predicting community violence from patients discharged from acute mental health units in england

ORIGINAL PAPER

Predicting community violence from patients dischargedfrom acute mental health units in England

Michael Doyle • Stuart Carter • Jenny Shaw •

Mairead Dolan

Received: 22 April 2010 / Accepted: 23 February 2011 / Published online: 10 March 2011

� Springer-Verlag 2011

Abstract

Purpose To investigate the validity of risk factors and

established risk measures in predicting community vio-

lence in an acute mental health sample up to 20 weeks

post-discharge.

Method Prospective cohort follow-up study conducted

between January 2006 and August 2007. Baseline assess-

ments were conducted while participants were inpatients.

The measures were rated following interview with the

participants, record review and speaking to someone who

knows the person well (e.g. friend, relative, carer). Baseline

measures were then compared with frequency and severity

of violence in the community post-discharge at 20 weeks.

Results In the 20-week period post-discharge, 29 (25.4%)

of the 114 participants were violent. All the risk measures

and measures of impulsiveness and anger were predictive

of violence where p \ 0.05. The HCR-20 total, psychop-

athy and clinical factors were strongly correlated with the

frequency of violence where p \ 0.05.

Conclusions The risk factors and risk measures that have

been found to be predictive in forensic samples are also

predictive in acute mental health samples, although the

effects are not as large. Future research needs to be con-

ducted with a larger sample to include investigation of

differences in risk factors based on gender and social

support. Services and clinicians need to consider how to

integrate findings into useful frameworks to support deci-

sions and contribute to managing risk. This should assist in

identifying interventions aimed at preventing community

violence.

Keywords Violence � Risk assessment � Acute

psychiatry � Community

Introduction

In the UK the concept of violence risk assessment in

mentally disordered patients has gained considerable pub-

lic, political and clinical attention over the past two dec-

ades [1]. Assessing risk of violence is a major concern to

mental health professionals when planning the discharge of

people with mental disorder and clinicians in acute mental

health services are increasingly expected to provide evi-

dence-based assessments of violence risk [2, 3]. In forensic

services the need for evidence-based guidance on violence

is clear, although in many ways the risk in non-forensic

services is greater, due to fewer community resources, less

intense supervision, more unpredictable prognoses and

greater frequency of violence [4–6]. Despite this, there are

M. Doyle (&)

Community Based Medicine, Adult Forensic Mental Health

Services, Greater Manchester West NHS Mental Health

Foundation Trust, University of Manchester,

Room 2.311 2nd Floor, Jean McFarlane Building,

University Place, Manchester M13 9PL, UK

e-mail: [email protected]

S. Carter

Bolton Primary Care Mental Health Service,

Bolton Primary Care Trust, Bolton, UK


J. Shaw

Department of Psychiatry, Lancashire Care NHS Trust,

University of Manchester, Manchester, UK


M. Dolan

Centre for Forensic Behavioural Science, Monash University,

Clayton, VIC, Australia


123

Soc Psychiatry Psychiatr Epidemiol (2012) 47:627–637

DOI 10.1007/s00127-011-0366-8

limited studies in the UK of acute non-forensic services

investigating which risk factors and scales are most pre-

dictive of post-discharge violence.

Several recent studies in the UK have investigated the

factors and risk measures that predict post-discharge vio-

lence in forensic samples. Coid et al. [7, 8] investigated

risk factors and five structured risk assessment instruments

associated with offending post-discharge from medium

secure forensic units in England. Predictors of future

offending included male gender, young age, early onset

offending and a diagnosis of personality disorder and all

risk instruments demonstrated moderate predictive validity.

Gray et al. [9] conducted a similar study and compared the

predictive accuracy of established risk assessment instru-

ments, the Violence Risk Appraisal Guide (VRAG) [10],

the Historical, Clinical and Risk Management 20-items

(HCR-20) [11] and the Psychopathy Checklist—Screening

Version (PCL:SV) [12] in predicting reconviction in a

sample of offenders with intellectual disabilities. They

found that all the instruments demonstrated good predictive

validity over a 5-year follow-up period.

