Annals Of Work Exposures and Health最新文献

Drilling is a common task throughout various industry sectors. When holes are drilled in silica-containing materials such as concrete, brick, and stone, respirable dust containing respirable crystalline silica (RCS) can be generated. Prolonged exposure to RCS can lead to silicosis, lung cancer, chronic obstructive pulmonary disease. Current good control practice includes the application of local exhaust ventilation (LEV) directly to the drills. This is via an external extraction system fitted to a shroud which extracts the dust generated during drilling. For several years, integrated LEV has been available for drills. Integrated LEV offers advantages over external LEV by providing increased portability, interlocked control, reduced trip hazards and reduced costs. However, little is known regarding the control effectiveness of integrated systems. The capture efficiency of 4 different integrated extractors and 1 external extraction system for controlling dust was measured using a real time respirable dust monitor. Furthermore, for 1 drill personal air sampling was conducted to assess the exposure potential to respirable dust from drilling repeatedly into concrete over a 1-h period using no LEV, integrated LEV, and external LEV options. The capture efficiency for respirable dust for 4 integrated drill LEV units ranged between 98.6% and 99.6%. This was comparable to the respirable dust capture efficiency provided by a self-sealing shroud fitted to an external extraction unit which provided efficiencies between 99.4% and 99.8%. The personal exposure testing showed respirable dust exposure was reduced by 87.6% for integrated LEV and 93.3% for external LEV fitted with a self-sealing shroud. The reason for the lower efficiency for the integrated LEV was attributed to dust generated as their dust bins were emptied more often throughout the tests. Given the size of the integrated LEV dust bins and the frequency at which they may require emptying, appropriate control measures to protect workers from dust exposure should be considered-eg emptying the units outdoors in conjunction with RPE.

Background: Antibiotics are handled in large amounts at hospitals at many different wards due to the wide range of bacterial infections that are treated. Unnecessary use and occupational exposure to antibiotics should be avoided due to the risk of bacterial resistance development and adverse health effects including skin and respiratory hypersensitivity reactions in persons handling these drugs.

Objectives: To develop a wipe test method for sampling and quantification of surface contaminations of antibiotics, to assess the current contamination levels in Swedish hospitals, and to propose hygienic guidance values for antibiotics based on these measurements.

Methods: A screening wipe test method and subsequent mass spectrometric analysis of 6 of the most frequently used antibiotics in healthcare was developed and applied in a screening campaign of 16 hospital wards. Wipe tests were sampled from surfaces such as workbenches, floors, storage shelves and handles in medicine rooms, patient rooms, rinsing rooms, utility rooms and corridors.

Results: Antibiotics were detected in most of the samples (cefotaxime 84% positive samples, piperacillin 81%, cloxacillin 65%, metronidazole 53%, ciprofloxacin 20%, and penicillin V 14%). Median values ranged from not detected up to 160 pg/cm2 for the 6 different compounds and the highest results from an individual wipe sample were 27 × 106 pg/cm2 (cefotaxime) and 3.0 × 106 pg/cm2 (piperacillin). For cloxacillin, piperacillin, and metronidazole, lower levels of contamination were observed in medicine rooms when closed systems were used compared with samples collected in rooms where preparations were made without closed systems. Comparison of contamination levels showed that there were significant differences between different surface categories. Out of the most frequently detected antibiotics, ie cloxacillin, piperacillin, and cefotaxime, highest median values were found for surface categories floor and storage whereas lower median values were found for workbenches.

Conclusion: A widespread environmental contamination of antibiotics was observed in hospital wards that potentially can contribute to the development of antibiotic-resistant bacteria as well as health impacts of exposed personnel. Probable sources include compounding, handling and administration of drug tablets, antibiotic contaminated waste as well as other sources such as excretions from patients and contaminated drug vials. Current surface cleaning routines do not sufficiently reduce spills and leakage regardless of source.

