Prevalent occupational exposures and risk of lung cancer among women: Results from the application of the Canadian Job-Exposure Matrix (CANJEM) to a combined set of ten case-control studies.

BACKGROUND: Worldwide, lung cancer is the second leading cause of cancer death in women. The present study explored associations between occupational exposures that are prevalent among women, and lung cancer. METHODS: Data from 10 case-control studies of lung cancer from Europe, Canada, and New Zealand conducted between 1988 and 2008 were combined. Lifetime occupational history and information on nonoccupational factors including smoking were available for 3040 incident lung cancer cases and 4187 controls. We linked each reported job to the Canadian Job-Exposure Matrix (CANJEM), which provided estimates of probability, intensity, and frequency of exposure to each selected agent in each job. For this analysis, we selected 15 agents (cleaning agents, biocides, cotton dust, synthetic fibers, formaldehyde, cooking fumes, organic solvents, cellulose, polycyclic aromatic hydrocarbons from petroleum, ammonia, metallic dust, alkanes C18+, iron compounds, isopropanol, and calcium carbonate) that had lifetime exposure prevalence of at least 5% in the combined study population. For each agent, we estimated lung cancer risk in each study center for ever-exposure, by duration of exposure, and by cumulative exposure, using separate logistic regression models adjusted for smoking and other covariates. We then estimated the meta-odds ratios using random-effects meta-analysis. RESULTS AND CONCLUSIONS: None of the agents assessed showed consistent and compelling associations with lung cancer among women. The following agents showed elevated odds ratio in some analyses: metallic dust, iron compounds, isopropanol, and organic solvents. Future research into occupational lung cancer risk factors among women should prioritize these agents.

Characterizing the Burden of Occupational Chemical Exposures by Sociodemographic Groups in the United States, 2021.

Objectives. To estimate the number and prevalence of workers in the United States exposed to chemical hazards available in the Canadian job-exposure matrix (CANJEM) database and examine exposure disparities across sociodemographic groups. Methods. We merged US worker demographic data from the Current Population Survey with CANJEM to characterize the burden and sociodemographic distribution of 244 chemical exposures in the United States in 2021. An interactive version of the full data set is available online (https://deohs.washington.edu/us-exposure-burden). Results. Of the chemical exposures examined, the most prevalent were cleaning and antimicrobial agents (14.7% of workforce estimated exposed), engine emissions (12.8%), organic solvents (12.1%), polycyclic aromatic hydrocarbons (10.1%), and diesel engine emissions (8.3%). Racial and ethnic minoritized groups, persons with lower educational attainment, foreign-born noncitizens, and males were generally overrepresented in exposure to work-related chemical hazards. Conclusions. In the United States, marginalized sociodemographic groups are estimated to experience an inequitable burden to many chemical exposures because of occupational segregation. Data from this analysis can inform occupational and public health research, policy, and interventions aimed at reducing the burden of disease and health inequities in the United States. (Am J Public Health. 2024;114(1):57-67. https://doi.org/10.2105/AJPH.2023.307461).

Occupational environment and ovarian cancer risk.

OBJECTIVES: To investigate employment in an occupation or industry and specific occupational exposures in relation to ovarian cancer risk. METHODS: In a population-based case-control study conducted in Montreal, Canada (2011-2016), lifetime occupational histories were collected for 491 cases of ovarian cancer and 897 controls. An industrial hygienist coded the occupation and industry of each participant's job. Associations with ovarian cancer risk were estimated for each of several occupations and industries. Job codes were linked to the Canadian job-exposure matrix, thereby generating exposure histories to many agents. The relationship between exposure to each of the 29 most prevalent agents and ovarian cancer risk was assessed. Odds ratios and 95% confidence intervals (OR (95% CI)) for associations with ovarian cancer risk were estimated using logistic regression and controlling for multiple covariates. RESULTS: Elevated ORs (95% CI) were observed for employment ≥10 years as Accountants (2.05 (1.10 to 3.79)); Hairdressers, Barbers, Beauticians and Related Workers (3.22 (1.25 to 8.27)); Sewers and Embroiderers (1.85 (0.77 to 4.45)); and Salespeople, Shop Assistants and Demonstrators (1.45 (0.71 to 2.96)); and in the industries of Retail Trade (1.59 (1.05 to 2.39)) and Construction (2.79 (0.52 to 4.83)). Positive associations with ORs above 1.42 were seen for high cumulative exposure versus never exposure to 18 agents: cosmetic talc, ammonia, hydrogen peroxide, hair dust, synthetic fibres, polyester fibres, organic dyes and pigments, cellulose, formaldehyde, propellant gases, aliphatic alcohols, ethanol, isopropanol, fluorocarbons, alkanes (C5-C17), mononuclear aromatic hydrocarbons, polycyclic aromatic hydrocarbons from petroleum and bleaches. CONCLUSIONS: Certain occupations, industries and specific occupational exposures may be associated with ovarian cancer risk. Further research is needed to provide a more solid grounding for any inferences in this regard.

