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MSC CLINICAL NEURODEVELOPMENTAL SCIENCES
ASSESSMENT COVER SHEET ID NUMBER: 1773321 MODULE CODE: 7PCFLDST ASSESSMENT TITLE: Period Prevalence of Co-occurring Epilepsy in Service-users with Neuropsychiatric Conditions: Evidence from South London and Maudsley Hospital Trusts. WORD COUNT: 8,686
Institute of Psychiatry, Psychology and
Neuroscience
Department of Forensics and Neurodevelopmental Sciences
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“Period Prevalence of Co-occurring Epilepsy in Service-users with
Neuropsychiatric Conditions: Evidence from South London and Maudsley
Hospital Trusts”
Student Number: 1773321
A dissertation submitted to the Institute of Psychology, Psychiatry and
Neuroscience, Department of Forensic and Neurodevelopmental Science,
King’s College London, in partial fulfilment of MSc Clinical
Neurodevelopmental Sciences
Dissertation Supervisor: Dr. Jonathan O’Muircheartaigh
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Acknowledgements
I would like to thank my supervisor, Dr. Jonathan O’Muircheartaigh, for his advice, support
and expertise throughout the entirety of this project. Likewise, I would like to thank both
Megan Pritchard and Amelia Jewell for their help in generating the Hospital Cohort from
CRIS. All your help was greatly appreciated and valued.
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Period Prevalence of Co-occurring Epilepsy in Service-users with Neuropsychiatric
Conditions: Evidence from South London and Maudsley Hospital Trusts.
Abstract
Common psychiatric and neurodevelopmental disorders, such as schizophrenia, bipolar
disorder, major depression and autism spectrum disorders, can co-occur with epilepsy and
seizures. Past studies have typically investigated the commonality of these conditions in
service-users with epilepsy, a bias within the literature. Whilst this approach has yielded
important findings, there has been a paucity of investigations surrounding the prevalence of
comorbid epilepsy in individuals diagnosed with neuropsychiatric disorders. The present
study aimed to resolve this and establish the prevalence of epilepsy in patients referred to
South London and Maudsley (SLaM) psychiatric services. Here, the Clinical Record
Interactive Search (CRIS) was used to extract ICD-10 diagnostic codes and socio-
demographic information from 35,092 service-users. As a main result, and contrary to past
findings, the prevalence of epilepsy in patients diagnosed with schizophrenia, bipolar
disorder, recurrent depression or a depressive episode were similar to the general London
population (0.44%), with proportions ranging from 0.36-0.45%. Comparatively, epilepsy was
substantially more prevalent in service-users with autism spectrum disorder compared to the
general London population, although the current estimate was smaller than past findings.
Results showed that for all disorders intellectual disability was significantly associated with
an epilepsy diagnosis, suggesting that the low proportions could represent the smaller
prevalence of intellectual disability within the hospital cohort. Alternatively, the results may
reflect an under-reporting of ICD-10 epilepsy codes in patient records, as post-hoc analysis
revealed higher numbers of patients receiving medications primarily used to treat seizures,
indicating a higher prevalence than those recorded using ICD codes.
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Contents Page
Acknowledgements ..................................................................................................................... 5
Abstract ...................................................................................................................................... 6
1. Introduction.......................................................................................................................... 11
1.1 The Prevalence of Psychiatric and Neurodevelopmental Comorbidities in PWE ......... 11
1.1.1 Depression in PWE ................................................................................................. 11
1.1.2 Bipolar Disorder in PWE ........................................................................................ 12
1.1.3 Schizophrenia in PWE ............................................................................................ 13
1.1.4 Neurodevelopmental Disorders in PWE: Autism Spectrum Disorder .................... 13
1.2 A Bi-directional Relationship: Comorbid Epilepsy in People with a Neuropsychiatric
Diagnosis.............................................................................................................................. 14
1.2.1 Comorbid epilepsy in Psychiatric Patients with Depression .................................. 14
1.2.2 Comorbid epilepsy in Neuropsychiatric Patients with ASD................................... 15
1.2.3 Comorbid epilepsy in Psychiatric Patients with BD ............................................... 16
1.2.4 Comorbid epilepsy in Psychiatric Patients with Schizophrenia ............................. 16
1.3 Rationale, Aims & Hypotheses ...................................................................................... 17
2. Methods ................................................................................................................................ 19
2.1 Study Setting: The Clinical Record Interactive Search ................................................. 19
2.2 Procedure ....................................................................................................................... 20
2.3 Study Population: Inclusion and Exclusion Criteria ...................................................... 20
2.4 Study Design and Measures ........................................................................................... 22
2.4.1 Socio-demographic Variables ................................................................................. 22
2.4.2 Exposure Measure: The identification of Psychiatric Disorders ............................ 23
2.4.3 Outcome Measure: Identification of Diagnosed Epilepsy ...................................... 23
2.4.4 Anti-Epileptic Drugs as an Indicator of Unreported Epilepsy ................................ 23
2.4.5 Intellectual Disability as a Risk Factor for Epilepsy .............................................. 24
2.5 Statistical Analysis ......................................................................................................... 24
3. Results .................................................................................................................................. 26
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3.1 Sample Characteristics of the Full Cohort ..................................................................... 26
3.2 Sample Characteristics Stratified by Psychiatric Diagnoses ......................................... 28
3.3 The Prevalence of Comorbid Epilepsy .......................................................................... 29
3.4 ID as a Risk Factor for Epilepsy .................................................................................... 31
3.5 Post-Hoc Analysis .......................................................................................................... 32
4. Discussion ............................................................................................................................ 34
4.1 The Prevalence of Epilepsy and Comparisons to the General Population..................... 34
4.2 Epilepsy Variability Between Psychiatric and Neurodevelopmental Disorders............ 36
4.3 The Importance of ID in Epilepsy ................................................................................. 36
4.3 Under-Reporting of Epilepsy codes in ePJS .................................................................. 37
4.4 Limitations ..................................................................................................................... 38
4.5 Strengths ........................................................................................................................ 39
4.6 Clinical Significance and Future Research .................................................................... 40
5. Conclusion ........................................................................................................................... 41
References: ............................................................................................................................... 42
Appendix A: Selected Representative Studies Investigating Psychiatric Comorbidity in PWE
.................................................................................................................................................. 50
Appendix B: Selected Representative Studies Investigating Comorbid Epilepsy in Patients
with Psychiatric and Neurodevelopmental Disorders ............................................................. 58
Appendix C: STATA Output for Demographic Characteristics, Proportions and Chi-Squared
Tests ......................................................................................................................................... 62
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Table of Figures
Figure 1. Flow Chart of the Extraction and Identification Process.......................................... 22
Table of Tables
Table 1. ICD-10 Codes and Corresponding Diagnosis ............................................................ 21
Table 2. Demographic Characteristics of Cohort .................................................................... 27
Table 3. Distribution of Neurodevelopmental and Psychiatric Disorders Within the Cohort . 28
Table 4. Distribution of Sex Stratified by Neurodevelopmental and Psychiatric Disorder ..... 29
Table 5. The Mean and Median for ‘Age at First Diagnosis at SLaM’ in each
Neurodevelopmental and Psychiatric Disorder ....................................................................... 29
Table 6. The Prevalence of Comorbid Epilepsy in Each Neuropsychiatric Population .......... 30
Table 7. The Proportion of ID in Each Neuropsychiatric Population...................................... 31
Table 8. Epilepsy Prevalence Rates Following Post-Hoc Analysis of AEDs as a Proxy
Measure of Seizure Status ........................................................................................................ 33
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Abbreviations
Abbreviation Meaning First Use
SLaM South London and Maudsley 6
CRIS Clinical Record Interactive Search 6
PWE People with Epilepsy 10
BD Bipolar Disorder 11
RR Risk Ratio 11
HR Hazard Ratio 11
ASD Autism Spectrum Disorder 12
OR Odds Ratio 12
ID Intellectual Disability 13
IRR Incident Rate Ratio 13
GABA GABAergic or y-aminobutyric acid 14
IP Interictal Psychosis 16
PIP Postictal Psychosis 16
NIHR National Institute for Health Research 18
IoPPN Institute of Psychiatry, Psychology and Neuroscience 18
ePJS Electronic Patient Journey System 18
EHRs Electronic Health Records 18
WHO World Health’s Organisation 18
BRC Biomedical Research Centre 19
NLP Natural Language Processing 19
cDoB Cleaned Date of Birth 21
Ethnicity Cleaned Ethnicity 21
AEDs Anti-Epileptic Drugs 22
NICE National Institute for Health and Clinical Excellence 23
SD Standard Deviation 23
IQR Inter-Quartile Range 23
X2 Chi-Squared 23
CIs Confidence Intervals 24
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1. Introduction
Epilepsy is a chronic and paroxysmal neurological disorder characterised by an ‘enduring
predisposition’ of epileptic seizures (Fisher et al, 2014). These seizures are defined by the
transient occurrence of symptoms that are the result of abnormal, excessive or synchronous
neural activity in the brain (Fisher et al, 2005). Epilepsy is a common condition that affects
people of all ages, gender and socioeconomic status, with prevalence estimates ranging from
0.4 to 0.8% (Fisher, 2014). It is the third most common neurological cause for years lived
with disability and is a substantial public health burden (Vos et al, 2015).
One of the most salient clinical challenges surrounding epilepsy is the issue of comorbidity,
the co-occurrence of two or more conditions in the same individual (Sarfati & Gurney, 2016).
Epilepsy often occurs alongside many neuropsychiatric disorders, with consistent evidence
highlighting a higher prevalence of psychiatric conditions in people with epilepsy (PWE)
compared to the general population (Kanner, 2016). This suggests an overlapping biological
basis and shared mutual susceptibility. In addition, it highlights significant clinical
implications. PWE and neuropsychiatric comorbidities experience greater levels of
psychiatric burden and disability compared to single disorder presentations (Kessler, Lane,
Shahly, & Stang, 2012). Therefore, understanding the relationship and clinical impact of
these comorbidities is essential in order to improve our health services.
