低收入成年人睡眠轨迹和全因死亡率
Kelsie M Full1,2, Hui Shi2, Loren Lipworth1
1Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee.
JAMA network open
|February 27, 2025
概括
随着时间的推移,保持健康的睡眠时间对于降低死亡风险至关重要. 低于最佳的睡眠轨迹与美国成年人全因死亡风险增加29%有关.
科学领域:
- 流行病学 流行病学
- 睡眠科学 睡眠科学
- 公共卫生 公共卫生
背景情况:
- 短时间和长时间的睡眠时间与不良健康结果有关,包括心血管疾病 (CVD),2型糖尿病和死亡率.
- 不断变化的睡眠时间模式对死亡风险的影响,特别是在不同的人口群体中,仍未得到充分探索.
研究的目的:
- 为了检查5年睡眠时间轨迹与所有原因和特定原因死亡率在美国成年人中的关联.
- 研究基于性别,种族和社会经济地位的这些关联的潜在变化.
主要方法:
- 利用了来自南方社区队列研究的数据,其中包括46928名年龄在40-79岁之间的成年人.
- 在基线和5年随访期间评估睡眠时间 (短,健康,长) 以确定9个睡眠轨迹.
- 使用Cox比例危险回归来分析与国家死亡指数相关的死亡结果.
主要成果:
- 近三分之二 (66.4%) 的参与者表现出不理想的5年睡眠轨迹.
- 与最佳轨迹相比,低于最佳的睡眠轨迹与所有原因死亡风险增加29%有关.
- 长期,短期和短期等特定轨迹显示出所有原因和CVD特定死亡率的最大风险.
结论:
- 这项研究强调了与在5年内保持低于最佳睡眠时间相关的显著死亡风险.
- 研究结果强调了持续,健康的睡眠时间对长期健康和生存的重要性.
- 观察到与睡眠轨迹相关的死亡风险差异因种族和收入而异,这表明可能需要针对性的公共卫生干预措施.
相关概念视频
Longitudinal Research
11.8K
Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
11.8K
Longitudinal Studies
108
Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
108
Truncation in Survival Analysis
145
Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
145
Life Tables
68
A life table is a statistical tool that summarizes the mortality and survival patterns of a population, providing detailed insights into the likelihood of survival or death across different age intervals within a cohort. By organizing data on survival probabilities and mortality rates, life tables offer a clear snapshot of population dynamics over time. They are extensively used in demography, public health, actuarial science, and ecology to analyze life expectancy, design health interventions,...
68
Introduction To Survival Analysis
157
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
The primary goal of survival analysis is to estimate survival time—the time...
157
Applications of Life Tables
44
Life tables are versatile across various fields, providing a quantitative basis for analyzing mortality and survival rates. Whether used by demographers, actuaries, epidemiologists, or sociologists, life tables offer valuable insights into the dynamics of life and death, facilitating informed decisions in public health, insurance, conservation, and beyond. Their broad applicability highlights the interconnectedness of demographic data with practical outcomes in everyday life and strategic...
44


