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Background: We developed an algorithm to quantitatively estimate endotoxin exposure for farmers in the Biomarkers of Exposure and Effect in Agriculture (BEEA) Study., Methods: The algorithm combined task intensity estimates derived from published data with questionnaire responses on activity duration to estimate task-specific cumulative endotoxin exposures for 13 tasks during four time windows, ranging from "past 12 months" to "yesterday/today." We applied the algorithm to 1681 participants in Iowa and North Carolina. We examined correlations in endotoxin metrics within- and between-task. We also compared these metrics to prior day full-shift inhalable endotoxin concentrations from 32 farmers., Results: The highest median task-specific cumulative exposures were observed for swine confinement, poultry confinement, and grind feed. Inter-quartile ranges showed substantial between-subject variability for most tasks. Time window-specific metrics of the same task were moderately-highly correlated. Between-task correlation was variable, with moderately-high correlations observed for similar tasks (e.g., between animal-related tasks). Prior day endotoxin concentration increased with the total metric and with task metrics for swine confinement, clean other animal facilities, and clean grain bins., Significance: This study provides insight into the variability and sources of endotoxin exposure among farmers in the BEEA study and summarizes exposure estimates for future investigations in this population., (© 2023 Wiley Periodicals LLC.)

Background/objective: Farmers conduct numerous tasks with potential for endotoxin exposure. As a first step to characterize endotoxin exposure for farmers in the Biomarkers of Exposure and Effect in Agriculture (BEEA) Study, we used published data to estimate task-specific endotoxin concentrations., Methods: We extracted published data on task-specific, personal, inhalable endotoxin concentrations for agricultural tasks queried in the study questionnaire. The data, usually abstracted as summary measures, were evaluated using meta-regression models that weighted each geometric mean (GM, natural-log transformed) by the inverse of its within-study variance to obtain task-specific predicted GMs., Results: We extracted 90 endotoxin summary statistics from 26 studies for 9 animal-related tasks, 30 summary statistics from 6 studies for 3 crop-related tasks, and 10 summary statistics from 5 studies for 4 stored grain-related tasks. Work in poultry and swine confinement facilities, grinding feed, veterinarian services, and cleaning grain bins had predicted GMs > 1000 EU/m 3 . In contrast, harvesting or hauling grain and other crop-related tasks had predicted GMs below 100 EU/m 3 ., Significance: These task-specific endotoxin GMs demonstrated exposure variability across common agricultural tasks. These estimates will be used in conjunction with questionnaire responses on task duration to quantitatively estimate endotoxin exposure for study participants, described in a companion paper., (© 2023 Wiley Periodicals LLC.)

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 <50 ng/m3 and had similar concentrations to each other. Overall, 67% of the participants reported at least one of these tasks. The most prevalent task was stored seed or grain (64%). The highest median glucan scores were observed for poultry confinement and swine confinement; these tasks were reported by 2% and 8% of the participants, respectively. The correlation between scores for the same task but different time windows was high for swine confinement and poultry confinement, but low for clean grain bins. Task-specific scores had low correlation with other tasks. Prior day glucan concentration was associated with the total glucan 'past 1 day' score and with swine confinement and clean grain bin task scores., 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., (Published by Oxford University Press on behalf of The British Occupational Hygiene Society 2022.)

