A Bayesian assessment of occupational health surveillance in workers exposed to silica in the energy and construction industry

• Published in: Environmental science and pollution research international 2019-10, Vol.26 (29), p.29560-29569
• Main Authors: Abad, Alberto, Gerassis, Saki, Saavedra, Ángeles, Giráldez, Eduardo, García, Julio F., Taboada, Javier
• Format: Article
• Language: English
• Subjects: Aged; Aquatic Pollution; Atmospheric Protection/Air Quality Control/Air Pollution; Bayes Theorem; Bayesian analysis; Body Mass Index; Body size; Construction industry; Construction Industry - statistics & numerical data; Current trends in Sustainable Energy; Earth and Environmental Science; Ecotoxicology; Engineering; Environment; Environmental Chemistry; Environmental conditions; Environmental Health; Environmental science; Exploration; Exposure; Female; Geographical variations; Health risks; Health surveillance; Humans; Illnesses; Industry - statistics & numerical data; Learning algorithms; Lungs; Machine learning; Male; Materials and Environment; Medical personnel; Medical records; Middle Aged; Models, Statistical; Occupational diseases; Occupational Diseases - chemically induced; Occupational Diseases - diagnosis; Occupational Diseases - epidemiology; Occupational exposure; Occupational Exposure - adverse effects; Occupational Health; Probabilistic inference; Silica; Silicon dioxide; Silicon Dioxide - toxicity; Spain - epidemiology; Waste Water Technology; Water Management; Water Pollution Control; Workers; Workplace
• ISSN: 0944-1344; 1614-7499
• DOI: 10.1007/s11356-018-2962-6

Medical records generated during occupational health surveillance processes have large amounts of unexploited information that can help to reduce silica-related health risks and many occupational diseases. The methodology applied in this study consists in analyzing through machine learning techniques a database with 70,000 medical examinations from workers in the energy and construction industry in Spain. First, a general unsupervised Bayesian model is built and node force analysis is used to identify the factors with the greatest impact on the worker’s health surveillance process. Second, a predictive Bayesian model is created and mutual information is employed to assess the more relevant factors affecting the medical capability of workers exposed to silica dust. The lung auscultation and the breathing exploration are the two factors that influence the most the medical capability of silica-exposed employees. Probabilistic inference shows a remarkable gender effect, where women present more resilience towards occupational diseases than men showing a higher proportion of normal results in certain key factors, such as body mass index (♀49.73%, ♂25.17%) or spirometry (♀53.73%, ♂48.91%). Finally, environmental conditions demonstrate to have a major influence on spatial variability of occupational diseases. The design of health prevention programs based on geographical variations can be crucial to the attainment of an ongoing and sustained healthier workforce with a reduction in the number of chronic workplace illnesses.