Swine workers may be occupationally exposed to Staphylococcus aureus (S. aureus) during time spent inside swine barns. Exposure may occur by inhaling S. aureus-containing particles or by touching contaminated surfaces or infected animals. Despite strong evidence that swine production work is a risk factor for increased nasal carriage of S. aureus, pathways of worker exposure within the swine barn setting have not been well characterized. We developed a Markov chain model to address this research gap by first describing the fate and transport of S. aureus-containing particles within a swine finishing barn. We defined 7 possible physical locations in and around the barn in which S. aureus-containing particles may exist and used published data to determine the probability that a particle will transition from any of these locations to the other locations during a 1-s time interval. We then used our model to estimate worker exposure to S. aureus during a period of 1 s to 30 min spent inside the swine barn. Finally, we modified inputs to simulate interventions to protect workers, such as ventilation controls, respirator use, and handwashing. Increasing the ventilation rate (ie the rate at which outdoor air replaces indoor air in the barn) in our model from the recommended rate for cold weather to the rate for mild weather resulted in a 59% decrease in the number of S. aureus-containing particles in the worker's respiratory system after 30 min. Increasing ventilation rates further to the recommended rate for hot weather resulted in an additional 58% decrease. Models simulating floor and surface cleaning prior to the worker's entry into the barn had little impact on the air concentration of S. aureus (<1% change) but reduced worker exposure to facial membranes by up to 13%. Simulations of N-95 respirator wearing had the greatest impact on worker exposure. As modeled, a well-fitting N-95 respirator may reduce worker inhalation exposure from 1,772 to 72 S. aureus-containing particles after 30 min in the barn, a 96% reduction. In our model, a poorly fitting N-95 respirator reduced exposure by about 30%, indicating that the type and fit of respirator worn has an important impact on the level worker protection.

Objectives: Studies addressing the epidemiology of silicosis in the artificial stone benchtop industry have shown that this industry includes a large number of migrant workers in Victoria, Australia. The objective of the current analyses was to compare characteristics of migrant workers in the industry with nonmigrant workers.

Methods: Data were derived from artificial stone benchtop workers who had health assessments through a regulator-funded screening program between 2019 and 2024. Migrant workers were defined as workers born outside Australia or had used an interpreter during the assessment. Participant characteristics, lung function, and silicosis prevalence were summarized by migrant status and compared between groups.

Results: There were 1,040 workers (n = 536 migrant workers). Migrant workers were older at assessment than nonmigrant workers (median age 39 versus 33 years, P < 0.001). About 1 quarter of migrant workers used an interpreter (23.8%) and 52% spoke English at home. Silicosis prevalence was higher in migrant compared with nonmigrant workers (23% versus 15%, risk-ratio 1.54, and 95% confidence interval 1.16 to 2.04) and migrant workers who used an interpreter had double the risk of silicosis than those who did not (46% versus 18%, risk-ratio 2.24, 95% confidence interval 1.61 to 3.10). Prelegislative changes, experience of carrying out dry processing was reportedly higher in nonmigrant than migrant workers. Fewer jobs among migrant workers than nonmigrant workers were reported using recommended respirators (44 versus 53%) or ventilation (24% versus 30%).

Conclusions: The risk of silicosis in the artificial stone benchtop industry differed by migration status and was higher among those with lower English language proficiency. As the use of appropriate respirators or ventilation was lower among migrant workers, this suggests the need for improved occupational health and safety practices among migrant workers, making sure the messages are communicated in a manner that is language and culturally appropriate.

Occupational exposure to airborne particles and bioaerosols in dental clinics is a potential hazard to dental health workers. Current studies on airborne particles and bioaerosols in dental clinics are limited and methodologically diverse, leaving gaps in the understanding of airborne particles in real-life dental settings. The aim of the study was to investigate the size, concentration, and composition of particles produced during dental procedures, and determine the exposure levels of dental personnel to respirable particles and bioaerosols in dental clinical environments with different characteristics. The study included two conventional dentist offices and one specialty clinic. The number concentration and size distribution of particles released during different dental procedures were monitored in real-time in dental procedure rooms. Personal samplers were used in parallel to collect the respirable and inhalable particle fractions. Total bacterial and total fungal DNA concentrations were quantified in the inhalable particle fraction by droplet digital polymerase chain reaction. Particle morphology and chemical composition were analyzed using scanning electron microscopy. The highest geometric mean value of the respirable particle mass concentration (0.06 mg/m3) was below the Norwegian occupational exposure limit for respirable dust of 5 mg/m3. Real-time sampling indicated that particle number concentrations were elevated during working hours in two clinics, with peak levels observed in one clinic coinciding with air polishing activities. The results also showed significant variations in bacterial and fungal DNA concentration levels (P < 0.0001). Many collected particles originated from powders used in dental treatments. Despite low respirable particle mass concentrations, increased levels of ultrafine particles during dental procedures highlight potential health risks to dental professionals. These findings also underscore the importance of advanced ventilation and safety measures to mitigate occupational exposure in dental environments.

