“From zero to hundred”:

Defining Aggressive Challenging Behaviour in

Adults with Intellectual Disabilities

PSBS0015 MSc Final Research Project

Candidate Number: NDBY8

Word Count: 6214

Abstract

Aggression is a behaviour that could lead to many social issues. People with

intellectual disability (ID) have a higher chance of engaging in aggressive behaviour.

However, definition of aggression and its associations within routinely collected data

has not been previously ascertained. The current study investigated prevalence of

aggression and its association with demographic and clinical characteristics after

defining aggression in adults with ID with/without aggression using the Clinical

Record Interactive Search (CRIS) databases in South London and the Maudsley

(SLaM) National Health Service (NHS) Foundation Trust. Significant difference was

found between cases with and without aggression in diagnosis of comorbid physical

and mental illness, medication use and number of contacts with professionals. The

results demonstrated that aggression in adults with ID was significantly associated

with male, younger age, mild/moderate level of ID, severe mental illness (SMI),

substance misuse, psychotropics, laxatives and number of psychology contacts at

episode level. This finding has highlighted mental health need in this population and

anti-aggressive function of antipsychotics. Work from current study has provided

valid definition of aggression and valuable insights into the profile of aggression in

adults with ID, which can further contribute to designing of treatments and targeting

interventions for aggression in adults with ID. Further study could investigate the

associations with different severity levels and types of aggression in this population.

Contents

Introduction ........................................................................................... 4

Methodology ......................................................................................... 9

Setting & Study Design ........................................................................ 9

Participants .......................................................................................... 9

Inclusion Criteria & Measurement ...................................................... 10

Identification of ID and Autism ......................................................... 13

Aggression ......................................................................................... 13

Psychiatric Diagnosis ......................................................................... 13

Medication and Physical Health ......................................................... 14

Statistical Analysis ............................................................................. 14

Results ................................................................................................. 16

Sensitivity of the Aggression App ....................................................... 16

Demographic Characteristics ............................................................. 18

Univariate Analysis ............................................................................ 18

Regression Analysis........................................................................... 22

Discussion .......................................................................................... 24

Summary of Results ........................................................................... 24

Strengths and Weakness ................................................................... 25

Prevalence of Aggression .................................................................. 26

Clinical Characteristics of Aggression ................................................ 27

Implications ........................................................................................ 29

Conclusion .......................................................................................... 30

Acknowledgement .............................................................................. 31

Reference ............................................................................................ 32

Introduction

Aggression is a behaviour that presents in many species and often with an adaptive

function. In social psychology, aggression is defined as any behaviour directed

towards others that has an intention to cause harm, whereas the target does not

expect to be harmed and is motivated to avoid the harm (Stangor, 2014). The most

recent World Health Organisation (WHO) report on interpersonal violence estimated

that more than 1.3 million people die because of violence each year, accounting for

2.5% of the global mortality. According to Siegel and Victoroff (2009), there can be

two main types of aggression. One is hostile aggression, including affective,

impulsive and reactive aggression, usually triggered when a threatening stimulus is

perceived or real, and motivated by a desire to express emotions. The other type is

instrumental aggression, which is motivated by reaching a specific goal. Instrumental

aggression is usually planned and takes time to occur, whereas hostile aggression

can be a spontaneous response against negative emotions. In psychological and

behavioural studies, aggression is usually measured by observation, behavioural

self-reports, nominations, or archival reports. Researchers have been investigating

aggression in different populations to figure out the function of aggression in human

being.

Aggressive behaviour has been found linked to dysfunction in specific brain regions

that are associated with emotion regulation, including the limbic system, orbitofrontal

and dorsolateral prefrontal, anterior cingulate cortex and their activity imbalance

(Davidson et al.,2000; Bufkin and Luttrell, 2005). The limbic system is crucial for

emotional control, especially the amygdala, which has been found activated when

processing negative emotions in response to threat and punishment in the

environment and during arousal (Cardinal et al., 2002; Rodrigues et al., 2009). For

example, fMRI studies showed that activity in the amygdala seems to be greater

when the individual was presented with fearful facial expressions (Whalen et al.

2001). Impairment in amygdala can lead to deficits in specifically recognising fear,

and inability in predicting fear in the others (Cardinal et al, 2021). An association was

found between fearful-faces-related amygdala reactivity and impulsive aggression

(Cunha-Bang et al., 2017). Moreover, the relationship between amygdala and

adjustment of aggression has been supplied with evidence from studies with healthy

subjects (Matthies et al., 2012), neuropsychiatric disorders (Tebarte van Elst et al.,

2000a, 2003, 2007) and adolescents with conduct disorder (Sterzer et al., 2007,

Balia, 2020). Amygdala volumes was found to decline 16%-18% among healthy

individuals with higher scores in aggression (Matthies et al., 2012; Pardini et al.,

2014; Ruthirakuhan et al., 2018). What is worthy noting in clinical studies was that

the removing amgdala can help reduce aggression because of increased tolerance

towards provocation and decline in automatic arousal level (Gouveia et al., 2019).

Orbitofrontal and dorsolateral prefrontal cortex (OFC, DLPFC) and anterior cingulate

cortex (ACC) are also involved in self-control and regulation of negative emotions

like fear and anger. According to Davidson and colleagues (2000), these three areas

suppress negative emotion through inhibitory interconnections to the amygdala.

Impairments in OFC, DLPFC or ACC can make the individual less able to supress

negative emotions when responding to external events. Therefore, individuals with

such impairments can be hypersensitive and overreactive to triggering events. LPFC

and OFC are also linked to impulsivity control and aggression (Gansler et al., 2011).

fMRI evidence suggested that individuals with larger activation in the left prefrontal

cortex and right ACC were more capable of controlling their negative emotions

effectively compared to the ones with lower levels of activation (Dougherty et al.,

1999). Patients with damage in OFC were found to exhibit poor impulse control, a

lack of interpersonal sensitivity and therefore aggressive outbursts (Bufkin & Luttrell,

2005). Decreased ability to regulate emotion can increase the propensity for impulse

aggression and violence. The neurobiological explanation of aggression can be

important underpinning mechanism underlying many psychiatric disorders

characterised by emotional dysregulation provoked by negative stimuli (Siever,

2008).

There is strong evidence for the link between verbal and physical aggressiveness

and a range of psychiatric disorders. A systematic review reported that nearly 20% of

patients in acute psychiatric wards may display violent behaviours (Lozzino et al.,

2015). Genovese and colleagues (2017) observed 3800 psychiatric outpatients and

found that almost half reported their present subjective anger to be at moderate-to-

severe level and more than 20% showed moderate-to-severe levels of overt

aggression. Studies conducted among criminal offenders demonstrated that

attacking violence and sexual offences were more closely related to past or current

mental health difficulties than to other types of crimes (Silver et al., 2008; Siever,

2008). Therefore, aggression seems to have higher presence in individuals with

mental health problems. Healthcare staff working in clinical mental health settings

also report facing aggressive incidents initiated by patients during routine care (Kerr

et al., 2017; Bizzarri et al., 2020). For example, Jonker et al (2008) undertook a

survey in mental health nurses working in inpatient wards. The respondents reported

being regularly exposed to aggression in general. Male gender, substance use,

schizophrenia and lifetime history of violence were found to be the main risk factors

associated with aggressiveness and violent behaviours (Amore et al., 2008; Lozzino

et al., 2015). Tsiouris and colleagues (2011) undertook a large-scale observation

into the relationship between aggression and mental disorders using the Institute for

Basic Research – Modified Overt Aggression Scale (IBR-MOAS), providing a profile

of aggression in different mental disorders. According to their research, bipolar

disorder and impulse control disorder were found to be strongly related to all

domains of aggression assessed in the scale. Psychotic disorder appeared to be

associated with four domains except physical aggression against self (PASLF),

whereas anxiety was mostly linked to PASLF and verbal aggression against self

(VASLF). Depression was associated with VASLF, personality disorders with verbal

aggression against others (VAOTH), VASLF and PASLF; Obsessive Compulsive

Disorders (OCD) with physical aggression against objects (PAOBJ); and autism with

physical aggression against others (PAOTH), PAOBJ and PASLF.

