Annals of Occupational Hygiene最新文献

Respiratory symptoms among hairdressers are often ascribed to the use of bleaching powders that contain persulfate salts. Such salts can act as allergens and airway irritants but the mechanisms behind the negative health effects are not fully known. In order to understand why some hairdressers experience respiratory symptoms during, and after, sessions of hair bleaching, it is of importance to characterize how exposure occurs. In this work we used time and particle size resolved instrumentation with the aim to measure the concentration of particles that hairdressers are exposed to during sessions of hair bleaching. We also used filter samples to collect particles for quantitative determination of persulfate (S2O8(2-)) content and for analysis by light microscopy. Two different types of bleaching powders were used, one marked as dust-free and one without this marking (denoted regular). The time resolved instrumentation revealed that particles <10 µm were emitted, specifically when the regular powder was prepared and mixed with hydrogen peroxide. In contrast to other research our work also revealed that supercoarse particles (>10 µm) were emitted during application of the bleaching, when both the regular and the dust-free powders were used. The measured level of persulfate, sampled in the breathing zone of the hairdressers, was on average 26 µg m(-3) when the regular powder was used and 11 µg m(-3) when the dust-free powder was used. This indicates that use of dust-free powder does not eliminate exposure to persulfates, it only lowers the concentration. We show that the site of sampling, or position of the hairdresser with regards to the hair being bleached, is of high importance in the determination of persulfate levels and exposure. This work focuses on the physical and chemical characterization of the particles released to the air and the results are important for accurate exposure assessments. Accurate assessments may in turn lead to a better understanding of why some hairdressers experience respiratory symptoms from hair bleaching sessions.

Cost-efficient assessments of job exposures in large populations may be obtained from models in which 'true' exposures assessed by expensive measurement methods are estimated from easily accessible and cheap predictors. Typically, the models are built on the basis of a validation study comprising 'true' exposure data as well as an extensive collection of candidate predictors from questionnaires or company data, which cannot all be included in the models due to restrictions in the degrees of freedom available for modeling. In these situations, predictors need to be selected using procedures that can identify the best possible subset of predictors among the candidates. The present study compares two strategies for selecting a set of predictor variables. One strategy relies on stepwise hypothesis testing of associations between predictors and exposure, while the other uses cluster analysis to reduce the number of predictors without relying on empirical information about the measured exposure. Both strategies were applied to the same dataset on biomechanical exposure and candidate predictors among computer users, and they were compared in terms of identified predictors of exposure as well as the resulting model fit using bootstrapped resamples of the original data. The identified predictors were, to a large part, different between the two strategies, and the initial model fit was better for the stepwise testing strategy than for the clustering approach. Internal validation of the models using bootstrap resampling with fixed predictors revealed an equally reduced model fit in resampled datasets for both strategies. However, when predictor selection was incorporated in the validation procedure for the stepwise testing strategy, the model fit was reduced to the extent that both strategies showed similar model fit. Thus, the two strategies would both be expected to perform poorly with respect to predicting biomechanical exposure in other samples of computer users.

Background: We published a meta-analysis of the association between work as a motor vehicle mechanic and mesothelioma in 2004. Since then, several relevant studies on this topic have been published. Thus, to update the state-of-the-science on this issue, we conducted a new systematic review and meta-analysis.

Methods: A comprehensive PubMed literature search through May 2014 was conducted to identify studies that reported relative risk estimates for mesothelioma among motor vehicle mechanics (in general), and those who were engaged in brake repair (specifically). Studies were scored and classified based on study characteristics. Random-effects meta-analyses generated summary relative risk estimates (SRREs) and corresponding 95% confidence intervals (CI). Heterogeneity of results was examined by calculating Q-test P-values (P-H) and I (2) estimates. Sub-group and sensitivity analyses were conducted for relevant study characteristics and quality measures.

Results: Ten case-control studies, one cohort study, and five proportionate mortality ratio (PMR)/standardized mortality odds ratio (SMOR) studies were identified and included in the quantitative assessment. Most meta-analysis models produced SRREs below 1.0, and no statistically significant increases in mesothelioma were observed. The SRRE for all studies was 0.80 (95% CI: 0.61-1.05) with significant heterogeneity (P-H <0.001, I (2) = 62.90). A similar SRRE was observed among the five Tier 1 studies with the highest quality ratings (SRRE = 0.76, 95% CI: 0.46-1.25), with no heterogeneity among studies (P-H = 0.912, I (2) = 0.00). Meta-analysis of the Tier 2 (n = 5) and Tier 3 (n = 6) studies resulted in SRREs of 1.09 (95% CI: 0.76-1.58) and 0.73 (95% CI: 0.49-1.08), respectively. Restricting the analysis to Tiers 1 and 2 combined resulted in an SRRE of 0.92 (95% CI: 0.72-1.29). The SRRE specific to brake work (n = 4) was 0.64 (95% CI: 0.38-1.09).

Conclusions: This meta-analysis of the epidemiologic studies provides evidence that motor vehicle mechanics, including workers who were engaged in brake repair, are not at an increased risk of mesothelioma.

