Epilepsy and psychiatric comorbidity: A nationally

representative population-based study*Dheeraj Rai, yMichael P. Kerr, zSally McManus, xVesna Jordanova, *Glyn Lewis,

and{Traolach S. Brugha

*Academic Unit of Psychiatry, School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom;

yDepartment of Psychological Medicine and Neurology, Cardiff University, Cardiff, United Kingdom; zNatCen Social

Research, London, United Kingdom; xCentre for Mental Health, Department of Medicine, Imperial College London, London,

United Kingdom; and{Department of Health Sciences, University of Leicester, Leicester, United Kingdom

SUMMARY

Purpose: In a nationally representative population-based

study in England, we estimated the burden of psychiatric

and neurodevelopmental comorbidities in people with

epilepsy. We investigated whether any overrepresenta-

tion of comorbidities could be explained by epilepsy being

a chronic medical or neurologic condition, or by the con-

founding effect of demographic and socioeconomic fac-

tors or other health conditions.

Methods: The Adult Psychiatric Morbidity Survey 2007

comprised detailed interviews with 7,403 individuals living

in private households in England. Doctor-diagnosed epi-

lepsy (and asthma, diabetes, and migraine, chronic condi-

tions for comparison) was ascertained by self-report, and

extensive diagnostic and screening interviews were used

to assess psychiatric and neurodevelopmental conditions.

Key Findings: The estimated lifetime prevalence of epi-

lepsy in the adult (‡16 years old) population of England

was 1.2% (95% confidence interval [CI] 1.0–1.5). Almost

one-third of the people with epilepsy had an International

Classification of Diseases, Tenth Revision (ICD-10) anxi-

ety or depressive disorder (compared with one in six people

without epilepsy). Among these, social phobia and agora-

phobia, generalized anxiety disorder, depression, and

measures of suicidality had strong associations with epi-

lepsy, which remained robust after accounting for poten-

tial confounders. These associations were consistently

stronger than those in people with asthma or diabetes,

and similar to those in people reporting migraine or

chronic headaches. Epilepsy was also strongly associated

with autism spectrum disorders (odds ratio [OR] 7.4, 95%

CI 1.5–35.5) and possible eating disorders, and these asso-

ciations were not evident in people with asthma, diabetes,

or migraine.

Significance: Psychiatric and neurodevelopmental condi-

tions were overrepresented in people with epilepsy.

These associations were stronger than with other non-

neurologic chronic conditions, and not explained by con-

founding. Some overlap in the psychopathology observed

in epilepsy and migraine cannot rule out the presence of

common pathways of psychiatric comorbidity in neuro-

logic conditions. However, associations of epilepsy with

conditions such as autism spectrum disorders point to

comorbidities specific to epilepsy that may not be shared

by other neurologic conditions.

KEY WORDS: Epilepsy, Psychopathology, Epidemiology,

Comorbidity, Neuropsychiatry.

Epilepsy is the most common serious neurologic disorder,and it affects >50 million people worldwide (World HealthOrganization 2005; Ngugi et al., 2010). Psychiatric disor-ders are commonly encountered in people with epilepsy,and these may negatively influence the course of epilepsy,lead to inadequate response to treatment, and contribute to apoor quality of life as well as increased mortality (Christensenet al., 2007; Thapar et al., 2009). However, psychopathol-

ogy is frequently unrecognized and untreated in people withepilepsy (Hermann et al., 2000), and many questions remainabout both the extent and the nature of the relationshipsbetween epilepsy and specific psychiatric conditions. Agood understanding of the burden of these comorbidities isessential for better recognition and treatment, and to informthe training needs of clinicians. This has the potential totranslate into improved care of people with epilepsy. Inaddition, observed associations may illuminate understand-ing of common mechanisms behind epilepsy and specificforms of psychopathology.

Population-based studies in adults have often focused oncomorbid depression in epilepsy (reviewed in Tellez-Zentenoet al., 2007), but potentially important relationships withother conditions such as anxiety (Beyenburg et al., 2005)

Accepted March 12, 2012; Early View publication May 11, 2012.Address correspondence to Dr. Dheeraj Rai, Academic Unit of Psychia-

try, School of Social and Community Medicine, University of Bristol,Oakfield House, Oakfield Grove, Clifton, Bristol BS8 2BN, U.K. E-mail:dheeraj.rai@bristol.ac.uk

Wiley Periodicals, Inc.ª 2012 International League Against Epilepsy

Epilepsia, 53(6):1095–1103, 2012doi: 10.1111/j.1528-1167.2012.03500.x

FULL-LENGTH ORIGINAL RESEARCH

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and neurodevelopmental disorders (Garcia-Morales et al.,2008; Bolton et al., 2011) have not been adequately exam-ined. The reported prevalence of comorbid psychopathologyin epilepsy also varies widely across studies, reflectingmethodologic limitations including possible selection (dueto recruitment of epilepsy cases from unrepresentative clini-cal populations) (Swinkels et al., 2005; Tellez-Zentenoet al., 2007; Garcia-Morales et al., 2008) or measurement(lack of diagnostic assessment tools to ascertain psychopa-thology) (Gilliam et al., 2003; Gaitatzis et al., 2004b; Laceyet al., 2009; Ottman et al., 2011) biases. Furthermore, stud-ies without adequate control populations (Gilliam et al.,2003; Gaitatzis et al., 2004b) may have led to inaccurateestimates of the risk of psychopathology in people with epi-lepsy compared with the general population.

