Effects of occupational risk factors on dental diseases in industrial workers: a machine learning approach

Meng, X; Rainbow, Y; Tan, M; Shi, Y; Li, Z; Ball, J; Sun, J

Effects of occupational risk factors on dental diseases in industrial workers: a machine learning approach

Meng, X Rainbow, Y Tan, M Shi, Y Li, Z Ball, J Sun, J

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Global Health Journal, 2026
Issue Date:: 2026-03

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Field	Value	Language
dc.contributor.author	Meng, X
dc.contributor.author	Rainbow, Y
dc.contributor.author	Tan, M
dc.contributor.author	Shi, Y
dc.contributor.author	Li, Z
dc.contributor.author	Ball, J
dc.contributor.author	Sun, J https://orcid.org/0000-0002-0097-2438
dc.date.accessioned	2026-03-10T04:21:50Z
dc.date.available	2026-03-10T04:21:50Z
dc.date.issued	2026-03
dc.identifier.citation	Global Health Journal, 2026
dc.identifier.issn	2096-3947
dc.identifier.uri	http://hdl.handle.net/10453/193812
dc.description.abstract	Objective Dental disease is a longstanding global health problem, yet the role of occupational exposures in its development is underexplored. Using a large occupational health dataset of 66,449 industrial workers, this study investigates the relationship between occupational risk factors and dental diseases. This study investigates the relationship between occupational risk factors and dental diseases in a large industrial workforce. Methods A cross-sectional study was conducted on a dataset comprising 66,449 industrial workers to examine associations between occupational risk factors and the prevalence of dental diseases, including residual roots, missing teeth, dental caries, periodontal disease, deposits on dental hard tissues, and other dental diseases. Machine learning was employed to evaluate the relationship between occupational exposures and dental disease outcomes. Results Noise exposure (alone and in combination with dust) was associated with a higher predicted likelihood of periodontal disease in machine learning analyses, although these estimates should be interpreted cautiously given the low prevalence of periodontal disease in this screening dataset. Age and sex were associated with selected oral disease outcomes. Oral disease patterns also varied by industry sector, with exploratory associations between dental caries and employment in the power supply and metal structure manufacturing sectors. Enterprise size was associated with several oral health outcomes. Conclusion This study identifies associations between demographic and occupational exposures and screening-detected oral disease outcomes in an industrial workforce. Future research should validate these findings in prospective studies and evaluate workplace-based prevention strategies that address potentially modifiable exposures and barriers to oral healthcare.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Global Health Journal
dc.relation.isbasedon	10.1016/j.glohj.2026.03.001
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Effects of occupational risk factors on dental diseases in industrial workers: a machine learning approach
dc.type	Journal Article
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.date.updated	2026-03-10T04:21:48Z
pubs.publication-status	Published

Abstract:

Objective Dental disease is a longstanding global health problem, yet the role of occupational exposures in its development is underexplored. Using a large occupational health dataset of 66,449 industrial workers, this study investigates the relationship between occupational risk factors and dental diseases. This study investigates the relationship between occupational risk factors and dental diseases in a large industrial workforce. Methods A cross-sectional study was conducted on a dataset comprising 66,449 industrial workers to examine associations between occupational risk factors and the prevalence of dental diseases, including residual roots, missing teeth, dental caries, periodontal disease, deposits on dental hard tissues, and other dental diseases. Machine learning was employed to evaluate the relationship between occupational exposures and dental disease outcomes. Results Noise exposure (alone and in combination with dust) was associated with a higher predicted likelihood of periodontal disease in machine learning analyses, although these estimates should be interpreted cautiously given the low prevalence of periodontal disease in this screening dataset. Age and sex were associated with selected oral disease outcomes. Oral disease patterns also varied by industry sector, with exploratory associations between dental caries and employment in the power supply and metal structure manufacturing sectors. Enterprise size was associated with several oral health outcomes. Conclusion This study identifies associations between demographic and occupational exposures and screening-detected oral disease outcomes in an industrial workforce. Future research should validate these findings in prospective studies and evaluate workplace-based prevention strategies that address potentially modifiable exposures and barriers to oral healthcare.

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http://hdl.handle.net/10453/193812