Objectives: The COLCHIC database contains workplace exposure results of chemical samples collected by the French prevention network since 1987. We aimed to investigate potential associations between exposure levels and ancillary variables in COLCHIC across a broad range of chemical agents in order to provide insight into how to best interpret and exploit the information in this national database., Methods: We selected personal and area measurements in COLCHIC and collected outside respiratory personal protective equipment (PPE), restricted to chemical agents that had at least 1000 samples available. We used Tobit models to estimate associations between exposure concentrations and sample year, sampling duration, PPE, workforce size, collective protective equipment, origin of request, and reason for request for each chemical agent for the period 1987-2019. Models for period 2002-2019 also included type of process (open/closed) and exposure frequency. We used separate models for each combination of agent, period, and personal or area samples. We then applied Bayesian meta-analytical methods to assess average effects and effect heterogeneity of exposure factors across agents., Results: COLCHIC contained 720 282 exposure results (62% personal and 38% area samples) to 77 agents, including 346 766 results for the more recent period 2002-2019 (67% personal and 33% area samples). Sample year and duration, PPE, and process type had the strongest and most consistent associations with exposure levels across agents. Personal and area exposure levels decreased yearly (6% for the entire period and 9% since 2002), and 30-min samples were approximately twice as high as 240-min samples. Workers wearing PPE were exposed to levels 1.7 times higher on average than those without PPE for both area and personal samples. Personal exposure levels associated with enclosed or semi-enclosed processes were approximately 20-30% lower compared with open processes. The associations for the other exposure variables were weaker and more inconsistent between agents. Between-agent heterogeneity of estimated effects, based on 80% prediction intervals, was lowest for sampling duration, time trends, and the presence of PPE., Conclusions: Sampling duration, time trends, and the presence of PPE are important factors to take into account when analyzing COLCHIC and had similar associations with exposure levels across agents. Other variables generally showed weaker associations or variable effects. These results will be used to adjust exposure estimates for the French working population from measurements stored in COLCHIC., (© The Author(s) 2021. Published by Oxford University Press on behalf of the British Occupational Hygiene Society.)

Surveys of the French working population estimate that approximately 15% of all workers may be exposed to at least three different chemical agents, but the most prevalent coexposure situations and their associated health risks remain relatively understudied. To characterize occupational coexposure situations in France, we extracted personal measurement data from COLCHIC and SCOLA, two large administrative occupation exposure databases. We selected 118 chemical agents that had ≥100 measurements with detected concentrations over the period 2010-2019, including 31 carcinogens (IARC groups 1, 2A, and 2B). We grouped measurements by work situations (WS, combination of sector, occupation, task, and year). We characterized the mixtures across WS using frequent itemset mining and association rules mining. The 275,213 measurements extracted came from 32,670 WS and encompassing 4692 unique mixtures. Workers in 32% of all WS were exposed to ≥2 agents (median 3 agents/WS) and 13% of all WS contained ≥2 carcinogens (median 2 carcinogens/WS). The most frequent coexposures were ethylbenzene-xylene (1550 WS), quartz-cristobalite (1417 WS), and toluene-xylene (1305 WS). Prevalent combinations of carcinogens also included hexavalent chromium-lead (368 WS) and benzene-ethylbenzene (314 WS). Wood dust (6% of WS exposed to at least one other agent) and asbestos (8%) had the least amount of WS coexposed with other agents. Tasks with the highest proportions of coexposure to carcinogens include electric arc welding (37% of WS with coexposure), polymerization and distillation (34%), and construction drilling and excavating (34%). Overall, the coexposure to multiple chemical agents, including carcinogens, was highly prevalent in the databases, and should be taken into account when assessing exposure risks in the workplace.

Background: The observed deficit of lung cancer in farmers has been partly attributed to exposure to organic dusts and endotoxins based largely on surrogate metrics. To move beyond these surrogates for etiological studies, we characterized task-based and time-weighted average (TWA) exposure to inhalable endotoxin, (1 → 3)-β-D-glucan, and dust in Iowa farmers., Methods: We collected 320 personal inhalable dust samples from 32 farmers during 69 sample days in 2015 and 2016. Samples were collected using Button aerosol samplers and analyzed for endotoxin using a kinetic chromogenic amebocyte lysate assay, and for (1 → 3)-β-D-glucan using a Limulus endpoint assay. We assessed relationships between bioaerosol concentrations and selected tasks and farm characteristics using linear mixed-effects models., Results: Bedding work, hog handling, and working in barn/confinement buildings, grain bins, and grain elevators were associated with higher endotoxin exposure. We found a monotonic trend between higher endotoxin concentrations and increasing number of animals. Bedding work, cleaning, and feed/grain storage work were associated with higher (1 → 3)-β-D-glucan concentrations. The median concentrations by task spanned one order of magnitude for inhalable dust and two orders of magnitude for endotoxin and (1 → 3)-β-D-glucan. Pearson correlations between endotoxin and glucan concentrations were 0.22 for TWA exposure and 0.56 for task samples., Conclusions: This characterization of exposure factors that influence bioaerosol concentrations can support the development of refined bioaerosol exposure metrics for future etiologic analyses of cancer and other health outcomes in farmers., Competing Interests: Declaration of competing interest None., (Copyright © 2020. Published by Elsevier GmbH.)

