Overview
The PedsQL includes more items than is manageable to value in the preference elicitation exercise required to construct the PedsUtil scoring system. With 23 items, each ranging 5 severity levels from “Never” to “Almost always”, the PedsQL in its entirety would generate 523 health states, which are feasibly too many to value. Therefore, it is necessary to reduce the length of the original instrument to a core set of dimensions and items to form the PedsUtil health state classification system.
Factor analysis aims to identify unobserved latent variables that explain patterns of correlation among observed variables (14). There are different types of factor analysis, namely confirmatory and exploratory factor analyses (15, 16). The confirmatory approach allows for a specific dimensional structure (i.e., the conceptual model) to be tested so that theoretically or clinically driven decisions can be integrated into the general method of item assessment at the outset, rather than as an afterthought. The exploratory approach, in contrast, does not incorporate any prior assumptions about the dimension structure into the analysis, which can be problematic since it does not guarantee that the identified dimension structure will be clinically coherent (15). Because the PedsQL is a well validated instrument with an established dimensional structure, confirmatory factor analysis was used to identify the core dimension structure of the PedsUtil health state classification system. This approach to constructing a health state classification system from an existing non-preference-based measure has been previously used to derive preference-based measures of adult generic HRQoL and condition-specific HRQoL (13, 14), such as the development of the SF-6D from the SF-36 (7, 16) and the QLU-C10D from the QLQ-C30 (17).
The PedsQL
The PedsQL 4.0 Generic Core Scales is a modular instrument that measures generic HRQoL in children and adolescents from ages 2 to 18 years (11, 12). It was developed through focus groups and cognitive interviews, and consists of child self-report and parent proxy-report versions. The child self-report version includes age groups 5–7 years, 8–12 years and 13–18 years. The parent proxy-report version includes age groups 2–4 years, 5–7 years, 8–12 years, and 13–18 years. The items in the different versions differ only in developmentally appropriate vocabulary and first or third person tense. A 5-point response scale (0 = Never; 1 = Almost never; 2 = Sometimes; 3 = Often; 4 = Almost always) is used for child self-report ages 8 years and older, and for all age groups with the parent proxy-report. For child self-report ages 5 to 7 years, a 3-point response scale (0 = Not at all; 2 = Sometimes; 4 = A lot) is used to increase comprehension. The PedsQL Infant Scales, which is validated to evaluate HRQoL for infants 1–24 months (18), was not included in this analysis as it consists of different items and dimensions than the PedsQL Generic Core Scales.
Overall, there are 21 to 23 multi-level items that fall under 4 different dimensions or subscales: 1) Physical Functioning; 2) Emotional Functioning; 3) Social Functioning; and 4) School Functioning (Table 1). The items are reverse-scored and linearly transformed to a 0 to 100 scale (0 = 100; 1 = 75; 2 = 50; 3 = 25; 4 = 0), where higher scores indicate better HRQoL. The total HRQoL score is calculated as the sum of the completed item scores divided by the number of items answered (11).
Table 1
Dimensions | Number of Items |
2–4 years | 5–7 years | 8–12 years | 13–18 years |
Physical Functioning | 8 | 8 | 8 | 8 |
Emotional Functioning | 5 | 5 | 5 | 5 |
Social Functioning | 5 | 5 | 5 | 5 |
School Functioning | 3 | 5 | 5 | 5 |
Total | 21a | 23 | 23 | 23 |
a PedsQL School Functioning contains 2 fewer items for children 2-4 years to reflect developmentally appropriate items.
Data Source
This study analyzed data from the Longitudinal Study of Australian Children (LSAC), which is a study that follows a population-representative sample of 10,000 children and their families from all parts of Australia (19). The LSAC was approved by the Australian Institute of Family Studies Ethics Committee, and families provided written informed consent to participate. The LSAC delivers a comprehensive national dataset on children as they age, and is one of the very few large-scale nationally representative surveys of children in the world. The LSAC adopted a dual cohort cross-sectional sequential design. Data are collected from 2 cohorts every 2 years. The first cohort (infant cohort) consists of 5,000 children who were 0–1 years old in 2003. The second cohort (child cohort) consists of 5,000 children who were 4–5 years old in 2003 (20).
For this study, a cross-sectional master dataset (n = 45,207) was created using data from the first 7 waves (2003-04 to 2015-16) of the LSAC. The dataset contains fully completed responses to the parent proxy-report version of the PedsQL for children aged 2 to 17 years. While the PedsQL is validated for children up to 18 years, LSAC data were only available for children up to 17 years at the time of this analysis. For children 2–3 years old, responses to only 19 of the 21 PedsQL items were available; the 2 items on school absence were not collected as part of the LSAC. Consequently, all analyses conducted in this study for children aged 2–3 years only utilized the 19 PedsQL items that were administered. For all other age groups, responses to the full PedsQL were available. Special healthcare needs status of LSAC participants were also categorized in this study as children with special healthcare needs or typically functioning children. A child was considered to have special healthcare needs if a parent reported that their child had “a condition which has lasted or is expected to last for at least 12 months, which causes the child to use medicine prescribed by a doctor, other than vitamins, or use more medical care, mental health or educational services” (20). Data from the last available wave for each child was used to determine special healthcare needs status since younger children are less likely to be identified as having special healthcare needs.
