Asthma Phenotypes, Risk Factors, and Measures of Severity in a National
Sample of US Children
Colleen F. Kelley, MD*‡; David M. Mannino, MD‡§; David M. Homa, PhD‡; Amanda Savage-Brown, PhD‡; and Fernando Holguin, MD*‡
ABSTRACT. Objective. To examine a nationally rep-resentative sample of US children aged 6 to 16 years old and determine whether there are differences in risk fac-tors and measures of severity between children with different asthma phenotypes.
Methods. We analyzed data from the Third National Health and Nutrition Examination Survey. We used questionnaire and skin-prick testing data to separate children into the following mutually exclusive catego-ries: atopic asthma, nonatopic asthma, resolved asthma, frequent respiratory symptoms with no asthma diagno-sis, and normal. We used multivariate regression to de-termine whether demographic or potential risk factors varied between phenotypes and whether measures of severity varied by phenotype.
Results. We found that 4.8% of children had atopic asthma, 1.9% had nonatopic asthma, 3.4% had resolved asthma, and 4.3% had frequent respiratory symptoms. Risk factors varied by phenotype, for example, the mean BMI was higher among children with nonatopic asthma, prenatal maternal smoking was a risk factor for resolved asthma, and child care attendance was a risk factor for frequent respiratory symptoms with no asthma diagno-sis. Patients with atopic and nonatopic asthma were sim-ilar for most measures of asthma severity (medication use, health status, and lung function impairment). In contrast, patients with resolved asthma had fewer symp-toms but a similar level of lung function impairment to that seen in patients with current asthma, whereas chil-dren with frequent respiratory symptoms but no asthma diagnosis had normal lung function.
Conclusions. Asthma risk factors and measures of se-verity vary between children with different asthma phenotypes. Pediatrics 2005;115:726–731; asthma, atopy, allergy, children, lung function.
ABBREVIATIONS. NHANES III, Third National Health and Nu-trition Examination Survey; SES, socioeconomic status; PIR,
pov-erty/income ratio; FEV1, forced expiratory volume in 1 second;
FVC, forced vital capacity; ED, emergency department.
A
sthma is a chronic inflammatory disorder of the airways that causes recurrent episodes of wheezing and other respiratory symptoms in an estimated 5 million children in the United States.1,2Asthma prevalence, morbidity, andmortal-ity has increased in the United States since 1980 for reasons that are not clear.3,4
For decades, clinical differences in asthma presen-tation have been recognized and led to its descrip-tion as a heterogeneous disorder.5 Subtypes
origi-nally identified were referred to as intrinsic and extrinsic asthma, but this terminology has since been abandoned and relabeled as atopic and nonatopic asthma, often delineated on the basis of positive skin tests to common allergens or the presence of antibod-ies in the blood.5–9 In addition to these 2 clinical
phenotypes, another phenotype describing children whose asthma resolves as they age has been reported and linked in the literature to premature birth and prenatal maternal smoking.8,10Finally, many studies
have addressed the prevalence and risk factors of “undiagnosed asthma” defined by frequent respira-tory symptoms or wheezing.11–14
Although many studies have described the simi-larities and differences between asthma phenotypes for immunologic markers and airway lability,6,15–17
fewer have described the epidemiologic and clinical characteristics of asthma across different phenotypes in a generalizable sample.8,18 We analyzed data
among children who were aged 6 through 16 years from the Third National Health and Nutrition Exam-ination Survey (NHANES III) and classified them into 5 respiratory phenotypes: current physician-di-agnosed atopic asthma, current physician-diphysician-di-agnosed nonatopic asthma, resolved physician diagnosed asthma, frequent respiratory symptoms with no asthma diagnosis, and normal. Potential asthma risk factors and measures of severity were assessed across these phenotypes.
