Chang Timothy S, Gangnon Ronald E, David Page C, Buckingham William R, Tandias Aman, Cowan Kelly J, Tomasallo Carrie D, Arndt Brian G, Hanrahan Lawrence P, Guilbert Theresa W
Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin, 5795 Medical Sciences Center, 1300 University Ave, Madison, WI 53706, USA.
Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin, 603 Warf Office Building, 610 Walnut St, Madison, WI 53706, USA.
J Biomed Inform. 2015 Feb;53:320-9. doi: 10.1016/j.jbi.2014.12.005. Epub 2014 Dec 20.
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.
地理分布的环境因素会影响哮喘等疾病的负担。我们的目标是在控制空间变异的同时，从大型电子健康记录（EHR）数据集中识别与哮喘诊断相关的稀疏环境变量。获取了威斯康星大学家庭医学、内科和儿科部门在三年期间199220名5至50岁患者的EHR数据集。每位患者的家庭住址被地理编码到3456个地理普查街区组中的一个。从商业数据库中获取了一千多个街区组变量。我们开发了一种稀疏空间环境分析（SASEA）方法。使用这种方法，首先通过稀疏主成分分析对环境变量进行降维。然后使用逻辑薄板回归样条建模从稀疏主成分中识别与哮喘相关的街区组变量。EHR数据集中患者的住址分布在威斯康星州的大部分地区。逻辑薄板回归样条建模捕捉了哮喘的空间变异。通过模型选择确定的四个稀疏主成分包括家中食物、养狗情况、家庭规模和可支配收入变量。在农村地区，来自显著稀疏主成分的养狗情况和租户居住的住房单元与哮喘有关。我们的主要贡献是在空间建模中纳入了稀疏性。SASEA将稀疏主成分依次添加到逻辑薄板回归样条建模中。这种方法允许使用EHR和环境数据集将地理分布的环境因素与哮喘联系起来。SASEA可应用于其他具有环境风险因素的疾病。