1.1 The Prevalence of Psychiatric and Neurodevelopmental Comorbidities in PWE
Neuropsychiatric comorbidity in PWE has been supported by a number of epidemiological
studies, with approximately 30-50% experiencing some form of psychiatric illness (Gaitatzis,
Carroll, Majeed, & Sander, 2004). Population-based studies have corroborated these trends
identifying a lifetime risk of 35%, with patients most commonly diagnosed with mood and
anxiety disorders (Tellez-Zenteno, Patten, Jette, Williams, & Wiebe, 2007).
1.1.1 Depression in PWE
Depression is the most common psychiatric disorder in PWE and is a significant risk factor
for morbidity (Agrawal & Govender, 2011). Comorbid depression is often under-recognised
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and poorly managed which adversely affects treatment outcomes and quality of life (Kobau,
Gilliam, & Thurman, 2006). The prevalence of depression in PWE range from 20-55% in
those with recurrent seizures, whilst smaller rates are found in those with controlled epilepsy
(3-9%) (Kanner, 2003). Although the precise prevalence is difficult to establish due to
methodological heterogeneity, a recent systematic review of population-based studies
estimated a pooled prevalence of 23.1% (Fiest et al, 2013). Subsequently, there is a
consensus that depression is more common in PWE compared to the general population
(Weatherburn, Heath, Mercer, & Guthrie, 2017) (see appendix A table 1 for selected
representative studies). Approximately 6.7% of adults in the general population experience at
least one major depressive episode, which is substantially lower compared PWE (Ahrnsbrak,
Bose, Hedden, Lipari & Park-Lee, 2017).
1.1.2 Bipolar Disorder in PWE
A mood disorder that has received less attention is bipolar disorder (BD). As with depression,
population-based studies on PWE have identified an association between the two conditions
(appendix A table 2 illustrates selected representative studies). For instance, a large survey
conducted in America showed that 12.2% of PWE screened positively for BD symptoms
against the Mood Disorder Questionnaire (Hirschfeld et al, 2000; Ettinger, Reed, Goldberg,
& Hirschfeld, 2005). Of those who screened positively, 49.7% were found to have a formal
diagnosis (Ettinger, Reed, Goldberg, & Hirschfeld, 2005). This rate was six-to-seven times
higher compared to someone without epilepsy, as BD affects approximately 1-2% of the
general population (Akiskal et al, 2000; Judd & Akiskal, 2003). Similar associations have
been confirmed across other population-based studies (e.g. Adelow et al, 2012; Bakken et al,
2014; & Chang, Liao, Hu, Shen & Chen, 2013).
These trends have been reflected in hospital settings. Retrospective cohorts using the Oxford
Record Linkage Study and the English National Linked Hospital Episode Statistics found that
PWE showed an elevated risk for BD, with significantly higher risk ratios (RR) of 3.0 and 3.6
respectively (Wotton & Goldacre, 2014). This corroborated a previous record-linkage study
conducted in Finland which found similar trends, albeit a higher hazard ratio (HR) of 6.3
(Clarke et al, 2012). Although methodologically different, both studies support a link
between epilepsy and BD.
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1.1.3 Schizophrenia in PWE
Previously, the relationship between schizophrenia and epilepsy was confounded by small
sample sizes and a lack of standardised definitions (Fruchter et al, 2014). However, the
association between these two conditions is becoming better understood. Population-based
studies have identified an overrepresentation of schizophrenia in PWE (see appendix A table
3), who are approximately two-times more likely to be diagnosed compared to those without
epilepsy (Qin, Xu, Laursen, Vestergaard, & Mortensen, 2005). More recent population-based
studies have identified an even higher risk. For instance, a Finnish registry-based population
study of 23,404 individuals found that PWE were 8.5-times more likely to have
schizophrenia (Clarke et al, 2012). Due to this greater vulnerability, the prevalence of
schizophrenia in PWE is estimated at 5.6% (Selassie et al, 2014). This is substantially higher
compared to the general population, with schizophrenia prevalent in 0.48% of individuals
(Simeone, Ward, Rotella, Collins, & Windisch, 2015).
This complex relationship may be mediated by the subtype of epilepsy and ongoing seizure
activity. A population-based study investigating the longitudinal association between
epilepsy and schizophrenia found that only individuals with severe, treatment-refractory
epilepsy were at an increased risk of later schizophrenia. However, those with treated
epilepsy did not show an elevated risk (Fruchter et al, 2014).
1.1.4 Neurodevelopmental Disorders in PWE: Autism Spectrum Disorder
Consistent evidence suggests that the prevalence of autism spectrum disorder (ASD) is higher
in PWE compared to the general population (Tuchman & Cuccaro, 2011). This has been
identified across a number of population-based studies (see appendix A table 4). For instance,
Selassie and colleagues (2014) found an increased odds ratio (OR) of 22.2 in 64,188 PWE.
Large prospective cohorts reported elevated proportions, identifying that approximately 5%
of children with epilepsy and 8.1% of non-institutionalised adults with epilepsy met the
criteria for ASD (Berg, Plioplys, & Tuchman, 2011; Rai et al, 2012). The strongest level of
evidence, a recent systematic review examining the prevalence of ASD in PWE, identified a
pooled prevalence of 6.3% (Strasser, Downes, Kung, Cross & De Haan, 2018). This figure is
considerably higher than the reported prevalence of ASD in the general population, estimated
at 0.75% (Baxter et al, 2015).
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The association between these conditions is moderated by socio-demographic and clinical
characteristics of service-users. In the same review, ASD was more prevalent in individuals
who were younger, had intellectual disability (ID) and/or specific epilepsy syndromes such as
infantile spasms and complex partial seizures (Strasser et al, 2018). Thus, it appears that
established determinants for ASD in the general population, such as ID, translate to important
risk factors in PWE (Berg, Plioplys, & Tuchman, 2011).
1.2 A Bi-directional Relationship: Comorbid Epilepsy in People with a Neuropsychiatric
Diagnosis
The evidence reviewed until now reflects the dominant neurology perspective in the field,
examining the prevalence of neuropsychiatric comorbidities in PWE from neurology units.
Critically, this would be more useful for a neurologist rather than a psychiatrist and
represents a bias within the literature, as complex partial seizures that are refractory to
medication are over-represented in these settings (Hesdorffer et al, 2012). However, there is a
paucity of research investigating the impact of comorbid epilepsy from a psychiatric
perspective, even though there is a growing body of evidence supporting bi-directional
relationships between epilepsy and the common disorders reviewed.
1.2.1 Comorbid epilepsy in Psychiatric Patients with Depression
Psychiatric disorders can both precede or follow the onset of epilepsy. For instance, evidence
supports a bi-directional relationship between depression and epilepsy (see appendix B table
5). Using a primary care cohort, Josephson and colleagues (2017) reported that incident
depression was associated with an increased hazard of developing later epilepsy (HR, 2.55).
Likewise, in a matched longitudinal cohort study using the UK General Practice Research
Database, an increased onset of depression was found both before and after an epilepsy
diagnosis, with an increased incident rate ratio (IRR) of 2.04 and 2.55 after 3-years
respectively. (Hesdorffer et al, 2012).
It has been hypothesised that two potential pathways explain this bi-directionality. Firstly, the
relationship may be explained through chronic stress exposure, as stressful life events
increase the likelihood of developing depression (Hoppe & Elger, 2011). Alternatively,
depression may facilitate the onset of epilepsy through proposed mechanisms of action
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including: hyperactivity of the hypothalamic-pituitary-adrenal axis, and disturbances of
glutamate neurotransmitters (Kanner et al, 2011).
1.2.2 Comorbid epilepsy in Neuropsychiatric Patients with ASD
Likewise, for ASD, evidence supports bi-directionality (see appendix B table 6). The most
recent prevalence estimate, from a cross-sectional study utilising the American National
Survey of Children’s Health, identified a seven-fold increased risk of epilepsy in those with
ASD compared to the general population (8.6% vs 1.2%) (Thomas, Hovinga, Rai, & Lee,
2017). However, epilepsy estimates have been highly variable, ranging from 6-27%
depending on the study (Jeste & Tuchman, 2015). Much of this variance can be attributed to
methodological heterogeneity. Furthermore, most studies were limited by small sample sizes
that decreased the authors’ generalisability and power to make rigorous conclusions. Despite
this, the co-occurrence of epilepsy in ASD is well-established and mediated by the presence
of ID (Viscidi et al, 2013). A meta-analysis comparing the prevalence of epilepsy between
groups stratified by IQ (,40, 40-50, 50-70, >70), found that epilepsy rates increased as IQ
declined (Amiet et al, 2013). As such, ID is argued to be a key factor to explain the bi-
directional association between epilepsy and ASD.
As with depression, two primary hypotheses attempt to explain the convergent
pathophysiological pathways of epilepsy and ASD. Firstly, their shared mutual aetiology may
be the result of imbalance and dysregulation of excitation/inhibitory GABAergic or y-
aminobutyric acid (GABA) receptor functions, which have been found to underlie both
conditions (Jeste et al, 2014). Alternatively, primary epilepsy may impact synaptic plasticity,
the ability of synapses to strengthen or weaken over time, which subsequently predisposes
cognitive delays and behavioural impairments (Brooks-Kayal et al, 2013). However, it is
likely that both mechanisms play a role (Jeste & Tuchman, 2015).
Although the bi-directional association between epilepsy and ASD is well-supported, more
hospital-based studies are needed in order to corroborate and update prevalence rates. This
would improve our understanding of the clinical impact of comorbid epilepsy in these
settings. Likewise, more hospital-based studies are needed to investigate epilepsy in patients
with depression.
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1.2.3 Comorbid epilepsy in Psychiatric Patients with BD
On the other hand, few studies have investigated epilepsy in BD populations, with past
findings limited through examining depression, rather than mania or BD specific-depression
(Mazza, et al, 2007). Although sparse, current estimates suggest a higher prevalence of
epilepsy in those with BD. For instance, a recent case-control study identified that the odds of
developing epilepsy were 2.53-times higher in BD cases compared to controls. Comorbid
epilepsy was over-represented, affecting 3.33% of individuals with BD compared to 1.29% of
controls (Sucksdorff et al, 2015) (see appendix B table 7).
