全球645个地区的降雨事件和每日死亡率:两阶段时间序列分析
Cheng He1, Susanne Breitner-Busch2,3, Veronika Huber2,3
1Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany cheng.he@helmholtz-munich.de.
概括
极端降雨事件显著增加了所有原因,心血管和呼吸系统问题的日常死亡风险. 适度降雨可能提供保护作用,风险因气候和植被而异.
科学领域:
- 环境流行病学环境流行病学
- 公共卫生 公共卫生
- 气候科学是气候科学.
背景情况:
- 每日降雨特征越来越被认为是影响公共健康的环境因素.
- 了解降雨强度,持续时间,频率和死亡率之间的特定关联对于公共卫生干预至关重要.
研究的目的:
- 调查每日降雨模式 (强度,持续时间,频率) 与全球所有原因,心血管和呼吸道死亡率之间的关系.
- 量化与不同回归周期的极端降雨事件相关的死亡风险.
主要方法:
- 采用两阶段时间序列分析,使用来自34个国家的645个地点 (1980-2020) 的每日死亡率数据.
- 降雨事件按回归期 (1,2,5年) 分类,并使用连续相对强度指数生成强度-响应曲线.
- 考虑14天的滞后期来评估累积死亡风险.
主要成果:
- 极端降雨事件 (5年回归期) 与所有原因 (RR 1.08),心血管 (RR 1.05) 和呼吸道 (RR 1.29) 死亡率的增加显著相关.
- 中等到大雨显示出保护作用 (RR <1),而极端强度增加死亡风险 (RR >1).
- 关联因气候类型,基线降雨变化和植被覆盖率而改变,降雨变化较低或植被稀缺的地区风险更高.
结论:
- 每日降雨强度,特别是极端事件,对公共健康构成重大风险,增加各种原因的死亡率.
- 降雨对健康的影响是复杂的,观察到的非线性影响,受当地环境和气候条件的影响.
- 这些发现凸显了需要制定适应气候变化的公共卫生战略,考虑降雨模式对死亡率的影响.
相关概念视频
Introduction To Survival Analysis
185
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...
185
Comparing the Survival Analysis of Two or More Groups
156
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...
156
Actuarial Approach
63
The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
63
Applications of Life Tables
50
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...
50
Assumptions of Survival Analysis
99
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
99
Life Tables
79
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,...
79


