BACKGROUND: Striking increases in the prevalence and morbidity of asthma among inner city children have been documented. OBJECTIVE: To establish and evaluate a large-scale, school-based case-detection process designed to efficiently and reliably identify inner city children with asthma. METHODS: A bilingual, seven-question, self-administered, parental asthma screening survey was developed. Clinical validation was achieved in a sample of 675 consecutive parents bringing a child to the school-based Breathmobile Program for initial evaluation, using a comprehensive evaluation by a physician specialist (ie, allergist) as the standard. Survey response patterns were used to construct a novel seven-model, tiered scoring algorithm and an abbreviated algorithm that predict the probability of a child being clinically classified as "yes asthma" or "no asthma." A systematic survey distribution process administered by a single coordinator was developed, and the impact of a classroom-oriented incentive offering a 25 dollars school supply gift certificate for survey return rates of >or= 80% was evaluated. RESULTS: A total of 636 parents provided one or more survey responses and information sufficient for clinical classification. The scoring algorithm correctly identified children with asthma (>or= 80% probability) with a sensitivity of 86.5%, a specificity of 83.6%, and a misclassification rate of 14.3% (91 of 636 children). The sensitivity for identifying persistent asthma was 91.3%. Asthma prevalence estimates derived using survey results from a larger sampling of the general population were similar to rates previously reported for comparable populations. The inclusion of an inexpensive incentive increased the median survey return rates from 35.3 to 65% (z= -11.9; p <.001). The screening process has been used to conduct 27,526 surveys at inner city schools. CONCLUSIONS: The Breathmobile case-detection process offers a validated, comprehensive, large-scale method with which to identify children with asthma at their school sites.
BACKGROUND: Striking increases in the prevalence and morbidity of asthma among inner city children have been documented. OBJECTIVE: To establish and evaluate a large-scale, school-based case-detection process designed to efficiently and reliably identify inner city children with asthma. METHODS: A bilingual, seven-question, self-administered, parental asthma screening survey was developed. Clinical validation was achieved in a sample of 675 consecutive parents bringing a child to the school-based Breathmobile Program for initial evaluation, using a comprehensive evaluation by a physician specialist (ie, allergist) as the standard. Survey response patterns were used to construct a novel seven-model, tiered scoring algorithm and an abbreviated algorithm that predict the probability of a child being clinically classified as "yes asthma" or "no asthma." A systematic survey distribution process administered by a single coordinator was developed, and the impact of a classroom-oriented incentive offering a 25 dollars school supply gift certificate for survey return rates of >or= 80% was evaluated. RESULTS: A total of 636 parents provided one or more survey responses and information sufficient for clinical classification. The scoring algorithm correctly identified children with asthma (>or= 80% probability) with a sensitivity of 86.5%, a specificity of 83.6%, and a misclassification rate of 14.3% (91 of 636 children). The sensitivity for identifying persistent asthma was 91.3%. Asthma prevalence estimates derived using survey results from a larger sampling of the general population were similar to rates previously reported for comparable populations. The inclusion of an inexpensive incentive increased the median survey return rates from 35.3 to 65% (z= -11.9; p <.001). The screening process has been used to conduct 27,526 surveys at inner city schools. CONCLUSIONS: The Breathmobile case-detection process offers a validated, comprehensive, large-scale method with which to identify children with asthma at their school sites.
Authors: Sara C Sadreameli; Rachel O Alade; Peter J Mogayzel; Sharon McGrath-Morrow; John J Strouse Journal: Pediatr Allergy Immunol Pulmonol Date: 2017-12-01 Impact factor: 1.349
Authors: Michelle N Eakin; Cynthia S Rand; Andrew Bilderback; Mary E Bollinger; Arlene Butz; Veni Kandasamy; Kristin A Riekert Journal: J Allergy Clin Immunol Date: 2011-11-21 Impact factor: 10.793
Authors: Aravind Yadav; Fernando F Corrales-Medina; James M Stark; S Shahrukh Hashmi; Mary P Carroll; Keely G Smith; Kristen M Meulmester; Deborah L Brown; Cindy Jon; Ricardo A Mosquera Journal: Pediatr Allergy Immunol Pulmonol Date: 2015-09-01 Impact factor: 1.349
Authors: Robbie D Pesek; Perla A Vargas; Jill S Halterman; Stacie M Jones; Andy McCracken; Tamara T Perry Journal: Ann Allergy Asthma Immunol Date: 2010-02 Impact factor: 6.347
Authors: Timothy J Downs; Laurie Ross; Suzanne Patton; Sarah Rulnick; Deb Sinha; Danielle Mucciarone; Maria Calvache; Sarah Parmenter; Rajendra Subedi; Donna Wysokenski; Erin Anderson; Rebecca Dezan; Kate Lowe; Jennifer Bowen; Amee Tejani; Kelly Piersanti; Octavia Taylor; Robert Goble Journal: Environ Res Date: 2009-09-16 Impact factor: 6.498