METHODS Study Population
The National Center for Health Statistics of the Centers for Disease Control and Prevention (Atlanta, GA) conducted NHANES III from 1988 through 1994.19 NHANES III was
ap-proved by the Institutional Review Board of the National Center for Health Statistics. Survey participants completed extensive questionnaires in the household and underwent comprehensive
From the *Rollins School of Public Health, Emory University, Atlanta, Georgia; ‡Air Pollution and Respiratory Health Branch, Division of Envi-ronmental Hazards and Health Effects, National Center for EnviEnvi-ronmental Health, Centers for Disease Control and Prevention, Atlanta, Georgia; and §Division of Pulmonary and Critical Care Medicine, University of Kentucky Medical Center, Lexington, Kentucky.
Accepted for publication Jul 28, 2004. doi:10.1542/peds.2004-0529 No conflict of interest declared.
Reprint requests to (D.M.M.) Division of Pulmonary and Critical Care Medicine, University of Kentucky Medical Center, 800 Rose St, MN 614, Lexington, KY 40536. E-mail: dmannino@uky.edu
physical examination, including pulmonary function testing, at a specially equipped mobile examination center. A knowledgeable proxy, usually a parent or a guardian, completed questionnaires for participants who were younger than 17 years.
Participants and Demographics
We limited the analysis to children who were aged 6 to 16 years and had data on age, gender, race, and BMI. NHANES III partic-ipants underwent a physical examination that included allergy skin testing for children agedⱖ6 years and pulmonary function testing for children agedⱖ8 years. Allergens tested included 3 indoor allergens (house mite, cat, and cockroach), 6 outdoor aller-gens (ragweed, perennial rye,Alternaria, Bermuda grass, Russian thistle, and white oak), and 1 food allergen (peanut). Laboratory analysis, including serum cotinine levels, was completed on chil-dren who wereⱖ4 years of age.19
Phenotype Definition
To delineate children who had asthma from those who did not have asthma, we used a positive answer to the question, “Has a doctor ever told you that your child has asthma?” Children then were classified as currently having asthma when the respondent answered positively when asked, “Does the child still have asthma?” Among this group, the children were stratified further by their allergen skin test status. Those with a positive reaction (wheal diameterⱖ3 mm than saline control) for 1 or more aller-gens were classified as having atopic asthma. Those who reported a history of positive skin testing or eczema but did not undergo skin testing (n⫽5) were also included in this group. Those with negative skin testing were classified as having nonatopic asthma. Patients with current asthma but no skin testing were excluded from the study (n⫽48). Those who had a previous diagnosis but denied having current asthma were classified as having resolved asthma. Children without a diagnosis of asthma but who reported moderate or severe respiratory symptoms (see below) were clas-sified as having frequent respiratory symptoms. Finally, all chil-dren who did not meet the previous criteria were classified as normal.
Variable Definitions
We classified the race/ethnicity of the participating children as white, black, Mexican-American, or other. We used education of the responding adult and poverty/income ratios (PIRs) as proxies for socioeconomic status (SES). The PIR is determined on the basis of the family income and number of people in the household.19
Age- and gender-specific percentiles were calculated for BMI.20
Children were classified as having been exposed to prenatal ma-ternal smoke when the respondent answered positively to the question, “Did mother smoke while pregnant with child?” (asked only of children 11 years and younger). Finally, children were classified as having attended child care before age 4.
Serum cotinine levels were determined using high-perfor-mance liquid chromatography atmospheric-pressure chemical ionization tandem mass spectrometry, as described elsewhere.21
Those with cotinine levels ⬎15 ng/mL were considered active smokers.21For PIR, BMI, education, and smoke exposure, we used
continuous data in the regression models.
