Leading Risk Factors for Diseases of the Circulatory and Musculoskeletal Systems in Metallurgical Workers

Introduction: It is important to assess health impact of the entire set of heterogeneous risk factors and identify the key ones in order to develop personalized measures for prevention of non-occupational diseases in workers.

Objective: To establish the major risk factors for circulatory and musculoskeletal disorders in employees of a metallurgical enterprise.

Materials and methods: We used anonymized data from a special assessment of working conditions and production control of the forging departments of the forging-rod and forging-press shops of a metallurgical enterprise producing titanium alloys for the years 2016–2020. To determine the presence of personal biological and behavioral risk factors for diseases of the circulatory and musculoskeletal systems, we examined anonymized data of the periodic medical check-up conducted in 2020 of 146 male blacksmiths working with hammers and presses. The mean age of the workers was 35.9 ± 8.8 years (range: 21 to 57 years) and their mean length of current employment was 18.9 ± 9.7 years (range: 2 to 41 years). The method of simple and multiple logistic regression was used to build models for predicting the likelihood of a disease; odds ratios were calculated with a 95 % confidence interval. The quality of the models was assessed using the maximum likelihood estimation.

Results: We determined the factors allowing prediction of the disease likelihood. The prevalence of circulatory diseases correlated with age, length of employment, and the body mass index, whole-body and hand-arm vibration, noise exposure, high ambient temperature, thermal radiation, and heavy physical work. The prevalence of musculoskeletal diseases, in its turn, had a somewhat weaker statistically significant association with the same risk factors. Models predicting the likelihood of circulatory and musculoskeletal diseases have been built. The best predictive model for circulatory diseases included the combined effect of the body mass index and heavy physical work while that for musculoskeletal disorders – of the body mass index and hand-arm vibration.

Conclusion: Multiple logistic regression used to analyze statistical relationships between work-related risk factors and disease prevalence in employees in specific occupations allowed identification of the leading factors contributing the most to the disease development and can be recommended for solving practical problems in occupational medicine.

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