3,409
Views
15
CrossRef citations to date
0
Altmetric
Original Articles

Historical Context and Recent Advances in Exposure-Response Estimation for Deriving Occupational Exposure Limits

, , &
Pages S7-S17 | Received 01 Apr 2015, Accepted 23 Jul 2015, Published online: 09 Nov 2015

REFERENCES

  • Castleman, B.I., and G.E. Ziem: Corporate influence on threshold limit values. Am. J. Ind. Med. 13(5):531–559 (1988).
  • Ziem, G.E., and B.I. Castleman: Threshold limit values: historical perspectives and current practice. J. Occup. Med. 31(11):910–918 (1989).
  • National Research Council: Risk Assessment in The Federal Government: Managing The Process. Washington, DC: National Academies Press, 1983.
  • National Research Council: Understanding Risk: Informing Decisions in a Democratic Society. Washington, DC: National Academies Press, 1996.
  • National Research Council:Science and Decisions: Advancing Risk Assessment. Washington, DC: National Academies Press, 2009.
  • “Occupational Safety and Health Administration: Hazard Communication Standard; Final Rule”. Federal Register 7717574–17896.
  • National Research Council: Science and Judgement in Risk Assessment. Washington, DC: National Academies Press, 1994.
  • Waters, M., L. McKernn, A. Maier, M. Jayjock, V. Schaeffer, and L. Brosseau: Exposure estimation and interpretation of occupational risk: Enhanced information for the occupational risk manager. J. Occup. Environ. Hyg. Supplement 1: S99–S111 (2015).
  • Dankovic, D.A., B.D. Naumann, A. Maier, M.L. Dourson, and L. Levy: The scientific basis of uncertainty factors used in setting occupational exposure limits. J. Occup. Envrion. Hyg. Supplement 1: S55–S68 (2015).
  • Lehman, A.J., and O.G. Fitzhugh: 100-fold margin of safety. Assoc. Food Drug Off. US Q. Bull. 18:33–35 (1954).
  • Wignall, J.A., A.J. Shapiro, F.A. Wright et al.: Standardizing benchmark dose calculations to improve science-based decisions in human health assessments. Environ. Health Perspect. 122(5):499–505 (2014).
  • Crump, K.S.: A new method for determining allowable daily intakes. Fund. Appl. Toxicol. 4(5):854–871 (1984).
  • Akaike, H.: A new look at the statistical model identification. IEEE Trans. Automatic Control 19:716–723 (1974).
  • U.S. Environmental Protection Agency:“The Risk Assessment Guidelines of 1986.” Washington, DC: U.S. Environmental Protection Agency, 1986.
  • U.S. Environmental Protection Agency:“Benchmark Dose Technical Guidance.” [Online] Available at http://www.epa.gov/raf/publications/pdfs/benchmark_dose_guidance.pdf, 2012).
  • Kodell, R.L., and R.W. West: Upper confidence limits on excess risk for quantitative responses. Risk Anal. 13(2):177–182 (1993).
  • Crump, K.S.: Calculation of benchmark dose from continuous data. Risk Anal. 15:79–89 (1995).
  • Raftery, A.E.: Bayesian model selection in social research. Sociol. Methodol. 25:111–163 (1995).
  • Buckland, S.T., K.P. Burnham, and N.H. Augustin: Model selection: an integral part of inference. Biometrics 53:603–618 (1997).
  • Hoeting, J.A., D. Madigan, A.E. Raftery, and C.T. Volinsky: Bayesian model averaging: a tutorial (with comments by M. Clyde, David Draper and E. I. George, and a rejoinder by the authors). Statist. Sci. 14:382–417 (1999).
  • Bailer, A.J., L.T. Stayner, R.J. Smith, E.D. Kuempel, and M.M. Prince: Estimating benchmark concentrations and other noncancer endpoints in epidemiology studies. Risk Anal. 17: 771–779 (1997).
  • Kang, S.H., R.L. Kodell, and J.J. Chen: Incorporating model uncertainties along with data uncertainties in microbial risk assessment. Regul. Toxicol. Pharmacol. 32(1):68–72 (2000).
  • Noble, R.B., A.J. Bailer, and R. Park: Model-averaged benchmark concentration estimates for continuous response data arising from epidemiological studies. Risk Anal. 29(4): 558–564 (2009).
  • Piegorsch, W.W., L. An, A.A. Wickens, R. Webster West, E.A. Peña, and W. Wu: Information‐theoretic model‐averaged benchmark dose analysis in environmental risk assessment. Environmetrics 24(3):143–157 (2013).
  • Shao, K., and J.S. Gift: Model uncertainty and Bayesian model averaged benchmark dose estimation for continuous data. Risk Anal. 34(1):101–120 (2014).
  • Simmons, S.J., C. Chen, X. Li et al.: Bayesian model averaging for benchmark dose estimation. Environ. Ecol. Statist. 22(1):5–16 (2015).
