Banks_Honors Thesis 2016

Running head: PREDICTIVE VALIDITY OF THE HCR-20V3 1

Assessing the Predictive Validity of the HCR-20V3 in Predicting Civil Psychiatric

Patients’ Short-term Violence Risk

Meghan Banks

Fordham University

PREDICTIVE VALIDITY OF THE HCR-20V3

Abstract

Assessment and prevention of violence are critical in psychiatric settings. Clinicians have

to make decisions about their patients’ violence risk; however, their unaided clinical

judgment often results in a number of false positives. As a result, there is a considerable

need for an accurate violence screening measure. The present study assessed the

predictive accuracy of the HCR-20 Version 3 (HCR-20V3) Summary Risk ratings (SSRs)

in gauging civil psychiatric patients’ violence risk. The sample consisted of 63 civil

psychiatric patients that were admitted to an urban public psychiatric hospital. Four raters

completed the HCR-20V3 ratings within 3 weeks after the patients’ hospital admission.

Within the study period, 59 percent of civil psychiatric patients were involved in at least

one aggressive incident. There was not a significant difference in aggression occurrence,

frequency and severity between low, moderate, and high risk patients on Case

Prioritization. There was not a significant difference in aggression severity between low,

moderate, and high risk patients who engaged in severe physical harm. Although there

was not a significant difference in aggression frequency, there was a significant different

difference in aggression severity between low, moderate, and high risk patients who

engaged in imminent violence. Case Prioritization and Severe Physical Harm ratings had

weak and non-significant predictive validity. However, Imminent Violence ratings had

moderate predictive validity that approached significance. The results suggest that the

HCR-20V3 has limited predictive accuracy in identifying civil psychiatric patients’

violence risk. The implications of these results for clinical practice are discussed.

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Assessment and prevention of violence are critical in psychiatric care,

management, and treatment (Chu, Daffern, & Ogloff, 2013). Clinicians often have to

make decisions about their patients’ violence risk, especially when presented with

behavioral emergencies that require psychiatric hospitalization (McNiel, Gregory, Lam,

& Binder, 2003). As a result, clinicians need to accurately gauge patients’ violence risk at

the time of psychiatric hospital admission. Although violence-screening measures are

frequent in psychiatric settings, there are well-known limitations of clinicians’ unaided

clinical judgment in assessing violence risk (McNiel, Lam, & Binder, 2000; McNiel et

al., 2003). Clinicians have modest predictive validity in assessing psychiatric patients’

violence risk; however, their assessments result in a greater likelihood of false positives

than any other type of error (Borum, 1996; Otto, 1992). A major reason for clinicians’

inability to accurately gauge psychiatric patients’ violence risk is because there is no

explicit professional standard for violence risk assessment. Consequently, there is a

considerable need for an accurate measure of violence risk, especially at the time of

hospital admission.

There are several reasons why an accurate violence-screening measure is critical

in psychiatric settings. First, psychosis is a significant factor for the occurrence of

violence within psychiatric settings (Douglas, Guy, & Hart, 2013). Psychiatric patients

are more psychotic at the time of hospitalization, and in turn have a higher propensity

towards violent behavior. Second, violence within the first few days of a psychiatric

hospitalization is a salient predictor of the length of that hospitalization (McNiel &

Binder, 1991). In relation to length of hospitalization, hospital staff often expects

violence from newly admitted patients and is less prepared for violent attacks from

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patients whose stays are longer (Cooper & Mendonca, 1989; Davis, 1991). Third,

psychiatric patient violence is one of the most frequent causes of staff injuries (Carmel &

Hunter, 1989; McNiel et al., 2003). Among staff working in psychiatric settings, 44

percent of clinical staff and 72 percent of nursing staff have been subjected to psychiatric

patient violence (Bowers et al., 2011). As a result, violence inflicted onto staff members

can result in staff absence and hinder the efficiency of the psychiatric services provided

to patients. Fourth, the base rate for physical violence within psychiatric settings is

roughly 50 percent, indicating that about half of all psychiatric patients commit a

physically violent attack during hospitalization (Bowers et al., 2011). Finally, among the

psychiatric patients who engage in violence, 45 percent are involved in repeated violent

attacks with a mean rate of 5.6 attacks (Bowers et al., 2011). Thus, clinicians need to

quickly assess psychiatric patients’ violence risk in order to make decisions regarding the

level of security that is needed during hospitalization, whether to initiate civil

commitment proceedings, and whether to warn or protect third parties from any

threatened violent behavior (McNiel et al., 2003).

In view of these potential obstacles, researchers have developed several violence-

screening measures that differ in terms of content, focus, and length. Four measures have

been most commonly used and studied with regard to improving clinicians’ decision

making about psychiatric patients’ violence risk: the Violence-Screening Checklist (VSC;

McNiel & Binder, 1994), the Brøset Violence Checklist (BVC; Almvik, Woods, &

Rasmussen, 2000), The Dynamic Appraisal of Situational Aggression (DASA; Ogloff &

Daffern, 2006), and the Historical-Clinical Risk Management Version 3 (HCR-20V3;

Douglas, Hart, Webster, & Belfrage, 2014).

