To apply a conceptually based noncategorical definition in the analysis of a nationally representative sample of US children to determine the prevalence and sociodemographic characteristics of US children with chronic conditions.
Data on 30,032 children, aged 0 through 17 years, from the 1994 National Health Interview Survey Disability Supplement were analyzed using a noncategorical conceptual definition of chronic conditions and a method that parallels that outlined in the development of the Questionnaire for Identifying Children with Chronic Conditions. We determined the percentages of children with chronic conditions overall and in 3 conceptual domains: (1) functional limitations, (2) dependence on compensatory mechanisms, and (3) service use or need beyond routine care for age.
Content that corresponded to 35 of 39 items on the Questionnaire for Identifying Children with Chronic Conditions assessing consequences was available in the National Health Interview Survey Disability Supplement data set. An estimated 10.3 million children (14.8%) had chronic conditions; 7.0% of the children met enrollment criteria in a single conceptual domain, 5.2% in 2 domains, and 2.6% in all 3 domains. Significant sociodemographic correlates of having a chronic condition were being school-aged or older, male, white, living with a responsible adult with less than a college education, and having a family income below the poverty index (all P<.01).
We established the feasibility of operationalizing a noncategorical conceptual definition by using a large-scale data set and provided an estimate of the prevalence of chronic conditions among US children. We confirmed that several sociodemographic correlates of chronic conditions in samples identified through diagnostic checklists were related to the presence of chronic conditions among these children.
THERE ARE many important gaps in available information about children who have chronic conditions and disabilities. Estimates of the numbers of children with chronic conditions in the United States range from 4% to more than 30%1- 5 and vary greatly depending on the definition and methods used to collect the information.3,5,6 Several large health surveys have been conducted by the US federal government. However, with a few exceptions, children with chronic conditions have not been targeted specifically in these surveys. Much of the current knowledge about the epidemiology of chronic conditions in children in the United States is based on statistics derived from the 1981 and 1988 Child Health Supplements, special health topic surveys appended to the National Health Interview Survey (NHIS).7,8 Although the NHIS is one of the best examples of a large-scale, population-based morbidity survey, the 1981 and 1988 Child Health Supplements included relatively few children with chronic conditions and disabilities and depended primarily on a disease-specific checklist method for identification.
Today, the disease-specific approach used in previous versions of the NHIS is no longer consistent with public policy. Several investigators have highlighted problems with identification using diagnostic checklists9,10 and emphasize noncategorical methods.11- 13 This more generic approach defines children with chronic conditions by examining the consequences of diverse medical, behavioral, or cognitive disorders rather than by using diagnostic labels.7,8 Such noncategorical definitions have become increasingly important in service planning and in the determination of program eligibility for Title V programs, state child health insurance plans, and Supplemental Security Income, among others.
The theoretical framework developed by Stein et al10 provides a definition of chronic conditions in children that is independent of diagnostic labels or etiology. This definition is based on a noncategorical approach that focuses on consequences of presumed conditions and uses a 12-month expected or actual duration criterion. The consequences of a health condition or disability in children may be measured in many ways. In this definition, 3 essential domains or components have been conceptualized: (1) functional limitations, (2) dependence on compensatory mechanisms, and (3) service use or need beyond routine care. This definition relies on consequence-based criteria, not diagnoses of specific conditions or groups of conditions, to measure these domains. The term "condition" includes a range of diagnosed or undiagnosed impairments, disorders, derangements, illnesses, and handicaps. Physical, cognitive, psychological, and behavioral conditions are included.
The 3 essential types of consequences in the Stein et al10 definitional framework have been converted into measurable constructs and incorporated into the Questionnaire for Identifying Children with Chronic Conditions (QuICCC), a practical instrument with good test-retest reliability, as well as content, convergent, and criterion-related validity.14 We previously applied this operational definition in secondary analyses of data derived from 3 community-based samples with diverse sociodemographic characteristics.15 Using these data, we reported estimates of the percentages of children younger than age 18 years identified by the overall definition as well as by its 3 conceptual domains or consequences in a small national sample.6 We also identified sociodemographic characteristics of children meeting the overall definition and its 3 domains or consequences in that sample.