In North America several studies have investigated post-

discharge violence from non-forensic mental health

patients. Klassen and O’Connor [13] identified five risk

factors on admission that related prospectively to violence

following discharge. These were early family quality,

current intimate relationship, arrest history, admissions

history and assault in the presenting problem. In Canada,

Douglas and colleagues [14] examined the validity of the

HCR-20 and the PCL:SV in predicting post-discharge

violence in a civil non-forensic sample. They found that the

HCR-20 and the PCL:SV significantly predicted violence

up to 626 days post-discharge. These findings were sup-

ported further in a similar study, where the HCR-20 and

PCL:SV significantly predicted post-discharge violence in

both males and female acute mental health patients [15]. In

the USA, in order to overcome some of the methodological

problems of previous studies, the MacArthur Violence Risk

Assessment Study (MacVRAS) [16] was designed as the

most comprehensive and robust study of its type to

investigate the factors that influence whether a person

discharged from a non-forensic psychiatric facility will be

violent post-discharge. They identified different types of

risk factors—historical, dispositional, contextual and clin-

ical—at baseline while participants were inpatient and then

examined how accurate they were at predicting violent

behaviour up to 20 weeks post-discharge. The findings

from this study identified a number of robust predictors of

post discharge violence, including psychopathy, prior

arrests, substance abuse, anger regulation problems and

recent violent behaviour [17]. A study modelled on the

MacVRAS was conducted with a representative sample of

discharges from forensic and non-forensic services in the

North West of England [4]. The findings supported the use

of the HCR-20 as a valid structured professional guideline

while the VRAG, PCL:SV and measures of anger and

impulsiveness were also found to significantly predict

community violence up to 24 weeks post discharge. As this

included both forensic and non-forensic patients, it remains

unclear if there are specific factors that predict post-dis-

charge community violence from those discharged from

acute mental health units in England, and despite the

impressive findings previous studies have a number of

limitations. Most only consider reconvictions as the out-

come and this is highly likely to result in a significant

under-estimating of the true prevalence of violence [4, 18,

19]. The samples in the UK were restricted to forensic and/

or learning disability or a mix of forensic and non-forensic

patients who may not be representative of the majority of

people discharged from acute mental health facilities. To

date, no studies in the UK have used a rigorous prospective

design to investigate risk factors for post-discharge vio-

lence even though it is estimated that nearly one in five of

those discharged from acute mental health units may be

violent in the 6 months post discharge [4]. There are a

multitude of reasons why people may be violent [4, 20], so

this study aimed to investigate the validity of a range of

historical, dispositional and clinical factors to predict vio-

lence up to 20 weeks post-discharge. The study also aimed

to evaluate the predictive validity of established risk

measures that have been found to be predictive in forensic

services to investigate if they have similar validity in a

non-forensic acute sample.

Method

The prospective cohort follow-up design chosen was

modelled on the MacVRAS to evaluate the predictive

validity of historical, dispositional and clinical risk factors

and to test the hypothesis that the non-forensic participants

with high baseline scores on the VRAG, HCR-20 and VRS

will be significantly more likely to be violent up to

20 weeks post-discharge than participants with low scores.

All patients resident during the study period in three non-

forensic acute mental health units at Greater Manchester

West Mental Health NHS Foundation Trust were included

in the study. This allowed effective communication and

tracking of individuals in one specific mental health care

organisation and in a geographical area served by inte-

grated electronic information systems. Patients excluded

from the study were those (1) who were under 18 or over

65, (2) unable to provide informed consent (3) patients

unavailable due to leave or absence from ward, (4) with

primary learning disability diagnosis, (5) who cannot read

or understand English.

628 Soc Psychiatry Psychiatr Epidemiol (2012) 47:627–637

123

Procedure

The North West of England Multi-site Research Ethics

Committee approved the study and written informed con-

sent was obtained from all participants. Responsible

Medical Officers covering each of the three units were

contacted and asked for approval to include their patients in

the study. Researchers conducted baseline assessment

while participants were inpatients by collecting data on

participants by interview, case note review and liaison with

clinician who knew the participant well (e.g. primary

nurse) in order to score instruments. The total baseline

assessment took between 2 and 4 h after interview, record

review and liaison with staff. Violent behaviour in the

community was measured up to 20 weeks post discharge.

The community measures were rated following interview

with the participants, record review and speaking to

someone who knows the person well (e.g. friend, profes-

sional, carer). Baseline measures were then compared with

frequency and severity of violence in the community post-

discharge.

Baseline assessment measures

Demographic, diagnostic and substance use data were

recorded at baseline for each of the participants based on

interview with participant and review of case records.

Validated scales were used to measure different types of

risk factor categorised into whether they were Disposi-

tional (anger, impulsiveness and psychopathy), Historical

(Historical items of the HCR-20 and VRS) or Clinical

(symptoms, alcohol and drug use). In addition, the pre-

dictive validity of established violence risk instruments, the

HCR-20, VRAG and VRS were also investigated.

Historical Clinical and Risk Management 20 items

The HCR-20 [11] is a broadband violence risk assessment

instrument. The HCR-20 takes its name from these three

scales—Historical, Clinical, Risk Management—and from

the number of items (20). The items are scored 0, 1 and 2.

Scores range from 0 to 40. The HCR-20 is sensitive to

change, as the C and R items are dependent on current

functioning and context to reflect different circumstances at

different time points. In this study the historical items were

rated at baseline while the clinical and risk management

items were rated based on the 2-week period prior to dis-

charge as this has been found to be the optimal time to

assess these items in previous studies [4].

Violence Risk Appraisal Guide The VRAG [10] was

developed to identify variables that predict violent recidi-

vism. The VRAG has 12 items which are attributed integer

weights ranging from -5 to ?12 and scores range from

-26 to ?38. The VRAG was designed for use with

forensic populations and as such three of the items rely

upon ratings of index offence. In this study, those partici-

pants who did not have an index offence were given the

lowest score possible for the index offence related items.

