Delphine Bosson-Rieutort1,2, Philippe Sarazin2, Dominique J Bicout3, Vikki Ho1,4, Jérôme Lavoué1,5. 1. Health Innovation and Evaluation Hub, University of Montreal Hospital Research Centre (CRCHUM), Montréal, Québec, Canada. 2. Chemical and Biological Hazards Prevention, Institut de recherche Robert-Sauvé en santé et en sécurité du travail, Montréal, Québec, Canada. 3. Biomathematics and epidemiology EPSP-TIMC, UMR 5525 TIMC CNRS Grenoble Alpes University, Faculté de Médecine de Grenoble, Bât Jean Roget, La Tronche & VetAgro Sup Veterinary Campus of Lyon, Marcy l'Etoile, France. 4. Department of Social and Preventive Medicine Université de Montréal Montréal, Montréal, Québec, Canada. 5. Department of Occupational and Environmental Health, Université de Montréal, Montréal, Québec, Canada.
Abstract
OBJECTIVES: The occupational environment represents an important source of exposures to multiplehazards for workers' health. Although it is recognized that mixtures of agents may have differenteffects on health compared to their individual effects, studies generally focus on the assessment ofindividual exposures. Our objective was to identify occupational co-exposures occurring in the United States using the multi-industry occupational exposure databank of the Occupational Safety and Health Administration (OSHA). METHODS: Using OSHA's Integrated Management Information System (IMIS), measurement data from workplace inspections occurring from 1979 to 2015 were examined. We defined a workplace situation (WS) by grouping measurements that occurred within a company, within the same occupation (i.e. job title) within 1 year. All agents present in each WS were listed and the resulting databank was analyzed with the Spectrosome approach, a methodology inspired by network science, to determine global patterns of co-exposures. The presence of an agent in a WS was defined either as detected, or measured above 20% of a relevant occupational exposure limit (OEL). RESULTS: Among the 334 648 detected exposure measurements of 105 distinct agents collected from 14 513 US companies, we identified 125 551 WSs, with 31% involving co-exposure. Fifty-eight agents were detected with others in >50% of WSs, 29 with a proportion >80%. Two clusters were highlighted, one for solvents and one for metals. Toluene, xylene, acetone, hexone, 2-butanone, and N-butyl acetate formed the basis of the solvent cluster. The main agents of the metal cluster were zinc, iron, lead, copper, manganese, nickel, cadmium, and chromium. 68 556 WS were included in the analyses based on levels of exposure above 20% of their OEL, with 12.4% of co-exposure. In this analysis, while the metal cluster remained, only the combinations of toluene with xylene or 2-butanone were frequently observed among solvents. An online web application allows the examination of industry specific patterns. CONCLUSIONS: We identified frequent co-exposure situations in the IMIS databank. Using the spectrome approach, we revealed global combination patterns and the agents most often implicated. Future work should endeavor to explore the toxicological effects of prevalent combinations of exposures on workers' health to prioritize research and prevention efforts.
OBJECTIVES: The occupational environment represents an important source of exposures to multiplehazards for workers' health. Although it is recognized that mixtures of agents may have differenteffects on health compared to their individual effects, studies generally focus on the assessment ofindividual exposures. Our objective was to identify occupational co-exposures occurring in the United States using the multi-industry occupational exposure databank of the Occupational Safety and Health Administration (OSHA). METHODS: Using OSHA's Integrated Management Information System (IMIS), measurement data from workplace inspections occurring from 1979 to 2015 were examined. We defined a workplace situation (WS) by grouping measurements that occurred within a company, within the same occupation (i.e. job title) within 1 year. All agents present in each WS were listed and the resulting databank was analyzed with the Spectrosome approach, a methodology inspired by network science, to determine global patterns of co-exposures. The presence of an agent in a WS was defined either as detected, or measured above 20% of a relevant occupational exposure limit (OEL). RESULTS: Among the 334 648 detected exposure measurements of 105 distinct agents collected from 14 513 US companies, we identified 125 551 WSs, with 31% involving co-exposure. Fifty-eight agents were detected with others in >50% of WSs, 29 with a proportion >80%. Two clusters were highlighted, one for solvents and one for metals. Toluene, xylene, acetone, hexone, 2-butanone, and N-butyl acetate formed the basis of the solvent cluster. The main agents of the metal cluster were zinc, iron, lead, copper, manganese, nickel, cadmium, and chromium. 68 556 WS were included in the analyses based on levels of exposure above 20% of their OEL, with 12.4% of co-exposure. In this analysis, while the metal cluster remained, only the combinations of toluene with xylene or 2-butanone were frequently observed among solvents. An online web application allows the examination of industry specific patterns. CONCLUSIONS: We identified frequent co-exposure situations in the IMIS databank. Using the spectrome approach, we revealed global combination patterns and the agents most often implicated. Future work should endeavor to explore the toxicological effects of prevalent combinations of exposures on workers' health to prioritize research and prevention efforts.