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Modeling Historic Arsenic Exposures and Spatial Risk for Bladder Cancer

Author

Listed:
  • Joseph Boyle

    (Virginia Commonwealth University)

  • Mary H. Ward

    (National Cancer Institute)

  • Stella Koutros

    (National Cancer Institute)

  • Margaret R. Karagas

    (Department of Epidemiology, Dartmouth Geisel School of Medicine)

  • Molly Schwenn

    (Maine Cancer Registry)

  • Alison T. Johnson

    (Vermont Department of Health)

  • Debra T. Silverman

    (National Cancer Institute)

  • David C. Wheeler

    (Virginia Commonwealth University)

Abstract

Arsenic is a bladder carcinogen though less is known regarding the specific temporal relationship between exposure and bladder cancer diagnosis. In this study, we modeled time-varying mixtures of arsenic exposures at many historic temporal windows to evaluate their association with bladder cancer risk in the New England Bladder Cancer Study. We used arsenic exposure estimates up to 60 years prior to study entry and compared the goodness of fit of models using these mixtures to those using summary measures of arsenic exposures. We used the Bayesian index low rank kriging multiple membership model (LRK-MMM) to estimate the associations of these mixtures with bladder cancer and estimate cumulative spatial risk for bladder cancer using participants’ residential histories. We found consistent evidence that modeling arsenic exposures as a time-varying mixture provided better fit to the data than using a single arsenic exposure summary measure. We estimated several positive though not significant associations of the time-varying arsenic mixtures with bladder cancer having odds ratios (ORs) of 1.03–1.14 and identified many significant and positive associations for an interaction among those who consumed water from a private dug well (ORs 1.28–1.60). Arsenic exposures 40–50 years before study entry received elevated importance weights in these mixtures. Additionally, we found two small areas of elevated cumulative spatial risk for bladder cancer in southern New Hampshire and in south central Maine. These results emphasize the importance of considering time-varying mixtures of exposures for diseases with long latencies such as bladder cancer.

Suggested Citation

  • Joseph Boyle & Mary H. Ward & Stella Koutros & Margaret R. Karagas & Molly Schwenn & Alison T. Johnson & Debra T. Silverman & David C. Wheeler, 2024. "Modeling Historic Arsenic Exposures and Spatial Risk for Bladder Cancer," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 16(2), pages 377-394, July.
    • Handle: RePEc:spr:stabio:v:16:y:2024:i:2:d:10.1007_s12561-023-09404-7
    DOI: 10.1007/s12561-023-09404-7
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    References listed on IDEAS

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    1. Michael B. Teitz & Polly Bart, 1968. "Heuristic Methods for Estimating the Generalized Vertex Median of a Weighted Graph," Operations Research, INFORMS, vol. 16(5), pages 955-961, October.
    2. David C. Wheeler & Salem Rustom & Matthew Carli & Todd P. Whitehead & Mary H. Ward & Catherine Metayer, 2021. "Bayesian Group Index Regression for Modeling Chemical Mixtures and Cancer Risk," IJERPH, MDPI, vol. 18(7), pages 1-19, March.
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