Violence Risk Scale The VRS [21] was developed to

assess an individual’s risk for violent recidivism, by

assessing both the Static and Dynamic factors. The VRS-

Version 2 consists of six Static or historical factors, and 20

Dynamic factors. However, unlike the Clinical items of the

HCR-20, the baseline VRS dynamic item scores are rated

on ‘lifetime’ rather ‘current’ functioning, and only change

when rating is repeated in response to specific treatment

programmes. Each item is rated on a four-point scale 0–3

against descriptive criteria, with total scores ranging from 0

to 78.

Dispositional factors

Novaco Anger Scale

The NAS [22] is a 60-item self-report instrument that

includes 48 items that measures the cognitive, arousal and

behavioural domains of anger. The scale includes a 12-item

anger regulation domain that provides information on how

the respondent manages their anger .

Barratt Impulsiveness Scale

The BIS [23] is a 30-item likert-type self-report impulsivity

measure that has three sub factors of impulsiveness: (1)

motor—acting without thinking, (2) cognitive—making

quick decisions, (3) non-planning—lack of concern for the

future. For this study each of the items was rated on a four-

point scale, 0–3 and scores range from 0 to 90.

Psychopathy Checklist: Screening Version

The 12 items of the PCL:SV [12] are divided into part

1—interpersonal and affective symptoms—and part 2,

social deviance symptoms. Each item is scored on a 3-point

scale: 0 = No ‘‘item does not apply’’; 1 = ‘‘maybe

applies’’; 2 = Yes ‘‘definitely applies’’. Scores range from

0 to 24.

Clinical factors

Positive and Negative Syndrome Scale (PANSS)

The PANSS [24] is a 30-item rating instrument evaluating

the presence/absence and severity of positive, negative and

Soc Psychiatry Psychiatr Epidemiol (2012) 47:627–637 629

123

general psychopathology of schizophrenia. All 30 items are

rated on a 7-point scale, 1 = absent and 7 = extreme.

Brief Michigan Alcohol Screening Test (MAST) and Drug

Abuse Screening Test (DAST)

The 10-item Brief MAST was derived from the longer 25

weighted-item MAST to identify problem drinkers in time-

limited situations [25]. The DAST was designed to provide

a brief instrument for clinical screening of drug abuse and

treatment evaluation research [26]. The 28 self-report

weighted items tap various consequences that are com-

bined in a total DAST score to yield a quantitative index of

problems related to drug use. The scales are based on self-

report historical information.

Historical factor measures

In order to measure putative historical risk factors a num-

ber of subscales of existing risk scales were used. These

included the Historical ten items of the HCR-20 [11] and

the six Static factors of the VRS [21].

Following training in the use of all the research mea-

sures, inter-rater reliability checks were conducted for 20

cases on the historical items of the HCR-20 and the

PCL-SV. Intra-class correlation (ICC) coefficients were

satisfactory for the clinically rated historical items of the

HCR-20 (0.97), PCL-SV total (0.97), PCL-SV factor 1

(0.85) and PCL-SV factor 2 (0.8). The inter-rater reli-

ability between three raters based on seven cases was 0.99

for the VRAG, 0.85 and 0.83 for the clinical and risk

management items of the HCR-20 and 0.96 for the VRS.

The PANSS was completed by a researcher following

interview with participants and based on independent

ratings of seven cases using the PANSS the ICC was

0.73.

Violent outcome

Violent behaviour in the community was measured up to

20 weeks post discharge. The sources of information were

the self-report of the participant, case records and collateral

report. The MacArthur Community Violence Instrument

[27] was used (with participant and collateral) and the

existing integrated electronic information systems and

paper records as necessary. For the purposes of this study,

violence was defined as in a similar recent study [28] as

any acts that include battery, sexual assaults, assaultative

acts or threats made with a weapon in hand. All violent

incidents recorded were screened by two researchers to

ensure they met the definitional criteria. Baseline measures

were then compared with frequency of violence in the

community post-discharge.

Data analysis

Analysis was conducted using SPSS for windows version

15. Descriptive statistics at baseline of age, gender, legal

status, diagnosis, ethnicity, previous violence and sub-

stance misuse are reported. Fisher’s exact test was used

with a chi squared statistic to compare differences between

violent and non-violent groups against these variables.

Receiver Operating Characteristic (ROC) analysis was

used to evaluate the predictive validity of the risk mea-

sures. Correlation coefficients were calculated to measure

association between risk measures and frequency of vio-

lence. Logistic regression procedures were used to examine

relationship between violence and risk factors when con-

trolling for age and gender.

Results

Sample description

A total of 162 inpatients from the three units were invited

to participate. Of these, 38 (23.5%) declined. Of the 124

participants initially recruited, 10 were lost to the study

either due to withdrawing consent (n = 6), death (2) and

lost to follow-up (2). Therefore, 114 participants success-

fully completed baseline and follow-up measures at

20 weeks post-discharge. The mean length of time as

inpatient was 77.8 days (SD 98.34) ranging from 1 to

518 days and the median length of stay was 37 days. The

mean number of days as inpatient between baseline

assessment and discharge was 43.54 (SD 59.43) with a

median of 20 days. Ninety-four (82.5%) of the participants

were discharged within 10 weeks of baseline assessment.