  • Wheeler, M.W., and A.J. Bailer: Properties of model-averaged BMDLs: a study of model averaging in dichotomous response risk estimation. Risk Anal. 27(3):659–670 (2007).
  • Wheeler, M.W., and A.J. Bailer: Comparing model averaging with other model selection strategies for benchmark dose estimation. Environ. Ecol. Statist. 16(1):37–51 (2009).
  • Morales, K.H., J.G. Ibrahim, C.J. Chen, and L.M. Ryan: Bayesian model averaging with applications to benchmark dose estimation for arsenic in drinking water. J. Am. Statist. Assoc. 101: 9–17 (2006).
  • Wheeler, M., and A.J. Bailer: Monotonic Bayesian semiparametric benchmark dose analysis. Risk Anal. 32(7):1207–1218 (2012).
  • Guha, N., A. Roy, L. Kopylev, J. Fox, M. Spassova, and P. White: Nonparametric Bayesian methods for benchmark dose estimation. Risk Anal. 33(9):1608–1619 (2013).
  • Piegorsch, W.W., H. Xiong, R.N. Bhattacharya, and L. Lin: Nonparametric estimation of benchmark doses in environmental risk assessment. Environmetrics 23(8):717–728 (2012).
  • Piegorsch, W.W., H. Xiong, R.N. Bhattacharya, and L. Lin: Benchmark dose analysis via nonparametric regression modeling. Risk Anal. 34(1):135–151. (2013).
  • Wheeler, M.W., K. Shao, and A.J. Bailer: (2015)Quantile benchmark dose estimation for continuous endpoints. Environmetrics 26(5):363–372 .
  • Lin, L., W.W. Piegorsch, and R. Bhattacharya: (2015)Nonparametric benchmark dose estimation with continuous dose-response data. Scand. J. Statist. 42(3):713–731.
  • Wheeler, M.W., and A.J. Bailer: An empirical comparison of low-dose extrapolation from points of departure (PoD) compared to extrapolations based upon methods that account for model uncertainty. Regul. Toxicol. Pharmacol. 67(1):75–82 (2013).
  • Clewell, H.J., and K.S. Crump: Quantitative estimates of risk for noncancer endpoints. Risk Anal. 25(2):285–289 (2005).
  • Budtz-Jorgensen, E., N. Keiding, and P. Grandjean: Benchmark dose calculation from epidemiological data. Biometrics 57(3):698–706 (2001).
  • Park, R.M., and L.T. Stayner: A search for thresholds and other nonlinearities in the relationship between hexavalent chromium and lung cancer. Risk Anal. 26(1):79–88 (2006).
  • Royston, P., G. Ambler, and W. Sauerbrei: The use of fractional polynomials to model continuous risk variables in epidemiology. Int. J. Epidemiol. 28(5):964–974 (1999).
  • Thurston, S.W., E.A. Eisen, and J. Schwartz: Smoothing in survival models: an application to workers exposed to metalworking fluids. Epidemiology 13(6):685–692 (2002).
  • Zeka, A., E.A. Eisen, D. Kriebel, R. Gore, and D.H. Wegman: Risk of upper aerodigestive tract cancers in a case-cohort study of autoworkers exposed to metalworking fluids. Occup. Environ. Med. 61(5):426–431 (2004).
  • U.S. Environmental Protection Agency:“Help Manual for Benchmark Dose Software Version 2.1.2.” [Online] Available at http://www.epa.gov/ncea/bmds/, 2011).
  • Wheeler, M.W., and A.J. Bailer: Model averaging software for dichotomous dose response risk estimation. J. Statist. Softw. 26(5)(2008).
  • Piegorsch, W.W.: Model uncertainty in environmental dose-response risk analysis. Statist. Publ. Pol. 1(1):78–85 (2014).
  • West, R.W., W.W. Piegorsch, E.A. Pena, et al.: The impact of model uncertainty on benchmark dose estimation. Environmetrics 23(8):706–716 (2012).
  • Sand, S., C.J. Portier, and D. Krewski: A signal-to-noise crossover dose as the point of departure for health risk assessment. Environ. Health Perspect. 119(12): 766–1774 (2011).
  • Burzala, L., and T.A. Mazzuchi: Uncertainty Modeling in Dose Response Using Nonparametric Bayes: Bench Test Results. In Uncertainty Modeling in Dose Response: Bench Testing Environmental Toxicity, R.M. Cooke (ed.), Hoboken, NJ: John Wiley & Sons, Inc., 2009. pp. 111–146.
  • Checkoway, H., N. Pearce, and D. Kriebel: Research Methods in Occupational Epidemiology. New York: Oxford University Press, 2004.
  • Arrighi, H.M., and I. Hertz-Picciotto: The evolving concept of the healthy worker survivor effect. Epidemiology 5(2):189–196 (1994).
  • Park, R.M., and W. Chen: Silicosis exposure-response in a cohort of tin miners comparing alternate exposure metrics. Am. J. Ind. Med. 56(3):267–275 (2013).
  • Stayner, L., K. Steenland, M. Dosemeci, and I. Hertz-Picciotto: Attenuation of exposure-response curves in occupational cohort studies at high exposure levels. Scand. J. Work Environ. Health 29(4):317–324 (2003).