Aggression may have a different function when presenting in individuals with

Intellectual disability (ID), which is characterized by intellectual adaption deficits with

onset in childhood (American Psychiatric Association, 2013). Challenging behaviours

in individuals with ID include aggressive behaviours towards others, self-injury,

destructive, and stereotypical behaviours (Bowring et al., 2019). Aggressive

challenging behaviour is a vital problem for individuals with ID and Autistic Spectrum

Disorder (ASD) (Visser and Teunisse, 2014), with a community prevalence of 8.3%

in adults with ID (Bowring et al., 2019). The high prevalence of aggressive behaviour

among adults with ID can pose negative impact not only on the individual’s personal

outcomes and life quality, but also on the mental health and wellbeing of their family

and caregivers.

Prevalence and types of aggressive behaviours in individuals with ID vary in different

research settings. Hogue and colleagues (2006) conducted a clinical-record-based

comparison using the Behaviour Rating Scale of the Emotional Problems Scales

(BRS-EPS) in three forensic services with different levels of security. The results

showed no difference in externalising behaviours including verbal aggression, non-

compliance or hyperactivity between offenders at different levels of security, but in

higher secure care, more offenders received higher scores on the sub-scales

measuring physical aggression and on the internalising behaviour problems

subscales. In inpatient settings, Tenneij and Koot (2008) investigated aggressive

behaviour through staff observation and reported that 72% of the behaviours were

outward directed mainly to staff (i.e., externalised) with equal distribution in both

genders. Hastings and colleagues (2013) also highlighted that context has the

strongest association with understanding aggressive challenging behaviour in this

population and social contextual processes are most responsible for the

maintenance of such behaviours.

Research investigating other underpinning correlates of aggressive behaviours has

produced equivocal results. A meta-analysis concluded that male gender,

severe/profound degree of ID and Autism diagnosis are the most significant

predictors of aggression in individuals with ID (McClintock et al., 2003). The

longitudinal cohort study by Cooper and colleagues (2009) partially supported the

results but also showed contradictions. They assessed additional factors such as

lifestyle, disabilities, development, support, problem behaviours and physical and

mental health in adults with ID through teo years and reported that female gender,

attention deficit hyperactivity disorders (ADHD), not having Down syndrome, not

living with a family carer, lower ability and urinary incontinence were linked to

aggressive behaviour in adults with ID. Tyrer and colleagues (2006) interviewed

adults with ID who were 20 years’ old and over using questions from Disability

Assessment Schedule (Holmes et al. 1982) and carer report of frequent and/or

severe physical aggression to others. They found that physical aggression happens

more fequently in men, younger adults, those with more severe ID and individuals

living in institutional settings, while lower prevalence in people with Down Syndrome.

Overall, the profile of aggression in people with ID seems to be inconsistent across

studies. Whilst predictors of aggression may differ between studies, there are certain

commonalities including more severe ID, living away from home and having ID due

to aetiologies other than Down Syndromes. It is likely that the inconsistencies are

related to methodological limitations of the studies and as yet there is not a unifying

theory that leads to generalisable conclusions.

Previous literature has identified risk factors and impact of challenging behaviour in

people with ID in different settings, but definition of aggression and its associations

within routinely collected clinical data has not been previously ascertained.

Aim and objectives

The aim of the current study is to define and validate aggressive behaviours in adults

with ID based on the Clinical Record Interactive Search (CRIS) databases in South

London and the Maudsley (SLaM) National Health Service (NHS) Foundation Trust.

A meaningful definition of aggressive behaviours was derived from structured CRIS

fields and open text using Natural Language Processing algorithms (NLP).

We investigated structured records of patients contributing data to CRIS in order to

draw a profile of aggressive behaviours in adults with ID in one mental health trust

(SLaM) by comparing the records with aggression documented and those without.

The first hypothesis was that adults with ID and aggression within episode will have

higher rates of comorbid physical and mental health issues (e.g. autism, ADHD,

severe mental illness or genetic disorder) compared to those with ID and no

aggression in episode. Second, we hypothesised that they would be more likely to

take medications regarding their physical and mental health issues. Third, that

people with ID and aggression are more likely to have increased number of

psychology and psychiatry contacts within their episodes compared to those without

aggressive challenging behaviour.

Methodology

Setting & Study Design

This study is a retrospective cohort design using routinely collected clinical records

from the Clinical Record Interactive Search (CRIS) database at SLaM, an NHS

mental health trust providing secondary mental health care to residents of four

London boroughs including Lambeth, Southwark, Lewisham and Croydon) of around

1.36 million. The use of electronic clinical records across SLaM services started from

2006 and CRIS was launched in 2008 to allow researchers to investigate electronic

clinical records with a permission of secondary data analysis. It contains anonymous

data for more than 27,800 patients who are receiving or previously received mental

health care. Data specification plan was conducted with inclusion criteria that are

variables that might have an association with aggressive behaviour in people with ID.

CRIS Natural Language Processing apps, structured field information from clinical

records were the main source of data and ICD-10 diagnostic codes were used as

identifier for physical and mental illness. The choice of inclusion criteria was based

on previous studies and advice from clinicians.

Participants

Episode-level records of adults with ID and with/without autism aged 18 to 65 years

at the first point of contact with the service were included in the study (mean age =

37.20, SD = 16.57). The mean episode length of the sample was 392.27 days (SD =

382.64). Sixty-one percent (N = 958) of the sample were male. The sample was from

diverse ethnic backgrounds, among which 50.4% were White, 31% were black, 4.9%

were Asian, 3.6% were mixed and 3% were from other ethnic groups. The CRIS

Natural Language Processing (NLP) algorithms were used to recognise all episodes

in the Mental Health of Learning Disability (MHLD) team in SLaM between

01/01/2014 and 31/12/2018. Other developmental disorders and aggression

documented in episode were also identified through the search. We found that 73.52%

of the sample had a record of aggressive behaviour (N=1158), whereas 26.48% did

not have any record within episode (N=417).

Inclusion Criteria & Measurement

The outcome is records of aggressive behaviours including verbal, physical and

sexual aggression over the cohort period (01/01/2014 to 30/12/2018). The exposure

variable is active face-to-face contact episode with ID services within this period

(start from 01/01/2014 but not necessarily end before 30/12/2018). We developed a

data specification plan with the search strategy for inclusion criteria, relevant

identifiers, and allowable values from CRIS dataset (see table 1). Demographic

information including date of birth (month/year), gender and ethnicity were routinely

recorded in the NHS electronic clinical records. The index date for age was set as

age at the point of admission to the service or at death. Ethnicity was mainly divided

into five categories: White, Black, Asian, mixed and other.

Table 1. Data Extraction Plan

Inclusion Criteria Identifier Allowable Values Description Source of Variable

Date of Birth Month/Year - - Structured field

Gender male/female male/female - Structured field

Ethnicity White/Black/Asian/Mixed/Other

White/Black/Asian/Mixed/Other - Structured field

ID Diagnosis Diagnosis of ID All episodes in MHLD teams in SLAM (total = 14, as per data specification) between 01/01/2014 and 31/12/2018

For an episode to be logged, the patient would have to be accepted to the relevant MHLD team.

Structured field

Level of ID F7*/F7*.1 Most recent diagnosis (patient level at anytime)

Diagnosis of Intellectual disability based on ICD-10. In the ICD-10 coding system, F7*refers to mental retardation (i.e. intellectual disability). F7*.1 is used to identify the extent of impairment of behaviour of the individual, where there is significant impairment of behaviour requiring attention or treatment.