There are a number of common suggested links between epilepsy and BD. For instance, at
the neurotransmission level, animal models support the role of dopamine and serotonin in the
pathogenesis of BD (Loscher & Honack, 1996) and in the modulation of epileptic activity
(Clinckers, Smolders, Meurs, Ebinger & Michotte, 2004). However, given the sparsity of the
evidence base, these explanations are so far speculative.
1.2.4 Comorbid epilepsy in Psychiatric Patients with Schizophrenia
As in BD, there has been little investigation into comorbid epilepsy in service-users with
schizophrenia. Despite this, past findings support a strong bi-directional relationship (see
appendix B table 8). In a retrospective cohort study, the incidence of epilepsy was found to
be higher in patients with schizophrenia compared to the non-schizophrenia comparative
cohort (6.99 vs 1.19 per 1,000 person-years), with an adjusted HR of 5.88 (Chang et al,
2011). This risk was lower compared to previous findings (OR= 11.1%) (Makikyro et al,
1998), which likely reflects the differing measures of association used. Regardless, both
studies corroborate the strong relationship between epilepsy and schizophrenia.
Neuroimaging and genetic investigations indicate that abnormalities in neurodevelopment
may, at least partially, explain the co-occurrence of these two conditions. Past genetic studies
have identified that a rare genetic mutation, 15q13. 3 microdeletions, can lead to either
schizophrenia or epilepsy (Helbig et al, 2009; Masurel-Paulet et al, 2010 & Vassos et al,
2010). In addition, neuronal migration deficits, that occur in the developing foetal brain, and
dysregulation of dopamine and glutamate neurotransmitter systems have also been associated
with the neuropathology of both conditions (Cascella, Schretlen & Sawa, 2009; Kalkman,
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2011). Further evidence for shared susceptibility is shown through the classification of
interictal (IP) and postictal psychoses (PIP). IP is characterised as a psychotic disorder in
PWE. In PIP, psychotic symptoms occur after seizures and is followed by a lucid period of
delusions and hallucinations, positive symptoms associated with schizophrenia (Devinksy,
2008).
1.3 Rationale, Aims & Hypotheses
There is strong evidence that a variety of neuropsychiatric comorbidities are more prevalent
in PWE. However, the majority of the literature reflects the dominant neurological
perspective in the field, despite evidence supporting bi-directionality between epilepsy and a
number of psychiatric and neurodevelopmental conditions. Although the bi-directional
relationship between epilepsy-depression and -autism has been empirically supported, more
hospital-based studies are needed in order to assess the possible clinical impact of these
comorbidities in real-world psychiatric settings. Furthermore, there has been a lack of
research investigating the clinical impact of epilepsy in BD and schizophrenia populations in
particular.
As such, the present study aimed to investigate whether epilepsy is more common in service-
users with ASD, BD, schizophrenia, or depression and compare these against population rates
from the UK general population. To the best of my knowledge, the current study is one of
few investigations to compare and examine the prevalence of epilepsy across a range of
neuropsychiatric disorders, as most research adopting a psychiatric perspective tend to focus
on one specific condition. These findings aim to build on the growing evidence supporting bi-
directionality in depression and autism; whilst developing the sparse evidence base
surrounding epilepsy in individuals with BD and schizophrenia. This will be achieved
through utilising the Clinical Record Interactive Search (CRIS) system, which contains
patient-record information from South London and Maudsley (SLaM) hospital trusts. The
present study had two objectives: i) to determine the prevalence of comorbid epilepsy in
service-users from SLaM outpatient units with autism, bipolar, schizophrenia and depression
and ii) to determine whether the prevalence of epilepsy is higher in service-users with
neuropsychiatric conditions compared to the general population. As such, it was hypothesised
that there will be i) a difference in the prevalence of epilepsy between service-users with
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autism, bipolar, schizophrenia and depression (two-tailed) and that ii) the prevalence of
epilepsy will be higher in service-users with neuropsychiatric conditions compared to the
general population (one-tailed). These findings will have possible clinical implications in
terms of identification and management of comorbid epilepsy in psychiatric settings.
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2. Methods
2.1 Study Setting: The Clinical Record Interactive Search
The cohort of service-users assessed in the current study were identified from the Clinical
Record Interactive Search (CRIS). CRIS is a case-register system that generates anonymised
information from SLaM services via electronic clinical records (Stewart et al, 2009). SLaM is
one of the largest healthcare services across western Europe, delivering secondary and
tertiary mental health care to a population of approximately 1.3 million residents across four
London boroughs (Croydon, Lambeth, Lewisham and Southwark) (Chang, Chen, Broadbent,
Stewart & O’Hara, 2017). The trust consists of more than 230 services which provides
inpatient care for an estimated 5,300 service-users and treats an additional 45,000 service-
users in the community (‘About Us’, 2018). As such, all disorders covered within this study
are assessed and managed by a number of SLaM teams who provide inpatient and/or
outpatient psychiatric care (‘Service Finder’, 2018). SLaM has aligned strong collaborative
networks with the Institute of Psychiatry, Psychology and Neuroscience (IoPPN) and with the
National Institute for Health Research (NIHR) with the aim to provide high-quality research
that will, in the long-term, improve the quality of care for service-users.
All SLaM clinical records have been fully electronic since April 2006 through the Patient
Journey System (ePJS), which incorporates legacy data from earlier service-specific
electronic health records (EHRs). The CRIS application was developed in 2007-2008 and
conforms to the World Health Organisation’s (WHO) formal description of a psychiatric case
register, i.e. a ‘patient-centred longitudinal record of contacts’ within a defined population
and set of services (Stewart et al, 2009; WHO, 1983). However, its dynamic ability to update
source-files every 24-hours and the inclusion of both structured and unstructured (open-text)
anonymised data distinguishes CRIS from other case registries (Perera et al, 2016).
Anonymisation is ensured through data-processing pipelines that structure and de-identifies
ePJS fields. This results in the de-identification of open-text and the generation of a
pseudonymised identifier (Fernandes et al, 2013). To-date, approximately 250,000
anonymised patient records can be accessed with all clinical and socio-demographic
information. Patient records can be retrieved using search terms for structured fields (e.g.
diagnosis) or free text (e.g. clinical event notes).
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All SLaM care is represented on CRIS using an opt-out model which is advertised on public
materials and initiatives. However, few service-users opt-out with only three to-date (Perera
et al, 2016). CRIS received approval as an anonymised data resource for secondary analysis
by the Oxfordshire Research Ethics Committee C (08/ H0606/71+5) (Stewart et al, 2009).
2.2 Procedure
For CRIS access, a project application was sent to the Biomedical Research Centre (BRC) to
be reviewed by an oversight committee. A substantive contract and research passport were
also required to ensure that all researchers were bound to NHS code of confidentiality. Once
the project was approved, CRIS access was solely permitted within the SLaM security
firewall and was audited weekly to ensure that searches were being conducted as agreed in
the application. Further details of the CRIS security model and approval procedure are
described elsewhere (Fernandes et al, 2013).
The current study was approved under the BRC CRIS oversight committee (ref: CRIS 18-
026). Following approval, an extraction plan was generated to define the cohort. This
included an inclusion and exclusion criteria, variables of interest and whether natural
language processing (NPL) was required.
2.3 Study Population: Inclusion and Exclusion Criteria
The study sample consisted of a hospital population from SLaM. All male and female
service-users with primary or secondary ICD-10 diagnosis for BD, schizophrenia, depression
and/or ASD were included for analysis (see table 1). All service-users were required to have
an active referral in SLaM between the ages of 18-65 years. Service-users younger than 18
were excluded as the mean age of onset for BD, schizophrenia and depression occur in late
adolescence and early adulthood (Kessler et al, 2005; Rajji, Ismail & Mulsant, 2009). In
addition, these individuals would likely fall under the care of different paediatric services
which provided further rationale for exclusion. Older adults, i.e. service-users older than 65,
were excluded to minimise the impact of unmeasured confounders, as seizures become more
prevalent in this age group due to age-related conditions such as strokes (Leppik et al, 2006).
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Further confounders and exclusions included patients with a diagnosis of dementia or any
other organic causes (ICD-10 code: F0) and head injury (ICD-10 code: S0), as evidence
suggests that both diagnoses increase the risk of epilepsy (Cloyd et al, 2006; Lowenstein,
2009). Although no start date was applied, the window end date for all searches was 1st
January 2017. The extraction and identification processes are visually depicted in the flow
chart below (see figure 1).
Table 1. ICD-10 Codes and Corresponding Diagnosis
ICD-10 Code Diagnosis
F84 ASD
F33 Recurrent Depression
F32 Depressive Episode
F31 BD
F20 Schizophrenia
F0 Dementia or other Organic disorders*
S0 Head Injury*
G40 Epilepsy
* denotes exclusion criteria
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Figure 1. Flow Chart of the Extraction and Identification Process
2.4 Study Design and Measures
Although CRIS is prospective, the current study adopted a cross-sectional design as data was
obtained at a single time point. Subsequently, a cohort of service-users who met the above
criteria were identified and extracted, alongside other relevant socio-demographic
information.
2.4.1 Socio-demographic Variables
Socio-demographic information was extracted from ePJS forms, with variables pertaining to
cleaned date of birth (cDoB), gender and cleaned ethnicity (ethnicity) collected for
descriptive analysis. Age was calculated by subtracting the service-user’s date of first
CRIS Project Approval
Develop Extraction Plan
Variables Extracted:
Cleaned DoB
Gender
Cleaned ethnicity
Date of psychiatric diagnosis (F84, F33,
F32, F31 and F20)
Date of epilepsy Diagnosis (G40)
ID diagnosis
Anti-epilepsy specific medication
Identify service-users who may have unreported
epilepsy diagnosis
NPL identification of anti-epilepsy specific medication
prescription: Lamotragine, Valproate / Valproic,
Carbamazepine, Levetiracetam, Clobazam, Phenytoin,
Topiramate or Zonisamide
Include
Service-users with an active SLaM referral between
the ages of 18-65 years
Service-users with an F84, F33, F32, F31 or F20
diagnosis
Exclude
Service-users younger than 18 years
Service-users older than 65 years
Service-users with dementia or other organic
diagnoses (F0)
Service-users with head injury (S0)
1773321 23
psychiatric diagnosis from their cDOB and dividing this figure by 365. This created an
additional variable ‘age at first diagnosis at SLaM’.
