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Measuring Quality of Life in Children With Attention-deficit/Hyperactivity Disorder and Their Families
Development and Evaluation of a New Tool
Jeanne M. Landgraf, MA;
Michael Rich, MD, MPH;
Leonard Rappaport, MS, MD
Arch Pediatr Adolesc Med. 2002;156:384-391.
ABSTRACT
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Objective To psychometrically evaluate a new parent-completed questionnaire that
measures the effect of attention-deficit/hyperactivity disorder (ADHD) on
the everyday well-being of children and their families.
Setting Using a mail-out/mail-back method, the sample was drawn from the registry
of an outpatient developmental and behavioral program of a large tertiary
pediatric hospital. All children received medication for ADHD.
Participants Responses were received for 81 children of whom 60 (74%) were boys.
An even split of questionnaires was returned for children with ADHD primarily
inattentive (50%) and ADHD combined (50%). The condition of 70 patients (86%)
had been diagnosed for 1 year or longer; 69 patients (89%) reported receiving
medication.
Main Outcome Measure The ADHD Impact Module, HealthAct, Boston, Mass, developed with input
from families, measures the effect of the disorder on the child's emotional–social
well-being (Child Scale, 8 items) and the family (Home Scale, 10 items).
Results The scales exceeded standard criteria for item convergent and discriminant
validity. No floor effects and minimal (2%) ceiling effects were observed.
Cronbach  was 0.88 and 0.93 (Child and Home Scales), respectively.
Raw scale scores are transformed on a 0 through 100 continuum; a higher score
indicates more favorable findings. Statistically significant differences (P<.000) were observed for ADHD inattentive vs ADHD combined
on both scales (Child, 65.26 vs 48.86; Home, 72.79 vs 51.26). Better "success
at home" scores were reported by parents of ADHD inattentive children (Child
Scale, 62.12 vs 47.36, P = .00; Home Scale, 70.58
vs 47.01, P = .000).
Conclusions The ADHD Impact Module meets stringent psychometric standards. Further
validation is required, but current evidence suggests it is a promising new
questionnaire.
INTRODUCTION
ATTENTION-DEFICIT/hyperactivity disorder (ADHD) is one of the most common
pediatric conditions, yet its diagnosis, treatment, and outcomes remain complex
and subject to controversy.1 The prevalence
of the disorder among school-aged children is estimated to be between 3% and
11%2 and has been found to affect between 3
to 6 times as many male as female subjects.3
The disorder usually evidences itself in settings other than the clinician's
office. Thus, a multimodal approach to diagnosis—which includes an array
of clinical evaluations and empirical data about the frequency and intensity
of symptoms from parents, teachers, and others with varying levels of training
in child development and behavior—is often used.4
While this approach has resulted in warnings about misdiagnosis and overmedication,5 review of the literature has demonstrated little basis
for this concern.6
According to the Diagnostic and Statistical Manual
of Mental Disorders, Fourth Edition (DSM-IV), diagnostic criteria include
early onset (usually before the age of 7 years) of continuous symptoms of
inattention and/or hyperactivity, resulting in clinically significant social,
academic, or occupational impairment.7 The DSM-IV further divides ADHD into 3 subtypes: predominantly
hyperactive-impulsive, predominantly inattentive, and combined. Diagnosis
is often made by means of standardized ratings scales,8-9
symptom checklists, or structured interviews.10
There is considerable comorbidity of the disorder, most commonly with learning
disorders, conduct disorder, and oppositional defiant disorder.11-13
In addition to social and academic impairment, significant decrements
in health-related quality of life have been reported for children with attentional
problems and hyperactivity relative to peers.14-18
However, the effect of the disorder on the everyday functioning and well-being
of children remains largely unexplored in clinical practice.19
Current practice suggests that children with ADHD benefit from stimulant
medication20-21 and that effective
treatment often requires a comprehensive, multimodal approach that includes
behavior modification. Significant variation has been observed in clinicians'
choices and combinations of therapeutic agents.22-23
Yet, much current research is still focused on the objective measurement of
symptoms24 rather than outcomes of treatment.
While symptom checklists are useful in standardizing diagnoses and following
core symptoms, they are a crude measure for evaluating the effect of treatment
on the everyday lives of families and children with ADHD. Treatment may be
able to reduce the frequency of related symptoms and thereby bring a welcome
reprieve from the problems of living with ADHD. However, this may not necessarily
result in an improvement in the quality of life at home, at school, or with
peers. Rigorous measures are needed to document whether therapeutic interventions
for ADHD are improving the quality of everyday life for these children.