In order to check representativeness, the research sample

was compared with normal population from the research

sites on five indices: mean age, percentage of males, per-

centage of white caucasian patients and percentage of par-

ticipants diagnosed with schizophreniform disorders

(Schizophrenia, Schizo-affective disorder, Delusional dis-

order) or mania-bipolar disorder. The study sample was

slightly older than the normal population (40.5 years vs.

39.4), contained a greater proportion of males (62.3 vs.

53.5%) and a greater proportion of patients with a primary

diagnosis of mania-bipolar disorder (19.3 vs. 8.2%), and

more white caucasian patients (91.2 vs. 89.8%).

The majority of the sample were males (n = 71, 62.3%)

and nearly all were white Caucasian (104, 91.2%). The

mean age of the sample was 40.46 years (SD 11.4), median

40.5 years and the sample ranged from 18 to 63 years.

Over half (63, 55.3%) had a serious mental illness diag-

nosis of schizophreniform disorder or mania/bipolar dis-

order. Only five (4.4%) had a personality disorder primary


123

diagnosis, although 19 (16.7%) were adjudged to have

either a probable/definite personality disorder if comorbid

personality disorder included. The majority (72, 63.2%)

were found to have a possible—less serious history of

substance misuse, while nearly a third of the participants

(33, 28.9%) had a history of definite and serious substance

misuse. Over a third of the sample (44, 38.6%) were legally

detained in hospital against their will at time of baseline

assessment. Serious violence (three or more acts and/or one

serious act of violence) was considered and 21 (18.4%) of

the samples had a lifetime history of serious violence.

Thirty-one (27.2%) participants committed no violence,

serious or otherwise, in the 2 months preceding admission.

Prevalence and frequency of violence

In the 20-week period post-discharge, 29 (25.4%) of the

participants were violent. A greater proportion of females

(27.9%, n = 12) were violent compared with males (23.9%

n = 17) at 20 weeks (see Table 1) although this was not

statistically significant. The rate of violence reported varied

by source of information. Relying solely on participant

self-report, then only 14.9% of the sample would have been

identified as being violent in the 20 weeks post-discharge.

Using case records (16.7%) and the collateral informant

(21.1%) increased the rate of violent behaviour detected.

The 29 participants who were violent in the 20 weeks

post-discharge committed a total of 56 violent acts. The 17

males who were violent committed 33 acts while the 12

females committed 23 acts of violence. The highest num-

ber of acts of violence committed by one participant was

six and two participants committed five acts of violence.

The mean number of incidents per participant was 1.9 and

the median was one. Where the information was available,

the participants who were violent knew the victims of their

violence in 28 incidents (77.8%). This included 27.8%

(n = 10) who were described as a casual acquaintance,

22% (8) relative/close friend, 16.7% (6) part of a profes-

sional relationship and 11.1% (4) spouse/partner. The most

serious incident involved a serious physical assault

Table 1 Comparison of violent

and non-violent groups

a Participants with a score of 13

or higher on the PCL:SVb Definite and serious history of

substance misusec Violence in the 2 months

prior to admission

* p \ 0.05

Variable Violent

n = 29

(25.4%)

Non-violent

n = 85

(74.6%)

v2 Odds ratio 95% confidence intervals p

Lower Upper

Gender

Male 17 (23.9) 54 (76.1) 0.22 1.23 0.52 2.91 0.399

Female 12 (27.9) 31 (72.1)

Formal/Informal*

Formal 10 (22.7) 34 (77.3) 0.28 1.27 0.53 3.05 0.383

Informal 19 (27.1) 51 (72.9)

Ethnicity

White 27 (26) 77 (74) 0.17 1.4 0.28 7.02 0.509

Black and minority ethnic 2 (20) 8 (80)

Primary diagnosis

Serious mental illness 19 (30.2) 44 (69.8) 1.65 1.77 0.74 4.25 0.142

Other disorder 10 (19.6) 41 (80.4)

Primary/comorbid PD

Yes 9 (47.4) 10 (52.6) 5.78 3.38 1.21 9.42 0.021*

No 14 (18.4) 62 (81.6)

Definite/probable psychopatha

Yes 5 (35.7) 9 (64.3) 0.89 1.76 0.54 5.76 0.261

No 24 (24) 76 (76)

Substance misuseb

Yes 8 (24.2) 25 (75.8) 0.04 0.91 0.36 2.34 1.00

No 21 (25.9) 60 (74.1)

History of serious violence

Yes 7 (33.3) 14 (66.7) 0.85 1.61 0.58 4.5 0.255

No 22 (23.7) 71 (76.3)

Pre-admission violencec

Yes 13 (41.9) 18 (58.1) 6.11 3.02 1.23 7.42 0.015*

No 16 (14.3) 67 (80.7)


123

associated with threats with a knife and serious property

damage, although the majority of incidents were punching,

kicking and/or slapping.