Objectives: Daily driving of diesel-powered tractors has been linked to increased lung cancer risk in farmers, yet few studies have quantified exposure levels to diesel exhaust during tractor driving or during other farm activities. We expanded an earlier task-based descriptive investigation of factors associated with real-time exposure levels to black carbon (BC, a surrogate of diesel exhaust) in Iowa farmers by increasing the sample size, collecting repeated measurements, and applying statistical models adapted to continuous measurements., Methods: The expanded study added 43 days of sampling, for a total of 63 sample days conducted in 2015 and 2016 on 31 Iowa farmers. Real-time, continuous monitoring (30-s intervals) of personal BC concentrations was performed using a MicroAeth AE51 microaethelometer affixed with a micro-cyclone. A field researcher recorded information on tasks, fuel type, farmer location, and proximity to burning biomass. We evaluated the influence of these variables on log-transformed BC concentrations using a linear mixed-effect model with random effects for farmer and day and a first-order autoregressive structure for within-day correlation., Results: Proximity to diesel-powered equipment was observed for 42.5% of the overall sampling time and on 61 of the 63 sample days. Predicted geometric mean BC concentrations were highest during grain bin work, loading, and harvesting, and lower for soil preparation and planting. A 68% increase in BC concentrations was predicted for close proximity to a diesel-powered vehicle, relative to far proximity, while BC concentrations were 44% higher in diesel vehicles with open cabins compared with closed cabins. Task, farmer location, fuel type, and proximity to burning biomass explained 8% of within-day variance in BC concentrations, 2% of between-day variance, and no between-farmer variance., Conclusion: Our findings showed that farmers worked frequently near diesel equipment and that BC concentrations varied between tasks and by fuel type, farmer location, and proximity to burning biomass. These results could support the development of exposure models applicable to investigations of health effects in farmers associated with exposure to diesel engine exhaust., (Published by Oxford University Press on behalf of The British Occupational Hygiene Society 2020.)

The CANJEM job-exposure matrix compiles expert evaluations of 31 673 jobs from four population-based case-control studies conducted in Montreal. For each job, experts had derived indices of intensity, frequency, and probability of exposure to 258 agents. CANJEM summarizes the exposures assigned to jobs into cells defined by occupation/industry, agent, and period. Some cells may, however, be less populated than others, resulting in uncertain estimates. We developed a modelling framework to refine the estimates of sparse cells by drawing on information available in adjacent cells. Bayesian hierarchical logistic and linear models were used to estimate the probability of exposure and the geometric mean (GM) of frequency-weighted intensity (FWI) of cells, respectively. The hierarchy followed the Canadian Classification and Dictionary of Occupations (CCDO) classification structure, allowing for exposure estimates to be provided across occupations (seven-digit code), unit groups (four-digit code), and minor groups (three-digit code). The models were applied to metallic dust, formaldehyde, wood dust, silica, and benzene, and four periods, adjusting for the study from which jobs were evaluated. The models provided estimates of probability and FWI for all cells that pulled the sparsely populated cells towards the average of the higher-level group. In comparisons stratified by cell sample size, shrinkage of the estimates towards the group mean was marked below 5 jobs/cell, moderate from 5 to 9 jobs/cell, and negligible at ≥10 jobs/cell. The modelled probability of three-digit cells were slightly smaller than their descriptive estimates. No systematic trend in between-study differences in exposure emerged. Overall, the modelling framework for FWI appears to be a suitable approach to refine CANJEM estimates. For probability, the models could be improved by methods better adapted to the large number of cells with no exposure., (© The Author(s) 2019. Published by Oxford University Press on behalf of the British Occupational Hygiene Society.)

