脑卒中预测模型在工作场所健康状况评价中的应用。

Environmental and occupational health practice Pub Date : 2024-05-10 eCollection Date: 2024-01-01 DOI:10.1539/eohp.2024-0002-FS
Hiroshi Nakashima, Isamu Kabe, Satoko Iwasawa, Yuka Miyoshi, Itsumi Hashimoto, Noriyuki Yoshioka, Satoko Suzuki, Yutaka Sakurai, Masashi Tsunoda
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引用次数: 0

摘要

目标:对于职业卫生人员来说,工作场所的健康状况是一件重要的事情,需要一个单一的指标来表示这种健康状况。本研究以日本某非铁金属公司10个工作场所的员工为研究对象,应用脑卒中预测模型来呈现工作场所的健康状况。方法:应用日本公共卫生中心前瞻性研究的脑卒中预测模型,对2,807名无心血管病史的男性员工进行研究。我们还应用了日本动脉硬化纵向研究和Suita研究的模型进行验证。作为每个工作场所的每个员工的期望值,我们计算了每个工作场所员工预测的10年中风风险的平均值。为了调整年龄分布的差异,采用直接法对各工作场所的卒中风险进行年龄调整。期望值作为工地的代表值,95%置信区间采用自举法计算。通过Logistic回归分析,探讨某工地出现高危险性的原因。我们检验了最差工作场所的部分回归系数是否受到可变危险因素的影响。结果:三个模型预测了10个工作场所相似的卒中风险。即使在年龄调整后,在不同的工作地点也观察到预测中风风险的差异。三种预测模型均发现糖尿病对最差工作场所的偏回归系数有影响。结论:脑卒中预测模型是反映工作场所健康状况的综合工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of stroke prediction models to evaluation of worksite health status.

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.

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