AI Article Synopsis
- - The study aimed to investigate how physical and mental health, along with different job categories, influence the likelihood of occupational injuries and diseases using advanced statistical methods.
- - Data was collected from the National Health Insurance Research Database from 2002 to 2013, focusing on six major occupational categories and their specific subcategories.
- - The findings revealed that certain job subcategories, especially those involving civil service and private organizations, were more prone to injuries and diseases, and that mental health issues like disorders and obesity increased these risks.
Article Abstract
Purpose: The present study used a hierarchical generalized linear model to explore the effects of physical and mental health and occupational categories on occupational injuries and diseases.
Methods: The data were obtained from the Registry for Beneficiaries of the 2002-2013 National Health Insurance Research Database. The benefit categories involved adults with occupational injuries and diseases. Six major occupational categories and 28 subcategories were used. The main analysis methods were binary logistic regression (BLR) and hierarchical generalized linear model (HGLM).
Results: After adjustment for relevant factors, the three major occupation subcategories most likely to develop occupational injuries and diseases were Subcategory 12 "employees with fixed employers" of Category 1 "civil servants, employees in public or private schools, laborers, and self-employed workers"; Subcategory 2 "employees in private organizations" of Category 1; and "sangha and religionists" of Category 6 "other citizens." Conditions such as mental disorders and obesity increased the risk of occupational injuries and diseases.
Conclusion: A portion of the occupational categories had a higher risk of occupational injuries and diseases. Physical and mental health issues were significantly correlated with occupational injuries and diseases. To the authors' knowledge, this is the first study to use HGLM to analyze differences in occupational categories in Taiwan.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9914676 | PMC |
| http://dx.doi.org/10.3390/diagnostics13030381 | DOI Listing |
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