Objectives: The validity of surrogate measures of retrospective occupational exposure in population-based epidemiological studies has rarely been evaluated. Using toenail samples as bioindicators of exposure, we assessed whether work tasks and expert assessments of occupational metal exposure obtained from personal interviews were associated with lead and manganese concentrations., Methods: We selected 609 controls from a case-control study of bladder cancer in New England who had held a job for ≥1 year 8-24 months prior to toenail collection. We evaluated associations between toenail metal concentrations and five tasks extracted from occupational questionnaires (grinding, painting, soldering, welding, working near engines) using linear regression models. For 139 subjects, we also evaluated associations between the toenail concentrations and exposure estimates from three experts., Results: We observed a 1.9-fold increase (95% CI 1.4 to 2.5) in toenail lead concentrations with painting and 1.4-fold increase (95% CI 1.1 to 1.7) in manganese concentrations with working around engines and handling fuel. We observed significant trends with increasing frequency of both activities. For lead, significant trends were observed with the ratings from all three experts. Their average ratings showed the strongest association, with subjects rated as possibly or probably exposed to lead having concentrations that were 2.0 and 2.5 times higher, respectively, than in unexposed subjects (p trend <0.001). Expert estimates were only weakly associated with manganese toenail concentrations., Conclusions: Our findings support the ability of experts to identify broad contrasts in previous occupational exposure to lead. The stronger associations with task frequency and expert assessments support using refined exposure characterisation whenever possible., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2019. No commercial re-use. See rights and permissions. Published by BMJ.)

Purpose of Review: Population-based studies increasingly link task-based occupational questionnaire responses collected from subjects to exposure estimates via transparent, programmable decision rules. We reviewed recent applications and methodological developments of rule-based approaches., Recent Findings: Agent-specific decision rules require interviews incorporating work-task-based questions. Some studies have developed rules before the interviews took place, while others developed rules after the interviews were completed. Agreement between rule-based estimates and exposures assigned using job-by-job expert review were generally moderate to good (Kappa = 0.4-0.8). Rules providing quantitative intensity levels using measurement data or that integrate multiple independent exposure sources for the same job represent further advances to improve the characterization of occupational exposures in population studies. Decision rules have provided transparent and reproducible assessments, reduce job-by-job review, and facilitate sensitivity analyses in epidemiologic studies. Future studies should consider the development of decision rules concurrent with the questionnaire design to facilitate occupational exposure assessment efforts.

Background: Work histories generally cover all jobs held for ≥1 year. However, it may be time and cost prohibitive to conduct a detailed exposure assessment for each such job. While disregarding short-term jobs can reduce the assessment burden, this can be problematic if those jobs contribute important exposure information towards understanding disease aetiology., Objective: To characterize short-term jobs, defined as lasting more than 1 year, but less than 2 years, in a population-based study conducted in Montreal, Canada., Methods: In 2005-2012, we collected work histories for some 4000 participants in a case-control study of prostate cancer. Overall, subjects had held 19 462 paid jobs lasting ≥1 year, including 3655 short-term jobs. Using information from interviews and from the Canadian Classification and Dictionary of Occupations, we characterized short-term jobs and compared them to jobs held ≥2 years., Results: Short-term jobs represented <4% of subjects' work years on average. Forty-five per cent of subjects had at least one short-term job; of these, 49% had one, 24% had two, and 27% had at least three. Half of all short-term jobs had been held before the age of 24. Short-term jobs entailed more often exposure to fumes, odours, dust, and/or poor ventilation than longer jobs (17 versus 13%), as well as outdoor work (10 versus 5%) and heavy physical activity (16 versus 12%)., Conclusions: Short-term jobs occurred often in early careers and more frequently entailed potentially hazardous exposures than longer-held jobs. However, as they represented a small proportion of work years, excluding them should have a marginal impact on lifetime exposure assessment., (© The Author(s) 2019. Published by Oxford University Press on behalf of the British Occupational Hygiene Society.)

