Literature DB >> 27959476

Bogen's Critique of Linear-No-Threshold Default Assumptions.

Kenny S Crump.   

Abstract

In an article recently published in this journal, Bogen(1) concluded that an NRC committee's recommendations that default linear, nonthreshold (LNT) assumptions be applied to dose- response assessment for noncarcinogens and nonlinear mode of action carcinogens are not justified. Bogen criticized two arguments used by the committee for LNT: when any new dose adds to a background dose that explains background levels of risk (additivity to background or AB), or when there is substantial interindividual heterogeneity in susceptibility (SIH) in the exposed human population. Bogen showed by examples that SIH can be false. Herein is outlined a general proof that confirms Bogen's claim. However, it is also noted that SIH leads to a nonthreshold population distribution even if individual distributions all have thresholds, and that small changes to SIH assumptions can result in LNT. Bogen criticizes AB because it only applies when there is additivity to background, but offers no help in deciding when or how often AB holds. Bogen does not contradict the fact that AB can lead to LNT but notes that, even if low-dose linearity results, the response at higher doses may not be useful in predicting the amount of low-dose linearity. Although this is theoretically true, it seems reasonable to assume that generally there is some quantitative relationship between the low-dose slope and the slope suggested at higher doses. Several incorrect or misleading statements by Bogen are noted.
© 2016 Society for Risk Analysis.

Entities:  

Keywords:  Dose response; human; linearity; low dose; modeling; noncarcinogens; nonlinearity

Year:  2016        PMID: 27959476     DOI: 10.1111/risa.12748

Source DB:  PubMed          Journal:  Risk Anal        ISSN: 0272-4332            Impact factor:   4.000


  1 in total

Review 1.  Implications of nonlinearity, confounding, and interactions for estimating exposure concentration-response functions in quantitative risk analysis.

Authors:  Louis Anthony Cox
Journal:  Environ Res       Date:  2020-05-19       Impact factor: 6.498

  1 in total

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