Patient-focused measures of functional health status and health-related quality of life in pediatric orthopedics: A case study in measurement selection
© Furlong et al; licensee BioMed Central Ltd. 2005
Received: 23 November 2004
Accepted: 12 January 2005
Published: 12 January 2005
The objectives of this report are to review the assessment of patient-focused outcomes in pediatric orthopedic surgery, to describe a framework for identifying appropriate sets of measures, and to illustrate an application of the framework to a challenging orthopedic problem.
A detailed framework of study design and measurement factors is described. The factors are important for selecting appropriate instruments to measure health status and health-related quality of life (HRQL) in a particular context. A study to evaluate treatment alternatives for patients with neurofibromatosis type 1 and congenital tibial dysplasia (NF1-CTD) provides a rich illustration of the application of the framework. The application involves great variability in the instrument selection factors. Furthermore, these patients and their supportive caregivers face numerous complex health challenges with long-term implications for HRQL.
Detailed summaries of important generic preference-based multi-attribute measurement systems, pediatric health profile instruments, and pediatric orthopedic-specific instruments are presented. Age-appropriate generic and specific measures are identified for study of NF1-CTD patients. Selected measures include the Activities Scale for Children, Gillette Functional Assessment Questionnaire Walking Scale, Health Utilities Index, and Pediatric Inventory of Quality of Life.
Reliable and valid measures for application to pediatric orthopedics are available. There are important differences among measures. The selected measures complement each other. The framework in this report provides a guide for selecting appropriate measures. Application of appropriate sets of measures will enhance the ability to describe the morbidity of pediatric orthopedic patients and to assess the effectiveness of alternative clinical interventions. The framework for measurement of health status and HRQL from a patient perspective has relevance to many other areas of orthopedic practice.
Keywordshealth status health-related quality of life neurofibromatosis orthopedics pediatrics
Assessments of patient-focused health status and health-related quality of life (HRQL) are being recognized increasingly by clinicians, patient advocates, regulatory authorities, administrators and policy makers as primary measures of the need, efficacy, effectiveness and efficiency associated with health care services. These types of measures are commonly reported in the published literature of many health care disciplines although there are few published reports concerning orthopedic surgery. However, the orthopedic community is becoming interested in using evidence reported by patients and patients' family members to determine what is best for patients.
Patient-focused evidence (including patient-reported outcomes, PROs) refers to results from functional health status (FHS) and HRQL studies of orthopedic outcomes with measurements obtained from the perspective of patients or their non-clinician caregivers (e.g., parents of patients too young to self-report). The field of orthopedics is concerned with all musculo-skeletal problems from the top to the bottom of the human body. This broad anatomical range is associated with a wide range of functional problems. Some orthopedic problems are highly focused in regards to anatomy and associated with highly effective therapies. However, other orthopedic problems are of a diverse nature, the etiology is often poorly understood, and effective treatment strategies are sometimes elusive.
The breadth and depth of some particularly challenging orthopedic problems has stimulated an interest in patient-based perspectives of their own FHS and HRQL. These types of measures will be referred to as "health measures" for the purposes of this paper. Health measures can be used to assess the burden of morbidity associated with orthopedic diagnoses, and to assess the effectiveness and efficiency of therapies. The diversity of issues also led the American Academy of Orthopedic Surgeons and the Pediatric Orthopedic Society of North America to develop the Pediatric Outcomes Data Collection Instrument (PODCI), and led other researchers to develop function-specific instruments such as the Activities Scale for Kids (ASK). However, there are a large variety of potentially appropriate health measures and there are limits to the number of these measures that can be used in any given study. The most appropriate set of health measures should be selected on the basis of underlying study objectives and design criteria .
Objectives and designs vary greatly across orthopedic studies. In general, orthopedic procedures and programs are undertaken to improve the health of patients. The health of patients can be measured in different ways. Conventional clinical or physiological measures are generally very useful for diagnostic and therapeutic purposes. Conventional clinical measures may, however, provide an incomplete assessment of health. For example, clinical examination and gait measures have been used to describe the quality and quantity of limitations in walking ability associated with hip flexion contracture but these types of assessments provide little information about the importance of this contracture relative to that of other serious health problems. In addition, various measures may provide conflicting results: some indicating improvement while others suggest decline in patients' health. Therefore, in the interest of scientific rigor, protocols to assess the overall effectiveness of therapy should include a priori specification of the comprehensive measure of health and a credible assessment viewpoint for the purposes of primary analysis of study results. Secondary study objectives may involve use of data collected from other health measures or from other assessment perspectives.
FHS and HRQL measures are important for a variety of reasons that complement conventional clinical measures . FHS measures provide descriptive information and HRQL measures add some type of valuation about the desirability of overall health status. Valuation may be based on preference-based scores or by including only items identified as important to patients. HRQL is a more comprehensive concept than FHS as noted in this leading definition.
"Health-related quality of life is the value assigned to duration of life as modified by the impairments, functional states, perceptions, and social opportunities that are influenced by disease, injury, treatment or policy" .
It is generally accepted that a goal of therapy is to make patients feel better  and this is supported by statements of leading policy analysts such as "The goal of health care is to protect, promote, and maintain the health status of people"  and "Since the ultimate goal of all health care is to improve, restore, and preserve HRQL" . However, physiological measures may change without people feeling better and people may feel better without measurable change in physiological function. Furthermore there is often a need for trade-offs between treatment-related benefits and adverse side effects. There is an increasing awareness also that various stakeholders in clinical decisions often have differing opinions about disability and that the opinions of patients should count. Numerous valid and reliable questionnaires are now available to collect FHS and HRQL measurements from patients and their representatives. The measures may be used for discriminative (comparing groups at a point in time such as assessing the burden of illness), evaluative (assessing within-person change over time as in clinical trials), and predictive purposes (providing prognostic information) .
A series of studies from a randomized controlled clinical trial (RCT) and prospective cohort study of elective total hip arthroplasty are illustrative of these purposes. These examples, while not from a pediatric setting, are orthopedic and demonstrate how data can be used for a variety of purposes. In the RCT, Rorabeck and colleagues  undertook a discriminative analysis of a set of FHS and HRQL measures, and reported there were no differences in outcomes between cemented and non-cemented protheses. Evaluative analyses of data from the same trial have documented improvements according to six HRQL measures and the six-minute-walk test , and have shown important differences among results from four major generic HRQL measures . In the prospective cohort study by Mahon et al , waiting time for surgery was inversely related to baseline WOMAC (Western Ontario and McMaster University Osteoarthritis Index) scores for patients at time of referral to an orthopedic surgeon.
There are many factors to consider when designing studies using patient-focused health measures for orthopedic patients. Among the most important factors are the study objectives, types of patients, the dimensions and FHS constructs associated with the health problem under study, the appropriateness of the questionnaire for the age range of the patients, the viewpoint of the assessors answering the questionnaire, the method of collecting questionnaire information (self-completion or interviewer-administration), the period of time respondents are asked to consider when answering the questionnaire (i.e., assessment recall period), and measurement properties of assessment tools. Measurement properties include content and construct validity, test-retest and inter-rater reliability, responsiveness, and practical limitations of data collection. It takes many years, and often decades, for credible and relevant evidence to be accumulated about the measurement properties of instruments. Instruments with well-established measurement properties should be used whenever possible. Two prime issues for assessments of children are the developmental stage of the subjects and the most appropriate respondent for questionnaires.
The methods section of the paper presents a framework of important factors for consideration in designing studies to assess FHS and HRQL for orthopedic patients. The results were generated by applying the framework in the context of developing a proposal for a comprehensive international study of patients with neurofibromatosis type 1 (NF1) and congenital tibial dysplasia (CTD). The NF1-CTD protocol provides a rich illustration because it encompasses great variability in the instrument selection factors, and these patients with their supportive caregivers face numerous complex health status challenges with long-term implications for HRQL. Furthermore, there is little information in the published literature about the comprehensive health status and HRQL of patients with NF1-CTD. The rationale and approach to studying FHS and HRQL from a patient perspective have relevance to many other areas of orthopedics.
