Dataset Information

Estimation of the probability of exposure to machining fluids in a population-based case-control study.

ABSTRACT: We describe an approach for estimating the probability that study subjects were exposed to metalworking fluids (MWFs) in a population-based case-control study of bladder cancer. Study subject reports on the frequency of machining and use of specific MWFs (straight, soluble, and synthetic/semi-synthetic) were used to estimate exposure probability when available. Those reports also were used to develop estimates for job groups, which were then applied to jobs without MWF reports. Estimates using both cases and controls and controls only were developed. The prevalence of machining varied substantially across job groups (0.1->0.9%), with the greatest percentage of jobs that machined being reported by machinists and tool and die workers. Reports of straight and soluble MWF use were fairly consistent across job groups (generally 50-70%). Synthetic MWF use was lower (13-45%). There was little difference in reports by cases and controls vs. controls only. Approximately, 1% of the entire study population was assessed as definitely exposed to straight or soluble fluids in contrast to 0.2% definitely exposed to synthetic/semi-synthetics. A comparison between the reported use of the MWFs and U.S. production levels found high correlations (r generally >0.7). Overall, the method described here is likely to have provided a systematic and reliable ranking that better reflects the variability of exposure to three types of MWFs than approaches applied in the past. [Supplementary materials are available for this article. Go to the publisher's online edition of Journal of Occupational and Environmental Hygiene for the following free supplemental resources: a list of keywords in the occupational histories that were used to link study subjects to the metalworking fluids (MWFs) modules; recommendations from the literature on selection of MWFs based on type of machining operation, the metal being machined and decade; popular additives to MWFs; the number and proportion of controls who reported various MWF responses by job group; the number and proportion of controls assigned to the MWF types by job group and exposure category; and the distribution of cases and controls assigned various levels of probability by MWF type.].

SUBMITTER: Park DU

PROVIDER: S-EPMC4359797 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Estimation of the probability of exposure to machining fluids in a population-based case-control study.

Park Dong-Uk DU Colt Joanne S JS Baris Dalsu D Schwenn Molly M Karagas Margaret R MR Armenti Karla R KR Johnson Alison A Silverman Debra T DT Stewart Patricia A PA

Journal of occupational and environmental hygiene 20140101 11

We describe an approach for estimating the probability that study subjects were exposed to metalworking fluids (MWFs) in a population-based case-control study of bladder cancer. Study subject reports on the frequency of machining and use of specific MWFs (straight, soluble, and synthetic/semi-synthetic) were used to estimate exposure probability when available. Those reports also were used to develop estimates for job groups, which were then applied to jobs without MWF reports. Estimates using b ...[more]

PMID: 25256317

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Project description:ObjectivesOccupational exposures in population-based case-control studies are increasingly being assessed using decision rules that link participants' responses to occupational questionnaires to exposure estimates. We used a hierarchical process that incorporated decision rules and job-by-job expert review to assign occupational benzene exposure estimates in a US population-based case-control study of non-Hodgkin lymphoma.MethodsWe conducted a literature review to identify scenarios in which occupational benzene exposure has occurred, which we grouped into 12 categories of benzene exposure sources. For each source category, we then developed decision rules for assessing probability (ordinal scale based on the likelihood of exposure > 0.02 ppm), frequency (proportion of work time exposed), and intensity of exposure (in ppm). The rules used the participants' occupational history responses and, for a subset of jobs, responses to job- and industry-specific modules. For probability and frequency, we used a hierarchical assignment procedure that prioritized subject-specific module information when available. Next, we derived job-group medians from the module responses to assign estimates to jobs with only occupational history responses. Last, we used job-by-job expert review to assign estimates when job-group medians were not available or when the decision rules identified possible heterogeneous or rare exposure scenarios. For intensity, we developed separate estimates for each benzene source category that were based on published measurement data whenever possible. Frequency and intensity annual source-specific estimates were assigned only for those jobs assigned ≥75% probability of exposure. Annual source-specific concentrations (intensity × frequency) were summed to obtain a total annual benzene concentration for each job.ResultsOf the 8827 jobs reported by participants, 8% required expert review for one or more source categories. Overall, 287 (3.3%) jobs were assigned ≥75% probability of exposure from any benzene source category. The source categories most commonly assigned ≥75% probability of exposure were gasoline and degreasing. The median total annual benzene concentration among jobs assigned ≥75% probability was 0.11 ppm (interquartile range: 0.06-0.55). The highest source-specific median annual concentrations were observed for ink and printing (2.3 and 1.2 ppm, respectively).ConclusionsThe applied framework captures some subject-specific variability in work tasks, provides transparency to the exposure decision process, and facilitates future sensitivity analyses. The developed decision rules can be used as a starting point by other researchers to assess occupational benzene exposure in future population-based studies.

Project description:BackgroundFew epidemiological studies of air pollution have used residential histories to develop long-term retrospective exposure estimates for multiple ambient air pollutants and vehicle and industrial emissions. We present such an exposure assessment for a Canadian population-based lung cancer case-control study of 8353 individuals using self-reported residential histories from 1975 to 1994. We also examine the implications of disregarding and/or improperly accounting for residential mobility in long-term exposure assessments.MethodsNational spatial surfaces of ambient air pollution were compiled from recent satellite-based estimates (for PM2.5 and NO2) and a chemical transport model (for O3). The surfaces were adjusted with historical annual air pollution monitoring data, using either spatiotemporal interpolation or linear regression. Model evaluation was conducted using an independent ten percent subset of monitoring data per year. Proximity to major roads, incorporating a temporal weighting factor based on Canadian mobile-source emission estimates, was used to estimate exposure to vehicle emissions. A comprehensive inventory of geocoded industries was used to estimate proximity to major and minor industrial emissions.ResultsCalibration of the national PM2.5 surface using annual spatiotemporal interpolation predicted historical PM2.5 measurement data best (R2 = 0.51), while linear regression incorporating the national surfaces, a time-trend and population density best predicted historical concentrations of NO2 (R2 = 0.38) and O3 (R2 = 0.56). Applying the models to study participants residential histories between 1975 and 1994 resulted in mean PM2.5, NO2 and O3 exposures of 11.3 ?g/m3 (SD = 2.6), 17.7 ppb (4.1), and 26.4 ppb (3.4) respectively. On average, individuals lived within 300 m of a highway for 2.9 years (15% of exposure-years) and within 3 km of a major industrial emitter for 6.4 years (32% of exposure-years). Approximately 50% of individuals were classified into a different PM2.5, NO2 and O3 exposure quintile when using study entry postal codes and spatial pollution surfaces, in comparison to exposures derived from residential histories and spatiotemporal air pollution models. Recall bias was also present for self-reported residential histories prior to 1975, with cases recalling older residences more often than controls.ConclusionsWe demonstrate a flexible exposure assessment approach for estimating historical air pollution concentrations over large geographical areas and time-periods. In addition, we highlight the importance of including residential histories in long-term exposure assessments. For submission to: Environmental Health.

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Estimation of the probability of exposure to machining fluids in a population-based case-control study.

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Estimation of the probability of exposure to machining fluids in a population-based case-control study.

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