Two other important issues remain unexplored. First,because socioeconomic disadvantage and health problemsare common in people with epilepsy (Scambler & Hopkins,1980; Gaitatzis et al., 2004a) as well as psychiatric disor-ders (Muntaner et al., 2004; Prince et al., 2007), confound-ing by these factors may explain the observed relationships,but this has not been previously considered. Second, depres-sion and anxiety disorders are commonly associated withmany chronic conditions (Prince et al., 2007). Depressivesymptoms (assessed by a screening instrument) appeared tobe more common among people with epilepsy than amongpeople with asthma in one previous study (Ettinger et al.,2004), but this issue has not been explored in studies usingdiagnostic instruments, or for disorders other than depres-sion. Specifically, no study has investigated how comorbidpsychopathology in epilepsy compares with that in peoplewith chronic neurologic presentations such as migraines orchronic headaches. Therefore, it is largely unknownwhether epilepsy is associated with an increased risk of psy-chopathology in addition to the effect of having a chronicmedical or neurologic condition.

We hypothesized that epilepsy would be associated withpsychiatric and neurodevelopmental disorders, even afteradjusting for potential confounders. We also hypothesizedthat epilepsy would have stronger associations with depres-sive and anxiety disorders than similar associations in peo-ple with other chronic conditions such as asthma, diabetes,and migraine. We tested these hypotheses using face-to-faceinterview data from a large nationally representative popu-lation-based study in England, which used validated diag-nostic and screening measures to ascertain a range ofpsychiatric and neurodevelopmental conditions.

Method

The Adult Psychiatric Morbidity Survey (APMS) 2007The sample for APMS 2007 was designed to be represen-

tative of the population 16 years and older living in privatehouseholds in England. A multistage stratified probabilitysampling design was used. The sampling frame was Royal

Mail’s small user Postcode Address File (PAF), with post-code sectors (on average comprising 2,550 households) rep-resenting the primary sampling units. Postcode sectors werefirst stratified by Strategic Health Authority (SHA). All ofthe primary sampling units within each SHA were then fur-ther stratified on the basis of the proportion of persons innonmanual occupations and sorted by the proportion ofhouseholds without a car based on 2001 Census data. Postalsectors were then sampled from each stratum with a proba-bility proportional to size (where size was measured by thenumber of delivery points). In this way, 519 postal sectorswere selected. Within these selected postal sectors, 28delivery points were then randomly selected, yielding14,532 delivery points. Interviewers visited these addressesto identify private households with at least one person aged‡16 years. Within the potentially eligible sample of 12,694addresses, one person from each household where contactwas made was randomly chosen to take part in the survey,and 7,461 (57%) individuals agreed to be interviewed. Theinterviews were conducted in two phases. The first phaseincluded structured diagnostic and screening assessmentsfor a range of psychiatric and neurodevelopmental disor-ders, along with measures of general health and demo-graphics. The second phase interviews were carried out ona subsample by clinically trained research interviewersusing semistructured diagnostic instruments. The probabili-ties of selection to the second phase interviews were basedon respondents’ responses to screening questions in phase1. Fieldwork was completed between October 2006 andDecember 2007. Further details of the survey methodologyare available elsewhere (Brugha et al., 2009; McManuset al., 2009). Ethical approval for the survey was obtainedfrom the Royal Free Hospital and Medical School ResearchEthics Committee.

Ascertainment of epilepsy, asthma, diabetes, andmigraine

The first phase interviews included a section on generalhealth, in which participants were asked if they had any spe-cific physical health conditions listed on a show card. Thoseindicating that they had epilepsy/fits since the age of 16were then asked if they had received a diagnosis of epilepsyby a doctor, the age of onset of the problem, whether theyhad fits in the last year, and if they had been on medicationfor the same in the last year. Similar questions have beenemployed and found to be valid in other population-basedstudies of epilepsy in Canada and the United States (Tellez-Zenteno et al., 2007; Ottman et al., 2011). People whoreported that they had epilepsy since age 16 and hadreceived a diagnosis of epilepsy by a doctor were consideredcases of epilepsy in this study. A similar methodology wasused to identify people reporting a lifetime doctor-diag-nosed history of migraines or frequent headaches, asthma,and diabetes, respectively. Asthma and diabetes are chronicconditions known to have a higher burden of comorbid

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psychopathology (Prince et al., 2007), and we chose tostudy them as comparison groups against our findings forcomorbidity in epilepsy. Furthermore, migraines andchronic headaches are conditions that may be neurologic inorigin, but that may also reflect neurotic psychopathology,and we studied this group as a comparison group (Antonaciet al., 2011).