For many common childhood conditions, progress can be measured using
objective tools like a spirometer or blood glucose monitor and, if warranted,
treatment can be modified accordingly. Pediatricians, neurologists, or psychiatrists
treating inattention and hyperactivity have no objective tool with which to
monitor the outcomes of treatment. Within the time constraints of a brief
office visit, clinicians often must rely on the parent's response to a global
"how's it going" or use questionnaires that just examine core symptoms to
determine if therapeutic intervention is having an effect. To our knowledge,
nothing exists that enables the practitioner to "fine-tune" treatment for
ADHD based on the information that is exchanged in these encounters.
Given current prevalence estimates, reliable and valid tools are needed
to facilitate a more rigorous approach to evaluating interventions at the
time of the clinic visit. Measuring the outcome of care on the quality of
the child's everyday life in addition to current core symptom questionnaires
would enable clinicians treating ADHD to benchmark the outcome of their care
in terms that are increasingly meaningful to the families and children they
are treating.
The purpose of this study was to develop and evaluate a brief parent-completed
questionnaire that could be ultimately used by clinicians to document the
outcome of care specifically for children with ADHD. The measure can be completed
at home or while families are waiting to be seen by the physician. It was
designed to complement diagnostic information and other clinical data by assessing
the experiences and feelings of families and children with inattention and
hyperactivity and the degree to which the child's disorder affects the quality
of their lives at home and in general. Specifically, we were interested in
developing a brief tool that could answer questions such as: Is the family
limited in doing the usual things like entertaining at home or going to public
places? How worried are parents about their child's future or about the effects
of ADHD on other siblings? How do they feel about their ability to cope with
their child's ADHD? Is their child excluded from the usual activities with
friends such as sleepovers, parties, or just "hanging out"?
Given the focus of our tool, parents were identified as the appropriate
respondent (as opposed to teachers who are often used in ADHD studies). Further,
the parent was chosen as the primary respondent so that we might capture preschool-aged
children in addition to those attending school. Future efforts will explore
the development of a youth self-report.
METHODS
MODULE DEVELOPMENT AND DESCRIPTION
A multistaged approach was used in the development process of the ADHD
Impact Module (AIM), HealthAct, Boston, Mass. After the current literature
was reviewed, interviews were conducted with 3 families, 2 clinicians (M.R.
and L.R.) treating children with ADHD at a nationally recognized pediatric
hospital, and 1 licensed educator who was the director of a community-based
support program for children with behavioral issues and their parents.
The interviews lasted 40 to 90 minutes and were conducted independently
by the principal author (J.M.L.) who is trained in psychometrics and questionnaire
design, ethnomethodology, and observational research.25-28
These interviews build on the author's previous observational and measurement
work when developing the Child Health Questionnaire–a general quality-of-life
measure for children that has been evaluated for use in ADHD.14-18
Previous experience included lengthy observations at 2 ADHD clinics, interviews
with 2 recognized clinical experts, and 2 home observations and interviews
with families.
To further our current understanding of the effect of ADHD on children
and their families, observations were made at a family home and one-on-one
conversational interviews were held with 2 fathers, 2 mothers, an 11-year-old
boy with ADHD, and his 10-year-old sister. In describing everyday life, the
families and children identified aspects of home life that are significantly
influenced by ADHD, such as following through with homework and chores, the
child's ability to cope with everyday hassles, the child not getting invited
to sleepovers, parents feeling constrained about entertaining others in their
home, and overall how the family is managing.
The Likert method of summated ratings,29
which is based on testable scaling assumptions, was used in the design of
the AIM. This method uses a graduated response continuum (eg, "a lot" to "not
at all"). Items were written to correspond to each of the 2 principal constructs
that emerged from discussions with families—influence on the child and
influence on the parent-family. Whenever possible, items were constructed
using the phrasing provided by the families such as those identified in the
previous paragraph.
The AIM is composed of 2 core multi-item scales: the Child Scale and
the Home Scale were specifically constructed to capture the effect of ADHD
on the child and the quality of life at home, and 9-descriptor items to assess
treatment status and history and other related background information. The
Child Scale consists of 8 items that measure the well-being of the child (eg,
child does well following through with homework, feedback from teachers has
been positive, and my child seems comfortable with how things are going).
The Home Scale consists of 10 items that assess the influence on the family-parent
(eg, my child's ADHD limits what we can do as a family, my child's ADHD has
added stress to our home life, and I feel tired and worn out).