Determinants of respirable crystalline silica exposure in construction in western Canada.

Task-based respirable crystalline silica (RCS) exposure monitoring data was collected from construction work sites across 3 Canadian provinces: Alberta, British Columbia (BC), and Manitoba. In total 373 RCS samples were obtained from 70 worksites across 44 companies. Sampling was conducted between May 2015 and August 2020. The overall geometric mean (GM) RCS exposure was 0.045 mg/m3 (geometric standard deviation, GSD = 6.8). Alberta had the highest average exposure and the highest variability with GM of 0.060 mg/m3 (GSD = 9.3), the GM in BC was 0.044 (GSD = 4.3), and in Manitoba the GM was 0.033 (GSD = 7.0). A multivariable model was built using forward stepwise linear regression modeling. Province, task type, work environment (indoor vs. outdoor), construction material, sampling duration, and engineering control use were all statistically significant predictors of exposure level in partial F-tests (P < 0.05). Overall, the model explained 42% of the RCS concentration variability. Task type contributed most to the model's explanatory power. The task type with highest average exposure levels was demolition (GM 0.30 mg/m3, GSD 0.49). Breaking (GM 0.16 mg/m3, GSD 8.4) and grinding (GM 0.081 m/m3, GSD 7.4) also had high-exposure levels. Working outdoors was associated with exposure levels 39% lower than indoors. Exposure control measures such as local exhaust ventilation and wetting were also associated with lower exposure levels. Among construction materials, Cement, sand, and stone were associated with higher RCS exposure levels relative to the reference material, concrete. The results of this study indicate that workers in western Canada remain exposed to RCS at levels that exceed the health-based American Congress for Governmental Industrial Hygienists Threshold Limit Value of 0.025 mg/m3. Although there were some differences in exposure levels between the provinces, the determinants of exposure were similar in all 3. The overall GM RCS exposure was 0.045 mg/m3 (geometric standard deviation, GSD = 6.8). Alberta had the highest average exposure and the highest variability with GM of 0.060 mg/m3 (GSD = 9.3), the GM in BC was 0.044 (GSD = 4.3), and in Manitoba the GM was 0.033 (GSD = 7.0).

Evaluation of the updated SOCcer v2 algorithm for coding free-text job descriptions in three epidemiologic studies.

OBJECTIVES: Computer-assisted coding of job descriptions to standardized occupational classification codes facilitates evaluating occupational risk factors in epidemiologic studies by reducing the number of jobs needing expert coding. We evaluated the performance of the 2nd version of SOCcer, a computerized algorithm designed to code free-text job descriptions to US SOC-2010 system based on free-text job titles and work tasks, to evaluate its accuracy. METHODS: SOCcer v2 was updated by expanding the training data to include jobs from several epidemiologic studies and revising the algorithm to account for nonlinearity and incorporate interactions. We evaluated the agreement between codes assigned by experts and the highest scoring code (a measure of confidence in the algorithm-predicted assignment) from SOCcer v1 and v2 in 14,714 jobs from three epidemiology studies. We also linked exposure estimates for 258 agents in the job-exposure matrix CANJEM to the expert and SOCcer v2-assigned codes and compared those estimates using kappa and intraclass correlation coefficients. Analyses were stratified by SOCcer score, score distance between the top two scoring codes from SOCcer, and features from CANJEM. RESULTS: SOCcer's v2 agreement at the 6-digit level was 50%, compared to 44% in v1, and was similar for the three studies (38%-45%). Overall agreement for v2 at the 2-, 3-, and 5-digit was 73%, 63%, and 56%, respectively. For v2, median ICCs for the probability and intensity metrics were 0.67 (IQR 0.59-0.74) and 0.56 (IQR 0.50-0.60), respectively. The agreement between the expert and SOCcer assigned codes linearly increased with SOCcer score. The agreement also improved when the top two scoring codes had larger differences in score. CONCLUSIONS: Overall agreement with SOCcer v2 applied to job descriptions from North American epidemiologic studies was similar to the agreement usually observed between two experts. SOCcer's score predicted agreement with experts and can be used to prioritize jobs for expert review.