Measures of psychopathologyMeasures described below are detailed further in the

APMS reports (Brugha et al., 2009; McManus et al., 2009).

Depression and anxiety disordersThe revised Clinical Interview Schedule (CIS-R) (Lewis

et al., 1992), a structured psychiatric interview designed tobe used by trained lay interviewers was used to identifypeople meeting International Classification of Diseases,Tenth Edition (ICD-10) (World Health Organization 1992)diagnostic criteria for depressive episodes, social phobia,specific phobia, panic disorder, agoraphobia, generalizedanxiety disorder (GAD), and obsessive compulsive disorder(OCD). In addition, when CIS-R scores indicate significantpsychopathology (‡12) (Lewis et al., 1992), but diagnosticcriteria of any of the above disorders is not met, a diagnosisof mixed anxiety and depression is applied, although theICD-10 does not give specific diagnostic criteria for thiscategory (World Health Organization 1992). To avoidconfusion, we refer to these presentations as nonspecificpsychiatric morbidity.

SuicidalityRespondents were asked questions about a lifetime or

previous year history of suicidal thoughts, suicide attempts,and self-harm in the face-to-face interview.

Autism Spectrum Disorder (ASD)The Autism Diagnostic Observation Schedule, Module 4

(ADOS) (Lord et al., 2000), a semistructured diagnosticinstrument for ASD was used in the phase 2 subsample(Brugha et al., 2011). The subsample was selected fromphase 1 responses to a validated self-reported screening toolfor ASD (Brugha et al., 2011). The ADOS ratings corre-sponding to Diagnostic and Statistical Manual of MentalDisorders, 4th ed. (American Psychiatric Association,2000) criteria were summed to produce an overall score,and the recommended threshold of ‡10 was used to indicatea case of adult ASD (Brugha et al., 2011).

Psychotic disordersThe Schedule for Clinical Assessment in Neuropsychia-

try (SCAN) interview was administered in the phase 2subsample for a confirmatory diagnosis of psychoticdisorder in the preceding year (Wing et al., 1990). The sub-sample was selected based on phase 1 screening questions,which included current use of antipsychotic medication,

inpatient psychiatric admissions, self-reported diagnoses, orpsychotic symptoms, including auditory hallucinations(McManus et al., 2009).

Screening for eating disordersThe SCOFF (Morgan et al., 1999) questionnaire was used

to screen for eating disorders. We defined people with a pos-sible eating disorder if they scored ‡2 on the SCOFF (therecommended cutoff) and answered affirmative to an addi-tional item—‘‘did your feelings about food interfere withyour ability to work, meet personal responsibilities and/orenjoy a social life?’’—capturing the potential impact onsocial functioning related to eating problems.

Screening for posttraumatic stress disorder (PTSD)A brief self-report screening questionnaire, the Trauma

Screening Questionnaire (TSQ) (Brewin et al., 2002), wasused to indicate presence of trauma-related symptoms in thepast 2 weeks and that clinical assessment for PTSD waswarranted.

Screening for adult attention-deficit/hyperactivity disorder(ADHD)

The Adult Self-Report Scale-v1.1 (ASRS) (Kessler et al.,2005) was used to screen for symptoms suggestive ofADHD. A recommended score of ‡4 was considered to be apositive screen, indicating that a clinical assessment forADHD may be warranted.

Possible confoundersBecause previous studies have not studied confounding,

we identified a number of potential sociodemographic andhealth confounders a priori, since these are associated withboth epilepsy (Scambler & Hopkins, 1980; Gaitatzis et al.,2004a) and psychopathology (Muntaner et al., 2004; Princeet al., 2007). These included age, gender, marital status,employment status (currently working, or either economi-cally inactive or unemployed), tenure of accommodation(owner or renting property), highest educational qualifica-tion, household income, and occupational social class. Asomatic comorbidity variable was made in which respon-dents were categorized as having 0, 1, or 2 or more current co-morbid doctor-diagnosed somatic illnesses (items includedcancer, diabetes, stroke, angina or myocardial infarction,hypertension, bronchitis or emphysema, asthma, peptic ulceror other gastrointestinal problems, liver disease, bowel orcolon diseases, bladder problems or incontinence, arthritisand migraine, or frequent headaches). Similar variables werealso made for comorbid somatic illnesses in people withmigraine or frequent headaches, asthma, and diabetes.

AnalysisAll analyses were conducted using the survey commands

in STATA 10.1 for Windows (StataCorp LP, College Sta-tion, TX, U.S.A.). Data were weighted to account for study

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design and nonresponse, so that the results were representa-tive in terms of age, sex, region, and area characteristics ofthe household population 16 years or older in England(McManus et al., 2009). First, sample weights were appliedto account for the different probabilities of selecting respon-dents in different-sized households. Second, to reducehousehold nonresponse bias, a household level weight wascalculated from a logistic regression model using inter-viewer observation and area-level variables (collected fromCensus 2001 data) available for responding and nonre-sponding households. The nonresponse weight for eachhousehold was calculated as the inverse of the probability ofresponse estimated from the model, multiplied by the house-hold’s selection weight. Finally, weights were applied usingcalibration weighting based on age, sex, and region toweight the data up to represent the structure of the nationalpopulation, taking into account the differential nonresponsebetween regions and age-by-sex groups. The populationcontrol totals used were the Office for National Statistics2006 midyear household population estimates. For psycho-sis and ASDs, (conditions ascertained in phase 2 inter-views), additional weights were designed to take intoaccount the probability of selection of participants for phase2 interviews. Complete details of the weighting proceduresare available elsewhere (McManus et al., 2009).