The release of the 1994 National Health Interview Survey on Disability (NHIS-D) and related surveys by the National Center for Health Statistics (NCHS) has provided an opportunity to assess whether it would be possible to apply the conceptual definition of chronic conditions in children to a large-scale database by using an operationalization similar to the one developed for the QuICCC. The NHIS-D was designed to identify persons in the United States with health conditions or disabilities, and provides detailed information that can be used to form estimates on prevalence in children by using a population-based sample. The 1994 NHIS-D contains information on the characteristics and functioning of 30,032 children younger than 18 years.
The developers of the NHIS-D included many of the QuICCC items in the survey. They were used along with other items to identify children whose caretakers would be reinterviewed in a "Followback Survey." However, items selected by the NCHS reflect a range of options for identifying children who have chronic conditions and disabilities, and they were designed to address the needs of many different potential users of these data. Therefore, the algorithm employed by NCHS in identifying children with conditions (and reflected in a summary item eligibility status [ELSTATUS] in the data set) does not represent an underlying theoretical or conceptually based definition. It includes some QuICCC items that measure the special health needs of children, a list of developmental disabilities diagnoses, and questions about activities of daily living, functional limitations, early child development, school problems, service and benefit eligibility, and perceptions of the child's health status. In contrast, our approach applies a conceptually based noncategorical definition to determine the prevalence and sociodemographic characteristics of US children with chronic conditions. To classify children as having a chronic condition, we selected individual variables from the NHIS-D that fit the previously published QuICCC criteria12 and sorted the criteria according to the 3 definitional components or domains as well. Analyses using the NHIS-D data set have allowed us to (1) test the feasibility of using the conceptual definition developed by Stein et al10 and its components as operationalized in the development of the QuICCC in analyzing a large-scale data set and (2) elaborate on and further develop preliminary estimates of the proportion and characteristics of US children who have health conditions or disabilities by using this consequence-based definition.
Data for these analyses came from the 1994 NHIS and several related questionnaires.14 The NHIS is conducted on a continual basis and its questionnaires are widely accepted and respected as among the best-available standardized instruments for household surveys about health. The NHIS Core Interview asks essentially the same questions every year, although it recently underwent major revision. The NHIS household response rate in 1994 was 94.1%. The 1994 NHIS Supplements covered 5 special health topics, including disability. The NHIS-D was designed to identify people likely to have disabling conditions and to provide some basic description of their characteristics and use of services. It contains sections that ask about sensory, communication, and mobility problems, ability to perform activities of daily living, the types of services and benefits received, the special health needs of children, and early childhood development. The response rate for the 1994 NHIS-D was 92.5% of those who completed the Core Interview, which yielded an overall response rate of 87.0%.
The NHIS has a complex multistage probability sampling design that involves a sample selected from the civilian noninstitutionalized population living in the United States. It is the best known source of data from which to make population estimates about health status and behavior. Weights are used to reduce bias in the sampling process and to ensure that the sample is representative of the population for sex, age, and race. This process keeps sampling errors to a minimum and represents a strength of using this data set for population estimates. Details on estimation procedures and statistical and sample design for the 1994 survey can be found in Current Estimates From the National Health Interview Survey, 1994.16
Questions for these analyses were selected from both the 1994 NHIS-D and Core Interviews based on the item content of the QuICCC as given in Table 1. Of the total 39 QuICCC items, 35 (90%) were either reproduced in the NHIS-D or could be generated from responses to 1 or more items. Among these 35 items, 16 QuICCC items were virtually identical to individual NHIS-D survey items; the other 19 questions were matched in general content, but in most cases were represented by multiple NHIS-D items. Only 4 QuICCC items could not be matched. Because we could not reproduce all 39 QuICCC items, we determined the estimated effect of excluding these questions by using data from a nationally representative sample of 1388 children for whom survey data were collected in developing the QuICCC, and determined that only 8 (3%) of the 256 children identified with the full QuICCC survey would have been excluded if those 4 questions were omitted. Thus, previous data indicate that 97% of the children identified by the QuICCC would be expected to be identified by the algorithm that was developed to implement the definition in this data set. It was determined that a child had a chronic condition if any question that was identical to the QuICCC or fit the algorithm in Table 1 in the appendix was answered affirmatively, thereby indicating that the child experienced at least 1 consequence in at least 1 domain. We also sorted the selected items into the 3 conceptual domains: functional limitations, dependence on compensatory mechanisms, and service use or need above routine care, and categorized children according to whether they had at least 1 consequence in each domain. Additional information about scoring procedures may be obtained from us on request.
