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Keywords:

  • Epilepsy;
  • Executive Functioning;
  • Quality of Life;
  • Children

Abstract

  1. Top of page
  2. Abstract
  3. METHOD
  4. RESULTS
  5. DISCUSSION
  6. Acknowledgments
  7. REFERENCES

Summary: Purpose: Based on prior research indicating poor health-related quality of life (HRQOL) in children with attention-deficit/hyperactivity disorder, we investigated (1) whether executive functioning deficits were related to poor HRQOL in children with epilepsy, (2) how important these variables were in comparison to known predictors of HRQOL such as neurological factors, and (3) the extent to which clinical-level impairments in executive dysfunction predispose children to low HRQOL.

Method: Data included scores on the Behavior Rating Inventory of Executive Function (BRIEF) and HRQOL scales (The Impact of Childhood Illness Scale [ICI] and Hague Restrictions in Epilepsy Scale [HARCES]) for 121 children (mean age = 11.9, SD = 3.6) from a tertiary center serving children with severe epilepsy.

Results: Correlations between the BRIEF and ICI total and subscore domains (child, parent, family, and treatment) were generally significant and moderate (e.g., r ≥ 0.30, p ≤ 0.001). BRIEF Global Executive Composite, number of antiepileptic drugs (AEDs), number of prior AEDs, and adaptive level all emerged as significant and unique predictors of HRQOL (R2= 0.36, adj. R2= 0.33, p < 0.0001). A clinically elevated BRIEF was associated with a twofold risk of low HRQOL (odds ratio = 2.21, p = 0.03).

Conclusions: Executive dysfunction appears to exert a broad adverse influence on HRQOL in children with epilepsy, with clinical-level impairments in executive dysfunction contributing to a twofold increase in the likelihood of poor HRQOL. The constellation of executive dysfunction, low adaptive level, high medication load, and a history of several failed AEDs are risk factors for poor HRQOL in children with epilepsy.

Executive functioning refers to critical functions such as planning, inhibition, set shifting, self-monitoring, organization, working memory, and initiating and sustaining motor and mental activity. Executive dysfunction is associated with behavioral disturbance, social dysfunction, and reduced educational and occupational attainment (Baron, 2004; Lezak et al., 2004). Health-related quality of life (HRQOL) is defined as encompassing the individual's well-being in psychological, social, occupational, and educational domains (Duncan, 1990; Austin et al., 1996). Because these domains are among those adversely impacted by executive dysfunction, it should follow that childhood disorders associated with executive dysfunction might be at increased risk for decreased HRQOL. Emerging research suggests that this is indeed the case: disorders such as attention-deficit/hyperactivity disorder (ADHD) and head injury, both associated with executive dysfunction as core symptoms (Barkley, 1997; Baron, 2004), present the most compelling argument for an association between executive dysfunction and HRQOL. Research involving these disorders indicate an increased risk of poor HRQOL in several domains, including physical functioning, social functioning, parental emotional health and activities, family activities, and family cohesion (Graetz et al., 2001; Klassen et al., 2004; Matza et al., 2004; Topolski et al., 2004; Escobar et al., 2005; Horneman et al., 2005).

Studies examining the impact of executive dysfunction on the HRQOL of children with epilepsy are entirely lacking, despite the ubiquitous nature of executive problems in children with epilepsy presenting for treatment at tertiary centers. In one study, 40–50% of children with epilepsy had clinically significant problems with planning or working memory, key elements of executive functioning (Slick et al., 2006). In another study, almost two-thirds of children with epilepsy were found to have ADHD symptoms (Thome-Souza et al., 2004). If there is a link between executive dysfunction and HRQOL in epilepsy, this would mean that a significant proportion of children are at risk for poor HRQOL by virtue of their executive deficits, in addition to their increased risk of poor HRQOL secondary to medical, psychological, cognitive, and sociodemographic factors. These include factors such as low IQ, low adaptive functioning, psychosocial difficulties, high seizure frequency, intractability, polydrug therapy, longstanding epilepsy duration, parental maladjustment, low family income, and older age (Devinsky et al., 1999; Sabaz et al., 2001; Sherman et al., 2002; Buelow et al., 2003; Miller et al., 2003; Sabaz et al., 2003; Williams et al., 2003; Sillanpaa et al., 2004).