In descriptive analysis, we first studied sociodemo-graphic characteristics of people with epilepsy and com-pared them with people without epilepsy. We thenestimated the prevalence of each psychiatric condition inpeople with epilepsy. We used weighted logistic regres-sion models to estimate odds ratios (ORs) and 95% confi-dence intervals (95% CIs) of the association of epilepsywith each comorbid condition. As recommended by the lit-erature, we used multiple measures of socioeconomicposition to minimize residual confounding due to thesefactors (Galobardes et al., 2007), and included all of thepotential confounders in adjusted models to estimateadjusted ORs and 95% CIs. There was a higher proportionof missing data for income and occupational class, butomission of these variables in our final adjusted modelspresented here had no substantial effect on the results. Foranalysis of neurodevelopmental conditions (ASD andADHD), we used verbal IQ estimated using the NationalAdult Reading Test (Crawford et al., 1988) as an addi-tional confounder. In separate comparative analyses, weused a similar methodology to estimate associations inpeople with asthma, diabetes, and migraine.

Results

Doctor-diagnosed epilepsy, present since age 16, wasreported by 101 people corresponding to a weighted preva-lence of 1.2% (95% CI 1.0–1.5) in the general population ofEngland. About 3% of our original sample had missing datain covariates, and we included 94 people with epilepsy and

7,071 people without epilepsy with complete data in ouranalysis. People with epilepsy were more likely to bedivorced or separated (as compared to being married), havelower educational qualifications, live in rented accommoda-tion, and be economically inactive compared with peoplewithout epilepsy (Table 1).

Generalized anxiety disorder (12.5%), nonspecific psy-chiatric morbidity (12.2%), and depressive disorders(9.6%) were the most common psychiatric comorbidities inpeople with epilepsy (Table 2). When compared with therest of the population without epilepsy—and after adjustingfor potential confounders—social phobias, agoraphobia,generalized anxiety disorder, and depression were the com-mon mental disorders with the strongest independent asso-ciations with epilepsy (Table 2). People with epilepsy alsohad strong relationships with all measures of lifetime andrecent suicidal thoughts and behavior. These associations

Table 1. Characteristics of people with epilepsy as

compared to the general populationwithout

epilepsy in England.

Epilepsy

(n = 94)

%

No epilepsy

(n = 7,071)a

% OR (95% CI)

Age (years)

16–34 32.1 30.8 Ref

35–54 45.1 35.7 1.2 (0.7–2.2)

55 or more 22.9 33.5 0.7 (0.3–1.3)

Gender

Female 52.4 51.6 1.0 (0.7–1.6)

Marital status

Married/cohabiting 54.7 63.1 Ref

Single/widowed 34.1 29.6 1.3 (0.8–2.3)

Divorced/separated 11.2 7.3 1.8 (1.0–3.2)

Household income

Tertile 1 (highest) 33.0 36.1 Ref

Tertile 2 25.6 32.7 0.9 (0.5–1.6)

Tertile 3 (lowest) 41.4 31.2 1.5 (0.9–2.5)

Employment

Economically inactive 49.5 38.7 1.6 (1.0–2.4)

Occupational social classb

I/II 37.4 38.7 Ref

III 45.8 40.6 1.2 (0.7–2.0)

IV/V 16.9 20.7 0.8 (0.4–1.7)

Highest educational qualification

Degree level 21.8 27.9 Ref

A/0 Level 45.9 46.1 1.3 (0.7–2.4)

No qualifications 32.3 26.1 1.6 (0.9–2.9)

Tenure of accommodation

Rented accommodation 43.5 29.4 1.9 (1.2–3.0)

Comorbid physical illness

None 55.8 57.1 Ref

1 25.1 25.3 1.0 (0.6–1.6)

2 or more 19.2 17.6 1.1 (0.6–1.9)

OR (95% CI), odds ratio with 95% confidence interval.aLower n’s for income (n = 5,744) and for occupational class (n = 6,809).bOccupational social class based on U.K. Registrar General’s classification

(I/II = professionals or intermediate professions, III = nonmanual and manualskilled occupations, IV/V = partially skilled or unskilled occupations).

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were largely similar with those observed in the group ofpeople reporting migraines, or chronic headaches, bar somedifferences, although the CIs overlapped. Specifically, theassociation with social phobias appeared to be much stron-ger, and nonspecific psychiatric morbidity, panic disorderand specific phobias appeared to be weaker in people withepilepsy as compared to those reporting migraines orchronic headaches (Table 3). In contrast, similar associa-tions were either absent or much weaker in people withasthma and diabetes (Table 3). The associations betweenepilepsy and all measures of suicidality were attenuatedafter adjusting for concurrent depression or anxietydisorders (Table 4).