Background: While the expert-based occupational exposure assessment approach has been considered the reference method for retrospective population-based studies, its implementation in large study samples has become prohibitive. To facilitate its application and improve upon it we developed, in the context of a Montreal population-based study of prostate cancer (PROtEuS), a hybrid approach combining job-exposure profiles (JEPs) summarizing expert evaluations from previous studies and expert review. We aim to describe the hybrid expert method and its impacts on the exposures assigned in PROtEuS compared to those from a previous study coded using the traditional expert method., Methods: Applying the hybrid approach, experts evaluated semi-quantitative levels of confidence, concentration and frequency of exposure to 313 agents for 16,065 jobs held by 4005 subjects in PROtEuS. These assessments were compared to those from a different set of jobs coded in an earlier study of lung cancer, conducted on the same study base, for 90 blue-collar occupations and 203 agents. Endpoints evaluated included differences in the number of exposures and in the distribution of ratings across jobs, and the within-occupation variability in exposure., Results: Compared to jobs from the lung cancer study, jobs in PROtEuS had on average 0.3 more exposures. PROtEuS exposures were more often assigned definite confidence ratings, but concentration and frequency levels tended to be lower. The within-occupation variability in ratings assigned to jobs were lower in PROtEuS jobs for all metrics. This was particularly evident for concentration, although considerable variability remained with over 40% of occupation/agent cells in PROtEuS exposed at different levels. The hybrid approach reduced coding time by half, compared to the traditional expert assessment., Conclusions: The new hybrid expert approach improved on efficiency and transparency, and resulted in greater confidence in assessments, compared to the traditional expert method applied in an earlier study involving a similar set of jobs. Assigned ratings were more homogeneous with the hybrid approach, possibly reflecting clearer guidelines for coding, greater coherence between experts and/or reliance on summaries of past assessments. Nevertheless, significant within-occupation variability remained with the hybrid approach, suggesting that experts took into account job-specific factors in their assessments.

Objectives: The CANJEM general population job-exposure matrix summarizes expert evaluations of 31 673 jobs from four population-based case-control studies of cancer conducted in Montreal, Canada. Intensity in each CANJEM cell is represented as relative distributions of the ordinal (low, medium, high) ratings of jobs assigned by the experts. We aimed to apply quantitative concentrations to CANJEM cells using Canadian historical measurements from the Canadian Workplace Exposure Database (CWED), taking exposure to wood dust as an example., Methods: We selected 5170 personal and area wood dust measurements from 31 occupations (2011 Canadian National Occupational Classification) with a non-zero exposure probability in CANJEM between 1930 and 2005. The measurements were taken between 1981 and 2003 (median 1989). A Bayesian hierarchical model was applied to the wood dust concentrations with occupations as random effects, and sampling duration, year, sample type (area or personal), province, and the relative proportion of jobs exposed at medium and high intensity in CANJEM cells as fixed effects., Results: The estimated geometric mean (GM) concentrations for a CANJEM cell with all jobs exposed at medium or high intensity were respectively 1.3 and 2.4 times higher relative to a cell with all jobs at low intensity. An overall trend of -3%/year in exposure was observed. Applying the model estimates to all 198 cells in CANJEM with some exposure assigned by the experts, the predicted 8-hour, personal wood dust GM concentrations by occupation for 1989 ranged from 0.48 to 1.96 mg m-3., Conclusions: The model provided estimates of wood dust concentrations for any CANJEM cell with exposure, applicable for quantitative risk assessment at the population level. This framework can be implemented for other agents represented in both CANJEM and CWED.

Objectives: We developed a job-exposure matrix called CANJEM using data generated in population-based case-control studies of cancer. This article describes some of the decisions in developing CANJEM, and some of its performance characteristics., Methods: CANJEM is built from exposure information from 31673 jobs held by study subjects included in our past case-control studies. For each job, experts had evaluated the intensity, frequency, and likelihood of exposure to a predefined list of agents based on jobs histories and descriptions of tasks and workplaces. The creation of CANJEM involved a host of decisions regarding the structure of CANJEM, and operational decisions regarding which parameters to present. The goal was to produce an instrument that would provide great flexibility to the user. In addition to describing these decisions, we conducted analyses to assess how well CANJEM covered the range of occupations found in Canada., Results: Even at quite a high level of resolution of the occupation classifications and time periods, over 90% of the recent Canadian working population would be covered by CANJEM. Prevalence of exposure of specific agents in specific occupations ranges from 0% to nearly 100%, thereby providing the user with basic information to discriminate exposed from unexposed workers. Furthermore, among exposed workers there is information that can be used to discriminate those with high exposure from those with low exposure., Conclusions: CANJEM provides good coverage of the Canadian working population and possibly that of several other countries. Available in several occupation classification systems and including 258 agents, CANJEM can be used to support exposure assessment efforts in epidemiology and prevention of occupational diseases.