Prerequisite for Imputing Non-detects among Airborne Samples in OSHA's IMIS Databank: Prediction of Sample's Volume.

INTRODUCTION: The US Integrated Management Information System (IMIS) contains workplace measurements collected by Occupational Safety and Health Administration (OSHA) inspectors. Its use for research is limited by the lack of record of a value for the limit of detection (LOD) associated with non-detected measurements, which should be used to set censoring point in statistical analysis. We aimed to remedy this by developing a predictive model of the volume of air sampled (V) for the non-detected results of airborne measurements, to then estimate the LOD using the instrument detection limit (IDL), as IDL/V. METHODS: We obtained the Chemical Exposure Health Data from OSHA's central laboratory in Salt Lake City that partially overlaps IMIS and contains information on V. We used classification and regression trees (CART) to develop a predictive model of V for all measurements where the two datasets overlapped. The analysis was restricted to 69 chemical agents with at least 100 non-detected measurements, and calculated sampling air flow rates consistent with workplace measurement practices; undefined types of inspections were excluded, leaving 412,201/413,515 records. CART models were fitted on randomly selected 70% of the data using 10-fold cross-validation and validated on the remaining data. A separate CART model was fitted to styrene data. RESULTS: Sampled air volume had a right-skewed distribution with a mean of 357 l, a median (M) of 318, and ranged from 0.040 to 1868 l. There were 173,131 measurements described as non-detects (42% of the data). For the non-detects, the V tended to be greater (M = 378 l) than measurements characterized as either 'short-term' (M = 218 l) or 'long-term' (M = 297 l). The CART models were complex and not easy to interpret, but substance, industry, and year were among the top three most important classifiers. They predicted V well overall (Pearson correlation (r) = 0.73, P < 0.0001; Lin's concordance correlation (rc) = 0.69) and among records captured as non-detects in IMIS (r = 0.66, P < 0.0001l; rc = 0.60). For styrene, CART built on measurements for all agents predicted V among 569 non-detects poorly (r = 0.15; rc = 0.04), but styrene-specific CART predicted it well (r = 0.87, P < 0.0001; rc = 0.86). DISCUSSION: Among the limitations of our work is the fact that samples may have been collected on different workers and processes within each inspection, each with its own V. Furthermore, we lack measurement-level predictors because classifiers were captured at the inspection level. We did not study all substances that may be of interest and did not use the information that substances measured on the same sampling media should have the same V. We must note that CART models tend to over-fit data and their predictions depend on the selected data, as illustrated by contrasting predictions created using all data vs. limited to styrene. CONCLUSIONS: We developed predictive models of sampled air volume that should enable the calculation of LOD for non-detects in IMIS. Our predictions may guide future work on handling non-detects in IMIS, although it is advisable to develop separate predictive models for each substance, industry, and year of interest, while also considering other factors, such as whether the measurement evaluated long-term or short-term exposure.

Estimating evaporation rates and contaminant air concentrations due to small spills of non-ideal aqueous organic solvent mixtures in a controlled environment.

Although small spills of non-ideal organic solvent mixtures are ubiquitous undesirable events in occupational settings, the potential risk of exposure associated with such scenarios remains insufficiently investigated. This study aimed to examine the impact of non-ideality on evaporation rates and contaminant air concentrations resulting from small spills of organic solvent mixtures. Evaporation rate constants alphas (α) were experimentally measured for five pure solvents using a gravimetric approach during solvent evaporation tests designed to simulate small spills of solvents. Two equations were used for estimating contaminants' evaporation rates from aqueous mixtures assuming either ideal or non-ideal behavior based on the pure-chemical alpha values. A spill model also known as the well-mixed room model with exponentially decreasing emission rate was used to predict air concentrations during various spill scenarios based on the two sets of estimated evaporation rates. Model predictive performance was evaluated by comparing the estimates against real-time concentrations measured for the same scenarios. Evaluations for 12 binary non-ideal aqueous mixtures found that the estimated evaporation rates accounting for the correction by the activity coefficients of the solvents (median = 0.0318 min-1) were higher than the evaporation rates estimated without the correction factor (median = 0.00632 min-1). Model estimates using the corrected evaporation rates reasonably agreed with the measured values, with a median predicted peak concentrations-to-measured peak concentrations ratio of 0.92 (0.81 to 1.32) and a median difference between the predicted and the measured peak times of -5 min. By contrast, when the non-corrected evaporation rates were used, the median predicted peak concentrations-to-measured peak concentrations ratio was 0.31 (0.08 to 0.75) and the median difference between the predicted and the measured peak times was +33 min. Results from this study demonstrate the importance of considering the non-ideality effect for accurately estimating evaporation rates and contaminant air concentrations generated by solvent mixtures. Moreover, this study is a step further in improving knowledge of modeling exposures related to small spills of organic solvent mixtures.