The 9 descriptive items include (1) 1 item to determine the child's
medication status during the 2 weeks prior to completion of the AIM; (2) 1
item to gauge the frequency with which a parent may experience "success" at
home in helping their child refocus or regain self-control; (3) 4 items to
assess parental attributions regarding ADHD (is it a health issue, discipline
issue, parenting issue,or behavioral issue); (4) date of diagnosis; (5) how
long the child has been receiving medication for ADHD; and (6) whether the
child or the family have attended an ADHD support group. Sample items from
the AIM are shown in Table 1.
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Table 1. Sample of the AIM*
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The core AIM scales were evaluated for readability and ease of completion
using the Flesch-Kincaid method, a gold-standard reading comprehension program.30 Results indicated that the they can be completed
by individuals with at least a fifth-grade reading level and would be easy
to read by 75% of the readers at this level.
The AIM was reviewed by the 2 clinical authors (M.R. and L.R.) both
of whom actively treat children with ADHD at a nationally recognized pediatric
hospital. In addition, 2 families of children with ADHD were asked to complete
the AIM to determine if there were problematic items. Recommended enhancements
and clarifications were incorporated. Questionnaire development guidelines
suggest that typically it takes someone 30 to 45 seconds to complete each
multiple-choice or true-false item.31 Once
finalized, the AIM was piloted in a sample of families of children who had
been diagnosed with ADHD by a trained clinician using standard diagnostic DSM-IV criteria. Based on the pilot study results and the
aforementioned guidelines, we estimate that most people can complete the AIM
in 4 to 7 minutes.
SAMPLING STRATEGY
Our convenience sample was drawn from a patient registry of children
who had or were receiving stimulant medication treatment for ADHD from an
outpatient development and behavioral program of a large pediatric hospital
in the greater Boston area. All children in the registry had been formally
diagnosed with ADHD by a team composed of a developmental behavioral pediatrician,
a PhD-level psychologist, and an MA- or PhD-level educational specialist.
The standard diagnostic process used in the clinic includes extensive educational,
neurodevelopmental, and psychological testing to identify alternative causes
of and/or comorbidities of activity and attention problems. Each child also
receives a physical examination and vision and hearing tests. Parental histories
were obtained separately by the psychologists and the pediatrician (L.R.).
The Clinical Attention Problems Scale measures the frequency of activity and
attention by asking the parent and teacher to respond to a series of 12 statements
and their applicability to their child in the morning and afternoon. Response
options range from "not true," "somewhat or sometimes true," "very often,"
or often true").32-33 Consensus
of diagnosis was established by the full team using DSM-IV criteria and evaluating results of the extensive evaluations. The
institutional review board of the hospital approved the project under their
quality improvement program.
The eligible patient population consisted of 259 families living in
Rhode Island, Massachusetts, and New Hampshire. A letter prepared and signed
by the clinic director (L.R.) and a postage-paid business reply envelope accompanied
each questionnaire. To maintain confidentiality, per the terms of institutional
review board approval, there was no follow-up to nonrespondents. No family
identifiers were used and voluntary completion of the form constituted informed
consent.
ANALYTIC METHODS
Scores for the Child Scale and the Home Scale were computed separately
by summing the items within each scale and deriving an overall mean score.
The raw mean scale score was then transformed on a 0 through 100 continuum
with higher scores indicating less negative influence or better functioning
and well-being. Using standard scoring conventions, a higher score is more
favorable. Multitrait analysis was used to assure that grouping the items
into the 2 separate scales as we did was appropriate. All computations were
performed using the Revised Multitrait Analysis Program for a disk operating
system.34 Multitrait analysis is a confirmatory
factor analytic method that was originally used in the construction of achievement
tests and has been applied to the development of patient-based measures in
the health care field since the early 1970s.35
It has been used as the method of choice in the evaluation of a wide array
of published instruments.35
As mentioned, items were written and grouped a priori to correspond
specifically to 2 key concepts—influence on the child and influence
on the parent-family. Multitrait analysis was chosen as the principal method
of analysis because it allowed us to test the strength of our scaling assumptions
(ie, grouping of items). This method extends traditional factor analysis by
examining the discriminatory power of items in addition to their convergence
to confirm that the hypothesized sets of items (ie, scales), indeed, measure
separate and unique constructs. In other words, one might expect to see a
relationship between items from the Child Scale and the Home Scale, but the
correlation across items from the 2 scales should be low enough to support
grouping them as expected to derive 2 independent scale scores. Multitrait
analysis provides a reliable way to assess the strength of these correlations.