ICD-10 Diagnostic Code

Number of episodes during care

- Episode level dataset - Structured field

Start date of episode and end date/ derived date – 31/12/2018

day/month/year Episode level dataset - Structured field

Psychiatric diagnosis

Non-affective psychotic disorders

F2* Date of earliest diagnosis, date of earliest dx in episode

Diagnosis of non-affective psychotic disorders based on ICD-10. F2* represent a group of disorders including schizophrenia, schizotypal and delusional disorders in the ICD-10 coding system.

ICD-10 Diagnostic Code

Affective disorders (split bipolar/mania and depression

F3* Date of earliest diagnosis, date of earliest dx in episode

Diagnosis of mood (affective) disorders based on ICD-10. F3* represents disorders in which the fundamental disturbance is a change in affect or mood to depression (with or without associated anxiety) or to elation in the ICD-10 coding system*.

ICD-10 Diagnostic Code

Personality disorders F6* Date of earliest diagnosis, date of earliest dx in episode

Diagnosis of personality disorders based on ICD-10. F6*represents disorders of adult personality and behaviour in the ICD coding system.

ICD-10 Diagnostic Code

Substance misuse F10-F19 Date of earliest diagnosis, date of earliest dx in episode

Diagnosis of substance misuse based on ICD-10. F10-19 represents mental and behavioural disorders due to psychoactive substance use in the ICD-10 coding system

ICD-10 Diagnostic Code

Other Developmental Disorders

F8* Date of earliest diagnosis, date of earliest dx in episode

Diagnosis of substance misuse based on ICD-10. F80-

F89 represents disorders of psychological

ICD-10 Diagnostic Code

development. Within this category, F84

represents diagnosis of pervasive developmental disorders.

Positive on Aggression App y/n Within the episode are they positive? Count/document count per year as a proportion from start date to episode end 31/12/2018

Aggressive behaviour in patients, including verbal, physical and sexual aggression. Positive mentions include reported to be quite aggressive towards…, violence and aggression, requires continued management and continuous to reduce in terms of incidence etc.

NLP

Positive for Autism on App y/n Ever - NLP

Positive for Hyperkinetic Disorder on App

y/n Ever - NLP

Genetic Disorder

Down Syndrome Q90 Date of earliest diagnosis, date of earliest dx in episode

Diagnosis of Down Syndrome according to ICD-10. Q90 represents Down Syndrome in the ICD-10 coding system.

ICD-10 Diagnostic Code

Medication BNF 4.*Central Nervous

System

BNF 4.1 Hypnotics/anxiolytics

BNF 4.2 Antipsychotics

BNF 4.3 Antidepressants

BNF 4.4 ADHD

BNF 4.7 Analgesics

BNF 4.8 Anti-epileptic

BNF 5.* Infections

BNF 1.6 Laxatives

BNF 6.2 Thyroid

0/1 medication in episode Medications that are prescribed currently and in the past. It does not calculate daily dose of a drug.

British National Formulary (BNF) Drug code

Psychology involvement Number of psychology contacts per 6 months

Number of attended events per 6 months within episode dates

- Structured field

Nurse involvement Number of nurse contacts per 6 months

Number of attended events per 6 months within episode dates

- Structured field

Psychiatry involvement Number of psychiatry contacts per 6 months

Number of attended events per 6 months within episode dates

- Structured field

Footnotes: Cases with active face-to-face contact episode between 01/01/2014 to 30/12/2018 with LD services will be included in the dataset. Episode should start after 01/01/2014 but not necessarily end before 30/12/2018. *description from ICD-10 version (2016)

Identification of ID and Autism

Diagnosis of ID and autism was identified through CRIS. The patients would have to

have been admitted to the relevant MHLD team when an episode was logged.

Acceptance to the MHLD service would require clinician review of eligibility based on

standardised definitions of intellectual disability, such as IQ scores lower than 70,

functional ability in the intellectual disability range and issues arising during the

developmental period. Therefore, these were considered as the most trustworthy

criteria to make sure that individuals would have a valid diagnosis of ID. Autism was

also identified through NLP in the same way to ascertain whether an individual had

ever been diagnosed with autism in CRIS. Structured WHO ICD-10 diagnostic codes

F70-79/F70-79.1 (mild to profound mental retardation) were used to identify the level

of ID with F79 for unspecified mental retardation. Episode-level data such as number

of episodes during care, start and end dates of the episode before 31st December

2018 were also retrieved from the electronic records.

Aggression

We are primarily interested in externalising aggressive behaviours. Therefore, words

such as hitting out, aggression/live, kicking, screaming, throwing things, verbal

abuse, abusive behaviour, biting and punching will be recognised from the records.

Within the episode, the positive records of aggression derided from free text using

NLP were collected. More specifically, the positive mentions included ‘quite

aggressive towards…’, ‘violence’ and ‘aggression’, ‘requires continued management’

and ‘continuous to reduce in terms of incidence’ and more. We calculated the count

of positive mentions as a proportion from start date to the end as an accuracy

estimate of the Aggression NLP App. The App would be used in further analysis if

accuracy rate was higher than 85%, which was decided after consulting with

researchers and clinicians in the research group.

Psychiatric Diagnosis

Other psychiatric diagnoses were defined through ICD-10 diagnostic codes. We

extracted data on 1. Non-affective psychotic disorders identified by the code F2*,

representing schizophrenia, schizotypal and delusional disorders; 2. Affective

disorders including split bipolar/mania and depression identified by codes F3*; 3.

Personality disorders identified by F6* and 4. substance misuse identified by F10-19.

Psychology, psychiatry and behaviour support specialist involvements were

calculated by number of attended contacts every 6 months within episode dates. We

explored this category in order to examine whether such health professional

involvement were effective in reducing aggressive behaviour.

Psychotropic drugs including antidepressants (BNF4.3), anxiolytics/hypnotics

(BNF4.1), stimulants (BNF4.4), antipsychotics (BNF4.2), and mood stabilizers were

counted to whether the use of psychotropics could affect aggression in this

population.

Medication and Physical Health

Medication records were used as a proxy measure of physical health because

physical health was not well-recorded in CRIS. Records of medications prescribed

currently and in the past can provide some evidence on the individual’s physical

health status suggestive of target conditions and side effects. Medication prescription

was recognised through British National Formulary (BNF) drug codes. Prescription of

Analgesics (BNF 4.3) would imply a possible experience of pain in the individual,

whereas Infections (BNF5*), Laxatives (BNF1.6), Anti-epileptic (BNF4.8) and Thyroid

(BNF6.2) would indicate possible presence of infection, constipation, epilepsy and

hyperthyroidism respectively.

Statistical Analysis

Prior work was already completed that had identified the population likely to have ID

and the search described here was limited to this group rather than the whole CRIS.

After extracting the dataset, the variables in the data extraction plan were reviewed

and adjusted accordingly after consultation with clinicians on the reliability of the data

for each variable in the dataset. We excluded data from the Autism and Hyperkinetic

NLP Apps from the analysis considering the possibility of oversensitivity issue.

Instead, lifetime diagnosis of autism and hyperkinetic disorders were defined as

having pervasive developmental disorder (ICD-10 code: F84.x) and hyperkinetic

disorder (ICD-10 code: F90.x) recorded in the structured field. Mental illnesses were

combined into categories of Severe Mental Health Illnesses (SMI) and Common

Mental Disorders (CMD). The SMI category included diagnosis of schizophrenia,

mania and bipolar disorder, whereas depression, behavioural syndromes,

personality disorders, neurotic, stress-relate or somatoform disorders, and other

mood disorders were combined into the CMD category. Total numbers of contacts at

episode level were used for categories of psychology, nurse and psychiatry

engagement.

Statistical analysis was undertaken in the IBM SPSS software. As the original

dataset was stored in Excel spreadsheets, it was exported and encoded into SPSS

prior to data analysis. Gender was coded in 0/1, using male as the reference

category. Ethnicity were coded as ‘1’ for ‘White’, ‘2’ for ‘black’, ‘3’ for ‘asian’, ‘4’ for

‘mixed’ and ‘5’ for ‘other’. Different levels of ID were merged into ‘0’ for

‘other/unspecific/not recorded’, ‘1’ for ‘mild/moderate’ and ‘2’ for ‘severe/profound’.

