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

Journal of Occupational and Environmental Hygiene

Volumn 12, Issue , 2015, Pages S7-S17

  Wheeler, M W ^a   Park, R M ^a   Bailer, A J ^b   Whittaker, C ^a  

^a NATIONAL INSTITUTE FOR OCCUPATIONAL SAFETY AND HEALTH   (United States)

^b MIAMI UNIVERSITY   (United States)

Author keywords

benchmark dose; critical effect; dose response; exposure response; modeling; occupational

Indexed keywords

EPIDEMIOLOGY; HISTORY; HUMAN; NO-OBSERVED-ADVERSE-EFFECT LEVEL; OCCUPATIONAL EXPOSURE; OCCUPATIONAL HEALTH; PROCEDURES; RISK ASSESSMENT; STANDARDS; STATISTICAL MODEL;

EPIDEMIOLOGIC METHODS; HISTORY, 20TH CENTURY; HUMANS; MODELS, STATISTICAL; NO-OBSERVED-ADVERSE-EFFECT LEVEL; OCCUPATIONAL EXPOSURE; OCCUPATIONAL HEALTH; RISK ASSESSMENT;

EID: 84947554336     PISSN: 15459624     EISSN: 15459632     Source Type: Journal    
DOI: 10.1080/15459624.2015.1076934     Document Type: Article

References (52)