A strong association between epilepsy and autism spec-trum disorders was observed even after accounting for soci-odemographic and health covariates (OR 6.2) and increasedfurther after adjusting for verbal IQ (OR 7.4, 95% CI 1.5–35.5). Epilepsy was also associated with a 70% increase inodds of a psychotic disorder, although CIs were wide(adjusted OR 1.7, 95% CI 0.2–14.2). The associations withscreen-positive adult ADHD were attenuated when covari-ates were introduced in the models. Finally, epilepsy wasassociated with an almost threefold increase in odds ofscreen-positive eating disorder. None of these conditionsappeared to have significant associations in people withasthma, diabetes, or migraine.

Discussion

In this comprehensive, nationally representative popula-tion-based study, we examined the burden and relationshipsof a range of comorbid psychiatric conditions in epilepsy,including several that have never been investigated previ-ously in adult populations. Almost one third of the peoplewith epilepsy met ICD-10 diagnostic criteria for an anxietyor depressive disorder in this study, compared with aboutone in six people in the general population without epilepsy.After adjustment for confounders, people with epilepsy hadsignificantly elevated odds of having social phobias, agora-phobia, generalized anxiety disorder, and depression and allmeasures of suicidality. These associations were consis-tently stronger than similar relationships in people withasthma or diabetes, and comparable to people reportingmigraines or chronic headaches. People with epilepsy had amore than sevenfold increase in odds of having an autismspectrum disorder and an almost threefold increase in theodds of being screen positive for eating disorder, comparedwith people without epilepsy. These associations were notobserved in people with asthma, diabetes, or migraine.

To our knowledge, this nationally representative popula-tion-based study represents the most detailed examinationof the psychiatric and neurodevelopmental comorbidities ofepilepsy ascertained by face-to-face interviews using

Table 2. Prevalence of psychiatric conditions in people with epilepsy, and results of logistic regression analysis

comparing psychopathology in people with epilepsy with the general population of England without epilepsy.

Mental disorder

Prevalence in people

with epilepsy % (95% CI) Crude OR (95% CI) Adjusted OR (95% CI)

Any depressive or anxiety disorder 30.6 (21.5–41.4) 2.3 (1.4–3.8)** 1.9 (1.2–3.2)*

Depression and an anxiety disorder 4.2 (2.2–7.2) 4.1 (2.1–8.2)*** 3.1 (1.5–6.3)**

Depression 9.6 (5.3–16.9) 3.7 (1.9–7.1)*** 2.7 (1.4–5.4)**

Generalized anxiety disorder 12.5 (7.6–20.1) 3.3 (1.8–5.9)*** 2.6 (1.5–4.7)**

Social phobia 6.0 (2.7–12.8) 7.1 (3.0–16.7)*** 5.2 (2.1–13.1)***

Specific phobias 1.8 (0.6–5.7) 2.0 (0.6–6.7) 1.2 (0.3–4.5)

Panic disorder No observation – –

Agoraphobia 4.9 (2.7–11.4) 4.7 (1.8–12.5)** 3.2 (1.2–8.7)*

Obsessive compulsive disorder 3.1 (1.5–6.3) 2.9 (1.3–6.4)** 1.8 (0.8–4.4)

Nonspecific psychiatric morbidity 12.2 (6.4–21.9) 1.5 (0.8–3.1) 1.3 (0.6–2.6)

Suicide and self-harm

Suicidal thoughts lifetime 26.5 (18.0–37.1) 2.3 (1.4–3.8)** 2.0 (1.2–3.2)**

Suicidal thoughts in last year 12.2 (6.9–20.8) 3.3 (1.7–6.2)*** 2.5 (1.3–4.8)**

Suicide attempts lifetime 12.5 (6.7–21.2) 3.0 (1.5–5.9)** 2.3 (1.1–4.6)*

Suicide attempts last year 4.1 (1.5–10.5) 7.3 (2.4–21.9)*** 4.6 (1.6–13.6)*

Deliberate self-harm 12.7 (7.2–21.5) 2.9 (1.5–5.5)** 2.3 (1.2–4.5)*

Neurodevelopmental/other conditions

Autism spectrum disorder 8.1 (2.2–25.9) 9.3 (2.0–42.4)** 7.4 (1.8–30.6)*

Psychotic disorder 1.1 (0.1–7.2) 2.7 (0.4–20.8) 1.7 (0.2–15.8)

Eating disorder (SCREEN) 5.0 (1.9–12.6) 3.4 (1.2–9.7)* 2.9 (1.1–7.7)*

Posttraumatic stress disorder (SCREEN) 4.9 (2.0–11.4) 1.8 (0.7–4.4) 1.2 (0.5–2.9)

Attention deficit disorder (SCREEN) 15.4 (9.2–24.7) 2.0 (1.1–3.7)* 1.6 (0.9–3.0)

Adjusted OR, model adjusted for age, gender, marital status, highest educational qualification, employed or economically inactive, and number of comorbidphysical illnesses. Verbal IQ score further included in the final model for association between autism spectrum disorder and attention deficit disorder screen(it had no confounding effect on other conditions under study).

