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 syndromes (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 (MOA) 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 (8 h 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 (8 h TWA) and a NOAEC of 0.5 ppm (8 h TWA). Allowing for possible subclinical effects in bone marrow not apparent in studies of peripheral blood endpoints, an OEL of 0.25 ppm (8 h TWA) is proposed.

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.

Objective: We studied the risk of 11 cancers of a priori interest in petroleum refinery workers. Methods: Iterative searches identified 36 studies for the 11 cancer sites. Statistical heterogeneity and publication bias were assessed to enhance interpretation of meta-relative risks. Results: Statistical heterogeneity was marked for mesothelioma, but was largely due to study quality. Higher quality studies showed a meta-relative risk (RR) of 3.22, (95% prediction interval 1.45 to 7.23). Melanoma (meta-RR = 1.23) and acute lymphoid leukemia (meta-RR = 1.51), showed results consistent with higher risk, but both were driven by one or two studies. Eight other cancer outcomes showed summary meta-RR's consistent with unity. Conclusions: Most cancer outcomes are consistent with background risk in refinery workers. This work has clarified an excess mesothelioma risk, conditional on study quality stratification. Continued surveillance is warranted for melanoma and ALL.

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.

Epidemiologic findings play an important role in benzene risk assessment, which is utilized to guide the selection of recommended benzene exposure levels to prevent adverse health effects. For decades, excess leukemia risk, especially that in the Pliofilm (R) cohort, has been the focus of benzene risk assessment. While more stringent benzene standards, often <= 1 ppm, have been promulgated to protect workers from developing leukemia, recent epidemiologic studies have reported elevated risk of myelodysplastic syndrome (MDS). This report aims to examine whether the use of new data on MDS is scientifically warranted in future benzene risk assessments. First, we reviewed current benzene guidelines, regulations, and underlying risk assessments in developed countries. Second, we examined current epidemiologic literature on benzene and MDS, which identified seven studies with simultaneous measures of MDS risk and benzene exposure and 17 studies on MDS in populations potentially exposed to benzene. Next, we examined the potential of the MDS data to serve as the basis of future benzene risk assessments, by comparing its quality and risk estimates with those used in current benzene standards. We conclude from the current literature that there is strong evidence that MDS can be caused by benzene, and the MDS data from the pooled petroleum study should be further examined in future benzene risk assessments. We recommend that future MDS-based benzene risk assessment use total MDS as the endpoint, take into consideration the full exposure period, and examine a range of benzene exposure metrics, including the role of peak, intermittent benzene exposures.

Objective: Due to the sparse data on benzene exposure and myelodysplastic syndrome (MDS) subtypes, we studied this relationship in patients from 29 hospitals in Shanghai, China. Methods: We recruited 604 cases of MDS and 1193 controls matched on age, sex, and admission date. We interviewed subjects for information on workplace and lifestyle exposures, and developed semi-quantitative exposure estimates. Results: Benzene exposure showed a direct exposure-response pattern with refractory cytopenia with multilineage dysplasia, a less certain association with refractory cytopenia with unilineage dysplasia, and no association with other MDS subtypes. A different pattern was observed with farm residence and smoking, which was primarily related to refractory anemias. Conclusions: This research demonstrates the importance of MDS subtype specification for more robust etiologic insights. Our data suggests that subtypes with non-erythroid dysplasia are associated with benzene exposure.

ABSTRACT Cases of lymphohematopoietic cancer from three petroleum industry cohorts, matched to controls from the respective cohort, were pooled into single study. Average benzene exposure was quantitatively estimated in ppm for each job based on measured data from the relevant country, adjusted for the specific time period, site and job exposure characteristics and the certainty of the exposure estimate scored. The probability of dermal exposure and of peak exposure was also assessed. Before risk was examined, an exposure estimate comparison and rationalisation exercise was performed across the studies to ensure accuracy and consistency of approach. This article evaluates the final exposure estimates and their use in the risk assessments. Overall benzene exposure estimates were low: 90% of participants accumulated less than 20 ppm-years. Mean cumulative exposure was estimated as 5.15 ppm-years, mean duration was 22 years, and mean exposure intensity was 0.2 ppm. 46% of participants were allocated a peak exposure (>3 ppm at least weekly). 40% of participants had a high probability of dermal exposure (based on the relative probability of at least weekly exposure). There were differences in mean intensity of exposure, probability of peak, and/or dermal exposure associated with job category, job site, and decade of exposure. Terminal Operators handling benzene-containing products were the most highly exposed group, followed by Tanker Drivers carrying gasoline. Exposures were higher around 1940–1950 and lower in more recent decades. Overall confidence in the exposure estimates was highest for recently held jobs and for white-collar jobs. We used sensitivity analyses, which included and excluded case-sets on the basis of exposure certainty scores, to inform the risk assessment. The above analyses demonstrated that the different patterns of exposure across the three studies are largely attributable to differences in jobs, site types, and time frames rather than study. This provides reassurance that the previous rationalisation of exposures achieved inter-study consistency and that the data could be confidently pooled.

