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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

Background Benzene exposure has been associated with increased risk of leukaemia and myelodysplastic syndrome. Existing studies are sparse for other lymphohaematopoietic cancer subtypes, such as myeloproliferative disease (MPD) and the related chronic myeloid leukaemia (CML). We pooled data from three petroleum worker nested case–control studies to address this gap. To our knowledge, this is the first study to systematically examine the relationship between MPD and quantitative benzene exposure. Methods There were 28 cases and 122 matched controls for CML and 30 MPD cases with 124 matched controls. Two haematopathologists identified each case and provided a diagnosis certainty score. Blinded data-driven assessments estimated benzene exposure for each job held by study participants. Statistical analyses included conditional logistic regression and penalised smoothing splines. Results Benzene exposures were low, and mean average exposure intensity for CML cases was 0.3 ppm and for MPD cases 0.17 ppm. Categorical analyses showed no increased risk of CML or MPD with benzene exposure. There was no significantly increased risk identified for more highly exposed terminal workers. Some association was seen in spline analyses between increased risk of MPD and benzene exposure experienced in the 2–20 years before diagnosis and with peak exposures considered with cumulative exposure as a continuous variable. Conclusions No convincing association was identified between MPD or CML and low exposure to benzene. The greater risk for exposures experienced in the 20 years before diagnosis needs investigating in more powerful studies with a wider range of exposure to benzene, and the biological plausibility further examined from a mechanistic viewpoint.

The epidemiologic literature on benzene exposure and leukemia in the MEDLINE and TOXNET databases was examined through October 2004 using the keywords "benzene", "leukemia" and "adverse health effects". This search was complemented by reviewing the reference lists from extant literature reviews and criteria documents on benzene. Published studies were characterized according to the type of industry studied and design, exposure assessment, disease classification, and control for confounding variables. Study design consisted of either cohort studies or case-control studies, which were further categorized into population-based and nested case-control studies. Disease classification considered the source of diagnostic information, whether there was clinical confirmation from medical records or histopathological, morphological and/or cytogenetic reviews, and as to whether the International Classification of Diseases (ICD) or the French-American-British (FAB) schemes were used (no studies used the Revised European-American Lymphoma (REAL) classification scheme). Nine cohort and 13 case-control studies met inclusion criteria for this review. High and significant acute myeloid leukemia risks with positive dose response relationships were identified across study designs, particularly in the "well-conducted" cohort studies and especially in more highly exposed workers in rubber, shoe, and paint industries. Risks for chronic lymphocytic leukemia (CLL) tended to show elevations in nested case-control studies, with possible dose response relationships in at least two of the three studies. However, cohort studies on CLL show no such risks. Data for chronic myeloid leukemia and acute lymphocytic leukemia are sparse and inconclusive.

Epidemiological studies have been cited in the literature as evidence both for and against the human cancer risks predicted by high-exposure rodent studies. However, there has been little overall consistency in the ways that these animal-to-human comparisons have been made. This review examines some examples of these types of comparisons and describes the methods and techniques used by different investigators. Eleven "key decision areas" that need to be addressed are identified and recommendations for consistent, logical, and statistically appropriate approaches that might be taken to standardize the process are provided. In general, it is suggested that investigators provide the most useful information when they use logical, transparent, and statistically valid comparisons to pursue limited and focused objectives, such as directly testing the validity of an existing regulatory guidance value. Other recommendations include selecting biologically plausible extrapolative models that fit the data and drawing conclusions that are consistent with the study results and objectives.

This article describes effective strategies for the identification and valid assessment of mortality due to mesothelioma.We manually reviewed all death certificates for mention of mesothelioma for all International Classification of Diseases (ICD) revisions. We tested the accuracy of our ascertainment method by comparing New Jersey death certificate data from our health status registry with histologically confirmed cases from the New Jersey State Cancer Registry.We found reasonably good agreement between death certificate diagnoses and histologically confirmed cases, κ coefficient 0.86 (95% confidence interval, 0.76 to 0.95). Most mesothelioma deaths in our test and North American cohorts were coded to unspecified anatomical sites.Limiting ascertainment to pleura and peritoneum ICD codes underestimates mesothelioma deaths. Reviewing all ICD codes that could contain mesothelioma is the only effective method for complete capture of mesothelioma diagnoses.

(2000). PETROLEUM WORKER STUDIES AND BENZENE RISK ASSESSMENT. Journal of Toxicology and Environmental Health, Part A: Vol. 61, No. 5-6, pp. 433-437.

A call for risk assessment approaches that better characterize and quantify uncertainty has been made by the scientific and regulatory community. This paper responds to that call by demonstrating a distributional approach that draws upon human data to derive potency estimates and to identify and quantify important sources of uncertainty. The approach is rooted in the science of decision analysis and employs an influence diagram, a decision tree, probabilistic weights, and a distribution of point estimates of carcinogenic potency. Its results estimate the likelihood of different carcinogenic risks (potencies) for a chemical under a specific scenario. For this exercise, human data on formaldehyde were employed to demonstrate the approach. Sensitivity analyses were performed to determine the relative impact of specific levels and alternatives on the potency distribution. The resulting potency estimates are compared with the results of an exercise using animal data on formaldehyde. The paper demonstrates that distributional risk assessment is readily adapted to situations in which epidemiologic data serve as the basis for potency estimates. Strengths and weaknesses of the distributional approach are discussed. Areas for further application and research are recommended.

