Journal of Affective Disorders 140 (2012) 222–232

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Journal of Affective Disorders

j ourna l homepage: www.e lsev ie r .com/ locate / jad

Review

Psychosocial predictors of depression and anxiety in patients with epilepsy:A systematic review

Milena Gandy a,⁎, Louise Sharpe a,⁎⁎, Kathryn Nicholson Perry b

a The School of Psychology, University of Sydney, Australiab The Health Services and Outcomes Research Group/School of Psychology, University of Western Sydney, Australia

a r t i c l e i n f o

⁎ Correspondence to: M. Gandy, School of Psycholo⁎⁎ Correspondence to: L. Sharpe, School of Psycholog

E-mail addresses: gandym@tcd.ie (M. Gandy), loui

0165-0327/$ – see front matter © 2011 Elsevier B.V. Adoi:10.1016/j.jad.2011.11.039

a b s t r a c t

Article history:Received 30 September 2011Received in revised form 25 November 2011Accepted 25 November 2011Available online 22 December 2011

Background: People with epilepsy (PWE) have a high chance of experiencing depression andanxiety disorders over their lifetime. However, those most at risk are unknown. Psychosocialvariables have been suggested as potentially important risk factors. A systematic review wasconducted in order to critically assess available evidence regarding the psychosocial predictorsof depression and anxiety in adults with epilepsy.Methods: Electronic databases searched were MEDLINE, PsycINFO and Web of Science. Studieswere included if they assessed depressive or anxiety symptoms using a validated questionnaire,and controlled for the role of potentially important epilepsy factors. Eleven studies wereidentified and assessed for research standards using the Quality Index Scale (QIS).Results: Ten of the eleven studies found at least one significant predictor of depression and allsix studies that assessed anxiety found one or more significant predictors.Limitations: Overall QIS score was only 7.5 out of 15, indicating significant design limitations ofmany included studies. There was also large variability between studies in measures used toassess psychosocial variables.Conclusion: Studies did not support the importance of attributional theory and stigma in thedevelopment of depression in epilepsy. There was inconsistent support for the role of illnessrepresentations but likely support for the role of stress and self-efficacy. Consistent supportwas found for the role of coping strategies and perceived social support. Given that psychosocialfactors are potentially modifiable, a better understanding of their role in the development ofdepression in people with epilepsy is urgently needed to guide effective treatments.

© 2011 Elsevier B.V. All rights reserved.

Keywords:DepressiveAnxiousPsychopathologyRiskQuality Index

Contents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2231.1. Consequences of psychopathology in PWE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2231.2. Identifying those at risk of mood disorders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223

1.2.1. Neuroepilepsy variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2231.2.2. Medication variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2231.2.3. Sociodemographic variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2241.2.4. Psychosocial variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 224

2. Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2243. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225

gy A18, University of Sydney, NSW 2006, Australia. Tel.: +61 29351 4063.y A18, University of Sydney, NSW 2006, Australia. Tel.: +61 293514558.se.sharpe@sydney.edu.au (L. Sharpe).

ll rights reserved.

223M. Gandy et al. / Journal of Affective Disorders 140 (2012) 222–232

3.1. Do psychosocial variables predict depression? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2253.1.1. Attributional theory constructs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2253.1.2. Impact of epilepsy constructs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2253.1.3. Mood-related constructs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2253.1.4. Stigma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2253.1.5. Social support . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2283.1.6. Coping strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2283.1.7. Illness representations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2283.1.8. Self-efficacy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2283.1.9. Stress . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 228

3.2. Do psychosocial variables predict anxiety? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2293.2.1. Health locus of control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2293.2.2. Impact of epilepsy constructs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2293.2.3. Mood-related constructs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2293.2.4. Stigma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2293.2.5. Coping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2293.2.6. Illness representations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229

4. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2294.1. Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229

4.1.1. Study design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2294.1.2. Lack of theoretical framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2294.1.3. Internal validity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2294.1.4. External validity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 230

4.2. Main findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2304.2.1. Overstated importance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2304.2.2. Inconsistent support . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2304.2.3. Consistent support . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231

5. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231Role of funding source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231Conflict of interest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231

1. Introduction

1.1. Consequences of psychopathology in PWE

Research indicates that people with epilepsy (PWE) are athigher risk for developing depression and anxiety comparedwith healthy controls and those with other medical condi-tions (Barry et al., 2008; Beyenburg et al., 2005; Hermann etal., 2000; Kanner, 2003a). Depression affects up to 30% ofPWE in population-based studies and up to 50% of patientsin tertiary settings (Kanner, 2003a,b). Untreated depressionresults in greater medical costs, worse response to epilepsytreatment, and more adverse antiepileptic drug AED effects(Barry et al., 2008; Cramer et al., 2003, 2004). Depressionand anxiety have a significant negative impact on the qualityof life of PWE, over and above that associated with seizurefrequency (Boylan et al., 2004; Brandt et al., 2010; Zeber et al.,2007). Anxiety and depressive disorders frequently co-occur,which results in more significant clinical consequences forPWE, including greater suicide risk (Kanner, 2009b). However,it is still unclear who is most at risk of developing thesedisorders (Barry et al., 2008).

1.2. Identifying those at risk of mood disorders

Hermann et al. (2000) suggested four domains of potentialrisk factors: (1) neuroepilepsy variables, such as seizure type;(2) medication variables (3) sociodemographic factors

and (4) psychosocial factors. Baker (2002) noted thatidentifying the relative contribution of these risk factorshas resulted in few conclusive findings. However, it continuesto be emphasized as an important direction for futureresearch (Barry et al., 2008).

1.2.1. Neuroepilepsy variablesThere are several reviews of the relationship between

depression and epilepsy. Those reviews addressing neuroe-pilepsy variables have produced mixed findings and therole of seizure variables remains inconclusive (Hermann et al.,2000; Lambert and Robertson, 1999; Marsh and Rao, 2002;Seethalakshmi and Krishnamoorthy, 2007). Recent reviewshave investigated this relationship based on the assumptionthat there are common neuropsychiatric pathways(Hesdorffer and Lee, 2009; Kanner, 2003a,b, 2004, 2005,2009a; Kanner and Balabanov, 2002). However the evidencefor this still remains tentative. While common neurologicalpathways cannot be ruled out as contributing to psychopatho-logy in PWE, these theories are at best insufficient to explainthe high levels of co-morbidity.