1. Castleman, B.I, G.E. Ziem.: Corporate influence on threshold limit values. Am. J. Ind. Med. 13 (5): 531-559 (1988).
2. Ziem, G.E, B.I. Castleman.: Threshold limit values: historical perspectives and current practice. J. Occup. Med. 31 (11): 910-918 (1989).
3. National Research Council.: Risk Assessment in The Federal Government: Managing The Process. Washington, DC: National Academies Press 1983.
4. National Research Council.: Understanding Risk: Informing Decisions in a Democratic Society. Washington, DC: National Academies Press 1996.
5. National Research Council: Science and Decisions: Advancing Risk Assessment. Washington, DC: National Academies Press 2009.
6. " Occupational Safety and Health Administration: Hazard Communication Standard; Final Rule ". Federal Register 77 17574-17896.
7. National Research Council.: Science and Judgement in Risk Assessment. Washington, DC: National Academies Press 1994.
8. Waters, M., L. McKernn., A. Maier., M. Jayjock., V. Schaeffer, 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).
9. Dankovic, D.A., B.D. Naumann., A. Maier., M.L. Dourson, L. Levy.: The scientific basis of uncertainty factors used in setting occupational exposure limits. J. Occup. Envrion. Hyg. Supplement 1: S55-S68 (2015).
10. Lehman, A.J, O.G. Fitzhugh.: 100-fold margin of safety. Assoc. Food Drug Off. US Q. Bull. 18: 33-35 (1954).
11. 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).
12. Crump, K.S.: A new method for determining allowable daily intakes. Fund. Appl. Toxicol. 4 (5): 854-871 (1984).
13. Akaike, H.: A new look at the statistical model identification. IEEE Trans. Automatic Control 19: 716-723 (1974).
14. U.S. Environmental Protection Agency.:" The Risk Assessment Guidelines of 1986." Washington, DC: U.S. Environmental Protection Agency, 1986.
15. U.S. Environmental Protection Agency, Benchmark Dose Technical Guidance http://www.epa.gov/raf/publications/pdfs/benchmark-dose-guidance.pdf(2012).
16. Kodell, R.L, R.W. West.: Upper confidence limits on excess risk for quantitative responses. Risk Anal. 13 (2): 177-182 (1993).
17. Crump, K.S.: Calculation of benchmark dose from continuous data. Risk Anal. 15: 79-89 (1995).
18. Raftery, A.E.: Bayesian model selection in social research. Sociol. Methodol. 25: 111-163 (1995).
19. Buckland, S.T., K.P. Burnham, N.H. Augustin.: Model selection: an integral part of inference. Biometrics 53: 603-618 (1997).
20. Hoeting, J.A., D. Madigan., A.E. Raftery, 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).
21. Bailer, A.J., L.T. Stayner., R.J. Smith., E.D. Kuempel, M.M. Prince.: Estimating benchmark concentrations and other noncancer endpoints in epidemiology studies. Risk Anal. 17: 771-779 (1997).
22. Kang, S.H., R.L. Kodell, J.J. Chen.: Incorporating model uncertainties along with data uncertainties in microbial risk assessment. Regul. Toxicol. Pharmacol. 32 (1): 68-72 (2000).
23. Noble, R.B., A.J. Bailer, R. Park.: Model-averaged benchmark concentration estimates for continuous response data arising from epidemiological studies. Risk Anal. 29 (4): 558-564 (2009).
24. Piegorsch, W.W., L. An., A.A. Wickens., R. Webster. West, E.A. Penã, W. Wu.: Information-theoretic model-averaged benchmark dose analysis in environmental risk assessment. Environmetrics 24 (3): 143-157 (2013).
25. Shao, K, J.S. Gift.: Model uncertainty and Bayesian model averaged benchmark dose estimation for continuous data. Risk Anal. 34 (1): 101-120 (2014).
26. Simmons, S.J., C. Chen., X. Li. et al.: Bayesian model averaging for benchmark dose estimation. Environ. Ecol. Statist. 22 (1): 5-16 (2015).
27. Wheeler, M.W, 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).
28. Wheeler, M.W, A.J. Bailer.: Comparing model averaging with other model selection strategies for benchmark dose estimation. Environ. Ecol. Statist. 16 (1): 37-51 (2009).
29. Morales, K.H., J.G. Ibrahim., C.J. Chen, 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).
30. Wheeler, M, A.J. Bailer.: Monotonic Bayesian semiparametric benchmark dose analysis. Risk Anal. 32 (7): 1207-1218 (2012).
31. Guha, N., A. Roy., L. Kopylev., J. Fox., M. Spassova, P. White.: Nonparametric Bayesian methods for benchmark dose estimation. Risk Anal. 33 (9): 1608-1619 (2013).
32. Piegorsch, W.W., H. Xiong., R.N. Bhattacharya, L. Lin.: Nonparametric estimation of benchmark doses in environmental risk assessment. Environmetrics 23 (8): 717-728 (2012).
33. Piegorsch, W.W., H. Xiong., R.N. Bhattacharya, L. Lin.: Benchmark dose analysis via nonparametric regression modeling. Risk Anal. 34(1):135-151. (2013).
34. Wheeler, M.W., K. Shao, A.J. Bailer.: (2015) Quantile benchmark dose estimation for continuous endpoints. Environmetrics 26 (5): 363-372.
35. Lin, L., W.W. Piegorsch, R. Bhattacharya.: (2015) Nonparametric benchmark dose estimation with continuous dose-response data. Scand. J. Statist. 42 (3): 713-731.
36. Wheeler, M.W, 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).
37. Clewell, H.J, K.S. Crump.: Quantitative estimates of risk for noncancer endpoints. Risk Anal. 25 (2): 285-289 (2005).
38. Budtz-Jorgensen, E., N. Keiding, P. Grandjean.: Benchmark dose calculation from epidemiological data. Biometrics 57 (3): 698-706 (2001).
39. Park, R.M, 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).
40. Royston, P., G. Ambler, W. Sauerbrei.: The use of fractional polynomials to model continuous risk variables in epidemiology. Int. J. Epidemiol. 28 (5): 964-974 (1999).
41. Thurston, S.W., E.A. Eisen, J. Schwartz.: Smoothing in survival models: an application to workers exposed to metalworking fluids. Epidemiology 13 (6): 685-692 (2002).
42. Zeka, A., E.A. Eisen., D. Kriebel., R. Gore, 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).
43. 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).
44. Wheeler, M.W, A.J. Bailer.: Model averaging software for dichotomous dose response risk estimation. J. Statist. Softw. 26 (5)(2008).
45. Piegorsch, W.W.: Model uncertainty in environmental dose-response risk analysis. Statist. Publ. Pol. 1 (1): 78-85 (2014).
46. 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).
47. Sand, S., C.J. Portier, 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).
48. Burzala, L, 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.
49. Checkoway, H., N. Pearce, D. Kriebel.: Research Methods in Occupational Epidemiology. New York: Oxford University Press, 2004.
50. Arrighi, H.M, I. Hertz-Picciotto.: The evolving concept of the healthy worker survivor effect. Epidemiology 5 (2): 189-196 (1994).
51. Park, R.M, W. Chen.: Silicosis exposure-response in a cohort of tin miners comparing alternate exposure metrics. Am. J. Ind. Med. 56 (3): 267-275 (2013).
52. Stayner, L., K. Steenland., M. Dosemeci, 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).