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Open Application of Statistical and Machine Learning Models to Explore the Impact of Environmental Exposures on Health and Disease: An Asthma Use Case

Author

Listed:
  • Bo Lan

    (UNC Highway Safety Research Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA)

  • Perry Haaland

    (Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA)

  • Ashok Krishnamurthy

    (Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC 27517, USA
    Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA)

  • David B. Peden

    (Division of Allergy, Immunology and Rheumatology, Center for Environmental Medicine, Asthma & Lung Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
    Department of Pediatrics, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA)

  • Patrick L. Schmitt

    (Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC 27517, USA)

  • Priya Sharma

    (Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC 27517, USA)

  • Meghamala Sinha

    (Oregon State University, Corvallis, OR 97331, USA)

  • Hao Xu

    (Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC 27517, USA)

  • Karamarie Fecho

    (Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC 27517, USA
    Copperline Professional Solutions, LLC, Pittsboro, NC 27312, USA)

Abstract

ICEES (Integrated Clinical and Environmental Exposures Service) provides a disease-agnostic, regulatory-compliant approach for openly exposing and analyzing clinical data that have been integrated at the patient level with environmental exposures data. ICEES is equipped with basic features to support exploratory analysis using statistical approaches, such as bivariate chi-square tests. We recently developed a method for using ICEES to generate multivariate tables for subsequent application of machine learning and statistical models. The objective of the present study was to use this approach to identify predictors of asthma exacerbations through the application of three multivariate methods: conditional random forest, conditional tree, and generalized linear model. Among seven potential predictor variables, we found five to be of significant importance using both conditional random forest and conditional tree: prednisone, race, airborne particulate exposure, obesity, and sex. The conditional tree method additionally identified several significant two-way and three-way interactions among the same variables. When we applied a generalized linear model, we identified four significant predictor variables, namely prednisone, race, airborne particulate exposure, and obesity. When ranked in order by effect size, the results were in agreement with the results from the conditional random forest and conditional tree methods as well as the published literature. Our results suggest that the open multivariate analytic capabilities provided by ICEES are valid in the context of an asthma use case and likely will have broad value in advancing open research in environmental and public health.

Suggested Citation

  • Bo Lan & Perry Haaland & Ashok Krishnamurthy & David B. Peden & Patrick L. Schmitt & Priya Sharma & Meghamala Sinha & Hao Xu & Karamarie Fecho, 2021. "Open Application of Statistical and Machine Learning Models to Explore the Impact of Environmental Exposures on Health and Disease: An Asthma Use Case," IJERPH, MDPI, vol. 18(21), pages 1-14, October.
  • Handle: RePEc:gam:jijerp:v:18:y:2021:i:21:p:11398-:d:668141
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