Published online December 3, 2007
PEDIATRICS Vol. 121 No. 1 January 2008, pp. e118-e126 (doi:10.1542/peds.2007-0480)
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ARTICLE

Assessment of Children's Health-Related Quality of Life in the United States With a Multidimensional Index

Alan E. Simon, MDa, Kitty S. Chan, PhDb and Christopher B. Forrest, MD, PhDc

a Centers for Disease Control and Prevention, National Center for Health Statistics, Hyattsville, Maryland
b Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
c Institute to Transform and Advance Children's Healthcare, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania


   ABSTRACT
TOP
 ABSTRACT
METHODS
 RESULTS
 DISCUSSION
 CONCLUSIONS
 REFERENCES
 
OBJECTIVE. Using nationally representative data, we examined biological, medical system, and sociodemographic factors that are associated with health-related quality of life as measured by a multidimensional index that accounts for a wide range of child health domains.

METHODS. Children aged ≥6 years (N = 69031) were drawn from the 2003/2004 National Survey of Children's Health. A random 25% sample was used to create a 12-item index of health-related quality of life with a range of 0 to 100, based on the conceptual framework of the Child Health and Illness Profile. Bivariate and multivariable regression analyses were conducted to identify the unadjusted and independent associations of key biological, medical system, and sociodemographic variables with health-related quality of life.

RESULTS. The index mean was 72.3 (SD: 14.5), median value was 73.7, and range was 11.1 to 99.9. Only 0.2% of children had a score at the ceiling. In multivariable regression analysis, the following variables were independently associated with lower health-related quality of life: biological factors (greater disease burden, severe asthma, and overweight status); medical system factors (unmet medical needs, lack of a regular health care provider, Medicaid insurance, or being uninsured previously during the year); and sociodemographic factors (older age groups, lower family education, single-mother family, having a smoker in the household, black race, and poverty).

CONCLUSIONS. Health-related quality of life in the United States is poorest for children and youth in lower socioeconomic status groups, those with access barriers, adolescents compared with children, and individuals with medical conditions. A multidimensional health-related quality-of-life index is an alternative to conventional measures (eg, mortality) for national monitoring of child health.

Key Words: quality of life • child health • National Survey of Children's Health

Abbreviations: HRQoL—health-related quality of life • IOM—Institute of Medicine • NSCH—National Survey of Children's Health • CHIP—Child Health and Illness Profile

Monitoring of child health at the national level has traditionally focused on morbidity and mortality measures, reportable infectious diseases, chronic conditions, limitations in activities, and behavioral risk factors.1–3 However, these measures do not provide an adequate understanding of child health, particularly for the large majority of children without a long-term health problem.

In contrast, measures of health-related quality of life (HRQoL) provide a broader view of child health, encompassing aspects of perceived health, health behavior, and well-being. Therefore, HRQoL has the potential to describe the health of children in the general population more comprehensively than conventional health measures and provide better identification of specific groups with high rates of unrecognized conditions, social and emotional problems, and poor functioning.4 Furthermore, with a broader definition of child health, it may be possible to capture more fully the variability in children's health. For example, Riley et al5 found that more than half of adolescents had poor health in at least 1 domain of HRQoL. Having comprehensive measurements of child health in large populations may be useful for evaluating policy decisions, identifying health disparities, and tracking population trends.6,7

National estimates of child HRQoL would additionally allow for examination of the effects of other factors on HRQoL across a broad socioeconomic and geographic population. Associations that are observed using nationally representative data could help clinicians and policy makers identify important determinants of children's HRQoL that may be targets for intervention. Relative effects on HRQoL may also help to prioritize efforts to intervene. Furthermore, whereas other studies have examined HRQoL in general populations,8 national population-based estimates would provide perhaps the most appropriate reference group against which to compare subgroups.

Several measures of generic HRQoL have been developed for children and adolescents6,8–11; however, few studies have provided nationally representative estimates of children's HRQoL in the United States. Those that exist have been limited by small samples12 or have not examined the associations of children's HRQoL with important independent variables such as insurance status and presence of medical conditions.13 In part, the lack of nationally representative estimates of child HRQoL may be attributable to the length of the measurement tools,14 given the cost and administrative difficulties of large-scale data collection efforts.

