Introduction
Attention-deficit/hyperactivity disorder (ADHD) is a disorder characterized by hyperactivity, impulsivity, and inattention that affects between 3 and 7% of school-age children (APA
2000). A worldwide pooled prevalence of 5.29% has been reported (Polanczyk et al.
2007). Impairment of ADHD affects cognitive and psychosocial functioning (Barkley
2002; Biederman and Faraone
2005; Nijmeijer et al.
2008; Escobar et al.
2008) as well as the quality of life (QoL) in patients and their families (Johnston and Mash
2001; Sawyer et al.
2002; Klassen et al.
2004; Matza et al.
2004; Escobar et al.
2005; Riley et al.
2006b).
Treatment options for ADHD include psychostimulants, especially in combination with behavioral therapy (MTA study) (Jensen et al.
2001) or atomoxetine, which is a non-stimulant treatment option for ADHD (Cheng et al.
2007). In most of the studies evaluating the efficacy of these medications, questionnaires such as the ADHD Rating Scale (ADHD-RS) (DuPaul et al.
1998a; Faries et al.
2001) or the clinical global impression (CGI) (Guy
1976; NIMH
1985) have been used as outcome measures for the core symptoms of ADHD.
Health-related QoL has received increasing attention both from clinicians and from investigators in children and adolescents with ADHD (Harpin
2005; Hakkart-van Roijen et al.
2007; Yang et al.
2007; Bastiaens
2008). Health-related QoL is a multidimensional concept that reflects the subjective physical, social, and psychological aspects of health and is distinct from symptoms of the disorder and objective functional outcomes (Wallander et al.
2001). It strongly depends on the subjectively perceived impact of the disorder (and of the respective treatment) on the level of physical, psychological, and social functioning (Leidy et al.
1999; Revicki et al.
2000). Some psychometric instruments are available to assess the health-related QoL, including the Child Health and Illness Profile, Child Edition (CHIP-CE) (Riley et al.
2001; Riley et al.
2006b) and the Child Health Questionnaire (CHQ) (Landgraf et al.
1996). These questionnaires are generic scales that assess QoL aspects that go beyond the core symptoms of the disorder and reflect various dimensions of QoL. CHIP-CE has child-, adolescent- and parent-rated versions, allowing the assessment of the patient’s QoL both from the parent’s and from the patient’s perspective. The possibility to assess QoL from different perspectives is a promising characteristic of this instrument for assessing QoL in children and adolescents (Schmidt et al.
2001).
A number of studies have shown improvement in health-related QoL in children and adolescents treated with atomoxetine (Michelson et al.
2001; Buitelaar et al.
2004; Perwien et al.
2004; Matza et al.
2006; Brown et al.
2006; Perwien et al.
2006; Prasad et al.
2007; Wehmeier et al.
2007,
2008). These studies have used the CHQ, the CHIP-CE, or other QoL instruments.
Up to now, the psychometric properties of the CHIP-CE were mostly studied in non-ADHD populations using cross-sectional data only. Only Riley et al. (
2006b) discuss some psychometric properties of this generic scale in an ADHD population. They found that internal consistency reliability was good-to-excellent (Cronbach’s α > 0.70) for all CHIP-CE domains and sub-domains and that almost no ceiling and floor effects were observed. A factor analysis of the sub-domains yielded a 12-factor solution. The domain-level factor analysis identified six factors, the four domains of Satisfaction, Comfort, Resilience and Risk avoidance and in addition the two sub-domains of the Achievement domain. Moderate to high correlations between the CHIP-CE scales and measures of ADHD and family factors were found. The HRQoL of children in this sample was considerably lower than that of community youth. However, this analysis has some limitations. First, the patients were not required to have been diagnosed formally with ADHD but only the clinical judgment of the investigator if the patient has hyperactive/inattentive/impulsive symptoms/problems and had not been formally diagnosed with ADHD or a hyperactive/inattentive/impulsive syndrome in the past was required for inclusion into the study. Another analysis of the study data showed that 11.5% of patients did not fulfill strict ADHD criteria (Döpfner et al.
2006). In addition, only cross-sectional data were analyzed making any statements about score sensitivity for changes over time impossible.
The objectives of the present combined analysis were to evaluate the psychometric properties of the CHIP-CE at baseline and over time and to assess the correlation between parameters related to QoL and those related to ADHD core symptoms using the individual patient data of five clinical trials studying atomoxetine in children and adolescents with ADHD.
Methods
Study design and procedures
Individual patient-level data from five clinical trials (four European and one Canadian, all of which were studies of atomoxetine using the CHIP-CE) with similar inclusion and exclusion criteria and similar duration (8–12 weeks’ follow-up) were included in the combined analysis. More details about the trials are reported elsewhere (Escobar et al.
2010). Thus, all data from clinical trials studying atomoxetine and using the CHIP-CE in the Lilly data base were included. The total number of patients included in the combined analysis was 794. Three of these studies were randomized, double-blind trials comparing atomoxetine with placebo: Study 1 (
n = 99) (Svanborg et al.
2009), Study 2 (
n = 149) (Escobar et al.
2007; Montoya et al.