2.4.2 Exposure Measure: The identification of Psychiatric Disorders
To ensure that all service-users had a formal psychiatric diagnosis, ICD-10 codes were
extracted from structured text-fields. Through using ICD-10 codes (see table 1 above), it can
be assured that diagnoses were made from qualified practitioners who would have used
formal assessments to make an informed diagnosis. This measure has been used reliably in a
number of previous CRIS studies (e.g. Roberts et al, 2016). However, due to a lack of
standardisation and time constraints, free-text data was excluded as it would not be possible
to review accurately. All psychiatric measures were binary/categorical and displayed as the
individual’s date of diagnosis at SLaM. Depression was characterised as either a depressive
episode or recurrent depression, two diagnostic subtypes within the ICD-10 (WHO, 1993).
2.4.3 Outcome Measure: Identification of Diagnosed Epilepsy
Diagnosed epilepsy was identified through using the structured ICD-10 code G40. As with
psychiatric exposures, no free-text data was included in the extraction due to time constraints.
The epilepsy measure was binary/categorical, with G40 identification depicted by the date of
diagnosis at SLaM.
2.4.4 Anti-Epileptic Drugs as an Indicator of Unreported Epilepsy
The prescription of anti-epileptic drugs (AEDs) was a proxy for non-reported epilepsy
diagnoses, as psychiatric services may be more exclusive for mental health and psychiatric F-
codes. Subsequently, epilepsy/G40 codes may be irregularly reported in these settings. AEDs
was extracted using the NLP application, which identifies unstructured text and converts this
information into structured outputs. Positively, this approach was found to be an accurate
method for extracting medication use from clinical narratives, as a recent evaluation of
pharmacotherapy application using NLP yielded a 90% precision and recall rate (Perera et al,
2016). In the current study, the identification of AEDs involved mapping patient records from
the Maudsley Hospital pharmaceutical database with ePJS structured medication fields and
unstructured events/ correspondence, such as letters and discharge summaries. Through using
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a combination of NLP and structured fields, high accuracy and precision can be assured. The
AEDs used in the study included: Lamotrigine, Valproate /Valproic acid, Carbamazepine,
Levetiracetam, Clobazam, Phenytoin, Topiramate, Zonisamide. All these AEDs are
recommended by the National Institute for Health and Clinical Excellence (NICE, 2018) and
are unlikely to be used as treatments for other disorders, with the exception of Lamotrigine
and Valproate which are used in BD (NICE, 2018). Although this is a coarse approach that
should be interpreted with caution, using this proxy should be indicative of an epilepsy
diagnosis.
2.4.5 Intellectual Disability as a Risk Factor for Epilepsy
The diagnostic status for ID was also extracted. Evidence supports that the risk of epilepsy in
ASD is a function of ID severity (Amiet et al, 2008). Although the odds of ID are higher in
patients with schizophrenia, BD and depression (Morgan et al, 2012), it is unknown whether
ID is associated with epilepsy in these psychiatric populations. A diagnosis of ID was
identified through two pathways: extraction of the F7 ICD-10 code and/or any use of SLaM
ID services found in structured ePJS forms. There are 18 SLaM ID mental health services in
which referral was assumed to reflect the presence of an ID.
2.5 Statistical Analysis
All statistical analyses were conducted using STATA (version 15) statistical software.
Descriptive statistics were generated to describe the distribution of gender, ethnicity and ‘age
at first psychiatric diagnosis at SLaM’ of the total cohort. Further descriptive analyses for
gender and age were produced through stratifying the cohort by psychiatric diagnosis.
Stratified tables for ‘age at first psychiatric diagnosis at SLaM’ displayed the mean, standard
deviation (SD), median and inter-quartile range (IQR). All other tables reported the
frequency (N) and proportion (%) of respective characteristics.
The prevalence of diagnosed epilepsy from ICD-10 codes were presented as a percentage of
the total sample size, calculated and displayed in tables for both the full cohort and each
psychiatric and neurodevelopmental disorder. To assess whether the prevalence of epilepsy in
each neuropsychiatric diagnosis significantly differed from one another, a chi-squared test
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(X2) was conducted in a four (depression vs ASD vs BD vs schizophrenia) by two (epilepsy
vs no epilepsy) level design. As patients can have multiple co-occurring disorders, to
establish robustness, the same test was repeated after excluding those with multiple
psychiatric diagnoses.
Following this, the prevalence of ID in the full cohort and in each neuropsychiatric condition
were calculated and displayed in tables. Similarly, to identify whether ID was associated with
an epilepsy diagnosis, four individual X2 tests were conducted for each neuropsychiatric
disorder with two levels (ID vs non-ID and epilepsy vs no epilepsy). ORs were generated
from the STATA output through dividing the odds epilepsy and ID by the odds of epilepsy
without ID.
For post-hoc exploratory analysis, the individuals prescribed AEDs with no G40 diagnosis
were added to the sample of individuals with confirmed epilepsy. X2 tests were repeated to
assess the possibility of not recording a diagnosis. All diagnostic proportions were presented
with 95% confidence intervals (CIs) and reported to two decimal places.
In the context of this study an alternative approach, such as a logistical regression, would not
have been appropriate. Modelling the determinants of epilepsy and predicting its likelihood
was not a key aim of the hypotheses. The bi-directionality of the exposure and outcome limits
the usefulness of the logistic regression, as there was a lack of temporality between variables,
i.e. it was unknown which disorder was diagnosed first. In addition, a large number of
epilepsy diagnoses were not expected. This would result in low statistical power and
therefore unreliable test statistics (ORs), as the group size would further decrease as
additional variables were inputted into the model. As the aim was to describe the strength of
the relationship between the neuropsychiatric disorders and epilepsy, a X2 test was deemed
sufficient. All STATA outputs are shown in appendix C.
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3. Results
The criterion for statistical significance was set at alpha level of p<0.05, with all values two-
tailed. However, in order to counteract the problem of multiple comparisons, Bonferroni
correction was applied to the critical p-value for a more conservative analysis. The correction
compensates for the increased risk of type-1 error by dividing the critical value (.05) by the
number of tests conducted (8). Thus, corrected significance was set at p<0.00625.
Assumptions for X2 tests were mostly satisfied and should be assumed unless explicitly
stated, as the majority of tests contained a number of observations greater than five in each
cell.
3.1 Sample Characteristics of the Full Cohort
Data from 35,092 service-users (15,582 males, 19,502 females, 2 other and 6 not specified)
with confirmed ICD-10 psychiatric and neurodevelopmental diagnoses were extracted
alongside their socio-demographic information. Ethnicity and age at first SLaM diagnosis
were coded into categorical groups for ease of analysis in STATA. In the full cohort, the
mean age at first SLaM diagnosis was 37.10 years (SD = 12.29, min 8, max 73) and consisted
of mainly white British service-users (44.07%), followed by other ethnic groups (9.91%),
other white groups (8.69%), African (7.94%) and other black backgrounds (7.80%).
Information pertaining to ethnicity was missing for 7.15% of the cohort, with the remaining
14.43% made up of service-users from Irish, Caribbean, Asian and mixed backgrounds. A
full description of the cohort is summarised in table 2.
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Table 2. Demographic Characteristics of Hospital Cohort
Characteristic Frequency (N) Percentage (%)
Age at first Slam Diagnosis (years)a
10 years or less 2,773 7.90%
21-30 9,257 26.38%
31-40 9,426 26.86%
41-50 7,965 22.70%
51-60 4,438 12.65%
61-70 1,203 3.43%
71-80 9 0.03%
Not Specified 21 0.06%
Ethnicity
White British 15,466 44.07%
Irish 696 1.98%
Other White Group 3,051 8.69%
Mixed Backgroundb
839 2.39%
Asianc
1,917 5.46%
Caribbean 1,613 4.60%
African 2,786 7.94%
Other Black Background 2,736 7.80%
Other Ethnic Group 3,478 9.91%
Not Specified 2,510 7.15%
Gender
Male 15,582 44.40%
Female 19,502 55.57%
Other 2 0.01%
Not specified 6 0.02%
a Mean = 37.10 years, SD = 12.29, min 8, max 73
b Mixed background includes: White and Asian, White and Black African, White and
Black Caribbean and any other mixed background
c Asian includes: Indian, Pakistani, Bangladeshi and Chinese
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3.2 Sample Characteristics Stratified by Psychiatric Diagnoses
Based on ICD-10 diagnostic codes, the most common psychiatric diagnosis was a depressive
episode (F32), which affected 56.02% of the cohort (95% CI =55.50%, 56.54%). Individuals
with F32 diagnoses were predominantly female (12,050 females vs 7,602 males), with a
mean age of 36.75 years at first SLaM diagnosis (SD=12.44, median= 36, IQR= 19). The
second most prevalent diagnosis was recurrent depression (F33) which affected 19.68% of
service-users (95% CI=19.27%, 20.10%). Individuals with F33 diagnoses were mostly
female (4,474 females vs 2,431 males), with a mean age of 39.80 years at first SLaM
diagnosis (SD=11.61, median= 40, IQR=18). This was followed by schizophrenia (F20) with
17.26% diagnosed (95% CI= 16.87%, 17.66%). Service-users with F20 were mostly male
(3,892 males vs 2,166 females), with a mean age of 37.63 years (SD=11.47, median=37,
IQR=18). BD (F31) was the fourth most common diagnosis, affecting 11.96% (95% CI=
11.62%, 12.30%). Service-users with F31 diagnoses were predominately female (2,560
females vs 1,637 males), with a mean age of 37.38 years at first SlaM diagnosis (SD=11.97,
median= 36, IQR=18). The least common diagnosis was ASD (F84), with 5.63% affected
individuals (95% CI= 5.40%, 5.88%). The F84 subgroup consisted mostly of males (1,510
males vs 466 females), with a mean age of 28.96 years (SD=12.11, median=25, IRQ= 17).
