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674880 
Journal Article 
Risk of benzene-induced leukemia: A sensitivity analysis of the pliofilm cohort with additional follow-up and new exposure estimates 
Crump, KS 
1994 
1 
Journal of Toxicology and Environmental Health
ISSN: 0098-4108 
42 
2 
219-242 
English 
8207757 
WOS:A1994NT04600008 
This report updates the risk assessment by Crump and Allen (1984) for benzene-induced leukemia that was used by OSHA (1987) to support its reduction of the permissible exposure limit (PEL) to 1 ppm and that also was the basis for EPA's (1985) interim “unit risk” for benzene. The present study derives new risk estimates using data from follow-up through 1987 (whereas the earlier assessment only had follow-up available through 1978), and using new exposure estimates for this cohort developed by Paustenbach et al. (1992) that account for a number of factors that were unknown or not fully evaluated in earlier exposure assessments. There was a significant excess of acute myelocytic or acute monocytic leukemia (AMML, the only forms of acute nonlymphatic leukemia observed) in this cohort, and this end point also exhibited a strong dose-response trend. AMML was the only hematopoietic or lymphatic cancer that was clearly linked to benzene exposure. However, quantitative estimates of risk based on modeling either AMML or all leukemia differed by only 20%. Differences between the two Pliofilm plant locations in the occurrence of AMML were not statistically significant (.12 ≤ p ≤ .21) after differences in levels of benzene exposure were taken into account. The Paustenbach et al. exposures predicted a quadratic dose response, based on a measure of exposure that weighted intensity of exposure more heavily than duration of exposure. The best-fitting quadratic models predicted an additional lifetime risk of a benzene-related death from 45 yr of exposure to 1 ppm of between 0.020 and 0.036 per thousand. Statistical confidence intervals (90%) on these estimates were barely wide enough to include risk estimates based on linear dose response models. These linear models predicted risks of between 1.6 and 3.1 per thousand.