Further, it is recommended that "tests of scaling assumptions should be conducted
before new items or scales are relied upon in formal studies."35(p4)
Specifically, the Revised Multitrait Analysis Program performs tests
of scaling assumptions using the following criteria. First, each item in a
hypothesized scale must be substantially linearly related to the underlying
concept being measured (tests of item internal consistency). An item-scale
correlation, corrected for overlap, of 0.40 or above has been recommended.36
Second, each item should correlate significantly higher with its hypothesized
scale than with other scales in the same matrix (tests of item discriminant
validity). To satisfy the item discriminant validity criterion, the correlation
between an item and its hypothesized scale must be significant. The convention,
based on an SE of 0.04, is a magnitude of 2 SEs higher than its correlation
with other scales.37 For new scales, however,
it is acceptable to extend the criteria for tests of discriminant validity
such that a "success" is counted if the correlation of an item to its hypothesized
scale was at least 1 SE higher than correlations with other scales.36
Reliability is a function of the average correlation among items. Since
it is possible for a measure to appear quite stable over time, but not be
internally consistent, test-retest is not recommended as a method of choice
to estimate reliability.38 Thus, the internal
consistency reliability for the 2 AIM Impact scales were estimated using Cronbach
coefficient.39 It has been noted that scales
with reliabilities of at least 0.70 and higher are sufficiently reliable for
use with group comparisons.40 Reliability estimates
of 0.90 or higher are suggested for use at the patient-specific level.37, 39 The coefficient represents
the average of all possible split-half reliability estimates adjusting for
scale length and has been shown to approximate test-retest estimates when
scaling assumptions are met.41-43
Respondents had to have answered at least half of the items for each
of the scales to be included in the analysis. Given that this was a newly
developed measure and we were evaluating its psychometric properties, if a
patient did not complete more than half of the items per scale, their entire
form would be excluded from any analysis.
The discriminatory power of an instrument—its ability to discriminate
within and across groups—is determined in part by the distribution of
scores observed for a given sample. The more scores are spread across the
continuum, the greater the chances that a true and measurable difference can
be found. Thus, to be truly useful, the full range of the measure should be
observed with few people scoring at either the lowest (floor) or the highest
end (ceiling) of the continuum.
We hypothesized that the negative impact on quality of life would be
greater for (1) children with ADHD combined compared with those with ADHD
primarily inattentive; (2) parents reporting no success at home in getting
their child to refocus or gain control of their behavior vs those reporting
success; and (3) children not taking medication for their ADHD compared with
those receiving medication. To assess discriminant validity of the AIM, differences
in scale scores between each of these groups were examined for significance
using the t test for independent samples.
RESULTS
SAMPLE CHARACTERISTICS
Responses were received from 81 (31%) of the 259 families surveyed. Table 2 gives the demographic profile of
the responding families and patients. Mothers (76 [94%]) preponderantly completed
the questionnaires. The majority (60 [74%]) of the patients were boys, who
were distributed relatively evenly in age and school grade. An even split
was observed between children whose parents reported a diagnosis (n = 80)
of ADHD primarily inattentive (40 [50%]) and those whose parents reported
a diagnosis of ADHD combined (40 [50%]). A large majority (70 [86%]) of the
sample (n = 70) had been diagnosed with ADHD 1 year or longer prior to completing
the AIM. Parents reported that 49 (63%) of the 78 children for whom medication
status was provided were taking their medication as directed during the 2
weeks prior to completing the AIM. Fifteen children (19%) had taken a short
drug holiday ( 4 days), 5 (6%) had taken a lengthy drug holiday ( 5
days), and 9 (11%) were not taking medication at the time the questionnaire
was completed.
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Table 2. Clinical Characteristics of the 81 Children With Attention-deficit/Hyperactivity
Disorder (ADHD)
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Figure 1 profiles parental
attributions regarding ADHD. A strong majority (66%-70%) feel that ADHD is
a health or behavioral issue. Yet, 45% to 48% of the parents also attribute
ADHD to parenting and discipline issues. These findings underscore the importance
of additional education and management for children and their families. Specifically,
what is needed is further education about the neurological basis of ADHD and
the special skills necessary in parenting a child with this condition.
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Parental attribution about attention-deficit/hyperactive disorder
as it relates to their child's and their family's quality of life. Percentages
may not total 100 because of rounding.
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INSTRUMENT PERFORMANCE
Respondents had to have answered at least half of the items for each
of the scales to be included in the analysis. Using this criterion, no one
was omitted from the analysis. Minimal missing data were observed in 1% to
2% of the responses for only 3 items in the Child Scale (child has received
positive feedback, child seems comfortable with how things are going, and
child adapts well to unexpected changes) and 2 items in the Home Scale (limits
me from entertaining and I am frustrated that my child's ADHD is unmanageable).