Comparison of violent and non-violent groups

There was no significant difference in the rate of post-

discharge violence based on age, although the violent

group were younger (39.28 vs. 40.86 years). There were no

significant differences between the violent and non-violent

groups based on ethnicity, legal status, lifetime history of

serious violence, history of serious substance misuse or the

presence of a serious mental illness. Table 1 shows that

those participants with a diagnosis of a personality disorder

(either primary or comorbid) were significantly more likely

to be violent than those without, with an odds ratio of 3.38,

(CIs 1.21–9.42), as were those participants who were vio-

lent in the 2 months preceding current admission with an

odds ratio of 3.02 (CIs 1.23–7.42). Only four (3.5%) of the

sample met the criteria for psychopathy with a score of 18

and above on the PCL:SV, 14 (12.3%) were rated possibly

a psychopath scoring 13 or above. Of the 100 participants

who scored 12 or less on the PCL:SV, 24% were violent

compared with 35.7% of those scoring 13 and above

although this was non significant.

Comparison of violent and non-violent groups based

on risk measures and historical, dispositional

and clinical factors

The mean scores on all the risk measures were significantly

different between the violent and non-violent groups. The

Table 2 Comparison of violent and non-violent groups based on risk measures and historical, dispositional and clinical factors

Variable Violent (s.d.) Non-violent (s.d.) t-test Sig. 95% confidence intervals

Lower Upper

Risk measures

HCR-20 total 15.24 (8.08) 10.37 (6.26) -3.123 0.002** -7.959 -1.774

VRAG total -8.14 (11.47) -14 (8.62) -2.895 0.005** -9.874 -1.850

VRS total 21.83 (14.17) 13.56 (10.18) -3.398 0.001** -13.081 -3.445

Historical factors

HCR-20 historical 7.97 (4.50) 5.58 (3.37) -3.014 0.003** -3.960 -0.818

VRS static factors 5.03 (3.21) 3.89 (3.10) -1.696 0.093 -2.473 0.192


NAS total 94.64 (20.9) 83.46 (19.62) -2.569 0.012* -19.80 -2.554

Cognitive 33.18 (6.82) 29.36 (6.46) -2.673 0.009** -6.655 -0.988

Arousal 32.93 (7.65) 28.49 (7.27) -2.762 0.007** -7.627 -1.254

Behavioural 28.54 (7.96) 25.62 (7.12) -1.822 0.071 -6.090 0.257

Regulation 25.39 (3.44) 25.63 (3.76) 0.296 0.768 -1.356 1.832

BIS total 46.07 (8.46) 41.39 (8.69) -2.515 0.013* -8.360 -0.992

Motor 15.41 (4.95) 13.15 (4.23) -2.297 0.023* -4.208 -0.310

Non-planning 18.72 (5.20) 16.64 (5.49) -1.784 0.077 -4.392 0.230

Cognitive 11.93 (3.59) 11.60 (3.15) -0.477 0.634 -1.730 1.058

PCL:SV total 6.93(5.9) 4.64 (4.9) -2.074 0.040* -4.489 -0.102

Interpersonal 2.45 (2.8) 1.66 (2.2) -1.560 0.122 -1.792 0.213

Social deviance 4.48 (3.3) 2.98 (3.1) -2.224 0.028* -2.848 -0.164

Clinical factors

PANSS total 67.45 (15.17) 66.05 (14.64) -0.441 0.660 -7.696 4.894

Positive 16.24 (7.84) 15.78 (7.64) -0.281 0.779 -3.740 2.810

Negative 10.41 (3.78) 11.18 (4.51) 0.817 0.416 -1.087 2.612

General 34.14 (5.63) 34.21 (6.30) 0.056 0.956 -2.542 2.690

Aggressive 6.66 (3.35) 4.88 (2.01) -3.398 0.001** -2.807 -0.739

Non-parametric scales U Sig.

MAST 6.52 (9.03) 4.79 (8.11) -0.1043 0.17 – –

DAST 1.24 (3.45) 1.65 (3.58) -0.1172 0.6 – –

* p \ 0.05, ** p \ 0.01, *** p \ 0.001


123

VRS total score demonstrated the largest difference where

t = -3.398, df, 112, p \ 0.001 (Table 2).

The mean scores of the historical factors were compared

between the violent and non-violent groups. The mean

scores on the HCR-20 Historical items were significantly

different between violent and non-violent groups where

t = -3.014, df, 112, p = 0.003. The group mean scores

based on the VRS static scale were not significantly

different.

When the mean dispositional factor scores of the violent

and non-violent group were compared, there were signifi-

cant differences in the NAS total, and on the cognitive and

arousal subscales, whereas the Motor subscale of the BIS

and BIS total were significantly different between the

violent and non-violent groups (Table 2). The NAS

Arousal subscale mean score demonstrated the largest

difference between violent and non-violent group where

t = -2.762, df 110, p = 0.007. The PCL:SV total score

and the Social Deviance subscale demonstrated a signifi-

cant difference in mean scores between the groups,

whereas the Interpersonal subscale did not.