Statistical Analysis – Confirmatory Factor Analysis
Four main competing conceptual models of the PedsUtil health state classification system were developed a priori, drawing on published literature and expert opinion (Table 2). The first conceptual model, Model A, was the original 4-dimension structure of the PedsQL: Physical Functioning, Emotional Functioning, Social Functioning, and School Functioning. The second, Model B, was comprised of 5 dimensions: Physical Functioning, Emotional Functioning, Social Functioning, School Functioning, and School Absence. Model B was specified based on previously published literature that found model fit to be better when School Functioning items were split into 2 separate dimensions (11, 12, 21); specifically, the 5-item School Functioning dimension was split into one dimensions with 3 items measuring school cognitive functioning and a second dimension with 2 items measuring school absence as related to illness. The third conceptual model, Model C, included 6 dimensions, 2 of which were single-item dimensions: Physical Functioning, Pain, Fatigue, Emotional Functioning, Social Functioning, and School Functioning. Model C was constructed based on expert opinion and by referencing other available preference-based HRQoL instruments for children (8–10, 22, 23) that suggested that the items measuring Pain and Fatigue (originally in the Physical Functioning dimension) may be clinically important to distinguish as independent dimensions. Lastly, Model D was composed of 7 dimensions, and was formed by combining Models B and C: Physical Functioning, Pain, Fatigue, Emotional Functioning, Social Functioning, School Functioning, and School Absence.
Table 2. Summary of Dimension Structures for Models A-D
Itema
Problems with...
|
Model A
(4 Dimensions)
|
Model B
(5 Dimensions)
|
Model C
(6 Dimensions)
|
Model D
(7 Dimensions)
|
Phys 1. Walking
|
Physical
|
Physical
|
Physical
|
Physical
|
Phys 2. Running
|
Physical
|
Physical
|
Physical
|
Physical
|
Phys 3. Participating in exercise
|
Physical
|
Physical
|
Physical
|
Physical
|
Phys 4. Lifting something heavy
|
Physical
|
Physical
|
Physical
|
Physical
|
Phys 5. Taking a bath or shower
|
Physical
|
Physical
|
Physical
|
Physical
|
Phys 6. Doing chores
|
Physical
|
Physical
|
Physical
|
Physical
|
Phys 7. Having hurts or aches
|
Physical
|
Physical
|
Pain
|
Pain
|
Phys 8. Low energy level
|
Physical
|
Physical
|
Fatigue
|
Fatigue
|
Emot 1. Feeling afraid or scared
|
Emotional
|
Emotional
|
Emotional
|
Emotional
|
Emot 2. Feeling sad or blue
|
Emotional
|
Emotional
|
Emotional
|
Emotional
|
Emot 3. Feeling angry
|
Emotional
|
Emotional
|
Emotional
|
Emotional
|
Emot 4. Trouble sleeping
|
Emotional
|
Emotional
|
Emotional
|
Emotional
|
Emot 5. Worrying
|
Emotional
|
Emotional
|
Emotional
|
Emotional
|
Soc 1. Getting along with others
|
Social
|
Social
|
Social
|
Social
|
Soc 2. Others not wanting to be friends
|
Social
|
Social
|
Social
|
Social
|
Soc 3. Getting teased
|
Social
|
Social
|
Social
|
Social
|
Soc 4. Unable to do things others can do
|
Social
|
Social
|
Social
|
Social
|
Soc 5. Keeping up with other children
|
Social
|
Social
|
Social
|
Social
|
School 1. Paying attention in class
|
School
|
School
|
School
|
School
|
School 2. Forgetting things
|
School
|
School
|
School
|
School
|
School 3. Keeping up with schoolwork
|
School
|
School
|
School
|
School
|
SchAbs 1. Missing school because sick
|
School
|
School Absence
|
School
|
School Absence
|
SchAbs 2. Missing school to go to doctor
|
School
|
School Absence
|
School
|
School Absence
|
Abbreviations: Phys, Physical Functioning; Emot, Emotional Functioning; Soc, Social Functioning; School, School Functioning; SchAbs, School Absence.
a Item descriptions are summarized in the table (exact wording of items not displayed).
The 4 conceptual models were evaluated using the robust weighted least squares estimation method, which is recommended for modeling ordinal data (24). Only multi-item dimensions entered the analysis because confirmatory factor analysis requires at least 2 items in each dimension in order to estimate the measurement model. Therefore, single-item dimensions (i.e., Pain and Fatigue) that were included in the conceptual models because they were deemed clinically important were not empirically tested. Because the PedsUtil health state classification should ideally be applicable across diverse pediatric populations, analyses were stratified by age group and child special healthcare needs status in order to identify a common dimension structure across all subgroups. Age groups were stratified by 2-year age intervals (from 2 to 17 years) to reflect the study design of the LSAC. Special healthcare needs status was defined as children with special healthcare needs or typically functioning children.
Model fit was evaluated in this study by examining standardized factor loadings and various fit indices including the comparative fit index (CFI) (25), Tucker-Lewis Index (TLI) (26), and the root mean square error of approximation (RMSEA) (27). Previously established guidelines suggest adequate fitting models have CFI and TLI values ≥0.90 and RMSEA values ≤0.08 (28). Modification indices and residual correlations were examined to re-specify the models to improve model fit when necessary. Models were modified and re-fit until a clinically coherent model was achieved across all subgroups that also adequately fit the data. All analyses were conducted in Mplus v8 (24). This study was determined to be exempt by the University of Michigan Institutional Review Board (IRBMED # HUM00182088).