1.2.2. Medication variablesAll AEDs can trigger psychiatric symptoms in PWE (Barry

et al., 2008). The specific level of risk AEDs pose for the deve-lopment of depression or anxiety, however, remains unclear(Barry et al., 2008; Vazquez and Devinsky, 2003). Patientsmost at risk for iatrogenically-triggered depression are

224 M. Gandy et al. / Journal of Affective Disorders 140 (2012) 222–232

those with a prior and/or family psychiatric history, suggestingthat AEDsmay act in combinationwith other risk factors (Barryet al., 2008). Despite the suggestion that the polytherapy in-creases the likelihood of depression (Hermann et al., 2000)there is limited evidence to support such claims (Grabowska-Grzyb et al., 2006; Mensah et al., 2006; Seminario et al., 2009).

1.2.3. Sociodemographic variablesSimilarly, research assessing the role of sociodemographic

factors is yet to yield conclusive findings. For instance, astudy comparing PWE with major depression and thosewith no psychiatric history did not find differences on anysociodemographic variables, including education, work,housing and marital status (Schmitz et al., 1999).

1.2.4. Psychosocial variablesPsychosocial variables have received less attention and

there are no reviews in this area to date. However,Hermann et al. (2000) noted that some studies included intheir review on neuropsychiatric variables assessed psycho-social factors and in 79% of those studies there was at leastone positive finding. They concluded that psychosocialfactors have the best chance of identifying correlates ofdepression. The aim of this systematic review was to assessthe available evidence regarding whether psychosocial factorspredict depression and anxiety in PWE; and if so, which factorsare most strongly supported by the evidence.

Fig. 1. Search process utilized and outcomes obtained in identifying articles for the revarticle.

2. Methods

A literature search of published studies was conducted inJanuary 2011. The electronic databases MEDLINE, PsycINFOand Web of Science were searched using different combina-tions of the following keywords: epilepsy, depression,depressive symptoms, mood, anxiety, anxious, affective,risk factors and predictors. There were no limits placedon the years for searched articles. All medical SubjectHeading (MeSH) terms were exploded to broaden the searchfor relevant studies. In addition, the ancestry method ofreviewing the references of empirical studies and reviewsfor other relevant articles was used to identify further studies.Tomeet the inclusion criteria, the study needed to (1) involveadults with a diagnosis of epilepsy; (2) use establishedmeasures of anxiety and/or depression; (3) perform multi-variate analyses to predict depression and/or anxiety; (4)use psychosocial predictive factors, which were defined asdynamic, theoretically driven psychological and/or socialcharacteristics; and (6) be written in English.

Fig. 1 displays the process of study selection for the re-view. 516 articles were identified through the search strate-gy. All titles and abstracts were reviewed to determine theirrelevance by one reviewer (MG). Reasons for exclusion areoutlined in Box A. A random selection of 25% (129) of ab-stracts was then assessed by an independent assessor (LS)to ensure inter-rater reliability, where there was 100%

iew. *Included poster presentations and published abstracts with no associated

225M. Gandy et al. / Journal of Affective Disorders 140 (2012) 222–232

agreement. Following this, 43 abstracts were retained, andfull copies of the papers were reviewed. Two raters (MGand LS) assessed each article for eligibility, as outlined inBox B. Disagreement between reviewers involved one arti-cle which was resolved by consensus. This study was ex-cluded due to lack of multivariate analysis. Inter-raterreliability had a kappa coefficient of 0.91.

Articles were reviewed by both assessors for quality utiliz-ing a modified version of the Quality Index Scale (QIS)(Downs and Black, 1998). This modified version was used ina systematic review of depressive symptoms among mothersof childrenwith epilepsy (Ferro and Speechley, 2009). The QIShas good reliability and validity for measuring the methodo-logical quality of health research (Downs and Black, 1998;Olivo et al., 2008; Sanderson et al., 2007; Wang et al., 2008).Items were scored either 0 (no/unable to determine) or 1(yes) with a maximum score of 15. The standard of reporting(0–7), external validity (0–3), internal validity (0–4) andstudy power (0–1) were assessed, higher scores indicatedstudies with higher methodological quality.

3. Results

There were 11 studies that met criteria for inclusion thedetails of which are summarized in Table 1. The years of pub-lication for these studies ranged from 1989 to 2010. The totalmean QIS was 7.5 (SD=3.1; range 4–13), 3.5 (SD=2.02,range 0–6) for reporting, 1.2 (SD=1.08, range 0–3) for exter-nal validity, and 3.27 (SD=0.90, range 2–4) for internal va-lidity. Only one study reported a power calculation (Kempet al., 1999).

3.1. Do psychosocial variables predict depression?

All studies were analyzed to determine which psychoso-cial factors have been found to predict depression. Table 2includes a summary of the mean QIS scores for the constructsunder review.

3.1.1. Attributional theory constructsThree of the four studies that investigated health-related

locus of control (HLOC) failed to find it to be a significant pre-dictor of depression (Asadi-Pooya et al., 2007; Endermann,1997; Hermann and Whitman, 1989). The other study,found HLOC in combination with activities of daily living(ADL), anxiety, self-esteem and happiness, accounted for38.5% of the variance in depression (Smith et al., 1991). How-ever, the analysis did not report individual contributions ofvariables. Further, the inclusion of mood-related variables islikely to have inflated the amount of variance accounted for(Sherbourne and Wells, 1997; Wood et al., 2010). The studyby Endermann (1997) also investigated the role of helpless-ness beliefs, described as judgments about hypothetical neg-ative epilepsy-specific outcomes. This study used a single-item measure that they termed “pre-attributional judgment”assessed at baseline (T1) to predict depression at discharge(T2). Patients who reported that their epilepsy was less likelyto change had higher levels of depression at discharge. It wasunclear from the article whether this remained significantwhen T1 depression was included in the analysis. This studyalso compared patients who developed clinical levels of

depression between T1 and T2 (n=9) with patients whowere never depressed (n=31). The group who developeddepression was more likely to endorse the item poor seizurecontrol that only happens to me as opposed to others. Pa-tients who remained depressed at both time-points(n=18) reported lower epilepsy-specific HLOC and scoredhigher on the pre-attributional variable: “distinctiveness ofnegative epilepsy-specific outcomes” when compared withpatients who recovered between T1 and T2 (n=19).

3.1.2. Impact of epilepsy constructsReisinger and Dilorio (2009) found that greater activity

restriction was a significant predictor of higher levels of de-pression at baseline but not follow up. A similar construct,ADL was assessed by Smith et al. (1991), however, the indi-vidual contribution of this variable was not reported.Reisinger and Dilorio (2009) also found patient financial dis-satisfaction predicted depression at baseline and 6 monthsbut not 3 months. Similarly, Hermann and Whitman (1989)found that less adequate financial status was a significantpredictor of depression. Lower levels of medication self-management was found to predict greater depression levelsat 3 and 6 months but not at baseline, whereas, lower life-style self-management was a significant predictor at baselineonly (Reisinger and Dilorio, 2009). Baker et al. (1996) foundthat impact of epilepsy predicted 3.33% of variance in depres-sion. A similar construct, poorer adjustment to epilepsy, wasalso found to significantly predict depression (1989). Al-though there was some evidence from each study for a rela-tionship between greater impact of epilepsy and increaseddepression, each study used a different measure and resultswere rarely consistent across time points.