Classical statistical methods for analyzing exposure data with values below the detection limits are well described in the occupational hygiene literature, but an evaluation of a Bayesian approach for handling such data is currently lacking. Here, we first describe a Bayesian framework for analyzing censored data. We then present the results of a simulation study conducted to compare the β-substitution method with a Bayesian method for exposure datasets drawn from lognormal distributions and mixed lognormal distributions with varying sample sizes, geometric standard deviations (GSDs), and censoring for single and multiple limits of detection. For each set of factors, estimates for the arithmetic mean (AM), geometric mean, GSD, and the 95th percentile (X0.95) of the exposure distribution were obtained. We evaluated the performance of each method using relative bias, the root mean squared error (rMSE), and coverage (the proportion of the computed 95% uncertainty intervals containing the true value). The Bayesian method using non-informative priors and the β-substitution method were generally comparable in bias and rMSE when estimating the AM and GM. For the GSD and the 95th percentile, the Bayesian method with non-informative priors was more biased and had a higher rMSE than the β-substitution method, but use of more informative priors generally improved the Bayesian method's performance, making both the bias and the rMSE more comparable to the β-substitution method. An advantage of the Bayesian method is that it provided estimates of uncertainty for these parameters of interest and good coverage, whereas the β-substitution method only provided estimates of uncertainty for the AM, and coverage was not as consistent. Selection of one or the other method depends on the needs of the practitioner, the availability of prior information, and the distribution characteristics of the measurement data. We suggest the use of Bayesian methods if the practitioner has the computational resources and prior information, as the method would generally provide accurate estimates and also provides the distributions of all of the parameters, which could be useful for making decisions in some applications.

Current recommendations for nanomaterial-specific exposure assessment require adaptation in order to be applied to complicated manufacturing settings, where a variety of particle types may contribute to the potential exposure. The purpose of this work was to evaluate a method that would allow for exposure assessment of nanostructured materials by chemical composition and size in a mixed dust setting, using carbon black (CB) and amorphous silica (AS) from tire manufacturing as an example. This method combined air sampling with a low pressure cascade impactor with analysis of elemental composition by size to quantitatively assess potential exposures in the workplace. This method was first pilot-tested in one tire manufacturing facility; air samples were collected with a Dekati Low Pressure Impactor (DLPI) during mixing where either CB or AS were used as the primary filler. Air samples were analyzed via scanning transmission electron microscopy (STEM) coupled with energy dispersive spectroscopy (EDS) to identify what fraction of particles were CB, AS, or 'other'. From this pilot study, it was determined that ~95% of all nanoscale particles were identified as CB or AS. Subsequent samples were collected with the Dekati Electrical Low Pressure Impactor (ELPI) at two tire manufacturing facilities and analyzed using the same methodology to quantify exposure to these materials. This analysis confirmed that CB and AS were the predominant nanoscale particle types in the mixing area at both facilities. Air concentrations of CB and AS ranged from ~8900 to 77600 and 400 to 22200 particles cm(-3), respectively. This method offers the potential to provide quantitative estimates of worker exposure to nanoparticles of specific materials in a mixed dust environment. With pending development of occupational exposure limits for nanomaterials, this methodology will allow occupational health and safety practitioners to estimate worker exposures to specific materials, even in scenarios where many particle types are present.

Sound pressure levels were measured on a military ship during firing of a Heavy Machine Gun (HMG). Measurements were made at three locations on the ship's bridge (the wheelhouse) and one location on the starboard bridge wing. The highest peak sound pressure levels measured on the bridge wing and on the bridge were 160.7 dB(C) (2170 Pa) and 122.7 dB(C) (27.3 Pa), respectively. The highest sound exposure levels measured on the bridge wing and on the bridge corresponding to one round being fired were 127.8 dB(A) and 88.9 dB(A), respectively. The ship's structure provided about 40 dB attenuation in the transmitted noise. The operator of the weapon would be required to wear some form of hearing protection. Based on the measured peak noise levels, there would be no requirement for bridge crew to wear any hearing protection during firing of a HMG. However, crew exposure to noise on the bridge is likely to exceed the upper exposure action value corresponding to 85 dB(A) after about 11 750 rounds.

Objective: The purposes of this study were (i) to examine the relationship between reported hearing protector use and social desirability bias, and (ii) to compare results of the Marlowe-Crowne social desirability instrument when administered using two different methods (i.e. online and by telephone).

Methods: A shortened version of the Marlowe-Crowne social desirability instrument, as well as a self-administered instrument measuring use of hearing protectors, was administered to 497 participants in a study of hearing protector use. The relationship between hearing protector use and social desirability bias was examined using regression analysis. The results of two methods of administration of the Marlowe-Crowne social desirability instrument were compared using t-tests and regression analysis.

Results: Reliability (using Cronbach's alpha) for the shortened seven-item scale for this sample was 0.58. There was no evidence of a relationship between reported hearing protector use and social desirability reporting bias, as measured by the shortened Marlowe-Crowne. The difference in results by method of administration (i.e. online, telephone) was very small.