For a complete summary of psychiatric distributions see tables 3-5.
Table 3. Distribution of Neurodevelopmental and Psychiatric Disorders Within the Cohort
Diagnosis Frequency (N) Percentage (%) 95% CI’s
ASD 1,977 5.63% 5.40-5.88%
BD 4,197 11.96% 11.62-12.30%
Recurrent
Depression
6,907 19.68% 19.27-20.10%
Depressive Episode 19,658 56.02% 55.50-56.54%
Schizophrenia 6,058 17.26% 16.87-17.66%.
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Table 4. Distribution of Sex Stratified by Neurodevelopmental and Psychiatric Disorder
Diagnosis Females (%) Males (%) Other (%) Not
Specified
(%)
ASD 446 (23.57%) 1,510 (76.38%) 1 (0.05%) -
BD 2,560 (61.00%) 1,637 (39.00%) - -
Recurrent
Depression
4,474 (64.77%) 2,431 (35.20%) - 2 (0.03%)
Depressive Episode 12,050
(61.30%)
7,602 (38.67%) 1 (0.01%) 5 (0.03%)
Schizophrenia 2,166 (35.75%) 3,892 (64.25%) - -
Table 5. The Mean and Median for ‘Age at First Diagnosis at SLaM’ in each
Neurodevelopmental and Psychiatric Disorder
Diagnosis Mean (years) SD (years) Median (years) IQR (years)
ASD 28.96 12.11 25 17
BD 37.38 11.97 36 18
Recurrent
Depression
39.80 11.61 40 18
Depressive
Episode
36.75 12.44 36 19
Schizophrenia 37.63 11.47 37 18
3.3 The Prevalence of Comorbid Epilepsy
In the full hospital cohort, 0.54% (95% CI=0.47, 0.62) of service-users were diagnosed with
epilepsy. Epilepsy was most prevalent in individuals with ASD, affecting 3.34% of the
subpopulation (95% CI= 2.63, 4.23). This was followed by BD (95% CI = 0.29, 0.71) and
recurrent depression (95% CI = 0.32, 0.64), with comorbid epilepsy found in 0.45% of
respective service-users. Epilepsy was fourth most common in individuals with a depressive
episode, with 0.44% affected (95% CI= 0.35, 0.54). Epilepsy was least common in
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schizophrenia, with 0.36% found to have a comorbid G40 diagnosis (95% CI= 0.21, 0.51). A
breakdown of epilepsy distributions can be found in table 6.
Table 6. The Prevalence of Comorbid Epilepsy in Each Neuropsychiatric Population
Diagnosis Number of Epilepsy
diagnoses (N)
Percentage of
Epilepsy (%)
95% CIs
ASD 66 3.34% 2.63- 4.23%
BD 19 0.45% 0.29- 0.71%
Recurrent
Depression
31 0.45% 0.32- 0.64%
Depressive Episode 86 0.44% 0.35- 0.54%
Schizophrenia 22 0.36% 0.21-0.51%
Total Cohort 190 0.54% 0.47- 0.62%
In order to compare the prevalence of comorbid epilepsy between different psychiatric and
neurodevelopmental populations, a X2 test was conducted. Statistical analysis revealed a
significant association between the different psychiatric diagnoses and the frequency of
epilepsy X2 (4) =277.22, p<.001. As such, service-users with ASD were more likely to be
diagnosed with epilepsy (3.34%) compared to other psychiatric conditions (0.36-0.45%).
To ensure that the results were robust to multiple-comorbidity, a second X2 test was
calculated with individuals presenting with multiple psychiatric diagnoses excluded (N= 66).
Statistical analysis revealed no changes, as the significant association remained X2 (4) =
202.18, p<.001.
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3.4 ID as a Risk Factor for Epilepsy
ID was prevalent is 1.30% (95% CI= 1.19, 1.43) of the total cohort. When stratified by ICD-
10 diagnoses, ID was most common in individuals with ASD with 13.71% affected (95% CI=
12.26, 15.30). This was followed by: schizophrenia with 1.07% (95% CI= 0.84, 1.37), BD
with 0.93% (95% CI= 0.68, 1.27) and recurrent depression with 0.90% (95% CI= 0.70, 1.15).
ID was found to be least common in individuals with a depressive episode, with 0.55%
affected (95% CI= 0.46, 0.67). For a full summary and distribution of ID see table 7.
Table 7. The Proportion of ID in Each Neuropsychiatric Population
Diagnosis ID Frequency (N) Percentage (%) 95% CIs
ASD 271 13.71% 12.26- 15.30%
BD 39 0.93% 0.68- 1.27%
Recurrent
Depression
62 0.90% 0.70- 1.15%
Depressive Episode 109 0.55% 0.46- 0.67%
Schizophrenia 65 1.07% 0.84- 1.37%
Total Cohort 457 1.30% 1.19- 1.43%
To establish whether comorbid ID was a risk factor for epilepsy in these psychiatric and
neurodevelopmental populations, four individual X2 tests were carried out. Using the output
generated from X2, additional ORs were calculated. However, ORs should be interpreted with
caution given the small sample sizes. With regards to ASD, a significant association was
found between the presence of ID and the frequency of epilepsy X2 (1) = 233.23, p<.001.
This seems to represent the fact, based on ORs, that the odds of epilepsy were 26.13 times
higher if individuals with ASD had ID.
Similar significant associations were found for all psychiatric disorders. A significant
association was found between ID and epilepsy in individuals with a depressive episode X2
(1) = 446.74, p<.001, with the odds of epilepsy 43.78 times higher in those with comorbid
ID. Likewise, for service-users with recurrent depression, a significant association was found
between the frequency of ID and epilepsy X2 (1) = 164.58, p<.001, with the odds of epilepsy
36.17 times higher in individuals with an ID. A further significant association was found
between epilepsy and ID in individuals with schizophrenia X2 (1) = 97.54, p<.001. This result
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appears to represent the fact that the odds of epilepsy were 29.29 times higher if service-users
had comorbid ID.
With regards to BD, the assumptions for X2 were not satisfied as less than 80% of the cells
had a frequency of five observations. As such, a Fisher’s exact test was conducted which
found a significant association between ID and epilepsy (p<.001), with the odds of epilepsy
21.57 times higher if service-users presented with ID.
3.5 Post-Hoc Analysis
Post-hoc analyses were conducted to assess the possibility of non-recorded epilepsy within
the hospital cohort. 1861 service-users with a BD (F31) diagnosis, who were prescribed
either Lamotrigine and/or Valproate/Valproic acid, were excluded from post-hoc analysis as
these AEDs are used in the treatment of BD. However, 394 patients were included and
indicatively assigned a pseudo-positive G40 status. When implementing these changes, the
prevalence of individuals with diagnosed epilepsy, or a pseudo-positive diagnosis, increased
to 1.66% of the total cohort (95% CI = 1.54, 1.80). Proportions increased for all psychiatric
and neurodevelopmental conditions. The largest increase was found in individuals with BD,
with 4.60% indicated as having possible epilepsy (95% CI= 4.00, 5.28). However, this was
expected and may have nothing to do with epilepsy. The second largest change was in
schizophrenia, with the proportion increasing to 2.16% (95% CI= 1.82, 2.56). Individuals
with recurrent depression showed the third largest increase, with 1.39% of service-users now
suggestive of epilepsy (95% CI= 1.13, 1.69). The fourth biggest increase was in ASD with
4.15% implied as having possible epilepsy (95% CI= 3.35, 5.12). Excluding BD, epilepsy
remained most prevalent in the ASD group. The smallest increment was in those with a
depressive episode, with suggested epilepsy increasing to 0.96% (95% CI= 1.13, 1.69). For a
breakdown of these changes see table 8.
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Table 8. Epilepsy Prevalence Rates Following Post-Hoc Analysis of AEDs as a Proxy
Measure of Seizure Status
Diagnosis Frequency of
Epilepsy (%)
95% CIs Change in
Prevalencea
ASD 82 (4.15%) 3.35- 5.12% 0.84%
BD 193 (4.60%) 4.00- 5.28% 4.15%
Recurrent
Depression
96 (1.39%) 1.13- 1.69% 0.94%
Depressive Episode 188 (0.96%) 0.83- 1.10% 0.52%
Schizophrenia 131 (2.16%) 1.82- 2.56% 1.8%
Total Cohort 584 (1.66%) 1.54- 1.80% 1.12%
a Arrows denote direction of change
An identical 4x2 X2 test was conducted to assess whether any significant association
remained between the frequency of epilepsy and the neurodevelopmental and psychiatric
disorders. Statistical analysis revealed that the association remained highly significant X2 (4)
= 341.74, p<.001. This finding seems to represent the large disparity between the prevalence
rates of epilepsy in ASD compared to those with a depressive episode. A second X2 test
excluding those with multiple psychiatric diagnoses also showed a significant association X2
(4) = 296.13, p<.001, highlighting independence.
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4. Discussion
The present study aimed to investigate whether epilepsy is more common in service-users
with ASD, BD, schizophrenia or depression and compare these proportions to the UK general
population. Past research investigating the comorbidity of epilepsy and mental health
problems has tended to focus on the prevalence of psychiatric disorders in PWE in neurology
units. Critically, these patients are typically over-represented with complex partial seizures
that are refractory to medication (Hesdorffer et al, 2012), representing a bias within the
literature. However, there has been a paucity of hospital-based investigations surrounding the
prevalence of epilepsy in service-users with neuropsychiatric impairments. Through utilising
CRIS, clinical information was extracted and analysed to establish the prevalence of epilepsy
within SLaM psychiatric services.
