A Prospective Cohort Study of Absconsion Incidents in Forensic Psychiatric Settings: Can We Identify Those at High-Risk?
A Prospective Cohort Study of Absconsion Incidents in Forensic Psychiatric Settings: Can We Identify Those at High-Risk?
Alexis E. Cullen 0 1 2
Amelia Jewell 0 1 2
John Tully 0 1 2
Suzanne Coghlan 0 1 2
Kimberlie Dean¤ 0 1 2
Tom Fahy 0 1 2
0 a Current Address: School of Psychiatry, Faculty of Medicine, University of New South Wales , Sydney , Australia ¤b Current Address: Justice Health & Forensic Mental Health Network , Matraville NSW , Australia
1 Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London , London , United Kingdom
2 Editor: Gabriele Fischer, Medical University of Vienna , AUSTRIA
Incidents of absconsion in forensic psychiatric units can have potentially serious consequences, yet surprisingly little is known about the characteristics of patients who abscond from these settings. The few previous studies conducted to date have employed retrospective designs, and no attempt has been made to develop an empirically-derived risk assessment scale. In this prospective study, we aimed to identify predictors of absconsion over a two-year period and investigate the feasibility of developing a brief risk assessment scale. The study examined a representative sample of 135 patients treated in forensic mediumand low-secure wards. At baseline, demographic, clinical, treatment-related, and offending/ behavioural factors were ascertained from electronic medical records and the treating teams. Incidents of absconsion (i.e., failure to return from leave, incidents of escape, and absconding whilst on escorted leave) were assessed at a two-year follow-up. Logistic regression analyses were used to determine the strongest predictors of absconsion which were then weighted according to their ability to discriminate absconders and non-absconders. The predictive utility of a brief risk assessment scale based on these weighted items was evaluated using receiver operator characteristics (ROC).
Data Availability Statement: Data are owned by a
third party, Biomedical Research Centre (BRC)
Clinical Records Interactive Search (CRIS) tool,
which provides access to anonymised data derived
from SLaM electronic medical records. These data
can only be accessed by permitted individuals (AEC
AJ) from within a secure firewall (i.e., remote access
is not possible and the data cannot be sent
elsewhere), in the same manner as the authors.
Funding: The South London and Maudsley NHS
Foundation Trust, UK supported the salary of AEC to
conduct the project. The funders had no role in study
During the two-year follow-up period, 27 patients (20%) absconded, accounting for 56
separate incidents. In multivariate analyses, four factors relating to offending and behaviour
emerged as the strongest predictors of absconsion: history of sexual offending, previous
absconsion, recent inpatient verbal aggression, and recent inpatient substance use. The
weighted risk scale derived from these factors had moderate-to-good predictive accuracy
design, data collection and analysis, decision to
publish, or preparation of the manuscript.
Competing Interests: The authors have declared
that no competing interests exist.
(ROC area under the curve: 0.80; sensitivity: 067; specificity: 0.71), a high negative
predictive value (0.91), but a low positive predictive value (0.34).
Potentially-targetable recent behaviours, such as inpatient verbal aggression and
substance use, are strong predictors of absconsion in forensic settings; the absence of these
factors may enable clinical teams to identify unnecessarily restricted low-risk individuals.
Incidents of absconsion, defined as being absent from hospital without permission, are
fortunately rare in forensic psychiatric settings . However, these rare incidents can have
potentially tragic outcomes including the occurrence of further incidents of serious violence; and
during the past decade there have been several high profile cases of this nature in the UK. Such
cases can lead to public inquires [2,3] and attract considerable media attention, both of which
are likely to undermine the public’s confidence in the ability of these services to safely treat
offenders with mental health problems. Furthermore, these cases may further increase stigma
towards people with mental illness in the general population by enhancing the link between
mental illness and risk of harm. In addition to the risk posed to members of the public by
patients who abscond, it is important to emphasise the increased risk to the patients
themselves. The UK National Confidential Inquiry recently reported that 25% of all psychiatric
inpatient suicides occurred among those who had absconded from hospital . Despite the
potentially negative consequences associated with absconsion incidents, relatively little is
known about the characteristics of patients who are most likely to abscond from forensic
settings. The absence of rigorous research findings limits the ability of clinicians to make
informed decisions about leave and how to manage the level of risk. As such, in an attempt to
reduce the potential for high-impact events, clinicians may be unnecessarily restricting or
denying leave to patients who are actually at low risk for absconsion events.
To date, nine studies (summarised in Table 1) have attempted to identify patient
characteristics associated with absconsion from forensic psychiatric services. These studies have
observed that several demographic factors, such as younger age, male sex, ethnicity, length of
stay, and legal section are associated with absconsion [5,6,7,8,9,10]; however, findings are
inconsistent across studies. With regards to diagnosis, perhaps unsurprisingly, psychopathy
and personality disorder appear to be more common among those who abscond from forensic
settings [7,11], a finding which is consistent with that of an early study which showed that
absconders are characterised by higher scores on the Minnesota Multiphasic Personality
Inventory than non-absconders . In contrast, one reasonably consistent finding is that the
presence of psychotic disorder is not associated with higher likelihood of absconsion [5,8,9,13]. A
number of offending and behavioural factors have also been found be more common among
patients who abscond relative to those who do not, including, a violent or acquisitive index
offence [5,7,11], an admission precipitated by an index offence , a greater number of
previous convictions , prior substance use problems [9,10,13], and higher scores on the
Historical, Clinical, Risk-Management—20 (HCR-20 ) violence risk assessment tool . Finally,
several studies have reported that previous absconsion is associated with increased likelihood
of later absconsion [5,6,10,13], indicating that past behaviour is a predictor of future behaviour,
or at least that the factors associated with the past behaviour are still present and increase the
risk of future behaviour.