We aimed to determine whether executive functioning deficits were associated with decreased HRQOL in children with severe epilepsy, and if so, whether these were major predictors of HRQOL compared to other known predictors of HRQOL in pediatric epilepsy. The rationale was that any predictor of HRQOL, to have clinical utility, would have to have at least a moderate association with HRQOL, and that this association would remain when other known predictors of pediatric HRQOL such as neurological variables and adaptive level were simultaneously considered.

METHOD

  1. Top of page
  2. Abstract
  3. METHOD
  4. RESULTS
  5. DISCUSSION
  6. Acknowledgments
  7. REFERENCES

Participants

Data consisted of archival records of consecutive referrals of children seen in the Neuropsychology Service of the Psychology Department at BC's Children's Hospital (BCCH) comprising behavior and quality-of-life scales routinely collected as part of the standard neuropsychological assessment. Because BCCH is the only tertiary medical center for pediatric epilepsy in the province of British Columbia, children generally represent the more severe end of the epilepsy spectrum, with all surgical candidates and candidates for vagal nerve stimulation (VNS) referred for assessment, along with nonsurgical cases with severe epilepsy (excepting children with profound intellectual delays or who are otherwise untestable because of multiple severe handicaps). Data for 121 cases (48% girls) were retrieved in accordance with hospital and university review boards. Exclusion criteria included progressive neurological or serious medical condition other than epilepsy and lack of parental fluency in English due to the written language requirements of the standardized questionnaires.

Demographic and clinical characteristics of the sample are shown in Table 1, and 1989 ILAE (International League Against Epilepsy, 1989) classifications in Table 2. Most patients were classified as having symptomatic localization-related epilepsy and early age of onset. The median number of failed antiepileptic drug (AED) trials was 3, indicating that the sample included a significant number of children with intractable seizures. In the sample, 26% had been treated with epilepsy surgery previously and 3% were being treated with VNS.

Table 1. Demographic and clinical characteristics of the sample (N = 121)
 MedianMeanSDRange
  1. The sample age range extends slightly beyond the normative sample range for the BRIEF (5–18 years) due to the unavailability of BRIEF versions for preschoolers and adults at the time of data collection. Age, age at onset, duration of epilepsy, and test–retest interval are shown in years. Seizure frequency refers to the number of seizures in the month preceding the evaluation. Number of prior AEDs refers to the number of AEDs tried unsuccessfully prior to the current evaluation. Adaptive level refers to SIB-R scores (normative mean of 100, SD = 15).

Age12.211.93.64.12–20.2
Age at onset 3.0 4.54.10–15.5
Duration of epilepsy 6.7 7.44.20.23–17.9
Number of AEDs  1 1.51.10–5
Number of failed AEDs  3 3.93.10–13
Seizure frequency  2103.5 376.0  0–>2,000
Adaptive level (SIB-R)75.577.534.6 4–155
Table 2. ILAE classifications
 Percent of sample
Localization-related epilepsies
 Idiopathic 0  
 Symptomatic73.2 
 Cryptogenic8.7
Generalized epilepsies 
 Idiopathic2.4
 Cryptogenic or symptomatic6.3
 Symptomatic2.4
Undetermined whether focal or generalized1.6
Special syndromes0.8
Unclassifiable1.6
Unspecified3.1

All the measures described below were administered to all children as part of the standard clinical workup. The specific HRQOL measures selected were used because of their ease of administration and brevity; both take 5–10 min to administer and can be completed independently by a parent while the child is being assessed.

Data on adaptive behavior level were also collected in order to provide information on the functional level of the sample, and because adaptive behavior is a strong predictor of HRQOL in pediatric epilepsy (Sherman et al., 2002). We chose adaptive functioning a priori over IQ scores because data could be obtained on all patients, including those unable to partake in standardized testing because of low functional levels, and because IQ scores reflected a number of different instruments which would have introduced additional error variance (e.g., patients were tested with WPPSI-R, WISC-III, WAIS-III, WPPSI-III, and WISC-IV, among others). Further, unlike IQ tests, adaptive scales provide an ecologically valid (i.e., real world) estimate of general level of functioning while providing an easily interpretable metric in a format akin to that used for IQs (i.e., standard scores with a mean of 100 and standard deviation [SD] of 15, with higher scores indicating better functional independence).