A framework of "Common Criteria" (i.e. a series of questions) has been developed to inform the use and evaluation of biomonitoring data in the context of human exposure and risk assessment. The data-rich chemical benzene was selected for use in a case study to assess whether refinement of the Common Criteria framework was necessary, and to gain additional perspective on approaches for integrating biomonitoring data into a risk-based context. The available data for benzene satisfied most of the Common Criteria and allowed for a risk-based evaluation of the benzene biomonitoring data. In general, biomarker (blood benzene, urinary benzene and urinary S-phenylmercapturic acid) central tendency (i.e. mean, median and geometric mean) concentrations for non-smokers are at or below the predicted blood or urine concentrations that would correspond to exposure at the US Environmental Protection Agency reference concentration (30 µg/m(3)), but greater than blood or urine concentrations relating to the air concentration at the 1 × 10(-5) excess cancer risk (2.9 µg/m(3)). Smokers clearly have higher levels of benzene exposure, and biomarker levels of benzene for non-smokers are generally consistent with ambient air monitoring results. While some biomarkers of benzene are specific indicators of exposure, the interpretation of benzene biomonitoring levels in a health-risk context are complicated by issues associated with short half-lives and gaps in knowledge regarding the relationship between the biomarkers and subsequent toxic effects.

A quantitative exposure-estimating algorithm for benzene and total hydrocarbons was developed for a case control study of petroleum marketing and distribution workers. The algorithm used a multiplicative model to adjust recently measured quantitative exposure data to past scenarios for which representative exposure measurement data did not exist. This was accomplished through the development of exposure modifiers to account for differences in the workplace, the materials handled, the environmental conditions, and the tasks performed. Values for exposure modifiers were obtained empirically and through physical/chemical relationships. Dates for changes that altered exposure potential were obtained from archive records, retired employee interviews, and from current operations personnel. Exposure modifiers were used multiplicatively, adjusting available measured data to represent the relevant exposure scenario and time period. Changes in exposure modifiers translated to step changes in exposure estimates. Though limited by availability of data, a validation exercise suggested that the algorithm provided accurate exposure estimates for benzene (compared with measured data in industrial hygiene survey reports); the estimates generally differed by an average of less than 20% from the measured values. This approach is proposed to quantify exposures retrospectively where there are sufficient data to develop reliable current era estimates and where a historical accounting of key exposure modifiers can be developed, but where there are insufficient historic exposure measurements to directly assess historic exposures.

Objectives This study’s purpose was to conduct a more in-depth analysis of the potential association between lung cancer, occupational exposures and smoking using data on cohort members from a Canadian petroleum company and refined statistical analyses. Methods Information on various exposures including asbestos and petroleum coke dust, as well as job type and operating segment were collected via manual and computerised company records. We performed life-table analyses, Poisson regression and restricted cubic splines to model exposure–response patterns while controlling for smoking status and age. Model diagnostics included the assessment of dispersion and offset parameters. Results These analyses show that lung cancer risk is strongly related to age and smoking, and to a lesser extent to province of last residence. When controlling for these covariates, there is suggestive evidence that maintenance work may also be related to lung cancer risk. Some analyses also indicate that asbestos exposure may be associated with lung cancer risk, although a clear exposure–response trend is not seen. Other exposures, including petroleum coke dust, were not strongly related to lung cancer risk, particularly when expressed as a continuous measure. Conclusions These data suggest that maintenance work may be associated with lung cancer incidence, although exposures to the single agents studied did not emerge as strong predictors of lung cancer incidence. Maintenance work may be a surrogate for general exposures to several agents (eg, polycyclic aromatic hydrocarbons, metals, welding fumes, radiation, etc), although these results may be affected by residual confounding due to smoking or other socio-demographic factors.