Objectives: Exposure determinant modeling can help industrial hygienists understand where, when, and how to control occupational exposures for their particular work environment. Yet, in practice, the ability to evaluate exposure determinants is degraded by selection bias (where only a subset of all exposed workers is sampled) and the statistical issue of "small n, large p" (few samples but many exposure determinants). This study explored the application of the causal inference framework and machine learning algorithms in exposure determinant modeling using a "small n, large p" example of potential determinants of heavy metal concentrations among informal electronic waste recycling workers.

Methods: As a case study, we used a multivariable logistic regression model to construct inverse probability weights to account for selection bias into a video substudy of 41 of 226 possible workers monitored for exposures to heavy metals. Forty-four determinants of biomarkers (eg tool use, job tasks, and personal protective equipment use) were quantified through video monitoring. Concentrations of heavy metals in blood (Pb and Mn) and urine (Ni and Cu) were sampled. We identified the best-performing biomarker determinant model by comparing the leave-one-out cross-validation root-mean-squared error (LOOCV-RMSE) of 5 models: 2 traditional models (multivariate linear regression and forward selection), and 3 machine learning algorithms (LASSO, boosted regression trees, and random forests). Using the best-performing model, we estimated reductions in heavy metal concentrations through hypothetical workplace controls to identify the most important determinant of biomarker concentrations.

Results: The random forest model had the lowest LOOCV-RMSE and was used as the final biomarker determinant model. Stopping workers from bending their backs while dismantling e-waste was the most important determinant of heavy metal concentrations. Using blood Pb as an example, this translated to an estimated reduction of 0.81 µg/dL (95% confidence interval: 0.66, 0.98) in comparison with maintaining the status quo. Using a traditional regression model (forward selection without inverse probability weights), back bending was not identified as an important determinant of blood Pb.

Discussion: Our causal inference approach with machine learning algorithms overcomes the common limitations of exposure determinant modeling and produces easy-to-interpret estimates of biomarker concentration reductions from hypothetical workplace controls. This can aid industrial hygienists in choosing the most effective hazard controls that can be contextualized to their particular work setting.

Background: During the Deepwater Horizon disaster in 2010, oil spill response and cleanup (OSRC) workers were exposed to crude oil, including benzene, toluene, ethylbenzene, xylene, and n-hexane (BTEX-H). Growing evidence links these exposures to cardiovascular disease (CVD). Lipid and C-reactive protein levels are used to assess CVD risk and may serve as mediators of the observed associations with CVD. However, few studies have assessed associations of oil spill cleanup-related exposures with blood levels of lipids and C-reactive protein (CRP).

Objective: This study examined associations of oil spill cleanup-related exposure to each individual BTEX-H chemical, total (aggregate sum) BTEX-H, and the BTEX-H mixture with blood lipids and CRP among OSRC workers in the Gulf Long-term Follow-up (GuLF) Study.

Methods: Subjects comprised 2,544 OSRC workers who completed a home visit (May 2011 to May 2013) and had CVD biomarker measurements. Cumulative exposures to BTEX-H (ppb-days) were estimated using a job-exposure matrix that linked air measurements with self-reported Deepwater Horizon work histories. Study biomarkers were lipids, including low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), total cholesterol, and high-sensitivity CRP (hsCRP), which were measured in blood samples collected during the home visit. Multivariable linear regression was used to estimate mean differences and 95% confidence intervals (CI) for associations of quartiles of BTEX-H with lipid and hsCRP levels. We log-transformed hsCRP due to a non-normal distribution. We used quantile g-computation to assess the joint effect of the BTEX-H mixture.

Results: Each BTEX-H chemical was associated with elevations in total cholesterol up to 3 yr after exposure, with the strongest effect estimates in the top quartile, ranging from 2.3 to 5.1 mg/dL. A one quartile simultaneous increase in the BTEX-H mixture was associated with a 1.7 mg/dL increase in total cholesterol. While trends were less consistent for hsCRP, most estimates were above the null and a one quartile increase in exposure to the BTEX-H mixture was associated with a 3% increase in hsCRP.

Conclusion: Our study suggests that oil spill cleanup-related BTEX-H exposures were associated with elevations in some CVD biomarkers.