Measures

Executive function ratings

Measuring executive dysfunction in children poses a challenge. Laboratory measures such as the Wisconsin Card Sorting Test (Kongs et al., 2000) are not always sensitive to executive dysfunction by virtue of being administered in a structured, quiet, one-on-one testing environment that reduces the need for self-initiated organization and problem solving (Strauss et al., 2006). Thus, specialized questionnaires completed by family members to assess executive functioning in daily life are increasingly used in clinical assessment. The most well known is the Behavior Rating Inventory of Executive Function (BRIEF; Gioia et al., 2000). The BRIEF is composed of 86 items constituting eight clinical scales named for the domains of executive function assessed: Working Memory, Inhibit, Initiate, Plan/Organize, Organization of Materials, Monitor, Emotional Control, and Shift. These scales each contribute to one of two supraordinate scales, namely the Behavioral Regulation Index (BRI) and the Metacognition Index (MCI), which then combine to form the overall score, the Global Executive Composite (GEC). The parent form is based on a large representative normative sample, and the test demonstrates good basic psychometric properties and validity (Strauss et al., 2006).

Quality-of-life scales

The Impact of Childhood Illness Scale (ICI; Hoare and Russell, 1995) is a 30-item parent-rated questionnaire that is divided into four sections: (i) impact of the disorder and its treatment, (ii) impact on the child's development and adjustment, (iii) impact on parents, and (iv) impact on the family. For each item, the parent rates how often the particular problem or situation occurs (frequency score) as well as the amount of concern each one causes (importance score). Scores for the two domains range from 0 to 60, with the total score ranging from 0 to 120. Higher scores reflect worse quality of life. The ICI was designed to be suitable for children with a variety of illnesses or disabilities (e.g., “Because of my child's illness, she may stop breathing;”“Because of her illness, my child has special problems with reading or math;”“My child's illness limits how often we go out as a family”). The parent rates each item according to the degree of perceived frequency or impact on the parent and/or child. Validity for the scale is good (Sherman et al., 2002), and it shows good cross-cultural applicability (Adewuya and Oseni, 1995; Roccella et al., 2005).

The Hague Restrictions in Epilepsy Scale (HARCES; Carpay et al., 1997) is a 10-item parent-rated scale that measures the number of restrictions imposed because of seizures. Items reflect the frequency with which the child takes part in activities such as swimming, riding a bicycle, staying elsewhere overnight, and participating in physical education. The scale focuses on the extent to which epilepsy affects a child's ability to take part in everyday childhood activities that contribute to HRQOL—activities in which developmentally critical exposures to peers and to age-appropriate activities occur. As would be expected because of seizure-related restrictions, the ability to participate in these activities is often restricted in children with epilepsy. The parent rates each item according to the degree to which the child's activity is limited by epilepsy; scores range from 10 (no disability) to 40 (severe disability). Internal reliability is good (r = 0.89) and test–retest stability is high (r = 0.93; Carpay et al., 1997). Validity is reported to be strong (Sherman et al., 2002), and the scale has shown utility in tracking recovery after epilepsy surgery (Van Empelen et al., 2004).

Neurological variables and adaptive level

Neurological variables included age at onset of epilepsy, epilepsy duration, seizure frequency (number of seizures in the previous month), number of AEDs, and number of prior AEDs (an index of intractability reflecting the number of failed pharmacological treatments). Seizure counts were estimated based on chart review prior to the child's neuropsychological assessment, and then confirmed by parents via interview on the day of testing. Neurological characteristics of the sample are shown in Table 1.

The Scales of Independent Behavior—Revised (SIB-R) is a measure of adaptive behavior that provides information on an individual's ability to function independently in the home and community. Parents rate their children in several domains, including motor skills, social interaction and communication skills, personal living skills, and community living skills. The SIB-R Broad Independence standard was recorded for each participant. Reliability and validity are adequate to high (Bruininks et al., 1997; Strauss et al., 2006). The SIB-R level for the sample is shown in Table 1.

Statistical approach and significance testing

Because of the exploratory nature of the study and the need to balance Type I and Type II error, the alpha level for all significance tests was set at 0.05 a priori. However, only correlations of medium effect size (r of approximately 0.30 or larger) were interpreted as evidence of significant associations between variables.

RESULTS

  1. Top of page
  2. Abstract
  3. METHOD
  4. RESULTS
  5. DISCUSSION
  6. Acknowledgments
  7. REFERENCES

Executive functioning and HRQOL: correlational analyses

Table 3 shows means and SDs for the BRIEF index and clinical scales, as well as total and subscale scores for HRQOL scales. On the BRIEF, T scores of 65 or over are indicative of clinical-level executive impairment (i.e., scores 1.5 SD above the normative mean; Gioia et al., 2000). Although group means were below this cutoff, a substantial proportion of children (45.2%) were identified as having clinical-level impairments as defined by an elevated GEC.

