Dataset Information

TREXMO plus: an advanced self-learning model for occupational exposure assessment.

ABSTRACT: In Europe, several occupational exposure models have been developed and are recommended for regulatory exposure assessment. Only some information on the substance of interest (e.g., vapor pressure) and the workplace conditions (e.g., ventilation rate) is required in these models to predict an exposure value that will be later used to characterize the risk. However, it has been shown that models may differ in their predictions and that usually, one of the models best fits a given set of exposure conditions. Unfortunately, there are no clear rules on how to select the best model. In this study, we developed a new modeling approach that together uses the three most popular models, Advanced REACH Tool, Stoffenmanger, and ECETOC TRAv3, to obtain a unique exposure prediction. This approach is an extension of the TREXMO tool, and is called TREXMO+. TREXMO+ applies a machine-learning technique on a set of exposure data with the measured values to split them into smaller subsets, corresponding to exposure conditions sharing similar characteristics. For each subset, TREXMO+ then establishes a regression model with the three REACH tools used as the exposure predictors. The performance of the new model was tested and a comparison was made between the results obtained by TREXMO+ and those obtained by conventional tools. TREXMO+ model was found to be less biased and more accurate than the REACH models. Its prediction differs generally from measurements by a factor of 2-3 from measurements, whereas conventional models were found to differ by a factor 2-14. However, as the available test dataset is limited, its results will need to be confirmed by larger-scale tests.

SUBMITTER: Savic N

PROVIDER: S-EPMC7397525 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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TREXMO plus: an advanced self-learning model for occupational exposure assessment.

Savic Nenad N Lee Eun Gyung EG Gasic Bojan B Vernez David D

Journal of exposure science & environmental epidemiology 20200203 3

In Europe, several occupational exposure models have been developed and are recommended for regulatory exposure assessment. Only some information on the substance of interest (e.g., vapor pressure) and the workplace conditions (e.g., ventilation rate) is required in these models to predict an exposure value that will be later used to characterize the risk. However, it has been shown that models may differ in their predictions and that usually, one of the models best fits a given set of exposure ...[more]

PMID: 32015431

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Project description:To estimate the current occupational cancer burden due to past exposures in Britain, estimates of the number of exposed workers at different levels are required, as well as risk estimates of cancer due to the exposures. This paper describes the methods and results for estimating the historical exposures. All occupational carcinogens or exposure circumstances classified by the International Agency for Research on Cancer as definite or probable human carcinogens and potentially to be found in British workplaces over the past 20-40 years were included in this study. Estimates of the number of people exposed by industrial sector were based predominantly on two sources of data, the CARcinogen EXposure (CAREX) database and the UK Labour Force Survey. Where possible, multiple and overlapping exposures were taken into account. Dose-response risk estimates were generally not available in the epidemiological literature for the cancer-exposure pairs in this study, and none of the sources available for obtaining the numbers exposed provided data by different levels of exposure. Industrial sectors were therefore assigned using expert judgement to 'higher'- and 'lower'-exposure groups based on the similarity of exposure to the population in the key epidemiological studies from which risk estimates had been selected. Estimates of historical exposure prevalence were obtained for 41 carcinogens or occupational circumstances. These include exposures to chemicals and metals, combustion products, other mixtures or groups of chemicals, mineral and biological dusts, physical agents and work patterns, as well as occupations and industries that have been associated with increased risk of cancer, but for which the causative agents are unknown. There were more than half a million workers exposed to each of six carcinogens (radon, solar radiation, crystalline silica, mineral oils, non-arsenical insecticides and 2,3,7,8-tetrachlorodibenzo-p-dioxin); other agents to which a large number of workers are exposed included benzene, diesel engine exhaust and environmental tobacco smoke. The study has highlighted several industrial sectors with large proportions of workers potentially exposed to multiple carcinogens. The relevant available data have been used to generate estimates of the prevalence of past exposure to occupational carcinogens to enable the occupational cancer burden in Britain to be estimated. These data are considered adequate for the present purpose, but new data on the prevalence and intensity of current occupational exposure to carcinogens should be collected to ensure that future policy decisions be based on reliable evidence.