Objectives: While several monocyclic aromatic hydrocarbons are classified as definite or possible carcinogens to humans, little data exist on their role in prostate cancer (PCa). We examined occupational exposure to benzene, toluene, xylene (BTX) and styrene and PCa risk in a population-based case-control study in Montreal, Canada., Methods: Cases aged ≤75 years diagnosed with PCa in 2005-2009 (n=1920) and population controls frequency-matched on age (n=1989) provided detailed work histories. Experts evaluated the certainty, frequency and concentration of exposure to monocyclic aromatic hydrocarbons in each job lasting ≥2 years. Logistic regression estimated OR and 95% CIs for PCa risk, adjusting for potential confounders., Results: Exposures to BTX were highly intercorrelated, except for durations of exposure at substantial levels. Ever exposure to any BTX was associated with overall PCa (OR 1.27, 95% CI 1.05 to 1.53), while the OR for styrene was 1.19. However, increases in risk were largely confined to low-grade tumours, with ORs of 1.33 (95%CI 1.08 to 1.64) and 1.41 (95% CI 0.85 to 2.31) for ever exposure to any BTX and styrene, respectively, and a duration response pattern for any BTX. Risks for low-grade tumours were elevated among men exposed ≥25 years at substantial levels of benzene (OR 2.32) and styrene (OR 2.44). Some cumulative exposure categories showed increased risks but without clear trends., Conclusion: Exposure to any BTX was associated with higher risks of overall PCa. Prolonged exposures at the substantial level to benzene and styrene increased risks of low-grade tumours. These novel findings were independent from PCa screening., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2018. No commercial re-use. See rights and permissions. Published by BMJ.)

Exposure to diesel particulate matter (DPM) is frequently assessed by measuring indicators of carbon speciation, but these measurements may be affected by organic carbon (OC) interference. Furthermore, there are still questions regarding the reliability of direct-reading instruments (DRI) for measuring DPM, since these instruments are not specific and may be interfered by other aerosol sources. This study aimed to assess DPM exposure in 2 underground mines by filter-based methods and DRI and to assess the relationship between the measures of elemental carbon (EC) and the DRI to verify the association of these instruments to DPM. Filter-based methods of respirable combustible dust (RCD), EC, and total carbon (TC) were used to measure levels of personal and ambient DPM. For ambient measurements, DRI were used to monitor particle number concentration (PNC; PTrak), particle mass concentration (DustTrak DRX and DustTrak 8520), and the submicron fraction of EC (EC 1; Airtec). The association between ambient EC and the DRI was assessed by Spearman correlation. Geometric mean concentrations of RCD, respirable TC (TC R ) and respirable elemental EC (EC R ) were 170 µg/m 3 , 148 µg/m 3 , and 83 µg/m 3 for personal samples, and 197 µg/m 3 , 151 µg/m 3 , and 100 µg/m 3 for ambient samples. Personal measurements had higher TC R: EC R ratios compared to ambient samples (1.8 vs. 1.50) and weaker association between EC R and TC R . Among the DRI, the measures of EC 1 by the Airtec (ρ = 0.86; P < 0.001) and the respirable particles by the DustTrak 8520 (ρ = 0.74; P < 0.001) showed the strongest association with EC, while PNC showed a weak and non-significant association with EC. In conclusion, this study provided important information about the concentrations of DPM in underground mines by measuring several indicators using filter-based methods and DRI. Among the DRI, the Airtec proved to be a good tool for estimating EC concentrations and, although the DustTrak showed good association with EC, interferences from other aerosol sources should be considered when using this instrument to assess DPM.