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Violence-Screening Checklist (VSC)

McNiel and Binder (1994) developed the Violence-Screening Checklist (VSC) to

aid in the assessment of violence risk in newly admitted psychiatric patients. The VSC

contains five items that are each assigned to a one-point value: (a) history of physical

attacks or fear-inducing behavior in the two weeks prior to hospital admission; (b)

absence of suicidal behavior (e.g., attempts or gestures within two weeks before

admission); (c) schizophrenia or mania diagnosis; (d) male gender; (e) currently married

or cohabitating (McNiel & Binder, 1994). Scores of 3 or higher indicate that the patient is

at high risk for future violent behavior.

McNiel and Binder (1994) applied the VSC to sample of 338 patients admitted to

a short-term psychiatric until of a university-based hospital between 1989 and 1990. The

sample consisted of 156 females (46%) and 182 males (54%) who primarily identified as

White (71%). Most participants were diagnosed with schizophrenia (27%) and other

forms of psychosis (55%). Among the participants, 92 percent were involuntarily

committed. Patients rated as high risk on the VSC were significantly more likely to

exhibit physical attacks and aggressive behavior in the hospital than their low risk

counterparts, X2 (2, N = 338) = 27.43, p < .0001. When the outcome was violent physical

attacks, the VSC had a sensitivity of 55 percent, a specificity of 64 percent, and a total

predictive accuracy of 62 percent. Although the results were modest, the VSC performed

better than most studies of clinical judgment in assessing violence risk (McNiel &

Binder, 1994).

McNiel, Gregory, Lam, and Binder (2003) evaluate the predictive validity of the

VSC using a sample of 100 patients admitted to a short-term psychiatric unit. The sample

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consisted of 53 females and 47 males with a mean age of 45.7 years (SD = 17.2). Among

the patients, 22 percent were married or cohabiting and 78 percent were single, divorced,

or widowed. The patients were identified as Caucasian (54%), Asian (19%), African

American (18%), and other ethnic backgrounds (9%). Most patients were diagnosed with

mood disorders (29%), schizophrenia (26%), substance-related disorders (27%), and

other forms of psychosis (20%). Patients were involuntarily civilly committed due to

dangerousness to others (22%), involuntarily civilly committed due to dangerousness to

self and/or grave disability (66%), or hospitalized voluntarily (11%). The VSC overall

had a significant and moderate association with the likelihood of later violence (r = .38, p

= .01). The study further assessed correlations between the items of the VSC and future

violence: history of physical attacks and/or fear-inducing behavior within 2 weeks before

hospital admission (r = .26, p < .001), absence of suicidal behavior (r = .40, p < .001),

schizophrenia or mania diagnosis (r = .39, p < .001), male gender (r = .18, p = .67), and

currently married or cohabitating (r = -.15, p = .14). At a cut-off score of 3, the VSC had

a sensitivity of 64 percent, a specificity of 80 percent, and a positive predictive value of

76 percent. The overall predictive accuracy (based on the Area Under the Curve or the

AUC statistic) was .74. Consistent with previous research, the VSC was moderately

effective in identifying patients who would and would not become violent when a score

of 2 or less was considered low risk and 3 or more was considered high risk (McNiel &

Binder, 1994; McNiel et al., 2003).

Nicholls, Ogloff, and Douglas (2004) evaluated whether there were sex

differences in the predictive accuracy of the VSC using a retrospective follow-up study.

The sample consisted of 268 involuntary psychiatric patients with 106 females and 162

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males who were primarily diagnosed with schizophrenia and affective disorders.

Psychiatric patients’ psychosocial histories, institutional behavior, release plan, and

intake were used to complete their VSC scores. At a cut-off score of 3, 117 males and 25

females were considered “high risk” on the VSC. The VSC had a sensitivity of 87

percent, a specificity of 22 percent, and a positive predictive value of 47 percent for male

psychiatric patients. The VSC had a sensitivity of 29 percent, a specificity of 49 percent,

and a positive predictive value of 47 percent for female psychiatric patients. Although the

VSC had excellent sensitivity in predicting male psychiatric violence, it had relatively

poor specificity. Furthermore, the VSC had a small and non-significant predictive

accuracy in gauging female psychiatric patient violence risk (Nicholls, Ogloff, &

Douglas, 2004).

Brøset Violence Checklist (BVC)

The Brøset Violence Checklist (BVC; Almvik et al., 2000) is a six-item violence

risk measure intended to identify patients at high risk for physical violence. The BVC

measures 6 behaviors and mental states: confusion, irritability, boisterous behavior,

verbal threatening, physical threatening, and attacking objects (Almvik et al., 2000).

Scores of 1 and 2 suggest that psychiatric patient violence risk is moderate and

preventative measures should be taken. Scores of 3 or higher suggest that psychiatric

patient violence risk is very high, immediate preventative measures are required, and

plans for handling an attack should be initiated (Almvik et al., 2000).