Conclusions: This is the first published study to measure social desirability bias in reporting of hearing protector use among farmers. Findings of this study do not support the presence of social desirability bias in farmers' reporting of hearing protector use, lending support for the validity of self-report in hearing protector use in this population.

Permethrin is a synthetic pyrethroid insecticide widely used in agriculture. Farm workers are thus regularly exposed during spraying season. To help interpret routine biomonitoring results, a proper knowledge of the time courses of biomarkers of exposure is necessary. The kinetics of biomarkers of exposure to permethrin has recently been documented in volunteers exposed to permethrin under controlled conditions but there is a lack of detailed time profiles following real exposure conditions. This study aimed at obtaining data on the excretion time courses of permethrin metabolites in agricultural workers following typical exposure conditions in the field. Twelve workers exposed to permethrin were recruited from a corn production farm in the Montérégie, Quebec, Canada. They provided all their urine voided over a period of 3 days following the onset of a spraying episode of permethrin or work in a treated area. Three major metabolites of permethrin, trans- and cis- 3-(2,2-dichlorovinyl)-2,2-dimethylcyclopropane-l-carboxylic acid metabolites (trans-DCCA, cis-DCCA), and 3-phenoxybenzoic acid (3-PBA), were analyzed. For the applicator, a progressive rise in excretion values was observed with a single peak being reached 29h following the onset of the 3.5h exposure and ensuing elimination with a half-life of 6.4h for trans-DCCA and 8.7h for 3-PBA. In the other workers (supervisor and corn pickers), excretion profiles were generally more compatible with multiple entries in the treated area during the 3-day sampling period and rapid elimination between exposure episodes. In general, 3-PBA was found in slightly higher levels than trans-DCCA, except for the applicator and a harvester. For both trans-DCCA and 3-PBA in a given worker, excretion values expressed as creatinine-normalized concentrations were less variable than those expressed as excretion rates per kilogram body weight. Time-dependent variability in excretion values of workers confirms the need for serial urine sampling of at least two biomarkers of exposure, with minimally pre-exposure, end-of-shift sample the day of onset of exposure, and following morning void.

Most studies on the transmission of infectious airborne disease have focused on patient room air changes per hour (ACH) and how ACH provides pathogen dilution and removal. The logical but mostly unproven premise is that greater air change rates reduce the concentration of infectious particles and thus, the probability of airborne disease transmission. Recently, a growing body of research suggests pathways between pathogenic source (patient) and control (exhaust) may be the dominant environmental factor. While increases in airborne disease transmission have been associated with ventilation rates below 2 ACH, comparatively less data are available to quantify the benefits of higher air change rates in clinical spaces. As a result, a series of tests were conducted in an actual hospital to observe the containment and removal of respirable aerosols (0.5-10 µm) with respect to ventilation rate and directional airflow in a general patient room, and, an airborne infectious isolation room. Higher ventilation rates were not found to be proportionately effective in reducing aerosol concentrations. Specifically, increasing mechanical ventilation from 2.5 to 5.5 ACH reduced aerosol concentrations only 30% on average. However, particle concentrations were more than 40% higher in pathways between the source and exhaust as was the suspension and migration of larger particles (3-10 µm) throughout the patient room(s). Computational analyses were used to validate the experimental results, and, to further quantify the effect of ventilation rate on exhaust and deposition removal in patient rooms as well as other particle transport phenomena.

This study aims to quantify dust exposure among the workers in four different industrial settings: rice mills, flour mills, oil mills, and tea factories and to compare the obtained data with the permissible exposure limit (PEL) of Indian Union Ministry of Labour as well as to compare the dust exposure across activities and seasons. RespiCon(TM) particle sampler was used for collecting dust concentration in the breathing zone of the workers. In total, 149 workers participated in the study and 204 samples were collected. Samples were collected in the vicinity of different processing operations. Samples in the rice mills were collected for two consecutive years in two seasons; however samples from other industries were collected for 1 year. The results indicate that geometric mean (GM) of dust exposure was significantly (P < 0.0001) different among industrial settings. Respirable dust were 8.22, 5.76, 2.98, and 6.34mg m(-3) and total dust exposure were 81.05, 111.02, 56.68, and 39.85mg m(-3) in the rice mills, oil mills, flour mills, and tea factories, respectively. Considerable variations in dust exposure were observed in different activities in the rice and oil mills; however variation was relatively less in the flour mills and tea factories. In the rice mills, dust concentration was higher in winter than those obtained in autumn and it is significantly different (P < 0.05) for inhalable dust and total dust. Positive correlation was obtained in thoracic dust (r (2) = 0.94) and inhalable dust (r (2) = 0.97) with total dust and thoracic dust with inhalable dust (r (2) = 0.89). The results show that majority of the workers are exposed to higher level of respirable dust as compared to the PEL, while total dust exposure to all the workers were higher than the PEL; thus, immediate reduction of dust exposure among the workers is necessary for preventing respiratory system impairment.