Table 3. Means, SDs, and ranges for BRIEF and HRQOL scales
 MeanSDRange
  1. BRIEF scores are shown as T scores (Mean = 50, SD = 10); ICI and HARCES scores are in raw score format.

BRIEF (T scores)
 GEC61.813.63–95
 BRI59.113.722–95
 MCI62.914.02–92
 Emotional Control55.812.722–83
 Inhibit58.514.627–98
 Initiate60.413.86–96
 Monitor60.413.25–91
 Organization of Materials51.811.67–73
 Plan/Organize62.814.35–95
 Shift60.412.438–94
 Working Memory64.613.87–101
ICI (raw scores)
 ICI Total45.025.50–103
 ICI Cumulative Frequency22.112.70–49
 Frequency Child9.45.10–19
 Frequency Parent2.92.30–8
 Frequency Family6.55.60–26
 Frequency Treatment3.42.50–10
 ICI Cumulative Importance22.913.80–59
 Importance Child9.95.40–20
 Importance Parent3.02.60–10
 Importance Family5.95.20–20
 Importance Treatment4.13.00–10
 HARCES21.39.70–40

Table 4 shows correlations between the executive functioning and HRQOL measures. Most of the correlations between the BRIEF GEC and ICI scores were significant and of moderate magnitude, suggesting a link between increasing executive dysfunction and worse quality of life. Similar results were obtained for the BRIEF BRI and MCI. Among BRIEF clinical scales, moderate correlations with HRQOL were found in all cases except for organization of materials and plan/organize, which showed minimal associations with HRQOL. Overall, significant correlations were more prevalent for frequency ratings than for importance ratings from the ICI. In contrast, the HARCES scores were minimally associated with executive function as measured by the BRIEF.

Table 4. Correlations between the BRIEF and HRQOL scales
 GECBRIMCIECInhibInitMonOrgMPlanShiftWM
  1. Correlations in bold were significant at p ≤ 0.001.

  2. GEC, Global Executive Composite; BRI, Behavioral Regulation Index; MCI, Metacognition Index; EC, Emotional Control; Inhib, Inhibit; Init, Initiate; Mon, Monitor; OrgM, Organization of Materials; Plan, Plan/Organize; WM, Working Memory.

ICI Total0.360.360.300.300.320.310.280.030.210.320.36
ICI Cumulative Frequency0.390.400.330.330.360.360.310.050.240.360.42
Frequency Child0.390.330.390.270.250.440.330.080.290.330.47
Frequency Parent0.300.320.270.260.280.310.260.030.160.300.34
Frequency Family0.310.380.230.300.380.200.250.010.150.330.31
Frequency Treatment0.260.330.160.290.320.190.150.070.150.240.23
ICI Cumulative Importance0.290.320.280.260.270.270.250.010.190.290.30
Importance Child0.310.280.330.220.200.340.270.030.250.280.31
Importance Parent0.260.260.280.200.230.290.250.060.160.260.30
Importance Family0.220.280.170.240.260.130.18−0.050.110.250.21
Importance Treatment0.180.230.130.200.220.150.120.020.090.140.19
 
HARCES0.110.100.120.060.130.140.04−0.110.080.100.21

Determining the relative importance of HRQOL predictors: multiple regression analyses

While BRIEF scores were shown to be moderate predictors of HRQOL according to correlational analyses, we also wanted to know how much variance executive functioning accounted for in comparison to other known predictors of HRQOL. Table 5 shows correlations with HRQOL involving neurological variables and adaptive functioning, moderate-to-strong predictors of HRQOL based on prior research (Sherman et al., 2002). As expected, number of AEDs, number of failed AEDs, and adaptive level were all significant bivariate predictors of ICI scores. Adaptive level was a particularly strong predictor, with sizable correlations shown for child, parent, and family frequency ratings (rs =−0.46, −0.62, and −0.47, respectively), indicating worse HRQOL with lower adaptive level. Seizure frequency was not associated with HRQOL, and age at onset and duration were only minimally associated with some aspects of HRQOL as measured by the ICI.

Table 5. Correlations between neurologic variables, adaptive functioning, and HRQOL scales
 Age at onsetDurationSeizure frequencyNumber of AEDsNumber of failed AEDsAdaptive level
  1. Correlations in bold were significant at p ≤ 0.001. Adaptive level refers to SIB-R broad independence scores.