OR, odds ratio; 95% CI, 95% confidence interval.*p < 0.05; **p < 0.01, ***p < 0.001.

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standardized validated measures of psychopathology. Inaddition, our ability to control for appropriate confounders,and compare our results with similar associations in peoplewith asthma, diabetes, and migraines offers a significantadvantage in comparison to previous literature.

Some limitations of our study should be noted. The ascer-tainment of epilepsy in this household survey was by self-report; therefore, the possibility of some reporting bias

cannot be excluded. In addition, we had no information onthe nature of the diagnostic process, seizure type, frequency,or treatment of epilepsy. However, these limitations arecommon to almost all previous population-based studies onthis topic (Ettinger et al., 2004; Gaitatzis et al., 2004b;Tellez-Zenteno et al., 2007; Ottman et al., 2011), andyielded an internationally comparable prevalence of epi-lepsy. Furthermore, due to the recognized challenges in thediagnosis of epilepsy it is possible that a proportion of oursample classified as epilepsy actually have an alternativediagnosis such as non–epileptic attack disorder; however,this potential misclassification is common to all population-identified epilepsy samples. Population-based studies withmore robust measures of epilepsy and detailed informationon the various types of epilepsy are required to overcomethis limitation (Berg et al., 2010; Thurman et al., 2011).Similarly, the comparison conditions included in this studywere ascertained by self-report, although an effort was madeto ascertain if these had been diagnosed by a doctor. Despitethe large sample, as expected, the number of people withepilepsy was relatively low, which limited our statisticalpower, particularly evident through wide CIs of associationswith other rare conditions. Response rates in national sur-veys are falling throughout the world (Groves, 2002), andthe 7,461 individuals interviewed in the APMS constituted57% of the eligible sample, comparable to other population-based studies on this topic (Ettinger et al., 2004). We useddetailed weighting procedures to minimize the likelihood ofnonresponse bias (McManus et al., 2009). Finally, thecross-sectional design, although important to estimate the

Table 3. Logistic regression analysis of associations of anxiety, depression, and suicidality in people with asthma,

diabetes, and migraine or chronic headaches as compared to the general population of England (without these

respective conditions).

Mental disorder

Asthma (n = 886)

Adjusted OR

(95% CI)

Diabetes (n = 406)

Adjusted OR

(95% CI)

Migraine (n = 936)

Adjusted OR

(95% CI)

Any depressive or anxiety disorder 1.7 (1.3–1.9)*** 1.0 (0.7–1.3) 2.3 (1.9–2.8)***

Depression and an anxiety disorder 1.6 (1.1–2.3)* 0.8 (0.5–1.4) 3.1 (2.1–4.7)***

Depression 1.4 (1.0–2.0) 0.9 (0.5–1.5) 2.5 (1.7–3.8)***

Generalized anxiety disorder 1.2 (0.9–1.6) 1.0 (0.6–1.7) 2.1 (1.6–2.9)***

Social phobia 0.8 (0.4–1.6) – 1.9 (1.0–3.5)

Specific phobias 1.5 (0.8–2.9) 0.1 (0.0–1.0) 2.7 (1.5–5.0)**

Panic disorder 1.3 (0.7–2.4) 2.2 (1.0–5.0) 1.9 (1.0–3.4)*

Agoraphobia 1.1 (0.6–2.2) 0.4 (0.1–1.3) 2.3 (1.3–3.9)**

Obsessive compulsive disorder 1.7 (0.9–3.2) 1.0 (0.4–2.6) 1.8 (1.0–3.2)

Nonspecific psychiatric morbidity 1.7 (1.3–2.1)*** 0.9 (0.6–1.3) 1.8 (1.4–2.2)***

Suicide and self-harm

Suicidal thoughts lifetime 1.5 (1.2–1.8)*** 1.0 (0.7–1.4) 2.2 (1.8–2.6)***

Suicidal thoughts in last year 1.5 (1.1–2.1)* 1.1 (0.7–1.8) 2.5 (1.8–3.5)***

Suicide attempts lifetime 1.5 (1.1–2.1)** 1.1 (0.6–1.9) 1.9 (1.3–2.6)**

Suicide attempts last year 3.5 (1.6–7.7)** 1.3 (0.3–6.8) 4.3 (1.8–10.1)**

Deliberate self-harm 1.5 (1.1–2.0)* 0.5 (0.2–1.1) 1.5 (1.0–2.2)*

Adjusted OR, model adjusted for age, gender, marital status, highest educational qualification, employed or economically inactive, and number of comorbidphysical illnesses.

OR, odds ratio; 95% CI, confidence interval.*p < 0.05; **p < 0.01; ***p < 0.001.

Table 4. Evidence of mediation of the relationship

between epilepsy and suicidality by anxiety and

depressive disorders (model 2) and symptoms

(model 3).