In the conceptual definition of Stein et al,10 the term "functional limitations" refers to condition-related restrictions or impairments in age-appropriate function, activities, or social roles in the general areas of physical, cognitive, or emotional growth and development. The QuICCC domain of functional limitations includes 15 items (see Table 1). To operationalize this domain in this study, we selected NHIS-D variables that indicated whether the child was restricted in the type or amount of activities he or she could participate in; had a developmental delay (physical, mental, emotional/behavioral, or speech/language); or had difficulty in hearing, seeing, communicating, getting along with others, eating, dressing, bathing, or using the toilet. "Dependence on compensatory mechanisms" reflects the use of accommodations, devices, or personal assistance to compensate for or minimize a limitation in function, activity, or social role. The QuICCC has 12 items in this domain. We operationalized this domain using NHIS-D questions about use of regular prescription medicines; the need to avoid certain foods or to follow a doctor-ordered special diet; use of mobility, hearing, or visual aids; or the need for equipment and/or help from another person to feed oneself, dress, bathe, use the toilet, or perform other activities. Finally, service use or need beyond routine care for age includes 12 items specifying the use of or need for medical, psychological, or educational services beyond what is routine or usual for the child's age. For our analyses, the service use domain consisted of NHIS-D items that assessed overnight hospitalization, regular visits to their general practitioner or specialist, psychological counseling, physical or occupational therapy, other medical or nursing procedures, special classes or services at school, or home teaching.
The NHIS-D data set contains information on 30,032 children younger than 18 years. Their mean age was 8.3 years; 51% were male. Ethnic distribution was 64% non-Hispanic white; 16% non-Hispanic black; 14% Hispanic (any race); and 5% other. Educational level among parents or other responsible adult family members was 13%, less than a high school education; 34%, high school graduates; 25%, attended some college; and 28%, bachelor's degree or higher. Approximately 20% of the children lived in households with incomes below the poverty index.
Analyses were conducted using both SPSS/PC+ Version 5.0 (SPSS Inc, Chicago, Ill)17 and SUDAAN Release 7.0 (Research Triangle Institute, Research Triangle Park, NC),18 The latter is a statistical software program used to analyze clustered or correlated data from complex sample surveys such as the NHIS. We analyzed the raw survey data and reanalyzed it using a weighting variable provided in the data set to adjust the sample to reflect the age-sex-race distribution of the total US population. Because of the survey's multistage sampling design, weights should be used to make proper estimates from NHIS data.
Using the identified items, we gave each child in the sample an overall "yes-no" categorical determination indicating whether the child met the definition of having a chronic condition based on the presence of a consequence in at least 1 domain, as well as a yes-no rating within each of the 3 separate consequence domains. We then calculated the percentages of children meeting the definitional criteria as a whole and each of the domains individually and in all possible combinations of 2 or more domains. We also calculated the percentages of identified children across several sociodemographic characteristics of the children or their families including age group, sex, race/ethnicity, parental education, and family income. We examined the bivariate relationships between the specified sociodemographic variables and the presence of a chronic condition using cross-tabulation, with χ2 tests used to determine significance. To maintain consistency across variables and calculate similar summary statistics for the identified groups (ie, proportions) in these analyses, we recoded continuous variables (child age, parental education) into discrete categories. The child's age was grouped into categories representing 4 developmental stages having potential health policy implications: infant/toddler (aged 0-3 years), preschool (aged 4-6 years), school-age/latency (aged 7-11 years), and adolescent (aged 12-17 years). Parental education was the highest grade achieved by the "responsible adult family member" and was grouped for this analysis into 4 categories: less than high school, high school graduate, some college, and college graduate or beyond. To represent socioeconomic status, we selected the variable that NCHS had generated to indicate if the annual family income was below the poverty index. To determine the independent correlates of having a chronic condition, we then included all of the sociodemographic characteristics that had significant bivariate relationships with the presence of a chronic condition in a logistic regression analysis.
