Related Experiment Video
Updated: Dec 8, 2025
03:53
Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses
Published on: November 10, 2023
1.7K
Excess mortality due to COVID-19 in Germany
Andreas Stang1, Fabian Standl2, Bernd Kowall2
1Institute of Medical Informatics, Biometry and Epidemiology, University Hospital Essen, Germany; School of Public Health, Department of Epidemiology, Boston University, Boston, United States.
The Journal of Infection
|September 21, 2020
Summary
Excess mortality was observed in Germany during the first wave of the SARS-CoV-2 pandemic, particularly in older age groups. A sensitivity analysis adjusting for demographics indicated a slight deficit in deaths.
Area of Science:
- Epidemiology
- Public Health
- Demography
Background:
- The first wave of the SARS-CoV-2 (COVID-19) pandemic significantly impacted global health systems.
- Understanding excess mortality is crucial for assessing the pandemic's true burden.
Purpose of the Study:
- To estimate excess mortality in Germany during the initial phase of the SARS-CoV-2 pandemic (weeks 10-23, 2020).
- To analyze age-specific mortality patterns during this period.
Main Methods:
- Analysis of weekly age-specific death counts from 2016 to week 26, 2020.
- Estimation of expected weekly deaths using 2016-2019 averages.
- Calculation of standardized mortality ratios (SMR) and 95% confidence intervals.
Main Results:
- Excess mortality was observed in individuals aged 60-69, 80-89, and 90+ during weeks 10-23, 2020.
- The overall net excess deaths were +8,071, with SMR of 1.03.
- A sensitivity analysis accounting for demographic changes showed an overall SMR of 0.98, indicating a deficit of 4,926 deaths.
Conclusions:
- Excess mortality in Germany during the first SARS-CoV-2 wave was concentrated in specific older age groups and lasted approximately two months.
- Factors such as younger infection age, reduced contact rates, and effective pandemic management may have contributed to the observed mortality patterns.
Related Concept Videos
01:51Vaccinations
50.5K
Overview
50.5K
00:52Pareto Chart
7.5K
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...
7.5K
01:12Relative Risk
1.5K
Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
1.5K
01:21Causality in Epidemiology
1.3K
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
1.3K
01:29Bias in Epidemiological Studies
1.1K
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:
1.1K
01:26Factors Affecting the Risk of Infection
13.1K
The hosts' susceptibility to infection depends on several factors. The integrity of the skin and mucous membranes helps protect the body against microbial attacks. When the skin is altered, the chance of infection, limb loss, and even death increases.
The integrity and count of the white blood cells help the body resist pathogens and fight infection. When impaired, it reduces the body's resistance to pathogens. The acidic pH levels of the gastrointestinal, genitourinary tracts, and skin...
The integrity and count of the white blood cells help the body resist pathogens and fight infection. When impaired, it reduces the body's resistance to pathogens. The acidic pH levels of the gastrointestinal, genitourinary tracts, and skin...
13.1K