将贝叶斯模型与动态人口组合在一起,以估计由于极端温度而导致的过度死亡
Garyfallos Konstantinoudis1, Anthony Hauser2, Julien Riou2
1Grantham Institute for Climate Change and the Environment, Imperial College London, London, UK.
Spatial and spatio-temporal epidemiology
|November 28, 2025
概括
在2022年夏天,极端高温导致80岁以上的瑞士人超过487例过度死亡. 较低的温度门对于热适应政策至关重要,特别是在长时间的热浪期间.
科学领域:
- 环境健康 环境健康
- 流行病学 流行病学
- 生物统计学 生物统计学
背景情况:
- 极端高温事件越来越被认为是严重的公共卫生威胁,导致死亡率上升.
- 有效的热适应政策,包括及时的热警告,需要精确监测热归因死亡负担.
- 识别易受伤害的人群和了解温度值对于减轻与热有关的死亡至关重要.
研究的目的:
- 评估与2022年夏季瑞士极端高温相关的过度死亡率.
- 为了确定特定的人群容易受到与热有关的死亡率.
- 为了估计温度值,与开发和完善热适应策略相关.
主要方法:
- 利用2011-2022年全国死亡率和人口数据,按年龄,性别,日期和州分层.
- 开发了贝叶斯集团建模方法,结合动态人口数据来预测预期死亡率.
- 通过比较预测和观察到的死亡,控制国家节日和时空效应等共变量,并考虑COVID-19大流行影响来计算过剩死亡率.
主要成果:
- 在2022年夏天在瑞士,在80岁以上的个人中观察到共487例过度死亡 (95%可信度区间:10-935).
- 分析显示,在超过四天的极端高温期间,最老年人群的最低过度死亡温度值与环境温度的第70百分位相对应.
- 该研究强调,即使较低的温度值在长时间的极端高温期间也变得显著.
结论:
- 这些发现强调了极端高温带来的大量死亡负担,特别是在瑞士的老年人群中.
- 确定的温度值表明需要修订热适应政策,考虑到热事件的持续时间.
- 将长期热浪期间较低温度值的见解纳入公共卫生战略对于预防与热有关的死亡率至关重要.
相关概念视频
Parametric Survival Analysis: Weibull and Exponential Methods
990
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
990
Distributions to Estimate Population Parameter
5.0K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
5.0K
Estimating Population Standard Deviation
3.3K
When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
3.3K
Estimating Population Mean with Unknown Standard Deviation
8.7K
In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
William S. Gosset (1876–1937) of the...
8.7K
Statistical Methods for Analyzing Epidemiological Data
875
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:
875
Mechanistic Models: Compartment Models in Individual and Population Analysis
226
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
226