3.1.3. Mood-related constructsFour studies assessed constructs that highly-overlapped

with depression. Happiness and depression can be argued tobe on opposite ends of the same construct (McGreal andJoseph, 1993). Furthermore, self-esteem (Franck and DeRaedt, 2007) and anxiety (Sherbourne and Wells, 1997) arehighly associatedwith depression. Three studies reported a sig-nificant relationship between depression and anxiety (Baker etal., 1996; Lee et al., 2010; Smith et al., 1991). Smith et al. (1991)found that lower levels of happiness and self-esteem signifi-cantly predicted higher levels of depression. One furtherstudy found that greater neuroticism explained 52% of the var-iance in depression (Endermann and Zimmermann, 2009).However, there is considerable evidence to support the factthat neuroticism is a trait that underpins negative affectivity(Andrews et al., 2002) including low mood, and therefore theimportance of this finding is open to debate.

3.1.4. StigmaOnly one out of three studies found increased perceived

stigma to predict depression. Reisinger and Dilorio (2009)found higher levels of stigma predicted depression at base-line, 3 and 6 month follow up. These findings were inconsis-tent with Hermann and Whitman (1989) and Baker et al.(1996) who failed to find that stigma was an independentpredictor of depression. Comparison of studies is complicatedby the utilization of different measures of stigma within avariety of settings.

Table 1Summary of studies reviewed.

Author year (country) Studydesign

Sample Outcomemeasure

Psychosocial measures Significant predictors in multivariate analysis QIS

Lee et al. (2010) (Korea) Crosssectional

N=150 (18–60 years M=34.7).Epilepsy >1 year. Remission (24.7%).Unspecified recruitment area.

BDI Daily Hassle Scale, Stress Coping StyleChecklist, Epilepsy, Self-Efficacy Scale,Social Support Scale, Beck Anxiety Scale.

Depression related to level of stress(β=0.278, pb0.001), social support(β=−0.065, p=0.04), anxiety (β=0.182,p=0.001) and self-efficacy (β=−0.007, pb0.001)

6

Reisinger and Dilorio (2009)(United States)

Prospective N=319 (18–75 M=43.2) Epilepsy>1 year. Recruited from epilepsyclinics.

CES-D Unvalidated measure of activityrestriction due to seizure condition,Epilepsy Self Management Scale,Epilepsy Self-Efficacy Scale,Personal Resource Questionnaire85 Part 2, Epilepsy Stigma Scale,Patient Satisfaction Questionnaire,Self-Reported Medication Taking Scale.

Depression related to: activityrestriction (β=2.371, p=0.021),lifestyle self-management (β=−0.368,p=0.010), self-efficacy (β=−0.051,p=0.001), social support (β=−0.103,p=0.0001), stigma (β=0.101, p=0.009)and financial patient satisfaction (β=−0.264,p=0/0001) at baseline. Medicationself-management (β=−0.468, p=0.010),social support (β=−0.155, p=0.0001)stigma (β=0.117, p=0.021) at 3 months.Medication self-management (β=−0.403,p=0.004), social support (−0.109, p=0.001)and stigma (0.141, p=0.004) at 6 months

9

Endermann and Zimmermann (2009)(Germany)

Crosssectional

N=36 (18–35 years M=25.6)difficult to treat epilepsy and mildcognitive impairments. Recruitedfrom short term residential care.

HADS Neo-Five-Factor Inventory Neuroticism related to depression (β=0.73,p=b.001) and anxiety (β=0.67, pb .001)

9

Asadi-Pooya et al. (2007)(United States)

Crosssectional

N=200 (23–66 years M=40.3).Inpatients and outpatients. Epilepsy>1 year. Well controlled 60 (31%)and refractory epilepsy 133 (69%)a

HADS Form C of the multidimensionalhealth locus of control

Anxiety predicted by powerful otherhealth locus of control (p=0.042,r2=0.021, t=−2.042, df=1,198)

4

Goldstein et al. (2005)(United Kingdom)

Crosssectional

N=43 (M=36.07 years) chronicepilepsy, specialized epilepsyneurology clinics

HADS Ways of Coping, IllnessPerception Questionnaire

Depression predicted by escape avoidance(β=0.431, p=0.003) and self-controllingcoping (β=0.296, p=0.038). Anxietypredicted by escape avoidance (β=0.345,p=0.012), distancing coping (β=−0.277,p=0.032) and Illness Identity representations(β=−0.335, p=0.014)

10

Kemp et al. (1999)(United Kingdom)

Crosssectional

N=94; 3 groups (21 recentlydiagnosed; 47 chronic patients caredfor in hospital; 26 chronic patientscared for by GP).

MHIb Revised Ways of Coping Checklist.Unvalidated measure of illnessrepresentations

Mental Health Index predicted by problemfocused coping (β=0.28, pb .01) andself-illness representations (β=−0.24,pb .05). Psychological distress predictedby Illness Identity (label; β=−0.27, pb0.1).

12

Endermann (1997) (Germany) Prospective N=77 (16–62). Inpatients withrefractory epilepsy

GDS Unvalidated Epilepsy-SpecificControl-Beliefs Questionnairebased on the multidimensionalhealth locus of control. Unvalidatedmeasure of helplessness beliefs.

Depression prior to discharge to hospital(T2; mean 96 days SD=70) predicted bypreattributional judgmental consistency ofnegative epilepsy specific event measuredat (T1) entry to hospital (β=0.25, pb0.05).From reported analysis unclear whether thisremained significant once T1 depression andmedication variables were included inthe analysis.

5

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Table 1 (continued)

Author year (country) Studydesign

Sample Outcomemeasure

Psychosocial measures Significant predictors in multivariate analysis QIS

Baker et al. (1996)(United Kingdom)

Crosssectional

N=586 (completed HADS).Community sample.

HADS The Impact of Epilepsy Scale, The AdverseDrug Events Profile, the Stigma Scale

Depression predicted by anxiety (r2=0.50078,F=587.12, p=0.001) and impact of epilepsy(r2=0.53401, F=335.19, p=0.001). Anxietypredicted by depression (r2=0.50078,F=587.82, p=0.001), impact of epilepsy(r2=0.53453, F=335.90, p=0.001),stigma (r2=0.8883, F=118.65, p=0.001)and Adverse Events Profile (r2=0.59171,F=104.89, p=0.001).

