相关实验视频
Updated: Jun 25, 2025

09:36
Assessment of Child Anthropometry in a Large Epidemiologic Study
Published on: February 2, 2017
27.1K
探索韩国肥胖的差异,使用分层年龄-周期-队列分析与交叉分类的随机效应模型
Chang Kyun Choi1, Jung-Ho Yang2, Sun-Seog Kweon3
1Division of Cancer Early Detection, National Cancer Control Institute, National Cancer Center, Goyang, Korea.
Journal of Korean medical science
|May 28, 2024
概括
韩国的肥胖患病率显示出与年龄相关的模式,以及最近出生群体之间的差异越来越大. 公共卫生战略应针对戒烟和体育活动,特别是在农村地区.
科学领域:
- 公共卫生 公共卫生
- 流行病学 流行病学
- 生物统计学 生物统计学
背景情况:
- 调查年龄,周期和出生队列对韩国肥胖患病率的影响.
- 旨在为预防肥胖提供有效的公共卫生策略.
- 使用来自韩国国家健康和营养检查调查 (KNHANES) 的数据.
研究的目的:
- 分析年龄,月经和出生队列对韩国人口肥胖率的影响.
- 确定与肥胖差异相关的主要人口和生活方式因素.
- 为有针对性的公共卫生干预提供证据.
主要方法:
- 采用分层年龄周期队列 (APC) 分析与交叉分类随机效应建模.
- 在大型数据集 (35,736名男性,46,756名女性,2007-2021年) 上使用多变量混合后勤回归.
- 评估APC因素与生活方式/社会经济变量之间的相互作用.
主要成果:
- 揭示了逆转的U形年龄对肥胖的影响,因吸烟史和体力活动而改变.
- 确定了2020-2021年的积极趋势和2014年的负面趋势的时期效应.
- 显示1980年代后出生群体中肥胖率下降,但1960年后出生的人中与吸烟,不活动和农村居住相关的差异增加.
结论:
- 突出了最近的韩国出生队伍中肥胖差异的增加.
- 强调需要针对性健康政策,重点关注戒烟和体育活动.
- 建议针对农村地区和1960年以后出生的人士采取具体干预措施.
更多相关视频
相关概念视频
Statistical Methods for Analyzing Epidemiological Data
361
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:
361
Bias in Epidemiological Studies
246
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:
246
Cross-Sectional Research
11.3K
In cross-sectional research, a researcher compares multiple segments of the population at the same time. If they were interested in people's dietary habits, the researcher might directly compare different groups of people by age. Instead of following a group of people for 20 years to see how their dietary habits changed from decade to decade, the researcher would study a group of 20-year-old individuals and compare them to a group of 30-year-old individuals and a group of 40-year-old...
11.3K
Comparing the Survival Analysis of Two or More Groups
177
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
177
Study Designs in Epidemiology
214
Epidemiological study designs are fundamental tools for investigating the distribution, determinants, and control of health conditions in populations. They help researchers understand the relationships between exposures and outcomes, and they broadly fall into two categories: "observational" and "experimental" studies.
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and...
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and...
214
Confounding in Epidemiological Studies
164
Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This...
164