In this study, we made nationally representative estimates of child HRQoL in the United States and identified key biological, medical system, and sociodemographic variables that may affect HRQoL. Preexisting measures of child HRQoL are not included in nationally representative surveys; therefore, to make national estimates, we created a multidimensional index of HRQoL from an existing nationally representative data source by mapping the best question available in a current survey to represent each of the domains of HRQoL. At the same time, we intended for this index to overcome 3 major barriers to measuring child HRQoL in large populations. First, high costs associated with population monitoring may be reduced by use of a short index, rather than a longer instrument. Second, some measures of child health have significant ceiling effects in the general population, suggesting that large numbers of children have perfect health and well-being.9 This ceiling exists despite evidence that children face numerous health challenges2,5,15; hence, there may be much unexamined variation in health at the maximum values of these measures. Third, many measures of HRQoL may not capture a full range of health states for children. Lacking from many measures of child HRQoL are "environmental and behavioral characteristics that influence subsequent health,"8 a focus that has been embraced by the Institute of Medicine (IOM) in their definition of child health.16


   METHODS
TOP
 ABSTRACT
 METHODS
RESULTS
 DISCUSSION
 CONCLUSIONS
 REFERENCES
 
Data Source
The data source for this study was the National Survey of Children's Health (NSCH), a national telephone survey conducted by the National Center for Health Statistics.17 This survey provides state- and national-level data on children's health, as well as their families and neighborhoods,17,18 although no prespecified measures of HRQoL exist within the survey. The caregivers of 102353 children who were younger than 18 years were interviewed between January 2003 and July 2004. Weighting of the sample allows generalization at the state and national levels.17 The Johns Hopkins Bloomberg School of Public Health Committees on Human Research approved this research project.

Sample
Children who were younger than 6 years (n = 33322) were excluded from the analysis because parents of these children were not asked questions in all of the 12 domains of HRQoL conceptualized. Of the remaining children (N = 69031), a random 25% sample was used to develop our index (n = 17258). The remaining 75% of the sample (n = 51773) was used for independent validation of the index. Once validated, the full 100% sample (N = 69031) was used for analysis to maximize the accuracy of our estimates for children's HRQoL in the United States. Both the 75% and 100% samples had 3.8% missing data for the index, overall.

Development of HRQoL Index
The conceptual framework of HRQoL from the Child Health and Illness Profile (CHIP) was chosen because it is a population-based measure of child health, developed explicitly for evaluating the health of children using large surveys. In addition, the framework closely fits the IOM definition of child health by including current factors that may affect future health, such as risk-taking behaviors, school performance, and behaviors that disrupt social development. The framework of the CHIP includes 5 domains and 12 subdomains, which are described in Table 1.19


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  		TABLE 1 Items Chosen for HRQoL Index

 
Each of the 299 items in the NSCH survey instrument was assessed by each author to determine whether it represented a measure of HRQoL. The authors then convened to reach consensus on each of the questions. Then each author independently assigned the items to 1 of the 12 subdomains used in the CHIP framework. Again, to resolve disagreements, the authors met to reach consensus about the assignment of each question. The categorization of survey items yielded a minimum of 1 item and a maximum of 7 items in each of the 12 subdomains.

Within each subdomain, 1 item was chosen from all items categorized into that subdomain by applying the following criteria to data from the 25% sample: (1) missing data <1%, (2) variability of responses within item, (3) minimal skewness of data, (4) minimal ceiling and floor effect, (5) best conceptual fit with subdomain, and (6) highest factor loading when >3 items were present within a subdomain. This yielded an index that has good face validity, with questions reflecting the concepts of the subdomains. The results of this process are presented in Table 1.

Using the 12 items selected, we created an index of HRQoL. The number of responses for each item in the index varied between 2 and 8 (see Table 1), but the value of the response categories within each item were adjusted to give each of the 12 items an equal value. The items were summed and rescaled to give an index score between 0 and 100.

Validation of the HRQoL Index
After construction of the index, the remaining 75% of the sample was used for known-group, construct validation. On the basis of past studies of both HRQoL and other health markers, we expected that the following would be related to lower HRQoL index scores: (1) presence of a health condition and greater impact of health condition6,20–26; (2) lower family income13,27–30; (3) children of single-parent families as compared with children of families with ≥2 parents12,13; (4) Hispanic ethnicity6,13,31,32; and (5) having unmet need for medical care.6,33 Unmet need was determined in our data set using a composite of 3 variables that together identified those who needed care but did not receive it. Reliability estimates across the items of the index were not calculated, because the index is multidimensional.

National Estimates and Factors Related to Children's HRQoL in the United States
Using the 100% sample, we generated population estimates of children's HRQoL in the United States by calculating the mean, median, and SD for our index using the survey weights provided in the database.17 We chose to explore the relationship of HRQoL with a core set of variables that have previously been associated with child health. These variables are categorized into 3 main areas: biological determinants, medical system factors, and individual- and family-level sociodemographic factors.