Almvik, Woods, and Rasmussen (2000) assessed the predictive validity of the

BVC in four psychiatric hospitals in Norway. A sample of 109 patients was admitted

during a 2-month period in 1997. The sample consisted of 52 male and 57 female

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psychiatric patients that were mostly involuntarily admitted (60%). Within the study

period, four male and eight female psychiatric patients were involved in a total of 33

violent incidents. When examining the distribution of BVC scores between violent and

non-violent patients, violent patients had a mean score of 2.5 (SD = 1.95) and non-violent

patients had a mean score of 0.32 (SD = 0.74). Further, there was a significant difference

between the means, indicating that violent patients scored significantly higher on the

BVC than their non-violent counterparts, t = 8.88, df = 64.39, p < .001. The study further

assessed the significance of differences in the six BVC items between violent and non-

violent patients by chi-square analysis: confusion (X2 = 43.47, p < .001), irritability (X2 =

151.40, p < .001), boisterousness (X2 = 163.54, p < .001), verbal threats (X2 = 147.17, p <

.001), physical threats (X2 = 111.93, p < .001), and attacks on objects (X2 = 176.32, p

< .001). At a cut-off score of 2, the BVC had a sensitivity of 63 percent and a specificity

of 92 percent. The BVC had an AUC of .82, indicating that the BVC had an excellent

predictive validity in gauging future psychiatric patient violence (Almvik et al., 2000).

Abderhalden, Needham, Miserez, Almvik, Dassen, and Fischer (2004) conducted

a multicenter prospective study to assess the predictive validity of the BVC in three Swiss

psychiatric hospitals. A sample of 219 patients was admitted into six acute psychiatric

wards. The sample consisted of 133 male patients (61%) and 86 female patients (39%)

who were primarily diagnosed with substance use and schizophrenic disorders. Nurses in

charge of each patient completed the BVC at the end of every shift, following a 2-week

run-in period. Within the study period, psychiatric patients committed 28 physical violent

attacks against persons. Of the 28 attacks, 4 were committed before the first rating, 14

during the prediction period, and 10 after the prediction period. The BVC had a

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sensitivity of 64 percent, a specificity of 94 percent, and a positive predictive value of 11

percent. The BVC had an AUC of .88, indicating an excellent predictive validity

(Abderhalden, Needham, Miserez, Almvik, Dassen, & Fisher, 2004). Further, the results

demonstrated the general applicability of the BVC in psychiatric settings. Under periods

of extreme workload, nurses were able to complete 93 percent of the BVC forms within

less than 3 minutes (Abderhalden et al., 2004). Thus, the BVC is an effective and

efficient measure in predicting short-term psychiatric patient violence in routine care.

Abderhalden, Needham, Dassen, Halfens, Haug, and Fischer (2006) conducted a

prospective study in order to assess the predictive validity of the BVC in two Swiss

psychiatric hospitals. In order to further improve the accuracy of the BVC, the study

combined the measure with a Visual Analogue Scale (VAS). The sample consisted of 300

patients admitted during a six-month period. For three days, psychiatric nursing staff

provided assessments twice daily at 10 a.m. and 6 p.m. At the end of the study period, the

nurses combined patients’ BVC scores with the maximum value of the 12-point VAS

scale. Psychiatric patients committed 37 physical attacks within the study period. Of the

37 attacks, 27 were directed at patients. The BVC had an AUC of .90, indicating an

excellent predictive accuracy. However, when the BVC was combined with the VAS, the

predictive dropped to an AUC of .83. Thus, the BVC overall is an effective measure in

gauging short-term psychiatric patient violence, but the inclusion of the VAS appears to

hinder its predictive accuracy.

Hvidhjelm, Sestoft, Skovgaard, and Bjorner (2014) assessed the predictive

validity of the BVC in a sample of 156 Danish forensic psychiatric patients. The sample

consisted of 150 male patients (96%) and 6 female patients (4%) that were primarily

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diagnosed with schizophrenic (83%) and behavioral disorders (5%). The patients’ mean

age was 38.4 years (SD = 11.1). Patients were assessed three times daily with the BVC

for 24 months. All violent incidents were recorded using the Staff Observation

Aggression Scale-Revised (SOAS-R; Nijman et al., 1999), with scores of 9 or more

indicating a violent attack. Within the study period, 419 physically violent incidents

occurred. Most violent incidents were provoked by the denial of something requested by

a patient (54.4%) and staff members were the most frequent targets of violence (78.8%).

In total, 76 patients (51%) had one or more violent incident. At a cut-off score of 2, the

BVC had a sensitivity of 80 percent, a specificity of 99 percent, and a positive predictive

value of 23 percent. At a cut-off score of 3, the BVC had a sensitivity of 95 percent, a

specificity of 99 percent, and a positive predictive value of 37 percent. The BVC across

all cut-off scores had an AUC of .92, indicating an excellent predictive validity in

gauging psychiatric patient violence risk. Thus, the BVC had a good sensitivity and

specificity in discriminating the potential for violent and non-violent acts in psychiatric

patients (Hvidhjelm, Sestoft, Skovgaard, & Bjorner, 2014).