Project description:ObjectivesThe Network on the Coordination and Harmonisation of European Occupational Cohorts (OMEGA-NET) was set up to enable optimization of the use of industrial and general population cohorts across Europe to advance aetiological research. High-quality harmonized exposure assessment is crucial to derive comparable results and to enable pooled analyses. To facilitate a harmonized research strategy, a concerted effort is needed to catalogue available occupational exposure information. We here aim to provide a first comprehensive overview of exposure assessment tools that could be used for occupational epidemiological studies.MethodsAn online inventory was set up to collect meta-data on exposure assessment tools. Occupational health researchers were invited via newsletters, editorials, and individual e-mails to provide details of job-exposure matrices (JEMs), exposure databases, and occupational coding systems and their associated crosswalks to translate codes between different systems, with a focus on Europe.ResultsMeta-data on 36 general population JEMs, 11 exposure databases, and 29 occupational coding systems from more than 10 countries have been collected up to August 2021. A wide variety of exposures were covered in the JEMs on which data were entered, with dusts and fibres (in 14 JEMs) being the most common types. Fewer JEMs covered organization of work (5) and biological factors (4). Dusts and fibres were also the most common exposures included in the databases (7 out of 11), followed by solvents and pesticides (both in 6 databases).ConclusionsThis inventory forms the basis for a searchable web-based database of meta-data on existing occupational exposure information, to support researchers in finding the available tools for assessing occupational exposures in their cohorts, and future efforts for harmonization of exposure assessment. This inventory remains open for further additions, to enlarge its coverage and include newly developed tools.

Project description:PurposeThis study aims to perform a classification and rigorous numerical evaluation of the risks of occupational exposure in the health environment related to the administration of transcranial magnetic stimulation (TMS) treatment. The study investigates the numerically estimated induced electric field that occurs in the human tissues of an operator caused by exposure to the variable magnetic field produced by TMS during treatments. This could be a useful starting point for future risk assessment studies and safety indications in this context.MethodsWe performed a review of the actual positions assumed by clinicians during TMS treatments. Three different TMS coils (two circular and one figure-of-eight) were modeled and characterized numerically. Different orientations and positions of each coil with respect to the body of the operator were investigated to evaluate the induced electric (-E) field in the body tissues. The collected data were processed to allow comparison with the safety standards for occupational exposure, as suggested by the International Commission on Non-Ionizing Radiation Protection (ICNIRP) 2010 guidelines.ResultsUnder the investigated conditions, exposure to TMS shows some criticalities for the operator performing the treatment. Depending on the model of the TMS coil and its relative position with respect to the operator's body, the numerically estimated E-field could exceed the limits suggested by the ICNIRP 2010 guidelines. We established that the worst-case scenario for the three coils occurs when they are placed in correspondence of the abdomen, with the handle oriented parallel to the body (II orientation). Working at a maximum TMS stimulator output (MSO), the induced E-field is up to 7.32 V/m (circular coil) and up to 1.34 V/m (figure-of-eight coil). The induced E-field can be modulated by the TMS percentage of MSO (%MSO) and by the distance between the source and the operator. At %MSO equal to or below 80%, the figure-of-eight coil was compliant with the ICNIRP limit (1.13 V/m). Conversely, the circular coil causes an induced E-field above the limits, even when powered at a %MSO of 30%. Thus, in the investigated worst-case conditions, an operator working with a circular coil should keep a distance from its edge to be compliant with the guidelines limit, which depends on the selected %MSO: 38 cm at 100%, 32 cm at 80%, 26.8 cm at 50%, and 19.8 cm at 30%. Furthermore, attention should be paid to the induced E-field reached in the operator's hand as the operator typically holds the coil by hand. In fact in the hand, we estimated an induced E-field up to 10 times higher than the limits.ConclusionsOur numerical results indicate that coil positions, orientations, and distances with respect to the operator's body can determine the levels of induced E-field that exceed the ICNIRP limits. The induced E-field is also modulated by the choice of %MSO, which is related to the TMS application. Even under the best exposure conditions, attention should be paid to the exposure of the hand. These findings highlight the need for future risk assessment studies to provide more safety information for the correct and safe use of TMS devices.

Dataset Information

TREXMO plus: an advanced self-learning model for occupational exposure assessment.

Publications

TREXMO plus: an advanced self-learning model for occupational exposure assessment.

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