to return, escape
Case control Absconsion and
(N = 36 vs. 150) escape
Note. PCL-R: Psychopathy Checklist–Revised; MHA: Mental Health Act; HCR-20: Historical, Clinical, Risk-Management– 20
History of absconsion; history of substance misuse and
dependence; history of non-compliance; history of sexually
inappropriate behaviour; history of childhood conduct problems
Younger age; shorter duration of stay; treated on
medium-/highdependency wards; previous absconsion; offending history;
treatment non-compliance antagonism to hospital rules;
impulsive/aggressive behaviour; deteriorated mental state; family/
friends disagree with detention; history of acting out behaviour;
anxiety conflict regarding transfer; judged at high-risk of
Minnesota Multiphasic Personality Inventory (MMPI ) scores
Male gender; African-Caribbean ethnicity; legal status; referral
from prison/police custody; offending prior to admission; history
Malingering; non-psychotic disorder; not treated with
antipsychotics; violent index offence; PCL-R  total, factor 1,
and factor 2 scores
Shorter length of stay prior to absconsion; legal classification of
psychopathy; legal classification of psychopathy; section 3 of
MHA; property index offence
Younger age; personality disorder diagnosis; MHA section
Younger age; transfer from prison; non-psychotic disorder;
number of previous convictions; unemployment; history of alcohol
problems; sibling position
Longer length of stay; history of unsuccessful absconsion
attempts; co-morbid substance use disorder; higher HCR-20 
scores; fewer violent offences
Factors associated with absconsion
Whilst the aforementioned studies have helped to identify patient characteristics associated
with absconsion, our current knowledge is limited by several methodological issues. Firstly, all
studies conducted in forensic settings to-date have been retrospective in nature. This
introduces the possibility that reverse causality may have contributed to some of these findings. For
example, it is conceivable that patients who abscond may later receive a higher score on the
HCR-20 as a result of this behaviour, rather than high HCR-20 scores being a true risk factor
for absconsion. A second limitation relates to the fact that many studies have focused to a large
extent on static, historical factors as opposed to dynamic factors. As such, it is not known if the
risk of absconsion might be reduced by targeting risk factors that are potentially amenable to
treatment. Finally, although previous studies have identified a range of factors that may
increase the risk for absconsion, there has been no attempt to develop a statistically-derived
risk assessment tool. This is surprising when one considers that there are now 25 different
violence risk assessment scales in existence .
The current study examines data collected in a two-year follow-up of a sample of patients
treated in medium- and low-secure forensic psychiatric services in the UK. The primary aim
of the study was to advance existing knowledge by identifying patient characteristics
prospectively associated with absconsion from forensic psychiatric units. Based on previous research,
we examined a range of demographic, clinical, treatment-related, and offending/behavioural
factors to determine those distinguishing patients who abscond from forensic settings from
those who do not. An additional aim of the study was to investigate the feasibility of developing
a brief absconsion risk assessment scale for use in forensic secure units.
The current study was conducted within the South London and Maudsley (SLaM) National
Health Service (NHS) Foundation Trust which provides secondary mental health care to
individuals residing in four southeast London boroughs (Lambeth, Southwark, Lewisham, and
Croydon). The sample comprised patients examined in a census conducted in November 2011,
which included all individuals receiving treatment in SLaM forensic inpatient services within a
two-week period. At the time of the census, SLaM forensic inpatient services were provided by
two medium-secure units (eight medium-secure wards in total) and one low-secure unit
consisting of a single ward. The medium-secure units provided a variety of services and included a
psychiatric intensive care unit, a specialist personality disorder service, and a female only ward.
In total, 135 patients were examined in the census.
Census data were obtained using the Biomedical Research Centre (BRC) Clinical Records
Interactive Search (CRIS) tool, which provides access to anonymised data derived from
SLaM electronic medical records . The CRIS tool has been described in detail previously
[16,17,18]. In brief, CRIS facilitates the searching and retrieval of anonymised data from the
medical records of over 165,000 patients who have been in contact with SLaM services from
2006 onwards (i.e., when electronic records were implemented across the trust). CRIS was
approved as a dataset for secondary analysis by the Oxfordshire Research Ethics Committee C
The aim of the census was to obtain detailed demographic, clinical, treatment-related, and
offending/behavioural data for a representative sample of forensic inpatients and to follow this
cohort longitudinally, thereby allowing us to identify predictors of adverse outcomes such as
absconsion. A pilot study determined that several variables of interest, particularly those
relating to offending and behaviour, were not systematically recorded within electronic medical
records and could not be easily extracted using CRIS. We therefore developed a census form to
capture additional variables of interest which were completed at the patient’s ward round with
input from the multidisciplinary team. Census forms were then uploaded to the electronic
medical records system and extracted using CRIS, thereby preserving patient anonymity.
Potential risk factors
All potential risk factors were obtained at the time of the census. Several factors were extracted
directly from structured fields within electronic medical records, including: sex, ethnicity,
date-of-birth, and admission/discharge dates (used to calculate length of stay for the current
SLaM forensic episode which was subsequently recoded into a binary variable: 18 months
vs. > 18 months). Primary diagnosis was determined from the most recent Mental Health
Review Tribunal report uploaded to the electronic medical records system and a binary variable
was subsequently derived (psychotic disorder vs. other disorder). HCR-20 assessments  are
regularly completed in SLaM forensic services; total scores on the most recent assessment were
obtained where available.