To provide exposure information for epidemiology studies conducted in Shanghai from 2001 to 2008, we completed retrospective exposure assessments (EA) of benzene and other hazards. Interviewers administered questionnaires to subjects from Shanghai area hospitals. An initial exposure screening by EA staff members, blinded as to case-control status, stratified jobs into exposed, unexposed, or uncertain categories prior to review by a separate expert panel (EP). Resources for the EA included job/industry-specific questionnaire responses by subjects, short-term benzene area concentration measurements from a Shanghai regulatory agency database, Chinese literature for qualitative and short-term quantitative measurements, on-site investigations, summaries of technology changes, and selected task simulations with concurrent benzene concentration measurements. An EP in Shanghai completed semi-quantitative benzene exposure assignments, with categories of 0 to 4 corresponding to intensity ranges of none,1, 1 to 10,10 to 100, and100 mg/m(3). For other hazards, sources included the EP's knowledge of the industries and Chinese and Western literature. For benzene, 20% of the EAs selected by a stratified random process were evaluated by two alternate methods. The study database of potential cases and controls included 18,857 jobs from the subjects' work histories. From 818 individuals initially screened as probably benzene exposed, 964 jobs underwent further review. From subjects with final diagnoses, 755 jobs qualified for inclusion in the final database for any study. For other exposures, the EA considered 17,893 jobs from 7654 subjects for possible exposures and were in the final study database. Of these, 2565 individuals had exposures of study interest from their 4909 exposed jobs. The prevalent exposures included agricultural chemicals, petroleum products, and metals. The EA involved extensive information assembly and exposure assignment by an EP and periodic reviews. The methods described went beyond those typically applied in past general population studies and may have provided improved information for the epidemiologic analyses. However, sufficient, reliable measured historical data are lacking to evaluate this conclusion.

We report results of a hospital-based case control study of 137 consecutive patients diagnosed with aplastic anemia (AA) in participating hospitals over a 4-year period. Diagnoses were made by a single laboratory, subjects were age- and gender-matched to two controls and interviewed concerning previous disease, work histories and exposures to potential etiologic agents. Analysis was conducted on two distinct subgroups: severe aplastic anemia (SAA) and moderate aplastic anemia (MAA). In univariate regression models, the strongest associations were observed for exposure to benzene and SAA (OR=3.12, 95% CI=1.12-8.65) and life on a farm and MAA (OR=3.08, 95% CI=1.44-6.56). Benzene exposure did not show a strong dose-response relationship with either subtype. When accounting for all of the potential confounders we considered in conditional regression models, the previous relationships persisted. Other explanatory variables included hair-dye use for MAA and farm exposures, such as livestock for SAA, although most of these additional variables fell just short of statistical significance. Adjusted R-squared values were only 10% for each subtype, leaving 90% of AA occurrence unexplained. Our results suggest that: (a) benzene exposure is more strongly related to SAA than MAA, (b) farm and livestock exposures are related to both forms of AA, confirming some previous results, and (c) a large percentage of AA remains unexplained, which may indicate that individual susceptibility has a major influence on AA occurrence.

In 1987 a Canadian company implemented an exposure tracking and health information system. The exposure tracking method aligned closely with published concepts for describing workplace exposure, with over 1800 similar exposure groups being used to describe occupational exposures. The database has been actively maintained and is subject to a number of quality checks. Recently, the company initiated a cancer morbidity study, with one objective being to examine whether the exposure tracking data could be used to reconstruct exposure estimates for the cohort. Five agents--hydrogen sulfide, petroleum coke/spent catalyst, hydrocarbon solvents and fuels, hydrocarbon lubricants, and an index for exposure to operations derived from noise exposure--were selected for development of occupational exposure estimates for each cohort member. The cohort consisted of workers first employed between January 1964 and December 1994 and who were employed for at least 1 year. Work history records were associated with a similar exposure group, using human resources data and knowledge of local industrial hygienists. Only employees with90% duration of their work history assigned were kept in the cohort (25,292 people out of a possible 25,617). For each similar exposure group inventory, the substances were identified that contributed to each of the five agents being studied. Exposure estimates before 1987 were modified using historic occupational exposure limits. Rules were created to sum the exposure from multiple substances found in any one similar exposure group. The validity of exposure estimates was tested via comparison with results documented in industrial hygiene survey reports. Industrial hygienists who were unaware of the derived exposure estimates evaluated several hundred industrial hygiene surveys and prepared benchmark information. The two lists were then evaluated for concordance, which was found to be significantly different from that occurring by chance. We conclude that the process described can create valid exposure estimates for use in epidemiology studies.

Among numerous studies of occupational groups with varied chemical exposures (e.g., farmers, petroleum workers, and rubber workers), some have reported excess risk for non-Hodgkin's lymphoma (NHL), multiple myeloma, and other cancers of the B-lymphocyte cell line. While not conclusive, these studies raise questions about the effects of chemical exposures on the lymphocytic versus myeloid cell lines. Almost 70 occupational cohort studies were identified that addressed B-cell cancer risks in 9 major industrial categories, in order to look for common patterns across industries. This effort was substantially limited by the inconsistent nature of lymphohematopoietic (LH) classification schemes across studies and over time, and the relative paucity of B-cell-specific results in studies for any given industry. Taking these limitations into consideration, a descriptive, graphical analysis suggested a pattern of B-cell cancer elevations in the rubber and "general chemical" industries, but no consistent patterns in petroleum production/distribution or petrochemical production. The limited data sources, which lack detail about differences in hazard and exposure for different types of products/chemicals, did not allow a comprehensive look at possible common exposures associated with B-cell cancer elevations across industries. This study suggests that evaluation of possible associations between specific chemical exposures and B-cell malignancies would require additional studies with clear and common definitions of B-cell outcomes. The article concludes by giving an example of a possible common framework for categorizing NHL, the diseases for which most classification issues arise.