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).

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.

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.

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.

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.

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.

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.

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.

A hybrid expert approach for retrospective assessment of occupational exposures in a population-based case-control study of cancer.

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.

Estimating the population prevalence of traditional and novel occupational exposures in Federal Region X.

OBJECTIVE: Federal Region X is an administrative region in the northwestern United States comprised of the states of Alaska (AK), Idaho (ID), Oregon (OR), and Washington (WA). Quantifying the number of workers in this region exposed to harmful circumstances in the workplace, and projected changes over time will help to inform priorities for occupational health training, risk reduction, and research. METHODS: State data for WA, ID, OR, and AK were used to estimate number of workers by occupation, in 2014 and 2024. These data were merged with a Canadian job-exposure matrix (CANJEM) which characterizes chemical exposures, and O*NET, which ranks occupations with particular physical, ergonomic, and psychosocial exposures. RESULTS: Of the exposures considered, psychosocial and ergonomic exposures were the most prevalent among the regional workforce, though traditional chemical exposures are still common and increasing. CONCLUSIONS: Exposure surveillance will inform prioritization of risk reduction strategies, ultimately leading to a decrease in occupational injury and illness. Findings from this analysis will help to prioritize occupational health training and research in the region.

Correction of odds ratios in case-control studies for exposure misclassification with partial knowledge of the degree of agreement among experts who assessed exposures.

OBJECTIVES: Estimates of association between exposures and diseases are often distorted by error in exposure classification. When the validity of exposure assessment is known, this can be used to adjust these estimates. When exposure is assessed by experts, even if validity is not known, we sometimes have information about interrater reliability. We present a Bayesian method for translating the knowledge of interrater reliability, which is often available, into knowledge about validity, which is often needed but not directly available, and applying this to correct odds ratios (OR). METHODS: The method allows for inclusion of observed potential confounders in the analysis, as is common in regression-based control for confounding. Our method uses a novel type of prior on sensitivity and specificity. The approach is illustrated with data from a case-control study of lung cancer risk and occupational exposure to diesel engine emissions, in which exposure assessment was made by detailed job history interviews with study subjects followed by expert judgement. RESULTS: Using interrater agreement measured by kappas (κ), we estimate sensitivity and specificity of exposure assessment and derive misclassification-corrected confounder-adjusted OR. Misclassification-corrected and confounder-adjusted OR obtained with the most defensible prior had a posterior distribution centre of 1.6 with 95% credible interval (Crl) 1.1 to 2.6. This was on average greater in magnitude than frequentist point estimate of 1.3 (95% Crl 1.0 to 1.7). CONCLUSIONS: The method yields insights into the degree of exposure misclassification and appears to reduce attenuation bias due to misclassification of exposure while the estimated uncertainty increased.

Occupational exposures to leaded and unleaded gasoline engine emissions and lung cancer risk.

OBJECTIVES: To determine whether occupational exposure to gasoline engine emissions (GEE) increased the risk of lung cancer and more specifically whether leaded or unleaded GEE increased the risk. METHODS: Two population-based case-control studies were conducted in Montreal, Canada. The first was conducted in the early 1980s and included many types of cancer including lung cancer. The second was conducted in the late 1990s and focused on lung cancer. Population controls were used in both studies. Altogether, there were 1595 cases and 1432 population controls. A comprehensive expert-based exposure assessment procedure was implemented and exposure was assessed for 294 agents, including unleaded GEE, leaded GEE and diesel engine emissions (DEE). Logistic regression analyses were conducted to estimate ORs between various metrics of GEE exposure and lung cancer, adjusting for smoking, DEE and other potential confounders. RESULTS: About half of all controls were occupationally exposed to GEE. Irrespective of the metrics of exposure (any exposure, duration of exposure and cumulative exposure) and the type of lung cancer, and the covariates included in models, none of the point estimates of the ORs between occupational exposure to leaded or unleaded GEE and lung cancer were above 1.0. Pooling two studies, the OR for any exposure to leaded GEE was 0.82 (0.68-1.00). CONCLUSIONS: Our results do not support the hypothesis that occupational exposure to GEE increases the risk of lung cancer.

Burden of lung cancer attributable to occupational diesel engine exhaust exposure in Canada.

