Park Nayoung, Woo Hyekyung
Department of Health Administration, Kongju National University, Gongju, Republic of Korea.
Front Public Health. 2025 Jun 4;13:1562280. doi: 10.3389/fpubh.2025.1562280. eCollection 2025.
Various mental disorders are becoming increasingly prevalent worldwide. Young adults are particularly vulnerable to mental health issues amid rapid lifestyle changes and socioeconomic pressures. This study adopted hybrid machine learning methods, combining existing statistical analysis and machine learning, to determine which factors affect young adults' mental health, considering recent changes. We used 4-year data (2019-2022) derived from the Community Health Survey, and the final study sample included 141,322 young people aged 19-34. We selected variables based on a literature review and feature selection and performed complex sample logistic regression analysis. New variables that had not previously been discussed (unmet medical needs, chewing difficulty, and accident/addiction experiences) were derived and found to significantly impact depression and subjective stress. These factors' impact on mental health was generally greater than that of the theoretical background variables. In conclusion, this study emphasizes the need to consistently monitor various factors in today's rapidly changing environment when devising policies aimed at managing young adults' mental health.
在全球范围内，各种精神障碍正变得越来越普遍。在生活方式迅速变化和社会经济压力的背景下，年轻人尤其容易受到心理健康问题的影响。本研究采用混合机器学习方法，将现有的统计分析和机器学习相结合，以确定在考虑到近期变化的情况下，哪些因素会影响年轻人的心理健康。我们使用了来自社区健康调查的4年数据（2019 - 2022年），最终的研究样本包括141322名年龄在19至34岁之间的年轻人。我们基于文献综述和特征选择来选择变量，并进行了复杂样本逻辑回归分析。得出了一些之前未被讨论过的新变量（未满足的医疗需求、咀嚼困难以及事故/成瘾经历），并发现这些变量对抑郁和主观压力有显著影响。这些因素对心理健康的影响通常大于理论背景变量。总之，本研究强调在制定旨在管理年轻人心理健康的政策时，有必要在当今快速变化的环境中持续监测各种因素。