Concordance of Occupational Exposure Assessment between the Canadian Job-Exposure Matrix (CANJEM) and Expert Assessment of Jobs Held by Women.

OBJECTIVES: To compare the exposure data generated by using the Canadian job-exposure matrix (CANJEM) with data generated by expert assessment, for jobs held by women. METHODS: We selected 69 occupational agents that had been assessed by experts for each of 3403 jobs held by 998 women in a population-based case-control study of lung cancer. We then assessed the same agents among the same jobs by linking their occupation codes to CANJEM and thereby derived probability of exposure to each of the agents in each job. To create binary exposure variables, we dichotomized probability of exposure using two cutpoints: 25 and 50% (referred to as CANJEM-25% and CANJEM-50%). Using jobs as units of observation, we estimated the prevalence of exposure to each selected agent using CANJEM-25% and CANJEM-50%, and using expert assessment. Further, using expert assessment as the gold standard, for each agent, we estimated CANJEM's sensitivity, specificity, and kappa. RESULTS: CANJEM-based prevalence estimates correlated well with the prevalences assessed by the experts. When comparing CANJEM-based exposure estimates with expert-based exposure estimates, sensitivity, specificity, and kappa varied greatly among agents, and between CANJEM-25% and CANJEM-50% probability of exposure. With CANJEM-25%, the median sensitivity, specificity, and kappa values were 0.49, 0.99, and 0.46, respectively. Analogously, with CANJEM-50%, the corresponding values were 0.26, 1.00, and 0.35, respectively. For the following agents, we observed high concordance between CANJEM- and expert-based assessments (sensitivity ≥0.70 and specificity ≥0.99): fabric dust, cotton dust, synthetic fibres, cooking fumes, soldering fumes, calcium carbonate, and tin compounds. We present concordance estimates for each of 69 agents. CONCLUSIONS: Concordance between CANJEM and expert assessment varied greatly by agents. Our results indicate which agents provide data that mimic best those obtained with expert assessment.

Predicting first-order evaporation rate constant alpha (α) from small spills of organic solvents in a controlled environment.

Exposures to vapors generated by small spills of organic solvents are common in the occupational hygiene practice. In these scenarios, contaminant mass release is exponentially decreasing, driven by an evaporation rate constant alpha (α). Knowing α is fundamental for adequately modeling peak concentrations and/or short-term exposures that occur and for achieving efficient occupational risk analysis and management. The purpose of this study was to measure alpha experimentally using a gravimetric approach in a controlled environment during solvent evaporation tests designed to simulate small spills of solvents. The effects of several factors on α were evaluated. Equations based on regression models derived from the experimental data were proposed for predicting α. Predictions were externally validated against experimental data. A total of 183 tests was performed. Data analyses found that alpha (α) values increased with vapor pressure, spill surface area-to-spill volume ratio, and air speed across the spill. Larger α were associated with petri dish containers compared to watch glasses. Three regression models were created for predicting α. They had four variables in common, namely vapor pressure, molecular weight, air speed above the liquid, and surface tension of the liquid. The fifth variable was either spill volume, spill surface area, or spill surface area-to-spill volume ratio. The R2 of the regression models were equal to 0.98. External validation showed mean relative errors of -32.9, -32.0, and -25.5%, respectively, with associated standard deviations of the relative errors of 17.7, 33.3, and 26.0%, respectively, and associated R2 of 0.92, 0.65, and 0.87, respectively. The proposed equations can be used for estimating α in exposure scenarios similar to those evaluated in this study. Moreover, these models constitute a step further in the improvement of knowledge on estimating evaporation rates for small spills of organic solvents.

Exposure Determinants in the French Database COLCHIC (1987-2019): Statistical Modeling across 77 Chemicals.

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.

Impact of Variability in Job Coding on Reliability in Exposure Estimates Obtained via a Job-Exposure Matrix.

