Exposure to benzene in a pooled analysis of petroleum industry case-control studies.

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.

Acute myeloid and chronic lymphoid leukaemias and exposure to low-level benzene among petroleum workers.

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.

Ensuring comparability of benzene exposure estimates across three nested case-control studies in the petroleum industry in support of a pooled epidemiological analysis.

BACKGROUND: Three case-control studies each nested within a cohort of petroleum workers assessed exposure to benzene in relation to risk of haematopoietic cancers. These studies have each been updated and the cases will be pooled to derive a more powerful study. The benzene exposure of new leukemia cases and controls was estimated in accordance with each respective study's original methods. An essential component of the process of pooling the data was comparison and rationalisation of the exposure estimates to ensure accuracy and consistency of approach. This paper describes this process and presents comparative estimates before and after appropriate revision took place. The original petroleum industry studies, in Canada, the UK and Australia, were conducted at different points in time by different study teams, but the industry used similar technology in similar eras in each of these countries. METHODS: A job history for each subject giving job title, dates of starting and leaving the job and location of work, was assembled. For each job or task, the average benzene exposure (Base Estimate (BE) in ppm) was derived from measurements collected at applicable worksites. Estimates of exposure intensity (workplace exposure estimates (WE)) were then calculated for each line of work history by adjusting the BEs for site- and era-specific exposure-related variables such as loading technology and percentage benzene in the product. To ensure that the exposure estimates were comparable among the studies, the WEs were allocated to generic Job Categories, e.g. Tanker Driver (by technology used e.g. bottom loading), Motor Mechanic. The WEs were stratified into eras, reflecting technological changes in the industry. The arithmetic mean (AM), geometric mean (GM) and range of the stratified WEs were calculated, by study, for each generic Job Category. These were then compared. The AMs of the WEs were regarded as substantially similar if they were within 20% in all three studies in one era or for at least two studies in two eras. If the AM of the WE group differed by more than 20%, the data were examined to see whether the difference was justified by differences in local exposure conditions, such as an enclosure versus open work area. Estimates were adjusted in the absence of justification for the difference. RESULTS: Reconciliation of differences resulted in changes to a small number of underlying BEs, particularly the background values, also the BEs attributed to some individuals and changes to the allocation of jobs between Job Categories. Although the studies covered some differing sectors of the industry and different time periods, for 22 Job Categories there was sufficient overlap, particularly in the downstream distribution sector, to make comparisons possible. After adjustment 12 Job Categories were judged to be similar and 10 were judged to be justifiably different. Job-based peak and skin exposure estimates were applied in a uniform way across the studies and a single approach to scoring the certainty of the exposure estimates was identified. CONCLUSIONS: The revised exposure estimates will be used in the pooled analysis to examine the risk of haematopoietic cancers and benzene exposure. This exercise provided an important quality control check on the exposure estimates and identified similarly exposed Job Categories that could be grouped for risk assessment analyses.

Health Watch exposure estimates: do they underestimate benzene exposure?

A nested case-control study found that the excess of leukemia, identified among the male members of the Health Watch cohort, was associated with benzene exposure. Exposure had been retrospectively estimated for each individual occupational history using an algorithm in a relational database. Benzene exposure measurements, supplied by Australian petroleum companies, were used to estimate exposure for specific tasks. The tasks carried out within each job, the products handled, and the technology used, were identified from structured interviews with contemporary colleagues. More than half of the subjects started work after 1965 and had an average exposure period of 20 years. Exposure was low; nearly 85% of the cumulative exposure estimates were at or below 10 ppm-years. Matched analyses showed that leukemia risk increased with increasing cumulative benzene exposures and with increasing exposure intensity of the highest-exposed job. Non-Hodgkin lymphoma and multiple myeloma were not associated with benzene exposure. A reanalysis reported here, showed that for the 7 leukemia case-sets with greater than 16 ppm-years cumulative exposure, the odds ratio was 51.9 (5.6-477) when compared to the 2 lowest exposed categories combined to form a new reference category. The addition of occasional high exposures, e.g. as a result of spillages, increased exposure for 25% of subjects but for most, the increase was less than 5% of total exposure. The addition of these exposures reduced the odds ratios. Cumulative exposures did not range as high as those in comparable studies; however, the recent nature of the cohort and local handling practices can explain these differences.

Estimating mean exposures from censored data: exposure to benzene in the Australian petroleum industry.

