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Objectives Farmers may be exposed to glucans (a cell component of molds) through a variety of tasks. The magnitude of exposure depends on each farmer’s activities and their duration. We developed a task-specific algorithm to estimate glucan exposure that combines measurements of (1→3)-β-D-glucan with questionnaire responses from farmers in the Biomarkers of Exposure and Effect in Agriculture (BEEA) study. Methods To develop the algorithm, we first derived task-based geometric means (GMs) of glucan exposure for farming tasks using inhalable personal air sampling data from a prior air monitoring study in a subset of 32 BEEA farmers. Next, these task-specific GMs were multiplied by subject-reported activity frequencies for three time windows (the past 30 days, past 7 days, and past 1 day) to obtain subject-, task-, and time window-specific glucan scores. These were summed together to obtain a total glucan score for each subject and time window. We examined the within- and between-task correlation in glucan scores for different time frames. Additionally, we assessed the algorithm for the ‘past 1 day’ time window using full-shift concentrations from the 32 farmers who participated in air monitoring the day prior to an interview using multilevel statistical models to compare the measured glucan concentration with algorithm glucan scores. Results We focused on the five highest exposed tasks: poultry confinement (300 ng/m3), swine confinement (300 ng/m3), clean grain bins (200 ng/m3), grind feed (100 ng/m3), and stored seed or grain (50 ng/m3); the remaining tasks were Conclusions This study provides insight into the variability and key sources of glucan exposure in a US farming population. It also provides a framework for better glucan exposure assessment in epidemiologic studies and is a crucial starting point for evaluating health risks associated with glucans in future epidemiologic evaluations of this population.

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Objectives To evaluate occupational exposures in case-control studies, exposure assessors typically review each job individually to assign exposure estimates. This process lacks transparency and does not provide a mechanism for recreating the decision rules in other studies. In our previous work, nominal (unordered categorical) classification trees (CTs) generally successfully predicted expert-assessed ordinal exposure estimates (i.e. none, low, medium, high) derived from occupational questionnaire responses, but room for improvement remained. Our objective was to determine if using recently developed ordinal CTs would improve the performance of nominal trees in predicting ordinal occupational diesel exhaust exposure estimates in a case-control study. Methods We used one nominal and four ordinal CT methods to predict expert-assessed probability, intensity, and frequency estimates of occupational diesel exhaust exposure (each categorized as none, low, medium, or high) derived from questionnaire responses for the 14983 jobs in the New England Bladder Cancer Study. To replicate the common use of a single tree, we applied each method to a single sample of 70% of the jobs, using 15% to test and 15% to validate each method. To characterize variability in performance, we conducted a resampling analysis that repeated the sample draws 100 times. We evaluated agreement between the tree predictions and expert estimates using Somers' d, which measures differences in terms of ordinal association between predicted and observed scores and can be interpreted similarly to a correlation coefficient. Results From the resampling analysis, compared with the nominal tree, an ordinal CT method that used a quadratic misclassification function and controlled tree size based on total misclassification cost had a slightly better predictive performance that was statistically significant for the frequency metric (Somers' d: nominal tree = 0.61; ordinal tree = 0.63) and similar performance for the probability (nominal = 0.65; ordinal = 0.66) and intensity (nominal = 0.65; ordinal = 0.65) metrics. The best ordinal CT predicted fewer cases of large disagreement with the expert assessments (i.e. no exposure predicted for a job with high exposure and vice versa) compared with the nominal tree across all of the exposure metrics. For example, the percent of jobs with expert-assigned high intensity of exposure that the model predicted as no exposure was 29% for the nominal tree and 22% for the best ordinal tree. Conclusions The overall agreements were similar across CT models; however, the use of ordinal models reduced the magnitude of the discrepancy when disagreements occurred. As the best performing model can vary by situation, researchers should consider evaluating multiple CT methods to maximize the predictive performance within their data.

Inspectors from the US Occupational Safety and Health Administration (OSHA) have been collecting industrial hygiene samples since 1972 to verify compliance with Permissible Exposure Limits. Starting in 1979, these measurements were computerized into the Integrated Management Information System (IMIS). In 2010, a dataset of over 1 million personal sample results analysed at OSHA's central laboratory in Salt Lake City [Chemical Exposure Health Data (CEHD)], only partially overlapping the IMIS database, was placed into public domain via the internet. We undertook this study to inform potential users about the relationship between this newly available OSHA data and IMIS and to offer insight about the opportunities and challenges associated with the use of OSHA measurement data for occupational exposure assessment.We conducted a literature review of previous uses of IMIS in occupational health research and performed a descriptive analysis of the data recently made available and compared them to the IMIS database for lead, the most frequently sampled agent.The literature review yielded 29 studies reporting use of IMIS data, but none using the CEHD data. Most studies focused on a single contaminant, with silica and lead being most frequently analysed. Sixteen studies addressed potential bias in IMIS, mostly by examining the association between exposure levels and ancillary information. Although no biases of appreciable magnitude were consistently reported across studies and agents, these assessments may have been obscured by selective under-reporting of non-detectable measurements. The CEHD data comprised 1 450 836 records from 1984 to 2009, not counting analytical blanks and erroneous records. Seventy eight agents with1000 personal samples yielded 1 037 367 records. Unlike IMIS, which contain administrative information (company size, job description), ancillary information in the CEHD data is mostly analytical. When the IMIS and CEHD measurements of lead were merged, 23 033 (39.2%) records were in common to both IMIS and CEHD datasets, 10 681 (18.2%) records were only in IMIS, and 25 012 (42.6%) records were only in the CEHD database. While IMIS-only records represent data analysed in other laboratories, CEHD-only records suggest partial reporting of sampling results by OSHA inspectors into IMIS. For lead, the percentage of non-detects in the CEHD-only data was 71% compared to 42% and 46% in the both-IMIS-CEHD and IMIS-only datasets, respectively, suggesting differential under-reporting of non-detects in IMIS.IMIS and the CEHD datasets represent the biggest source of multi-industry exposure data in the USA and should be considered as a valuable source of information for occupational exposure assessment. The lack of empirical data on biases, adequate interpretation of non-detects in OSHA data, complicated by suspected differential under-reporting, remain the principal challenges to the valid estimation of average exposure conditions. We advocate additional comparisons between IMIS and CEHD data and discuss analytical strategies that may play a key role in meeting these challenges.