Diagnosis of mental and physical diseases and uses of medication were coded into

‘0’ for ‘no’ and ‘1’ for ‘yes’.

We used descriptive analysis to characterise the cohort and we undertook a validity

check by running comparisons between different cohorts (i.e. people with comorbid

mental illness with/without aggression in episode) in the SPSS software. Chi-square

association tests were undertaken at the beginning of data analysis to explore the

association between aggression in episode and sociodemographic and clinical

variables. Normality of distribution for the numeric variables were assessed using the

Shapiro-Wilk test. The tested variable would not be normally distributed when

Shapiro-Wilk test was significant. We would use a parametric test (e.g., t-test) for

variables with normal distribution or a non-parametric test (e.g., Mann-Whitney U test)

for those without normal distribution. Either of the above tests was used to compare

age, length of episode and number of psychology, nurse or psychiatry contacts

between cases with or without aggression based on data distribution of the variable.

The variables with significant association to aggression were picked out for further

regression analysis. Afterwards, we conducted logistic regression analysis to

examine the contribution of each variable to a binary variable aggressive behaviour

(yes=aggression in episode).

Results

Sensitivity of the Aggression App

481 records were screened for the sensitivity estimation of the Aggression App.

During the process, a traffic light system was used to identify whether the record was

accurate for an inclusion of aggressive behaviour; accurate mentions were marked in

green as a ‘Yes’, those with mention of the word ‘aggression’, but no actual records

of aggressive incidents were marked in red as ‘No’ and those with unclear

`information on the aggression incidents were marked in amber as ‘Maybe’ (See

table). Hand annotation was added for the amber marks ‘Maybe’ for further

discussion with the wider group of researchers and clinicians and were finalised with

either ‘Yes’ or ‘No’. Within the 481 records checked and marked in colours, 380 were

‘Yes’ and 38 were ‘No’. The accuracy rate of the Aggression App was estimated as

90.90%, higher than the present level of 85%. Therefore, the App was deemed

reliable for use in further analyses.

Table 2. Examples of Aggression App Sensitivity Estimation

Patient ID Data Source Date Context String Evidence of Aggression

10040628

Summary of Need

27/06/2012

She will swear by using gestures (the V sign) as well as verbally. Swearing may be directed to those who are around her. Physical aggression – In this category of behaviour, ZZZZZ has the potential to hit staff. She may threaten to do so to begin with by waving and shaking her fist. This may then lead to actual hits, if she is close enough to do so.

Yes

10035773 Attachment

20/07/2015

After his father’s death, ZZZZZ acted as his mother’s carer as she was diagnosed with dementia, until her death in 2003. It seems this was a stressful period for ZZZZZ , and he described her extreme levels of confusion and agitation during this period. It seemed that ZZZZZ ’s mother at times did not recognise him and responded to him aggressively, escalating at points to threatening him with a knife. ZZZZZ also had a pet dog, Buster, who died in 2003. The loss of Buster at that time, so soon after the deaths of both parents, was significant for ZZZZZ .

No

10038459 Event 08/05/2012

I met witht he staff membmer on duty, Lynda White, who is a bank staff member. She works regulalry in the house and has known ZZZZZ for a long time. Completed the CBI - a total score of 14 for physical aggression and 14 for verbal aggression ---------------------------09 May 2012 09:21, Anne Parris

Maybe (Not sure what CBI stands for and the marking

criteria of the scale)

Demographic Characteristics

Table 3 indicates the demographic characteristics of patients with and without

aggression documented in episode. Statistically significant associations were found

between aggression and gender, 2(1) = 27.28, (p < .001), with males more likely to

have aggressive behaviours documented in episode. There was significant

association between ethnic groups and aggression, (2(4) = 32.93, p < .001). This

suggested significant association between ethnic group and aggression. In the group

of cases with aggression, half were White (53%, N = 590), a third were Black (33.2%,

N = 369), 6.5% were Asian (N = 72), 4.1% were mixed (N = 46), and 3.2% were from

other ethnic groups (N = 36).

Testing for normality, age variable was not normally distributed based on results

from Shapiro-Wilk test, W (1575) = 0.911, p < .001. We found a statistically

significant difference (U = 205898, p < 0.001) between the age at first contact with

the service for cases with aggression compared to those without.

Table 3. Demographic Characteristics of patients with/without aggression

Demographic Characteristics

Aggression (N=1158)

No Aggression (N=417)

Overall (N=1575)

Mean (SD) Mean (SD) Mean (SD) Mann-Whitney U p-value

Age 39.99 (16.20) 45.35 (16.32) 37.20 (16.57) 205899 < .001

N (%) N (%) N (%) Chi-square X2 p-value

Gender 27.279 < .001 Male 749 (64.7%) 209 (50.1%) 958 (60.8%) Female 409 (35.3%) 208 (49.9%) 617 (39.2%)

Ethnic Group (*missing data: 92/1575)

32.925 < .001

White 590 (53.0%) 204 (55.1%) 794 (53.5%) Black 369 (33.2%) 118 (31.9%) 487 (32.8%) Asian 72 (6.5%) 28 (7.6%) 100 (6.7%) Mixed 46 (4.1%) 10 (2.7%) 56 (3.8%) Other 36 (3.2%) 10 (2.7%) 46 (3.1%)

*Missing data is excluded from the analysis

Univariate Analysis

Episode length variable was not normally distributed according to Shapiro-Wilk test,

W (1575) = 0.844, P <.001. We found a significant difference (U = 342457, p < .001)

between the episode length for cases with aggression compared to those without

aggression.

More than half of the cases with mild or moderate ID demonstrated incidents of

aggression in episode (N=736, 63.6%, see table 4) whilst only 13.4% of cases with

severe or profound ID had aggression documented (N=155). (X2 (1, N = 1575) =

77,56, p < .001) (see figure 1).

Table 4. Level of ID in patients showing/not showing aggression in episode

Aggression (N=1158)

No Aggression (N=417)

Overall (N=1575)

Mean (SD) Mean (SD) Mean (SD) Mann-Whitney U p-value

Length of Episode 458.85 (406.56)

207.37 (218.28)

392.27 (382.64)

342457 < .001

N (%) N (%) N (%) Chi-square X2 p-value

Level of ID 77.556 < .001 Mild/Moderate 736 (63.6%) 177 (42.4%) 913 (58%) Severe/Profound 155 (13.4%) 50 (12%) 205 (13%) Other/Not Recoded/Unspecific

267 (23.1%) 190 (45.6%) 457 (29%)

Figure 1. Number of cases with different levels of ID with/without aggression

Aggression was found to have significant association with diagnosis of comorbid

physical and mental health issues (see table 5) including pervasive developmental

disorders [X2 (1, N = 1575) = 46.01, p < .001], hyperkinetic disorders [X2 (1, N =

1575) = 9.96, p = .002], treatment resistant depression [X2 (1, N = 1575) =

45.20, p < .01], SMI [X2 (1, N = 1575) = 27.12, p < .001], substance misuse

[X2 (1, N = 1575) = 6.110, p = 0.013], genetic, metabolic or chromosomal

abnormality [X2 (1, N = 1575) = 5.75, p = .017] and epilepsy [X2 (1, N = 1575) =

7.45, p = .006]. However, CMD and dementia did not have a significant statistical

association with aggression [X2 (1, N = 1575) = 2.22, p = .136; X2 (1, N = 1575)

= .32, p = .574 respectively].

