人口年龄结构对过度死亡率的依赖性 P-分数
Niklas Ullrich-Kniffka1, Jonas Schöley2
1University of Rostock, Ulmenstr. 69, 18057, Rostock, Germany. niklas.ullrich@uni-rostock.de.
Population health metrics
|September 28, 2024
概括
过度死亡率P-score是比较欧洲国家流行病负担的可靠指标. 它的排名在很大程度上不受人口结构的影响,主要反映了真正的过度死亡率差异.
科学领域:
- 人口统计学 人口统计学
- 流行病学 流行病学
- 公共卫生 公共卫生
背景情况:
- 过度死亡率P-score通过将观察到的死亡人数与预期的死亡人数进行比较来衡量流行病负担.
- 它的年龄依赖对于准确的跨国比较至关重要,特别是在COVID-19大流行期间.
研究的目的:
- 为了正式和经验性地评估人口结构偏差的P-分数.
- 评估P-score适用于在COVID-19大流行期间比较欧洲国家过度死亡率的适用性.
主要方法:
- 根据联合国世界人口前景和人类发展指数数据,计算了欧洲国家 (2021-2023) 的P-score.
- 通过李-卡特预测模型估计预期死亡人数.
- 使用Kitagawa类型分析分解P-score差异,并比较年龄标准化和经典P-score之间的等级相关性.
主要成果:
- P-分数是年龄特定的过度死亡的平均值,加权于预期死亡.
- 在欧洲P分数比较中,人口结构的差异起到了边际作用.
- 过度死亡效应是主要因素,年龄标准化和经典P-score的国家排名相似.
结论:
- 对于欧洲的过度死亡率比较来说,P-score非常适合,因为它主要反映了死亡率差异,而不是结构性偏差.
- 由于死亡分布的潜在变化,不应将研究结果推断为全球比较.
- 在特定的比较场景中,可以使用年龄标准化来减轻P-score偏差.
相关概念视频
Survival Curves
110
Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
110
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
Life Histories
17.8K
Overview
17.8K
Parametric Survival Analysis: Weibull and Exponential Methods
374
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...
374
Applications of Life Tables
51
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...
51
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