7

Upton and Thompson (1992)(United Kingdom)

Crosssectional

N=137 intractable epilepsy 60% recruitedfrom epilepsy clinics and 40% fromcommunity

HADS The Ways of Coping Checklist Depression predicted by wish fulfillingfantasy (β=0.34, pb0.001).Self blame (β=0.20, pb0.05) andcognitive restructuring (β=−0.21,pb0.005) anxiety predicted by wishfulfilling fantasy (β=0.36, pb0.001)and cognitive restructuring (β=−0.21,pb .05).

13

Smith et al. (1991)(United Kingdom)

Crosssectional

N=100 Medically refractory partialseizures. 80 attended a RCT for new AED, 14evaluated for surgery of intractable TLE, 6neurology outpatient department.

HADS Nottingham Health Profile,Self-Esteem Scale, External Locusof Control Scale, Mood Profile,Happiness Scale, Social Questionnaire

Depression predicted by group includingactivities of daily living, anxiety, self-esteem,locus of control and happiness(r2=0.385, F=13.9, pb0.0001). Anxietypredicted by group including, activities ofdaily living, depression, self-esteem, locus ofcontrol and happiness (r2=0.302, F=8.30,pb0.0001)

4

Hermann and Whitman (1989)(United States)

Crosssectional

N=102 Patients being assessed forsuitability for focal resection ofepileptogenic lesion.

CES-D Perceived Stigma Scale, The PerceivedLimitations, The Adjustment to Seizures,Vocational Adjustment, and Financial Statusscale of the Washington Psychosocial SeizureInventory, Life Experience Survey, The SocialSupport Questionnaire (no. of individualsavailable to provide social support), Rotter'sInternal/External Control of Reinforcementscale.

Depression predicted by stressful life events(p=0.013), poor adjustment to epilepsy(p=0.0002) and less adequate financialstatus (p=0.007)

8

Beck Depression Inventory (BDI); Center for Epidemiological Studies Depression Scale (CES-D); the German Depression Scale (GDS); the Hospital Anxiety and Depression Scale (HADS), Mental Health Inventory (MHI);Quality Index Scale (QIS).

a It was recognized that these numbers do not match those of the sample size of 200 published by the authors.b The MHI includes items relating to depression and was thus used in this discussion.

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Table 2Mean QIS scores for constructs reviewed for depression.

Construct domain Number of studies Overall QIS mean (SD) Trial reporting mean (SD) Internal validity mean (SD) External validity mean (SD)

Attributional theory 4 5.25(1.89)

1.5(1.73)

3.29(0.95)

0.5(1)

Impact of epilepsy 4 7.25(2.5)

3.25(1.71)

3.25(0.92)

0.75(0.96)

Mood-related 4 6.50(2.08)

3(1.63)

2.5(0.58)

1(1.15)

Stigma 3 8.3(1.53)

4(1)

3.33(1.15)

1(1)

Social support 3 8(2)

4(1)

3.67(0.58)

0.33(0.58)

Coping 3 11.67(1.53)

5.33(0.58)

2(1)

0.9(0.99)

Illness representations 2 11(1.41)

5(0)

4(0)

1.5(0.71)

Self-efficacy 2 8(2.83)

4(1.41)

3.22(0.97)

0.5(0.71)

Stress 2 7(1.41)

3.5(0.71)

3.22(0.97)

1.44(1.01)

Standard deviation (SD).

228 M. Gandy et al. / Journal of Affective Disorders 140 (2012) 222–232

3.1.5. Social supportTwo out of three studies found decreased social support pre-

dicted depression. In Lee et al.'s (2010) study it accounted for6.8% of the variance in depressionwhen assessed in a regressionanalysis with self-efficacy, stress, anxiety, age and duration ofepilepsy. Reisinger and Dilorio's (2009) prospective studyfound that decreased levels of social support predicted depres-sion at baseline, 3 and 6 months. These resultswere inconsistentwith Hermann andWhitman (1989) who found that the size ofsocial network was not an independent predictor of depression.

3.1.6. Coping strategiesGoldstein et al. (2005) found that increased escape-avoidant

coping and self-controlling coping predicted depression, account-ing for 16.3% and8.7%of variance, respectively. Kempet al. (1999)found that higher levels of problem-focused coping predictedlower levels of mental health. Finally, Upton and Thompson(1992) found that increased levels of wish-fulfilling fantasy, self-blame and lower cognitive-restructuring predicted 3.13%,11.86% and 4.03% of the variance in depression, respectively.

3.1.7. Illness representationsOne out of two studies found that illness representations

predicted depression. Kemp et al. (1999) found that illness

Table 3Mean QIS scores for constructs reviewed for anxiety.

Construct domain Number of studies Overall QIS mean (SD) Trial report

Attributional theory 2 4(0)

0.5(.71)

Impact of epilepsy 2 5.50(2.12)

2(1.41)

Mood-related 2 6.67(2.52)

3(2)

Stigma 1 7 3Coping 2 11.50

(2.12)5.50(0.71)

Illness representations 1 10 5

Standard deviation (SD).

representations improved the prediction of mental healthby 9%, with self-illness relationship being the independentcontributor. They also improved prediction of psychologicaldistress scores by 14%, with higher Illness Identity scoresbeing the independent contributor. Goldstein et al. (2005)failed to find illness representations improved the predictionof depression when entered into the third step of their re-gression analysis.

3.1.8. Self-efficacyTwo studies found a positive relationship with self-

efficacy, cross-sectionally. Lee et al. (2010) found that lowerself-efficacy predicted 3.9% of the variance of depression.Reisinger and Dilorio (2009) also found decreased self-efficacy to be a significant predictor of depression at baselinebut not at 3 or 6 month follow-ups.

3.1.9. StressTwo studies found stress predicted depression. Lee et al.

(2010) found that higher levels of stress (daily hassles)were the most significant predictor of depression, account-ing for 38.8% of variance. Hermann and Whitman (1989)found that a greater number of stressful life events predicteddepression.

ing mean (SD) Internal validity mean (SD) External validity mean (SD)

2.5(0.71)

1(1.41)

2.5(0.71)

1(1.41)

2.33(0.58)

1.33(1.15)

2 24(0)

2(1.4)

4 1

229M. Gandy et al. / Journal of Affective Disorders 140 (2012) 222–232

3.2. Do psychosocial variables predict anxiety?

Six studies assessed predictors of anxiety; the QIS scoremeans for each of the reviewed constructs are included inTable 3.

3.2.1. Health locus of controlThe two studies to assess HLOC did not provide strong

support for a relationship. Although Asadi-Pooya et al.(2007) found a statistically significant relationship betweenanxiety and higher scores relating to powerful-other HLOC,they noted that it was too small to be clinically significant.Furthermore, the contribution of HLOC cannot be assessedfrom Smith et al.'s (1991) analysis.

