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Descriptive Analytics for Occupational Health: Is Benzene Metabolism in Exposed Workers More Efficient at Very Low Concentrations?

In: Causal Analytics for Applied Risk Analysis

Author

Listed:
  • Louis Anthony Cox Jr.

    (Cox Associates)

  • Douglas A. Popken

    (Cox Associates)

  • Richard X. Sun

    (Cox Associates)

Abstract

The occupational risks to workers from noxious substances inhaled in air depend on the concentrations inhaled and on what happen to the inhaled substances—for example, whether they are swiftly detoxified and eliminated from the body without doing harm, or whether they are metabolized to form toxic concentrations of metabolites in target tissues. Descriptive analytics applied to data on inhaled concentrations and metabolites formed can be used to clarify how efficiently the body produces toxic metabolites at low exposure concentrations. This chapter applies descriptive analytics methods introduced in Chaps. 1 – 3 , including interaction plots, nonparametric regression, CART trees, and Bayesian networks, to data on benzene metabolites in Chinese factory workers in an effort to resolve a recent puzzle in the literature on low dose benzene toxicology. For readers who do not care to pursue this topic further, we recommend quickly examining the figures to see how plots and visualizations of patterns in the data can be displayed and used to gain insight into the dependencies among variables.

Suggested Citation

  • Louis Anthony Cox Jr. & Douglas A. Popken & Richard X. Sun, 2018. "Descriptive Analytics for Occupational Health: Is Benzene Metabolism in Exposed Workers More Efficient at Very Low Concentrations?," International Series in Operations Research & Management Science, in: Causal Analytics for Applied Risk Analysis, chapter 0, pages 285-311, Springer.
    • Handle: RePEc:spr:isochp:978-3-319-78242-3_4
    DOI: 10.1007/978-3-319-78242-3_4
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