Measures of Severity
Severity of respiratory symptoms was classified as no symp-toms when the respondent reported no episodes of coughing, wheezing, or upper respiratory tract infection in the past 12 months. Mild symptoms were defined as 1 to 11 episodes, mod-erate symptoms as 12 to 300 episodes, and severe symptoms as
⬎300 episodes (typically reported as “daily”) in the past 12 months. Symptom aggravation was determined by positive an-swers to questions that asked whether respiratory symptoms were brought on by pollen, house dust, animals, and exercise or cold air. Skin-test positivity was stratified into indoor allergen, outdoor allergen, and food allergen groups as defined above. Respondents were asked to list any prescription medications that the children were using and the diagnosed condition for these medications. We searched for medication that is typically used for asthma (eg, inhaled bronchodilators, inhaled steroids) and classified these as inhaled steroids, inhaled bronchodilators, or other medication used for asthma. Indicators of health status were determined by
the occurrence of at least 1 hospitalization for wheezing in the past 12 months, at least 1 emergency department (ED) or unscheduled doctor’s office visit for wheezing in the past 12 months, and⬎5 school absences in the past 12 months. Also, the respondent clas-sified the child’s health status according to his or her impression. This is reported as excellent/very good or as good/fair/poor. Finally, the examining doctor also classified the child’s health status according to his or her opinion. This is also reported as excellent/very good or as good/fair/poor.
Spirometry was conducted on children who were 8 years or older using a dry rolling seal spirometer in the mobile examina-tion center. Procedures for testing were based on the 1987 Amer-ican Thoracic Society Recommendations.22To obtain spirometry
acceptable according to the protocol, we performed 5 to 8 forced expirations. Several measures of lung function were used: the forced expiratory volume in 1 second (FEV1), the forced vital
capacity (FVC), and the FEV1/FVC ratio. Published prediction
equations based on NHANES III data23were used to determine
which participants had a low FEV1, defined as ⬍80% of the
predicted value. The FEV1/FVC ratio was also dichotomized with
0.80 as the cutoff.
Statistical Analysis
We calculated all estimates using the appropriate sampling weight to represent US children who were 6 to 16 years of age. For analyses, we used both SAS and SUDAAN.24,25Weighted
percent-ages are reported stratified by phenotype for variables and out-come measures except where geometric mean is indicated. We developed logistic and linear regression models to predict asthma risk factors by comparing children with each phenotype, sepa-rately, with normal children. We developed similar models to examine the outcomes of asthma severity, but, in addition, com-pared children in the asthma phenotype groups with children with atopic asthma. Variables included in these models were age, gender, race/ethnicity, PIR, education, BMI, cotinine level, prena-tal maternal smoking, and child care attendance. Categorical out-come variables were dichotomized as follows: ethnicity, white versus all others; respiratory symptoms, severe or moderate ver-sus mild or no symptoms; adult respondent’s impression of health status, excellent or very good versus less than very good; and physician’s impression of health status, excellent or very good versus less than very good.P⬍.05 for the regression coefficient was considered significant.
RESULTS
Of the 13 944 children who participated in NHANES III, 8261 were younger than 6 years and excluded from the analysis. We also excluded 390 for missing BMI, 48 children with current asthma but no skin testing, and 1 for missing cotinine levels. This resulted in an analytic sample of 5244 children who represented⬃39.6 million US children. The 439 chil-dren who were 6 years and older and excluded from the study sample were similar to the children who were included with regard to age, gender, race/ ethnicity, reported prenatal smoking, and child care attendance (P⬍.05 for all).
edu-cation level, children with resolved asthma had a higher prevalence of prenatal maternal smoking, and children with frequent respiratory symptoms had a higher prevalence of child care attendance and a higher mean parental education level (Table 2; the confidence intervals for these associations are avail-able from the authors).
Among children with atopic asthma, almost all (96.5%) reacted to at least 1 indoor allergen (Table 2).
This finding can be contrasted with 41.1% of normal children who reacted to at least 1 indoor allergen (Table 2).