3.2.2. Impact of epilepsy constructsBaker et al. (1996) found that greater impact of epilepsy

accounted for 3.38% of the variance in anxiety scores. In-creased adverse events related to AEDs were a significantpredictor; but explained only 0.29% of the variance. The con-tribution of ADL cannot be established from the results ofSmith et al.'s (1991) study.

3.2.3. Mood-related constructsBaker et al. (1996) found that higher levels of depression

accounted for 50% of the variance in anxiety. Smith et al.(1991) predicted anxiety using a group of variables that in-cluded happiness, self-esteem and depression which collec-tively accounted for 30.02% of the variance. Endermann andZimmermann (2009) found that increased levels of neurot-icism explained 48% of the variance in anxiety.

3.2.4. StigmaBaker et al. (1996) included that stigma in their variables

found to significantly predict anxiety, including depression.However, when calculated, it only contributed to explaininga minimal 0.26% of the variance.

3.2.5. CopingTwo studies found that coping predicted anxiety.

Goldstein et al. (2005) found that higher escape-avoidanceand decreased distancing predicted greater anxiety, account-ing for 21.5% and 8.6%, respectively. Upton and Thompson(1992) found that increased use of wish-fulfilling fantasycoping predicted higher levels of anxiety, accounting for12.58% of the variance.

3.2.6. Illness representationsGoldstein et al. (2005) found that increased levels of

Illness Identity independently predicted anxiety and in-creased the variance explained by their analysis by a fur-ther 10.1%

4. Discussion

Despite the considerable variation in the studies de-signs including outcome measures and psychosocial factors10 out of 11 found at least one psychosocial variable pre-dicted depression. Similarly, each of the seven studiesfound that at least one psychosocial factor predicted anxi-ety. These results suggest that psychosocial variables are

more consistently related to depression and anxiety thanpreviously investigated variables. However, before consid-ering the results, the limitations that pervade the literatureneed to be acknowledged, since they render conclusionspreliminary.

4.1. Limitations

4.1.1. Study designThe majority of studies (9/11) used cross-sectional de-

signs. Although cross-sectional studies are an importantstep for identifying predictors, they are limited to correla-tional inferences, such that causation cannot be inferred. An-other significant limitation was the small sample size inmany studies. Four studies had a sample size less than 100and only Kemp et al. (1999) reported a power calculation.These small sample sizes may have resulted in the studiesbeing underpowered.

4.1.2. Lack of theoretical frameworkGoldstein et al. (2005) was the only study to outline a

priori hypotheses. This study, as well as the other copingand illness representation papers, appear to be grounded inthe soundest theoretical frameworks. They used the self-regulation model (SRM) proposed by e.g. Leventhal et al.(1998) to select variables. The SRM is amongst the most in-fluential models in understanding and predicting how peopleadapt to chronic illness (Sharpe and Curran, 2006). Many ofthe remaining studies had insufficient theoretical back-ground fromwhich they derived the selection of psychosocialvariables. The rationale of these studies was often describedas exploratory.

Four studies were significantly limited by the use of anxietyor depression (or relatedmood variables) to predict each other(Baker et al., 1996; Endermann and Zimmermann, 2009; Lee etal., 2010; Smith et al., 1991). These concepts are highly relatedand share over-lapping items. For instance, in the study byBaker et al. (1996) 50% of variance in depressionwas explainedby anxiety, with only 7% of variance being explained by otherfactors. These relationships simply reveal high levels of comor-bidity between anxiety and depression in PWE (Kanner,2009b), and therefore are relatively uninformative.

This same issue is also somewhat problematic for some ofthe variables assessing impact of epilepsy. For example,Hermann and Whitman (1989) found that poorer adjustmentto epilepsy predicted depression. However, depression is anindication of the fact that people fail to adjust to illness(Sharpe and Curran, 2006) and hence the theoretical impor-tance of these findings is open to question. Some factors, inclu-ded in the construct we described as adjustment to illness (e.g.activity restriction, ADL or financial impact) are arguably moreindependent. However, what people are able to do is alsostrongly associated with both their physical capacity and theirmood state. Hence, the degree to which these psychosocial pre-dictors are independent from depression is open to debate.

4.1.3. Internal validityFor almost all constructs each study used different mea-

sures. These often included unvalidated and non epilepsy-specific measures which may have lacked construct validity.This was also the case for outcome measures. There are

230 M. Gandy et al. / Journal of Affective Disorders 140 (2012) 222–232

various factors associated with the nature and treatment ofepilepsy that complicate the detection of psychopathology(Gilliam et al., 2006). As a result the Neurological DisordersDepression Inventory for Epilepsy (NDDI-E) (Gilliam et al.,2006) has been validated as a screener for depression thatexcludes illness factors and common AED side-effects.Whereas, the HADS, utilized in most studies, has not yetbeen validated in PWE (Barry et al., 2008). Further, no studiesused psychiatric diagnoses based on structured interviews.Future research should therefore consider the validity ofmeasures and include formal diagnoses of depressive andanxiety disorders.

4.1.4. External validityThe overall external validity was relatively poor (1.2; SD

1.08). Many studies recruited specific samples, such as inpa-tients (Asadi-Pooya et al., 2007; Endermann, 1997) and pa-tients being assessed for surgery (Hermann and Whitman,1989; Smith et al., 1991) with the majority including patientswith refractory epilepsy only (Endermann, 1997; Endermannand Zimmermann, 2009; Goldstein et al., 2005; Hermann andWhitman, 1989; Smith et al., 1991; Upton and Thompson,1992). Only two studies included patients with well-controlled epilepsy (Asadi-Pooya et al., 2007; Lee et al.,2010). Three studies excluded participants with a history ofpsychiatric diagnosis (Goldstein et al., 2005; Lee et al.,2010; Reisinger and Dilorio, 2009) and one of these also ex-cluded those on psychotropic medication (Goldstein et al.,2005). Two studies failed to exclude those with learning dif-ficulties (Asadi-Pooya et al., 2007; Lee et al., 2010). There wasalso a lack of consistency between methods used to diagnoseand distinguish epilepsy symptoms. For instance, Baker et al.(1996) and Upton and Thompson (1992) included partici-pants with a diagnosis from a GP, as opposed to a hospitalspecialist, which could lead to contamination of their samplewith misdiagnosed epilepsy. In addition, only two studies ex-plicitly noted consecutive sampling (Hermann andWhitman,1989; Upton and Thompson, 1992), which means that otherstudies can be considered to be a sample of convenience.