For a third item in the Home Scale—effect on siblings—7 parents
indicated by written comment that the item was "not applicable" because they
had an only child. Given the hypothesized linearity of items, which is the
theoretical underpinning of the Likert approach, and the low miss rate, values
were not imputed for the omitted items. The SE for the entire group, which
is based on sample size, was 11. This is not surprising given the small sample
(n = 81). The average SE for groups of 200 or more ranges from 0.04 to 0.06.
Thus, in this sample, items had to work harder at meeting the scaling criteria.
Item Internal Consistency and Item Discriminant Validity
Item correlations for the Child Scale ranged from 0.53 to 0.71 and for
the Home Scale from 0.61 to 0.85. Eighty-eight percent of the items in the
Child Scale and 100% of items in the Home Scale met the item-discriminant
criteria. These findings confirmed that our scoring approach (ie, summing
the items and deriving 2 independent scale scores) was appropriate.
Floor and Ceiling Effects
A relatively even distribution of responses was observed across both
scales. No floor effects were observed for either scale. A minimal ceiling
effect (2%) was observed for the Home Scale only. This finding suggests that
the AIM has potential discriminatory power and any differences observed in
mean scale scores would not be due to chance occurrence.
Reliability
The Cronbach coefficient observed for the Child Scale was 0.88.
The Cronbach coefficient for the Home Scale was 0. 93. These coefficients
substantially exceed the recommended criteria (0.70) for group-level comparisons.
They also provide evidence that the scales may be sufficiently robust for
patient-specific reporting—a distinct advantage for both families and
practitioners. As such, the instrument would provide practitioners with a
tool to assess individualized treatment strategies and determine their benefits
and limitations for the child and his or her family.
Discriminatory Power
Analyzed by the t test for independent samples,
scores on both the Child and the Home Scales were significantly worse for
children with ADHD combined than for children with ADHD primarily inattentive.
Mean scale scores for the 2 groups are given in Table 3. The mean Child Scale score for children with ADHD combined
was 48.86 vs 65.26 for children with ADHD primarily inattentive (P = .000). The mean Home Scale score was 51.26 vs 72.79 (P = .000), respectively.
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Table 3. Comparison of AIM Scale Score by Diagnostic Group and Success
With Interventions at Home*
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Also given in Table 3 are
the significant differences observed across the Child and Home Scales for
parents reporting success with behavioral interventions at home as compared
with those reporting no success. Better scores were observed for parents reporting
success vs no success on both the Child Scale (62.12 vs 47.36, P = .001) and the Home Scale (70.58 vs 47.01, P
= .000).
An interesting pattern was observed (but not tested for significance)
for the item "success at home with behavioral interventions." Of those reporting
success (n = 39), more were parents of children with ADHD primarily inattentive
(n = 30) relative to parents of children with ADHD combined (n = 9). For those
indicating no success (n = 40), a relatively equal distribution was observed
across the parent-reported diagnostic subgroups (ADHD primarily inattentive
[n = 19] and ADHD combined [n = 21]).
These data were inadequate to determine significant statistical differences
in quality of life between those patients taking medication and those not
taking medication. Only 9 children (11%) were not actively taking medication
and 5 (56%) of these represented the ADHD primarily inattentive group. The
same pattern was observed for the 20 children on drug holiday (25%) with 12
(60%) representing the ADHD primarily inattentive group. Thus, of the 29 patients
who were not receiving medications or who were on a drug holiday, 17 patients
(59%) were categorized as having ADHD primarily inattentive. It was impossible
to control for the confounding effect of the diagnosis and potential skewing
of these data owing to the small sample size.
COMMENT
This pilot test of the AIM provides clinicians with a promising new
option for gathering standardized information on the effect of ADHD and its
treatment on the everyday life of affected children and their families. A
multimodal treatment strategy has long been advocated for treating children
with attention difficulties. To date, however, there has not been a way for
measuring and comparing the benefits of different treatment options as they
occur in the applied clinical setting. In addition, current questionnaires
only examine the core symptoms of ADHD that are certainly important but probably
inadequate from a clinical viewpoint.
Currently, physicians must rely on symptom checklists and information
obtained in brief and often hurried encounters with parents to determine whether
and to what degree an intervention is improving the child's life at home.
Finally, we are unaware of any available tool that assesses the effect of
ADHD on parents. Thus, current assessment strategies fail to fully gauge the
effect that ADHD and its treatment may have on the everyday quality of life
for children with ADHD and their families. If we are to truly understand the
effect of ADHD interventions on the life of the child and his or her family,
the outcome of treatment must be routinely monitored over time—with
measurement occurring parallel to the clinical encounter.
