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

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.