Census forms were completed by the multidisciplinary clinical teams and used to capture a
broad range of potential risk factors, including: current leave status, Mental Health Act section
(collapsed to civil vs. forensic), recent episode of acute illness (psychosis, mania, or depression),
current medication, concerns regarding medication compliance, and engagement in
psychological therapy. Census forms included three items to determine the presence of learning
disability, personality disorder (any), and psychopathy based on previously-completed
assessments (if available) in the patient’s clinical record (i.e., assessments typically completed
by forensic psychiatrists and psychologists as part of routine clinical care). For each diagnosis,
clinical teams were asked whether the diagnosis was (i) not present, (ii) likely to be present but
not formally confirmed (i.e., the clinical team suspect diagnosis is present but there are no
recent/relevant assessments available in the patient’s clinical record to confirm this), or (iii)
definitely present as confirmed using a validated assessment tool. Validated tools to assess
personality disorder included the Minnesota Multiphasic Personality Inventory (MMPI ) and
the Structured Clinical Interview for DSM-IV Axis II Personality Disorders (SCID-II ),
and for psychopathy, included the Psychopathic Checklist Revised (PCL-R ) and
Psychopathic Checklist Screening Version (PCL-SV ). We initially undertook analyses at all three
levels (not present, likely present, and definitely present) for all three variables; however, for
learning disability and personality disorder, statistical power was improved by collapsing across
levels and no information was lost in doing so. These two variables were therefore subsequently
collapsed to form binary variables (not present vs. likely or definitely present). Psychopathy
was retained as a three-parameter variable owing to the fact that the association with
absconsion was not consistent across the probable and definite diagnostic groups (see Table 2).
Clinical teams additionally provided information on the current index offence, categorised as
violent (e.g., assault, grievous/actual bodily harm, murder/manslaughter), acquisitive, drug,
sexual, and other offences. These data were subsequently collapsed to violent vs. other; as it was
not possible to determine whether sexual index offences were violent in nature, sexual offences
were included in the ‘other’ category along with acquisitive, drug, and other offences. Clinical
teams also indicated whether the patient had a history of sexual offending and whether the
patient had previously absconded. The census form was also used to determine whether the
patient had used substances in an inpatient setting and whether the patient had exhibited
aggression as an inpatient (none; verbal aggression only; actual or attempted physical violence);
both items were rated for the 12-month period prior to census completion.
In January 2014 (approximately 26 months after initial census completion), incidents of
absconsion were ascertained for all patients in the census cohort using CRIS. Specifically, CRIS
was used to perform free-text searches of ‘events’ and ‘ward progress notes’ (i.e., entries made
by clinical teams at multiple times throughout the day) within electronic medical records. The
following search terms were used to identify entries containing any of the following key words:
‘abscon ’, ‘escap ’, ‘fail to return’, and ‘AWOL’ (i.e., absence without official leave); each
retrieved entry was then manually cleaned to identify those relating to genuine incidents of
absconsion. Incidents were categorised into three types, (i) absconsion (i.e., running away from
a member of staff, or refusing to return to the unit with a member of staff, whilst on escorted
leave), (ii) escape (i.e., a successful or unsuccessful attempt to escape from the perimeter of the
hospital), and (iii) failure to return from unescorted leave. In order to increase statistical
power, these events were then subsequently collapsed to create a binary variable (absconsion
vs. no absconsion) for each patient. In sensitivity analyses, we additionally examined the ability
of the final weighted risk score to predict each of the three outcomes separately.
Note. LoS: Length of stay for current episode; MHA: Mental Health Act; OR: odds ratio. Missing data: ethnicity (n = 6); leave status (n = 4); MHA section
(n = 5); learning disability (n = 5); recent episode of acute illness; (n = 4); personality disorder (n = 4); psychopathy (n = 4).
All analyses were performed using Stata version 12. Univariable logistic regression analyses
were initially performed to identify factors that distinguished patients who had absconded at
least once during the follow-up period from those with had not. In order to derive a
parsimonious regression model, factors that were significant predictors of absconsion in univariable
analyses (p < 0.05) were then entered into a backward stepwise logistic regression model using a
probability of 0.10 for variable entry and removal. Multicollinearity was assessed via the
correlation matrix and indicated no issues with multicollinearity (r<0.30 for all correlations between
the four independent variables retained in the final model). Factors retained in the
multivariable model were then weighted according to their ability to discriminate between absconders
and non-absconders using a methodology employed in the development of the Violence Risk
Appraisal Guide (VRAG) . Specifically, for each risk factor included in the final model, the
total sample was stratified by the presence of that risk factor and absconsion rates were
calculated within each stratum (absent vs. present). For each stratum, a weighting of one was then
assigned for every full 5% plus or minus the base rate of absconsion in the total sample. A total
score was derived for each participant by summing weighted scores for each risk factor
included in the final model.
Several measures were used to assess the predictive accuracy of the final risk assessment
scale. A receiver operator characteristic (ROC) analysis was first performed on the total scores,
thus, for each possible score of the scale the true positive rate was plotted against the false
positive rate. The area under the curve (AUC) statistic was subsequently derived, indicating the
probability that a randomly selected absconder would obtain a higher risk classification on the
risk scale than a randomly selected non-absconder . The ROC analysis was also used to
determine an optimal cut-off point on the risk assessment scale, that is, a cut-off yielding the
best trade-off between sensitivity (the proportion of absconders correctly classified as
highrisk) and specificity (the proportion of non-absconders correctly classified as low-risk). The
positive (PPV) and negative predictive values (NPV) were subsequently computed at the
optimal cut-off, indicating the probability that a high-risk patient will abscond and that a low-risk
patient will not abscond, respectively. Consistent with Fazel and colleagues [24,25], we also
computed the diagnostic odds ratio, defined as the odds that an absconder will be classified as a
being at high-risk relative to the odds that a non-absconder will be classified at high-risk.