2007), and Study 3 (
n = 139) (Curatolo et al.
2007). The fourth study was a randomized, open-label study of atomoxetine versus standard of care (Study 4,
n = 201) (Prasad et al.
2007), and the last one was an open-label atomoxetine study (Study 5,
n = 206) (Dickson et al.
2007), where all patients received atomoxetine.
All patients met the DSM-IV diagnostic criteria for ADHD and had a symptom severity of at least 1.5 standard deviations (SD) above norm values for the ADHD-RS (ADHD subscale of the SNAP in Study 3). The diagnosis was confirmed using the Kiddie-Schedule for Affective Disorders and Schizophrenia for School-Aged Children-Present and Lifetime Version (K-SADS-PL) in all studies except in Study 5. In Studies 2 and 3, basal CGI-S scores for ADHD were at least 4 or higher. The double-blind treatment period was between 8 and 12 weeks in the placebo-controlled studies (8 weeks for Study 3, 10 weeks for Study 1, and 12 weeks for Study 2). Studies 2 and 4 included only medication-naïve patients. Study 3, which was carried out in Italy, did not explicitly require medication-naïve patients, but at the time of recruitment, there were no ADHD drugs available in that country.
The primary scale on which this combined analysis was based is the Child Health and Illness Profile-Child Edition-Parent Form (CHIP-CE-Parent Form) (Riley et al.
2001), a 76-item generic health-related quality of life (HR-QoL) questionnaire, covering a total of five domains (Satisfaction, Comfort, Risk avoidance, Resilience, and Achievement) and twelve sub-domains (satisfaction with health (SH), satisfaction with self (SS), physical comfort (PC), emotional comfort (EC), restricted activity (RA), individual risk avoidance (IRA), threats to achievement (TA), family involvement (FI), physical activity (PA), social problem solving (SPS), academic performance (AP), and peer relations (PR)) that were developed in non-ADHD samples. The CHIP-CE scores are standardized to
t-scores, i.e., to a mean (±SD) of 50 (±10), based on the norm values, which were derived from a sample of 1,049 school children from the United States, with higher scores indicating better health. Riley et al. (
2004a) found that its domains (Satisfaction, Comfort, Risk Avoidance, Resilience, and Achievement) measure structurally distinct, interrelated aspects of health. Furthermore, they summarized that the domain reliability was high with an internal consistency between 0.79 and 0.88 and a retest reliability between 0.71 and 0.85 as measured by the intra-class correlation ICC.
Efficacy on core ADHD symptoms was assessed using the Attention Deficit/Hyperactivity Disorder Rating Scale-IV, Parent Version (ADHD-RS), which evaluates all 18 symptoms of ADHD according to the DSM-IV diagnostic criteria (Guy
1976; DuPaul et al.
1998b). Improvement is indicated by a decrease in the score. The ADHD-RS comprises a total score, a hyperactive/impulsive sub-score, and an inattentive sub-score.
Statistical analysis
The demographic data were analyzed using descriptive statistics. The number of missing items per evaluation was computed and also analyzed descriptively as a continuous variable. The proportion of evaluations without missing items was presented for the CHIP-CE as a whole and for the domains and sub-domains. All visits and all five studies were pooled for this analysis. Inclusion of patients receiving active treatment and placebo in the analysis over time will increase the range of the changes and will thus lead to a wider basis for the evaluation. The item-total correlations (Spearman’s and Pearson’s correlation coefficients) were calculated for the total scores as well as for the domains and sub-domains. Furthermore, the sub-domains were correlated with the domains and the total score, and the domains were correlated with the total score. The items/sub-domains/domains were sorted by their Spearman’s correlation coefficient with the respective summary score. Only the Spearman’s correlation coefficient is reported here because it is similar to the Pearson’s correlation coefficient for these data. Cronbach’s alpha was computed for the items that were grouped into a sub-score and for all subsets of items that can be created by deleting one item within a sub-domain. The relative frequencies of floor effects (lowest possible value observed) and ceiling effects (highest possible value observed) for the sub-domains, domains, and total scores are provided. Correlations between domains of the CHIP-CE at baseline and at endpoint are shown. The same was done for the sub-domains. A factor analysis based on the sub-domains was performed additionally in order to explore the relationships between the sub-domains. Factor analyses using the varimax rotation on the 76 items with solutions allowing 5 or 12 factors were performed because the CHIP-CE has 5 domains and 12 sub-domains, as the goal was to replicate the factor structure seen in the normative sample. Only loadings >0.30 are presented. All analyses were done using the SAS statistical program.
Discussion
The objective of this combined analysis was to evaluate the psychometric properties of the CHIP-CE in a sample of children and adolescents with ADHD from clinical studies. The analyses were based on the data from five clinical trials of atomoxetine. The descriptive CHIP-CE baseline data of these studies confirmed the impairment in terms of QoL in this clinical trial population with moderate core symptoms severity. The psychometric evaluation of the CHIP-CE showed a low number of missing items, confirming that the questionnaire comprising 76 items is relatively easy to apply (Riley et al.