The selective nature of these studies makes comparison offindings more complex and may explain the inconsistency inresults. Rates of depression have been found to differ in thosewell-controlled versus refractory forms of epilepsy (2004)and it is possible that predictors of psychopathology differbetween these groups. Furthermore, the lack of control forseizure variables and learning difficulties may have con-founded findings. Exclusion of those with psychiatric histo-ries is also likely to have resulted in underestimated levelsof symptomotology and a restriction in the range withinsamples.

4.2. Main findings

Despite the limitations, there were three major findings,reported below.

4.2.1. Overstated importanceNumerous opinion papers emphasize the unpredictability

and uncontrollability of epilepsy and argue that epilepsy mayresult in a form of learned helplessness that results in depres-sion in accordance with Seligman's attributional theory. This

theory states that individuals who make internal, stableand global attributions about the cause of a negativeevent (such as epilepsy-related stressors) will become de-pressed (Miller et al., 1975). This review found little evi-dence for this predicted relationships. Although Smith etal. (1991) found that HLOC was associated with depressionand anxiety, in multivariate analyses it was entered with agroup of variables including anxiety and happiness, whichseem very likely to have made the major contribution. Fur-thermore, although one other study reported a significantrelationship with powerful others HLOC and anxiety, eventhe authors argued it was too small to be clinically signifi-cant (Asadi-Pooya et al., 2007). Although Endermann's(1997) study found some relationships between increasedHLOC, helplessness beliefs and higher levels of depression,this study had one of the lowest quality ratings of only 5.These findings were also based on single or 3-item unvali-dated questionnaires of attributional constructs and in-volved comparisons of very small groups (e.g. n=18–19).In the context of other, null findings (Asadi-Pooya et al.,2007; Hermann and Whitman, 1989), Endermann's resultstherefore do not provide compelling support for the role ofattributional theory constructs.

Stigma has been described as the largest burden faced byPWE (McCagh et al., 2009) and it has been suggested inmultiple reviews as a likely risk factor for the developmentof psychopathology (Antonak and Livneh, 1992; Baker,2002; Barry, 2003; Beyenburg et al., 2005; Hesdorffer andLee, 2009; Lambert and Robertson, 1999; Marsh and Rao,2002). However, increased levels of stigma predicted de-pression in only 1/3 studies and it was found to be associat-ed with very small proportion of the variance in anxiety inthe one study to assess this. These results therefore questionwhether stigma deserves the role it has been given in theliterature as a major contributor to psychopathology. Unfor-tunately, each study used a different measure of stigma,which made comparisons of the results difficult. In addition,depression and anxiety scores measured by Baker et al.(1996) were mainly accounted by each other, which limitedthe amount of variance that may have been accounted for byother variables.

Although the role of stigma and attributional theory re-main inconclusive, in the only two prospective studiesreviewed a role was found for both. Therefore, while the cur-rent evidence does not support the weight given to them, animportant role for each cannot yet be ruled out.

4.2.2. Inconsistent supportThere were mixed findings for the role of illness repre-

sentation and depression. Goldstein et al. (2005) failed tofind that illness representations predicted depression, al-though they did predict anxiety. It should be noted thatthis study had a sample of only 43 participants and was like-ly to be underpowered. Furthermore, it excluded patientswith a history of psychiatric diagnosis which may have re-duced the variance in depression. Kemp et al. (1999) foundthat illness representations, assessed through interview, sig-nificantly improved the prediction of the MHI. Future re-search that involves the use of validated measures, in alarge sample size of representative PWE is necessary to as-sess this area further.

231M. Gandy et al. / Journal of Affective Disorders 140 (2012) 222–232

4.2.3. Consistent supportConsistent evidence for the role of coping was found for

depression and anxiety, with each of the three studies findingat least one significant predictor. It is difficult to state themost important coping strategies as each study utilized dif-ferent measures, which assessed different coping strategies.The research was, however, consistent with predictions ofbroad definitions of coping, such that engagement strategies(problem-focused and cognitive-restructuring), have beenfound to be related to better illness adjustment (Livneh etal., 2001) whereas, disengagement strategies, includingwishful-thinking and avoidance, are related to poorer adjust-ment and psychopathology. Coping consistently predictedsome of the largest amounts of unique variance in psychopa-thology and was of the highest quality of research reviewed.As coping is a dynamic, evolving process rather than static itwill be important for research to assess this longitudinally(Livneh et al., 2001). This research would benefit fromusing consistent measures of coping across studies in orderto identify the most important specific coping strategies.

There was also consistent support for the role of lowerperceived social support and depression. Although onestudy failed to find a relationship it used a limited measureof the size of social network (Hermann and Whitman,1989). Lower levels of perceived social support have consis-tently been found to predict psychological distress betterthan size of social network in other illnesses (Barrera,1986). Reisinger and Dilorio (2009) provided strong evidencefor the role of decreased social support, which predicted de-pression over a 6 month period. Future research would bene-fit from assessing its contribution when considered inrelation to other psychosocial factors and its relationshipwith anxiety.

There was also consistent support for the role of stresshowever its definition differed greatly between studies. Onestudy assessed daily stress, and the other assessed the num-ber of stressful events of the past year. Further, the sampleof patients with intractable epilepsy being considered for re-section in Hermann and Whitman's (1989) study may haveexperienced increased stressful life events relating to poorseizure control, which limits generalizability. The role ofstress for predicting anxiety in PWE remains to be assessed.

Finally, there was consistent support for the role of de-creased self-efficacy for predicting depression in the twostudies that assessed this cross-sectionally (Lee et al., 2010;Reisinger and Dilorio, 2009). However, it should be notedthat it failed to remain a significant predictor prospectively(Reisinger and Dilorio, 2009). Further research is needed toassess the role of self-efficacy prospectively and in predictinganxiety.

5. Conclusions

On the one hand, this systematic review has highlightedthe fact that, in contrast to the research on other risk factors,psychosocial variables were found to consistently predict de-pression and anxiety across studies. This is potentially impor-tant because, unlike the other classes of predictors which areeither stable (neuroepileptic and sociodemographic vari-ables), or necessary (medication variables), psychosocial var-iables are amenable to change via psychosocial interventions

