Evaluating early-life asthma definitions as a marker for subsequent asthma in an electronic medical record setting.
Case definitions for asthma incidence in early life vary between studies using medical records to define disease. This study assessed the impact of different approaches to using medical records on estimates of asthma incidence by age 3 and determined the validity of early-life asthma case definitions in predicting school-age asthma.



Asthma diagnoses and medications by age 3 were used to classify 7103 children enrolled in Kaiser Permanente Georgia according to 14 definitions of asthma. School-age asthma was defined as an asthma diagnosis between ages 5 and 8. Sensitivity (probability of asthma by age 3 given school-age asthma), specificity (probability of no asthma by age 3 given no school-age asthma), positive and negative predictive values (probability of (no) school-age asthma given (no) asthma by age 3), and likelihood ratios (combining sensitivity and specificity) were used to determine predictive ability.



9.0-35.2% of children were classified as asthmatic by age 3 depending on asthma case definition. Early-life asthma classifications were more specific than sensitive and were better at identifying children who would not have school-age asthma (negative predictive values: 80.7-86.6%) than at predicting children who would have school-age asthma (positive predictive values: 43.5-71.5%).



Choice of case definition had a large impact on the estimate of asthma incidence. While ability to predict school-age asthma was limited, several case definitions performed similarly to clinical asthma prediction tools used in previous asthma research (e.g., the Asthma Predictive Index).
Department of Epidemiology, Rollins School of Public Health and Laney Graduate School, Emory University, Atlanta, GA, USA.|Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA.
Audrey Flak
AF
Pennington
Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA.|School of Community Health Sciences, University of Nevada Reno, Reno, NV, USA.
Matthew J
MJ
Strickland
Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA.
Karen A
KA
Freedle
Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA.
Mitchel
M
Klein
Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA.
Carolyn
C
Drews-Botsch
Kaiser Permanente Georgia Center for Clinical and Outcomes Research, Atlanta, GA, USA.|South Australian Health and Medical Research Institute, Adelaide, SA, Australia.
Craig
C
Hansen
School of Community Health Sciences, University of Nevada Reno, Reno, NV, USA.|Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA.
Lyndsey A
LA
Darrow
D001249:Asthma / Q000453:epidemiology*; D002648:Child; D002675:Child, Preschool; D015331:Cohort Studies; D057286:Electronic Health Records; D005260:Female; D006801:Humans; D015994:Incidence; D007223:Infant; D007231:Infant, Newborn; D008297:Male; D011153:Population; D011237:Predictive Value of Tests; D011379:Prognosis; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D014481:United States / Q000453:epidemiology
asthma; birth cohort; children; electronic medical records; epidemiology; prediction