CAPSULE SUMMARY
Asthma definitions vary widely across research studies and feature a trade-off between inclusivity and precision. Harmonizing definitions across multiple asthma birth cohort studies highlights these challenges.
Keywords: asthma, asthma prevalence, pediatric asthma, birth cohort
To The Editor:
Every birth cohort study investigating risk factors for the development of asthma relies on specific criteria for classifying children as asthmatic or not, but these criteria vary widely across studies, as there is no gold standard set of diagnostic criteria nor an objective test for asthma. Researchers make independent, yet informed, decisions about the data elements they need and are able to collect to achieve their research objectives given available resources. Cohorts with only questionnaire data available, such as The International Study of Asthma and Allergies in Childhood (ISAAC), rely on parent-reported physician diagnosis of asthma.1 Some studies have used lung function and airway hyperresponsiveness in their asthma definition criteria, while others have used reports of wheezing symptoms, asthma medication use, and health care data. Often, studies use a combination of the above to identify children with asthma. A comprehensive review of birth cohort asthma definitions found 60 different asthma definitions among 122 studies.2 In addition to the varying prevalence of asthma due to the definition used, identification of risk factors for asthma may also vary based on how the outcome is defined and the age at which it is applied, potentially leading to inconsistent findings across birth cohorts.
The Children’s Respiratory Research and Environment Workgroup (CREW) is a consortium of 12 U.S birth cohort studies3 which are part of the larger Environmental Influences on Child Health Outcomes (ECHO) program of the National Institutes of Health (http://echochildren.org/). The combined study participants of the CREW consortium represent a diverse national sample of children recruited over the past 30+ years into cohort studies specifically designed to study the role of early life environmental risk factors on asthma development. Some of the cohorts recruited participants from the general population, while others selected participants based on parental histories of asthma or allergy or parental atopy. One of the goals of CREW is to combine and harmonize extant data across all of the cohorts. Understanding the extent of heterogeneity in asthma definitions is an essential early step in the process towards building a harmonized definition of asthma.
We used data from 9 of the CREW cohorts to examine how the prevalence of asthma in middle childhood (age 5-10 years) varies by applying different outcome definitions, where possible. After examining all of the definitions used across all 9 cohorts (Table E1 in the Online Repository), 4 definitions of asthma were chosen for comparison across multiple cohorts:
DEFINITION 1: Positive response to “Has a doctor or other health professional ever told you your child has asthma?” at any time up to the assessment age.
DEFINITION 2: A parent-reported physician diagnosis of asthma as above plus a parental report of ≥ 1 wheezing episode in the past 12 months.
DEFINITION 3: A parent-reported physician diagnosis of asthma as above or report of asthma medication use (both controllers and rescue albuterol) in the past year.
DEFINITION 4: FEV1 reversibility ≥ 12% or bronchial hyperresponsiveness (methacholine PC20 ≤ 4 mg/ml).
A single analyst (TG) pooled the data and applied the four asthma definitions to each cohort. In order to ensure that observed differences were due to changes in definition and not to changing population sizes, the cohort analysis populations were restricted to those children who had data for all definitions possible for that cohort. If a cohort did not collect a given data element at all (e.g. clinical pulmonary function measurements), that definition was not used for that cohort.
Descriptive statistics were calculated as median with interquartile range (median, IQR:25th, 75th) for continuous variables, and frequency and percentages for categorical variables. We used a Venn diagram to graphically display the overlap in children defined as asthmatic using the different asthma definitions. All analyses were performed using R version 3.4.0 (http:/www.R-project.org).
Demographic characteristics of the cohorts are shown in Table I. Each cohort sent data for a particular time point, usually that for which a primary determination of asthma was previously done. Ages ranged from about 5 years to 7 years of age, with the exception of the WHEALS study which provided data from a 10-year assessment.
Table I.