Across the total sample (N = 135), the mean age at study commencement was 38.5 years and
the majority of patients were male (90%). Psychotic disorder was the most common primary
diagnosis (78%) while just over half of the sample had a primary or co-morbid diagnosis of a
personality disorder (51%). At the time of census completion, the median length of stay in
forensic inpatient wards for the total sample was 406 days (range: 7–2510) and approximately
one third of the cohort had been granted unescorted community leave (32%).
Absconsion data were obtained at the two-year follow-up for all patients examined in the
2011 census. In total, 27 patients (20%) absconded from a forensic ward on at least one
occasion during the follow-up period; between them, these patients were involved in 56 separate
incidents of absconsion with a median of one incident per patient (range: 1–8). Incidents were
most commonly classified as failures to return to the unit (36 incidents committed by 14
patients), followed by escapes (14 incidents committed by 7 patients), whilst relatively few
related to incidents of absconsion whilst on escorted leave (6 incidents committed by 3
patients). Given that only one of the nine forensic wards was a low-secure ward, incidents in
low-security appeared to be somewhat over-represented (21 incidents vs. 35 in
medium-security); of note, the majority of escapes were from low-secure wards.
Univariable associations between potential risk factors and absconsion
Univariable logistic regression analyses were conducted to examine the association between
potential risk factors and absconsion. As shown in Table 2, whilst absconders were somewhat
younger in age relative to non-absconders (mean age: 35.6 vs. 39.2 years), this association did
not achieve statistical significance (OR = 0.97, p = 0.15). Indeed, none of the demographic
factors (i.e., sex, ethnicity, length of stay, leave status, and forensic MHA section) were significant
predictors of absconsion (p > 0.05 for all factors). Of the clinical factors examined (Table 2),
absconsion was not significantly associated with a primary diagnosis of psychotic disorder,
having a definite or probable diagnosis of learning disability, a recent episode of acute illness,
or personality disorder (p > 0.05). Relative to those without psychopathy, the odds of
absconsion were higher among those patients who were noted by the clinical teams to have a likely
diagnosis of psychopathy but who had not formally been assessed (OR = 3.41). In contrast,
those definitely meeting criteria for psychopathy (assessed using the PCL-R or PCL-SV) were
less likely to abscond (OR = 0.28); however, neither parameter achieved statistical significance
(p > 0.05).
Note. HCR-20: Historical Clinical Risk-Management—20; OR: odds ratio. Missing data: medication non-compliance (n = 4); clozapine treatment (n = 5);
psychological therapy (n = 7); HCR-20 (n = 22); other index offence (n = 2); history of sexual offending (n = 8); previous absconsion (n = 3); inpatient
substance use (n = 4); inpatient aggression (n = 3).
Treatment-related factors and their association with absconsion are presented in Table 3.
Neither medication non-compliance, clozapine treatment, nor engagement in psychological therapy
were significant predictors of absconsion (p > 0.05). In contrast, absconsion was significantly
associated with a number of offending/behavioural factors. Patients who absconded were more
likely to have an ‘other’ index offence (OR = 3.00, p = 0.02), a history of sexual offending
(OR = 2.62, p = 0.04), and to have absconded previously (OR = 2.60, p = 0.04). With regards to
inpatient behaviour in the twelve months prior to census completion, those who had engaged in
substance use were significantly more likely to abscond (OR = 3.93, p = 0.01). Furthermore,
relative to patients who exhibited no aggression, the odds of absconsion were approximately four
times higher among patients who had been verbally aggressive (OR = 3.93, p = 0.01), but were
lower among patients who had been physically violent (OR = 0.69, p = 0.56).
Multivariable model and scale weighting
A stepwise logistic regression analysis was performed on the five factors that achieved statistical
significance in univariable analyses, namely, other index offence, history of sexual offending,
previous absconsion, inpatient substance use, and inpatient aggression (note, dummy variables
coding for verbal aggression and physical violence were both included in the multivariable model).
Physical violence and other index offence did not improve overall prediction and were therefore
excluded from the model in Step 1 and Step 2, respectively. The four remaining items were then
weighted according to their ability to discriminate absconders from non-absconders using the
method described above; briefly, after stratifying by each risk factor, a weight of one was assigned
for every full 5% ± the base rate of absconsion in the total sample. For example, the rate of
absconsion among those with and without a history of sexual offending was 29% and 13%, respectively;
as these rates are a full 5% plus and minus the absconsion rate in the total sample (20%) a history
of sexual offending received a weight of plus one whilst the absence of a history of sexual offending
was given a weight of minus one. As shown in Table 4, using this procedure, a history of sexual
offending and previous absconsion were assigned the same weighting whilst inpatient substance
use and verbal aggression were weighted more heavily. Possible minimum and maximum scores
on the weighted scale ranged from minus four to eight.
Predictive accuracy of the weighted scale
ROC analysis was performed on the total scores of the weighted scale to examine the predictive
accuracy of the final risk assessment scale and identify a suitable cut-off score. These analyses
(Fig 1) yielded an AUC statistic of 0.75 (95% CI = 0.63 to 0.87) and determined that a cut-off
score of one (i.e., zero = low-risk; one = high-risk) represented the best trade-off between
sensitivity and specificity. Applying this cut-off, 36% and 64% of the sample were classified as
high- and low-risk, respectively. The proportion of absconders correctly classified as high-risk
(sensitivity) and the proportion of non-absconders classified as low-risk (specificity) was 0.67
(95% CI = 0.45 to 0.84) and 0.71 (95% CI = 0.62 to 0.79). The probability that a patient
classified as high-risk would go on to abscond was low (PPV = 0.34; 95% CI = 0.21 to 0.49), however,
the likelihood that a low-risk patient would not abscond was high (NPV = 0.91; 95% CI = 0.82
to 0.96). Finally, the diagnostic OR indicated that the odds of being classified as high-risk were
approximately five times higher among absconders relative to non-absconders (OR = 4.97;
95% CI = 1.93 to 12.79).