Using this operationalization of the Stein et al10 conceptual definition, we determined that 4452 (14.8%) of 30,032 children in the NHIS-D sample met the enrollment criteria for having a chronic condition or disability. This percentage was stable after using the weights provided by NCHS to adjust the sample data to reflect the age-sex-race distribution in the overall US population. Extrapolating from these weighted analyses, there are an estimated 10.3 million children in this country who are experiencing consequences that suggest they have a chronic condition. Table 2 shows the percentages of children who met the criteria for having a chronic condition overall and within the 3 domains of this definition, both individually and in all possible combinations. Percentages using both unweighted raw data and weighted NHIS-D data are provided. We determined that more children were identified by meeting criteria in the service use domain than by functional limitations alone or by compensatory dependence alone. In addition, we determined that 7% of all children met the criteria in a single domain, 5.2% met criteria in 2 domains, and 2.6% met criteria in all 3 domains.
χ2 Analyses (using the weighted NHIS-D data) showed that each of the sociodemographic variables we selected—age group, sex, race/ethnicity, parental education, and family income—had a significant bivariate relationship with the presence of a chronic condition in this sample of children. We then used logistic regression analysis to determine which sociodemographic variables were uniquely associated with having a chronic condition when each of the other factors was controlled. The statistically significant independent variables were being school-aged or older, male, white, living with a responsible adult with less than a college education, and having a family income below the poverty index (all P<.01). Each of these factors was independently associated with a greater likelihood of meeting the definition of chronic condition as operationalized. The results of these analyses, including odds ratios for the various categories of the independent variables, are shown in Table 3.
We were able to operationalize the conceptual definition of chronic conditions in the children of Stein et al10 by using variables from the NHIS-D data set. We employed an algorithm to assess condition-related consequences in which 35 of the 39 items that were used in the QuICCC were reproduced by NHIS-D variables. To our knowledge, this is one of the first attempts to use a conceptual definition of chronic health conditions and develop an operationalization of the definition that could be implemented in a large scale data set. We used this operationalization to examine the prevalence of chronic health conditions and disabilities among US children younger than 18 years who were identified by using the overall definition and its 3 domains: functional limitations, dependence on compensatory mechanisms, and service use or need beyond routine care for age. Our findings suggested that almost half (47%) of the children who have chronic conditions have consequences in only 1 of the 3 domains, although others experience multiple types of consequences. There were substantial numbers of children in each potential combination of the 3 domains and in each individual domain. This suggests that each of the 3 individual conceptual domains of the definition makes a unique contribution to the identification of children with chronic conditions. Whether having problems in 1, 2, or 3 domains reflects increasing severity is an intriguing question that we plan to explore in future studies. Data from our earlier studies of the QuICCC in a national sample support the notion of increased severity with respect to children's adjustment, but not with regard to parental psychological distress.13 Other factors remain to be explored.
As noted previously, the prevalence rate of children with chronic conditions has varied greatly in previous investigations because of the range of methods used for identification. In analyses using data from a nationally representative sample of 1388 children, Westbrook et al6 applied a similar operationalization of the same conceptually based noncategorical definition as the one used in this article and reported a prevalence rate of about 18%. In comparison, the analyses presented here using the NHIS-D data set identified a slightly smaller overall percentage of children with a condition (14.8%). Much of the difference between the 2 rates seems to be due to a greater proportion of children in the earlier sample whose parents reported that they relied on compensatory mechanisms. Clearly, the much smaller sample used in the previous study is likely to have generated less stable prevalence estimates than those derived from the NHIS-D. Moreover, as noted earlier, we reproduced the content of only 35 of the 39 items in the QuICCC as used by Westbrook et al,6 and these items identified 97% of the children in their sample who had conditions based on the full 39-item set. Applying this 3% difference in identification rate to the NHIS-D data set, we estimated that the prevalence rate would have increased to about 15.3% if we had had data on all 39 items included in the QuICCC, but this is still a smaller proportion of the sample. Some variation in rates also may be due to differences between the complete QuICCC and NHIS-D adaptations of these items. The earlier study used the actual QuICCC to operationalize the conceptual definition and identify children with chronic conditions, rather than the NHIS-D survey instrument. It is well known to survey developers that changes in the wording and sequencing of questions can alter responses. It is therefore possible that differences in the wording or sequencing of questions in these 2 studies contribute to some of the variation in reported rates of chronic conditions.