(Brown and Schulberg, 1995), including Cognitive BehavioralTherapy which is now a recommended treatment to improvecoping skills and strategies in PWE with mood difficulties(Kerr et al., 2011). Therefore understanding which psychoso-cial variables are consistently associated with psychopatholo-gy in PWE has important management implications. On theother hand, the studies' differences in methodology and ana-lyses, as well as numerous limitations, preclude definitiveconclusions as to which psychosocial factors are most impor-tant. Furthermore, it is possible that psychosocial profiles ofPWE may differ in relation to the nature of their epilepsy.For instance, it may be that factors such as stigma or locusof control are important for the group of PWE who experi-ence poorly controlled epilepsy but are less important for pa-tients with infrequent seizures. From the current literature, itis premature to draw conclusions about these speculative re-lationships. Nonetheless, it must be considered that the var-ied nature of epilepsy of the samples studied could havecontributed to some of the inconsistencies in the findingsand future research should address these issues. These re-strictions not withstanding it does appear that the impor-tance placed on HLOC and stigma as likely risk factors forpsychopathology in PWE has been overstated within the lit-erature. Although there was consistent support for the roleof mood-related and impact of epilepsy variables, the overlapbetween items and constructs questions the usefulness ofthese findings. As it stands there is inconsistent support forthe role of illness representations but likely support for therole of stress and self-efficacy. More consistent, yet tentative,support was gained for the role of coping strategies and per-ceived social support. Overall, the major conclusion of this re-view is that psychosocial predictors are an important topicfor future research. However, it is important that future re-search draws on representative populations of PWE, usesstrong theoretical frameworks to develop testable hypothe-ses, controls for other likely confounders and assesses thecontribution of a range of psychosocial predictors, using val-idated measures and prospective designs. In addition, therole of psychosocial predictors play in those with a formal di-agnosis of a depressive or anxiety disorder remains an impor-tant area investigation.

Role of funding sourceMilena Gandy has been supported by scholarships from the Molly

McDonnell Foundation of the Epilepsy Society of Australia and the NationalHealth Research Council (NHMRC) of Australia. Professor Louise Sharpe issupported by an NHMRC Senior Research Fellowship.

Conflict of interestThe authors have no conflict of interests.

AcknowledgmentsThis review forms part of a project that was supported by the generous

scholarships of the Molly McDonnell Foundation of the Epilepsy Society ofAustralia and the National Health Research Council of Australia.

References

Andrews, G., Slade, T.I.M., Issakidis, C., 2002. Deconstructing current comor-bidity: data from the Australian national survey of mental health andwell-being. The British Journal of Psychiatry: The Journal of Mental Sci-ence 181, 306–314.

232 M. Gandy et al. / Journal of Affective Disorders 140 (2012) 222–232

Antonak, R.F., Livneh, H., 1992. A review of research on psychosocial adjust-ment to impairment among persons with epilepsy. Journal of Epilepsy 5,194–205.

Asadi-Pooya, A.A., Schilling, C.A., Glosser, D., Tracy, J.I., Sperling, M.R., 2007.Health locus of control in patientswith epilepsy and its relationship to anx-iety, depression, and seizure control. Epilepsy & Behavior 11, 347–350.

Baker, G.A., 2002. The psychosocial burden of epilepsy. Epilepsia 43 (suppl6), S26–S30.

Baker, G.A., Jacoby, A., Chadwick, D.W., 1996. The associations of psychopa-thology in epilepsy: a community study. Epilepsy Research 25, 29–39.

Barrera, M., 1986. Distinctions between social support concepts, measures,and models. American Journal of Community Psychology 14, 413–445.

Barry, J.J., 2003. The recognition and management of mood disorders as a co-morbidity of epilepsy. Epilepsia 44 (suppl 4), S30–S40.

Barry, J.J., Ettinger, A.B., Friel, P., Gilliam, F.G., Harden, C.L., Hermann, B.,Kanner, A.M., Caplan, R., Plioplys, S., Salpekar, J., Dunn, D., Austin, J.,Jones, J., 2008. Consensus statement: the evaluation and treatment ofpeople with epilepsy and affective disorders. Epilepsy & Behavior 13(supp 1), S1–S29.

Beyenburg, S., Mitchell, A.J., Schmidt, D., Elger, C.E., Reuber, M., 2005. Anxietyin patients with epilepsy: systematic review and suggestions for clinicalmanagement. Epilepsy & Behavior 7, 161–171.

Boylan, L.S., Flint, L.A., Labovitz, D.L., Jackson, S.C., Starner, K., Devinsky, O.,2004. Depression but not seizure frequency predicts quality of life intreatment-resistant epilepsy. Neurology 62, 258–261.

Brandt, C., Schoendienst, M., Trentowska, M., May, T.W., Pohlmann-Eden, B.,Tuschen-Caffier, B., Schrecke, M., Fueratsch, N., Witte-Boelt, K., Ebner, A.,2010. Prevalence of anxiety disorders in patients with refractory focalepilepsy — a prospective clinic based survey. Epilepsy & Behavior 17,259–263.

Brown, C., Schulberg, H.C., 1995. The efficacy of psychosocial treatments inprimary care - a review of randomized clinical trials. Gen. Hosp. Psychi-atry 17, 414–424.

Cramer, J.A., Blum, D., Reed, M., Fanning, K., 2003. The influence of comorbiddepression on seizure severity. Epilepsia 44, 1578–1584.

Cramer, J.A., Blum, D., Fanning, K., Reed, M., 2004. The impact of comorbiddepression on health resource utilization in a community sample of peo-ple with epilepsy. Epilepsy & Behavior 5, 337–342.

Downs, S.H., Black, N., 1998. The feasibility of creating a checklist for theassessment of the methodological quality both of randomised andnon-randomised studies of health care interventions. Journal of Epidemi-ology and Community Health 52, 377–384.

Endermann, M., 1997. Learned helplessness in epilepsy: a contribution to-ward the prediction of depression levels in a clinical setting. Journal ofEpilepsy 10, 161–171.

Endermann, M., Zimmermann, F., 2009. Factors associated with health-related quality of life, anxiety and depression among young adultswith epilepsy and mild cognitive impairments in short-term residentialcare. Seizure — European Journal of Epilepsy 18, 167–175.

Ferro, M.A., Speechley, K.N., 2009. Depressive symptoms among mothers ofchildren with epilepsy: a review of prevalence, associated factors, andimpact on children. Epilepsia 50, 2344–2354.

Franck, E., De Raedt, R., 2007. Self-esteem reconsidered: unstable self-esteem outperforms level of self-esteem as vulnerability marker for de-pression. Behaviour Research and Therapy 45, 1531–1541.

Gilliam, F.G., Barry, J.J., Hermann, B.P., Meador, K.J., Vahle, V., Kanner, A.M.,2006. Rapid detection of major depression in epilepsy: a multicentrestudy. The Lancet 5, 399–405.

Goldstein, L.H., Holland, L., Soteriou, H., Mellers, J.D.C., 2005. Illness representa-tions, coping styles and mood in adults with epilepsy. Epilepsy Research67, 1–11.

Grabowska-Grzyb, A., Jedrzejczak, J., Naganska, E., Fiszer, U., 2006. Risk factorsfor depression in patients with epilepsy. Epilepsy & Behavior 8, 411–417.

Hermann, B.P., Whitman, S., 1989. Psychosocial predictors of interictaldepression. Journal of Epilepsy 2, 231–237.

