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Spatiotemporal and spatial threshold models for relating UV exposures and skin cancer in the central United States

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  • Hatfield, Laura A.
  • Hoffbeck, Richard W.
  • Alexander, Bruce H.
  • Carlin, Bradley P.

Abstract

The exact mechanisms relating exposure to ultraviolet (UV) radiation and elevated risk of skin cancer remain the subject of debate. For example, there is disagreement on whether the main risk factor is duration of the exposure, its intensity, or some combination of both. There is also uncertainty regarding the form of the dose-response curve, with many authors believing only exposures exceeding a given (but unknown) threshold are important. This paper explores methods to estimate such thresholds using hierarchical spatial logistic models based on a sample of a cohort of x-ray technologists for whom self-reports of time spent in the sun and numbers of blistering sunburns in childhood are available. A preliminary goal is to explore the temporal pattern of UV exposure and its gradient. Changes would imply that identical exposure self-reports from different calendar years may correspond to differing cancer risks.

Suggested Citation

  • Hatfield, Laura A. & Hoffbeck, Richard W. & Alexander, Bruce H. & Carlin, Bradley P., 2009. "Spatiotemporal and spatial threshold models for relating UV exposures and skin cancer in the central United States," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 3001-3015, June.
    • Handle: RePEc:eee:csdana:v:53:y:2009:i:8:p:3001-3015
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    References listed on IDEAS

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    1. Montserrat Fuentes & Hae-Ryoung Song & Sujit K. Ghosh & David M. Holland & Jerry M. Davis, 2006. "Spatial Association between Speciated Fine Particles and Mortality," Biometrics, The International Biometric Society, vol. 62(3), pages 855-863, September.
    2. Banerjee, Sudipto & Gelfand, Alan E., 2006. "Bayesian Wombling: Curvilinear Gradient Assessment Under Spatial Process Models," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1487-1501, December.
    3. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
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    1. LeSage, James & Banerjee, Sudipto & Fischer, Manfred M. & Congdon, Peter, 2009. "Spatial statistics: Methods, models & computation," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 2781-2785, June.

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