Of all the clinical factors measured, only the mean

total of the PANNS Aggressive subscale significantly

differed between violent and non-violent groups, where

t = -3.398, df 112, p = 0.001.

Predictive validity of risk measures and historical,

dispositional and clinical factors

Based on ROC analyses, all the risk scales significantly

predicted post-discharge violence. The HCR-20 had the

largest AUC of 0.676 where p = 0.009 while the HCR-20

Historical 10 scale was the only measure of historical

factors that was found to significantly predict post-dis-

charge violence (Table 3). Using a median split at 10 on

the HCR-20, the odds ratio (OR) between the two groups in

terms of violence was 3.016 with a sensitivity of 68%

specificity 58.7% and a positive predictive value (PPV) of

35.4%.

The NAS total and the cognitive and arousal subscales

were significantly predictive of violence in the 20-week

period post-discharge, with the arousal subscale having the

largest AUC of 0.677 (Table 3). Using a median split of

83.5 on the NAS, the OR between the two groups was

2.986 with a sensitivity of 67.9%, specificity 56.1% and a

PPV of 34.5%.

The BIS total and the Motor subscale were significantly

predictive with respective AUCs of 0.661 and 0.626. Nei-

ther the PCL:SV total score or the PCL:SV interpersonal

subscales predicted post-discharge violence in this sample,

although the social deviance subscale was significantly

predictive where AUC = 0.642, p = 0.023. The PANNS

total and subscales did not predict post-discharge violence

except for the Aggressive subscale where AUC = 0.654,

p = 0.014 (Table 3).

The measures that were predictive of violence were

investigated using logistic regression to investigate uni-

variate predictive validity of each of the risk measures for

post-discharge violence. The procedure was repeated with

age and gender added as covariates to see if the scales and

factors remained predictive of violence. Table 4 shows the

risk scales and risk factor measures before and after age

and gender were added. In each case the scales and factors

remained predictive when age and gender were controlled

for (Table 4).

Table 3 Predictive validity of risk measures and historical, disposi-

tional and clinical factors

Variable AUC SE Sig. 95% confidence

intervals

Lower Upper

Risk measures

HCR-20 total 0.676 0.063 0.009** 0.554 0.799

VRAG total 0.645 0.066 0.031* 0.515 0.774

VRS total 0.662 0.062 0.016* 0.541 0.782

Historical factors

HCR-20 historical 0.658 0.062 0.011* 0.537 0.780

VRS static factors 0.612 0.058 0.072 0.498 0.726


NAS total 0.675 0.061 0.007** 0.555 0.796

Cognitive 0.658 0.063 0.016* 0.535 0.782

Arousal 0.677 0.061 0.007** 0.558 0.796

Behavioural 0.658 0.060 0.016* 0.541 0.776

Regulation 0.504 0.064 0.947 0.379 0.630

BIS total 0.661 0.059 0.010* 0.545 0.777

Motor 0.626 0.063 0.043* 0.504 0.749

Non-planning 0.606 0.063 0.091 0.488 0.723

Cognitive 0.511 0.062 0.857 0.389 0.633

PCL:SV total 0.620 0.061 0.053 0.502 0.739

Interpersonal 0.571 0.065 0.258 0.444 0.697

Social deviance 0.642 0.059 0.023* 0.527 0.757

Clinical factors

PANSS total 0.526 0.064 0.672 0.400 0.653

Positive 0.512 0.066 0.850 0.383 0.641

Negative 0.455 0.062 0.472 0.333 0.577

General 0.492 0.062 0.899 0.371 0.613

Aggressive 0.654 0.062 0.014* 0.533 0.775

MAST (alcohol) 0.577 0.062 0.219 0.456 0.697

DAST (drugs) 0.476 0.061 0.696 0.356 0.595

* p \ 0.05, ** p \ 0.01, *** p \ 0.001


123

Correlation between frequency of violence,

risk measures and historical, dispositional

and clinical factors

The association between the frequency of violence up to

20 weeks post-discharge was compared with the indepen-

dent variables. There was a strong correlation between the

HCR-20 total and frequency of violence (r = 0.435) but

not for the VRAG or the VRS risk measures (Table 5). Of

the dispositional factors, only the PCL:SV total and inter-

personal and social deviance subscales were significantly

correlated with frequency of violence. The PANSS total,

positive, and aggressive subscales were strongly correlated

with frequency of violence and the aggressive subscale had

the largest correlation coefficient of r = 0.616 of all the

independent variables. The clinical items of the HCR-20

rated at discharge were also significantly correlated where

r = 0.511, p = 0.011, although neither the MAST or the

DAST were correlated with the frequency of violence post-

discharge.

Discussion

As far as the authors are aware this is the first study of its

type using a rigorous prospective design in an acute mental

health sample in the UK. The findings from this study are

similar to those found in the MacVRAS [22] and this study

has demonstrated that the risk scales and risk factors that

have been found to be predictive of community violence in

studies in forensic samples are also predictive of post-

discharge violence in acute mental health patients in

England. The HCR-20, VRAG, VRS, NAS total and cog-

nitive and arousal subscales, and the BIS total and motor

subscale significantly predicted post-discharge violence.