Table 3 depicts the asthma severity measures of respiratory symptoms, symptom aggravation, medi-cation use, health status in the past 12 months, doc-tor’s impression of health status, adult respondent’s impression of health status, and lung function. When compared with normal children, a higher proportion
TABLE 1. Demographics and Potential Asthma Risk Factors of Children Included in Study*
Covariates n Weighted Population Weighted %
Age group, y
6–11 3234 22 200 000 56.1
12–16 2010 17 400 000 43.9
Gender
Male 2582 20 240 000 51.5
Female 2662 19 210 000 48.5
Race
White 1362 26 240 000 66.3
Black 1831 6 160 000 15.6
Mexican-American 1816 3 410 000 8.7
Other 235 3 760 000 9.5
Prenatal maternal smoking
Yes 634 5 340 000 13.5
No 2537 16 470 000 41.6
Missing 2073 17 790 000 44.9
Child care attendance
Yes 1669 14 150 000 35.7
No 3575 25 460 000 64.3
Asthma phenotype
Atopic asthma 225 1 890 000 4.8
Nonatopic asthma 72 760 000 1.9
Resolved asthma 155 1 360 000 3.4
Respiratory symptoms 212 1 690 000 4.3
Normal 4580 33 890 000 85.6
Total 5244 39 600 000
* Data are from the NHANES III and are rounded to the nearest 10 000. Percentages may not add up to 100% because of rounding.
TABLE 2. Covariates Among Children (6 –16 Years) Stratified by Asthma Phenotype*
Covariates Phenotypes
Atopic Asthma
Nonatopic Asthma
Resolved Asthma
Respiratory Symptoms
Normal
Mean age 11.5 10.4 10.8 10.8 10.9
Male gender 55.9 45.6 67.9 46.5 51.0
Race
White 66.0 74.3 63.1 75.3 65.9
Black 19.0 10.7 12.9 13.3 15.7
Mexican-American 6.2 9.8 7.1 7.5 8.9
Other 10.9 5.2 16.9 3.9 9.5
Mean PIR (n⫽4820) 2.0† 2.4 2.2 2.6 2.2
Mean education level of adult respondent, y (n⫽5205)
12.4† 12.2† 11.9 13.0† 12.0
Mean BMI percentile 62.7 68.5† 61.9 57.8 57.7
Mean cotinine level among nonsmokers (n⫽4369) 0.8 0.6 0.7 0.8 0.7
Prenatal maternal smoking:
Yes 10.5 14.4 20.7† 14.1 13.4
No 40.4 41.4 33.0 43.3 41.7
Missing 51.6 44.2 46.3 42.6 44.9
Child care attendance 39.9 37.6 36.6 49.4† 34.9
Skin test positivity (n⫽4782)
Indoor allergens 96.5† 0 67.6† 45.3 41.1
Outdoor allergens 78.0† 0 47.9† 32.6 33.4
Food allergen 23.8† 0 10.7 5.9 7.0
N 225 72 155 212 4580
Weighted % 4.8 1.9 3.4 4.3 85.6
* Data are from the NHANES III and reported as weighted percentages.
of those with atopic or nonatopic asthma reported symptom aggravation with exposure to pollen, dust, animals, and cold air or exercise; had a hospitaliza-tion or ED/unscheduled doctor’s office visit for wheezing; missed⬎5 days of school in the previous year; and had worse lung function (Table 3; confi-dence intervals are available from the authors). Ev-ery outcome except symptom aggravation by dust or pollen was similar between children with atopic and nonatopic asthma (Table 3; confidence intervals are available from the authors).
Children with resolved asthma were, in general, more similar to normal children than those with atopic asthma (Table 3). They did, however, have significantly lower lung function, as determined by the FEV1/FVC ratio, than did normal children (Table 3).
Children who reported frequent respiratory symp-toms had more symptom aggravation by pollen, dust, or animals and had a higher proportion report-ing hospitalization for wheezreport-ing or an ED visit for wheezing in the previous year when compared with normal children, consistent with a definition requir-ing frequent respiratory symptoms for inclusion (Ta-ble 3). Their lung function was similar to that of normal children and significantly better than that in children with atopic asthma (Table 3).
DISCUSSION
In this nationally representative sample of US chil-dren, we classified 4.8% of children as having atopic
asthma, 1.9% as having nonatopic asthma, 3.4% as having resolved asthma, and 4.3% as having fre-quent respiratory symptoms with no asthma diagno-sis. We found important similarities and difference for both potential risk factors and measures of sever-ity among our 4 asthma phenotypes. Children with nonatopic asthma had higher mean BMIs. Prenatal maternal smoking and child care attendance were associated with resolved asthma phenotype and fre-quent respiratory symptom phenotype, respectively. Children with atopic and nonatopic asthma share the highest burden of hospitalization, ED visits, and school absences. A substantial proportion of all 3 phenotypes of physician-diagnosed asthma demon-strated lung function impairment. Of note, the fre-quent respiratory symptoms phenotype had a slightly higher prevalence of certain adverse events (symptom aggravation, ED visits, and school ab-sences) than normal children but far less than those with a current physician diagnosis of asthma.