OBJECTIVES: The use of a job-exposure matrix (JEM) to assess exposure to potential health hazards in occupational epidemiological studies requires coding each participant's job history to a standard occupation and/or industry classification system recognized by the JEM. The objectives of this study were to assess the impact of inter-coder variability in job coding on reliability in exposure estimates derived from linking the job codes to the Canadian job-exposure matrix (CANJEM) and to identify influent parameters. METHOD: Two trained coders independently coded 1000 jobs sampled from a population-based case-control study to the ISCO-1968 occupation classification at the five-digit resolution level, of which 859 could be linked to CANJEM using both assigned codes. Each of the two sets of codes was separately linked to CANJEM and thereby generated, for each of the 258 occupational agents available in CANJEM, two exposure estimates: exposure status (yes/no) and intensity of exposure (low, medium, and high) for exposed jobs only. Then, inter-rater reliability (IRR) was computed (i) after stratifying agents in 4 classes depending, for each, on the proportion of occupation codes in CANJEM defined as 'exposed' and (ii) for two additional scenarios restricted to jobs coded differently: the first one using experts' codes, the other one using codes randomly selected. IRR was computed using Cohen's kappa, PABAK and Gwet's AC1 index for exposure status, and weighted kappa and Gwet's AC2 for exposure intensity. RESULTS: Across all agents and based on all jobs, median (Q1, Q3; Nagents) values were 0.68 (0.59, 0.75; 220) for kappa, 0.99 (0.95, 1.00; 258) for PABAK, and 0.99 (0.97, 1.00; 258) for AC1. For the additional scenarios, median kappa was 0.28 (0.00, 0.45; 209) and -0.01 (-0.02, 00; 233) restricted to jobs coded differently using experts' and random codes, respectively. A similar decreasing pattern was observed for PABAK and AC1 albeit with higher absolute values. Median kappa remained stable across exposure prevalence classes but was more variable for low prevalent agents. PABAK and AC1 decreased with increasing prevalence. Considering exposure intensity and all exposed jobs, median values were 0.79 (0.68, 0.91; 96) for weighted kappa, and 0.95 (0.89, 0.99; 102) for AC2. For the additional scenarios, median kappa was, respectively, 0.28 (-0.04, 0.42) and -0.05 (-0.18, 0.09) restricted to jobs coded differently using experts' and random codes, with a similar though attenuated pattern for AC2. CONCLUSION: Despite reassuring overall reliability results, our study clearly demonstrated the loss of information associated with jobs coded differently. Especially, in cases of low exposure prevalence, efforts should be made to reliably code potentially exposed jobs.

Simultaneous modeling of detection rate and exposure concentration using semi-continuous models to identify exposure determinants when left-censored data may be a true zero.

BACKGROUND: Most methods for treating left-censored data assume the analyte is present but not quantified. Biased estimates may result if the analyte is absent such that the unobserved data represents a mixed exposure distribution with an unknown proportion clustered at zero. OBJECTIVE: We used semi-continuous models to identify time and industry trends in 52,457 OSHA inspection lead sample results. METHOD: The first component of the semi-continuous model predicted the probability of detecting concentrations ≥ 0.007 mg/m3 (highest estimated detection limit, 62% of measurements). The second component predicted the median concentration of measurements ≥ 0.007 mg/m3. Both components included a random-effect for industry and fixed-effects for year, industry group, analytical method, and other variables. We used the two components together to predict median industry- and time-specific lead concentrations. RESULTS: The probabilities of detectable concentrations and the median detected concentrations decreased with year; both were also lower for measurements analyzed for multiple (vs. one) metals and for those analyzed by inductively-coupled plasma (vs. atomic absorption spectroscopy). The covariance was 0.30 (standard error = 0.06), confirming the two components were correlated. SIGNIFICANCE: We identified determinants of exposure in data with over 60% left-censored, while accounting for correlated relationships and without assuming a distribution for the censored data.

Modeling occupational exposure to solvent vapors using the Two-Zone (near-field/far-field) model: a literature review.