Data pertaining to 35,092 service-users with ASD, BD, schizophrenia, recurrent depression
or a depressive episode were extracted. As a main result, epilepsy was prevalent in 0.54% of
the hospital cohort. Epilepsy varied between each psychiatric and neurodevelopmental
disorder, with proportions ranging from 0.44% to 3.34%, and was significantly associated
with ID. However, for all disorders, especially in each psychiatric diagnosis, these estimates
appear to be likely under-estimations.
4.1 The Prevalence of Epilepsy and Comparisons to the General Population
Surprisingly, the prevalence of epilepsy in the total cohort (0.54%) and in patients with BD
(0.45%), schizophrenia (0.36%), recurrent depression (0.45%) and a depressive episode
(0.44%) were lower than anticipated. All epilepsy comorbidity proportions were considerably
smaller compared to past findings and were similar to the general London population,
indicating undermeasurement. However, expected trends were found in patients with ASD.
For service-users with BD, comorbid epilepsy was found in 0.45% which was considerably
smaller compared to past findings. For instance, a recent cross-sectional study found epilepsy
in 8% of participants recruited from the BD research network (Knott et al, 2016). However,
this figure likely over-estimated the true prevalence, as self-report questionnaires were used
to assess the participants’ lifetime history of epilepsy. Only 1.8% of the 1596 individuals had
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‘well-defined, expert-confirmed epilepsy’. However, a prevalence of 1.8% was four-times
higher than the current finding. This disparity was even greater when compared to a recent
population-based study using Finnish National Registers, which identified epilepsy in 3.3%
of individuals with BD (Sucksdorff et al, 2015). This was approximately seven-times higher
than the present estimate.
Regarding depression, the prevalence of epilepsy was similar between episodic and recurrent
subtypes (0.44-0.45%). However, these figures are difficult to compare against, as literature
investigating epilepsy in depression report HRs and IRRs rather than proportions. Despite
this ambiguity, the proportions identified were much smaller than expected, especially when
considering depression is the most common comorbidity seen in PWE (Agrawal & Govender,
2011). A slightly smaller prevalence was found for individuals with schizophrenia, with
epilepsy presenting in 0.36% of service-users, the smallest proportion established. However,
heterogeneity in measures of association also limit any meaningful comparisons (Chang et al,
2011; Makikyro et al, 1998).
All psychiatric proportions and CIs were likely under-estimations as they were similar to the
general population of London. Epilepsy statistics produced by the House of Commons
identified that London has the lowest rates of epilepsy across the UK, especially when
compared to the North of England and South Wales. In London, around 4.4 per 1,000
individuals are diagnosed with epilepsy, which equates to 0.44% of the London population
(House of Commons, 2010). Consequently, the hypothesis that ‘the prevalence of epilepsy
will be higher in service-users with neuropsychiatric conditions compared to the general
population’, was mostly rejected, as 0.44% falls within the CIs for each of the psychiatric
disorders. This overlap indicates non-significant differences between epilepsy in these
conditions and the general London population.
In service-users with ASD, 3.34% had confirmed epilepsy which was higher than the
prevalence found in the general London population, supporting past evidence (e.g. Thomas,
Hovinga, Rai, & Lee, 2017). In this respect, the hypothesis that ‘the prevalence of epilepsy
will be higher in service-users with neuropsychiatric conditions compared to the general
population’, was partially supported. However, the recorded prevalence of 3.34% was lower
than anticipated, especially when compared to past findings. For instance, a population-based
study in Finland found epilepsy in 6.6% of the ASD population (Jokiranta et al, 2014). This
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was similar to other estimates identified in Denmark (6.3%) (Schendel et al, 2016) and
America (8.6%) (Thomas, Hovinga, Rai & Lee, 2017). Other studies using ASD clinic
samples report even higher prevalence estimates which range from 28.2% to 37% (Valvo et
al, 2013; Yasuhara, 2010). Speculatively, this variation may be attributed to sample
heterogeneity, as ID was less prevalent in the hospital cohort compared to past study samples.
ID is argued to co-occur in 28% of individuals with ASD (Bryson, Bradley, Thompson &
Wainwright, 2008), over double the 13.71% found in our ASD group. This disparity is even
more apparent in Valvo and colleagues’ (2013) study, as over 55.8% of their ASD sample
had mild to severe ID, approximately four-times more prevalent. Assuming that ID is a
function of epilepsy in ASD (Amiet et al, 2008), a smaller proportion of ID represents a
smaller prevalence of epilepsy.
4.2 Epilepsy Variability Between Psychiatric and Neurodevelopmental Disorders
Patients with ASD had a significantly higher proportion of epilepsy diagnoses compared to
all psychiatric disorders as evidenced by X2. The CIs reported for depression (both episodic
and recurrent), BD and schizophrenia overlapped with one another, suggesting non-
significant differences between these prevalence estimates. However, none of these CIs
overlapped with the ASD range, providing further evidence that these patients had a
significantly greater proportion of epilepsy diagnoses. These findings remained after
excluding psychiatric and neurodevelopmental comorbidity. As such, the hypothesis that
there will be ‘a difference in the prevalence of epilepsy between service-users with autism,
bipolar, schizophrenia and depression’ was supported. One possible explanation for this
finding was the greater proportion of comorbid ID found in the ASD group, although this
warrants caution.
4.3 The Importance of ID in Epilepsy
As previously stated, ID was most prevalent in the ASD group affecting 13.71% of the
subpopulation. In a previous meta-analysis, the prevalence of epilepsy in people with ASD
was estimated at 8% in the absence of ID, compared to 20% in those with ID (Amiet et al,
2008). In the present study 23.18% of ASD and ID patients had confirmed epilepsy, similar
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to Amiet and colleagues’ (2008) estimation. On the other hand, epilepsy was identified in
0.88% of those without ID, which was considerably lower than past estimations. These
proportions significantly differed from one another, as evidenced by X2, corroborating that
epilepsy in ASD is strongly associated with ID (Clark et al, 2005; Amiet et al, 2008).
Interestingly, this association extended to all psychiatric conditions, as evidenced by highly
significant p-values and ORs. With all p-values below 0.1%, ORs were used to compare the
strength of the association between ID and epilepsy. Service-users with depression and ID
showed the greatest odds. Respectively, the odds of epilepsy were 36.17 and 43.78 times
higher in those with ID and recurrent or episodic depression compared to service-users
without ID. Elevated ORs were also found in individuals with schizophrenia and ID, with the
odds 29.29 times higher in these patients. ID was further associated with increased odds of
epilepsy in BD patients, with 21.57 times higher odds in those with ID compared to those
without. Therefore, the higher prevalence of epilepsy in the ASD group may, at least
partially, reflect the higher proportions of ID compared to other psychiatric disorders. To the
best of my knowledge, this is a novel finding as no past literature has identified ID as a risk
factor for epilepsy in schizophrenia, depression or BD. On the other hand, these findings
must be interpreted with caution, as the small sample sizes observed in most of the tests may
have over-inflated their respective ORs. Consequently, this requires further and more
vigorous replication in order to validate this result.
4.3 Under-Reporting of Epilepsy codes in ePJS
Alternatively, a potential under-reporting of ICD-10 epilepsy codes (G40) in ePJS may
underpin the low prevalence estimates reported. In post-hoc analysis, the addition of AEDs as
an indicator of an unreported diagnosis elevated proportions in all disorders, increasing the
prevalence by a range of 0.52% to 4.15%. As such, 1.66% of the hospital cohort presented
with either confirmed epilepsy or assigned pseudo-positive epilepsy. Although speculative, if
the prescription of AEDs was indicative of a diagnosis, the hypothesis that ‘the prevalence of
epilepsy will be higher in service-users with neuropsychiatric conditions compared to the
general population’ could now be fully accepted, as all prevalence estimates were higher
compared to the general London population (0.44%). This implies possible under-reporting
of G40 codes and misclassification bias within the main analysis.
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Whether AEDs was indicative of epilepsy requires caution, as the off-label use of
pharmaceutical drugs beyond approved indications can be of common practice.
Anticonvulsants, such as valproate, have been used effectively as mood-stabilisers in the
alternative treatment for BD (Solomon et al, 1996). Although patients with BD prescribed
valproate were excluded, other treatments included in post-hoc analysis may have been used
off-label to similar effects. An example of this includes topiramate, a potentially effective
treatment in patients with mood disorders unresponsive to traditional medication (Marcotte,
1998). This likely explains why the proportion of diagnosed and pseudo-positive epilepsy
was substantially higher in patients with BD compared to all other psychiatric and
neurodevelopmental disorders. This raises a clear question regarding the validity of the
proxy, as the residual confound of off-label use could have biased and misclassified service-
users.
4.4 Limitations
The questionable validity of AEDs as a proxy for epilepsy highlights additional limitations
within the study. Critically, the methodology used may have lacked sensitivity to detect all
epilepsy diagnoses within the hospital cohort. The sole use of a structured diagnostic label,
i.e. G40 codes, would have missed patients if their diagnoses were recorded in open-text
fields, for example in referral letters. This may have introduced differential misclassification
bias, as individuals would have been assigned an incorrect outcome status, which may
explain the low prevalence figures reported. However, analysing open-text data was not
possible due to time constraints.
Furthermore, the current study lacked generalisability to the wider UK population, as all
service-users were from SLaM psychiatric services. As London has the lowest prevalence of
epilepsy across the UK, it could be hypothesised that higher rates of epilepsy, within these
defined clinical populations, would be found elsewhere. In addition, the proportions of
epilepsy within each psychiatric and neurodevelopmental condition were relatively small,
especially when further stratifying by ID. As such, this may have led to exaggerated ORs and
unreliable trends. In this regard, a population-based study would have been more
comprehensive to assemble a larger and more representative cohort of the UK population.