Measure of suicidality

Model 1

OR (95% CI)

Model 2

OR (95% CI)

Model 3

OR (95% CI)

Suicidal thoughts lifetime 2.3 (1.1–4.6)** 1.6 (0.9–2.8) 1.5 (0.9–2.8)

Suicidal thoughts

in last year

2.5 (1.3–4.9)** 1.9 (0.9–3.8) 1.7 (0.8–3.5)

Suicide attempts lifetime 2.2 (1.1–4.6)* 1.8 (0.8–4.0) 1.7 (0.8–3.8)

Suicide attempts last year 4.3 (1.4–13.5)* 3.2 (1.0–10.4) 2.7 (0.8–9.1)

Deliberate self-harm 2.2 (1.1–4.2)* 1.8 (0.9–3.5) 1.6 (0.8–3.3)

Model 1: adjusted estimates after adjustment for confounders (age, gender,marital status, highest educational qualification, employed or economicallyinactive, tenure of accommodation, and number of comorbid physicalillnesses).

Model 2: Model 1 adjusted for presence of any anxiety or depressivedisorder to assess mediation by anxiety or depressive disorders.

Model 3: Model 1 adjusted for total CIS-R score to assess mediation byanxiety or depressive symptoms (this approach would ensure subthresholdpsychopathology is also captured).

OR, odds ratio; 95% CI, 95% confidence interval.*p < 0.05; **p < 0.01.

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burden of comorbidities, does not allow for directionalinferences. For instance, the relationship between epilepsyand depression may be bidirectional (Kanner, 2008), andlongitudinal studies are required to clarify causal pathwaysof our observations.

The prevalence estimates of depression and anxiety disor-ders in people with epilepsy in our study are consistent withthe relatively sparse population-based literature on this topic(Ettinger et al., 2004; Gaitatzis et al., 2004b; Tellez-Zentenoet al., 2007; Garcia-Morales et al., 2008). In addition, ourstudy clarifies several issues not adequately addressedpreviously. First, it is notable, that anxiety disorders, partic-ularly phobias and GAD, are common comorbidities inepilepsy. This is important, since these may be as disablingas depression (Rai et al., 2010), and are potentially treat-able. Second, the associations of epilepsy with depression,anxiety disorders, and suicidality were consistently strongerthan similar estimates in asthma and diabetes, indicatingthat epilepsy has specific and stronger relationships withthese comorbidities than other chronic physical conditions.Similarly, although people reporting migraines or chronicheadaches may actually represent the presence of neuroticpsychopathology rather than a primary neurologiccondition, (Antonaci et al., 2011) comparing the rates ofpsychopathology in people with epilepsy with this grouponly reinforce our results that depression, anxiety disorders,and suicidality are significant issues in people with epilepsy.Our observation that apart from depression, anxiety, andsuicidality, no other conditions we studied had significantrelationships with asthma, diabetes, or migraine suggeststhat epilepsy has unique associations with psychopathol-ogy that are not simply explained due to it being a chronic(neurologic) condition.

The associations between suicidality and epilepsy in ourstudy were largely mediated by comorbid depression oranxiety disorder. This contrasts with findings from a Dan-ish study (Christensen et al., 2007), where secondary-carediagnosis (ascertained mostly from inpatient records) ofpsychiatric conditions did not fully explain the risk ofsuicide in people with epilepsy. Two important issues mayexplain this discrepancy. First, depressive and anxietydisorders are mostly managed in primary care, and second,psychopathology is often undiagnosed in people withepilepsy (Hermann et al., 2000). Hence our comprehensiveface-to-face assessment of psychopathology was an inher-ent advantage as compared to the use of a registeredsecondary-care diagnosis.

Another notable contribution of our study is in relation tothe relatively high prevalence of nonspecific psychiatricmorbidity in people with epilepsy, but without a significantincrease in odds ratios as compared to the general popula-tion (Table 2). These presentations, where significant anxi-ety or depressive symptoms do not meet diagnostic criteriafor specific disorders, are reminiscent of the ‘‘atypical’’ pre-sentations of depression and anxiety that epileptologists

have previously termed as ‘‘dysthymic like disorders’’ or‘‘dysphoric disorder of epilepsy’’ (Kanner, 2003; Blumeret al., 2004). These presentations were among the mostcommon psychiatric morbidities in the general population,and were no more common in people with epilepsy thanthose without.

Our data also allowed us to estimate associations betweenepilepsy and several conditions that are relatively uncommonin the general population. Noteworthy among these was thestrong association between adult autism spectrum disordersand epilepsy. This relationship is widely noted in children,although pediatric population-based studies on this topic arealso limited (Tuchman & Rapin, 2002). Furthermore, arecent study suggests seizures may, in fact, arise in late ado-lescence or adulthood in people with ASD (Bolton et al.,2011), reinforcing our confidence in our results. In addition,in keeping with some previous literature, we found a 70%increase in odds of a psychotic disorder in people with epi-lepsy, albeit with wide CIs. Clinical studies have reported ahigher prevalence (2–9%) of psychotic disorders (Garcia-Morales et al., 2008) in people with epilepsy (particularlytemporal lobe epilepsy) than our modest 1% prevalence.However, it has been noted that previous reports may haveoverestimated this association by studying highly selectedclinical populations (Swinkels et al., 2005).