The Two-Zone model is used in occupational hygiene to predict both near-field and far-field airborne contaminant concentrations. A literature review was carried out on 21 scientific publications in which the Two-Zone model was used to assess occupational exposure to solvent vapors. Data on exposure scenarios, solvents, generation/emission rates, near- and far-field parameters, and model performance were collected and analyzed. Over the 24 exposure scenarios identified, 18 were evaluated under controlled conditions, 5 under normal workplace activities, and 1 was reported based on literature data. The scenarios involved a variety of tasks which consisted, mostly, of cleaning metal parts, spraying solvents onto surfaces, spilling liquids, and filling containers with volatile substances. Twenty-eight different solvents were modeled and the most commonly tested were benzene, toluene, and acetone. Emission rates were considered constant in 16 scenarios, exponentially decreasing in 6 scenarios, and intermittent in 2 scenarios. Four-hundred-and-forty-six (446) predicted-to-measured concentration ratios were calculated across the 21 studies; 441 were obtained in controlled conditions, 4 under normal workplace activities, and 1 was calculated based on the literature data. For controlled studies, the Two-Zone model predictive performance was within a factor of 0.3-3.7 times the measured concentrations with 93% of the values between 0.5 and 2. The model overestimated the measured concentrations in 63% of the evaluations. The median predicted concentration for the near-field was 1.38 vs. 1.02 for the far-field. Results suggest that the model might be a useful tool for predicting occupational exposure to vapors of solvents by providing a conservative approach. Harmonization in model testing strategies and data presentation is needed in future studies to improve the assessment of the predictability of the Two-Zone model. Moreover, this review has provided a database of exposure scenarios, input parameter values, and model predictive performances which can be useful to occupational hygienists in their future modeling activities.

Toward Understanding of Environmental Risk Factors in Systemic Sclerosis [Formula: see text].

IMPORTANCE: Systemic sclerosis (SSc) is a severe, chronic, and incurable autoimmune fibrotic skin disease with significant extracutaneous involvement. Low concordance rate in twin studies and unequal geographic distribution of SSc argues for importance of environment in disease initiation and progression. OBJECTIVE: In this manuscript we provide a summary of all investigated potential external risk factors for SSc. DATA SOURCES: A literature search in PubMed and EMBASE database was performed for studies published until January 1, 2020 by 2 reviewers (EN and LO) independently. FINDINGS: Occupational and/or environmental exposures to silica and organic solvents are associated with increased incidence and severity of SSc. Exposure to epoxy resins, asbestos, and particulate air pollution favors increased risk of SSc, but data are based on limited number of observational studies. There is insufficient evidence to conclude an association between SSc development and other occupational (eg, welding fumes) or personal exposures (eg, smoking, vitamin D deficiency). Association of SSc with silicone breast implants has been disproven. Infectious pathogens (eg, Helicobacter pylori and angiotropic viruses) and dysbiosis seem to play a role in SSc development and severity, but their role remains to be clarified. CONCLUSIONS AND RELEVANCE: It may be prudent to counsel our patients with SSc (or those at risk of SSc) to avoid occupations with exposure to silica, organic solvents, asbestos and epoxy resins; restraint from smoking, using cocaine or drugs with pro-fibrotic potential. While the association between low vitamin D and SSc remains to be confirmed, we believe that SSc patients should be encouraged to maintain healthy vitamin D levels as benefits outweigh the risks.

Evidence of Absence: Bayesian Way to Reveal True Zeros Among Occupational Exposures.

OBJECTIVES: Workplace exposure measurements typically contain some observations below limit of detection. The current paradigm for exposure data interpretation relies on the lognormal distribution, where censored observation are assumed to be present but not quantifiable. However, there are setting were such assumptions are untenable and true zero exposures cannot be ruled out. This issue can be non-trivial because decisions about compliance depend on the adequacy of the lognormal model. METHODS: We adapted previously described statistical models for mixture of true zeros and lognormal distribution to function within Bayesian procedure that overcomes historical limitations that precluded them from being used in practice. We compared the performance of the new models and the traditional lognormal model in simulation. Their implementation is illustrated in diverse datasets. RESULTS: The approach we propose involves estimating the proportion of true zeroes, and the geometric mean and standard deviation of the lognormal component of the mixture. This can be implemented in practice either based on the truncated lognormal model fit to the observed data, or on the censored Bernoulli-lognormal mixture model, which has the advantage of allowing for multiple censoring points. Both models can be implemented via a free online application. In simulations, when none of the censored values were zeros, all estimation procedures led to similar risk assessment. However, when all or most of the censored values were zeros, the traditional approach that assumes lognormal distribution performed noticeably worse than newly proposed methods, typically overestimating noncompliance. Application to real data suggests that we cannot rule out presence of true zero exposures in typical measurement series gathered by occupational hygienists. CONCLUSIONS: Forcing the usual lognormal model to data containing a large proportion of censored values can bias risk assessment if a substantial part of the censored points are true zeroes. The Bernoulli-lognormal model is a suitable and accessible model that can account for such challenging data, and leads to unbiased risk assessments regardless of the presence of true zeros in the data.