OBJECTIVE: To estimate the population attributable fraction (PAF) and number of incident and fatal lung cancers in Canada from occupational exposure to diesel engine exhaust (DEE). METHODS: DEE exposure prevalence and level estimates were used with Canadian Census and Labour Force Survey data to model the exposed population across the risk exposure period (REP, 1961-2001). Relative risks of lung cancer were calculated based on a meta-regression selected from the literature. PAFs were calculated using Levin's equation and applied to the 2011 lung cancer statistics obtained from the Canadian Cancer Registry. RESULTS: We estimated that 2.4% (95% CI 1.6% to 6.6%) of lung cancers in Canada are attributable to occupational DEE exposure, corresponding to approximately 560 (95% CI 380 to 1570) incident and 460 (95% CI 310 to 1270) fatal lung cancers in 2011. Overall, 1.6 million individuals alive in 2011 were occupationally exposed to DEE during the REP, 97% of whom were male. Occupations with the highest burden were underground miners, truck drivers and mechanics. Half of the attributable lung cancers occurred among workers with low exposure. CONCLUSIONS: This is the first study to quantify the burden of lung cancer attributable to occupational DEE exposure in Canada. Our results underscore a large potential for prevention, and a large public health impact from occupational exposure to low levels of DEE.

Diesel engine exhaust exposure in underground mines: Comparison between different surrogates of particulate exposure.

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 (EC1;Airtec). The association between ambient EC and the DRI was assessed by Spearman correlation. Geometric mean concentrations of RCD, respirable TC (TCR) and respirable elemental EC (ECR) were 170 µg/m3, 148 µg/m3, and 83 µg/m3 for personal samples, and 197 µg/m3, 151 µg/m3, and 100 µg/m3 for ambient samples. Personal measurements had higher TCR:ECR ratios compared to ambient samples (1.8 vs. 1.50) and weaker association between ECR and TCR. Among the DRI, the measures of EC1 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.

Occupational exposure to pesticides and other biocides and risk of thyroid cancer.

OBJECTIVES: To assess the associations between occupational exposure to biocides and pesticides and risk of thyroid cancer. METHODS: Using data from a population-based case-control study involving 462 incident thyroid cancer cases and 498 controls in Connecticut collected in 2010-2011, we examined the association with occupational exposure to biocides and pesticides through a job-exposure matrix. We used unconditional logistic regression models to estimate OR and 95% CI, adjusting for potential confounders. RESULTS: Individuals who were occupationally ever exposed to biocides had an increased risk of thyroid cancer (OR=1.65, 95% CI 1.16 to 2.35), and the highest risk was observed for the high cumulative probability of exposure (OR=2.18, 95% CI 1.28 to 3.73). The observed associations were similar when we restricted to papillary thyroid cancer and well-differentiated thyroid cancer. Stronger associations were observed for thyroid microcarcinomas (tumour size ≤1 cm). No significant association was observed for occupational exposure to pesticides. CONCLUSIONS: Our study provides the first evidence linking occupational exposure to biocides and risk of thyroid cancer. The results warrant further investigation.

Lifetime occupational exposure to metals and welding fumes, and risk of glioma: a 7-country population-based case-control study.

BACKGROUND: Brain tumor etiology is poorly understood. Based on their ability to pass through the blood-brain barrier, it has been hypothesized that exposure to metals may increase the risk of brain cancer. Results from the few epidemiological studies on this issue are limited and inconsistent. METHODS: We investigated the relationship between glioma risk and occupational exposure to five metals - lead, cadmium, nickel, chromium and iron- as well as to welding fumes, using data from the seven-country INTEROCC study. A total of 1800 incident glioma cases and 5160 controls aged 30-69 years were included in the analysis. Lifetime occupational exposure to the agents was assessed using the INTEROCC JEM, a modified version of the Finnish job exposure matrix FINJEM. RESULTS: In general, cases had a slightly higher prevalence of exposure to the various metals and welding fumes than did controls, with the prevalence among ever exposed ranging between 1.7 and 2.2% for cadmium to 10.2 and 13.6% for iron among controls and cases, respectively. However, in multivariable logistic regression analyses, there was no association between ever exposure to any of the agents and risk of glioma with odds ratios (95% confidence intervals) ranging from 0.8 (0.7-1.0) for lead to 1.1 (0.7-1.6) for cadmium. Results were consistent across models considering cumulative exposure or duration, as well as in all sensitivity analyses conducted. CONCLUSIONS: Findings from this large-scale international study provide no evidence for an association between occupational exposure to any of the metals under scrutiny or welding fumes, and risk of glioma.

Impact of aggregating exposure information from cases and controls when building a population-based job-exposure matrix from past expert evaluations.