Methods: a framework for orthopedic applications
This section identifies specific study design and measurement instrument factors that should be considered in selecting measures for use in studies, and presents some background information for illustrating the application of these factors.
Study design factors
Study design factors are specified in detailed protocols that clearly define the study objectives and conditions. Well-defined study objectives identify the study subjects, and type and number of measurements required. Study conditions describe practical issues including data collection sites, and budgets for time and resources.
Variability in study objectives
A highly focused, single objective study may require relatively few instruments while a broad-based, multi-faceted study will require numerous measures. For example, a RCT of efficacy for an experimental technique to achieve union of bone compared to a conventional technique might specify patients' reports from a single well-established walking ability scale as the primary outcome measure. However, an economic evaluation of the cost-effectiveness and cost-utility of the experimental technique would require at least two instruments: the single well-established walking ability scale to estimate effectiveness for the numerator in the cost-effectiveness ratio; and a utility-based scale of overall HRQL to estimate quality-adjusted life years for the numerator in the cost-utility ratio.
Age range of subjects
The age of study subjects affects the choice of measurement instruments and the method of collecting data. Each instrument is valid for a specific age range and the age range is quite small for many pediatric instruments. For example, developmental changes during childhood make it difficult for single instruments to be valid from infancy through adolescence. Many instruments are valid for one or more of the following age categories: infants, pre-school children, primary-school children, adolescents, young / middle-age adults, seniors and elderly. Inability to read well (e.g., young children or populations with high illiteracy rates), or see well (e.g., many elderly populations), or concentrate well (e.g., people on certain strong medications) inhibits use of self-complete questions. Well-designed interviewer-administered questionnaires pose clear, short, well-focused questions with readily understood and easily remembered response options (e.g., yes or no response options).
It is becoming widely accepted in many disciplines that studies of health care programs and technologies should include measures of patients' perceptions of their FHS and HRQL . Measurements of these types have been shown to vary by assessment viewpoint and, although many different viewpoints are valid, the patient perspective is considered to be one of the most important. It has been shown that children as young as 7 years can reliably complete interviewer-administered disease-specific and generic questionnaires about their own health .
Type of data collection sites
The locations and cultural characteristics of the study population will determine the language and sometimes the choice of assessors. Relatively few instruments have been carefully translated and culturally-adapted to facilitate use in a large variety of communities. Some cultures accept a variety of assessment viewpoints (e.g., patient, family, nurse, physician, and other allied health professionals) while other cultures recognize only a physician viewpoint for health assessments.
Mode of data collection
The literature suggests that there may be important effects due to mode of data collection  and, therefore, mode of administration should be standardized across subjects, assessors, and assessment points. In general, self-complete questionnaires tend to elicit reports of greater morbidity than interviewer-administered questionnaires. It is hypothesized that subjects may be less inhibited to report disabilities on self-complete questionnaires than to report disabilities to interviewers. The mode of data collection for a study may be determined by numerous factors other than the characteristics of study subjects mentioned above. A low budget study may rely on a mail survey, or distribution of questionnaires in a busy clinic setting, and use self-complete questionnaires. Alternatively, a study involving serial assessments may require follow-up data to be collected by telephone and therefore require use of questionnaires designed for interviewer-administration.
Assessment recall period
FHS questionnaires should have well-specified assessment periods to help ensure that the subjects and researchers know the period of time covered by the responses. Assessment recall period refers to the period of time the assessor is asked to consider when answering the questionnaire. The period should match the objectives of the study. For instance, if a new surgical technique is thought to reduce the peri-operative burden of morbidity then frequent assessments with a brief recall period (24 hours) might be suitable. Standard assessment periods include the past 24 hours, the past 1 week, the past 2 weeks and the past 4 weeks. Short assessment periods should be used in studies of patients whose FHS varies over time and in studies involving serial assessments to ensure that there is no overlap in assessment times. Relatively long recall assessment durations may be used when it can be assumed patients' health status is fairly stable. For example, a four-week recall assessment period was used to measure the HRQL of patients in a randomized clinical trial and economic evaluation of two alternative treatment strategies for patients with knee osteoarthritis [15, 16].
There are limits to the number and type of measures that respondents can be expected to complete without getting tired or frustrated, and/or that the study budget can afford in regards to both data collection and analysis. The evidence about the limits of respondent burden is sparse. All else being equal, studies involving serial assessments should expect to collect fewer measurements per assessment than single-assessment cross-sectional surveys. To maximize efficiency, instruments should be selected to provide complementary rather than overlapping information.
Types of measures
There are numerous definitions of FHS and HRQL. For the purposes of the paper, a FHS measure is defined as being descriptive in terms of functional ability and HRQL is defined as involving some form of valuation of that health status.
One published taxonomy  suggests that measures may be classified as specific or generic. Specific measures are focused on a specified health problem, disease, or age group of subjects. An example is the Western Ontario McMaster Osteoarthritis Index . Specific measures designed for evaluative purposes are often able to detect small but clinically important differences among subjects and be responsive to small but clinically important intra-subject changes over time. Generic measures are applicable to a broad range of subjects, including a wide variety of clinical groups and general populations. There are 2 types of generic measures: generic health profile instruments such as the Rand-36  and SF-36 ; and generic preference-based instruments. There are 2 types of generic preference-based instruments: direct measurement instruments, such as standard gamble ; and multi-attribute classification systems with preference-based scoring functions , such as the Health Utilities Index [22, 23] and the Quality of Well-Being Scale [24, 25]. A detailed description of direct measurement techniques is beyond the scope of this paper but provided in a recent review paper by Torrance and colleagues . Direct preference measurement is generally not practical for application in most clinical studies, especially those involving very young children. Customized instrumentation, usually relying on administration by highly skilled interviewers, must be developed for each study. Further, measurement questions are cognitively demanding. Direct preference measurement will, therefore, not be considered further in this paper.
Each multi-attribute system includes a descriptive classification scheme to describe and assess health status, and a preference-based valuation system. HRQL scores for health states defined by multi-attribute systems are calculated from models fitted from directly measured preference measurements (see below). Multi-attribute systems provide descriptive information about comprehensive health status, and interval-scale preference scores of overall HRQL from a community perspective on a scale where 0.00 is the score for being dead and 1.00 is the score for being in perfect health. Several multi-attribute systems define negative scores of overall HRQL to represent preferences for states considered worse than being dead. A few systems include single-attribute preference-based scales of morbidity. Single-attribute morbidity scales are defined such that the least desirable level within an attribute (dimension of health status such as vision) has a score of 0.00 (blind) and the most desirable level has a score of 1.00. The community perspective is most widely recommended for technology assessment and reference case economic evaluation analyses [26–29]. Interval-scale properties, and a score of 0.00 for being dead, are important features of HRQL scales for integrating the effects of morbidity and mortality in descriptive studies and in cost-utility economic evaluations. Interval-scale preference scores of HRQL may be either utilities (e.g., Health Utilities Index Mark 3) or values (e.g., EQ-5D). Utility preference measures are based on von Neumann-Morgenstern utility theory, include an element of risk attitude, and are therefore appropriate for decision problems with uncertainty. Value scores are preferences measured under certainty. Details about differences between utilities and values, and about direct preference measurement, appear in recent papers by Torrance et al [20, 30]. Uncertainty is an important factor in many orthopedic procedures and therefore utility scores are more appropriate than value scores in this context.
