1. Application of stroke prediction models to evaluation of worksite health status
- Author
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Hiroshi Nakashima, Isamu Kabe, Satoko Iwasawa, Yuka Miyoshi, Itsumi Hashimoto, Noriyuki Yoshioka, Satoko Suzuki, Yutaka Sakurai, and Masashi Tsunoda
- Subjects
expected value ,health status ,prediction model ,stroke risk ,worksite ,Industrial safety. Industrial accident prevention ,T55-55.3 ,Medicine (General) ,R5-920 - Abstract
Objectives: For occupational health staff, the health status of the worksite is an important matter, and a single index for presenting this health status is desired. We applied a stroke prediction model to employees of a Japanese non-iron metal company working at 10 worksites to present health status of the worksite. Methods: We applied a stroke prediction model of the Japan Public Health Center-based Prospective Study to 2,807 male employees without history of cardiovascular disease. We additionally applied models from the Japan Arteriosclerosis Longitudinal Study and from the Suita Study for validation. As the expected value for each employee at a worksite, we calculated the mean of employees’ predicted 10-year stroke risk for each worksite. To adjust difference in age distribution, the stroke risk of each worksite was age-adjusted using the direct method. The expected values were presented as the representative value of a worksite with the 95% confidence interval calculated using the bootstrap method. Logistic regression analysis was conducted to explore the reason why a worksite exhibits a high risk. We examined if partial regression coefficients of the worst worksite were affected by modifiable risk factors. Results: Three models predicted similar stroke risks for 10 worksites. Difference in the predicted stroke risk was observed among the worksites even after age-adjustment. Diabetes mellitus was found to affect partial regression coefficient of the worst worksite in any of three prediction models. Conclusion: The stroke prediction model was observed to be a comprehensive tool for presenting a worksite’s health status.
- Published
- 2024
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