Rechenmacher, Müller, Abderhalden, and Schulc (2014) also conducted a

prospective cohort study to assess the predictive validity of the BVC combined with the

VAS in four adult psychiatric hospitals in Austria. The sample consisted of 155 male

patients (49.6%) and 117 female patients (50.4%) that were primarily diagnosed with

mood (34.9%) and schizophrenic (20.7%) disorders. Psychiatric nursing staff rated

patients using the BVC-VAS between 10 a.m. and 11 a.m. and 6 p.m. and 7 p.m. At a

cut-off score of 7, the BVC-VAS had a sensitivity of 59 percent, a specificity of 97

percent, and a positive predictive value of 19 percent. The BVC-VAS had an AUC of .93,

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indicating an excellent predictive validity in differentiating high-risk from low-risk

psychiatric patients. However, there were 7 false negative and 44 false positive cases,

resulting in the BVC-VAS having a lower sensitivity than anticipated.

Dynamic Appraisal of Situational Aggression (DASA)

The Dynamic Appraisal of Situational Aggression (DASA; Ogloff & Daffern,

2006) is a brief structured violence risk instrument developed to assess violence risk in

psychiatric patients. The DASA takes less than five minutes to be complete in psychiatric

settings (Vojt, Marshall, & Thompson, 2010). The DASA, like the BVC, assumes that

physical violence is likely to occur in the presence of aggressive behaviors (Daffern &

Howells, 2007). The seven item instrument is composed of strictly dynamic violence risk

factors: negative attitudes, impulsivity, irritability, verbal threats, sensitive to perceived

provocation, easily angered when requests are denied, and unwillingness to follow

directions (Ogloff & Daffern, 2006). Each of the seven items is scored for its presence or

absence, with scores of 6 or 7 indicating that a patient likely presents an imminent risk

for physical violence and preventative measures need to be implemented. However,

scores of 4 or more should also be considered high risk in order to initiate immediate

intervention, and in turn, reduce the likelihood of the patient engaging in physical violent

behavior (Ogloff & Daffern, 2006).

Ogloff and Daffern (2006) assessed the predictive validity of the DASA in a

sample of 100 psychiatric patients during a 6-month study period. The sample consisted

of 78 male patients and 22 female patients admitted to the Thomas Embling Hospital who

were primarily diagnosed with schizophrenic or other psychotic disorders (77%). Nursing

staff rated patients using the DASA three times daily at 7 a.m., 1 p.m., and 9 p.m. Within

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the study period, psychiatric patients committed 111 physically violent incidents.

Specifically, 35 male patients (44.9%) and 15 female patients (68.2%) committed

physical violent acts. Among the violent patients, 18 percent had a DASA score of 1-3,

15 percent had a DASA score of 4-5, 55 percent had a DASA score of 6-7. And 11

percent had a DASA score of 0. When comparing nursing staff’s risk ratings with and

without the aid of the DASA, their accuracy increased when using the DASA. Further

differences in risk ratings with and without the aid of the DASA were significant X2 (2) =

33.76, p < .0001, indicating that risk ratings with the DASA were significantly more

accurate. The DASA had an AUC of .82, indicating that the measure had an good

predictive validity in differentiating violence risk between violent and non-violent

patients.

Daffern and Howells (2007) assessed the predictive validity of the DASA in

gauging imminent violence risk in patients admitted to a highly secure psychiatric

hospital. The sample consisted of 40 patients from the high secure Dangerous and Severe

Personality Disorder (DSPD) unit of the Rampton Hospital. Nursing staff rated patients’

violence risk at approximately 1 p.m. daily using the DASA during a four-month study

period. Within the study period, there were 85 incidents of physical violence. Violent

patients that had a DASA score of 0 committed 49 (57.6%) of the physically violent

incidents. At a cut-off score of 4.5, the DASA had a sensitivity of 13 percent and a

specificity of 98 percent. At a cut-off score of 6.5, the DASA had a sensitivity of 4.7

percent and a specificity of 99.6 percent. The DASA across all cut-off score had an AUC

of .65, indicating a modest, but lower than expected, predictive accuracy for violence risk

(Daffern & Howells, 2007). Overall, the DASA was a weak measure of violence risk in

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this study. The authors suggested that patients with a personality disorder are more likely

to engage in instrumental violence than reactive violence when compared with mentally

ill patients (Daffern & Howells, 2007). Thus, the DASA may be an effective measure in

predicting reactive violence but less effective in predicting instrumental violence.

Vojt et al. (2010) conducted a prospective cohort study to assess the predictive

validity of the DASA in a sample of psychiatric patients. The sample consisted of 20

patients admitted to a secure hospital in Northern Ireland. All of the patients were males

primarily diagnosed with a psychotic disorder (77%). The majority of the patients were

admitted due to a violent index offense (69%), while some patients were admitted due to

an elevated risk of violence (7.7%). Within the study period, 14 incidents of physical

violence occurred with an average SOAS-R score of 9, indicating that the patients’

physically violent attacks were in general severe. In terms of physical violence, the

DASA had an AUC of .65, indicating a moderate predictive accuracy in differentiating

high risk from low risk psychiatric patients. However, the DASA showed poor predictive

validity when capturing specific victim categories of psychiatric patient violence. The

DASA had an AUC of .48 and .55 for physical violence against staff members and

patients respectively. While the DASA may be moderately effective in predictive violent

incidents in general, it does not appear to be reliable in predicting the recipient patients’

violent behavior.