For different levels of each variable, we estimated mean HRQoL using survey weights and constructed 95% confidence intervals. In all analyses, results were considered significant at the P < .05 level. Effect sizes were calculated for each variable as the difference in HRQoL index score between the highest and lowest category within a variable, divided by the SD of the category with the highest HRQoL index score.34 We evaluated effect size using Cohen's criteria, with effect sizes of 0.2 considered as small, 0.5 as moderate, and ≥0.8 as large.34

We examined the independent association of each variable with HRQoL using multivariable linear regression modeling. Entry into the initial model was contingent on significance of a variable in bivariate analysis. We retained variables with categories that were significantly different from the reference category at the P < .01 level but removed variables that were colinear (correlated >0.8) with other variables. We tested the robustness of our findings by using Wald testing, forward and backward selection procedures, and forward and backward stepwise procedures. All variable selection procedures yielded identical results.

All analyses were conducted by using Stata 9.2 statistical software.35 SVY (survey) functions were used for weighting all analyses to provide population estimates and account for the complex survey design. There were 2 exceptions to this rule. First, non-SVY sampling-weighted analyses were conducted when using forward and backward selection and stepwise selection procedures. Second, robust estimation, which also is not supported in the SVY context, was conducted with non-SVY sampling weights on the initial and final models to ensure that results would not differ given that our index was not normally distributed.


   RESULTS
TOP
 ABSTRACT
 METHODS
 RESULTS
DISCUSSION
 CONCLUSIONS
 REFERENCES
 
Results of Validation
In bivariate analysis using the 75% sample, our HRQoL index varied as expected with each of the variables used for construct validation: condition impact (the presence or absence and overall severity of any health condition), family income, family structure, ethnicity, and having unmet need for medical care. Results using the 75% sample were nearly identical to those using the 100% sample for these variables; therefore, for concision, the results of the 100% analysis are presented for these variables as part of Table 2.


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  		TABLE 2 Bivariate Relationships

 
Results of National Estimates and Factors Related to Children's HRQoL in the United States
The weighted data represented the population of ~46755000 children in the United States between the ages of 6 and 17 years. Overall, the mean value of the index was 72.31, and the median value was 73.71. The range of the index was 11.11 to 99.99, and the SD was 14.48. Importantly, the distribution, although skewed slightly, had only 0.23% of children at the ceiling with a score of ≥99. The overall distribution of the index is shown in Fig 1.


Figure 1
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  		FIGURE 1 Distribution of HRQoL index.

 
Variables with largest effect sizes included asthma impact, condition impact, and unmet need. Lack of insurance, poverty, and minority status were associated with moderate negative effects on HRQoL. All other variables explored were significant but had smaller effect sizes. Among those, lower HRQoL was associated with overweight, lack of a regular health care provider, lower level of education in household, single-parent family structure, unemployment in the home, fewer total number of adults in the home, non–English language spoken in the home, presence of a smoker in the household, Hispanic ethnicity, and older age/gender groupings. Table 2 presents results for validation variables and variables with moderate to large effect sizes.

The index varied with most variables as would be expected; however, results of age/gender groupings should be noted (Table 3). In general, scores declined from a maximum in ages 6 to 7 until they reached their nadir in the 11- to 12-year range in girls and the 13- to 15-year range in boys, at which point they begin to increase slightly.


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  		TABLE 3 Age–Gender Groupings

 
In multivariable regression analyses, variables associated with lower HRQoL included greater impact of medical condition, presence and greater severity of asthma, overweight, unmet need, lack of a regular health care provider, older age–gender groupings, lower family education, single-parent family structure, presence of a smoker in the household, black race, and greater poverty. Also associated with lower HRQoL was having Medicaid for the entire year or having Medicaid or private insurance currently but being uninsured previously during the past 12 months. The direction of effects and relative effects among categories within each variable were generally consistent between bivariate and multivariate analyses. Our findings were largely robust to different methods of handling missing data (hotdeck multiple imputation and case-wise deletion) and estimation (weighted robust and weighted nonrobust). Results from the final model are shown in Table 4.


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  		TABLE 4 Adjusted Effects on HRQoL

 

   DISCUSSION
TOP
 ABSTRACT
 METHODS
 RESULTS
 DISCUSSION
CONCLUSIONS
 REFERENCES
 
We examined relationships to children's HRQoL using data from a nationally representative survey. In doing so, we identified factors that are associated with HRQoL and have not been or were incompletely described in previous studies. For example, to our knowledge, other studies have not described a relationship between smoking in the household and decreased HRQoL for children. For some groups, such as children with asthma, a causal connection seems likely; however, for many children, it is unknown whether smoking in the household may affect an aspect of HRQoL, such as self-esteem or risk-taking, in a yet undescribed manner.