Demographic characteristics of participating CREW birth cohorts.
| CCCEH (N=727) | TCRS (N=1246) | IIS (N=482) | COAST (N=259) | URECA (N=485) | CCAAPS (N=617) | EHAAS (N=438) | WHEALS (N=1258) | CAS (N=835) | |
|---|---|---|---|---|---|---|---|---|---|
| Race/Ethnicity | |||||||||
| Hispanic | 65% | 26% | 26% | 3% | 20% | 1% | 6% | 7% | 0% |
| Non-Hispanic White | 0% | 59% | 58% | 87% | 1% | 66% | 80% | 26% | 95% |
| Non-Hispanic Black | 35% | 2% | 2% | 4% | 72% | 16% | 7% | 63% | 2% |
| Other/Mixed | 0% | 13% | 14% | 6% | 7% | 16% | 8% | 5% | 4% |
| Sex | |||||||||
| Male | 48% | 49% | 48% | 57% | 51% | 55% | 54% | 50% | 49% |
| Female | 52% | 51% | 52% | 43% | 49% | 45% | 46% | 50% | 51% |
| Mean Age in Years at Asthma Assessment (SD) | 7.0 (6.0-9.0) | 6.2 (5.8-6.5) | 5.1 (5.0-5.2) | 6.0 (6.0-6.0) | 7.0 (6.9-7.2) | 6.8 (6.7-7.0) | 7.0 (7.0-7.0) | 10.2 (9.5-10.8) | 6.7 (6.6-6.8) |
| Type of Eligibility* | General | General | General | Family History | Family History | Family History | Family History | General | General |
| Percent w/Asthma in Original Cohort Publication | 31% | 10% | 12% | 28% | 29% | 16% | 8% | 13% | 10% |
Detailed eligibility criteria are given in Table E1 in the Online Supplement.
Only two definitions could be applied to all 9 cohorts: physician diagnosis (Definition 1) and physician diagnosis plus wheeze (Definition 2). As expected, prevalence based on Definition 2 (“current” versus “ever” asthma) was lower across all the cohorts (range from 2-13 percentage points). Physician diagnosis or asthma medication use (Definition 3) was applied to six of the nine cohorts, and always resulted in higher prevalence than Definition 1 (range from 1-6 percentage points). The definition based on tests of reversibility or hyperresponsiveness (Definition 4) could only be applied to three cohorts. Prevalence based on Definition 4 was the lowest of the four definitions for one cohort and the highest for two of the cohorts (Figure 1). Thus, in addition to varying in inclusivity, the different definitions identified distinct subsets of children as having asthma (Figure E1 in the Online Repository).
Figure 1.
Changes in asthma prevalence across each cohort for varying definitions of asthma. (Definition 1= Doctor diagnosis of asthma; Definition 2 = Doctor diagnosis of asthma + wheezing in the past 12 months; Definition 3 = Doctor diagnosis of asthma or asthma medication use in the past year; Definition 4 = FEV1 reversibility or bronchial hyperresponsiveness).
Without an objective test or even a gold-standard set of clinical diagnostic criteria for asthma, research studies are not consistent in operationalizing their definition of disease. In our application of 4 different asthma definitions to data from 9 of the CREW birth cohorts, we found substantial differences in asthma prevalence. Further, the definitions identified distinct groups of children, such that certain definitions miss children that others pick up. In addition, because of the availability of data, only 2 out of 4 of the definitions could be applied post-hoc to all studies, making harmonization of outcome definitions challenging.
As shown in the van Wonderen review article,2 researchers often choose combinations of variables to determine asthma in their study populations as they strive to identify all the asthmatics while simultaneously excluding the non-asthmatics. The URECA cohort, in particular, used a complex set of and/or criteria to identify the children with asthma in their population at seven years of age.4 An approach like this, e.g. creating standard “building blocks” that could be assembled depending on the research question, may hold promise, but is next to impossible to apply post hoc to datasets from other studies that did not ask the same questions or include the same procedures.
This work highlights the need to develop consensus guidelines for asthma definitions and to promote the use of standardized data collection elements to ensure the ability to compare studies and achieve reproducibility and rigor.