Chu, Daffern, and Ogloff (2013) compared the predictive validity of the BVC and

the DASA in assessing violence risk of 70 psychiatric patients. The sample consisted of

55 male and 15 female patients that had a mean age of 30.47 years (SD = 12.42). Patients

were primarily diagnosed with psychosis (80%), substance use (74.3%), and personality

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disorders (20%). When the outcome variable was limited to interpersonal violence, the

BVC had an AUC of .66, indicating a moderate to large effect size. At a cut-off score of

3, the BVC had a sensitivity of 52 percent, a specificity of 95 percent, and a positive

predictive value of 1 percent. At a cut-off score of 4, the DASA had a sensitivity of 81

percent, a specificity of 76 percent, and a positive predictive value of 4 percent. At a cut-

off score of 6, the DASA had a sensitivity of 57 percent, a specificity of 91 percent, and a

positive predictive value of 7 percent. The DASA had an AUC of .75, indicating a

comparable predictive validity to the BVC (Chu et al., 2013). Although both measures

demonstrated strong predictive validity for psychiatric patient violence, they both had

low positive predictive accuracy. Further research is needed to reduce the rate of false

positives using these measures (Chu et al., 2013).

Historical-Clinical-Risk Management-20 Version 3 (HCR-20V3)

The HCR-20 Version 3 (HCR-20V3; Douglas & Belfrage, 2014) is a structured

professional judgment (SPJ) tool designed to aid clinicians in assessing the likelihood of

violence in criminal offenders and psychiatric patients. The 20-item instrument includes

three subscales composed of violence risk factors: Historical (H), Clinical (C), and Risk

Management (R). The H subscale includes ten static violence risk factor items (e.g.,

Previous Violence, Substance Use), the C subscale includes five dynamic risk factor

items (e.g., Symptoms of Major Mental Disorder, Violent Ideation or Intent), and the R

subscale includes five risk factor items pertaining to future situational circumstances that

increase violence risk (e.g., Personal Support, Stress or Coping (Douglas & Belfrage,

2014; Douglas, Hart, Webster, & Belfrage, 2014).

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Rather than using cut-off scores, clinicians arrive at categorical estimations for the

presence and relevance of each risk factor. Presence of each risk factor is rated as not

present (N), possibly or partially present (P), or definitely present (Y). Relevance

estimates the extent to which each risk factor, if present, is pertinent to the specific

patient’s violence risk. Relevance of each risk factor is rate as low, moderate, or high.

Finally, clinicians formulate summary risk ratings (SRRs) to rate case prioritization,

severe physical harm, and imminent violence as low, medium, or high risk.

Hogan and Olver (2016) assessed the predictive validity of the HCR-20V3 in

identifying violent forensic psychiatric patients. The sample consisted of 99 forensic

psychiatric patients admitted into a psychiatric hospital in western Canada from January

2005 to July 2008. The sample was predominantly male (86%) with a mean age of 36.7

years (SD = 12.8). Patients were primarily diagnosed with schizophrenia and psychotic

disorders (61%) and substance/alcohol disorders (70%). Patients were either not

criminally responsible (NCR; 52%), convicted offenders admitted for psychiatric

treatment (24%), or admitted for other services (24%). Most patients had a previous

criminal charge (70%), documented history of institutional aggression (54%), and had

committed violent index offenses (71%). The HCR-20V3 Presence and Relevance ratings

had AUC values of .76 (p < .001) and .70 (p < .001) respectively, indicating moderate to

large and significant predictive validity. Case Prioritization and Imminent Violence risk

ratings had an AUC of .68 (p < .01) and .75 (p < .001) respectively, indicating a moderate

to large and significant predictive validity. However, the Severe Physical Harm risk

rating had an AUC of .44, indicating a weak and not significant predictive validity. Thus,

the HCR-20V3 demonstrated moderate predictive accuracy; however, there is limited

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research on the measure’s predictive validity for identifying violence in civil psychiatric

patients.

Despite differences among these validated violence screening measures, there is

little evidence that suggests one predicts violence risk better than the other. Because the

HCR-20V3 is widely used in assessing community violence and is often completed in

hospital settings prior to discharge, the present study assessed whether it would be useful

in also gauging short-term violence risk in civil psychiatric patients (Singh, Grann, &

Fazel, 2011).

The present study addressed four research questions. The first research question

addressed whether the HCR-20V3 Case Prioritization, Severe Physical Harm, and

Imminent Violence risk ratings would each be associated with aggression frequency and

severity among civil psychiatric patients during a 3 month follow-up period. The second

research question addressed whether the Case Prioritization rating would predict

aggression occurrence, frequency, and severity. The third research question addressed

whether the Severe Physical Harm risk rating would predict aggression severity. The

final research question addressed whether the Imminent Violence risk rating would

predict aggression frequency and severity.