Analyses by incident subtype
Sensitivity analyses were conducted to determine whether total scores on the weighted risk
scale predicted each of the three incident subtypes (i.e., failure to return, escape, and
absconsion). Logistic regression analyses indicated that scores on the weighted risk scale were
significantly associated with each of the outcome incident subtypes (p < 0.05 for all). Specifically, for
every one point increase on the weighted risk scale, the odds of a patient failing to return
increased by 1.21 fold (95% CI = 1.02 to 1.45), the odds of escaping or attempting to escape
was increased by 1.50 fold (95% CI = 1.15 to 1.95) and the odds of absconsion increased by
1.46 fold (95% CI = 1.02 to 2.09).
This is the first truly prospective cohort study to examine factors predicting absconsion in a
forensic psychiatric inpatient population. During the two-year follow-up, one in five patients
absconded from medium- and low-secure inpatient settings (with incidents occurring in
lowsecurity appearing to be disproportionately high), these incidents were largely failures to return
Fig 1. Receiver operator characteristic (ROC) curve for total scores on the four-item weighted
absconsion risk scale (AUC = 0.75).
to the ward whilst on community leave. We found that the strongest predictors of absconsion
were related to historical behaviour (a history of sexual offending and prior absconsion) and
recent inpatient behaviour (substance use and verbal aggression occurring during the past
twelve months). A weighted scale based these four items was found to have adequate predictive
accuracy as indicated by moderate-to-high AUC, sensitivity, and specificity values; however,
while the NPV was high, the PPV for the scale was very low. Such findings demonstrate that it
is at least possible to develop a brief, empirically-derived absconsion risk assessment scale
which could be trialled in forensic settings.
Our findings are broadly consistent with those of previous retrospective studies conducted
in forensic inpatient populations. In the current study, having an index offence classified as
‘other’ (which included sexual offences) was a significant predictor of absconsion in univariable
analyses, but was not retained in the final model after a history of sexual offending was
included. In contrast, three previous studies reported that a sexual index offence was not
significantly associated with absconsion [5,7,10]; yet Beer and colleagues found that a history of
sexually inappropriate behaviour was associated with absconsion at the trend level . Together,
these findings suggest that whilst sexual offending may be an important risk factor for
absconsion, index offence alone may not be particularly informative; thus, taking into consideration
any history of sexual offending may improve prediction of absconsion. Our finding that
absconsion was associated with a history of previous absconsion was unsurprising and is
consistent with all four previous studies that have included prior absconsion (actual or attempted)
as a potential risk factor [5,6,10,13]. However, the practical implications of identifying past
behaviour as a risk factor for future behaviour are limited as such behaviours cannot be
targeted with interventions. A novel finding from our study was that recent inpatient behaviours,
namely, substance use and verbal aggression, were associated with absconsion; indeed these
factors showed better ability to discriminate between absconders and non-absconders than
historical factors. Whilst ours is the first study to have examined these specific inpatient
behaviours, Brook and colleagues also reported that absconsion was associated with impulsive/
aggressive behaviour on the ward . Thus, it may be that recent behaviour provides a good
indication of current levels of impulsivity which might in turn contribute to absconding
In contrast with some previous forensic studies, but not all, absconsion was not predicted by
any of the demographic factors examined, although this may reflect a lack of statistical power.
Whilst some studies have reported that younger age [5,9,26], male sex , and ethnicity  are
associated with absconsion, others have not [5,6,7,9,10,13,26,27]. Whether such factors predict
absconsion may depend on the demography of the sample and the clinical setting. Indeed,
studies conducted in general psychiatric settings have yielded more consistent findings than
forensic studies , and have typically shown that absconders are more likely to be younger
in age [28,29,30,31,32,33]. A similar consideration may apply in regards to clinical factors;
previous studies in acute settings have observed higher rates of absconsion among patients with
schizophrenia [28,30,31,33], but absconsion was not predicted by a diagnosis of psychotic
disorder in the current study or in previous forensic studies [5,8,10,27]. Thus, psychotic diagnoses
may be of greater relevance in acute general psychiatry settings, possibly reflecting the fact that
acutely unwell patients in forensic settings will likely be given less leave and will therefore have
less opportunity to abscond. However, as noted above, whether a potential risk factor can be
found to predict an outcome depends on the prevalence (or variability) of the exposure in the
sample; as the majority of patients in our study had a primary diagnosis of psychotic disorder,
it is possible that we did not have sufficient numbers of non-psychotic patients to allow us to
determine whether psychotic disorder is indeed a risk factor for absconsion.
Given the findings of previous studies conducted in both forensic settings [7,11,26] and
general psychiatric hospitals [34,35,36], it was surprising that neither psychopathy nor personality
disorder predicted absconsion. Interestingly, we found that whilst those with a likely diagnosis
of psychopathy were three times more likely to abscond than those without psychopathy, none
of the patients with a definite diagnosis absconded. It may be that patients who were deemed to
require a formal assessment of psychopathy and who were known to fulfil psychopathy criteria
were placed under more restrictive leave conditions, thereby reducing the risk of absconsion.
However, those without a ‘definite’ formal diagnosis (but who might have some traits) might
not be subject to the same restrictions. Consistent with this hypothesis, we also found that
patients who had exhibited recent physical violence in an inpatient setting were less likely to
abscond than those who had not. Thus, patients deemed at particularly high-risk (i.e., those
with known psychopathy and those who have been physically violent) may be at lower risk of
absconding because they have fewer opportunities to do so or because their risk management
plans address a comprehensive range of risk scenarios.