1773321 39
The mean age at first SLaM diagnosis for a number of psychiatric and neurodevelopmental
conditions indicate possible referral bias in terms of severity. This is especially apparent in
service-users with ASD, who had a mean age of 28.96 years at first SLaM diagnosis. As ASD
is a neurodevelopmental disorder, characterised by deficits that occur during the
developmental period, this suggests a number of individuals were not seen at SLaM until
later on in their care. This further applies to service-users with BD and schizophrenia, as the
mean ages at first SLaM diagnosis were higher than previously established age of onsets
(Kessler et al, 2005; Rajji, Ismail & Mulsant, 2009).
4.5 Strengths
Despite these limitations, there were a number of important methodological strengths. As
clinical data was collected routinely in ePJS, this enabled the generation of a quick and low-
cost population data with diagnoses spanning over multiple years. As such, and through
directly drawing upon patient records, large volumes of clinical data that were ‘real-world’
and ‘real-time’ were extracted for analysis. There is also high confidence to the reliability and
validity of data pertaining to ICD-10 diagnoses, as this information would have been entered
in by trained health professionals working within SLaM services. As stated earlier, structured
ICD-10 codes have been used reliably in other CRIS studies evidencing its high accuracy
(Roberts et al, 2016). In addition, the use of NLP to identify AEDs should also be reliable
and valid, as past findings demonstrated high precision and sensitivity in identifying
medication use (Perera et al, 2016).
A further advantage is that the distribution of ethnicities found were similar to those
described in the 2011 census (Greater London Authority, 2013), demonstrating that the
extracted sample was representative of the South London population. This was additionally
enhanced through the large sample size extracted, as the current study was one of the largest
hospital-based studies to investigate epilepsy in individuals with psychiatric and
neurodevelopmental disorders.
1773321 40
4.6 Clinical Significance and Future Research
As epilepsy was prevalent in service-users with ASD, SLaM health professionals should be
aware of this heightened risk, especially in those with comorbid ID. Epilepsy should be
assessed and managed appropriately within this vulnerable population in order to avoid
preventable mortalities, as seen in the case of Connor Sparrowhawk whose death reignited
criticism towards the quality of care for people with ID (Salman, 2014). The appropriate
assessment and management of epilepsy also applies to those treating patients with
schizophrenia, BD and depression. The possible under-reporting of G40 codes in ePJS
highlights an area of possible improvement for clinical practice. If epilepsy diagnoses are not
clearly reported in EHRs, this may adversely affect the quality of care co-ordination between
agencies, i.e. the sharing of important clinical information between those involved in the
patients care. To validate whether G40 under-reporting is an issue within SLaM psychiatric
settings, future replications should incorporate open-text searches.
Further investigation is also required to establish whether ID is a risk factor for epilepsy in
depression, schizophrenia and BD clinical populations. ID has been found to be prevalent in
all these conditions (Morgan et al, 2012), which may drive the higher prevalence of epilepsy
reported in the majority of the literature. Although this trend was established in the present
study, the small sample sizes potentially over-estimated this association. As such, replication
using a more targeted ID sample is needed for validation. Stratification by ID severity would
also allow for more powerful interactions.
Future studies should also establish whether the bi-directional relationships between these
disorders and epilepsy can predict the severity of epilepsy. This has been shown from a
neurological perspective, as evidence suggests that severe-treatment refractory epilepsy
increases the risk of later schizophrenia but not in those who responded to treatment
(Fruchter et al, 2014). Although the present study was blind to epilepsy subtypes, future
cohort studies would yield novel insights into whether neuropsychiatric impairment can
predict the severity of epilepsy.
1773321 41
5. Conclusion
There has been a paucity of studies investigating the prevalence of comorbid epilepsy in
service-users with neuropsychiatric impairment, despite the growing body of evidence
supporting bi-directionality (e.g. Josephson et al, 2017). The present study aimed to resolve
this issue through establishing the prevalence of epilepsy among a South London population
of service-users diagnosed with ASD, BD, schizophrenia, recurrent depression or a
depressive episode. Contrary to the status-quo of the evidence base, the prevalence of
epilepsy across all psychiatric disorders were smaller than anticipated and similar to the
general London population, with the exception of ASD. Critically, this may reflect an under-
reporting of G40 codes and outcome insensitivity. From an applied perspective, an under-
reporting of G40 codes in ePJS highlights a potential area for improvement in terms of
clinical practice, as inconsistent reporting may adversely impact the quality of care co-
ordination between different agencies. Although this was evidenced post-hoc, there was
questionable validity regarding the use of AEDs as a proxy of an unreported epilepsy
diagnosis. As such, this investigation was more speculative and requires further validation
and confirmation that treatment is a proxy for the presence of epilepsy.
Alternatively, the small proportions of epilepsy may reflect the prevalence of ID within the
hospital cohort. For all psychiatric and neurodevelopmental disorders, comorbid ID was
significantly associated with an epilepsy diagnosis. This was a novel finding and could
explain why the prevalence of epilepsy was significantly higher in patients with ASD, as ID
was considerably more prevalent in this population.
1773321 42
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Appendix A: Selected Representative Studies Investigating Psychiatric Comorbidity in
PWE
Table 1. Methodology and Key Findings of Selected Studies Investigating Depression in
PWE
Authors Number of
Participants
Design Data source Statistics
(95%CI)
Key findings
Gaitatzis,
Carroll,
Majeed &
Sander
(2004)
1.3 million
patients from
211 general
practices.
Cross-
sectional
population-
based study
Data from
the UK
General
Practice
Database
(1995-1998)
Adjusted
Rate Ratio
(RR) = 2.59
(2.35-2.86)
Comorbid
depression
found in
18% of PWE
Tellez-
Zenteno et al
(2007)
36,984
respondents
with a
response rate
of 77%
Cross-
sectional
population-
based study
Data from
the Canadian
Community
Health
Survey
(2002)
Adjusted
Odds ratio
(OR) = 1.8
(1.0-3.1)
Comorbid
depression
was found in
17.4% of
PWE
compared to
3.9% in the
GP
Fiest et al
(2013)
N/A Systematic
review of
population-
based studies
23 eligible
articles using
14 unique
datasets
Estimated
pooled OR =
2.77 (2.09-
3.67)
Epilepsy
significantly
associated
with
depression
and was
highly
prevalent
(23.1%)
Weatherburn
et al (2017)
1.5 million
patients
registered to
Cross-
sectional
population-
Descriptive
analysis of
primary care
Adjusted OR
= 1.57 (1.49-
1.65)
16.3% of
PWE were
found to
1773321 51
314 primary
care
practices in
Scotland and
UK (2007)
based study electronic
records
have
depression
vs 9.5% of
those
without
Table 2. Methodology and Key Findings of Selected Studies Investigating BD in PWE
Authors Number of
Participants
Design Data source Statistics
(95%CI)
Key findings
Ettinger et al
(2005)
85,358
participants
selected on
demographic
variables
Cross-
sectional
population-
based study
MDQ
(screening
instrument
for bipolar 1
and II
symptoms)
OD for BD
symptoms
compared to
healthy
controls =
6.62 (4.86-
9.03)
Bipolar
symptoms
were evident
in 12.2% of
PWE
49.7% of
those
screened
positive had
a formal BD
diagnosis
Adelow et al
(2012)
1,885 cases
(PWE) with
new onset of
unprovoked
seizures
15,080
controls
randomly
selected
Population-
based case-
control study
Stockholm
Epilepsy
Register
(2000-2005)
Adjusted OR
= 2.7 (1.4-
5.3)
The odds for
unprovoked
seizures after
hospital
discharge for
BD
compared to
controls
1773321 52
from
Stockholm
county
register
Bakken et al
(2014)
33,571
registered
with at least
one
diagnosis of
epilepsy in
somatic
hospitals
3,705,938
people
without
epilespy
from general
Norwegian
population
Population-
based
prospective
cohort
The
Norwegian
Patient
Registry
(2008-2012)
Adjusted RR
of 2.29
(2.10-2.49)
The
proportion of
PWE
registered
with BD was
more than
twice as high
compared to
people
without
epilepsy
(1.50% vs
0.68%)
Chang et al
(2013)
938 PWE
and 518,748
participants
without
epilepsy
Population-
based
retrospective
cohort study
Identified
from the
National
Health
Insurance
Research
Database in
2000-2002
(tracked
them until
2008)
Hazard ratio
(HR) = 23.5
(11.4-48.3)
The epilepsy
cohort
showed a
higher risk
of BD
compared to
the non-
epileptic
cohort
Wotton &
Goldacre
50, million
patient
Population-
based
Identified
from the
RR for BD
in ORLS =
BD in PWE
occurs more
1773321 53
(2014) records
143,508 in
the epilepsy
cohort
retrospective
cohort study
Oxford
Record
Linkage
Study
(ORLS) and
English
National
Linked
Hospital
Episode
Statistics
(HES)
3.0 (1.7-5.1)
RR for BD
in all-
England =
3.6 (3.3-3.9)
frequently
than
expected by
chance
Clarke et al
(2012)
9653
families and
23,404
offspring
identified
Population-
based family
study
Linked two
national
registers: the
Finnish
Hospital
Discharge
Register and
the Finnish
Population
Registry
HR for BD
in PWE =
6.3 (2.4-
16.4)
BD occurred
in 1.9% of
PWE
compared to
0.3% of the
no epilepsy
group
PWE had a
6.3-fold
increased
risk of BD
1773321 54
Table 3. Methodology and Key Findings of Selected Studies Investigating Schizophrenia in
PWE
Authors Number of
Participants
Design Data source Statistics
(95%CI)
Key findings
Qin et al
(2005)
Cohort of
2,270,372
followed
from 1977-
2002
34,494
PWE
(1.5%)
Population-
based
prospective
cohort study
Danish
Longitudinal
Registers
RR = 2.48
(2.20-2.80)
Strong
association
between
schizophrenia
and epilepsy.