2004a,
2006b). The correlations between the items and the total score were stable over time as the item-total correlations showed a similar pattern at baseline and after the double-blind phase for the placebo-controlled studies. Smaller correlations were observed between changes from baseline values. The similarity of the correlations at baseline and at endpoint indicates that the total score was sensitive to the same items at both points in time, a result that could not be shown by the cross-sectional analysis by Riley et al. (
2004a,
2006b). The same holds true for the various domains. Interestingly, the item-total correlations varied widely for the Risk avoidance domain. Such a gap was not seen for any of the other domains. The item with the weakest correlation to the domain score “trouble paying attention at school” is closely related to the core symptoms of ADHD. Therefore, the low correlation with the Risk avoidance domain suggests that in the ADHD population, this item belongs to a different dimension than other items in this domain. Correlation patterns were similar at the end of the double-blind phase for the placebo-controlled studies. However, the weak correlation for item “trouble paying attention at school” was not as distinct as for the baseline assessment in the Risk avoidance domain. Weaker correlations were seen for the changes from baseline analyses.
The assessment of the item-sub-domain correlations yielded a similar pattern for the TA sub-domain, which is part of the Risk avoidance domain, for baseline and endpoint. The items for the PA sub-domain could be separated into two groups based on the correlations with three items that had a much higher correlation with the sub-domain than the other items. Items 44 (“How often did your child play active games or sports?”), 45 (“How often did your child play hard enough to start sweating and breathing hard?”), and 46 (“How often did your child run hard when he/she played or did sports?”) had much higher correlations compared with the items 31 (“How often did your child have trouble walking one block?”), 32 (“How often did your child have trouble walking up one flight of stairs?”), and 33 (“How often did your child have trouble running?”). A similar pattern, but with overall weaker correlations, was observed for the changes from baseline.
Correlations between sub-domains and domains and between domains and the total score were similar at baseline and endpoint. The correlations for change from baseline were usually slightly smaller. The RA and the PA sub-domains had lower correlations with their domains than most of the other domains at baseline, at endpoint, and also for the change from baseline. The same was found to be true for the Comfort domain regarding the correlation of the domain with the total score. The Achievement domain, the Satisfaction domain, and the Risk avoidance domain seem to be especially important components of the CHIP-CE scale in children and adolescents with ADHD, based on their strong correlation with the total score. The low correlation of the other two domains, Resilience and Comfort, might be caused by the fact that these contain sub-domains that are not affected by ADHD at baseline (PC, RA, and PA). This was not only observed in the present population of patients with ADHD, but also in a cross-sectional sample from the United States on which Riley et al. (
2007) based their analysis.
The internal consistency as measured by Cronbach’s alpha for all sub-domains was good at baseline and at endpoint, which confirms the findings from an observational study with ADHD patients (Riley et al.
2006b) as well as the results based on a community sample (Riley et al.
2004a). The internal consistency for changes from baseline to endpoint as measured by Cronbach’s alpha was moderate, except for AP where it was low. Therefore, the CHIP-CE is generally useful to track changes in QoL over time. The internal consistency of domains and sub-domains was robust against single missing items, except for changes in the TA sub-domain and the AP sub-domain. Results from those sub-domains should only be used if all items are available. Considerable ceiling effects were only observed for the RA domain, which is not surprising in a sample selected based on a psychiatric and not a physical condition. A similar profile of floor and ceiling effects was seen in an observational study in ADHD patients (Riley et al.
2006b). The RA domain had also most ceiling effect (6.3%) in a community sample (Riley et al.
2004a). The factor analysis allowing for 12 factors showed that the sub-domains generally load onto different factors; especially the sub-domains that are impaired in ADHD patients can be distinguished. However, this is not the case for the 5-factor solution based on the number of CHIP-CE domains, where the items from sub-domains that do
not belong to the same domain often load together on one factor. It is therefore advisable to use the sub-domains rather than the domains of the CHIP-CE when evaluating ADHD patients. This is supported by the factor analysis based on the sub-domains and the correlation analysis of the sub-domains, which showed that those sub-domains that belong to the same domain do not necessarily have a high correlation. Riley et al. (
2006a) also found a 12-factor solution in a cross-sectional naturalistic ADHD sample. This is an important difference to the results of CHIP-CE domains previously reported in a community sample (Riley et al.
2004a,
b; Rajmil et al.
2004). The correlation between the domains over time was stable in our analysis. The same holds true for the sub-domains. A cluster of between-sub-domain correlations was observed for nine sub-domains, which showed correlations of >0.3 with three or more sub-domains at baseline and/or at endpoint. In contrast, the three sub-domains FI, PA, and AP appeared to be less correlated with the others.
Possible limitations of this evaluation are the different designs of the studies on which this combined analysis was based, including different patient populations with respect to pre-treatment and comorbidities. Therefore, these results may not be directly transferable to epidemiological samples. Furthermore, it is difficult to assess how the proxy evaluation by the parents may have influenced the relationship between QoL and the core symptoms. The influence of the QoL of the parents or the parents’ diseases (such as ADHD) could not be assessed because these data were not obtained.
Acknowledgments
We thank Dr. Birgit Eschweiler for manuscript editing and support.