In addition, the fact that the earlier study limited participation to English-speaking families with telephones undoubtedly influenced its findings. The NHIS-D is conducted in person and has only an English-language version, but the interviewers who collect data are encouraged to administer it to the respondent using other household members as informal translators. The effects of this data collection method have not been formally assessed.
By using this definition in this study, we also identified several sociodemographic factors that were related to the presence of a chronic condition in children, including the child's age, sex, race/ethnic background, parental education, and family income. Previous research using diagnostic checklists has demonstrated that the prevalence of chronic conditions is slightly higher in older children, and statistically greater in boys and in white children compared with black children.5 Socioeconomic disadvantage also is typically associated with an increased risk of chronic or disabling conditions in children.19,20 Therefore, the pattern of results we attained was not unexpected.
Our findings differ somewhat from the patterns of sociodemographic characteristics that differed between children with and without chronic conditions in the study by Westbrook et al,6 which used the same conceptual definition. Older age, male sex, and lower family income were associated with children having a condition in both samples. However, nonwhite race was associated with a higher rate of chronic conditions in the earlier study and a lower rate in this study of the NHIS-D. Level of parental education had no relationship to a child having a condition in the earlier study, but educational levels of less than 4 years of college were associated with greater percentages of identified children in this study. We identified the significant independent variables using logistic regression analyses that statistically controlled for potentially interrelated factors. The smaller sample size in the earlier study precluded Westbrook et al6 from conducting similar analyses.
It is critical to acknowledge that the overall prevalence of chronic conditions in children and its variation in different sociodemographic groups is a function of the definition chosen and the conceptual elements that are included in its specific operationalization. However, the relative consistency in sociodemographic correlates across samples identified by diagnostic and noncategorical approaches is reassuring. Methodologies other than parent report (eg, physician report or direct physical examination of the child) also would be likely to yield different results.
There are many other potential definitional models for identifying children with chronic conditions and disabilities. Nevertheless, we clearly established the feasibility of using the conceptual definition of Stein et al10 to provide a prevalence estimate of chronic conditions in US children. We suggest that other conceptual definitions should be operationalized and tested in a similar fashion. This will help researchers and policy makers understand the implications of using different approaches and increase available information about methods for selecting and implementing operational definitions for research and programmatic purposes.
Accepted for publication July 15, 1998.
This work was supported by a Data Utilization and Enhancement grant MCJ-36D257-01-0 from the Maternal and Child Health Bureau (Title V, Social Security Act), Health Resources and Services Administration, Department of Health and Human Services, Bethesda, Md (Dr Stein).
All analyses, interpretations, or conclusions derived from these data are the responsibility of the authors and not the National Center for Health Statistics, which is responsible only for the quality of the data set.
Presented in part at the 37th Annual Meeting of the Ambulatory Pediatric Association, May 4, 1997, Washington, DC.
Corresponding author: Ruth E. K. Stein, MD, Department of Pediatrics, Albert Einstein College of Medicine/Montefiore Medical Center, Centennial I, 111 E 210th St, Bronx, NY 10467.
Editor's Note: Affected children and their families know that there is more than one way to define chronic conditions, and the policy implications are enormous.—Catherine D. DeAngelis, MD
Stein REK, Silver EJ. Operationalizing a Conceptually Based Noncategorical DefinitionA First Look at US Children With Chronic Conditions. Arch Pediatr Adolesc Med. 1999;153(1):68-74. doi:10.1001/archpedi.153.1.68