Table 5. Comorbid physical and mental health issues in patients with learning Disability showing/not showing aggression in episode

Comorbid Physical and Mental Health Issues

Aggression (N=1158)

No Aggression

(N=417)

Overall (N=1575)

N (%) N (%) N (%) Chi-square X2 p-value

F84.x Pervasive Developmental Disorders

461 (35.1%)

89 (34.3%)

550 (34.9%)

46.008 < .001

F90.x Hyperkinetic Disorders 79 (5.0%)

11 (7.7%)

90 (5.7%)

9.963 .002

Dementia 46 (4.0%)

14 (3.4%)

60 (3.8%)

.317 .574

Severe Mental Illness (SMI)* 183 (15.8%)

24 (5.8%)

207 (13.1%)

27.115 < .001

Schizophrenia 156 (13.5%)

21 (5.0%)

177 (11.2%)

- -

Mania 4 (0.3%) - 4 (0.3%) - - Bipolar 34 (2.9%) 3 (0.7%) 37 (2.3%) - -

Common Mental Disorders (CMD)** 251 (21.7%)

76 (18.2%) 327 (20.8%)

2.218 .136

Depression 117 (10.1%)

37 (8.9%) 154 (9.8%) - -

Other mood Disorders 14 (1.2%) 3 (0.7%) 17 (1.1%) - -

-

Behavioural Syndromes 7 (0.6%) 2 (0.5%) 9 (0.6%) - - Personality Disorders 46 (4.0%) 9 (2.2%) 55 (3.5%) - - Neurotic, Stress-related, or Somatoform Disorders

124 (10.7%)

46 (11.0%) 170 (10.8%)

- -

Substance Misuse 20 (1.7%) 16 (3.8%) 36 (2.3%) 6.110

.013

Genetic, Metabolic, or Chromosomal Disorders

231 (19.9%)

61 (14.6%) 292 (18.5%)

5.745 .017

Epilepsy 146 (12.6%)

32 (7.7%) 178 (11.3%)

7.446

.006

*&**People could have more than one diagnosis; therefore, SMI and CMD are not add-ups of different diagnoses.

Aggression was also found to be significantly related to medications (see table 6)

including antipsychotics (X2 (1, N = 1575) = 128.32, p < .01) and other psychotropic

medications ((X2 (1, N = 1575) = 244.80, p < .01), analgesics ((X2 (1, N = 1575) =

48.12), p < .01), antiepileptics ((X2 (1, N = 1575) = 70.36, p < .001), infection

medications (X2 (1, N = 1575) = 15.48, p < .001) and laxatives (X2 (1, N = 1575) =

29.22, p < .001). Medications for thyroid disease did not show significant association

with aggressive behaviour (X2 (1, N = 1575) = 2.40, p = .238).

Table 6. Medication taken by patients with ID showing/not showing aggression in episode

Medication

Aggression (N=1158)

No Aggression (N=417)

Overall (N=1575)

N (%) N (%) N (%) Chi-square X2 P-value

Psychotropics Antipsychotics 467 (40.3%) 42 (10.1%) 509 (32.3%) 128.316 < .001 Other 798 (68.9%) 103 (24.7%) 901 (57.2%) 244.802 < .001

Other Medications Analgesics 229 (19.8%) 22 (5.3%) 251 (15.9%) 48.115 < .001 Antiepileptics 316 (27.3%) 31 (7.4%) 347 (22.0%) 70.357 < .001 Infection Medication 64 (5.5%) 4 (1.0%) 68(4.3%) 15.483 < .001 Laxatives 105 (9.1%) 5 (1.2%) 110 (7.0%) 29.219

< .001

Thyroid Medication 45 (3.9%) 11 (2.6%) 56 (3.6%) 1.393 .238

In terms of clinical contacts, we found a significant difference between all

professional contacts for cases with aggression compared to those without

(Psychology U = 287889, p < .001); (Nursing U = 275780, p < .001); (Psychiatry U =

332543, p < .001) (see table 7).

Table 7. Health Professional Involvement in episode

Health Professional Involvement

Aggression (N=1158)

No Aggression (N=417)

Overall (N=1575)

Min Max Mean SD Min Max Mean SD Min Max Mean SD

Psychology Contacts

0 34 1.61 3.06 0 13 .85 1.61 0 34 1.41 2.77

Nurse Contacts

0 43 .77 3.62 0 20 .18 1.41 0 43 .62 2.90

Psychiatry Contacts

0 37 1.15 2.31 0 7 .49 .93 0 37 .97 2.06

Regression Analysis

Results from univariate analysis including Chi-square association and Mann-Whitney

U tests suggested that age, gender, level of ID, diagnosis of comorbid physical and

mental disorders use of medication and health professional contacts had significant

association with aggression in episode.

Logistic regression was undertaken to determine the effects of the above variables

on the likelihood that patients have aggression documented in episode. The logistic

regression model was statistically significant, X2 (19) = 420.268, p < .001. The model

explained 34.2% (Nagelkerke R2) of the variance in aggression and correctly

classified 79.1% of cases. The variables showing significant association with

aggression were age, male, black ethnicity, severe/profound level of ID, length of

episode, severe mental illness, diagnosis of substance misuse, use of non-

antipsychotic psychotropics in episode, use of laxatives in episode and psychology

contacts (see table 8).

With every year increase in age, the odds of showing aggressive behaviour was

multiplied by 0.989. Because 0.989 is less than 1, any odds being multiplied by

0.898 would be decreasing. Thus, as age increases, the odds of having aggression

incidence in episode decrease (p= .019). Younger adults are, therefore, more likely

to show aggressive behaviours within episode. Males were found to be 1.673 more

likely to have aggression in episode compared to females (p< .001).

Patients with severe/profound level of ID were 55.5% less likely to show aggression

in the episode compared to those with mild/moderate ID (p < .001). As for comorbid

diseases, those with SMI (p = .04) and substance misuse were more likely (x 1.787)

to have records of aggressive behaviour.

Furthermore, patients taking other psychotropics episode were almost three times

more likely to exhibit aggressive behaviour (p < .001) and taking laxatives was

associated with a threefold increase in the likelihood of showing aggressive

behaviour (p = .024). Patients with aggression were also found to have more

psychology contacts in the episode (p < .001). With every increase in psychology

contacts, the odds of aggression was multiplied by 1.111.

Table 8. Logistic model for aggression in episode in adults with ID

Logistic Regression Model for Aggression in Episode

Predictor Variables Odds Ratio (OR) 95% CI p-value

Age .989 .981 - .998 .019

Gender 1.663 1.266 - 2.183 < .001

Ethnicity Black Ethnicity .773 .555 – 1.078 .129 Asian Ethnicity .570 .320 – 1.014 .056 Mixed Ethnicity 1.176 .516 – 2.679 .699 Other Ethnicity .571 .244 – 1.338 .197

Level of ID Severe/Profound ID .445 .329 – .602 < .001 Unspecific/Other Level of ID .567 .364 – .882 .012

Length of Episode 1.001 1.000 -1.002 .001

F84 Pervasive Developmental Disorder 1.330 .939 – 1.884 .108

F90 Hyperkinetic Disorder 1.106 .521 – 2.347 .793

Severe Mental Illness (SMI) 1.787 1.036 – 3.082 .037

F1 Substance Misuse .415 .179 – .963 .041

G40 Epilepsy 1.095 .552 – 2.172 .769

Genetic, Metabolic Disorders and Chromosomal Abnormality

.971 .579 – 1.628 .911

Antipsychotics 1.310 .813 – 2.110 .267

Other Psychotropics 2.900 1.946 – 4.320 < .001

Analgesics 1.233 .708 – 2.146 .459

Antiepileptics 1.487 .897 – 2.465 .124

Infection Medications 1.884 .606 – 5.859 .274

Laxatives 3.077 1.161 – 8.156 .024

Psychology Contacts 1.111 1.063 – 1.161 < .001

Nurse Contacts .992 .939 – 1.048 .773

Psychiatry Contacts 1.054 .962 – 1.154 .258

Discussion

Summary of Results

To sum up, younger age, male, mild/moderate level of ID, a longer length of episode,

diagnosis of severe mental illness, use of non-antipsychotic psychotropics, use of

laxatives, and higher number of psychology contacts were found to have significant

independent association with aggressive behaviour in adults with ID. The prevalence

of aggression in adults with ID referred to the ID service within SLaM was 73.52%,

almost nine times higher than the prevalence in the community sample estimated by

a previous study (Bowering et al., 2019). These results support the following

hypotheses. First, we found significantly higher rates of comorbid diagnoses of

pervasive developmental disorder, hyperkinetic disorder, SMI, substance misuse,

genetic, metabolic, and chromosomal disorders, and epilepsy in cases with ID and

aggression. Second, we found significant associations between aggression and

other psychotropics, analgesics, antiepileptics, infection medications and laxatives.

