韩国老年男性的烟草使用:分类和回归树分析
Sung Seek Moon1, Hyeouk Chris Hahm2, Jinwon Lee2
1Diana R. Garland School of Social Work, Baylor University, Waco, TX, USA.
Korean journal of family medicine
|January 8, 2026
概括
年长的韩国男性.
科学领域:
- 老年学是一门学科.
- 公共卫生 公共卫生
- 行为科学 行为科学
背景情况:
- 烟草使用仍然是韩国老年男性的重大公共卫生问题.
- 以前的研究经常使用线性模型,可能缺少复杂的相互作用.
- 这项研究探讨了烟草使用预测因素中的等级模式.
研究的目的:
- 确定影响韩国老年男性吸烟的关键因素及其相互作用.
- 使用分类和回归树 (CART) 分析来获得细微的理解.
- 为戒烟提供有针对性的公共卫生干预信息.
主要方法:
- 对2023年韩国社区健康调查数据的分析.
- 包括34924名65岁及以上的韩国男性.
- 应用CART分析来识别预测模式.
主要成果:
- 年龄是最强的预测因素;低社会参与度的男性 ≤72.5岁显示高吸烟率 (24.8%).
- 较年轻的男性 (≤67.5岁) 具有较高的社会活动率是最低的 (12.9%).
- 对于≥72.5岁的男性来说,酒精使用是关键;不喝酒的人的比例很低 (9.3%),而喝酒的人≤78.5岁的比例更高 (22.0%).
结论:
- 促进社会参与对于减少年轻老年男性吸烟至关重要.
- 减少酒精消费对于老年男性至关重要,特别是那些≥72.5岁的男性.
- 需要针对特定年龄因素的量身定制策略,以便在这个人口群体中戒烟.
更多相关视频
07:22Glycemic Impact on Knee Osteoarthritis Symptoms on Physical, Radiographic, and Inflammatory Markers among Individuals Aged 50 and Over with Diabetes
Published on: March 7, 2025
709
06:22Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
Published on: September 19, 2025
423
相关概念视频
Statistical Methods for Analyzing Epidemiological Data
889
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
889
Cancer Survival Analysis
645
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
645
Observational Studies
10.7K
Observational studies are a type of analytical study where researchers observe events without any interventions. In other words, the researcher does not influence the response variable or the experiment's outcome.
There are three types of observational studies – Prospective, retrospective, and cross-sectional.
Prospective Study
Prospective studies, also known as longitudinal or cohort studies, are carried out by collecting future data from groups sharing similar characteristics. One...
There are three types of observational studies – Prospective, retrospective, and cross-sectional.
Prospective Study
Prospective studies, also known as longitudinal or cohort studies, are carried out by collecting future data from groups sharing similar characteristics. One...
10.7K
Survival Tree
382
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a...
Building a Survival Tree
Constructing a...
382
Regression Toward the Mean
6.8K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.8K