This remained the case when gender and age were con-

trolled for. The HCR-20 total, PCL:SV total and interper-

sonal and social deviance subscales, and the PANSS total

and positive symptom and aggression subscales were

significantly correlated with the frequency of post-dis-

charge violence. Despite these promising findings, consis-

tent with previous studies a high false positive rate for the

Table 4 Univariate analysis odds ratios for risk measures (Step 1) and adjusted odds ratios (Step 2) when covariates age and gender added

Measure Step 1 univariate analysis Step 2 covariates age and gender added

Odds ratio 95% confidence intervals p Adjusted odds ratio 95% confidence intervals p

HCR-20 1.101 1.030 1.176 0.004 1.104 1.031 1.181 0.005

VRAG 1.062 1.016 1.109 0.007 1.067 1.017 1.119 0.008

VRS 1.058 1.021 1.097 0.002 1.064 1.023 1.106 0.002

H10 1.178 1.050 1.320 0.005 1.190 1.055 1.343 0.005

NAS 1.027 1.005 1.050 0.015 1.029 1.006 1.052 0.013

BIS 1.067 1.012 1.126 0.016 1.066 1.011 1.125 0.019

PCL:SV soc deviance 1.151 1.012 1.309 0.032 1.151 1.007 1.316 0.039

PANSS aggressive 1.303 1.098 1.547 0.002 1.301 1.094 1.548 0.003

Table 5 Correlation between risk measures and historical, disposi-

tional and clinical factors with frequency of violence at 20 weeks

Scale Correlation r Sig p

Risk measures

HCR-20 total 0.435* 0.034

VRAG total 0.318 0.092

VRS total 0.359 0.056

Historical

HCR-20 historical 0.336 0.075

VRS static factors 0.166 0.390


NAS total 0.128 0.515

Cognitive 0.071 0.721

Arousal 0.091 0.645

Behavioural 0.187 0.340

Regulation 0.125 0.527

BIS total -0.164 0.395

Motor -0.099 0.610

Non-planning -0.157 0.415

Cognitive -0.051 0.792

PCL:SV total 0.391* 0.036

Interpersonal 0.373* 0.047

Social deviance 0.374* 0.046

Clinical factors

PANSS total 0.517** 0.004

Positive 0.512** 0.005

Negative 0.010 0.959

General 0.300 0.114

Aggressive 0.616*** 0.000

MAST (alcohol)a 0.011 0.953

DAST (drugs)a 0.071 0.715

a Spearmans rho

* p \ 0.05,** p \ 0.01, *** p \ 0.001


123

best performing scales was evident at the median split;

65% for the HCR-20 and 66% for the NAS, and the relative

predictive accuracy of the all the risk measures is moderate

when compared with the larger effects found in other

similar studies in forensic, mixed, prison and learning

disability samples [4, 8, 9]. For the purposes of this study

the sample comprised of discharged acute psychiatric

patients. It could be argued that discrimination between the

violent and non-violent groups should be greater in non-

forensic samples given that high ratings are less common.

However, the risk measures were developed and validated

with forensic services in mind and this may account for the

lower predictive accuracy. Nonetheless, the findings

remain significant and may have implications for future

research and clinical practice.

The sample was generally representative of the general

population resident at the respective sites and it was pos-

sible to make satisfactory ratings of historical, risk, dispo-

sitional and clinical factors based on a combination of

interview, record review, collateral information and self-

report questionnaires. In this study more than one in four of

those discharged from the acute mental health sites were

physically violent in the 20-weeks post discharge and this

was even higher in the female sample. This base rate of

violence is higher than previously recorded in the MacV-

RAS and a mixed forensic/non-forensic sample, where base

rates of violence were 18.7 and 18.8%, respectively [4, 22].

The relative increase in the rate of violence may be due to a

real increase in violence, especially amongst females, and it

reinforces the importance of structured approaches for

violence risk assessment in non-forensic settings. It is also

likely to be due to the fact that in this study, multi-disci-

plinary computerised clinical notes were used. This made it

much easier to identify incidents of violence. In a similar

previous study, only 8.9% of the violence detected resulted

from reviewing records, whereas the figure here was 16.7%

when using computerised records. Therefore, it is possible

that the rate of violence was due to improved detection

rather than being a real increase in the time since last study

and it is of note that the rate of violence detected from case

records surpassed that of the participants’ self report

(16.7 vs. 14.9%). The finding that females were more likely

to be violent than males is a unique finding in a study of this

type, although the difference was non-significant. Further

studies need to be conducted to include a larger sample

focusing primarily on examining gender differences. The

perpetrator of violence knew almost all the victims. This is

comparable to previous findings in this area [4, 22] and

supports the viewpoint that people with mental disorder are

unlikely to be violent against people at random and/or who

they do not know. As with previous studies most of the

victims were known to the perpetrator and the vast majority

of violent incidents were minor.

