在COVID-19大流行期间估计整体和特定原因的过度死亡率:方法论方法比较方法论方法
Claudio Barbiellini Amidei1, Ugo Fedeli1, Nicola Gennaro1
1Epidemiological Department, Azienda Zero, Veneto Region, 35131 Padova, Italy.
概括
在COVID-19期间估计过度死亡率需要仔细选择方法. 不同的方法会产生不同的结果,特别是当考虑流行病前的死亡原因趋势时.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
- 公共卫生 公共卫生
背景情况:
- 在全球范围内,COVID-19疫情造成了显著的过度死亡率.
- 研究中的方法差异阻碍了过度死亡率估计的可比性.
- 预先存在的特定原因死亡率趋势使准确的评估变得复杂.
研究的目的:
- 为了估计由于不同的统计方法而导致的过度死亡率数字的变化.
- 分析疫情前的趋势如何影响特定死因的过度死亡率估计.
- 为了比较预测方法来评估COVID-19大流行期间的过度死亡率.
主要方法:
- 2020年意大利威尼托地区的每月死亡数据与预测数据进行了比较.
- 使用了四种预测方法:2018-2019年平均死亡率,5年平均年龄标准化率,季节性自回归集成移动平均 (SARIMA) 模型和通用估计方程 (GEE) 模型.
- 分析了所有原因,循环系统疾病,癌症和神经/精神疾病的过度死亡率.
主要成果:
- 所有原因的过度死亡率估计在各种方法中从+9.5%到+17.2%不等.
- 循环系统疾病死亡率估计有显著差异,从-4.4%到+8.4%,受到大流行前趋势下降的影响.
- 癌症死亡率显示微小的变化,除了使用简单的年龄标准化率. 神经/精神疾病估计根据方法不同,SARIMA和GEE显示最小的变化.
结论:
- 过度死亡率估计的规模高度依赖于所选择的预测方法.
- 在没有趋势调整的情况下比较年龄标准化利率可能会导致不同的结果.
- 通用估计方程 (GEE) 模型似乎是对过度死亡率评估的通用和可靠选择.
相关概念视频
Statistical Methods for Analyzing Epidemiological Data
427
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:
427
Comparing the Survival Analysis of Two or More Groups
228
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...
228
Actuarial Approach
101
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,...
101
Kaplan-Meier Approach
195
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
195
Cancer Survival Analysis
402
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
402
Bias in Epidemiological Studies
380
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:
380