Patients taking those medications were more likely to have aggression documented

in their clinical record than those not taking these medications. This supported the

second hypothesis, implying a higher possibility of health issues including pain,

infections, epilepsy and constipation in those with aggression. Cases with ID and

aggression were found to have higher numbers of psychology, nurse and psychiatry

contacts than those without as hypothesised. This finding supported the third

hypothesis. Fourth, aggression was also significantly associated with demographic

factors, including age, gender and ethnic groups. Males and individuals with younger

age were more likely to display aggression. Those from a White ethnic background

had a higher likelihood of presenting aggressive behaviours in episode than

individuals from other ethnic backgrounds. In addition, cases with mild or moderate

ID and with longer episode lengths were found more likely to have aggression

documented in episode.

Strengths and Weakness

This study has several strengths. This is the first time in almost ten years that a

routine database has been used for investigating aggression in individuals with ID.

The use of a routine database provided a representative and large sample

containing people with different levels of ID who are in contact with community and

inpatient services. Clinical records can be an essential source of information that

reliably register and quickly gather patient demographic and clinical profiles at

different time points in a cost-efficient way. Routine records also enabled us to gain

valid and reliable measurements of clinical features captured by health professionals

working in clinical settings. The use of the Aggression App has supplied our study

with a comprehensive definition for aggression in the population with ID. Previous

studies on this topic mostly used observation, self-reports, or nominations by carers

to assess aggression in individuals with ID. In the current study, the Aggression App

based on electronic health records has gathered relevant information as listed above

and has included a validity check carried out by hand annotation, providing a

meaningful definition and sensitive evaluation of aggression.

There are also some limitations to the study. Firstly, patients in the cohort were

residents in South London because the database was based in the MHLD team in

SLaM. Clinical characteristics can vary by region. Thus, the sample might only be

representative for the region instead of a wider population, and results from the

current study might not be generalisable to other populations. Secondly, the

retrospective nature of the current database means that the data will be modified

over time with new records added to the dataset as the episode progresses. In this

case, the findings from the current study might not be replicable in the future. Thirdly,

the sample was biased, with more males than females in the record. This can be a

result of gender differences in the diagnosis of ID. Hence, the significant association

between gender and aggression can be affected by sampling bias. The age of the

whole sample has a positively skewed distribution with a median of 31.93, which

means the sample is relatively young. Lastly, although the study has included a

comprehensive definition of aggression, it is still a relatively brief overview on the

characteristics of cases with aggressive behaviour. We did not specify aggression

into detailed categories as it was done in Tsiouris et al. (2011). Therefore, we have

not included associations between clinical characteristics and more specific types of

aggression.

Prevalence of Aggression

The prevalence of aggression found in the current study provided further evidence

for higher likelihood of reported aggression in people living in institutional settings

than in community settings (Tyrer et al., 2006; Bowering et al., 2019). The results

together highlighted the influence of context on the individual’s behaviour. However,

this higher rate of aggression in institutional settings can result from selection bias,

where patients with ID would have a higher chance of having contacts with service

than those not having aggression. The result of aggression being more prevalent in

males and individuals with younger age is consistent with the pattern found in

studies on aggression with the general population (Betterncourt & Miller, 1996;

Björkqvist, 2018).

We found in the present study that aggression was more prevalent in cases with mild

or moderate levels of ID, in contrast with previous studies. According to the NICE

guidelines 2015, severe/profound ID was a risk factor for all aggression, including

physical, verbal and destructive aggression. When looking at specific types of

aggression, individuals with mild/moderate ID were more likely to have verbal

aggression than those with severe/profound ID (Crocker, 2006). However, studies

conducted in inpatient settings showed a higher possibility of physical aggression in

patients with mild/moderate ID (Ross, 1972), consistent with our current findings. It is

possible that aggression in patients with severe/profound level of ID was more

efficiently managed in inpatient settings than elsewhere, which implied a need for

improvement in interventions and management in other settings.

Clinical Characteristics of Aggression

Our study has found a significant distinction between the aggression and no

aggression groups in almost all the comorbid psychological and physical disorders

included, indicating a notable link between aggression and comorbid mental and

physical health issues. Interestingly, no significant differences were found between

the two groups regarding CMD diagnoses, while previous studies suggested

significant associations between aggression and depression, anxiety, personality

disorders and OCD respectively. The first possible explanation for this was that

combining independent variables into one might cause a decrease in internal

consistency and lack of accurate estimation for individual disorders. This could also

be a result of underdiagnosis for CMD in the population. In SMI, there are more

observable symptoms such as elevated mood and irritability in bipolar disorders and

positive symptoms in psychotic orders. At the same time, recognition of CMD, such

as affective disorders, rely more on expressive communication. Hurley’s (2008)

observation on depression in adults with ID pointed out that a significant number of

patients with ID were unable to meet the required number of DSM criteria for major

depression. Peña-Salazar and colleagues (2020) undertook a psychiatric

assessment in adult patients with ID with no prior psychiatric diagnosis and found

that 29.6% of the sample had a previously undiagnosed mental disorder.

Furthermore, affective disorders might be more related to self-injury than other

aggression types (Tsiouris et al., 2011).

However, only SMI and substance misuse had significant associations with

aggression after adjusting for covariates. This study also failed to find significant

independent associations between aggression and pervasive developmental

disorders and hyperkinetic disorder like previous studies found (e.g., Cooper et al.,

2009; McCathy et al., 2010). Diagnosis of other developmental disorders, including

pervasive developmental and hyperkinetic disorders, became insignificant for

aggression after controlling covariates. This might result from controlling for the level

of ID in the model because autism diagnosis was found to be more prevalent in

individuals with severe ID (McCathy et al., 2010). After controlling for mild/moderate

level of ID, the number of cases with pervasive developmental disorders diagnosis

may have reduced, and the association between autism and aggression became

non-significant.

Psychology contacts were found to be the only professional contacts that had a

significant independent association with aggression. For every increase in the

number of psychology contacts, the possibility of having aggression was multiplied

by 1.11. This means that cases with aggression almost all had at least one

psychology contact, suggesting a substantial role of psychological input in the

intervention and management of aggression in individuals with ID. Nevertheless,

there are still cases with aggression but zero psychology contact, suggesting a gap

between high mental health needs and low resource availability or access.

Researchers highlighted the increasing risk of mental health difficulties and the

corresponding need for mental health care in people with ID (Soltau et al., 2015;

Howlett et al., 2015). This finding has highlighted a high demand for a skilled mental

health workforce in the service.

Within the medication records, a significant contribution of psychotropics other than

antipsychotics was found in reducing aggression in this population. After controlling

for covariates, antipsychotics were found not significantly associated with aggression

anymore, whilst the association with other psychotropics remained significant. This

change has suggested that aggression might be treated adequately by

antipsychotics, whereas those taking other psychotropics still had aggression after

treatment. Consequently, indirect evidence was provided for the use of

antipsychotics in treating aggression in patients with ID.

Implications

Aggression is a vital issue in healthcare for people with ID, not only for the patients’

own wellbeing, but also for safety of their family and carers. Findings from our study

provided valuable insights into the profile of aggression in adults with ID, which could

be used in designing of management of behaviour and more targeting plans for

individuals in the future. According to the NICE Guideline 2015, interventions for

challenging behaviour in individuals with ID should be personalised based on their

own needs. There is broad agreement among psychiatrists that non-pharmacological

interventions should be the primary choice for treatment of aggression (Unwin, 2008).