ICI Total−0.120.170.030.320.32−0.41
ICI Cumulative Frequency−0.190.240.070.320.36−0.52
Frequency Child−0.140.350.090.300.33−0.46
Frequency Parent−0.240.200.030.120.22−0.62
Frequency Family−0.200.180.110.230.34−0.47
Frequency Treatment−0.040.02−0.010.450.25−0.11
ICI Cumulative Importance−0.060.10−0.010.290.26−0.28
Importance Child0.020.12−0.020.230.17−0.18
Importance Parent−0.070.05−0.020.110.12−0.33
Importance Family−0.130.110.050.240.31−0.32
Importance Treatment−0.02−0.01−0.070.410.21−0.08
 
HARCES0.080.120.120.360.23−0.39

To evaluate joint and unique associations of individual predictors with ICI scores, independent variables with at least moderate association with ICI total scores were selected. These were variables associated with a medium correlational effect size (e.g., r at or around + or −0.30). Based on the correlational analyses involving the ICI total score, these included BRIEF GEC, number of AEDs, number of prior AEDs, and adaptive level. Using these variables to predict ICI total scores, the regression model was significant (R2= 0.36, adj. R2= 0.33, p < 0.0001). When common variance among the four predictors was controlled for, all four predictors were uniquely and moderately related to HRQOL as indexed by the ICI, including the GEC (see Table 6). These results indicate that BRIEF ratings account for a significant amount of unique variance in HRQOL, even when other important predictors are taken into account.

Table 6. Summary of regression analysis for variables predicting ICI total
 BSEBetapZero-order rPartial rSemi-partial rTolerance
  1. PrevAEDs refer to number of prior failed AED trials.

Constant6.4013.89 
AEDs7.191.770.310.00010.230.360.310.98
PrevAEDs2.310.640.290.00010.320.320.280.94
SIB-R−0.180.07−0.240.007−0.38−0.25−0.210.75
BRIEF GEC0.520.160.280.0010.360.300.250.80

We also explored the pattern of relative prediction of ICI cumulative frequency and cumulative importance ratings. When AEDs, prior AEDs, adaptive level, and GEC were used as predictors of ICI cumulative frequency ratings, the regression model was significant (R2= 0.40, adj. R2= 0.38, p < 0.0001), and GEC emerged as a significant predictor of unique variance in ICI scores (partial r = 0.32, p < 0.001). A similar pattern was found for ICI cumulative importance ratings when GEC, number of AEDs, number of prior AEDs, and adaptive level were regressed on that variable (R2= 0.28, adj. R2= 0.27, p < 0.0001; partial r = 0.28, p < 0.002).

Clinical significance of executive dysfunction in predicting HRQOL: odds ratios

We also explored the extent to which executive dysfunction as measured by the BRIEF GEC served as a specific risk factor for low HRQOL using logistic regression. The group was divided according to GEC score (above or below the clinical cutoff noted above) to determine whether an elevated BRIEF GEC conferred a clinically significant risk of low HRQOL, defined as ICI scores above the sample mean. Having a clinically elevated GEC was associated with a twofold risk of poor HRQOL (odds ratio = 2.21, p = 0.03, 95% CI for odds ratio = 1.1–4.6).

DISCUSSION

  1. Top of page
  2. Abstract
  3. METHOD
  4. RESULTS
  5. DISCUSSION
  6. Acknowledgments
  7. REFERENCES

While we expected executive functioning to be associated with HRQOL based on prior research indicating poor HRQOL in children with ADHD, we were surprised at the extent to which BRIEF scales predicted HRQOL in the context of pediatric epilepsy. Unlike ADHD, pediatric epilepsy is a neurological condition defined by seizures, not behavioral disturbance; further, unlike ADHD, it is a chronic medical condition associated with several known neurological predictors of poor HRQOL that together would be presumed to account for a significant proportion of variance in HRQOL. Executive dysfunction was presumed to be a comparatively weaker predictor given that the central focus of children and families, particularly those struggling with intractability, was presumed to be the seizure disorder itself. This is not what we found; instead, executive dysfunction was an important predictor of HRQOL, equivalent to neurological predictors such as number of AEDs and number of failed AED trials, two markers indicative of the severity and extent of intractability of the seizure disorder. The results suggest that executive dysfunction is a significant barrier to the HRQOL of children and families and that executive dysfunction exerts a broad adverse influence on several components of HRQOL. In this study, clinical-level impairments in executive dysfunction were associated with a twofold increase in the likelihood of low HRQOL. In particular, the constellation of executive dysfunction, low adaptive level, high medication load, and a history of several failed AEDs appeared to contribute significantly to the risk of poor HRQOL in children with epilepsy.

