Sparse modeling of spatial environmental variables associated with asthma.

Geographically distributed environmental factors influence the burden of diseases such as asthma. Our objective was to identify sparse environmental variables associated with asthma diagnosis gathered from a large electronic health record (EHR) dataset while controlling for spatial variation. An EHR dataset from the University of Wisconsin's Family Medicine, Internal Medicine and Pediatrics Departments was obtained for 199,220 patients aged 5-50years over a three-year period. Each patient's home address was geocoded to one of 3456 geographic census block groups. Over one thousand block group variables were obtained from a commercial database. We developed a Sparse Spatial Environmental Analysis (SASEA). Using this method, the environmental variables were first dimensionally reduced with sparse principal component analysis. Logistic thin plate regression spline modeling was then used to identify block group variables associated with asthma from sparse principal components. The addresses of patients from the EHR dataset were distributed throughout the majority of Wisconsin's geography. Logistic thin plate regression spline modeling captured spatial variation of asthma. Four sparse principal components identified via model selection consisted of food at home, dog ownership, household size, and disposable income variables. In rural areas, dog ownership and renter occupied housing units from significant sparse principal components were associated with asthma. Our main contribution is the incorporation of sparsity in spatial modeling. SASEA sequentially added sparse principal components to Logistic thin plate regression spline modeling. This method allowed association of geographically distributed environmental factors with asthma using EHR and environmental datasets. SASEA can be applied to other diseases with environmental risk factors.

Evaluation of the modified asthma predictive index in high-risk preschool children.

BACKGROUND: Prediction of subsequent school-age asthma during the preschool years has proven challenging. OBJECTIVE: To confirm in a post hoc analysis the predictive ability of the modified Asthma Predictive Index (mAPI) ina high-risk cohort and a theoretical unselected population. We also tested a potential mAPI modification with a 2-wheezing episode requirement (m2API) in the same populations. METHODS: Subjects (n [ 289) with a family history of allergy and/or asthma were used to predict asthma at age 6, 8, and 11 years with the use of characteristics collected during the first 3 years of life. The mAPI and the m2API were tested for predictive value. RESULTS: For the mAPI and m2API, school-age asthma prediction improved from 1 to 3 years of age. The mAPI had high predictive value after a positive test (positive likelihood ratio ranging from 4.9 to 55) for asthma development at years 6,8, and 11. Lowering the number of wheezing episodes to 2(m2API) lowered the predictive value after a positive test(positive likelihood ratio ranging from 1.91 to 13.1) without meaningfully improving the predictive value of a negative test.Posttest probabilities for a positive mAPI reached 72% and 90%in unselected and high-risk populations, respectively. CONCLUSIONS: In a high-risk cohort, a positive mAPI greatly increased future asthma probability (eg, 30% pretest probability to 90% posttest probability) and is a preferred predictive test to them 2API. With its more favorable positive posttest probability,the mAPI can aid clinical decision making in assessing future asthma risk for preschool-age children.