Only 4 (3.5%) met the criteria for psychopathy as

measured by the PCL:SV. This is similar to base rate of

between 2.1 and 6.1% reported in the PCL:SV manual [12].

Compared with the findings from the MacVRAS and other

community violence studies, the predictive accuracy of

psychopathy was relatively moderate, although there was a

strong correlation between psychopathy and frequency of

violence. As reported in previous literature [29–31], the

presence of a personality disorder was linked with an

increased risk of community violence where those with a

personality disorder were over three-times more likely to

be violent than those without, although the type and nature

of the disorder and the causal mechanisms remain unclear

based on evidence from this study. These findings lend

support to the inclusion of PD and psychopathy in assess-

ments of future violence [32]. Any violence committed in

the 2 months prior to admission was a significant risk

factor for post discharge violence suggesting that propen-

sity for violence was pervasive over time and possibly

linked to personality factors. Nevertheless, it should be

noted that the majority of those violent in the 2 months

prior to admission (58.1%) were not violent in the

20 weeks post discharge. In future research and clinical

practice, it may be worth investigating whether the use of

risk tools might be best used to assess the risk of violence

‘recurring’ rather than ‘occurring’ for the first time [33]

whereby a tiered approach is used to determine if further

more detailed assessment is necessary where there is a

history and/or, suspected history of violence prior to

admission [2].

Numerous previous studies investigating risk factors for

violence have found a close link between substance misuse

and future violence [34–36]. In this study, a history of

substance misuse was not found to be associated with an

increased risk, possibly due to the widespread prevalence

of substance misuse in nearly two-thirds of the sample.

Caution is advised, however, as self-reports of substance

misuse are notoriously unreliable and a more fine-grained

assessment of substance misuse may have identified a link

given the strong evidence from previous studies. Substance

misuse should continue to be a focus for violence risk

assessment in clinical practice.

Based on the ROC analysis, the HCR-20 total was the

best performing risk scale and this is consistent with pre-

vious research in this area [5, 15] where the clinical and

risk management items are rated at discharge reflect current

mental state, behaviour, social functioning and context.

This supports the focus on more dynamic factors and

supports a prevention paradigm of risk management [37]

where risk assessments are conducted to assist formulation

and prevention of violent behaviour rather than simply

prediction. However, given the significant predictive

accuracy of the historical items of the HCR-20 and the


123

VRS, it would appear necessary to anchor violence risk

assessments by considering more static historical factors as

part of a tiered approach.

In terms of dispositional factors, anger was generally

more predictive of violence than impulsiveness and psy-

chopathy. This is consistent with previous findings [4, 38],

where anger and its component parts as measured by the

NAS have previously outperformed psychopathy. Of note

is the fact that both the NAS and BIS are based on self-

report and it is possible that participants were more honest

as this was a research project where participants were

reassured that their responses would not impact on their

stay or detention in hospital. From a clinical perspective,

other than PD, there were no specific diagnoses related to

future community violence and this is consistent with

previous similar studies [4, 27, 39]. None of the scales that

measured clinical risk factors independently predicted post

discharge violence, except for the PANSS aggressive

subscale. The aggressive subscale measures the degree of

anger, difficulty in delaying gratification and affective

instability. These three areas reflect components of anger

and impulsiveness, which have been found to be highly

predictive of violence in this and previous research in

mental health settings. Research has already identified

effective cognitive-orientated interventions for anger and

impulsiveness problems that may reduce the risk of vio-

lence [40–42]. If these interventions for anger and impul-

siveness problems can be developed into treatment

packages, then these could provide effective violence

prevention strategies.

The rigorous methodological design precluded the

recruitment of more participants in the study period,

although the method chosen has been recommended as the

most accurate way to achieve reliable findings and avoid

inherent biases of this type of study [27]. Nevertheless,

future studies should use this method with a view to

recruiting larger samples. The location and the level of

support the participants received on discharge were not

considered in this study and this should be focus of future

research to evaluate the effect of professional and social

support.

Based on our findings, it is likely that violence risk

management strategies will be more successful if structured

professional guidelines such as the HCR-20 are used to

guide clinical assessments and risk formulations. It is dif-

ficult to prove a causal link between risk factors and vio-

lence [43], but the overall findings highlight that in acute

mental health populations there is a need to focus on what

the person has done, (past and recent history of violence)

what the person is (angry, impulsive, aggressive) and what

the person has (personality disorder, psychopathy) [20] and

substance misuse should remain a focus in risk assessment

in both males and females. Although efforts have been

made to develop an evidence-based actuarial tool for cli-

nicians based on the findings of MacVRAS [28] further

validation studies are still required and the challenge

remains for clinicians in acute mental health services to

apply the findings from this study and others into practice.

Reliable and practical frameworks that support decision-

making in real-time clinical practice are necessary if studies

like this are to contribute to violence prevention.

Acknowledgments We are very grateful to Professor John Mona-

han from the University of Virginia, USA , for all his support and

advice with this work. This study was supported by a grant from the

Department of Health National Forensic Research and Development

programme.

Conflict of interest None.

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