Evidence of measurement properties: validity, reliability, and responsiveness
A valid measure is "sound and sufficient" . There are many ways to assess validity of measures. Assessments of FHS and HRQL measures should consider at least six types of validity: face validity, content validity, construct validity, convergent validity, discriminative validity, and predictive validity . Face validity requires that a measure appear on the surface to make sense in regards to being relevant and useful. Content validity requires that the measure include all important and relevant domains or dimensions of health status. Construct validity describes the extent to which a measure corresponds to theoretical concepts and convergent validity describes the association between related variables. Discriminant validity is a lack of correlation between dissimilar variables or groups. Predictive validity, one type of criterion validity, describes the relationship between current and future measurements.
A measure is reliable if it is sound and dependable . Reliability is assessed by tests of repeatability or reproducibility. Reliability is often assessed in terms of agreement between intra-subject test-retest measurements and inter-assessor measurements .
Responsiveness is also referred to as sensitivity to change. It is an important feature for determining a measure's ability to detect effects of treatments or natural changes over time (e.g., due to the aging process). Husted and colleagues reviewed the literature and defined two major types of responsiveness: internal and external responsiveness . Internal responsiveness describes the ability of a measure (instrument) to change and has been assessed using a variety of techniques including the magnitude of statistical significance tests (e.g., p < 0.05 versus p < 0.001), the mean change score divided by the standard deviation of scores at baseline (effect size), and a sensitivity coefficient calculated as the proportion of the variance in change scores due to treatment . It has also been assessed as the ratio of the mean change in patients' scores and the pooled standard deviation of the mean change scores , and as the mean change score among those who changed divided by the standard deviation of change scores among stable patients . External responsiveness is concerned with the relationship between change in a measurement and change in a reference measurement of health status. External responsiveness has been assessed using the receiver operating characteristic method, correlations (e.g., Pearson product moment correlation), and regression models. The minimum important difference (MID) is the smallest size of difference that is important from patients' or clinicians' perspectives. The MID between two measurements is a concept closely related to responsiveness when assessing change over time .
Ceiling and floor effects are undesirable properties that reduce the validity, reliability, and responsiveness of measures. A ceiling effect may occur when a large proportion of measurement observations are close to the upper bound of the measurement scale. A ceiling effect results in a positively skewed distribution of measurements, limited ability of the measure to discriminate among subjects at the upper end of the scale, and attenuated responsiveness to improvements in health in longitudinal studies. A floor effect may occur when a large proportion of measurement observations are close to the lower bound of the measurement scale. Floor effects create a negatively skewed distribution of measurements, limited ability of the measure to discriminate among subjects at the lower end of the scale, and decreased responsiveness to decrements in health in longitudinal studies. Many generic and specific measures of HRQL may be subject to ceiling effect problems in that they may not be able to describe patients or subjects with above average (supra-normal) health. Some measures are subject to floor effect problems. Some are subject to both. Typically floor effect problems are more serious in clinical studies (which often involve patients with disabilities) and ceiling effect problems may be more problematic in general population studies.
Limits of respondent burden
The limits of respondent burden depend upon many factors including the number of questions presented, how the questions are presented, the complexity of questions, the sophistication of respondents, and the respondents' interest in the questions. In general, the allowable length of questionnaire is shorter for mail and phone administration than for face-to-face interviewer administration . One set of guidelines specifies the following maximum lengths: 20 questions for phone surveys; 60 questions for mailed surveys; and 80 questions for face-to-face interview surveys . Another guideline recommends that telephone interviews not exceed 5 to 10 minutes . These guidelines are in general agreement with maximum recommended number of pages for self-administered questionnaires: 2 to 4 page upper limit for topics not especially salient ; 12 page upper limit for self-administered questionnaires ; and 4 to 6 page upper limit for mailed surveys . For mail-out surveys, the evidence suggests no effect of length on response rates for questionnaires varying from 3 to 9 pages [43, 44] but reduced response rates with questionnaires greater than 12 pages .
Availability of support services
Applications of FHS and HRQL measures are greatly facilitated by expert advice, detailed instructions and other services designed to support users of a measure. Supporting documentation is usually protected by copyright and should not be used without written permission of the original developers. Documentation obtained from third-party sources should be considered suspect because it is frequently invalid. Licensing fees are used to fund high quality, readily accessible service centers. Permission to use copyright materials is typically granted one study at a time. Support services may also include consultation about the most appropriate versions of questionnaires for use in a specific study. Application packages may include data collection instruments such as questionnaires, procedure manuals, coding algorithms and scoring systems, as well as background information about the conceptual and measurement properties of the instrument.
NF1-CTD: A case study
Recently there has been interest in using measures of FHS and HRQL to evaluate treatment alternatives for NF1-CTD. NF1 is one of the most common genetic disorders in childhood . It is estimated that at least 1 million people throughout the world have NF1 . NF1 has a wide range of clinical manifestations including abnormalities of the skin, nervous system, bones and soft tissues . Other conditions experienced by children with NF1 include short stature and neurologic problems such as learning disabilities or unspecified school performance problems (36%), frequent headaches (28%), mental retardation (6%), and reduced reproductive potential [46–49].
CTD is rare in the general population, approximately 1 per 140,000 . It has been estimated that approximately 1% of people with NF1 have CTD . CTD is diagnosed usually during the first year of life and fractures often occur before 3 years of age. Frequently, initial presentation is tibial bowing followed by subsequent fracture and pseudoarthrosis . There is no generally accepted standard for management of CTD although most surgeons would suggest initial treatment of either intramedullary fixation with bone grafting or resection and bone transplant. Surgical procedures for the treatment of CTD are fraught with complications and failure of union. For the treatment of CTD, pre-fracture bracing until skeletal maturity may be a better alternative than surgery. CTD is associated with severe complications due to nonunion or pseudoarthrosis after osteotomy and amputation may be required.
Conventional clinical measures of CTD include the Crawford classification system . These measures provide clinicians with important information used in diagnosis and management of well-established symptoms. A list of important concerns could be prepared by interviewing patients and members of their families. Standardized comprehensive tools that integrate multi-dimensional effects would also be useful in quantifying the number and extent of problems experienced by NF1-CTD patients, and other pediatric orthopedic patients with complex issues. The published literature on NF1 and NF1-CTD contains virtually no information based on FHS or HRQL measurements. The exception is a recent paper by Wolkenstein and colleagues  who reported results from 128 adult patients in France using the generic health profile SF-36 and a skin-disease-specific measure, Skindex-France.
Surveys of the published literature, experts in the fields, web sites and other sources of information were conducted to determine the dimensions of health that are affected by NF1-CTD, the types of FHS and HRQL measures that have been used, which measures should be considered as potentially useful for studies of NF1-CTD, the measurement properties of potentially useful measures, and the relative merits of various measures. A review of the on-line Quality of Life Instruments Database (QOLID) developed by Dr. Marcello Tamburini and the MAPI Research Institute , and correspondence with instrument developers, identified a short list of potentially useful measurement tools in each of the following categories: generic preference-based HRQL systems, major pediatric and other generic health profiles, and disease or function specific measures. Selected measures should have demonstrated properties in accordance with currently accepted criteria [12, 52, 53] and should provide commensurate measurements for patients across a wide age range. Problems with mobility, cognition, pain, emotion (including impacts of problems with self-image), self-care, vision, and fertility are aspects of health reported in the published literature to be compromised in NF1 patients.
Illustrative study design criteria
to document long-term health outcomes associated with the disease and its treatment;
to measure the burden of disease and treatment during active therapy;
to investigate the hypothesis that improved HRQL is associated with initial amputation compared with multiple limb-saving procedures;
to determine relationships of FHS and HRQL with conventional clinical variables used in diagnosis and management; and
to assess the measurement properties (e.g., construct validity, patient versus parent inter-rater reliability, and responsiveness to change) of selected FHS and HRQL measures in NF1-CTD patients.
These detailed objectives require the identification and assessment of leading FHS and HRQL measures for use in both cross-sectional and prospective longitudinal surveys.