Reliability estimates indicate that the AIM scales can be used with
confidence for group-level comparisons and that they approach or exceed the
minimum level for use to monitor the outcome of care at the individual patient
level. The next step is to assess reliability of the AIM in longitudinal prospective
studies and to further monitor its performance at the individual patient level.
Preliminary findings of discriminant validity, as evidenced by expected and
observed differences in mean scale scores for children with ADHD primarily
inattentive vs ADHD combined and parents reporting success at home vs no success,
confirm other published work in the field.
While these general findings underscore what has been reported from
large-scale studies, the AIM presents as a potentially promising new tool
that dimensions the influence of ADHD. Future efforts will examine data at
a practice or clinic level to assess the outcome of different treatment options.
Benchmarking care in this way will, in the long-term, provide the evidence
necessary for defining "best-practice" guidelines.
There are several limitations to this study that must be noted. First,
we acknowledge that the number of patients returning questionnaires was low.
To protect patient confidentiality and to fulfill the requirements of the
institutional review board, it was impossible to follow up with patients,
obtain demographic information about nonrespondents, or obtain current contact
information for those patients who might have moved since their initial visit
to the clinic. Given these constraints, we were unable to determine if our
respondents are representative of all patients and families listed in the
ADHD database from which our study sample was drawn.
Our principal interest was in evaluating the underlying conceptual structure
of a new tool to capture the effect of ADHD on children and families as opposed
to describing the effect of ADHD on families in general. The full range of
possible scores was observed across the AIM scales indicating both the high
and low effects on well-being and family life. Thus, there is no reason to
believe that the sample is skewed toward one end of the spectrum or the other.
Future efforts will focus on the design of clinic-based prospective studies
wherein we can further evaluate the performance of the AIM.
Further, although, our sample may be small relative to the percentage
of children diagnosed as having ADHD, traditional psychometric tests, such
as those discussed in this article, are sample sensitive. As such, criteria
are actually more stringent and difficult to achieve with samples of less
than 100. Despite the small sample, the effect size was large enough that
the AIM achieved the standards and performed well. These preliminary results
are encouraging and suggest that the AIM can be used with confidence as normative
data are collected and a larger prospective study is undertaken.
Second, our sample was obtained from an ADHD–specific clinic database
associated with an academic medical center and, thus, may not be generalizable
to other clinic settings. A review of clinic records between April 1, 2000,
and June 30, 2000, indicated that 54 children with ADHD were seen. Of these,
44 (82%) were diagnosed as having ADHD combined. Thus, we believe the pool
of respondents was drawn from a clinic database that reflects the general
profile for other clinics treating this affected population. Since patients
were not identified, it is impossible to link their responses on the AIM to
a standardized diagnostic tool. Thus, for evaluating the differences in scores
by diagnostic group, we were limited to using parental reports. Based on our
work with parent interviews and the standardized use of parent-completed ADHD
diagnostic tools, we feel confident that parents are accurate reporters. Future
efforts will focus on the correlation between scores on standard diagnostic
tools and the AIM.
Third, we did not ask parents to report comorbidities. However, it was
possible to retrieve the principal diagnoses for the clinic's current data
set. Findings indicate that 41% have problems associated with academic issues,
15% have behavioral issues, 14% have language problems, 8% have psychiatric
or emotional problems, 5% have motor or sensory problems, 5% have global cognitive
problems, 4% have environmental or family problems, 4% have problems with
organizational skills, approximately 3% have autism-social cognition problems,
and 1% have problems with specific organic causes. Comorbidities and their
effects on quality of life will be monitored as the measurement properties
of the AIM are further evaluated.
Questionnaire development is a complex and iterative process. Each application
of the AIM will provide further insight into its strengths and limitations.
Use of this tool in the clinical research setting may not only be useful to
practicing clinicians, but data that are generated will be an invaluable asset
in the interpretation of scores and, ultimately, with further study, the assessment
of practice guidelines. Future efforts will focus on key issues such as the
sensitivity of the AIM to changes over time and with drug treatment, relationship
with diagnostic tools, and the development of a normative database to determine
whether children of different ages or from different backgrounds score differently.
Investigations are in place to further assess the performance of the AIM with
an eye toward its ongoing use as an important tool to augment the diagnostic
process and further assist practitioners in benchmarking the care and treatment
of ADHD. Ultimately, information from the AIM will yield a richer understanding
about the success of various treatment options (what works and does not work)
in terms that are most meaningful to the children with ADHD and their families.
| What This Study Adds
Attention-deficit/hyperactivity disorder is a common pediatric condition
(3%-11%) with a significant negative effect on the quality of life for the
affected child and his or her family. Current practice guidelines recommend
comprehensive, multimodal treatment for ADHD, including stimulant medication
and behavioral modification, yet actual management practice varies widely.
