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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., (© The Author(s) 2022. Published by Oxford University Press on behalf of the British Occupational Hygiene Society.)

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., (© The Author(s) 2020. Published by Oxford University Press on behalf of the British Occupational Hygiene Society.)

Background: The aim was to investigate possible associations between glioma (an aggressive type of brain cancer) and occupational exposure to selected agents: combustion products (diesel and gasoline exhaust emissions, benzo(a)pyrene), dusts (animal dust, asbestos, crystalline silica, wood dust) and some other chemical agents (formaldehyde, oil mist, sulphur dioxide)., Methods: The INTEROCC study included cases diagnosed with glioma during 2000-2004 in sub-regions of seven countries. Population controls, selected from various sampling frames in different centers, were frequency or individually matched to cases by sex, age and center. Face-to-face interviews with the subject or a proxy respondent were conducted by trained interviewers. Detailed information was collected on socio-economic and lifestyle characteristics, medical history and work history. Occupational exposure to the 10 selected agents was assessed by a job exposure matrix (JEM) which provides estimates of the probability and level of exposure for different occupations. Using a 25% probability of exposure in a given occupation in the JEM as the threshold for considering a worker exposed, the lifetime prevalence of exposure varied from about 1% to about 15% for the different agents. Associations between glioma and each of the 10 agents were estimated by conditional logistic regression, and using three separate exposure indices: i) ever vs. never; ii) lifetime cumulative exposure; iii) total duration of exposure., Results: The study sample consisted of 1,800 glioma cases and 5,160 controls. Most odds ratio estimates were close to the null value. None of the ten agents displayed a significantly increased odds ratio nor any indication of dose-response relationships with cumulative exposure or with duration of exposure., Conclusion: Thus, there was no evidence that these exposures influence risk of glioma.

National occupational exposure databanks have been cited as sources of exposure data for exposure surveillance and exposure assessment for occupational epidemiology. Formaldehyde exposure data recorded in the U.S Integrated Management Information System (IMIS) between 1979 and 2001 were collected to elaborate a multi-industry retrospective picture of formaldehyde exposures and to identify exposure determinants. Due to the database design, only detected personal measurement results (n = 5228) were analyzed with linear mixed-effect models, which explained 29% of the total variance. Short-term measurement results were higher than time-weighted average (TWA) data and decreased 18% per year until 1987 (TWA data 5% per year) and 5% per year (TWA data 4% per year) after that. Exposure varied across industries with maximal estimated TWA geometric means (GM) for 2001 in the reconstituted wood products, structural wood members, and wood dimension and flooring industries (GM = 0.20 mg/m(3). Highest short-term GMs estimated for 2001 were in the funeral service and crematory and reconstituted wood products industries (GM = 0.35 mg/m(3). Exposure levels in IMIS were marginally higher during nonprogrammed inspections compared with programmed inspections. An increasing exterior temperature tended to cause a decrease in exposure levels for cold temperatures (-5% per 5 degrees C for T < 15 degrees C) but caused an increase in exposure levels for warm temperatures (+15% per 5 degrees C for T >15 degrees C). Concentrations measured during the same inspection were correlated and varied differently across industries and sample type (TWA, short term). Sensitivity analyses using TOBIT regression suggested that the average bias caused by excluding non-detects is approximately 30%, being potentially higher for short-term data if many non-detects were actually short-term measurements. Although limited by availability of relevant exposure determinants and potential selection biases in IMIS, these results provide useful insight on formaldehyde occupational exposure in the United States in the last two decades. The authors recommend that more information on exposure determinants be recorded in IMIS.