Background: In epidemiologic studies that rely on professional judgment to assess occupational exposures, the raters’ accurate assessment is vital to detect associations. We examined the influence of the type of questionnaire, type of industry, and type of rater on the raters’ ability to reliably and validly assess within-industry differences in exposure. Our aim was to identify areas where improvements in exposure assessment may be possible. Methods: Subjects from three foundries (n 5 72) and three textile plants (n 5 74) in Shanghai, China, completed an occupational history (OH) and an industry-specific questionnaire (IQ). Six total dust measurements were collected per subject and were used to calculate a subject-specific measurement mean, which was used as the gold standard. Six raters independently ranked the intensity of each subject’s current job on an ordinal scale (1–4) based on the OH alone and on the OH and IQ together. Aggregate ratings were calculated for the group, for industrial hygienists, and for occupational physicians. We calculated intra-class correlation coefficients (ICCs) to evaluate the reliability of the raters. We calculated the correlation between the subject-specific measurement means and the ratings to evaluate the raters’ validity. Analyses were stratified by industry, type of questionnaire, and type of rater. We also examined the agreement between the ratings by exposure category, where the subject-specific measurement means were categorized into two and four categories. Results: The reliability and validity measures were higher for the aggregate ratings than for the ratings from the individual raters. The group’s performance was maximized with three raters. Both the reliability and validity measures were higher for the foundry industry than for the textile industry. The ICCs were consistently lower in the OH/IQ round than in the OH round in both industries. In contrast, the correlations with the measurement means were higher in the OH/IQ round than in the OH round for the foundry industry (group rating, OH/ IQ: Spearman rho 5 0.77; OH: rho 5 0.64). No pattern by questionnaire type was observed for the textile industry (group rating, Spearman rho 5 0.50, both assessment rounds). For both industries, the agreement by exposure category was higher when the task was reduced to discriminating between two versus four exposure categories. Conclusions: Assessments based on professional judgment may reduce misclassification by using two or three raters, by using questionnaires that systematically collect task information, and by defining intensity categories that are distinguishable by the raters. However, few studies have the resources to use multiple raters and these additional efforts may not be adequate for obtaining valid subjective ratings. Thus, improving exposure assessment approaches for studies that rely on professional judgment remain an important research need.

Air pollution causes a wide range of adverse cardiovascular effects, from inflammation identified in biomarkers to increased mortality (1, 2). Its adverse effects have been associated with both short- and long-term exposure to particulate matter (1, 3), more specifically particulate matter from combustion rather than crustal sources (4, 5). Combustion-based particulate matter carries metals and reactive organic compounds, including polycyclic aromatic hydrocarbons (PAHs), on its surface (1). Particulate matter and PAH exposures are prevalent in occupational settings at levels 1–3 orders of magnitude higher than those in environmental settings, yet occupational studies have been less conclusive. Some studies have demonstrated elevated heart disease mortality with PAH-exposed jobs in industries with exposure to combustion productions, including aluminum smelting (6, 7), chimney sweeping (8), and gas work (9). However, heart disease mortality rates in occupational cohorts are typically lower than those in the general population (6, 10, 11). The strongest evidence thus far is from a cohort of European asphalt pavers: Burstyn et al. (12) reported monotonic exposure-response relations between quantitative PAH exposure and fatal heart disease in internal comparisons. Recent morbidity studies add to the growing occupational evidence, with adverse cardiovascular effects with particulate matter and PAH exposure being identified using biomarkers of cardiovascular disease, such as markers of inflammation, blood pressure, and heart rate variability (1, 13–15). In this paper, we examine heart disease mortality in a cohort of aluminum smelter workers in British Columbia, Canada. We expand on the previously reported study of acute myocardial infarction (AMI) mortality in this cohort (16) to include all causes of ischemic heart disease (IHD); and for the first time in an occupational setting, we examine heart disease exposure-response relations with both acute and chronic exposure to benzo[a]pyrene (B(a)P). We also report on the relative mortality risk for heart disease and other chronic diseases as compared with the British Columbia general population, and we examine exposure-response relations with chronic respiratory diseases and cerebrovascular disease.