The prevalence of NF1-CTD is relatively low. Patients will need to be recruited from numerous clinical centers in North America to generate precise estimates of FHS and HRQL. Questionnaires should be available in at least 3 major languages: English, French and Spanish. The survey population ranges in age from newborn into adulthood and linking results across the study objectives requires that at least some of the assessment tools be in common across the age range of study patients. To avoid potential confounding effects, data collection techniques should be consistent across subjects and measures.
The patient-focus will be represented by collecting measurements from all patients old enough to provide self-assessments, and from parents acting as proxy assessors for all children and adolescents. Self-complete questionnaires requiring minimal supervision should be used to eliminate the need for interviewers at each clinical center, to facilitate use of mail-out surveys, and to avoid potential "interviewer" effects. The number and type of measures per assessment, and the number of serial assessments per patient, should be sufficient to address all the study objectives within the limits of study resources and assessor burden. Measures of morbidity associated with NF1-CTD should be comparable with data on norms from surveys of general populations and other patient groups, and be useful for assessing the effectiveness and efficiency of health care services.
Existing patient-focused health measures
The HRQL measure should be comprehensive and preference-based, to facilitate a broad variety of comparisons. A pediatric health profile measure and other specific measures will be selected to complement the selected preference-based HRQL measure. FHS measures may be focused on one or more of the following: the population of interest (e.g., pediatrics); the major underlying disease (e.g., NF1); the major human function of most interest (e.g., walking ability); the medically-defined health problem of most interest (e.g., tibial dysplasia); the medical speciality most involved with treatment of the health problem (e.g., pediatric orthopedics).
Major Characteristics of Five Generic Preference-Based Multi-Attribute Systems
# Attributes (# levels per attribute)
4 (3 to 27)
7 (3 to 5)
8 (5 or 6)
6* (4 to 6)
# of unique health states
Mobility, Physical activity, Social activity, Symptoms / problems.
Sensation, Mobility, Emotion, Cognition,
Vision, Hearing, Speech, Ambulation, Dexterity, Emotion, Cognition, Pain.
Mobility, Self-care, Usual activities, Pain / discomfort, Anxiety / depression.
Physical functioning, Role limitations, Social functioning, Pain, Mental health, Vitality.
Types of HRQL scores and measurement
Utilities; VAS and SG combination
Utilities; VAS and SG combination
HRQL scoring model form
Modified linear additive
Modified linear additive
HRQL scale interval
0.00 to 1.00
-0.03 to 1.00
-0.36 to 1.00
-0.59 to 1.00
0.00 to 1.00
Questionnaire formats (languages)
2 SF-36 versions (numerous)
The major population-specific health profiles include the Child Health Questionnaire (CHQ), Pediatric Inventory of Quality of Life (PedsQL), Pediatric Evaluation of Disability Inventory (PEI) and TNO-AZL Pre-School Children Quality of Life questionnaire (TAPQOL). The PEI is limited to children age 0.5 – 7 years of age and requires a structured parent interview or clinician observation . TAPQOL [74, 51] is limited to children 0.5 to 5 years of age. Therefore, PEI and TAPQOL will not be discussed further.
The major pediatric disease-/function-/specialty-specific instruments include the PODCI (also referred to as the POSNA or Pediatric Orthopedic Society of North America instrument), ASK, Gillette Functional Assessment Questionnaire Walking Scale (FAQ walking scale), and Wee-FIM. Wee-FIM , a popular measure of functional independence, is not being considered because it involves clinician assessments rather than assessments from a patient or parent perspective. In general, disease-specific scales in orthopedics focus on pain and physical function because these factors are major areas of concern for orthopedic patients and no generic health measures have been developed specifically for orthopedic application . No relevant disease-specific measures or disease-specific preference-based tools were identified.
Major Characteristics of Two Pediatric Health Profile Systems
Child Health Questionnaire
Pediatric Inventory of Quality of Life
Parent Forms (PF)
Parent Form for Infants & Toddlers
Youth- Completion Form
Child Self-Report Forms
1) ages 5–7 years
2) ages 8–12 years
3) ages 13–18 years
Parent Report Forms
1) ages 2–4 years
2) ages 5–7 years
3) ages 8–12 years
4) ages 13–18 years
Measures of physical and psycho-social health concepts
Measures of core dimensions delineated by WHO
Age Bounds (years)
5 to 17
2 months to 5 years
11 to 17
5 to 18
2 to 18
98 (PF98) or 50 (PF50) or 28 (PF28) short form
87 (working on shorter form)
10 child & 4 parent concepts
8 infant & 5 parent concepts
10 child concepts
4 "Generic Core"
2 Global Physical & Global Psycho-Social
1 overall, for primary analyses
4 sub-scales, for secondary and descriptive analyses
Completion Time (minutes)
10 to 20 for PF50;
7 to 15 for PF28
20 to 30
15 to 30
Domains and Constructs of Forms for Two Pediatric Health Profile Systems
CHQ: Child Health Questionnaire
Parent Forms (PF)
Parent Form for Infants & Toddlers
Child Forms (3 versions: 5–7, 8–12, 13–18 yrs)
Parent Forms (4 versions: 2–4, 5–7, 8–12, 13–18 yrs)
1. Physical functioning – 6 items.
- e.g., Child is greatly limited in performing all physical activities, including self-care, due to physical health.
2. Role (school and activities) limitations due to emotional.*
3. Role limitations due to behavioral problems* – 3 items.
- e.g., Child is limited a lot in school work or activities with friends due to behavioral problems.
4. Role limitations due to physical problems – 2 items.
- e.g., Child is greatly limited in school work or activities with friends due to physical health.
5. Bodily pain – 2 items.
- e.g., Child has extremely severe, frequent and limiting body pain.
6. General behavior – 6 items.
- e.g., Child very often exhibits aggressive, immature, delinquent behavior.
7. Mental health – 5 items.
- e.g., Child has feelings of anxiety and depression all the time.
8. Self-esteem – 6 items.
- e.g., Child is very dissatisfied with abilities, looks, family/peer relationships and life overall.
9. General health perceptions – 6 items.
- e.g., Parent believes child's health is poor and likely to get worse.
10.Change in health – 1 item.
- e.g., Child's health is much worse now than 1 year ago.
1. Physical Functioning.
2. Behavior (perception).
3. Growth & Development.
5. Behavior (getting along).
6. Temperament & Mood.
7. General Health.
8. Change in Health.
1.. Physical Functioning – 8 items.
- hard to walk more than one block, hard to run, hard to do sports or exercises, hard to lift something heavy, hard to take a bath, hard to do chores around the house, hurt or ache, low energy.
2. Emotional Functioning – 5 items.
- feel afraid or scared, feel sad or blue, feel angry, trouble sleeping, worry about what will happen.
3. Social Functioning – 5 items.
- trouble getting around, other kids not wanting to be friends, teased, doing things other peers do, hard to keep up when playing with others.
4. School Functioning – 5 items.
- hard to concentrate, forget things, trouble keeping up with schoolwork, miss school because not well, miss school for doctor appointments.
Major Characteristics of Orthopedic-Specific Systems
FAQ walking scale
Activities Scale for Kids
Functional Walking Scale
Physical disability in terms of capacity or performance dimensions
Complete range of functional walking abilities
Age range (years)
2 – 18
2 – 18
# Domains; and Domains
Comorbidity, Upper Extremity & Physical Function, Transfers & Basic Mobility, Sports & Physical Function, Pain, Treatment Expectations, Happiness, Satisfaction with Symptoms.
Personal Care, Dressing, Eating & Drinking, Miscellaneous, Play, Locomotion, Standing Skills, Stairs, Transfers.