Hermann, B.P., Seidenberg, M., Bell, B., 2000. Psychiatric comorbidity inchronic epilepsy: identification, consequences, and treatment of majordepression. Epilepsia 41 (suppl 2), S31–S41.

Hesdorffer, D.C., Lee, P., 2009. Health, wealth, and culture as predominant fac-tors in psychosocial morbidity. Epilepsy & Behavior 15 (suppl 1), S36–S40.

Kanner, A.M., 2003a. Depression in epilepsy: a frequently neglected multi-faceted disorder. Epilepsy & Behavior 4 (suppl 4), S11–S19.

Kanner, A.M., 2003b. Depression in epilepsy: prevalence, clinical semiology,pathogenicmechanisms, and treatment. Biological Psychiatry 54, 388–398.

Kanner, A.M., 2004. Do epilepsy and psychiatric disorders share commonpathogenic mechanisms? A look at depression and epilepsy. ClinicalNeuroscience Research 4, 31–37.

Kanner, A.M., 2005. Depression in epilepsy: a neurobiologic perspective.Epilepsy Current 5, 21–27.

Kanner, A.M., 2009a. Depression and epilepsy: a review of multiple facets oftheir close relation. Neurologic Clinics 27, 865–880.

Kanner, A.M., 2009b. Psychiatric issues in epilepsy: the complex relation ofmood, anxiety disorders, and epilepsy. Epilepsy & Behavior 15, 83–87.

Kanner, A.M., Balabanov, A., 2002. Depression and epilepsy— how closely re-lated are they? Neurology 58 (suppl 8), S27–S39.

Kemp, S., Morley, S., Anderson, E., 1999. Coping with epilepsy: do illness rep-resentations play a role? British Journal of Clinical Psychology 38, 43–58.

Kerr, M.P., Mensah, S., Besag, F., de Toffol, B., Ettinger, A., Kanemoto, K., Kanner,A., Kemp, S., Krishnamoorthy, E., LaFrance Jr., W.C., Mula, M., Schmitz, B.,van Elst, L.T., Trollor, J., Wilson, S.J., 2011. International consensus clinicalpractice statements for the treatment of neuropsychiatric conditions asso-ciated with epilepsy. Epilepsia 52, 2133–2138.

Lambert, M.V., Robertson, M.M., 1999. Depression in epilepsy: etiology, phe-nomenology, and treatment. Epilepsia 40 (suppl 10), S21–S47.

Lee, S.A., Lee, S.M., No, Y.J., 2010. Factors contributing to depression in pa-tients with epilepsy. Epilepsia 51, 1305–1308.

Leventhal, H., Leventhal, E.A., Contrada, R.J., 1998. Self-regulation, health,and behavior: a perceptual-cognitive approach. Psychology & Health13, 717–733.

Livneh, H., Wilson, L.M., Duchesneau, A., Antonak, R.F., 2001. Psychosocialadaptation to epilepsy: the role of coping strategies. Epilepsy & Behavior2, 533–544.

Marsh, L., Rao, V., 2002. Psychiatric complications in patients with epilepsy:a review. Epilepsy Research 49, 11–33.

McCagh, J., Fisk, J.E., Baker, G.A., 2009. Epilepsy, psychosocial and cognitivefunctioning. Epilepsy Research 86, 1–14.

McGreal, R., Joseph, S., 1993. The depression-happiness scale. PsychologicalReports 73, 1279–1282.

Mensah, S.A., Beavis, J.M., Thapar, A.K., Kerr, M., 2006. The presence and clin-ical implications of depression in a community population of adults withepilepsy. Epilepsy & Behavior 8, 213–219.

Miller, W.R., Seligman, M.E.P., Kurlander, H.M., 1975. Learned helplessness,depression, and anxiety. The Journal of Nervous and Mental Disease161, 347–357.

Olivo, S.A., Macedo, L.G., Gadotti, I.C., Fuentes, J., Stanton, T., Magee, D.J.,2008. Scales to assess the quality of randomized controlled trials: a sys-tematic review. Physical Therapy 88, 156–175.

Reisinger, E.L., Dilorio, C., 2009. Individual, seizure-related, and psychosocialpredictors of depressive symptoms among people with epilepsy over sixmonths. Epilepsy & Behavior 15, 196–201.

Sanderson, S., Tatt, L.D., Higgins, J.P.T., 2007. Tools for assessing quality andsusceptibility to bias in observational studies in epidemiology: a system-atic review and annotated bibliography. International Journal of Epide-miology 36, 666–676.

Schmitz, E.B., Robertson, M.M., Trimble, M.R., 1999. Depression and schizo-phrenia in epilepsy: social and biological risk factors. Epilepsy Research35, 59–68.

Seethalakshmi, R., Krishnamoorthy, E.S., 2007. Depression in epilepsy: phe-nomenology, diagnosis and management. Epileptic Disorders 9, 1–10.

Seminario, N.A., Farias, S.T., Jorgensen, J., Bourgeois, J.A., Seyal, M., 2009. De-termination of prevalence of depression in an epilepsy clinic using abrief DSM-IV-based self-report questionnaire. Epilepsy & Behavior 15,362–366.

Sharpe, L., Curran, L., 2006. Understanding the process of adjustment to ill-ness. Social Science & Medicine 62, 1153–1166.

Sherbourne, C.D., Wells, K.B., 1997. Course of depression in patients with co-morbid anxiety disorders. Journal of Affective Disorders 43, 245–250.

Smith, D.F., Baker, G.A., Dewey, M., Jacoby, A., Chadwick, D.W., 1991. Seizurefrequency, patient-perceived seizure severity and the psychosocial con-sequences of intractable epilepsy. Epilepsy Research 9, 231–241.

Upton, D., Thompson, P.J., 1992. Effectiveness of coping strategies employedby people with chronic epilepsy. Journal of Epilepsy 5, 119–127.

Vazquez, B., Devinsky, O., 2003. Epilepsy and anxiety. Epilepsy & Behavior 4(suppl 4), S20–S25.

Wang, T., Collet, J.P., Shapiro, S., Ware, M.A., 2008. Adverse effects of medicalcannabinoids: a systematic review. Canadian Medical Association Jour-nal 178, 1669–1678.

Wood, A.M., Taylor, P.J., Joseph, S., 2010. Does the CES-D measure a continu-um from depression to happiness? Comparing substantive and artifactu-al models. Psychiatry Research 177, 120–123.

Zeber, J.E., Copeland, L.A., Amuan, M., Cramer, J.A., Pugh, M.J., Zeber, J.E.,Copeland, L.A., Amuan, M., Cramer, J.A., Pugh, M.J.V., 2007. The role of co-morbid psychiatric conditions in health status in epilepsy. Epilepsy & Be-havior 10, 539–546.