Although most aspects of executive functioning as measured by the BRIEF were related to HRQOL, not all dimensions were associated with HRQOL. Examination of the correlation matrix in Table 4 suggests that at the composite level, an index of self-control/inhibition (BRI) was related to almost all aspects of HRQOL. At the subscale level, the Working Memory scale was also related to most dimensions of HRQOL, particularly with regard to the frequency with which the child's life was adversely affected. Importantly, although clinical elevations on this scale are frequent in pediatric epilepsy samples (Slick et al., 2006), the Plan/Organize scale was not related to HRQOL. Similarly, ratings on the Organization of Materials scale were not related to HRQOL. This suggests that difficulties with planning/organization, although common in severe epilepsy, do not translate into poor HRQOL unlike other executive functioning domains such as emotional control, inhibition, initiation, monitoring, shifting, and working memory.

We did not find that seizure frequency was a strong predictor of quality of life; this may relate to the fact that seizure frequency was recorded in terms of seizure counts rather than parent ratings of severity, as in some studies (Sabaz et al., 2001; Sabaz et al., 2003). Parent ratings may be useful in tapping overall severity of a seizure disorder, but they also introduce common method variance that increases the association between the two variables. Instead, we found that the neurological variables that were most predictive of HRQOL were signs of intractability such as medication load and number of failed AED trials. In the latter case, it is possible that increasing failure of medications may lead parents to adopt a “learned hopelessness” regarding their child's HRQOL, and suggests that repeated medication trial failures have a cumulative adverse impact on HRQOL which is not accounted for by seizure frequency alone. As well, we found that unlike the ICI, the HARCES was not associated with executive dysfunction. The HARCES provides information on the child's access to age-appropriate activities in daily life; therefore, the extent of activity limitations in children with epilepsy appears to be based more on neurological and epilepsy-related factors than on limitations imposed by executive functioning problems.

Some caveats deserve mention. These include the fact that our sample was a tertiary care center sample referred for neuropsychological assessment, which may have biased the sample toward greater severity of epilepsy and executive dysfunction. Although relevant for health professionals working in tertiary care centers, the findings may therefore not apply to community referrals, or to children with less severe forms of epilepsy. Secondly, our sample included a small group of children who had a prior history of epilepsy surgery, as well as children awaiting epilepsy surgery and a small group of children who were being treated with VNS. It is possible that HRQOL predictors might differ in these groups as a function of surgery status, or that predictors may differ in groups receiving different treatments for epilepsy (e.g., epilepsy surgery vs. VNS). Further studies using larger samples would be helpful to delineate the nature and extent of treatment-related changes in HRQOL predictors. In addition, it would be helpful to determine the degree to which executive dysfunction predicts HRQOL when additional factors are taken into account; ideally, these would include psychosocial, demographic, and family variables.

Based on the results of this study, we may now add executive dysfunction to the list of known risk factors for poor HRQOL in children with epilepsy, along with such factors as low adaptive behavior level, low IQ, intractability of the epilepsy syndrome, psychosocial difficulties, AEDs, low family income, and early age at epilepsy onset (Devinsky et al., 1999; Sabaz et al., 2001; Sherman et al., 2002; Buelow et al., 2003; Miller et al., 2003; Sabaz et al., 2003; Williams et al., 2003; Sillanpaa et al., 2004). Other questions remain unanswered, such as the relationship between executive dysfunction and behavioral disorders such as ADHD and affective disorders such as depression and anxiety in pediatric epilepsy. Studies on the relationship between executive dysfunction and self-reported HRQOL would also be of interest. The results also suggest new possibilities for screening and treatment research, including determining whether it would be useful to screen for executive dysfunction to identify children at risk for poor HRQOL to facilitate early intervention, and whether treatments aimed explicitly at improving executive dysfunction (e.g., stimulant medication, behavioral interventions) might improve HRQOL in children with epilepsy.

Acknowledgments

  1. Top of page
  2. Abstract
  3. METHOD
  4. RESULTS
  5. DISCUSSION
  6. Acknowledgments
  7. REFERENCES

Acknowledgment:  This study was supported by a grant by the British Columbia Medical Services Foundation/Vancouver Foundation to the first author. Thanks to Dr. Esther Strauss for helpful comments on this manuscript.

REFERENCES

  1. Top of page
  2. Abstract
  3. METHOD
  4. RESULTS
  5. DISCUSSION
  6. Acknowledgments
  7. REFERENCES
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