挪威的非COVID-19死亡率过高 2020-202222年挪威的非COVID-19死亡率过高
Guttorm Raknes1,2,3, Stephanie Jebsen Fagerås4, Kari Anne Sveen5
1Department of Health Registry Research and Development, Norwegian Institute of Public Health, Postboks 973 Sentrum, NO-5808, Bergen, Norway. guttorm.raknes@fhi.no.
BMC public health
|January 23, 2024
概括
在挪威,在2020年3月至2022年12月期间,观察到过多的非COVID-19死亡,主要来自心血管疾病. 然而,在此期间,呼吸道疾病和痴呆症的死亡率有所下降.
科学领域:
- 公共卫生 公共卫生
- 流行病学 流行病学
- 死亡率研究 死亡率研究
背景情况:
- 在全球范围内,COVID-19大流行严重影响了死亡率.
- 了解非COVID-19死亡原因对于评估整体流行病影响至关重要.
- 疫情期间的过度死亡率可能受到直接COVID-19感染以外的因素的影响.
研究的目的:
- 调查挪威非COVID-19死亡原因的变化.
- 在大流行期间量化特定非COVID-19原因的过度死亡率.
- 将观察到的死亡率与基于大流行前趋势的预测水平进行比较.
主要方法:
- 使用挪威死亡原因登记处数据 (2010-2022) 进行基于人口的横截面研究.
- 对主要死亡原因群体的年龄标准化死亡率 (ASMR) 的分析.
- 使用基于2010-2019年数据的一般线性回归模型预测预期死亡和ASMR.
主要成果:
- 在2021-2022年因所有原因和心血管疾病观察到显著的过度死亡率.
- 心血管疾病和恶性瘤的年龄标准化死亡率 (ASMR) 增加.
- 呼吸道疾病 (不包括COVID-19) 和痴呆症的死亡人数比预期的要少.
结论:
- 挪威在2020年3月至2022年12月期间经历了相当高的非COVID-19死亡率.
- 过度的心血管死亡是这种死亡率增加的主要驱动因素.
- 肺部疾病和痴呆症的死亡率在大流行期间低于预期.
更多相关视频
相关概念视频
Actuarial Approach
79
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,...
79
Life Tables
105
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,...
105
Pareto Chart
6.7K
A Pareto chart is a bar graph or a combination of both line and bar graphs. The bar lengths represent the individual values or the frequency, while the lines represent the cumulative total values. In this chart, the longest bars are arranged on the left and the shortest bars on the right, which makes it easier to read and interpret the data. It can also be called a Pareto diagram or Pareto analysis.
The Pareto chart is named after the Italian economist Vilfredo Pareto, who described the Pareto...
The Pareto chart is named after the Italian economist Vilfredo Pareto, who described the Pareto...
6.7K
Survival Curves
160
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...
160
Applications of Life Tables
67
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...
67
Acute Respiratory Failure-II
238
Type I Respiratory Failure, or hypoxemic respiratory failure, occurs when the partial pressure of oxygen (PaO2) in arterial blood falls below 60 mmHg while breathing room air without a corresponding increase in arterial carbon dioxide levels (PaCO2). This condition highlights a significant impairment in the lungs' capacity to oxygenate the blood.
The underlying physiological abnormalities that contribute to hypoxemic respiratory failure include:
The underlying physiological abnormalities that contribute to hypoxemic respiratory failure include:
238