While several symptom checklists have been developed for diagnosis, there
are no instruments with which to measure the more global impact of treatment
on the child and his or her family. We describe the development and pilot
implementation of a parent-completed questionnaire that measures the effect
of ADHD and its treatment on the quality of life of affected children and
their families. The AIM met strict psychometric standards for item-convergent
and -discriminant validity and found significant differences in quality of
life between children with ADHD inattentive and ADHD combined. There is evidence
to indicate that this is a promising questionnaire that may be used to measure
and benchmark the outcomes of care for ADHD.
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AUTHOR INFORMATION
Accepted for publication December 6, 2001.
Development work on the AIM was funded in part by Eli Lilly & Company,
Indianapolis, Ind.
Corresponding author and reprints: Jeanne M. Landgraf, MA, HealthAct,
205 Newbury St, Fourth Floor, Boston, MA 02116 (e-mail: jml{at}healthact.com).
From HealthAct (Ms Landgraf), the Divisions of Adolescent/Young Adult
Medicine (Dr Rich) and General Pediatrics (Dr Rappaport), Children's Hospital,
and the Department of Pediatrics, Harvard Medical School (Drs Rich and Rappaport),
Boston, Mass.
REFERENCES
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1. Zametkin AJ, Ernst M. Problems in the management of attention-deficit–hyperactivity
disorder [review]. N Engl J Med. 1999;340:40-46.
FREE FULL TEXT
2. Szatmari P. The epidemiology of attention deficit hyperactivity disorder. Child Adolesc Psychiatr Clin N Am. 1992;1:361-371.
3. Brandenburg NA, Friedman RM, Silver SE. The epidemiology of childhood psychiatric disorders: prevalence findings
from recent studies. J Am Acad Child Adolesc Psychiatry. 1990;29:76-83.
ISI
| PUBMED
4. Taylor MA. Evaluation and management of attention-deficit hyperactivity disorder
[review]. Am Fam Physician. 1997;55:887-89, 897, 901 passim.
ISI
| PUBMED
5. Safer DJ, Zito JM, Fine EM. Increased methylphenidate usage for attention deficit disorder in the
1990s. Pediatrics. 1996;98:1084-1088.
FREE FULL TEXT
6. Goldman LS, Genel M, Bezman RJ, Slanetz PJ for the Council on Scientific Affairs, American Medical Association. Diagnosis and treatment of attention-deficit/hyperactivity disorder
in children and adolescents. JAMA. 1998;279:1100-1106.
FREE FULL TEXT
7. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, Fourth
Edition. Washington, DC: American Psychiatric Association; 1994.
8. Conners CK. Rating scales in attention-deficit/hyperactivity disorder: use in assessment
and treatment monitoring [review]. J Clin Psychiatry. 1998;59(suppl 7):24-30.
9. Vaughn ML, Riccio CA, Hynd GW, Hall J. Diagnosing ADHD (predominantly inattentive and combined type subtypes):
discriminant validity of the behavior assessment system for children and the
Achenbach Parent and Teacher Rating Scales. J Clin Child Psychol. 1997;26:349-357.
FULL TEXT
|
ISI
| PUBMED
10. Barkley RA. Attention Deficit Hyperactivity Disorder: A Handbook
for Diagnosis and Treatment. New York, NY: Guilford Press; 1990.
11. Cantwell DP. Attention deficit disorder: a review of the past 10 years. J Am Acad Child Adolesc Psychiatry. 1996;35:978-987.
FULL TEXT
|
ISI
| PUBMED
12. Burns GL, Walsh JA, Owen SM, Snell J. Internal validity of attention deficit hyperactivity disorder, oppositional
defiant disorder, and overt conduct disorder symptoms in young children: implications
from teacher ratings for a dimensional approach to symptom validity. J Clin Child Psychol. 1997;26:266-275.
FULL TEXT
|
ISI
| PUBMED
13. Tirosh E, Berger J, Cohen-Ophir M, Davidovitch M, Cohen A. Learning disabilities with and without attention-deficit hyperactivity
disorder: parents' and teachers' perspectives. J Child Neurol. 1998;13:270-276.
ISI
| PUBMED
14. Landgraf JM, Abetz L, Ware JE. The Child Health Questionnaire (CHQ): A User's Manual. Boston, Mass: Health Institute, New England Medical Center; 1996.