From what we found in the study, aggression has a close association with mental

health and psychology contact is the first line of treatment of routine care. These two

findings together inferred a great demand of mental health support for individuals

with ID and aggression.

At the same time, use of antipsychotic medications seemed to play a crucial role in

the treatment of aggression. Consideration of using pharmacotherapy should not be

encouraged in management of aggression unless other treatment is not successful

(Ali, Blickwedel & Hassiotis, 2014). Previous studies on pharmacotherapy for

aggressive behaviours in people with ID highlighted overuse issues of antipsychotic

medications in treating aggressive behaviour. Despite the overuse issue of

medications, our finding has provided evidence for a possible anti-aggression

function of anti-psychotic medications. Considering the significant cases in current

sample with diagnosis of psychotic disorders, it is possible that aggression in the

population can be a consequential behavioural outcome of psychotic disorders in this

population. We strongly believe that further research must be conducted on

exploring and understanding the display of aggression in people with ID across the

age span and disability levels given the devastating impact of this disorder.

Conclusion

In conclusion, current study has provided a validation on definition of aggression and

found strong links between aggression and some clinical characteristics in adults

with ID in routine database. Cases with and without aggression were found to have

significant difference in many clinical characteristics. The variables making major

independent contribution to aggression in the population included male, younger age,

mild/moderate ID, episode length, SMI, not having diagnosis of substance misuse,

use of non-antipsychotic psychotropics and more psychology contacts. The use of

antipsychotics seemed to be properly treated because it did not appear in the

prediction pathway for aggression. This finding can be meaningful evidence for

research on pharmacotherapy for aggression in people with ID. Psychology contacts

was also found to play an important role in management of aggression, implying a

significant mental health need for this population. At the same time, autism did not

appear to have association with aggression as in the previous research. Further

study should further explore the profile by investigating the associations with severity

and types of aggression.

Acknowledgement

I would like to thank my supervisor Professor Angela Hassiotis who gave me this

opportunity to work on this project. I got to learn a lot from this project about

challenging behaviour in intellectual disability. I would also like to thank Professor

Andre Strydom and his team (Mr James Smith, Mr Asaad Baksh and Mr Rory

Sheehan) from King’s College London for the support and guidance they provided

throughout the process. Thanks to South London and Maudsley Hospital for

providing dataset for this project.

Lastly, I would like to extend my heartfelt thanks to my family and friends, especially

my parents. Without their help, this project would not be successful.

Reference Ali, A., Blickwedel, J., & Hassiotis, A. (2014). Interventions for challenging behaviour

in intellectual disability. Advances in psychiatric treatment, 20(3), 184-192.

Amore, M., Menchetti, M., Tonti, C., Scarlatti, F., Lundgren, E., Esposito, W., & Berardi, D. (2008). Predictors of violent behavior among acute psychiatric patients: clinical study. Psychiatry and clinical neurosciences, 62(3), 247-255.

Balia, C. (2020). Aggressive behaviour in children and adolescents with Conduct Disorder or Oppositional Defiant Disorder: neuropsychological characterization and drug treatments. Preliminary analysis of data from the European MATRICS project.

Bettencourt, B., & Miller, N. (1996). Gender differences in aggression as a function of provocation: a meta-analysis. Psychological bulletin, 119(3), 422.

Bizzarri, J. V., Piacentino, D., Kotzalidis, G. D., Moser, S., Cappelletti, S., Weissenberger, G., Pompili, M., & Conca, A. (2020). Aggression and violence toward healthcare workers in a psychiatric service in Italy: A retrospective questionnaire-based survey. The Journal of nervous and mental disease, 208(4), 299-305.

Björkqvist, K. (2018). Gender differences in aggression. Current Opinion in Psychology, 19, 39-42.

Bowring, D., Totsika, V., Hastings, R., Toogood, S., & McMahon, M. (2017). Prevalence of psychotropic medication use and association with challenging behaviour in adults with an intellectual disability. A total population study. Journal of Intellectual Disability Research, 61(6), 604-617.

Bowring, D. L., Painter, J., & Hastings, R. P. (2019). Prevalence of challenging behaviour in adults with intellectual disabilities, correlates, and association with mental health. Current Developmental Disorders Reports, 6(4), 173-181.

Bufkin, J. L., & Luttrell, V. R. (2005). Neuroimaging studies of aggressive and violent behavior: current findings and implications for criminology and criminal justice. Trauma, Violence, & Abuse, 6(2), 176-191.

Cardinal, R. N., Parkinson, J. A., Hall, J., & Everitt, B. J. (2002). Emotion and motivation: the role of the amygdala, ventral striatum, and prefrontal cortex. Neuroscience & Biobehavioral Reviews, 26(3), 321-352.

Cardinale, E. M., Reber, J., O'Connell, K., Turkeltaub, P. E., Tranel, D., Buchanan, T. W., & Marsh, A. A. (2021). Bilateral amygdala damage linked to impaired ability to predict others' fear but preserved moral judgements about causing others fear. Proceedings of the Royal Society B, 288(1943), 20202651.

Cooper, S. A., Smiley, E., Jackson, A., Finlayson, J., Allan, L., Mantry, D., & Morrison, J. (2009). Adults with intellectual disabilities: prevalence, incidence

and remission of aggressive behaviour and related factors. Journal of Intellectual Disability Research, 53(3), 217-232.

Crocker, A. G., Mercier, C., Lachapelle, Y., Brunet, A., Morin, D., & Roy, M. E. (2006). Prevalence and types of aggressive behaviour among adults with intellectual disabilities. Journal of Intellectual Disability Research, 50(9), 652-661.

da Cunha-Bang, S., Fisher, P. M., Hjordt, L. V., Holst, K., & Knudsen, G. M. (2019). Amygdala reactivity to fearful faces correlates positively with impulsive aggression. Social neuroscience, 14(2), 162-172.

Davidson, R. J. (2000). Affective style, psychopathology, and resilience: brain mechanisms and plasticity. American Psychologist, 55(11), 1196.

Davidson, R. J., Putnam, K. M., & Larson, C. L. (2000). Dysfunction in the neural circuitry of emotion regulation--a possible prelude to violence. science, 289(5479), 591-594.

Dougherty, D. D., Shin, L. M., Alpert, N. M., Pitman, R. K., Orr, S. P., Lasko, M., Macklin, M. L., Fischman, A. J., & Rauch, S. L. (1999). Anger in healthy men: a PET study using script-driven imagery. Biological psychiatry, 46(4), 466-472.

Drieschner, K. H., Marrozos, I., & Regenboog, M. (2013). Prevalence and risk factors of inpatient aggression by adults with intellectual disabilities and severe challenging behaviour: A long-term prospective study in two Dutch treatment facilities. Research in Developmental Disabilities, 34(8), 2407-2418.

Edition, F. (2013). Diagnostic and statistical manual of mental disorders. Am Psychiatric Assoc, 21.

Gansler, D. A., Lee, A. K., Emerton, B. C., D'Amato, C., Bhadelia, R., Jerram, M., & Fulwiler, C. (2011). Prefrontal regional correlates of self-control in male psychiatric patients: Impulsivity facets and aggression. Psychiatry Research: Neuroimaging, 191(1), 16-23.

Genovese, T., Dalrymple, K., Chelminski, I., & Zimmerman, M. (2017). Subjective anger and overt aggression in psychiatric outpatients. Comprehensive psychiatry, 73, 23-30.

Gouveia, F. V., Hamani, C., Fonoff, E. T., Brentani, H., Alho, E. J. L., De Morais, R. M. C. B., De Souza, A. L., Rigonatti, S. P., & Martinez, R. C. (2019). Amygdala and hypothalamus: historical overview with focus on aggression. Neurosurgery, 85(1), 11-30.

Hastings, R. P., Allen, D., Baker, P., Gore, N. J., Hughes, J. C., McGill, P., Noone, S. J., & Toogood, S. (2013). A conceptual framework for understanding why challenging behaviours occur in people with developmental disabilities. International Journal of Positive Behavioural Support, 3(2), 5-13.

