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Predicting the Effects of Changes: Could Removing Arsenic from Tobacco Smoke Significantly Reduce Smoker Risks of Lung Cancer?

  • Louis Anthony CoxJr
Chapter
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 129)

The remainder of the book applies principles from earlier chapters to several challenging quantitative risk assessment (QRA) problems for complex, uncertain, and nonlinear systems. This chapter returns to the problem of predicting how removing a specific constituent (arsenic) from a complex mixture (cigarette smoke) would affect lung cancer risks. This goes beyond the bounding and portfolio QRAs in Chapters 8 and 10 by applying the systems dynamics model in Chapter 11 to obtain explicit quantitative results. Rather than only estimating bounds for the probable changes in consequences, this chapter predicts specific quantitative reductions in risk, contingent on specified assumptions about causal mechanisms. Quantitative sensitivity analysis shows how predicted risk reductions (under stated assumptions) and preventable fractions of risk change as key assumptions are changed.

Keywords

Cigarette Smoke Transition Rate Lung Cancer Risk Arsenic Exposure Epidermal Growth Factor Receptor Pathway 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Cox AssociatesDenverUSA

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