Search

Supplemental Digital Content is available in the text, Objective: This study updates the mortality experience of over 25,000 workers in a large Canadian petroleum company through December 31, 2006. Methods: Standardized mortality ratios were generated for all-cause and specific cause mortality. Results: All cause and all cancer mortality were favorable compared with the general Canadian population. Cancers of previous interest were largely consistent with expectation. There is a continuing excess of mesothelioma, which is of similar magnitude as the previous update, although based on larger numbers. This excess is mostly attributable to men who died in their 50s and 60s and who worked in the refining sector. Conclusion: Most causes of death show mortality rates lower than the Canadian general population. Given the excess of mesothelioma observed, this study supports ongoing vigilance in asbestos exposure control programs, as refineries continue to remove asbestos from their facilities.

Occupational exposure to benzene at levels of 10 ppm or more has been associated with increased risk of acute myeloid leukaemia (AML). The mode of action (MOA) for AML development leading to mortality is anticipated to include multiple earlier key events, which can be observed in hematotoxicity and genetic toxicity in peripheral blood of exposed workers. Prevention of these early events would lead to prevention of the apical, adverse outcomes, the morbidity and mortality caused by the myelodysplastic syndrome (MDS) and AML.. Incorporation of key event information should modify the risk model, but few modification approaches have been suggested. To that end, two approaches to risk model modification are described that use sub-linear and segmented linear increases in risk below key events, while maintaining a linear increase in AML mortality risk beginning at 2 ppm, the lowest observed adverse effect concentration (LOAEC) identified for hemato- and geno- toxicity in high quality studies of human occupational exposure. Below 2 ppm two different modification approaches to quantitative risk models were applied: a continuously decreasing slope model and a segmented modification in slope. These two approaches provide greater flexibility to incorporate MOA information in risk model development and selection.

This paper derives an occupational exposure limit for benzene using quality assessed data. Seventy-seven genotoxicity and 36 haematotoxicity studies in workers were scored for study quality with an adapted tool based on that of Vlaanderen et al 2008 (Environ Health. Perspect. 116 1700-5). These endpoints were selected as they are the most sensitive and relevant to the proposed mode of action and protecting against these will protect against benzene carcinogenicity. Lowest and No- Adverse Effect Concentrations (LOAECs and NOAECs) were derived from the highest quality studies (i.e. those ranked in the top tertile or top half) and further assessed as being “more certain” or “less certain”. Several sensitivity analyses were conducted to assess whether alternative “high quality” constructs affected conclusions. The lowest haematotoxicity LOAECs showed effects near 2 ppm (8h TWA), and no effects at 0.59 ppm. For genotoxicity, studies also showed effects near 2 ppm and showed no effects at about 0.69 ppm. Several sensitivity analyses supported these observations. These data define a benzene LOAEC of 2 ppm (8h TWA) and a NOAEC of 0.5 ppm (8h TWA). Allowing for possible subclinical effects in bone marrow not apparent in studies of peripheral blood endpoints, an OEL of 0.25ppm (8h TWA) is proposed., Shorter and updated version of this article

Understanding the Mode of Action (MOA) for a chemical can help guide decisions in development of Occupational Exposure Limits (OELs). Where sufficient information exists, it can provide the OEL developer the basis for selecting either a health-based or risk-based approach. To support the development of an OEL for benzene, scientific information relevant to MOA assessment for risk-based and health-based OEL approaches was reviewed. Direct-acting mutagenicity was considered as a basis for a risk-based OEL, versus MOAs consistent with a health-based approach: indirect mutagenicity via topoisomerase II inhibition, indirect mutagenicity via reactive oxygen species generation, or an immune-based bone marrow dysfunction. Based on the evidence against direct DNA reactivity, threshold expectations for remaining MOAs, and evidence for dose rate affecting acute myeloid leukemia and myelodysplastic syndrome risk, the weight of evidence favors a health-based OEL approach. In the case of benzene, development of an OEL based on observations of earlier key events (i.e., hematologic changes and genetic toxicity) is anticipated to provide protection from later adverse outcomes such as leukemia.

Background: There is a recognized need to improve the application of epidemiologic data in human health risk assessment especially for understanding and characterizing risks from environmental and occupational exposures. Although there is uncertainty associated with the results of most epidemiologic studies, techniques exist to characterize uncertainty that can be applied to improve weight-of-evidence evaluations and risk characterization efforts. Methods: This report derives from a Health and Environmental Sciences Institute (HESI) workshop held in Research Triangle Park, North Carolina, to discuss the utility of using epidemiologic data in risk assessments, including the use of advanced analytic methods to address sources of uncertainty. Epidemiologists, toxicologists, and risk assessors from academia, government, and industry convened to discuss uncertainty, exposure assessment, and application of analytic methods to address these challenges. Synthesis: Several recommendations emerged to help improve the utility of epidemiologic data in risk assessment. For example, improved characterization of uncertainty is needed to allow risk assessors to quantitatively assess potential sources of bias. Data are needed to facilitate this quantitative analysis, and interdisciplinary approaches will help ensure that sufficient information is collected for a thorough uncertainty evaluation. Advanced analytic methods and tools such as directed acyclic graphs (DAGs) and Bayesian statistical techniques can provide important insights and support interpretation of epidemiologic data. Conclusions: The discussions and recommendations from this workshop demonstrate that there are practical steps that the scientific community can adopt to strengthen epidemiologic data for decision making. Citation: Burns CJ, Wright JM, Pierson JB, Bateson TF, Burstyn I, Goldstein DA, Klaunig JE, Luben TJ, Mihlan G, Ritter L, Schnatter AR, Symons JM, Yi KD. 2014. Evaluating uncertainty to strengthen epidemiologic data for use in human health risk assessments. Environ Health Perspect 122:1160–1165; http://dx.doi.org/10.1289/ehp.1308062