An alternative explanation for the lack of association between ‘definite psychopathy’ and
absconsion relates to the fact that psychopathy ratings were made by the clinical teams using
previously-collected data (i.e., PCL-R or PCL-SV assessments not completed for the purposes
of this study). Thus, these assessments were carried out by different clinicians with varying skill
levels. Whilst it is dictated within our NHS trust that all forensic psychiatrists and psychologists
must undergo formal PCL-R/PCL-SV training before carrying out these assessments, we were
not able to examine the reliability of assessments across raters. However, as assessments were
typically completed by the multidisciplinary team (rather than individual clinicians) the
reliability and validity of these assessments are likely to be high. Finally, in contrast to hypotheses
and two previous forensic studies [5,13], none of the treatment-related factors we examined
were significantly associated with absconsion.
A secondary aim of the study was to assess the feasibility of developing a brief absconsion
risk scale. The sensitivity and specificity values (0.67 and 0.71, respectively) indicated that the
scale had moderate ability to classify absconders as high-risk and non-absconders as low-risk.
One major limitation was that the probability that a patient identified as being at high-risk for
absconsion would actually abscond was low (PPV = 0.37). However the negative predictive
value was high (NPV = 0.91). These findings indicate that the scale over-identifies those who
might be at high-risk of absconding, but that the measure can classify low-risk patients with
high levels of accuracy. Whilst this raises questions about the extent to which this scale can be
practically applied in a forensic setting, it is important to note that these values are very similar
to those reported for well-established violence risk assessment scales. In a recent meta-analysis,
Fazel and colleagues reported sensitivity and specificity values of 0.92 and 0.36, respectively,
and positive and negative predictive values of 0.41 and 0.91 for the ability of these tools to
predict violence . Thus, the predictive accuracy of our absconsion tool is similar to that
reported for widely-used violence risk assessment measures.
One important consideration when evaluating the predictive accuracy of the weighted risk
scale is the observational (i.e., naturalistic) nature of this study which likely affected the
findings. Not all patients included in the study had the same opportunity to abscond over the study
period, and this in turn is likely to be related to the risk of absconsion. For example, by
definition, patients in the low-secure ward were placed under less restrictive conditions and therefore
likely had greater opportunity to abscond. However, these patients were likely to be at lower
risk of absconsion and other adverse outcomes (i.e., in order to deem them suitable for
treatment in a low-secure environment); thus, potentially reducing the association between true
risk factors (e.g., psychopathy and physical violence) and absconsion. Even within a
mediumsecure setting, clinical interventions may have had an effect on the observed findings. It is
highly possible that clinical teams may have imposed additional restrictions on those at
highrisk for absconsion, or those who were acutely unwell, which may have prevented this outcome
The clinical utility and acceptability of using an absconsion risk screening tool with the PPV
and NPV of our instrument will depend on the clinical and security responses that are elicited
by high and low scores. If such a scale is used to classify high-risk individuals for the purposes
of identifying those who might benefit from a relatively benign psychological or supervisory
intervention then use of the tool may have some merit. However, in view of the poor PPV
value, we suggest that it would be inappropriate to infer that a high score should be the sole
basis for decisions about a patient’s security needs. On the other hand, it may be possible to use
the tool to screen out those at very low risk of absconding who might be eligible for less
restrictive leave conditions. Similarly, a study investigating the predictive validity of a
newly-developed violence risk assessment measure, demonstrating similar psychometric properties to the
absconsion risk scale developed in the current study, proposed that such an instrument could
be utilised to screen out patients at very low risk of violence prior to completing more detailed
assessments . Owing to the paucity of rigorously conducted studies in this area, decisions
about access to leave (which in the UK are made by clinical teams and the Ministry of Justice)
are not informed by absconsion-specific studies. Instead, decisions are based on professional
judgement which is likely influenced by a range of generic risk factors for other outcomes (e.g.,
violence) and/or by taking a generally and generically cautious approach to leave for all patients
irrespective of specific risk factors. Both of these approaches may mean that individuals who
are actually at low-risk for absconsion are given more restrictive leave conditions than are
actually warranted, using absconsion-specific risk tools to identify those at low-risk for this
outcome may help to reduce this possibility.
Despite the fact that our scale may not accurately identify those at high-risk of absconsion,
the results of the current study are clinically useful in that they provide suggestions for
interventions which may help to reduce the risk among forensic psychiatric patients. Two indices of
recent inpatient behaviour, verbal aggression and substance use, were found to be the strongest
predictors of absconsion. Previous work by our group has indicated that cognitive-skills
programmes targeting social problem-solving skills and thinking styles can be effective in reducing
these behaviours in forensic populations. Specifically, in a randomised controlled trial of the
Reasoning and Rehabilitation (R&R) programme, we observed that forensic psychiatric
patients randomised to R&R showed reductions in verbal aggression relative to those receiving
treatment as usual and that this effect was maintained at 12-months post-treatment .
Patients who completed the full R&R programme additionally demonstrated significant
reductions in substance use. The current findings suggest that reductions in these behaviours might
be associated with a reduction in absconsion; indeed, we also observed a significant effect of
R&R on the number of leave violations (which included absconsion incidents) with stronger
effects among those completed treatment . Thus, programmes such as R&R may help to
reduce the risk of absconsion and other impulsive/antisocial inpatient behaviours by
promoting adaptive thinking styles.