The risk of
schizophrenia
was 2.48
times higher
in PWE
compared to
those without
Clarke et
al (2012)
9653
families and
23,404
offspring
identified
Population-
based family
study
Linked two
national
registers: the
Finnish
Hospital
Discharge
Register and
the Finnish
Population
Registry
HR for
schizophrenia
in PWE = 8.5
(3.4-20.9)
Schizophrenia
occurred in
2.4% of PWE
compared to
0.3% of those
without
PWE had an
8.5-fold
increased risk
of having
schizophrenia
Selassie
et al
(2014)
64,188
PWE who
were treated
or released
Hospital-
based
retrospective
cohort study
Utilised the
South Carolina
state-wide
hospital
OR = 3.61
(3.34-3.89)
The odds of
developing
schizophrenia
higher in
1773321 55
from the
emergency
department
(ED) and
outpatient
department
(OPD) were
eligible
discharge and
ED visit
databases
PWE and
more
prevalent,
affecting
5.6% (95% CI
= 5.4-5.7%)
of PWE
Fruchter
et al
(2014)
868,208
screened
male
adolescents
Population
based
retrospective
cohort study
Linked data
from the
Israeli draft
board and the
Israeli
National
Psychiatric
Hospitalisation
Case Registry
Risk for
hospitalisation
for
schizophrenia
in treatment
refectory
epilepsy (HR
= 3.89, 95%
CI = 1.75-
89.67,
p<0.001)
People with
severe
epilepsy
showed
increased risk
of future
schizophrenia.
Successfully
treated
epilepsy did
not predict
risk of
schizophrenia
1773321 56
Table 4. Methodology and Key Findings of Selected Studies Investigating ASD in PWE
Authors Number of
Participants
Design Data source Statistics
(95%CI)
Key findings
Selassie et al
(2014)
64,188 PWE
who were
treated or
released
from the
emergency
department
(ED) and
outpatient
department
(OPD) were
eligible
Population-
based
retrospective
cohort study
Utilised the
South
Carolina
state-wide
hospital
discharge
and ED visit
databases
OR = 22.2
(16.8-29.3)
Despite
identifying a
low prevalence
of ASD (1.3%
(95% CI = 1.2-
1.4%)), the
odds of
developing
ASD were 22
times higher
compared to
those without
epilepsy
Berg,
Plioplys &
Tuchman
(2011)
555 children
were
included for
analysis. A
total of 56
children
were
identified by
parent report
of having
ASD
Community-
based
retrospective
cohort study
Children
were
recruited
from 16
child
neurologist
practices
from the
State of
Connecticut
from 1993-
1997
Overall
prevalence
of ASD =
5% (95% CI
= 3.2-6.9%)
Prevalence
of ASD in
children with
normal
cognitive
functioning
= 2.2% (0.8-
3.6%)
Prevalence
The prevalence
of ASD in
childhood onset
epilepsy was
estimated at 5%
1773321 57
of ASD in
children with
cognitive
impairment
(IQ <80) =
13.8% (8.0-
19.5%)
Rai et al
(2012)
7,403
individuals
living in
private
households
in England
Population-
based cross-
sectional
study
The Adult
Psychiatric
Morbidity
Survey
(2007)
Adjusted OR
= 7.4 (1.8-
30.6)
The prevalence
of ASD in non-
institutionalised
adults was
estimated at
8.1% (95% CI
= 2.2-25.9)
Strasser,
Downes,
Kung, Cross
& De Haan
(2017)
N/A Systematic
Review and
meta-
analysis of
studies
investigating
the
prevalence
of ASD in
PWE over
the past 15-
years
A total of 19
studies were
eligible for
analysis
An estimated
pooled
prevalence
of 6.3% (5.4-
7.1%)
Current
research
supports a high
prevalence of
ASD in PWE
1773321 58
Appendix B: Selected Representative Studies Investigating Comorbid Epilepsy in
Patients with Psychiatric and Neurodevelopmental Disorders
Table 5. Methodology and Key Findings of Selected Studies Investigating the Bi-Directional
Relationship Between Epilepsy in Depression
Authors Psychiatric
Condition
Number of
Participants
Design Data source Statistics
(95%CI)
Key findings
Josephson
et al
(2017)
Depression 10,595,709
patients
(229,164
developed
depression
& 97,177
developed
epilepsy)
UK
Hospital-
based
prospective
cohort
study
Data
obtained
from The
Health
Improvement
Network
(THIN)
HR of
those with
incident
epilepsy
developing
depression
= 2.04
(1.97-
2.09)
HR of
those
incident
depression
developing
epilepsy =
2.55 (2.49-
2.60)
Identified an
increased
hazard and
bi-directional
association
between
depression
and epilepsy
Hesdorffer
et al
(2012)
Depression 6.4 million
people
from more
than 480
general UK
practices
Population-
based
prospective
cohort
study
The UK
General
Practice
Research
Database
Incidence
Rate ratio
(IRR) after
epilepsy
diagnosis
(0-1yrs) =
1.9 (1.4-
2.7)
Found a 2-
way
relationship
between
depression
and epilepsy.
The
1773321 59
IRR after
epilepsy
diagnosis
(>1-2yrs)
= 2.5 (1.8-
3.6)
IRR (<2-
3yrs) = 2.4
(1.6-3.7)
incidence of
depression
significantly
increased
before and
after an
epilepsy
diagnosis
Table 6. Methodology and Key Findings of Selected Studies Investigating the Prevalence of
Epilepsy in ASD populations
Authors Psychiatric
Condition
Number of
Participants
Design Data source Statistics
(95%CI)
Key findings
Thomas,
Hovinga,
Rai & Lee
(2017)
ASD 95,677
surveys
collected
Population-
based
cross-
sectional
study
US National
Survey of
Children’s
Health
(2011-2012)
OR = 2.3
(0.9-6.1)
Epilepsy was
reported to
occur in 8.6%
of ASD cases
Viscidi et
al (2013)
ASD 5,815
participants
Population-
based
cross-
sectional
study
US National
Survey of
Children’s
Health
(2007)
Autism
Genetic
Resource
Prevalence
in NSCH
= 12.5%
Prevalence
of
Epilepsy
in AGRE
The average
prevalence of
epilepsy in
children with
ASD aged 2-
17yrs was
12.5%.
The
1773321 60
Exchange
(ARGE)
Simons
Simplex
Collection
(SSC)
Autism
Consortium
(AC)
= 5.3%
Prevalence
in SSC =
2.9%
Prevalence
in AC
studies =
6.7%
prevalence of
epilepsy
increased to
26% in
children aged
13yrs and
older
Table 7. Methodology and Key Findings of Sucksforff and Colleagues’ (2015) Study
Investigating Comorbid Epilepsy in Bipolar Disorder Cases
Authors Psychiatric
Condition
Number of
Participants
Design Data source Statistics
(95%CI)
Key findings
Sucksdorff
et al
(2015)
BD 1861 cases
with BD
aged up to
25 years
3643
matched
controls
Nested
case-
control
study
Finnish
National
Registers
Adjusted
OR = 2.53
(1.73-
3.70)
Epilepsy was
found in
3.33% of
cases vs
1.29% of
controls
1773321 61
Table 8. Methodology and Key Findings of Selected Population-Base Cohort Studies
Investigating Epilepsy in Schizophrenia
Authors Psychiatric
Condition
Number of
Participants
Design Data source Statistics
(95%CI)
Key findings
Chang et
al (2011)
Schizophrenia 5,195
patients with
incident
schizophrenia
20,776
controls
matched for
age and sex
Population-
based
retrospective
cohort
Taiwan
National
Health
Insurance
Database
(1999-
2008)
Adjusted
HR =
5.88
(4.71-
7.36)
The incidence
of epilepsy
was higher in
the
schizophrenia
cohort
compared to
non-
schizophrenia
cohort
Makikyro
et al
(1998)
Schizophrenia 11,017
participants
from the
unselected
general
population
Population-
based
prospective
cohort
28-year
follow-up
of the 1966
Northern
Finland
general
population
birth cohort
OR =
11.1 (1.2-
2.7)
Epilepsy
strongly
associated
with
schizophrenia.
The odds of
epilepsy were
11.1x higher
in those
diagnosed
with
schizophrenia
1773321 62
Appendix C: STATA Output for Demographic Characteristics, Proportions and Chi-
Squared Tests
Gender, Ethnicity and ‘Age at First diagnosis at SLaM’ Distributions in the Hospital Cohort
Mean, SD and Range of ‘Age at First SLaM at SLaM Diagnosis’
1773321 63
Sample Characteristics Stratified by Psychiatric Diagnoses
Distributions of Psychiatric Disorders
95% CIs For Neuropsychiatric Distributions
1773321 64
Distributions of Sex and ‘Age at First Diagnosis at SLaM’ Stratified by Neuropsychiatric
Condition
ASD
BD
1773321 65
Recurrent Depression
Depressive Episode
1773321 66
Schizophrenia
The Prevalence of Comorbid Epilepsy
Proportion of Epilepsy in the Total Cohort
95% CIs
1773321 67
Prevalence of Epilepsy Stratified by Psychiatric Diagnosis With 95% CIs
ASD
BD
1773321 68
Recurrent Depression
Depressive Episode
1773321 69
Schizophrenia
Chi-Squared Output for the Association Between Epilepsy and the Different
Neuropsychiatric Conditions
1773321 70
Output for the Repeat Chi-Squared Test Excluding Multiple Psychiatric Diagnoses
Intellectual Disability as a Risk Factor for Epilepsy
The Proportion of ID in Hospital Cohort
1773321 71
Proportions of ID Within Each Psychiatric and Neurodevelopmental Disorder
ASD
BD
1773321 72
Recurrent Depression
Depressive Episode
1773321 73
Schizophrenia
Chi-Squared Output
ASD
1773321 74
BD (X2)
BD Fisher’s Exact Output
Recurrent Depression
1773321 75
Depressive Episode
Schizophrenia
1773321 76
Post-Hoc Analysis
Prevalence of Epilepsy in the Total Cohort
Prevalence of Epilepsy Stratified by Psychiatric Disorders
1773321 77
Chi-Squared Output
1773321 78
Chi-Squared Output (without multiple psychiatric diagnoses)