Supplementary Material
ACKNOWLEDGMENTS
The Children’s Environment and Respiratory Workgroup (CREW) would like to acknowledge the following institutions, investigators and staff whose efforts contributed to the work presented in this manuscript (principal investigators are indicated by an asterisk):
Columbia Center for Children’s Environmental Health (CCCEH): Rachel Miller*, Howard Andrews, Julie Herbstman, Lori Hoepner, Frederica Perera, Matthew Perzanowski, Xinhua Liu, Judyth Ramirez, Janelle Rivera, Deliang Tang, Kylie Wheelock, Jaqueline Jezioro.
Tucson Children’s Respiratory Study (TCRS): Anne L. Wright*, Fernando D. Martinez*, Wayne Morgan, Debra A. Stern, Dean Billheimer, Brian Hallmark, Paloma Beamer, Nathan Lothrop, Lydia De La Ossa, Silvia Lopez, Marilyn Halonen, Amber Spangenberg, David Spies.
Infant Immune Study (IIS): Anne L. Wright*, Fernando D. Martinez*, Wayne Morgan, Debra A. Stern, Dean Billheimer, Brian Hallmark, Paloma Beamer, Nathan Lothrop, Heidi Erickson, Marilyn Halonen, Amber Spangenberg, David Spies
Childhood Origins of Asthma Study (COAST): Robert F. Lemanske, Jr.*, Daniel J. Jackson*, James E. Gern, Carole Ober, Ronald E. Gangnon, Michael D. Evans, Victoria Rajamanickam, Christopher Tisler, Lisa Salazar, Susan Doyle, Yury Bochkov, Rebecca Brockman-Schneider, Rose Vrtis, Kristine Grindle, Tressa Pappas, Elizabeth Anderson, Kathy Roberg, Kirsten Carlson-Dakes, Mark DeVries, Douglas DaSilva, Ronald Sorkness, Lance Mikus, Julia Bach
Urban Environment and Childhood Asthma Study (URECA):
Johns Hopkins University, Baltimore, MD: R Wood*, E Matsui, H Lederman, F Witter, S Leimenstoll, D Scott, M Cootauco, P Jones; Boston University School of Medicine, Boston, MA: G O’Connor*, W Cruikshank, M Sandel, A Lee-Parritz, C Jordan, E Gjerasi, P Price-Johnson, L Gagalis, L Wang, N Gonzalez, M Tuzova; Harvard Medical School, Boston, MA – D Gold, R Wright; Columbia University, New York, NY: M Kattan*, C Lamm, N Whitney, P Yaniv, M Pierce, Jaqueline Jezioro; Mount Sinai School of Medicine, New York, NY: H Sampson, R Sperling, N Rivers; Washington University School of Medicine, St Louis, MO: G Bloomberg*,L Bacharier*, Y Sadovsky, E Tesson, C Koerkenmeier, R Sharp, K Ray, J Durrange, I Bauer, A Freie, V Morgan; Statistical and Clinical Coordinating Center - Rho, Inc, Chapel Hill, NC: C Visness*, P Zook, M Yaeger, J Martin, A Calatroni, K Jaffee, W Taylor, R Budrevich, H Mitchell; Scientific Coordination and Administrative Center, University of Wisconsin, Madison, WI: W Busse*, J Gern**, P Heinritz, C Sorkness, K. Hernandez, Y. Bochkov, K Grindle, A Dresen, T Pappas, M. Renneberg, B. Stoffel; National Institute of Allergy and Infectious Diseases, Bethesda, MD: P Gergen, A Togias, E Smartt, K Thompson.
Cincinnati Childhood Allergy and Air Pollution Study (CCAAPS): Gurgit K. Khurana Hershey*, Patrick H. Ryan*, Jocelyn M. Biagini Myers*, Grace K. LeMasters*, Kristi Curtsinger, Liza Murrison*, Jeffrey W. Burkle, Christopher Wolfe, Zachary Flege, David Morgan, Kristina Keidel, Krista Tensing, Taylor Groeschen.
