ArticleFrontiers in public health2026
Predicted 10-year cardiometabolic risk among hospital staff: a cross-sectional study.
Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: Chronic diseases and cardiometabolic disorders are major public health and occupational health challenges in Taiwan, particularly among healthcare workers exposed to shift work, high occupational stress, and irregular schedules. Nurses and hospital staff are especially vulnerable to hypertension, type 2 diabetes mellitus, stroke, and cardiovascular disease risk. Using a localized digital risk assessment platform and machine learning-based data-driven modeling, this study aimed to evaluate chronic disease risk among medical center employees and identify high-risk occupational subgroups for precision health management. Participants/Methods: This retrospective cross-sectional study included 1,916 employees aged 35-70 at a Northern Taiwan medical center from 2019 to 2024. Ten-year predicted risks for coronary artery disease, stroke, type 2 diabetes mellitus, hypertension, and major adverse cardiovascular events were estimated using the Chronic Disease Risk Assessment Platform. To assess associations between predicted risk and demographic or occupational variables, we used ordinal logistic regression, generalized linear models, and decision tree algorithms. Results: Hypertension exhibited the highest overall predicted risk (38.6%), followed by type 2 diabetes mellitus and MACE. Male employees had higher odds of being classified into a higher predicted HTN risk category than female employees (odds ratio [OR] = 4.18, 95% confidence interval [CI]: 3.21-5.44, Conclusion: This study applied disease risk prediction models developed by the Taiwan Health Promotion Administration platform, integrating clinical indicators and machine learning analyses to identify healthcare workers at increased disease risk. The findings suggest that localized digital prediction tools may be integrated into institutional health surveillance systems to support continuous health monitoring, early risk identification, and proactive preventive care in healthcare settings.
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