A major limitation of the current study is the small sample which limited our ability to identify
significant predictors of absconsion (i.e., due to a lack of statistical power) and also causes
problems for model over-fitting in multivariable analyses. Statistical power was further reduced
by the fact that the study suffered from missing data (particularly problematic for HCR-20
scores); however, only three patients were missing data on variables that were included in the
final weighted risk scale. Furthermore, in this small sample we were not fully able to examine
different types of absconding behaviour separately. Whilst we were able to perform sensitivity
analyses to confirm that scores on the final weighted risk scale significantly predicted each of
the three outcome incident subtypes (failure to return, escape, and absconsion), the sample size
was not sufficiently large for us to be able to perform multivariate analyses to identify factors
associated with each of the outcomes. Given that these acts are heterogeneous in nature, it is
possible that these behaviours are associated with different risk factors. A further limitation
relates to the extent to which our findings can be generalised to other populations given that
the study was conducted in a single NHS trust. However, our results are largely consistent with
previous retrospective studies in forensic populations, thereby increasing our confidence that
such factors are relevant to other samples. The use of routinely-collected data (i.e., not obtained
for the purposes of this study) is a potential limitation. This is particularly problematic for
ratings of comorbid psychiatric conditions such as personality disorder and psychopathy where
there was a degree of uncertainty regarding the extent to which a patient met diagnostic
criteria. These diagnoses were rated as ‘not present’, ‘possibly present but not formally assessed’, or
‘definitely present as determined based on a formal assessment’ depending on whether a
recent/relevant assessment, using a validated tool, was available in the patient’s clinical record.
This uncertainty may have contributed to the lack of association between these diagnoses and
absconsion. However, the use of data collected by the treating team (as opposed to research
clinicians) adds to the external validity of the study as the risk scale is scored using ‘real world’
data and the lack of definite information on personality disorder and psychopathy reflects the
realities that many clinical teams face. One major limitation is that we were not able to perform
cross-validation analyses of the risk assessment tool in an independent sample, thus, these
findings should be interpreted with caution. Finally, as discussed above, one issue that is common
to all observational predictive studies, is that one cannot control for the effect of interventions
that may be implemented to reduce the risk of absconsion in specific high-risk groups. For
example, the fact that definite psychopathy and physical violence were found to be negatively
associated with absconsion may reflect the fact that patients with these characteristics had
more restrictive leave conditions and therefore had fewer opportunities to abscond.
This preliminary study represents the first attempt to conduct a truly prospective investigation
of absconsion in a forensic psychiatric inpatient setting. Our findings suggest that absconding
behaviour among forensic psychiatric inpatients is most strongly associated with recent
inpatient behaviours that may be indicative of impulsive, rule-breaking, and antisocial traits.
Psychological treatments aimed at promoting adaptive thinking styles may help to reduce these
traits and the risk of absconsion. We additionally attempted to develop an empirically-derived
scale to assess risk of absconsion in forensic settings; this scale had poor predictive ability but
showed potential for identifying those at low-risk for absconsion. Whilst we cannot
recommend that this tool is used in clinical practice without further external validation in an
independent sample, we have at least shown that it is possible to develop a brief risk assessment
tool and we hope this stimulates further research in this area.
Conceived and designed the experiments: AEC TF KD. Performed the experiments: AEC.
Analyzed the data: AEC. Wrote the paper: AEC AJ JT SC KD TF. Data collection: AEC AJ JT SC.
1. Stewart D , Bowers L ( 2010 ) Absconding from psychiatric hospitals: a literature review . London: Institute of Psychiatry, King's College London.
2. South West London Strategic Health Authority . ( 2006 ) The Independent Inquiry into the Care and Treatment of John Barrett . London: NHS London.
3. Niche Health & Social Care Consulting . ( 2012 ) An Independent Investigation into the Care and Treatment of Mr R . London: NHS London.
4. Hunt IM , Windfuhr K , Swinson N , Shaw J , Appleby L , Kapur N ( 2010 ) Suicide amongst psychiatric inpatients who abscond from the ward: a national clinical survey . BMC Psychiatry 10 : 14. doi: 10.1186/ 1471 - 244X -10-14 PMID: 20128891
5. Brook M , Dolan M , Coorey P ( 1999 ) Absconding of patients detained in an english special hospital . The Journal of Forensic Psychiatry 10 : 46 - 58 .
6. Dolan M , Snowden P ( 1994 ) Escapes from a medium secure unit . Journal of Forensic Psychiatry 5 : 275 - 286 .
7. Huws R , Shubsachs A ( 1993 ) A study of absconding by special hospital patients: 1976 to 1988 . The Journal of Forensic Psychiatry 4 : 45 - 58 .
8. Moore E , Hammond S ( 2000 ) When statistical models fail: problems in the prediction of escape and absconding behaviour from high-security hospitals . The Journal of Forensic Psychiatry 11 : 359 - 371 .
9. Morrow WR ( 1969 ) Escapes of Psychiatric Offenders . The Journal of Criminal Law , Criminology, and Police Science 60 : 464 - 471 .
10. Wilkie T , Penney SR , Fernane S , Simpson AI ( 2014 ) Characteristics and motivations of absconders from forensic mental health services: a case-control study . BMC Psychiatry 14 : 91. doi: 10.1186/ 1471 - 244X -14-91 PMID: 24669758
11. Gacono CB , Meloy JR , Speth E , Roske A ( 1997 ) Above the law: escapes from a maximum security forensic hospital and psychopathy . J Am Acad Psychiatry Law 25 : 547 - 550 . PMID: 9460041
12. Cooke G , Thorwarth C ( 1978 ) Prediction of elopement of mentally ill offenders using the MMPI . Criminal Justice and Behavior 5 : 151 - 157 .
13. Beer MD , Muthukumaraswamy A , Khan AA , Musabbir MA ( 2009 ) Clinical predictors and patterns of absconding in a low secure challenging behaviour mental health unit . Journal of Psychiatric Intensive Care 5 : 81 - 87 .
