Predictive value of a cross-cultural asthma case-detection tool in an elementary school population

• Published in: Pediatrics (Evanston) Vol. 114; no. 3; p. e307
• Main Authors: Galant, Stanley P, Crawford, Linda J R, Morphew, Tricia, Jones, Craig A, Bassin, Stanley
• Format: Journal Article
• Language: English
• Published: United States 01.09.2004
• Subjects: Algorithms; Asthma - diagnosis; Asthma - ethnology; California; Child; Child, Preschool; Cultural Diversity; European Continental Ancestry Group; Female; Hispanic Americans; Humans; Male; Poverty Areas; Predictive Value of Tests; Reproducibility of Results; Sensitivity and Specificity; Suburban Population; Surveys and Questionnaires; Urban Population; Vietnam - ethnology; Vietnam; California
• ISSN: 1098-4275
• DOI: 10.1542/peds.2003-0575-F

Bronchial asthma, which affects approximately 5 million US children, is vastly underdiagnosed and treated, particularly among minorities and those of low socioeconomic status. Because current methods of detecting those at greatest risk of asthma in a multicultural setting appear inadequate, we assessed the validity and reliability of a new asthma questionnaire across 3 dominant cultures in Orange County, California (white, Hispanic, and Vietnamese). Children in grades 1, 3, and 5 and their families, in 3 different schools representative of these major ethnic groups, were randomly selected to participate in the validation process. Two schools with low socioeconomic status and dominant Hispanic or Vietnamese minorities were designated inner-city schools, whereas the third school was a suburban school with predominately white students. Participants completed a 7-question, 11-element questionnaire in their primary language, followed by an asthma evaluation (history, physical examination, and spirometry) by an asthma specialist (who was blinded with respect to the results of the questionnaire), at their respective schools. The physician then made a determination regarding the presence and severity (according to National Institutes of Health guidelines) of asthma. Several weeks later, the entire student body was asked to complete the questionnaire at home and return it to school for analysis. Validation of each item was evaluated for sensitivity, specificity, and positive and negative predictive values, and application of univariate analyses provided an estimated probability of an asthma diagnosis by the asthma specialist. A "best-fit" algorithm was determined with all 11 elements, if possible, and an abbreviated algorithm that selected the fewest-question combination that yielded the best asthma predictability was established. Reliability was established with the percent agreement between the 2 questionnaires and the kappa statistic. Of the 401 children/families who participated in the validation analysis, 45% were Hispanic, 22% white, 19% Vietnamese, and 15% other. The overall prevalence of asthma specialist-diagnosed asthma was 28%, with 65% of cases being graded as intermittent and 35% as persistent. Sixty-two percent of the children had not been previously diagnosed with asthma. There were no significant differences among cultures in sensitivity or specificity for any of the individual questions or the complete or abbreviated algorithms. The abbreviated algorithm with 3 questions, ie, question 1 (asthma in the past 2 years), question 4 (cough, chest tightness, trouble breathing, or wheezing with exercise), and question 6 (same symptoms in the morning or day in the past 4 weeks) yielded comparable sensitivity and specificity for the complete algorithm in all groups. The abbreviated algorithm had >86% predictability in detecting children with persistent asthma and 56% predictability in detecting children with intermittent asthma. Reliability was also excellent, with percent agreement usually > 80% and kappa values of >.70. This asthma detection tool has been shown to be suitable for detecting persistent asthma in a multicultural inner-city population, as well as in a suburban setting. An abbreviated algorithm with 3 questions and >80% predictability in detecting persistent asthma seems ideal for evaluating large numbers of school-aged children. The school setting is an excellent site for identifying children with asthma. Although there is concern that subjects detected in the school setting might not have access to ongoing medical care, case detection is an important first step that could lead to earlier diagnosis and treatment. Reducing the barriers to good care in inner-city environments is the next step.