Health, N. C. C. f. M. (2015). Challenging behaviour and learning disabilities: prevention and interventions for people with learning disabilities whose behaviour challenges.

Hogue, T. E., Mooney, P., Morrissey, C., Steptoe, L., Johnston, S., Lindsay, W., & Taylor, J. (2007). Emotional and behavioural problems in offenders with intellectual disability: comparative data from three forensic services. Journal of Intellectual Disability Research, 51(10), 778-785.

Holmes, N., Shah, A., & Wing, L. (1982). The Disability Assessment Schedule: a brief screening device for use with the mentally retarded. Psychological medicine, 12(4), 879-890.

Howlett, S., Florio, T., Xu, H., & Trollor, J. (2015). Ambulatory mental health data demonstrates the high needs of people with an intellectual disability: results from the New South Wales intellectual disability and mental health data linkage project. Australian & New Zealand Journal of Psychiatry, 49(2), 137-144.

Hurley, A. (2008). Depression in adults with intellectual disability: symptoms and challenging behaviour. Journal of Intellectual Disability Research, 52(11), 905-916.

Iozzino, L., Ferrari, C., Large, M., Nielssen, O., & De Girolamo, G. (2015). Prevalence and risk factors of violence by psychiatric acute inpatients: a systematic review and meta-analysis. PloS one, 10(6), e0128536.

Jonker, E., Goossens, P., Steenhuis, I., & Oud, N. (2008). Patient aggression in clinical psychiatry: perceptions of mental health nurses. Journal of psychiatric and mental health nursing, 15(6), 492-499.

Kerr, K., Oram, J., Tinson, H., & Shum, D. (2017). Health care workers' experiences of aggression. Archives of psychiatric nursing, 31(5), 457-462.

Matthies, S., Rüsch, N., Weber, M., Lieb, K., Philipsen, A., Tuescher, O., Ebert, D., Hennig, J., & van Elst, L. T. (2012). Small amygdala–high aggression? The role of the amygdala in modulating aggression in healthy subjects. The World Journal of Biological Psychiatry, 13(1), 75-81.

McCarthy, J., Hemmings, C., Kravariti, E., Dworzynski, K., Holt, G., Bouras, N., & Tsakanikos, E. (2010). Challenging behavior and co-morbid psychopathology in adults with intellectual disability and autism spectrum disorders. Research in Developmental Disabilities, 31(2), 362-366.

McClintock, K., Hall, S., & Oliver, C. (2003). Risk markers associated with challenging behaviours in people with intellectual disabilities: a meta‐analytic study. Journal of Intellectual Disability Research, 47(6), 405-416.

Organization, W. H. (2014). Global status report on violence prevention 2014. World Health Organization.

Pardini, D. A., Raine, A., Erickson, K., & Loeber, R. (2014). Lower amygdala volume in men is associated with childhood aggression, early psychopathic traits, and future violence. Biological psychiatry, 75(1), 73-80.

Peña-Salazar, C., Arrufat, F., Santos, J. M., Fontanet, A., González-Castro, G., Más, S., Roura-Poch, P., & Valdés-Stauber, J. (2020). Underdiagnosis of psychiatric disorders in people with intellectual disabilities: Differences between psychiatric disorders and challenging behaviour. Journal of Intellectual Disabilities, 24(3), 326-338.

Pickard, H. (2015). Self-harm as violence: When victim and perpetrator are one. Women and violence, 71-90.

Rodríguez, A., Rey, B., Clemente, M., Wrzesien, M., & Alcañiz, M. (2015). Assessing brain activations associated with emotional regulation during virtual reality mood induction procedures. Expert Systems with Applications, 42(3), 1699-1709.

Ross, R. T. (1972). Behavioral correlates of levels of intelligence. American Journal of Mental Deficiency.

Ruthirakuhan, M., Lanctôt, K. L., Di Scipio, M., Ahmed, M., & Herrmann, N. (2018). Biomarkers of agitation and aggression in Alzheimer's disease: a systematic review. Alzheimer's & Dementia, 14(10), 1344-1376.

Sheehan, R., Hassiotis, A., Walters, K., Osborn, D., Strydom, A., & Horsfall, L. (2015). Mental illness, challenging behaviour, and psychotropic drug prescribing in people with intellectual disability: UK population based cohort study. Bmj, 351.

Siegel, A., & Victoroff, J. (2009). Understanding human aggression: New insights from neuroscience. International journal of law and psychiatry, 32(4), 209-215.

Siever, L. J. (2008). Neurobiology of aggression and violence. American journal of psychiatry, 165(4), 429-442.

Silver, E., Felson, R. B., & Vaneseltine, M. (2008). The relationship between mental health problems and violence among criminal offenders. Criminal Justice and Behavior, 35(4), 405-426.

Soltau, B., Biedermann, J., Hennicke, K., & Fydrich, T. (2015). Mental health needs and availability of mental health care for children and adolescents with intellectual disability in B erlin. Journal of Intellectual Disability Research, 59(11), 983-994.

Stangor, C., & Walinga, J. (2014). Introduction to psychology. BCcampus, BC Open Textbook Project.

Sterzer, P., Stadler, C., Poustka, F., & Kleinschmidt, A. (2007). A structural neural deficit in adolescents with conduct disorder and its association with lack of empathy. Neuroimage, 37(1), 335-342.

Tenneij, N., & Koot, H. M. (2008). Incidence, types and characteristics of aggressive behaviour in treatment facilities for adults with mild intellectual disability and severe challenging behaviour. Journal of Intellectual Disability Research, 52(2), 114-124.

Tsiouris, J. (2010). Pharmacotherapy for aggressive behaviours in persons with intellectual disabilities: treatment or mistreatment? Journal of Intellectual Disability Research, 54(1), 1-16.

Tsiouris, J., Kim, S., Brown, W., & Cohen, I. (2011). Association of aggressive behaviours with psychiatric disorders, age, sex and degree of intellectual disability: a large‐scale survey. Journal of Intellectual Disability Research, 55(7), 636-649.

Tyrer, F., McGrother, C., Thorp, C., Donaldson, M., Bhaumik, S., Watson, J., & Hollin, C. (2006). Physical aggression towards others in adults with learning disabilities: prevalence and associated factors. Journal of Intellectual Disability Research, 50(4), 295-304.

Unwin, G. L., & Deb, S. (2008). Use of medication for the management of behavior problems among adults with intellectual disabilities: A clinicians' consensus survey. American Journal on Mental Retardation, 113(1), 19-31.

van Elst, L. T., Hesslinger, B., Thiel, T., Geiger, E., Haegele, K., Lemieux, L., Lieb, K., Bohus, M., Hennig, J., & Ebert, D. (2003). Frontolimbic brain abnormalities in patients with borderline personality disorder: a volumetric magnetic resonance imaging study. Biological psychiatry, 54(2), 163-171.

van Elst, L. T., Ludaescher, P., Thiel, T., Büchert, M., Hesslinger, B., Bohus, M., Rüsch, N., Hennig, J., Ebert, D., & Lieb, K. (2007). Evidence of disturbed amygdalar energy metabolism in patients with borderline personality disorder. Neuroscience letters, 417(1), 36-41.

van Elst, L. T., Woermann, F., Lemieux, L., & Trimble, M. (2000). Increased amygdala volumes in female and depressed humans. A quantitative magnetic resonance imaging study. Neuroscience letters, 281(2-3), 103-106.

Visser, E., Berger, H., Prins, J., Lantman-De Valk, H. V. S., & Teunisse, J. (2014). Shifting impairment and aggression in intellectual disability and autism spectrum disorder. Research in Developmental Disabilities, 35(9), 2137-2147.

Whalen, P. J., Shin, L. M., McInerney, S. C., Fischer, H., Wright, C. I., & Rauch, S. L. (2001). A functional MRI study of human amygdala responses to facial expressions of fear versus anger. Emotion, 1(1), 70.