OBJECTIVES: To assess whether the inclusion of data from cases would bias a job-exposure matrix (JEM), we evaluated whether exposures were systematically different between cases and controls from a large historical case-control study. METHODS: Data included 10 381 jobs assessed for occupational exposure to 294 agents within a lung cancer case-control study. For each sex, 1 JEM was developed from case jobs, and 1 from control jobs: with occupation (four-digit occupational codes), time period (1945-1959, 1960-1984, 1985-1995) and agent axes. We estimated concordance in exposure status (defined as probability of exposure threshold ≥5%) and exposure metrics of probability and intensity of exposure, between the 2 JEMs. RESULTS: Of all hypothetical occupation-period-agent combinations, most had no or few observations. Among males there were 8136 common cells (24-occupational codes, 3-periods, 226-agents), containing sufficient observations for comparison with 92% concordance in exposure status; discordance was equally likely to be towards cases or controls. Females had 1710 common cells (9-occupational codes, 3-periods, 114-agents) with 93% concordance in exposure status; discordant cells were more likely to reflect greater exposure among cases. Among concordantly exposed cells, probability and intensity of exposures were highly correlated between the case JEM and control JEM (Kendall τ>0.50), and absolute differences were small (median difference in probability <1.5%, median ratio in intensity=1.00) for both sexes. CONCLUSIONS: Agreement between the case JEM and control JEM was high, suggesting that aggregating the case and control information in our study into a single JEM is justifiable given the benefits of increased sample size.

Occupation, industry, and the risk of prostate cancer: a case-control study in Montréal, Canada.

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.

Trends in OSHA Compliance Monitoring Data 1979-2011: Statistical Modeling of Ancillary Information across 77 Chemicals.

OBJECTIVES: The Integrated Management Information System (IMIS) is the largest multi-industry source of exposure measurements available in North America. However, many have suspected that the criteria through which worksites are selected for inspection are related to exposure levels. We investigated associations between exposure levels and ancillary variables in IMIS in order to understand the predictors of high exposure within an enforcement context. METHODS: We analyzed the association between nine variables (reason for inspection, establishment size, total amount of penalty, Occupational Safety and Health Administration (OSHA) plan, OSHA region, union status, inspection scope, year, and industry) and exposure levels in IMIS using multimodel inference for 77 agents. For each agent, we used two different types of models: (i) logistic models were used for the odds ratio (OR) of exposure being above the threshold limit value (TLV) and (ii) linear models were used for exposure concentrations restricted to detected results to estimate percent increase in exposure level, i.e. relative index of exposure (RIE). Meta-analytic methods were used to combine results for each variable across agents. RESULTS: A total of 511,047 exposure measurements were modeled for logistic models and 299,791 for linear models. Higher exposures were measured during follow-up inspections than planned inspections [meta-OR = 1.61, 95% confidence interval (CI): 1.44-1.81; meta-RIE = 1.06, 95% CI: 1.03-1.09]. Lower exposures were observed for measurements collected under state OSHA plans compared to measurements collected under federal OSHA (meta-OR = 0.82, 95% CI: 0.73-0.92; meta-RIE = 0.86, 95% CI: 0.81-0.91). A 'high' total historical amount of penalty relative to none was associated with higher exposures (meta-OR = 1.54, 95% CI: 1.40-1.71; meta-RIE = 1.18, 95% CI: 1.13-1.23). CONCLUSIONS: The relationships observed between exposure levels and ancillary variables across a vast majority of agents suggest that certain elements of OSHA's process of selecting worksites for inspection influence the exposure levels that OSHA inspectors encounter. Nonetheless, given the paucity of other sources of exposure data and the lack of a more demonstrably representative data source, our study considers the use of IMIS data for the estimation of exposures in the broader universe of worksites in the USA.

A Web-based Tool to Aid the Identification of Chemicals Potentially Posing a Health Risk through Percutaneous Exposure.

Occupational hygiene practitioners typically assess the risk posed by occupational exposure by comparing exposure measurements to regulatory occupational exposure limits (OELs). In most jurisdictions, OELs are only available for exposure by the inhalation pathway. Skin notations are used to indicate substances for which dermal exposure may lead to health effects. However, these notations are either present or absent and provide no indication of acceptable levels of exposure. Furthermore, the methodology and framework for assigning skin notation differ widely across jurisdictions resulting in inconsistencies in the substances that carry notations. The UPERCUT tool was developed in response to these limitations. It helps occupational health stakeholders to assess the hazard associated with dermal exposure to chemicals. UPERCUT integrates dermal quantitative structure-activity relationships (QSARs) and toxicological data to provide users with a skin hazard index called the dermal hazard ratio (DHR) for the substance and scenario of interest. The DHR is the ratio between the estimated 'received' dose and the 'acceptable' dose. The 'received' dose is estimated using physico-chemical data and information on the exposure scenario provided by the user (body parts exposure and exposure duration), and the 'acceptable' dose is estimated using inhalation OELs and toxicological data. The uncertainty surrounding the DHR is estimated with Monte Carlo simulation. Additional information on the selected substances includes intrinsic skin permeation potential of the substance and the existence of skin notations. UPERCUT is the only available tool that estimates the absorbed dose and compares this to an acceptable dose. In the absence of dermal OELs it provides a systematic and simple approach for screening dermal exposure scenarios for 1686 substances.