Method

Participants

The sample was comprised of 63 civil psychiatric patients that were admitted to

an urban public psychiatric hospital between February and December of 2013 (described

in Howe et al., 2015). The sample consisted of 47 male (73%) and 17 female (27%)

patients ranging in age from 18 to 70 years old with a mean age of 37.97 years (SD =

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13.15). Patients identified as White (14%, n = 9), Black (69% n = 44), Asian (2%, n = 1),

and mixed race/ethnicity (5%, n = 3), but some did not have race/ethnicity coded in their

charts (11%, n = 7). All patients were diagnosed with schizophrenia or schizoaffective

disorder (86%, n = 55) or a mood disorder (14%, n = 9). Patients were transferred from

acute hospitals (75%, n = 48), prison (22%, n = 14), or admitted from another location

(3%, n = 2).

Measures

The HCR-20V3 is the third edition of the HCR-20 (Douglas, Hart, Webster, &

Belfrage, 2013). The HCR-20V3 resembles the HCR-20 Version 2 in format and structure,

but differs in several specific risk factors and final SRRs. Along with presence ratings,

clinicians are also required to rate the relevance of each risk factor as low, moderate, or

high, which was not included in the preceding version. For statistical analysis purposes,

the presence and relevance ratings are converted into numerical ratings: 0 = not

present/low relevance, 1 = possibly or partially present/moderate relevance, and 2 =

present/high relevance. If a presence rating item is omitted or the item is rated as not

present, the relevance rating for the item is omitted as well. Presence and relevance

ratings are used to inform three SRRs: case prioritization, severe physical harm, and

imminent violence that are all rated as low, moderate, or high risk. Final qualitative

ratings can be converted into numerical ratings, with 1 = low risk, 2 = moderate risk, and

3 = high risk.

Procedure

Violence risk ratings for the HCR-20V3 were completed within 2 to 3 weeks after

hospital admission. The rating period for the clinical subscales was the preceding three

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months. If the patient was discharged at the time the instrument was coded, the rating

period started three months before the date of discharge. Research staff coded risk

management rating based on the discharge or release plans if the patient had existing

plans to be released into the community at the time of evaluation. Data regarding

aggressive incidents was extracted from the hospital’s database. Target of aggression was

coded as 1 = patient, 2 = ward staff (e.g., TA), 3 = professional staff (e.g., clinician), 4 =

visitor, 5 = multiple targets, and 6 = other. Setting of aggression was coded as 1 = ward,

2 = treatment program, and 3 = on grounds. Aggression was coded as 1 = present and 0

= not present. Aggression frequency was coded based on the number of aggressive acts

committed by each patient. Aggression severity was coded as 1 = minimal (e.g., verbal

aggression only). 2 = moderate (e.g., physical attack without injury; threat with a

weapon), and 3 = severe (e.g., life threatening or resulting in serious injury). Aggression

occurrence, frequency, and severity were assessed during a 3-month follow-up period.

Research staff included four raters: one forensic psychologist who routinely used

the HCR-20V3 in clinical practice and three MA-level clinical psychology doctoral

students who received both didactic and supervised training in the administration and

scoring of the instrument. Raters made independent ratings for each scale and scored

instrument based on the patient’s medical record information, chart reviews, and brief

interviews with the patient’s treatment team. Out of the 64 psychiatric patients, two raters

independently rated 35 patients, finding good inter-rater reliability (Howe et al., 2015).

The Spearman correlation test was conducted to determine whether the SRRs

were significantly associated with civil psychiatric patient aggression frequency and

severity. The Chi-Square test of independence was conducted to determine whether the

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Case Prioritization rating would significantly predict the occurrence of aggression during

a 3-month follow-up period. One-way ANOVA test was conducted to determine whether

the Case Prioritization and Imminence Violence risk ratings would significantly predict a

difference in aggression frequency and severity between the means of low, moderate, and

high risk patients. Another one-way ANOVA test was conducted to determine whether

the Severe Physical Harm risk ratings would significantly predict a difference in

aggression severity between the means of low, moderate, and high risk patients.

Results

During the study period, 38 of the 64 patients (59.38%) were involved in at least

one aggressive incident. Among the patients who were involved in an aggressive

incident, 6 had been rated as low risk on the HCR-20V3 Case Prioritization risk rating, 20

had been rated as moderate risk, and 12 had been rated as high risk on Case Prioritization

risk ratings. There was a weak, positive, and non-significant correlation between

aggression and Case Prioritization risk ratings, rs (61) = .10, p = .46. The occurrence of

aggression did not significantly differ by low, moderate, and high risk patients during the

3-month follow-up period, X2 (2, N = 63) = 1.14, p = .56 (Table 1).