Yes, Global Function, mean of sub-scales for domains 1 to 4 above
Yes, additive and not weighted
Completion Time (minutes)
10 – 20
5 – 12
Spanish Language version
Patient &/or Parent (Diagnostic, complications and aims section by clinician)
The choice of existing measures is based on a process of elimination considering the relative strengths of each instrument and the complementarities among measures. Neither the SF-36 nor the EQ-5D is valid for use in adolescent patients with orthopedic problems . A review of measurement of HRQL in children by Eiser & Morse [; see also [85, 86]] identified HUI and CHQ and PedsQL as the only 3 generic measures that fulfill all specified review criteria: established reliability and validity; suitable for self- and proxy-report; and brief (<30 items). PODCI outperformed CHQ physical functioning scale for orthopedic patients . However, PODCI has considerable problems with missing data, especially in upper extremity function and physical function and sports scales for children ages 2 to 5 years, associated with the use of "too young" response options . ASK is reported to be more sensitive to change in disability levels than HUI [Young N, personal email communication to W Furlong 2002-02-18]. The FAQ walking scale provides the most complete assessment of functional walking abilities, especially at the upper end of the scale .
In summary, there are few measures available for assessing subjects less than 5 years of age and even fewer for subjects less than 2 years of age. Most relevant measures are available in self-complete format only. Preliminary recommendations for the NF1-CTD study were that PedsQL be used as the generic health profile, HUI be the multi-attribute preference-based measure of HRQL utility scores for children age 5 years and older and that CHSCS-PS be the measure for children age 2 through 4 years, ASK be the measure of activity limitation, FAQ walking scale be the measure of walking ability, and that a small feasibility study of these instruments be completed with a convenience sample.
A pilot feasibility study surveyed 8 NF1 patients using HUI and FAQ walking scale measures. Questionnaires were completed by 6 NF1 patients and 3 parents. The combined HUI and FAQ walking scale questions took respondents an average of 13 minutes (range, 9–20 minutes) to complete. The patients were 11 to 50+ years old and had health problems ranging from mild to severe. One patient with tibial dysplasia and 2 patients with scoliosis were included.
HUI data were collected from 5 patients, 2 parents and both the patient and parents in one case. Health problems were reported in 7 of the 8 HUI3 attributes (vision, speech, ambulation, dexterity, emotion, cognition, and pain; no problems with hearing were reported). The attributes associated with the most morbidity, as assessed using HUI3 single-attribute utility scores , were pain (mean score = 0.81), speech (0.94), cognition (0.94), and emotion (0.94). For the 7 patients having complete data, 5 had two or more HUI2 and HUI3 attributes at less than full function. On the conventional utility scale in which being dead = 0.00 and in perfect health = 1.00, the HUI3 scores ranged from 0.45 to 1.00. The mean HUI3 score, 0.73, is similar to the mean score of 0.77 for adults with arthritis .
FAQ walking scale data were collected from 4 patients (1 of the 5 survey patients did not answer the question), 2 parents and both the patient and parents in one case. Five patients were reported to be at Level 10 (walks, runs, and climbs on level and uneven terrain and does stairs without difficulty or assistance), one patient to be at Level 8 (walks outside the home for community distances, is able to get around on curbs and uneven terrain in addition to level surfaces, but usually requires minimal assistance or supervision for safety), and one patient at Level 6 (walks more than 15–50 ft. outside the home but usually uses a wheelchair or stroller for community distances or in congested areas).
In summary, the feasibility study showed that the HUI and FAQ walking scale questions were acceptable to patients' families and that results, especially for HUI, reflected the large variability in HRQL of the sample of patients.
Choice of measures for illustrative study
No single measure will provide sufficient data to address all the important study objectives. A set of measures is required. The set of measures should provide complementary data of health status and preference-based scores of HRQL. Redundancy in measurement is reduced, and efficiency of measurement is increased, by selecting the most comprehensive generic measures and then supplementing these measures with the most appropriate set of specific measures.
it includes both generic health profile and preference-based scoring systems;
the preference-based scoring systems are well-validated;
it is the most comprehensive, compact and efficient of these types of systems;
it includes many of the most important domains in the context of NF1-CTD;
it is applicable for all people age 5 years and older;
well-developed data collection questionnaires are available to match the study design criteria;
HUI results facilitate integrating effects of morbidity and mortality, and cost-utility economic evaluations;
it has been used successfully in a variety of studies of musculoskeletal problems;
population norm data are available; and
a closely-related health status system, the CHSCS-PS, is available to assess children 2 through 4 years of age.
The HUI will provide a broad set of measures for comparisons with other populations and for estimating HRQL on a general scale such that dead = 0.00 and perfect health = 1.00. As a generic measure, HUI also has the ability to capture side effects and the effects of co-morbidities. However, these broad measures may not be responsive to small but important changes in health status. Therefore, HUI should be complemented by a set of instruments focused on pediatric, orthopedic and walking issues.
it includes domains, social and school function, which complement HUI and CHSCS-PS domains;
it is appropriate for children ages 2 through 18 years;
it is not overly burdensome in terms of data collection;
patient and parent assessment questionnaires are available; and
it can be interviewer-administered to facilitate data collection by telephone, if necessary.
Two specific measures should also be part of the set of instrumentation: the ASK and the FAQ walking scale. ASK is an orthopedic-specific instrument which has been shown to cover the most important domains in the context of musculoskeletal disorders, including the impact of limb lengthening surgery experienced by many children with tibial dysplasia. ASK is also attractive because it provides overall summary scores for both performance and capability measures, and is only moderately burdensome to complete. Walking ability is one of the most important aspects of health that is frequently compromised in NF1-CTD patients, and the FAQ walking scale is the most complete scale of functional walking ability currently available and it only requires asking one question.
CHQ is not recommended because it does not add much to the set of recommended measures and it is burdensome to complete. PODCI is not recommended because it is very burdensome to complete, it has been reported to have major problems with "missing data", and the system for collapsing questionnaire responses into summary scores is not well validated.
Children ages 2 to 5 years of age should be assessed by their parents using three questionnaires: the CHSCS-PS (12 questions), the PedsQL (23 questions); and the FAQ walking scale (1 question). It is expected that all three of these questionnaires can be completed in an average of 15 minutes.
Children and adolescents ages 5 to 17 years of age should be assessed by their parents using four questionnaires: the HUI (15 questions), the PedsQL (23 questions), the FAQ walking scale (1 question), and the ASK (30 questions). These four questionnaires are expected to be completed in an average of 20 to 30 minutes.
Children and adolescents older than 11 years should provide self-assessments using four questionnaires: the HUI (15 questions), the PedsQL (23 questions), the FAQ walking scale (1 question), and the ASK (30 questions). On average, it is expected that respondents will complete all four questionnaires in 20 to 30 minutes.
This paper highlights reasons why patient-focused measures of FHS and HRQL should be considered important tools in the field of orthopedic surgery. It has also noted that there is increasing competition for scarce health-care resources, that allocation decisions about these resources are being informed by evidence based on patient-focused health measures, and that these measures are being under-utilized by the orthopedic surgery community.
The orthopedic community faces numerous obstacles in utilizing FHS and HRQL measures. One major obstacle is that the multitude of existing measures makes it difficult to decide which measures may be appropriate for a specific application. A second obstacle is that most of the information about FHS and HRQL measures is not reported in the orthopedic literature. A third obstacle is that usually no one measure can capture all the important aspects associated with a specific orthopedic issue. The framework outlined in the paper provides guidance for selecting appropriate FHS and HRQL measures. The framework guides orthopedic investigators to combine their basic study criteria, including objectives and clinical context, with key criteria for FHS and HRQL measures from the published literature.
The results in this paper identify some major sources of information about health measures, identify some of the most widely used measures of FHS and HRQL, and provide summaries of key characteristics for selected measures in three major taxonomical classes: generic preference-based multi-attribute systems; generic pediatric health profile systems; and orthopedic-specific systems. It is clear that there are many important differences among measures both within and across taxonomical classes. All measures are not equal. There are sound factors for making judgements about which measures are most appropriate for a given application. A process of appraisal and elimination was used to select one measure from each taxonomical class for inclusion in the NF1-CTD study illustrative example, and a pilot study of the most readily available selected measures confirmed the feasibility of their use in a small sample of NF1-CTD patients.