Occupational Co-exposures to Multiple Chemical Agents from Workplace Measurements by the US Occupational Safety and Health Administration.

OBJECTIVES: The occupational environment represents an important source of exposures to multiplehazards for workers' health. Although it is recognized that mixtures of agents may have differenteffects on health compared to their individual effects, studies generally focus on the assessment ofindividual exposures. Our objective was to identify occupational co-exposures occurring in the United States using the multi-industry occupational exposure databank of the Occupational Safety and Health Administration (OSHA). METHODS: Using OSHA's Integrated Management Information System (IMIS), measurement data from workplace inspections occurring from 1979 to 2015 were examined. We defined a workplace situation (WS) by grouping measurements that occurred within a company, within the same occupation (i.e. job title) within 1 year. All agents present in each WS were listed and the resulting databank was analyzed with the Spectrosome approach, a methodology inspired by network science, to determine global patterns of co-exposures. The presence of an agent in a WS was defined either as detected, or measured above 20% of a relevant occupational exposure limit (OEL). RESULTS: Among the 334 648 detected exposure measurements of 105 distinct agents collected from 14 513 US companies, we identified 125 551 WSs, with 31% involving co-exposure. Fifty-eight agents were detected with others in >50% of WSs, 29 with a proportion >80%. Two clusters were highlighted, one for solvents and one for metals. Toluene, xylene, acetone, hexone, 2-butanone, and N-butyl acetate formed the basis of the solvent cluster. The main agents of the metal cluster were zinc, iron, lead, copper, manganese, nickel, cadmium, and chromium. 68 556 WS were included in the analyses based on levels of exposure above 20% of their OEL, with 12.4% of co-exposure. In this analysis, while the metal cluster remained, only the combinations of toluene with xylene or 2-butanone were frequently observed among solvents. An online web application allows the examination of industry specific patterns. CONCLUSIONS: We identified frequent co-exposure situations in the IMIS databank. Using the spectrome approach, we revealed global combination patterns and the agents most often implicated. Future work should endeavor to explore the toxicological effects of prevalent combinations of exposures on workers' health to prioritize research and prevention efforts.

Multi-exposures to suspected endocrine disruptors in electronic waste recycling workers: Associations with thyroid and reproductive hormones.

Electronic waste recycling (e-recycling) exposes workers to substances such as flame retardants and metals. Some of them are known or suspected endocrine disruptors that could affect hormonal homeostasis and eventually result in adverse health outcomes. Our aim was to measure biological concentrations of organophosphate ester (OPE) metabolites, polybrominated diphenyl ethers (PBDEs), mercury, lead and cadmium in e-recycling workers, and to explore associations with thyroid and sexual hormones. In a cross-sectional study, end-of-shift blood and urine spot samples were collected from 23 women and 77 men in six e-recycling facilities and one commercial recycling facility. Urinary concentrations of 15 OPE metabolites and mercury, and blood concentrations of 12 PBDE congeners, lead, cadmium, and thyroid (thyroxine [T4], triiodothyronine [T3], thyroid stimulating hormone [TSH]) and sexual (testosterone [T], estradiol, Follicle Stimulating Hormone [FSH], Luteinizing hormone [LH]) hormones were measured. E-recycling workers had higher concentrations of BDE209, all OPE metabolites, and lead than commercial recycling workers. In e-recycling workers, plasma geometric mean concentration of BDE209 was 18 ng/g lipids (geometric standard deviation [GSD]: 2.8) vs.1.7 ng/g lipids (GSD: 2.8) in commercial recycling, and urinary geometric mean concentration of diphenyl phosphate (DPhP), a major metabolite of triphenyl phosphate, was 1.7 ng/ml (GSD: 2.5), vs. 0.95 ng/ml (GSD: 2.0). In men, a two-fold increase in BDE209 was associated with 3.1% (95% Confidence interval: 0.07, 6.1) higher levels of total T4, and a two-fold increase in tert-butyl diphenyl phosphate (tb-DPhP) was associated with 18% (-29, -4.7) lower total T, 18% (-27, -6.9) lower free T and 13% (-25, 0.70) lower free T/estradiol ratio. In women, a two-fold increase in BDE153 was associated with 10% (-17, -3.2) lower free T3. To our knowledge, this is the first study to show associations between OPE metabolites and sex hormones in adults. Although some of our results are not conclusive and need replication, they suggest that prudent avoidance should be applied in risk management of flame retardants.