The association of increased BMI and asthma has been shown internationally in children and adults.26–28
Our study adds to this knowledge by examining the effect of BMI across asthma phenotype. Children with nonatopic asthma had a significantly higher mean BMI (68.5 vs 57.7; P ⬍ .05) than normal children. These results support the existence of a complex relationship between asthma and BMI that may vary by asthma phenotype; however, the true relationship needs to be examined in longitudinal studies.
In our analysis, we expected serum cotinine levels
TABLE 3. Measures of Asthma Severity for Children (6 –16 Years) Stratified by Respiratory Phenotype*
Covariates Phenotypes
Atopic Asthma
Nonatopic Asthma
Resolved Asthma
Respiratory Symptoms
Normal
Respiratory symptoms
Moderate/severe 24.9 29.9 9.0 100.0‡ 0.0
Symptom aggravation
Pollen 65.9† 43.0† 24.8‡ 47.6†‡ 17.2
Dust 50.1† 25.2†‡ 13.4‡ 23.4†‡ 8.4
Animals 43.9† 12.7†‡ 7.7‡ 12.2†‡ 4.4
Cold air/exercise 43.1† 41.2† 19.5‡ 24.7‡ 18.5
Medication use
Bronchodilators 36.4 41.0 0.2 1.7 0.8
Inhaled steroids 6.7 7.9 0.0 2.7 0.2
Any asthma medication 37.5 41.5 0.2 4.4 1.0
Health status in past 12 mo
Hospitalization for wheezing 7.8† 7.8† 0.2†‡ 1.0†‡ 0.1
Unscheduled visit for wheezing 51.4† 44.8† 13.1†‡ 12.4†‡ 4.8
⬎5 school absences 56.5† 43.0† 36.7‡ 33.2‡ 29.1
Doctor’s impression of health status (n⫽5073)
Good/fair/poor 19.2 19.2 6.3 10.7‡ 11.7
Adult respondent’s impression of health status
Good/fair/poor 51.7† 43.3† 22.9 38.8†‡ 22.4
Lung function (continuous data)
Mean FEV1% predicted (n⫽3874) 95.5† 96.9 98.2 98.6 100.4
Mean FEV1/FVC (n⫽3874) 81.6† 84.0 82.2† 86.7‡ 87.3
Lung function (categorical data)
FEV1/FVC⬍0.80 (n⫽3874) 35.3† 39.8† 39.0† 10.4‡ 9.8
FEV1⬍80% predicted (n⫽3874) 9.3† 10.0 6.9 4.7 3.3
* Data are from the NHANES III and are reported as weighted percentages, except where mean is indicated.
†P⬍.05 for adjusted linear or logistic regression coefficient comparing atopic, nonatopic, resolved, or frequent respiratory symptoms phenotype with normal phenotype (not done for outcomes of respiratory symptoms or medication use). Confidence intervals for estimates are available from the authors.
to emerge as a phenotypic indicator because invol-untary smoking by children has been linked to respi-ratory infections, middle ear disease, and asth-ma.29–31Current exposure to environmental tobacco
smoke, however, did not predict any of the respira-tory phenotypes in our cross-sectional analysis. Our data did concur with previous reports that children who are exposed in utero to tobacco smoke are at increased risk for wheezing that later resolves with age.10,32,33 This group of children with “resolved”
asthma had evidence that their respiratory disease had not truly resolved, but, perhaps, were less symp-tomatic at the time of the evaluation. Ominously, these children had lung function impairment similar to that seen in children with “current” asthma, sug-gesting that they may have ongoing lung inflamma-tion, airway remodeling, or scarring related to the previous asthma diagnosis, tobacco smoke exposure, or other factors.34
Another important finding in this analysis was that atopic and nonatopic asthma do not differ substantially with respect to most asthma severity measures, with the notable exception of symptom aggravation by dust or animals. This finding was surprising in that the literature documents differences in airway lability in children and risk factors in adults between atopic and nonatopic asthma.9,15However, there is evidence that
immunologic findings (eosinophilia and elevated se-rum immunoglobulin E) are similar among the 2 groups.6,17Our data support these findings in a new
arena and suggest that atopic and nonatopic asthma may be more similar than different clinically as well as epidemiologically.