A retrospective assessment of exposure to benzene was carried out for a nested case control study of lympho-haematopoietic cancers, including leukaemia, in the Australian petroleum industry. Each job or task in the industry was assigned a Base Estimate (BE) of exposure derived from task-based personal exposure assessments carried out by the company occupational hygienists. The BEs corresponded to the estimated arithmetic mean exposure to benzene for each job or task and were used in a deterministic algorithm to estimate the exposure of subjects in the study. Nearly all of the data sets underlying the BEs were found to contain some values below the limit of detection (LOD) of the sampling and analytical methods and some were very heavily censored; up to 95% of the data were below the LOD in some data sets. It was necessary, therefore, to use a method of calculating the arithmetic mean exposures that took into account the censored data. Three different methods were employed in an attempt to select the most appropriate method for the particular data in the study. A common method is to replace the missing (censored) values with half the detection limit. This method has been recommended for data sets where much of the data are below the limit of detection or where the data are highly skewed; with a geometric standard deviation of 3 or more. Another method, involving replacing the censored data with the limit of detection divided by the square root of 2, has been recommended when relatively few data are below the detection limit or where data are not highly skewed. A third method that was examined is Cohen's method. This involves mathematical extrapolation of the left-hand tail of the distribution, based on the distribution of the uncensored data, and calculation of the maximum likelihood estimate of the arithmetic mean. When these three methods were applied to the data in this study it was found that the first two simple methods give similar results in most cases. Cohen's method on the other hand, gave results that were generally, but not always, higher than simpler methods and in some cases gave extremely high and even implausible estimates of the mean. It appears that if the data deviate substantially from a simple log-normal distribution, particularly if high outliers are present, then Cohen's method produces erratic and unreliable estimates. After examining these results, and both the distributions and proportions of censored data, it was decided that the half limit of detection method was most suitable in this particular study.

Validation of exposure estimation for benzene in the Australian petroleum industry.

A nested case-control study was undertaken to investigate whether an excess of lympho-haematopoietic cancers in the Australian petroleum industry was associated with benzene exposure. The benzene exposures of the cases and controls were estimated using a quantitative algorithm based largely on exposures measured in the Australian petroleum industry. The algorithm was used to estimate, for each subject, the benzene exposure in parts per million (ppm) for each job held in the industry, and the cumulative exposure in ppm years. Because of the critical importance of the exposure assessment in this design of epidemiological study, particular attention was paid to the reliability of the inputs to the algorithm. The inputs [base estimates (BEs) of exposure and technology-specific exposure modifiers (EMs)] were compared to data from other sources including the occupational hygiene literature. Where such comparison data were available, they were generally found to confirm the values used in the algorithm, although four input values were changed as a result of the validation exercise. The integrity of the task-based algorithm was validated by employing it to calculate the exposures of the tanker drivers in the study and comparing these with measured daily exposure for tanker drivers in the Australian petroleum industry and exposure values found in the occupational hygiene literature. After adjustment for the mix of products carried by the Australian tanker drivers, the estimates from the algorithm were found to be comparable to the measured and literature values. This exercise provided evidence that the exposure assessment for the epidemiological study was reliable and that the results of the study can be used as the basis for evaluating the relationship between exposure to benzene and the risk of lympho-haematopoietic cancer.

Retrospective exposure assessment for benzene in the Australian petroleum industry.

An excess of lympho-haematopoietic (LH) cancers has been identified in the Australian petroleum industry through the Health Watch surveillance programme. A nested case-control study is being conducted to investigate this excess. This paper describes the methods used to provide quantitative estimates of benzene exposure for each of the subjects in the case-control study. Job histories were compiled for each subject from interviews and company employment records. Site visits and telephone interviews were used to identify the tasks included in each job title. Details about the tasks such as their frequency, the technology in use and about changes that had taken place over the years were also gathered. Exposure dated back to the late 1940s for a few subjects. Collaborating petroleum companies provided recent benzene exposure monitoring data. These were used to generate Base Estimates of exposure for each task, augmented with data from the literature where necessary. Past exposures were estimated from the Base Estimates by means of an exposure algorithm. The modifying effects of technological changes and changes to the product were used in the algorithm. The algorithm was then computed to give, for each job, for each subject, an estimate of average benzene exposure in ppm in the workplace atmosphere (Workplace Estimate). This value was multiplied by the years for which the job was held and these values summed to give an estimate of Cumulative Estimate of benzene in ppm-years. The occupational hygienists performing the exposure assessment did so without knowledge of the case or control status of subjects. Overall exposures to benzene in the Australian petroleum industry were low, and virtually all activities and jobs were below a time-weighted average of 5 ppm. Exposures in terminals were generally higher than at refineries. Exposures in upstream areas were extremely low. Estimates of Cumulative Estimate to benzene ranged from 0.005 to 50.9 ppm-years.

P-glycoprotein-associated Cl- currents are activated by cell swelling but do not contribute to cell volume regulation.

P-glycoprotein-associated Cl- conductance is activated by cell swelling; the ensuing Cl- efflux is thought to contribute to cell volume regulation. We tested this hypothesis in human breast cancer cells transfected with human mdr1 complementary DNA, which display P-glycoprotein-associated, swelling-activated Cl- currents. The Cl- electrochemical driving force favors Cl- efflux, but there was no appreciable Cl- loss or regulatory volume decrease (both assessed with fluorescent dyes) during the exposure to hyposmotic solution. Calculations indicate that the swelling-activated Cl- current is insufficient to cause a significant Cl- efflux. Hence, regulatory volume decrease is not a function of P-glycoprotein.