Background: Age, family history and ancestry are the only recognized risk factors for prostate cancer (PCa) but a role for environmental factors is suspected. Due to the lack of knowledge on the etiological factors for PCa, studies that are both hypothesis-generating and confirmatory are still needed. This study explores relationships between employment, by occupation and industry, and PCa risk., Methods: Cases were 1937 men aged ≤75 years with incident PCa diagnosed across Montreal French hospitals in 2005-2009. Controls were 1994 men recruited concurrently from electoral lists of French-speaking Montreal residents, frequency-matched to cases by age. In-person interviews elicited occupational histories. Unconditional logistic regression estimated odds ratios (OR) and 95 % confidence intervals (CI) for the association between employment across 696 occupations and 613 industries and PCa risk, adjusting for potential confounders. Multinomial logistic models assessed risks by PCa grade. Semi-Bayes (SB) adjustment accounted for the large number of associations evaluated., Results: Consistently positive associations-and generally robust to SB adjustment-were found for occupations in forestry and logging (OR 1.9, 95 % CI: 1.2-3.0), social sciences (OR 1.6, 95 % CI: 1.1-2.2) and for police officers and detectives (OR: 1.8, 95 % CI 1.1-2.9). Occupations where elevated risk of high grade PCa was found included gasoline station attendants (OR 4.3, 95 % CI 1.8-10.4) and textile processing occupations (OR 1.8, 95 % CI 1.1-3.2). Aside from logging, industries with elevated PCa risk included provincial government and financial institutions. Occupations with reduced risk included farmers (OR 0.6, 95 % CI 0.4-1.0) and aircraft maintenance workers (OR 0.1, 95 % CI 0.0-0.7)., Conclusions: Excess PCa risks were observed across several occupations, including predominantly white collar workers. Further analyses will focus on specific occupational exposures.

Introduction: At the time of the 1930 International Labour Office Conference on silicosis in Johannesburg, mining was the main sector affected by silicosis; however, other industries would later emerge as areas of concern., Methods: A search of the occupational hygiene and epidemiological literature was conducted to retrieve historical and current documents pertaining to silica exposure and associated hazards., Results: The potential risk of silicosis in construction became evident starting in the 1960s, and the body of literature including case reports, sampling surveys, and medical surveillance continues to grow to this day. Among non-construction activities, hydraulic fracturing and engineered countertop manufacturing have recently appeared as industries with a potential for overexposure, while mining remains the industrial sector with the highest prevalence of exposure., Conclusions: The risk of developing this "ancient disease" remains a current issue in many workplaces, and requires ongoing surveillance and prevention efforts., (© 2015 Wiley Periodicals, Inc.)

Reference values for the biological monitoring of occupational exposures are generally normalized on the basis of creatinine (CR) concentration or specific gravity (SG) to account for fluctuations in urine dilution. For instance, the American Conference of Governmental Industrial Hygienists (ACGIH(®)) uses a reference value of 1g/L for CR. The comparison of urinary concentrations of biomarkers between studies requires the adjustment of results based on a reference CR and/or SG value, although studies have suggested that age, sex, muscle mass, and time of the day can exert non-negligible influences on CR excretion, while SG appears to be less affected. The objective of this study was to propose reference values for urinary CR and SG based on the results of samples sent for analysis by occupational health practitioners to the laboratory of the Occupational Health and Safety Research Institute of Québec (IRSST). We analyzed a database containing 20,395 urinary sample results collected between 1985 and 2010. Linear mixed-effects models with worker as a random effect were used to estimate the influence of sex and collection period on urinary CR and SG. Median CR concentrations were 25-30% higher in men (1.6 g/L or 14.4 mmol/L) than in women (1.2 g/L or 10.2 mmol/L). Four percent of the samples for men and 12% for women were below the acceptable threshold for CR (4.4 mmol/L). For SG, 5% of samples for men and 12% for women were below the threshold of 1.010. The difference in SG levels between sexes was lower than for CR, with a median of 1.024 for men compared to 1.020 for women. Our results suggest that the normalization of reference values based on a standard CR value of 1 g/L as proposed by the ACGIH is a conservative approach. According to the literature, CR excretion is more influenced by physiological parameters than SG. We therefore suggest that correction based on SG should be favored in future studies involving the proposal of reference values for the biological monitoring of occupational exposures.