The paper shows that a set of relevant, valid, reliable, responsive and practical patient-focused health measures for use in an orthopedic study can be readily identified and selected from the published literature and information available on the worldwide web. We encourage orthopedic researchers to use the framework to identify and select appropriate patient-focused health measures in their future studies.
Conflict of Interest
W. Furlong and D. Feeny have a proprietary interest in Health Utilities Inc. which distributes copyright Health Utilities Index (HUI®) instrumentation and provides methodological advice on the use of HUI.
We are pleased to acknowledge funding from a Shriner's Foundation Planning Grant and support of the following members of the Planning Grant team: Dr. John Carey in the Department of Pediatrics at the University of Utah School of Medicine and member of the medical staff at Intermountain Shriners Hospital for Children for his role as Principal Investigator of the Shriner Foundation Planning Grant; and Dr. Jan Friedman and Patricia Birch in the Department of Medical Genetics at the University of British Columbia for providing the HUI and FAQ walking scale feasibility study data reported in this paper. The granting agency played no role in the design, interpretation, or analysis of the work reported here and have not reviewed or approved of this manuscript. Dr. James Wright's review comments identified some important points that we used to improve the paper.
- Laupacis A, Rorabeck CH, Bourne RB, Feeny D, Tugwell P, Sim DA: Randomized Trials in Orthopaedics: Why, How and When. J Bone Joint Surgery 1989, 71: 535–543.Google Scholar
- Matza LS, Swensen AR, Flood EM, Secnik K, Leidy NK: Assessment of health-related quality of life in children: a review of conceptual, methodological and regulatory issues. Value Health 2004, 7: 79–92. 10.1111/j.1524-4733.2004.71273.xPubMedView ArticleGoogle Scholar
- Patrick DL, Erickson P: Health Status and Health Policy: quality of life in health care evaluation and resource allocation. New York: Oxford University Press; 1993.Google Scholar
- Guyatt GH, Naylor CD, Juniper E, Heyland DK, Jaeschke R, Cook DJ: Users' Guide to the Medical Literature XII. How to Use Articles About Health-Related Quality of Life. JAMA 1997, 277: 1232–1237. 10.1001/jama.277.15.1232PubMedView ArticleGoogle Scholar
- Steinwachs DM, Wu AW, Cagney KA: Outcome research and quality of care. In Quality of Life and Pharmacoeconomics in Clinical Trials. Second edition. Edited by: Spilker B. Philadelphia: Lippincott-Raven Press; 1996:747–752.Google Scholar
- Osoba D: Health-related quality-of-life outcomes in clinical trials. In Assessing Quality of Life in Clinical Trials. Edited by: Fayers P, Hayes RD. Oxford: Oxford University Press; 2004:259–274.Google Scholar
- Guyatt GH, Feeny DH, Patrick DL: Measuring health-related quality of life. Ann Intern Med 1993, 118: 622–629.PubMedView ArticleGoogle Scholar
- Rorabeck CH, Bourne RB, Laupacis A, Feeny D, Wong C, Tugwell P, Leslie K, Bullas R: A double-blind study of 250 cases comparing cemented with cementless total hip arthroplasty. Cost effectiveness and its impact on health-related quality of life. Clin Orthop 1994, 298: 156–164.PubMedGoogle Scholar
- Laupacis A, Bourne R, Rorabeck C, Feeny D, Wong C, Tugwell P, Leslie K, Bullas R: The Effect of Elective Total Hip Replacement Upon Health-Related Quality of Life. J Bone Joint Surgery 1993, 75: 1619–1626.Google Scholar
- Feeny D, Wu L, Eng K: Comparing Short Form 6D, standard gamble, and Health Utilities Index Mark 2 and Mark 3 utility scores: results from total hip arthroplasty patients. Qual Life Res 2004, 13: 1659–1670. 10.1007/s11136-004-6189-2PubMedView ArticleGoogle Scholar
- Mahon JL, Bourne R, Rorabeck C, Feeny D, Stitt L, Webster-Bogaert S: Health-related quality of life and mobility in patients awaiting elective total hip arthroplasty in a prospective study. CMAJ 2002, 167: 1115–1121.PubMed CentralPubMedGoogle Scholar
- Revicki DA, Osoba D, Fairclough D, Barofsky I, Berzon R, Leidy NK, Rothman M: Recommendations on health-related quality of life research to support labeling and promotional claims in the United States. Qual Life Res 2000, 9: 887–900. 10.1023/A:1008996223999PubMedView ArticleGoogle Scholar
- Feeny D, Juniper EF, Ferrie PJ, Griffith LE, Guyatt G: Why not just ask the kids? Health-related quality of life in children with asthma. In Measuring Health-Related Quality of Life in Children and Adolescents Implications for Research and Practice. Edited by: Drotar D. Mahwah, NJ: Lawrence Erlbaum Associates Publishers; 1998:171–185.Google Scholar
- Grootendorst P, Feeny DH, Furlong W: Does it matter whom and how you ask? Inter- and intra-rater agreement in the Ontario Health Survey. J Clin Epidemiol 1997, 50: 127–136. 10.1016/S0895-4356(96)00314-9PubMedView ArticleGoogle Scholar
- Raynauld JP, Torrance GW, Band PA, Goldsmith CH, Tugwell P, Walker V, Schultz M, Bellamy N, Canadian Knee OA Study Group: A prospective, randomized, pragmatic, health outcomes trial evaluating the incorporation of hylan G-F 20 into the treatment paradigm for patients with knee osteoarthritis (Part 1 of 2): clinical results. Osteoarthritis Cartilage 2002, 10: 506–517. 10.1053/joca.2002.0798PubMedView ArticleGoogle Scholar
- Torrance GW, Raynauld JP, Walker V, Goldsmith CH, Bellamy N, Band PA, Schultz M, Tugwell P, Canadian Knee OA Study Group: A prospective, randomized, pragmatic, health outcomes trial evaluating the incorporation of hylan G-F 20 into the treatment paradigm for patients with knee osteoarthritis (Part 2 of 2): economic results. Osteoarthritis Cartilage 2002, 10: 518–527. 10.1053/joca.2001.0513PubMedView ArticleGoogle Scholar
- Bellamy N: Pain assessment in osteoarthritis: experience with the WOMAC osteoarthritis index. Semin Arthritis Rheumatol 1989, 18: 14–17. 10.1016/0049-0172(89)90010-3View ArticleGoogle Scholar
- Hays RD, Morales LS: The Rand-36 measure of health-related quality of life. Ann Med 2001, 33: 350–357.PubMedView ArticleGoogle Scholar
- Ware JE Jr: The SF-36 health survey. In Quality of Life and Pharmacoeconomics in Clinical Trials. Second edition. Edited by: Spilker B. Philadelphia PA: Lippincott-Raven Press; 1996:337–345.Google Scholar
- Torrance GW, Furlong W, Feeny D: Health utility estimation. Expert Rev Pharmacoeconomics Outcomes Res 2002, 2: 99–108. 10.1586/14737184.108.40.206View ArticleGoogle Scholar
- Hawthorne G, Richardson J: Measuring the value of program outcomes: a review of multiattribute utility measures. Expert Rev Pharmacoeconomics Outcomes Res 2001, 1: 215–228. 10.1586/14737220.127.116.11View ArticleGoogle Scholar