14. Webster CD , Douglas K , Eaves D ( 1997 ) HCR-20: Assessing Risk for Violence-Version 2 . Barnaby , British Columbia : Simon Fraser University, Mental Health , Law, and Policy Institute .
15. Singh JP , Desmarais SL , Van Dorn RA ( 2013 ) Measurement of predictive validity in violence risk assessment studies: a second-order systematic review . Behavioral Sciences & the Law 31 : 55 - 73 .
16. Stewart R , Soremekun M , Perera G , Broadbent M , Callard F , Denis M , et al. ( 2009 ) The South London and Maudsley NHS Foundation Trust Biomedical Research Centre (SLAM BRC) case register: development and descriptive data . BMC Psychiatry 9: 51. doi: 10.1186/1471-244X-9-51 PMID: 19674459
17. Chang CK , Hayes RD , Perera G , Broadbent MT , Fernandes AC , Lee WE , et al. ( 2011 ) Life expectancy at birth for people with serious mental illness and other major disorders from a secondary mental health care case register in London. PLoS One 6: e19590 . doi: 10.1371/journal. pone.0019590 PMID: 21611123
18. Hayes RD , Chang CK , Fernandes A , Broadbent M , Lee W , Hotopf M , et al. ( 2011 ) Associations between substance use disorder sub-groups, life expectancy and all-cause mortality in a large British specialist mental healthcare service . Drug Alcohol Depend 118 : 56 - 61 . doi: 10.1016/j.drugalcdep. 2011 . 02.021 PMID: 21440382
19. Butcher JN , Dahlstrom WG , Graham JR , Tellegen AM , Kreammer B ( 1989 ) The Minnesota Multiphasic Personality Inventory-2 (MMPI-2) Manual for Administration and Scoring . Minneapolis, MN: University of Minneapolis Press.
20. First MB , Gibbon M , Spitzer RL , Williams JBW , Benjamin LS ( 1997 ) Structured Clinical Interview for DSM-IV Axis II Personality Disorders, (SCID-II) Washington , D.C: American Psychiatric Press, Inc.
21. Hare R ( 2003 ) The Psychopathy Checklist-Revised . Toronto, ON, Canada: Multihealth Systems.
22. Hart SD , Cox D , Hare R ( 1995 ) The Hare PCL: SV: Psychopathy Checklist: Screening Version . New York: Multi -Health Systems.
23. Harris GT , Rice ME , Quinsey VL ( 1993 ) Violent recidivism of mentally disordered offenders: the development of a statistical prediction instrument . Criminal Justice and Behavior 20 : 315 - 335 .
24. Fazel S , Singh JP , Doll H , Grann M ( 2012 ) Use of risk assessment instruments to predict violence and antisocial behaviour in 73 samples involving 24 827 people: systematic review and meta-analysis . BMJ 345: e4692 . doi: 10.1136/bmj.e4692 PMID: 22833604
25. Singh JP , Grann M , Lichtenstein P , Langstrom N , Fazel S ( 2012 ) A novel approach to determining violence risk in schizophrenia: developing a stepped strategy in 13,806 discharged patients . PLoS One 7: e31727. doi: 10.1371/journal.pone.0031727 PMID: 22359622
26. Moore DL , Bergman BA , Knox PL ( 1999 ) Predictors of sex offender treatment completion . Journal of Child Sexual Abuse 7 : 73 - 88 .
27. Smith J , Quaynor E ( 1990 ) Absconding from a regional secure unit . The Journal of Forensic Psychiatry 1 : 245 - 250 .
28. Bowers L , Jarrett M , Clark N , Kiyimba F , McFarlane L ( 2000 ) Determinants of absconding by patients on acute psychiatric wards . J Adv Nurs 32 : 644 - 649 . PMID: 11012807
29. Dickens GL , Campbell J ( 2001 ) Absconding of patients from an independent UK psychiatric hospital: a 3-year retrospective analysis of events and characteristics of absconders . Journal of Psychiatric and Mental Health Nursing 8 : 543 - 550 . PMID: 11842482
30. John CJ , Gangadhar BN , Channabasavanna SM ( 1980 ) Phenomenology of 'escape' from a mental hospital in India . Indian J Psychiatry 22 : 247 - 250 . PMID: 22058474
31. Short J ( 1995 ) Characteristics of absconders from acute admission wards . Journal of Forensic Psychiatry 6.
32. Sommer G ( 1974 ) A short term study of elopement from a state mental hospital . Journal of Community Psychology 2 : 60 - 62 .
33. Muir-Cochrane E , Mosel K , Gerace A , Esterman A , Bowers L ( 2011 ) The profile of absconding psychiatric inpatients in Australia . J Clin Nurs 20 : 706 - 713 . doi: 10.1111/j.1365- 2702 . 2010 .03553.x PMID: 21320199
34. Farragher B , Gannon M , Ahmad I ( 1996 ) Absent without leave-can we predict those who go AWOL? Irish Journal of Psychological Medicine 13 : 28 - 30 .
35. Nussbaum D , Lang M , Chan B , Riviere R ( 1994 ) Characterization of elopers during remand: Can they be predicted? The METFORS experience . American Journal of Forensic Psychology : 17 - 37 .
36. Walsh E , Rooney S , Sloan D , McCauley P , Mulvaney F , O'Callaghan E , et al. ( 1998 ) Irish Psychiatric Absconders:characteristics and outcome . Psychiatric Bulletin 22 : 351 - 353 .
37. Cullen AE , Clarke AY , Kuipers E , Hodgins S , Dean K , Fahy T ( 2012 ) A multisite randomized trial of a cognitive skills program for male mentally disordered offenders: violence and antisocial behavior outcomes . Journal of Consulting and Clinical Psychology 80 : 1114 - 1120 . doi: 10.1037/a0030291 PMID: 23025249