.doi.org/10.1371/journal.pone .0055942

Received September 14, 2015 Revision received January 22, 2016 Accepted January 25, 2016 䡲

Mortality among forensic psychiatric patients in Finland.

Transitioning into the Community: Outcomes of a Pilot Housing Program for Forensic Patients.

A comparison of psychiatric diagnoses among HIV-infected prisoners receiving combination antiretroviral therapy and transitioning to the community.

Psychiatric assessment of patients with dementia in the community.

Changes in dynamic risk and protective factors for violence during inpatient forensic psychiatric treatment: predicting reductions in postdischarge community recidivism.

[Forensic psychiatric assessment of posttraumatic stress disorder].

Predictive validity of the Short-Term Assessment of Risk and Treatability for violent behavior in outpatient forensic psychiatric patients.

[Stalking: diagnostics, risk assessment, principles of treatment and forensic psychiatric assessment].

The forensic psychiatric report.

Forensic psychiatric patients' narratives of their offense.

Substance use disorders in forensic psychiatric patients.

The process approach to neuropsychological assessment of psychiatric patients.

Psychometric properties of the forensic inpatient quality of life questionnaire: quality of life assessment for long-term forensic psychiatric care.

Patients referred to community psychiatric nurses.

Specific risk factors of arsonists in a forensic psychiatric hospital.

Community care of psychiatric patients.

The factor structure of the Brief Psychiatric Rating Scale (expanded version) in a sample of forensic psychiatric patients.

Mortality, Rehospitalisation and Violent Crime in Forensic Psychiatric Patients Discharged from Hospital: Rates and Risk Factors.

Risk factors associated with psychiatric hospitalization among Iranian schizophrenic patients.

Violence and suicide risk assessment in the psychiatric emergency room.

The rating of psychiatric impairment in forensic practice: a review.

Caring Science and the Development of Forensic Psychiatric Caring.

Web-Based Violence Risk Monitoring Tool in Psychoses: Pilot Study in Community Forensic Patients.

Community approach to psychiatric illness.