- Feeny DH, Torrance GW, Furlong WJ: Health Utilities Index. In Quality of Life and Pharmacoeconomics in Clinical Trials. Second edition. Edited by: Spilker B. Philadelphia: Lippincott-Raven Press; 1996:239–252.Google Scholar
- Furlong WJ, Feeny DH, Torrance GW, Barr RD: The Health Utilities Index (HUI) system for assessing health-related quality of life in clinical studies. Ann Med 2001, 33: 375–384.PubMedView ArticleGoogle Scholar
- Patrick DL, Bush JW, Chen MM: Methods for measuring levels of well-being for a health status index. Health Serv Res 1973, 8: 228–245.PubMed CentralPubMedGoogle Scholar
- Kaplan RM, Anderson JP: The general health policy model: an integrated approach. In Quality of Life and Pharmacoeconomics in Clinical Trials. Second edition. Edited by: Spilker B. Philadelphia: Lippincott-Raven Publishers; 1996:309–322.Google Scholar
- Gold MR, Siegel JE, Russell LB, Weinstein MC, (Eds): Cost-Effectiveness in Health and Medicine. New York: Oxford University Press; 1996.Google Scholar
- CCOHTA (Canadian Coordinating Office for Health Technology Assessment): Guidelines for Economic Evaluation of Pharmaceuticals: Canada. 2nd edition. Ottawa: Canadian Coordinating Office for Health Technology Assessment; 1997. [http://www.ccohta.ca]Google Scholar
- Drummond MF, O'Brien B, Stoddart G, Torrance GW: Methods for the Economic Evaluation of Health Care Programmes. Second edition. Oxford: Oxford University Press; 1997.Google Scholar
- NICE (National Institute for Clinical Excellence): Guidance for Manufacturers and Sponsors. NICE Technology Appraisals Process Series No. 1. London: National Institute for Clinical Excellence; 2001. [http://www.nice.org.uk]Google Scholar
- Torrance GW, Feeny D, Furlong W: Visual analog scales: do they have a role in the measurement of preferences for health states? Med Decis Making 2001, 21: 329–334. 10.1177/02729890122062622PubMedView ArticleGoogle Scholar
- Last JM, (Eds): A Dictionary of Epidemiology. New York: Oxford University Press; 1983.Google Scholar
- Streiner DL, Norman GR: Health Measurement Scales: A Practical Guide to Their Development and Use. Oxford: Oxford University Press; 1991.Google Scholar
- Deyo RA, Diehr P, Patrick DL: Reproducibility and responsiveness of health status measures: statistics and strategies for evaluation. Control Clin Trials 1991, 12: 142S-158S.PubMedView ArticleGoogle Scholar
- Husted JA, Cook RJ, Farewell VT, Gladman DD: Methods for assessing responsiveness: a critical review and recommendations. J Clin Epidemiol 2000, 53: 459–468. 10.1016/S0895-4356(99)00206-1PubMedView ArticleGoogle Scholar
- Liang MH, Fossel AH, Larson MG: Comparisons of five health status instruments for orthopedic evaluation. Med Care 1990, 28: 632–642.PubMedView ArticleGoogle Scholar
- Farivar SS, Liu H, Hays RD: Half standard deviation estimate of the minimally important difference in HRQOL scores? Expert Rev Pharmacoeconomics Outcomes Res 2004, 4: 515–523. 10.1586/1473718.104.22.1685View ArticleGoogle Scholar
- Aday LA: Designing and Conducting Health Surveys. San Francisco: Jossey-Bass; 1989.Google Scholar
- Jackson W: Research Methods: Rules for Survey Design and Analysis. Scarborough, Canada: Prentice-Hall Canada Inc; 1988.Google Scholar
- Woodward CA, Chambers LW, Smith KD: Guide to improved Data Collection in Health and Health Care Surveys. Ottawa: Canadian Public Health Association; 1982.Google Scholar
- Sudman S, Bradburn NM: Asking Questions: A Practical Guide to Questionnaire Design. San Francisco: Jossey-Bass; 1982.Google Scholar
- Dillman DA: Mail and Telephone Surveys: The Total Design Method. New York: Wiley and Sons; 1978.Google Scholar
- Erdos PL: Professional Mail Surveys. New York: McGraw-Hill; 1978.Google Scholar
- Champion DJ, Sears AM: Questionnaire response rate: a methodological analysis. Soc Forces 1969, 47: 335–339.View ArticleGoogle Scholar
- Seth J, Roscoe AM: Impact of questionnaire length, follow-up methods and geographic location on response rate to a mail survey. J Appl Psychol 1975, 60: 252–254.View ArticleGoogle Scholar
- Stevenson AD, Birch PH, Friedman JM, Viskochil DH, Balestrazzi P, Boni S, Buske A, Korf BR, Niimura M, Pivnick EK, Schorry EK, Short PM, Tenconi R, Tonsgard JH, Carey JC: Descriptive analysis of tibial pseudoarthrosis in patients with neurofibromatosis type 1. Am J Med Genet 1999, 84: 413–419. 10.1002/(SICI)1096-8628(19990611)84:5<413::AID-AJMG5>3.0.CO;2-1PubMedView ArticleGoogle Scholar
- Crawford AH, Schorry EK: Neurofibromatosis in children: the role of the orthopaedist. J Am Acad Orthop Surg 1999, 7: 217–230.PubMedGoogle Scholar
- Clementi M, Milani S, Mammi I, Boni S, Monciotti C, Tenconi R: Neurofibromatosis type 1 growth charts. Am J Med Genet 1999, 87: 317–323. 10.1002/(SICI)1096-8628(19991203)87:4<317::AID-AJMG7>3.0.CO;2-XPubMedView ArticleGoogle Scholar
- Friedman JM: Epidemiology of neurofibromatosis type 1. Am J Med Genet (Sem Med Genet) 1999, 89: 1–6.View ArticleGoogle Scholar
- Ozonoff S: Cognitive impairment in neurofibromatosis type 1. Am J Med Genet (Sem Med Genet) 1999, 89: 45–52.View ArticleGoogle Scholar
- Wolkenstein P, Zeller J, Revuz J, Ecosse E, Leplége A: Quality of life impairment in neurofibromatosis type 1. Arch Dermatol 2001, 137: 1421–1425.PubMedView ArticleGoogle Scholar
- QOLID, the Quality of Life Instruments Database [http://www.qolid.org]
- Medical Outcomes Trust, Scientific Advisory Committee: Assessing Health Status and Quality-of-Life Instruments: Attributes and Review Criteria. Qual Life Res 2002, 11: 193–205. 10.1023/A:1015291021312View ArticleGoogle Scholar
- Hufford MR, Shiffman S: Methodological issues affecting the value of patient-reported outcomes data. Expert Rev Pharmacoeconomics Outcomes Res 2002, 2: 119–128. 10.1586/1473722.214.171.124View ArticleGoogle Scholar
- Sintonen H: An approach to measuring and valuing health states. Soc Sci Med 1981, 15: 55–65.Google Scholar
- Rabin R, de Charro F: EQ-5D: a measure of health status from the EuroQol Group. Ann Med 2001, 33: 337–343.PubMedView ArticleGoogle Scholar
- Hawthorne G, Richardson J, Osborne R: The Assessment of Quality of Life (AqoL) instrument: a psychometric measure of health-related quality of life. Qual Life Res 1999, 8: 209–224. 10.1023/A:1008815005736PubMedView ArticleGoogle Scholar
- Brazier J, Roberts J, Deverill M: The estimation of a preference-based measure of health status from the SF-36. J Health Econ 2002, 21: 271–292. 10.1016/S0167-6296(01)00130-8PubMedView ArticleGoogle Scholar
- Brazier JE, Roberts J: The estimation of a preference-based measure of health from SF-12. Medical Care 2004, 42: 851–859. 10.1097/01.mlr.0000135827.18610.0dPubMedView ArticleGoogle Scholar