Many construction activities can put workers at risk of breathing silica containing dusts, and there is an important body of literature documenting exposure levels using a task-based strategy. In this study, statistical modeling was used to analyze a data set containing 1466 task-based, personal respirable crystalline silica (RCS) measurements gathered from 46 sources to estimate exposure levels during construction tasks and the effects of determinants of exposure. Monte-Carlo simulation was used to recreate individual exposures from summary parameters, and the statistical modeling involved multimodel inference with Tobit models containing combinations of the following exposure variables: sampling year, sampling duration, construction sector, project type, workspace, ventilation, and controls. Exposure levels by task were predicted based on the median reported duration by activity, the year 1998, absence of source control methods, and an equal distribution of the other determinants of exposure. The model containing all the variables explained 60% of the variability and was identified as the best approximating model. Of the 27 tasks contained in the data set, abrasive blasting, masonry chipping, scabbling concrete, tuck pointing, and tunnel boring had estimated geometric means above 0.1mg m(-3) based on the exposure scenario developed. Water-fed tools and local exhaust ventilation were associated with a reduction of 71 and 69% in exposure levels compared with no controls, respectively. The predictive model developed can be used to estimate RCS concentrations for many construction activities in a wide range of circumstances.

We created an exposure database of respirable crystalline silica levels in the construction industry from the literature. We extracted silica and dust exposure levels in publications reporting silica exposure levels or quantitative evaluations of control effectiveness published in or after 1990. The database contains 6118 records (2858 of respirable crystalline silica) extracted from 115 sources, summarizing 11,845 measurements. Four hundred and eighty-eight records represent summarized exposure levels instead of individual values. For these records, the reported summary parameters were standardized into a geometric mean and a geometric standard deviation. Each record is associated with 80 characteristics, including information on trade, task, materials, tools, sampling strategy, analytical methods, and control measures. Although the database was constructed in French, 38 essential variables were standardized and translated into English. The data span the period 1974-2009, with 92% of the records corresponding to personal measurements. Thirteen standardized trades and 25 different standardized tasks are associated with at least five individual silica measurements. Trade-specific respirable crystalline silica geometric means vary from 0.01 (plumber) to 0.30 mg/m³ (tunnel construction skilled labor), while tasks vary from 0.01 (six categories, including sanding and electrical maintenance) to 1.59 mg/m³ (abrasive blasting). Despite limitations associated with the use of literature data, this database can be analyzed using meta-analytical and multivariate techniques and currently represents the most important source of exposure information about silica exposure in the construction industry. It is available on request to the research community.

A quantitative determinants-of-exposure analysis of respirable crystalline silica (RCS) levels in the construction industry was performed using a database compiled from an extensive literature review. Statistical models were developed to predict work-shift exposure levels by trade. Monte Carlo simulation was used to recreate exposures derived from summarized measurements which were combined with single measurements for analysis. Modeling was performed using Tobit models within a multimodel inference framework, with year, sampling duration, type of environment, project purpose, project type, sampling strategy and use of exposure controls as potential predictors. 1346 RCS measurements were included in the analysis, of which 318 were non-detects and 228 were simulated from summary statistics. The model containing all the variables explained 22% of total variability. Apart from trade, sampling duration, year and strategy were the most influential predictors of RCS levels. The use of exposure controls was associated with an average decrease of 19% in exposure levels compared to none, and increased concentrations were found for industrial, demolition and renovation projects. Predicted geometric means for year 1999 were the highest for drilling rig operators (0.238 mg m(-3)) and tunnel construction workers (0.224 mg m(-3)), while the estimated exceedance fraction of the ACGIH TLV by trade ranged from 47% to 91%. The predicted geometric means in this study indicated important overexposure compared to the TLV. However, the low proportion of variability explained by the models suggests that the construction trade is only a moderate predictor of work-shift exposure levels. The impact of the different tasks performed during a work shift should also be assessed to provide better management and control of RCS exposure levels on construction sites.