Although questions covering adult ADHD, eating disor-ders, and PTSD were administered using screening instru-ments, our study represents the first population-basedestimates of their possible relationship with epilepsy inadults. It has previously been suggested that the prevalence ofADHD in adults with epilepsy may be underestimated (Gar-cia-Morales et al., 2008), the rationale being that 25–30% ofthe pediatric population with epilepsy have comorbid ADHD,and that 50–75% of these children may continue to haveADHD in adulthood (Garcia-Morales et al., 2008). We foundpartial support for this assertion. More than 15% of adultswith epilepsy in our sample with epilepsy screened positivefor adult ADHD, representing an over twofold increase inodds when compared with the general population withoutepilepsy. This association, however, was largely explainedafter adjustment for confounders. We also found a significantassociation between epilepsy and screen-positive eating dis-orders, which has not been previously investigated. We didnot find evidence of a relationship between screen-positivePTSD and epilepsy in contrast with findings from one smallstudy (Rosenberg et al., 2000) of 35 patients with intractableseizures. Further exploration of the relationships betweenepilepsy and these conditions, using diagnostic instrumentsare obvious questions for future research.

Possible mechanismsEpilepsy has been a stigmatized condition throughout his-

tory, and negative societal (mis)perceptions, stereotyping,and discrimination against people with epilepsy continuesto be common (Scambler & Hopkins, 1980; Baxendale &

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O’Toole, 2007; de Boer et al., 2008; de Boer, 2010; TheLancet Neurology, 2010), and is known to propagate socialisolation and low self-esteem (Jacoby, 1994; Krauss et al.,2000). People with epilepsy often live in uncertainty and infear of having further seizures (Fisher et al., 2000). Theseissues could explain our results, and conditions associatedwith worry (GAD and other anxiety disorders), avoidance(phobias, particularly social phobia), stigma (depression,phobias), and low self-esteem (depression and suicidality,eating disorders) all had strong relationships with epilepsy.

The understanding of a common neurobiology betweenepilepsy and psychiatric comorbidities is also emerging(Tecott et al., 1995; Cowan & Kandel, 2001; Martin, 2002;Tuchman & Rapin, 2002; Jobe, 2003; Kanner, 2005),although most evidence is based on animal models orobserved indirectly (e.g., through efficacy of anticonvul-sants on mood disorders) (Grunze, 2008). Some overlap inthe psychopathology observed in epilepsy and migrainecannot rule out the presence of common pathways of comor-bidity in neurologic conditions. However, associations ofepilepsy with conditions such as autism spectrum disorderspoint to comorbidities specific to epilepsy that may not beshared by other neurologic conditions. Furthermore,advances in the neurosciences hold potential for elucidationof pathways between seizures, other neurologic conditions,and psychopathology (Cowan & Kandel, 2001; Martin,2002).

Implications and Future

Research

The causal pathways of our findings need further elucida-tion through well-designed cross-disciplinary longitudinalstudies using detailed measures of epilepsy (Thurman et al.,2011). The impoverishing ‘‘wall’’ (Cowan & Kandel, 2001;Baker et al., 2002; Martin, 2002) between neurology andpsychiatry is notable through the documentation of the‘‘sore lack’’ of epidemiologic data on epilepsy (The LancetNeurology, 2010), when detailed psychiatric morbidity sur-veys such as the APMS are being conducted. Future psychi-atric morbidity surveys hold potential as vehicles fordetailed population-based studies of epilepsy. In clinicalpractice, greater awareness of the range of psychiatric pre-sentations in people with epilepsy, and their effective identi-fication and management is warranted.

Acknowledgments

The APMS survey was carried out by the National Centre for SocialResearch in collaboration with the University of Leicester, and was com-missioned by The NHS Information Centre for health and social care withfunds from the Department of Health. The APMS data are available fromthe UK Data Archive, University of Essex, Colchester. DR is fundedthrough a clinical lecturer award funded by Severn Deanery, Bristol, U.K.We thank Professor Howard Meltzer for useful comments on the manu-script.

Disclosure

None of the authors has any conflict of interest to disclose.

We confirm that we have read the Journal’s position on issues involved inethical publication and affirm that this report is consistent with those guide-lines

Author Contributions

(1) All coauthors have been substantively involved in the study and/orthe preparation of the manuscript; (2) no undisclosed groups or personshave had a primary role in the study and/or in manuscript preparation (i.e.,there are no ‘‘ghost-writers’’); and (3) all coauthors have seen and approvedthe submitted version of the paper and accept responsibility for its content.Specific contributions: DR conceived this paper, conducted the analysis,wrote the first and subsequent drafts of the manuscript, and is the guarantor.MK, VJ, SM, GL, and TSB contributed significantly toward the scientificdevelopment of this article at all stages, and edited successive versions ofthe manuscript. SM and TSB made important contributions to the designand conduct of the parent APMS survey. DR had full access to the data andtakes responsibility for submission.

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