Table 1

Cross-Tabulation of Aggression by Case Prioritization

Case Prioritization Yes No X2

Low 6 4 1.14

Moderate 20 10

High 12 11

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Total 38 25

There were also no significant differences in aggression frequency between low,

moderate, and high risk patients, F (2, 62) = 0.57, p = .57. On average, patients rated as

low risk on Case Prioritization committed the least number of aggressive incidents (M =

0.40, SD = .52), ranging from 0 to 1.Patients rated as moderate risk on Case Prioritization

committed the greatest number of aggressive incidents (M = 1.27, SD = 2.96), ranging

from 0 to 12. Patients rated as high risk on Case Prioritization committed less aggressive

incidents than their moderate risk counterparts (M = 1.00, SD = 1.41), with the number of

incidents ranging from 0 to 5 (Table 2). Similarly, there were no significant differences in

aggression severity between low, moderate, and high risk patients on Case Prioritization,

F (2, 62) = .71, p = .49. Patients rated as high risk on Case Prioritization, on average, had

the highest level of aggression severity within the follow-up period (M = .96, SD = .79);

however, patients rated as low risk almost had a comparable aggression severity level as

their high risk counterparts (M = 0.90, SD = 1.20) (Table 3).

Table 2

Means and Standard Deviations of Aggression Frequency by Case Prioritization

Case Prioritization n M SD

Low 10 0.40 0.52

Moderate 30 1.27 1.05

High 23 1.00 1.41

Total 63 1.03 2.22

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Table 3

Means and Standard Deviations of Aggression Severity by Case Prioritization

Case Prioritization n M SD

Low 10 0.90 1.20

Moderate 30 0.63 0.93

High 23 0.96 1.07

Total 63 0.76 1.02

There was not a significant difference in aggression severity between low,

moderate, and high risk patients with regard to engaging in severe physical harm, F (2,

62) = 1.18, p = .32. However, patients rated as high risk for engaging in severe physical

harm had the highest level of aggression severity (M = 1.40, SD = 0.89) and patients rated

as low risk had the lowest overall mean aggression severity (M = 0.67, SD = 0.99) (Table

4).

Table 4

Means and Standard Deviations of Aggression Severity by Severe Physical Harm

Severe Physical Harm n M SD

Low 33 0.67 0.99

Moderate 25 0.84 1.07

High 5 1.40 0.89

Total 63 0.79 1.02

There was not a significant difference in aggression frequency between low,

moderate, and high risk patients in engaging in imminent violence, F (2, 62) = 0.47, p

= .63. Patients rated as high risk of engaging in imminent violence committed the greatest

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number of aggressive acts (M = 1.75, SD = 1.83); but there was no difference in the

frequency of aggressive acts between patients rated low and moderate risk of engaging in

imminent violence (Low: M = 0.93, SD = 2.41; Moderate: M = 0.93, SD = 2.16) (Table

5). There was a significant difference in aggression severity between low, moderate, and

high risk patients of engaging in imminent violence, F (2, 62) = 3.47, p = .03 (Table 6).

Patients rated as low risk in engaging in imminent violence had the lowest level of

aggression severity (M = 0.59, SD = .93). Patients rated as moderate risk in engaging in

imminent violence fell in between the patients rated as low and high risk in terms of

aggression severity ratings (M = 0.75; SD = 1.00). Patients rated as high risk in engaging

in imminent violence had the highest level of aggression severity (M = 1.63; SD = 1.06)

(Table 7).

Table 5

Means and Standard Deviations of Aggression Frequency by Imminent Violence

Imminent Violence n M SD

Low 27 0.93 2.42

Moderate 28 0.93 2.16

High 8 1.75 1.83

Total 63 1.03 2.22

Table 6

One-Way Analysis of Variance of Aggression Severity by Imminent Violence

Source df SS MS F p

Between groups 2 6.67 3.34 3.47 .04*

Within groups 60 57.64 0.961

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Total 62 64.32

Note. *p < .05, two-tailed.

Table 7

Means and Standard Deviations of Aggression Severity by Imminent Violence

Imminent Violence n M SD

Low 27 0.59 0.93

Moderate 28 0.75 1.05

High 8 1.63 1.06

Total 63 0.79 1.02

There was a weak, positive, and non-significant association between aggression

frequency and severity and Case Prioritization ratings, rs = .13, p = .32 and rs = .08, p

= .53, respectively. Similarly, there was a weak, positive, and non-significant association

between aggression frequency and severity and risk of engaging in serious physical harm

ratings, rs = .19, p = .19 and rs = .17, p = .19, respectively. There was a weak positive

association between aggression frequency and risk of engaging in imminent violence, rs =

.22, p = .08, that approached significance. However, there was a positive significant

association between aggression severity and risk of engaging in imminent violence, rs

= .25, p = .03 (Table 8).

Table 8

Spearman Correlations between SRRs and Aggression Frequency and Severity

SRRs Frequency Severity

Case Prioritization .13 .08

Severe Physical Harm .19 .17

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Imminent Violence .22 .25*

Note. *p < .05, two-tailed.

When predictive accuracy was gauged using Receiver Operating Characteristic

(ROC) curve analysis, Case Prioritization and Severe Physical Harm ratings had a weak

and non-significant predictive validity, AUC = .55, p = .49 and AUC = .60, p = .49,

respectively. However, Imminent Violence risk ratings had a moderate predictive validity

that approached significance, AUC = .63, p = .08 (Figure 1).