- Torrance GW, Feeny DH, Furlong WJ, Barr RD, Zhang Y, Wang Q: Multi-attribute preference functions for a comprehensive health status classification system: Health Utilities Index Mark 2. Med Care 1996, 34: 702–722. 10.1097/00005650-199607000-00004PubMedView ArticleGoogle Scholar
- Feeny D, Furlong W, Torrance GW, Goldsmith CH, Zhu Z, DePauw S, Denton M, Boyle M: Multi-attribute and single-attribute utility functions for the Health Utilities Index Mark 3 system. Med Care 2002, 40: 113–128. 10.1097/00005650-200202000-00006PubMedView ArticleGoogle Scholar
- UCSD: Health Outcomes Assessment Program http://medicine.ucsd.edu/fpm/hoap/qwb.htm and http://medicine.ucsd.edu/fpm/hoap/qwbsa.htm
- Feeny D, Blanchard C, Mahon JL, Bourne R, Rorabeck C, Stitt L, Webster-Bogaert S: Comparing community-preference based and direct standard gamble utility scores: evidence from elective total hip arthroplasty. Int J Technol Assess Health Care 2003, 19: 362–372. 10.1017/S0266462303000321PubMedView ArticleGoogle Scholar
- Feeny D, Furlong W, Saigal S, Sun J: Comparing directly measured standard gamble scores to HUI2 and HUI3 utility scores: group and individual-level comparisons. Soc Sci Med 2004, 58: 799–809. 10.1016/S0277-9536(03)00254-5PubMedView ArticleGoogle Scholar
- Horsman JR, Furlong WJ, Feeny DH, Torrance GW: The Health Utilities Index (HUI ® ): concepts, measurement properties and applications. Health Qual Life Outcomes 2003, 1: 54. 10.1186/1477-7525-1-54PubMed CentralPubMedView ArticleGoogle Scholar
- Health Utilities Inc. /Health-Related Quality of Life [http://www.healthutilities.com]
- Health Utilities Group /Health Utilities Index and Quality of Life [http://www.fhs.mcmaster.ca/hug]
- Nathan PC, Furlong W, Horsman J, Van Schaik C, Rolland M, Weitzman S, Feeny D, Barr RD: Inter-observer agreement of a comprehensive health status classification system for pre-school children among patients with Wilms' tumor or advanced neuroblastoma. Qual Life Res 2004, 13: 1707–1715. 10.1007/s11136-004-7624-0PubMedView ArticleGoogle Scholar
- Saigal S, Rosenbaum P, Feeny D, Furlong W, Stoskopf B, Hoult L: Multi-attribute health status classification system for pre-school children: Final Report to The Medical Council of Canada for Grant No. MA 12956. Hamilton, ON, McMaster University; 2000.Google Scholar
- Saigal S, Rosenbaum P, Stoskopf B, Hoult L, Furlong W, Feeny D, Hagan R: Development, reliability and validity of a new measure of overall health for pre-school children. Qual Life Res, in press.
- EuroQol Group [http://www.euroqol.org]
- Ware JE Jr, Sherbourne CD: The MOS 36-item short-form health status survey (SF-36): I. conceptual framework and item selection. Med Care 1992, 30: 473–483.PubMedView ArticleGoogle Scholar
- Ware JE, Snow KK, Kosinski M, Gandek B: SF-36 Health Survey manual and interpretation guide. Boston: New England Medical Center, The Health Institute; 1993.Google Scholar
- Wright JG: Quality of Life in Orthopedics. In Quality of Life and Pharmacoeconomics in Clinical Trials. Second edition. Edited by: Spilker B. Philadelphia: Lippincott-Raven Press; 1996:1039–1044.Google Scholar
- Fekkes M, Theunissen NCM, Brugman E, Veen S, Verrips EGH, Koopman HM, Vogels T, Wit JM, Verloove-Vanhorick SP: Development and psychometric evaluation of the TAPQOL: a health-related quality of life instrument for 1–5-year-old children. Qual Life Res 2000, 9: 961–972. 10.1023/A:1008981603178PubMedView ArticleGoogle Scholar
- Uniform Data System for Medical Rehabilitation [http://www.udsmr.org]
- Medical Outcomes Trust: Instruments [http://www.outcomes-trust.org/instruments.htm]
- The PedsQL™ Measurement Model for the Pediatric Quality of Life Inventory™ [http://www.pedsql.org]
- Varni JW, Seid M, Kurtin PS: PedsQL™ 4.0: reliability and validity of the Pediatric Quality of Life Inventory Version 4.0 Generic Core Scales in healthy and patient populations. Med Care 2001, 39: 800–812. 10.1097/00005650-200108000-00006PubMedView ArticleGoogle Scholar
- Varni JW, Burwinkle T, Seid M, Zellner J: The PedsQL as a population health measure: implications for states and nations. Quality of Life Newsletter 2002, 28: 4–6.Google Scholar
- Haynes RJ, Sullivan E: The Pediatric Orthopaedic Society of North America Pediatric Orthopaedic Functional Health Questionnaire: an analysis of normals. J Pediatr Orthop 2001, 21: 619–621. 10.1097/00004694-200109000-00013PubMedGoogle Scholar
- Young NL, Williams JI, Yoshida KK, Bombardier C, Wright JG: The context of measuring disability: does it matter whether capability or performance is measured? J Clin Epidemiol 1996, 49: 1097–1101. 10.1016/0895-4356(96)00214-4PubMedView ArticleGoogle Scholar
- Young NL, Williams JI, Yoshida KK, Wright JG: Measurement properties of the Activities Scale for Kids. J Clin Epidemiol 2000, 53: 125–137. 10.1016/S0895-4356(99)00113-4PubMedView ArticleGoogle Scholar
- Vitale MG, Levy DE, Johnson MG, Gelijns AC, Moskowitz AJ, Roye BP, Verdisco L, Roye DP: Assessment of quality of life in adolescent patients with orthopaedic problems: are adult measures appropriate? J Pediatr Orthop 2001, 21: 622–628. 10.1097/00004694-200109000-00014PubMedGoogle Scholar
- Eiser C, Morse R: The measurement of quality of life in children: past and future perspectives. J Dev Behav Pediatr 2001, 22: 248–256.PubMedView ArticleGoogle Scholar
- Connolly MA, Johnson JA: Measuring quality of life in paediatric patients. Pharmacoeconomics 1999, 16: 605–625.PubMedView ArticleGoogle Scholar
- Pickard AS, Topfer LA, Feeny DH: A structured review of studies on health-related quality of life and economic evaluation in pediatric acute lymphoblastic leukemia. J Natl Cancer Inst Monogr 2004, 33: 102–125. 10.1093/jncimonographs/lgh002PubMedView ArticleGoogle Scholar
- Vitale MG, Levy DE, Moskowitz AJ, Gelijns AC, Spellman M, Verdisco L, Roye DP: Capturing quality of life in pediatric orthopaedics: two recent measures compared. J Pediatr Orthop 2001, 21: 629–635. 10.1097/00004694-200109000-00015PubMedGoogle Scholar
- Daltroy LH, Liang MH, Fossel AH, Goldberg MJ, the Pediatric Outcomes Instrument Development Group: The PONSA Pediatric Musculoskeletal Functional Health Questionnaire: report on reliability, validity, and sensitivity to change. J Pediatr Orthop 1998, 18: 561–571. 10.1097/00004694-199809000-00001PubMedView ArticleGoogle Scholar
- Novacheck TF, Stout JL, Tervo R: Reliability and validity of the Gillette Functional Assessment Questionnaire as an outcome measure in children with walking disabilities. J Pediatr Orthop 2000, 20: 75–81. 10.1097/00004694-200001000-00017PubMedGoogle Scholar
- Grootendorst P, Feeny D, Furlong W: Health Utilities Index Mark 3: evidence of construct validity for stroke and arthritis in a population health survey. Med Care 2000, 38: 290–299. 10.1097/00005650-200003000-00006PubMedView ArticleGoogle Scholar
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