Bayesian Hierarchical Modelling of Individual Expert Assessments in the Development of a General-Population Job-Exposure Matrix.

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 current burden of cancer attributable to occupational exposures in Canada.

Exposure to occupational carcinogens is often overlooked as a contributor to the burden of cancer. To estimate the proportion of cancer cases attributable to occupational exposure in Canada in 2011, exposure prevalence and levels of 44 carcinogens were informed by data from the Canadian carcinogen exposure surveillance project (CAREX Canada). These were used with Canadian Census (between 1961 and 2011) and Labour Force Survey (annual surveys between 1976 and 2013) data to estimate the number of workers ever exposed to occupational carcinogens. Risk estimates of the association between each carcinogen and cancer site were selected mainly from published literature reviews. Population attributable risks were estimated using Levin's equation and applied to the 2011 cancer statistics from the Canadian Cancer Registry. It is estimated that 15.5 million Canadians alive in 2011 were exposed, during at least one year between 1961 and 2001, to at least one carcinogen in the workplace. Overall, we estimated that in 2011, between 3.9% (95% CI: 3.1%-8.1%) and 4.2% (95% CI: 3.3%-8.7%) of all incident cases of cancer were due to occupational exposure, corresponding to lower and upper numbers of 7700-21,800 cases. Five of the cancer sites - mesothelioma, non-melanoma skin cancer, lung, female breast, and urinary bladder - account for a total of 7600 to 21,200 cancers attributable to occupational exposures such as solar radiation, asbestos, diesel engine exhaust, crystalline silica, and night shift work. Our study highlights cancer sites and occupational exposures that need recognition and efforts by all stakeholders to avoid preventable cancers in the future.

Halogenated flame retardants and organophosphate esters in the air of electronic waste recycling facilities: Evidence of high concentrations and multiple exposures.

BACKGROUND: In response to a worldwide increase in production of electronic waste, the e-recycling industry is rapidly rowing. E-recycling workers are exposed to many potentially toxic contaminants, among which flame retardants (FRs), mainly suspected of being endocrine disruptors, are thought to be the most prevalent. OBJECTIVE: To conduct an exposure assessment of four chemical groups of FRs in Canadian e-recycling facilities, and to identify the main cofactors of exposure. METHODS: Personal air samples were collected over a workday for 85 workers in six e-recycling facilities, grouped into three facility sizes, and for 15 workers in control commercial waste facilities. Total particulate matter was measured by gravimetry with stationary air samples. FRs were collected on OSHA versatile samplers, which allow particulate and vapor phases collection. Fifteen polybrominated diphenyl ether congeners (PBDEs), nine novel brominated (NBFRs), two chlorinated (ClFRs), and fourteen organophosphate ester (OPEs) flame retardants were analysed by gas chromatography-mass spectrometry. Sociodemographic data, tasks performed and materials processed by participating workers were recorded. Tobit regressions were used to identify cofactors of exposure, and their conclusions were corroborated using semi-parametric reverse Cox regressions. RESULTS: Thirty-nine of the 40 FRs analysed were detected in at least one air sample in e-recycling, and workers in this industry were exposed on average to 26 (range 12 to 39) different substances. The most detected chemical group of FRs in e-recycling was PBDEs with geometric mean sums of all congeners ranging from 120 to 5100 ng/m3, followed by OPEs with 740 to 1000 ng/m3, NBFRs with 7.6 to 100 ng/m3, and finally ClFRs with 3.9 to 32 mg/m3. The most important cofactor of exposure was the size of the e-recycling facility, with the largest one presenting on average 12 times the concentrations found in the control facility. Among tasks as potential cofactors of exposure, manual dismantling and baler operation exposed workers to some of the highest concentrations of PBDEs and ClFRs. There was a reduction of up to 27% in exposure to FRs associated with a 3-year increase in seniority. Finally, particulate matter concentrations in e-recycling facilities were highly correlated with all chemical classes except OPEs, and were higher in the large facility. CONCLUSIONS: Among the FRs analysed, PBDE exposure was particularly high in e-recycling. Dust and particulate matter reduction strategies in these workplaces, together with training on proper working practices would certainly be important first steps to lower occupational exposures and prevent potential health effects.