15. Landgraf JM, Abetz L. Functional status and well-being of children representing three cultural
groups: initial self-reports using the CHQ-CF87. J Psychol Health. 1997;12:839-854
16. Landgraf JM, Ware JE, Rossi-Roh K. Measuring the Relative Health and Well-Being of Children
With Attention Deficit Hyperactivity Disorder: Results from the Children's
Health and Quality of Life Project. Boston, Mass: Health Institute, New England Medical Center; 1993.
17. Landgraf JM. The Child Health Questionnaire: a potential new tool to assess the
outcome of psychosocial treatment and care. In: Maruish M, ed. The Use of Psychological Testing
for Treatment Planning and Outcomes Assessment. Mahwah, NJ: Lawrence
A Erlbaum Associates; 1999:665-677.
18. Landgraf JM, Abetz L. Influences of socio-demographic characteristics on parental reports
of children's physical and psychosocial well-being: early experiences with
the Child Health Questionnaire. In Drotar D, ed. Measuring Health-Related Quality
of Life in Children and Adolescents: Implications for Research and Practice. Mahwah, NJ: Lawrence A Erlbaum Associates; 1998:105-126.
19. American Academy of Pediatrics. Clinical practice guideline: diagnosis and evaluation of the child
with attention-deficit/hyperactivity disorder. Pediatrics. 2000;105:1158-1170.
FREE FULL TEXT
20. Greenhill LL, Halperin JM, Abikoff H. Stimulant medications [review]. J Am Acad Child Adolesc Psychiatry. 1999;38:503-512.
FULL TEXT
|
ISI
| PUBMED
21. Spencer T, Biederman J, Wilens T, Harding M, O'Donnell D, Griffin S. Pharmacotherapy of attention-deficit hyperactivity disorder across
the life cycle [review]. J Am Acad Child Adolesc Psychiatry. 1996;35:409-432.
FULL TEXT
|
ISI
| PUBMED
22. Zito JM, Safer DJ, dosReis S, Magder LS, Gardner JF, Zarin DA. Psychotherapeutic medication patterns for youths with attention-deficit/hyperactivity
disorder. Arch Pediatr Adolesc Med. 1999;153:1257-1263.
FREE FULL TEXT
23. Zarin DA, Suarez AP, Pincus HA, Kupersanin E, Zito JM. Clinical and treatment characteristics of children with attention-deficit/hyperactivity
disorder in psychiatric practice. J Am Acad Child Adolesc Psychiatry. 1998;37:1262-1270.
FULL TEXT
|
ISI
| PUBMED
24. Teicher MH, Ito Y, Glod CA, Barber NI. Objective measurement of hyperactivity and attentional problems in
ADHD. J Am Acad Child Adolesc Psychiatry. 1996;35:334-342.
FULL TEXT
|
ISI
| PUBMED
25. Garfinkel H. What is ethnomethodology? In: Garfinkel H, ed. Studies in Ethnomethodology. Englewood Cliffs, NJ: Prentice-Hall International Inc; 1967:1-34.
26. Leiter K. A Primer on Ethnomethodology. New York, NY: Oxford University Press; 1980.
27. Green JL, ed, Wallat C, ed. Ethnography and Language in Educational Settings. Norwood, NJ: Ablex Publishing Corp, Greenwood Publishing Group Inc;
1981.
28. Lincoln YS, Guba EG. Naturalistic Inquiry. Beverly Hills, Calif: Sage Publications; 1985.
29. Likert RA. A technique for the measurement of attitudes. Arch Psychol. 1932;140:44-53.
30. Lapp D, Flood J. Teaching Reading to Every Child. New York, NY: McMillan Publishing Co, Inc; 1978.
31. Thorndike RM, Cunnigham GK, Thorndike RL, Hagen EP. Measurement and Evaluation in Psychology and Education. New York, NY: McMillan Publishing Co Inc; 1991.
32. Edelbrock C, Rancurello M. Childhood hyperactivity: an overview of rating scales and their applications. Clin Psychol Rev. 1985;5:429-445.
FULL TEXT
33. Edelbrock C. The Pediatric Developmental and Behavioral Homepage. Available at: http://www/dbpeds.org/handouts. Accessed
January 1, 2002.
34. Hayashi T, Hays RD. A microcomputer program for analyzing multi-trait–multimethod
matrices. Behav Res Methods Instrum Comput. 1987;19:345-348.
35. Ware JE, Harris WJ, Gandek B, Rogers BW, Reece PR. MAP-R for Windows: Multitrait Multi-Item Analysis
Program–Revised User's Guide. Boston, Mass: Health Assessment Lab; 1997.
36. Campbell DT, Fiske DW. Convergent and discriminant validation by the multitrait-multimethod
matrix. Psychol Bull. 1959;56:85-105.
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