Those who experience frequent respiratory symp-toms in the absence of an asthma diagnosis are often classified in the literature as having probable or un-diagnosed asthma.11–14In our study, this phenotype
was distinctly different from those with a physician diagnosis of asthma, particularly with regard to asthma severity measures and lung function. There-fore, it is possible that many children with frequent respiratory symptoms are not undiagnosed or have probable asthma but represent a process different from that of asthma. A small proportion of these children (⬍5%) were on “asthma” medications but for diagnoses other than asthma (Table 3).
This study is potentially limited by its cross-sec-tional design as it can identify only associations and cannot establish causation. In addition, the possibil-ity of misclassification exists as much of the data were self-reported by an adult proxy. Finally, the phenotypic definitions may not represent true differ-ences in asthma presentation. Asthma is a complex disorder that may display various characteristics from each of the defined phenotypes.
In conclusion, asthma, which is an important cause of morbidity in US children, probably represents sev-eral different clinical entities with different risk factors and outcomes. Better subclassifications of both children and adults with asthma may ultimately lead to better interventions and treatments. A particularly worrisome finding in this analysis is the high proportion of chil-dren with “resolved” asthma who also have lung func-tion impairment, suggesting that these children may be
at risk for lower lung function or the development of chronic obstructive pulmonary disease as adults and may merit close clinical monitoring.
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CARDIOLOGISTS SAY RANKINGS SWAY CHOICES ON SURGERY
“An overwhelming majority of cardiologists in New York say that, in certain instances, they do not operate on patients who might benefit from heart surgery, because they are worried about hurting their rankings on physician scorecards issued by the state, according to a survey released yesterday. The survey, which was sponsored by the School of Medicine and Dentistry at the University of Rochester, demonstrated the difficulty that many doctors have with the public disclosure of their performance data, an idea pioneered by New York State. In 1994, the state’s Department of Health began to compile data on cardiologists perform-ing coronary angioplasty, a less invasive procedure than bypass surgery for re-storing blood flow to the heart. Eighty-three percent of the cardiologists surveyed said that because the state reports the mortality rates of heart surgeons, patients who might benefit from angioplasty might not receive the procedure. In addition, 79% of the doctors said that the knowledge that mortality statistics would be made public had, at times, influenced their decision on whether to operate. . . . ‘I am really distressed by the responses we got from physicians,’ said Dr Craig R. Narins, an author of a report on the survey. A practicing cardiologist, Dr Narins said he was aware of general discomfort with the state’s physician rankings but surprised by the degree to which doctors disliked the system. According to the survey, about 75% of doctors said the reports did not serve to improve patient care in the state.”
Santora M.New York Times. January 11, 2005
DOI: 10.1542/peds.2004-0529
2005;115;726
Pediatrics
Fernando Holguin
Colleen F. Kelley, David M. Mannino, David M. Homa, Amanda Savage-Brown and
Sample of US Children
Asthma Phenotypes, Risk Factors, and Measures of Severity in a National
Services
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DOI: 10.1542/peds.2004-0529
2005;115;726
Pediatrics
Fernando Holguin
Colleen F. Kelley, David M. Mannino, David M. Homa, Amanda Savage-Brown and
Sample of US Children
Asthma Phenotypes, Risk Factors, and Measures of Severity in a National
http://pediatrics.aappublications.org/content/115/3/726
located on the World Wide Web at:
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by the American Academy of Pediatrics. All rights reserved. Print ISSN: 1073-0397.