In addition, whereas other studies have examined the effect of insurance on HRQoL in the general population of children in the United States,36 it had not previously been examined in a nationally representative sample. Although having private insurance all year was significantly different from all other insurance categories in bivariate and most other categories in multivariate analysis, estimates for all nonreference categories were very similar to one another. In future studies, insurance may require greater specificity in categorization given the increasing numbers of underinsured children.

Also, the importance of medical care can be observed. Both having unmet need for medical care and lack of a regular doctor or nurse were related to lower HRQoL, and these effects persisted even after accounting for both poverty status and insurance status. It is unclear whether these effects reflect the effect of medical care itself or are a proxy for parental organizational skills or parenting quality.

The effects of age and gender on HRQoL are varied in the literature, even when similar domains are measured.12,13 Our findings suggest that entry into adolescence (ages 11–12) may have negative effects on perceived health. Although these results should be taken with caution because of small effect sizes, future research could further explore this age range and potentially identify children who are at risk for negative health experiences during this transition.

To identify these relationships, we developed a multidimensional index of children's HRQoL based on the conceptual framework developed for the CHIP instruments. This index measures a definition of child health similar to the conceptualization used by the IOM and is still concise enough to be used in large-scale surveys. We found support for its construct validity on the basis of the known-group method and observed good variability and negligible ceiling effect, suggesting that a multidimensional index of children's HRQoL would be useful for population monitoring.

To enhance interpretability of index scores, we provided a general picture of the profile of child health for different ranges of the index, based on the median response of each item for each 10-point stratum of the index (Table 5). At a mean national value of 72.31, for example, a child would generally be considered by the caregiver to be in excellent health, but, at the same time, the caregiver would be "a little" concerned about the child's self-esteem and about having enough time with the child.


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  		TABLE 5 Median Responses to Questions Within Index Score Strata

 
Examining associated characteristics of children within each 10-point stratum of the score provides another means of understanding the implications of different ranges of the index (Table 6). For example, the median family income for families with children who score between 90 and 100 on the index is 300% to 400% of the federal poverty level. In contrast, the median family income for children who score between 40 and 50 is only 100% to 200% of the federal poverty level.


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  		TABLE 6 Median or Modal Value of Independent Variables by Index Score Categories

 
There are several limitations to this study. First, our index was based on caregiver responses to questions concerning the children. It is likely that a child's own responses would provide a better measure of HRQoL.37 Second, our index is validated only for children aged 6 to 18. Third, our index used a single item to represent each construct. Although a brief index has advantages for use in large populations, it limits the depth of coverage for each construct. Finally, our analysis used key variables from biological, medical system, and sociodemographic areas. Worthwhile arguments could be made to include other variables, although the variables included have strong face validity as important correlates to children's HRQoL.

Future research should replicate specific findings, including missing levels of data. Also, it should include examinations into additional factors, such as broader contextual factors, including neighborhood and regional characteristics. Furthermore, the effects of each variable on HRQoL within subpopulations should be explored. Finally, additional refinement and validation of our individual items and overall index are needed.


   CONCLUSIONS
TOP
 ABSTRACT
 METHODS
 RESULTS
 DISCUSSION
 CONCLUSIONS
REFERENCES
 
In this study, we have shown that a short, multidimensional index can be used to measure comprehensively children's HRQoL. Also, we were able to describe the variation in HRQoL of children in the United States and identify biological, medical system, and sociodemographic factors that seem to influence children's HRQoL. These findings suggest that a short index could be included on national surveys to examine population or national trends of child HRQoL. Furthermore, this would allow for a more cost-efficient description of the effects of health policy on areas of child health that extend beyond standard measures of morbidity and mortality.


   ACKNOWLEDGMENTS
 
We thank Marie Diener-West for statistical consultations and Stephen Blumberg and Jane Sisk for comments on drafts of the article. We also gratefully acknowledge the support of the Robert Wood Johnson Clinical Scholars Program.


   FOOTNOTES
 
Accepted Jun 8, 2007.

Address correspondence to Alan E. Simon, MD, National Center for Health Statistics, 3311 Toledo Rd, Room 3231, Hyattsville, MD 20782. E-mail: fpa8{at}cdc.gov

This project was conducted while Dr Simon was a Robert Wood Johnson Clinical Scholar at Johns Hopkins University. The findings and conclusions in this article are those of the authors and do not necessarily reflect the views of the National Center for Health Statistics.

The authors have indicated they have no financial relationships relevant to this article to disclose.


   REFERENCES
TOP
 ABSTRACT
 METHODS
 RESULTS
 DISCUSSION
 CONCLUSIONS
 REFERENCES
 

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PEDIATRICS (ISSN 1098-4275). ©2008 by the American Academy of Pediatrics




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