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Spatial Environmental Modeling of Autoantibody Outcomes among an African American Population

Author

Listed:
  • Rachel Carroll

    (Department of Public Health Sciences, Medical University of South Carolina, 67 President St, Charleston, SC 29425, USA)

  • Andrew B. Lawson

    (Department of Public Health Sciences, Medical University of South Carolina, 67 President St, Charleston, SC 29425, USA)

  • Delia Voronca

    (Department of Public Health Sciences, Medical University of South Carolina, 67 President St, Charleston, SC 29425, USA)

  • Chawarat Rotejanaprasert

    (Department of Public Health Sciences, Medical University of South Carolina, 67 President St, Charleston, SC 29425, USA)

  • John E. Vena

    (Department of Public Health Sciences, Medical University of South Carolina, 67 President St, Charleston, SC 29425, USA)

  • Claire Marjorie Aelion

    (School of Public Health and Health Sciences, University of Massachusetts Amherst, Amherst, MA 01003, USA)

  • Diane L. Kamen

    (Department of Medicine, Division of Rheumatology and Immunology, Medical University of South Carolina, 67 President St, Charleston, SC 29425, USA)

Abstract

In this study of autoimmunity among a population of Gullah African Americans in South Carolina, the links between environmental exposures and autoimmunity (presence of antinuclear antibodies (ANA)) have been assessed. The study population included patients with systemic lupus erythematosus (n = 10), their first degree relatives (n = 61), and unrelated controls (n = 9) where 47.5% (n = 38) were ANA positive. This paper presents the methodology used to model ANA status as a function of individual environmental influences, both self-reported and measured, while controlling for known autoimmunity risk factors. We have examined variable dimension reduction and selection methods in our approach. Following the dimension reduction and selection methods, we fit logistic spatial Bayesian models to explore the relationship between our outcome of interest and environmental exposures adjusting for personal variables. Our analysis also includes a validation “strip” where we have interpolated information from a specific geographic area for a subset of the study population that lives in that vicinity. Our results demonstrate that residential proximity to exposure site is important in this form of analysis. The use of a validation strip network demonstrated that even with small sample numbers some significant exposure-outcome relationships can be detected.

Suggested Citation

  • Rachel Carroll & Andrew B. Lawson & Delia Voronca & Chawarat Rotejanaprasert & John E. Vena & Claire Marjorie Aelion & Diane L. Kamen, 2014. "Spatial Environmental Modeling of Autoantibody Outcomes among an African American Population," IJERPH, MDPI, vol. 11(3), pages 1-16, March.
    • Handle: RePEc:gam:jijerp:v:11:y:2014:i:3:p:2764-2779:d:33827
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    References listed on IDEAS

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    2. Li, Baibing & Martin, Elaine B. & Morris, A. Julian, 2002. "On principal component analysis in L1," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 471-474, September.
    3. Fahrmeir, Ludwig & Kneib, Thomas, 2011. "Bayesian Smoothing and Regression for Longitudinal, Spatial and Event History Data," OUP Catalogue, Oxford University Press, number 9780199533022.
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