Discussion

The aim of the current study was to assess the predictive validity of the

HCR-20V3 Summary Risk Ratings (SRRs) in gauging violence risk in civil psychiatric

patients. As evident from previous research, there is a high base rate for patient

aggression within psychiatric settings (Bowers et al., 2011). In this study, more than half

of the sample committed at least one aggressive incident, however, severe aggression was

not very common. Thus, there were only a handful of moderately severe aggressive acts,

which were not life threatening.

However, contrary to expectations, the HCR-20V3 was not an effective measure at

predicting aggression within this civil psychiatric setting. The Case Prioritization SRR

demonstrated particularly weak predictive validity in gauging violence risk. Likewise, the

Severe Physical Harm SRR also demonstrated weak predictive validity in gauging

violence risk, and was not a significant predictor of severity of violence. However, the

pattern of findings indicated greater violence severity among those patients thought to be

at high risk for severe violence and lower levels of violence severity among those deemed

to be at low risk. The Imminent Violence SRR also demonstrated moderate predictive

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validity with regard to severity of violence, though it was still weakly associated with

aggression in this sample of civil psychiatric patients.

Although the HCR-20V3 SRRs’ showed limited predictive validity, the study had

several limitations. While the HCR-20V3 seems to have excellent predictive accuracy in

community and forensic settings, it demonstrated poor predictive accuracy within the

sample of civil psychiatric patients. Perhaps the HCR-20V3 risk factors were not relevant

to this sample of civil psychiatric patients. As well, the study’s definition of violence

encompassed both verbal and life threatening physical aggression, which may have been

too broad to be captured adequately by the HCR-20V3. Although the sample had a high

rate of aggression, its low aggression severity may have impacted the predictive validity

of the SRRs. This could also be due the study’s low threshold for identifying aggressive

behavior or that the facility primarily focused on highly aggressive patients that could not

be managed elsewhere. Finally, the study did not compare the predictive validity of the

BVC, DASA, VSC, and HCR-20V3 in gauging violence risk within the sample. As a

result, the poor predictive accuracy demonstrated could be due to the HCR-20V3,

inaccuracy in the ratings of the research team, or the civil psychiatric setting itself.

The sample size also limited the study’s statistical power. Although 38 out of 63

patients engaged in some type of aggressive behavior, the overall sample size of 63 was

still modest. As a result, the study was unable to look at potentially important variables,

such as the differential risk for violence between diagnostic categories or gender. As

well, the sample size was too small to analyze victim characteristics (e.g., patient versus

staff) or the setting of civil psychiatric patient violence (e.g., hospital, grounds,

community).

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The sample’s characteristics were an additional limitation. Patients were

hospitalized in a public psychiatric facility that provided long-term care. Although the

sample was representative of chronic psychiatric patients in most urban psychiatric

settings, there was little to no variability in diagnosis (e.g., all patients suffered from and

had active symptoms of a major mental disorder). This lack of variability could have

prevented an analysis of some items related to the SRRs (e.g., presence of a mental

disorder; Howe et al., 2015). Finally, the HCR-20V3 ratings were based on chart reviews

and interviews with the treatment teams, not clinical interviews with the patients

themselves. A reliance on charts to assess the final SRRs may have had an adverse

impact on information about patients’ future plans and propensity to commit an

aggressive act.

The study’s findings, while in general not significant, revealed some

counterintuitive trends of civil psychiatric patient violence. For example, low and

moderate risk patients were the most likely to engage in any aggression and accounted

for more aggressive incidents than high risk patients. This could be due to the facility’s

allocation of primary and aggressive treatment. High risk patients more likely to receive

the most aggressive interventions, which in turn, could have impacted the subsequent

severity of aggression and predictive accuracy of the HCR-20V3 within the sample.

Although the results suggest that the HCR-20V3 is not an accurate measure in

identifying high risk patient aggression, the current study underscores the aggressive

behavior of chronically ill patients in civil psychiatric settings. Future research should

compare the predictive accuracy of several violence screening measures in order to

determine which measure is the most effective in gauging short-term psychiatric patient

26


violence risk. By determining which measures provide the strongest predictive accuracy,

the number and severity of violent attacks committed by chronically ill patients can be

decreased. Future research should explore the predictive accuracy of the HCR-20V3 SRRs

in capturing violence risk across gender, diagnostic, victim, and setting categories. As

well, future research should explore the predictive accuracy in capturing civil psychiatric

patients’ unprovoked and retaliated forms of violence. Finally, clinicians need to focus on

the behavioral interventions provided to psychiatric patients rated as moderate risk.

Moderate risk psychiatric patients are still at a high risk for inpatient aggression, perhaps

because more aggressive interventions are prioritized to high risk patients. Thus, the

current study revealed the poor predictive accuracy of the HCR-20V3, but provides

clinical implications for future practice within psychiatric settings.

27


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Appendix

Figure 1. ROC curves of Summary Risk Ratings.

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