Two apparently contradictory findings in the literature on low-dose human metabolism of benzene are as follows. First, metabolism is approximately linear at low concentrations, e.g., below 10 ppm. This is consistent with decades of quantitative modeling of benzene pharmacokinetics and dose-dependent metabolism. Second, measured benzene exposure and metabolite concentrations for occupationally exposed benzene workers in Tianjin, China show that dose-specific metabolism (DSM) ratios of metabolite concentrations per ppm of benzene in air decrease steadily with benzene concentration, with the steepest decreases below 3 ppm. This has been interpreted as indicating that metabolism at low concentrations of benzene is highly nonlinear. We reexamine the data using non-parametric methods. Our main conclusion is that both findings are correct; they are not contradictory. Low-concentration metabolism can be linear, with metabolite concentrations proportional to benzene concentrations in air, and yet DSM ratios can still decrease with benzene concentrations. This is because a ratio of random variables can be negatively correlated with its own denominator even if the mean of the numerator is proportional to the denominator. Interpreting DSM ratios that decrease with air benzene concentrations as evidence of nonlinear metabolism is therefore unwarranted when plots of metabolite concentrations against benzene ppm in air show approximately straight-line relationships between them, as in the Tianjin data. Thus, an apparent contradiction that has fueled heated discussions in the recent literature can be resolved by recognizing that highly nonlinear, decreasing DSM ratios are consistent with linear metabolism.

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.

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.

Despite the theoretical success of obviating the need for hypothesis-generating studies, they live on in epidemiological practice. Cole asserted that “… there is boundless number of hypotheses that could be generated, nearly all of them wrong” and urged us to focus on evaluating “credibility of hypothesis”. Adopting a Bayesian approach, we put this elegant logic into quantitative terms at the study planning stage for studies where the prior belief in the null hypothesis is high (i.e., “hypothesis-generating” studies). We consider not only type I and II errors (as is customary) but also the probabilities of false positive and negative results, taking into account typical imperfections in the data. We concentrate on a common source of imperfection in the data: non-differential misclassification of binary exposure classifier. In context of an unmatched case-control study, we demonstrate—both theoretically and via simulations—that although non-differential exposure misclassification is expected to attenuate real effect estimates, leading to the loss of ability to detect true effects, there is also a concurrent increase in false positives. Unfortunately, most investigators interpret their findings from such work as being biased towards the null rather than considering that they are no less likely to be false signals. The likelihood of false positives dwarfed the false negative rate under a wide range of studied settings. We suggest that instead of investing energy into understanding credibility of dubious hypotheses, applied disciplines such as epidemiology, should instead focus attention on understanding consequences of pursuing specific hypotheses, while accounting for the probability that the observed “statistically significant” association may be qualitatively spurious.

Objective: This article describes effective strategies for the identification and valid assessment of mortality due to mesothelioma. Methods: 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. Results: We found reasonably good agreement between death certificate diagnoses and histologically confirmed cases, 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. Conclusions: 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.

Background: There is a recognized ned 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 inter-disciplinary 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.

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 mu g/m(3)), but greater than blood or urine concentrations relating to the air concentration at the 1 x 10(-5) excess cancer risk (2.9 mu g/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.

Background: High benzene exposure causes acute myeloid leukaemia (AML). Three petroleum case-control studies identified 60 cases (241 matched controls) for AML and 80 cases (345 matched controls) for chronic lymphoid leukaemia (CLL).Methods: Cases were classified and scored regarding uncertainty by two haematologists using available diagnostic information. Blinded quantitative benzene exposure assessment used work histories and exposure measurements adjusted for era-specific circumstances. Statistical analyses included conditional logistic regression and penalised smoothing splines.Results: Benzene exposures were much lower than previous studies. Categorical analyses showed increased ORs for AML with several exposure metrics, although patterns were unclear; neither continuous exposure metrics nor spline analyses gave increased risks. ORs were highest in terminal workers, particularly for Tanker Drivers. No relationship was found between benzene exposure and risk of CLL, although the Australian study showed increased risks in refinery workers.Conclusion: Overall, this study does not persuasively demonstrate a risk between benzene and AML. A previously reported strong relationship between myelodysplastic syndrome (MDS) (potentially previously reported as AML) at our study's low benzene levels suggests that MDS may be the more relevant health risk for lower exposure. Higher CLL risks in refinery workers may be due to more diverse exposures than benzene alone.

BACKGROUND:Benzene at high concentrations is known to cause acute myeloid leukemia (AML), but its relationship with other lymphohematopoietic (LH) cancers remains uncertain, particularly at low concentrations. In this pooled analysis, we examined the risk of five LH cancers relative to lower levels of benzene exposure in petroleum workers. METHODS:We updated three nested case-control studies from Australia, Canada, and the United Kingdom with new incident LH cancers among petroleum distribution workers through December 31, 2006, and pooled 370 potential case subjects and 1587 matched LH cancer-free control subjects. Quantitative benzene exposure in parts per million (ppm) was blindly reconstructed using historical monitoring data, and exposure certainty was scored as high, medium, or low. Two hematopathologists assigned diagnoses and scored the certainty of diagnosis as high, medium, or low. Dose-response relationships were examined for five LH cancers, including the three most common leukemia cell-types (AML, chronic myeloid leukemia [CML], and chronic lymphoid leukemia [CLL]) and two myeloid tumors (myelodysplastic syndrome [MDS] and myeloproliferative disease [MPD]). Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using conditional logistic regression, controlling for age, sex, and time period. RESULTS:Cumulative benzene exposure showed a monotonic dose-response relationship with MDS (highest vs lowest tertile, >2.93 vs ≤0.348 ppm-years, OR = 4.33, 95% CI = 1.31 to 14.3). For peak benezene exposures (>3 ppm), the risk of MDS was increased in high and medium certainty diagnoses (peak exposure vs no peak exposure, OR = 6.32, 95% CI = 1.32 to 30.2) and in workers having the highest exposure certainty (peak exposure vs no peak exposure, OR = 5.74, 95% CI = 1.05 to 31.2). There was little evidence of dose-response relationships for AML, CLL, CML, or MPD. CONCLUSIONS:Relatively low-level exposure to benzene experienced by petroleum distribution workers was associated with an increased risk of MDS, but not AML, suggesting that MDS may be the more relevant health risk for lower 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.