The Epidemiology of Home Allergens and Asthma Study (EHAAS): Diane R. Gold, Soma Datta, Sharon O’Toole, Conner Fleurat, Leanna Farnham.
Wayne County Health, Environment, Allergy and Asthma Longitudinal Study (WHEALS):
Henry Ford Health System, Detroit, MI: CC Johnson*, G Wegienka, S Havstad, E Zoratti, A Cassidy-Bushrow, A Levin, H Kim, K Woodcroft, A Sitarik, C Joseph, LK Williams, C Barone, K Bobbitt, S Zhang, J Campbell, K Bourgeois, M Aubuchon, J Ezell, K Jones; Augusta University, Augusta, GA: D Ownby
Childhood Allergy Study (CAS): D Ownby*, CC Johnson, C Joseph, E Zoratti, G Wegienka, S Havstad, K Woodcroft, E Peterson, S Hensley Alford, J McCullough, C Strauchman Boyer, S Blocki, G Birg, N Akkerman, K Wells, S Zhang, C Nicholas, A Jones, G Stouffer
Funding
CREW is funded by HHS/NIH grants 5UG3OD023282, 4UH3OD023282. Additional support was provided by individual cohorts’ grants/contracts:
Columbia University: P01ES09600, R01 ES008977, P30ES09089, R01 ES013163, R827027
Tucson Children’s Respiratory Study (TCRS): NHLBI 132523
Infant Immune Study (IIS): HL-56177
Childhood Origins of Asthma Study (COAST): P01 HL070831, U10 HL064305, R01 HL061879.
Urban Environment and Childhood Asthma Study (URECA): NO1-AI-25496, NO1-AI-25482, HHSN272200900052C, HHSN272201000052I, NCRR/NIH RR00052, M01RR00533, 1UL1RR025771, M01RR00071, 1UL1RR024156, UL1TR001079, 5UL1RR024992-02, NCATS/NIH UL1TR000040.
Cincinnati Childhood Allergy and Air Pollution Study (CCAAPS): R01 ES11170, R01 ES019890
The Epidemiology of Home Allergens and Asthma Study (EHAAS): R01 AI035786
Wayne County Health, Environment, Allergy and Asthma Longitudinal Study (WHEALS): R01 AI050681, R56 AI050681, R01 AI061774, R21 AI059415, K01 AI070606, R21 AI069271, R01 HL113010, R21 ES022321, P01 AI089473, R21 AI080066, R01 AI110450, R01 HD082147 and the Fund for Henry Ford Health System.
Childhood Allergy Study (CAS): R01 AI024156, R03 HL067427, R01 AI051598, Blue Cross Foundation Johnson, and the Fund for Henry Ford Hospital
Conflict of Interest Statement
All authors report grants from NIH during the conduct of study. D.J. Jackson reports grants from GlaxoSmithKline, as well as personal fees from Novartis, Vifor Pharma, Pfizer, Boehringer Ingelheim, and Commense outside the submitted work. R.F. Lemanske reports grants as part of the Clinical and Translational Science Award from NIH, Childhood Origins of ASThma (COAST), and AsthmaNet outside the submitted work. R.F. Lemanske also reports personal fees from LSU, University of Kentucky, and Thermo Fischer outside the submitted work. In addition, R.F. Lemanske reports royalties from Elsevier and Up To Date outside the submitted work. T. Hartert reports grants from the World Health Organization outside the submitted work. J.E. Gern reports personal fees from PREP Biopharm Inc, Regeneron, MedImmune, and Ena Pharmaceuticals, as well as personal fees and stock options from Meissa Vaccines Inc outside the submitted work. In addition, J.E. Gern has a patent “Methods of Propagating Rhinovirus C in Previously Unsusceptible Cell Lines” issued, and a patent “Adapted Rhinovirus C” pending. T. Gebretsadik, J. Biagini Myers, D.R. Gold, E.M. Zoratti, L.J. Martin, R. Miller, C.C. Johnson, G.K.K. Hershey, A. Wright, D.A. Stern, and C.M. Visness have nothing to disclose outside the submitted work.
Footnotes
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