Related Experiment Video
Updated: Dec 2, 2025
03:53
Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses
Published on: November 10, 2023
1.6K
Explaining among-country variation in COVID-19 case fatality rate
Gabriele Sorci1, Bruno Faivre2, Serge Morand3,4
1Biogéosciences, CNRS UMR 6282, Université de Bourgogne Franche-Comté, 6 Boulevard Gabriel, 21000, Dijon, France. gabriele.sorci@u-bourgogne.fr.
Scientific Reports
|November 4, 2020
Summary
COVID-19 case fatality rate (CFR) varied globally. Comorbidity factors like cardiovascular and cancer diseases, along with socio-economic elements such as GDP per capita and democracy levels, significantly influenced CFR across nations.
Area of Science:
- Epidemiology
- Public Health
- Health Economics
Background:
- The global spread of SARS-CoV-2 (COVID-19) has raised significant concerns regarding its associated mortality rate.
- Available data indicate temporal and spatial variations in the COVID-19 case fatality rate (CFR).
Purpose of the Study:
- To identify key factors contributing to the variability in COVID-19 CFR across different countries.
- To analyze the influence of comorbidity, demographic, economic, and political factors on temporal changes in CFR.
Main Methods:
- Utilized temporal CFR data from the European Centre for Disease Prevention and Control.
- Compiled country-specific data on comorbidities, demography, economy, and political regimes.
- Employed statistical analysis to associate these factors with CFR variations.
Main Results:
- Significant variations in temporal CFR trajectories were observed among countries.
- Higher DALYs (Disability-Adjusted Life Years) for cardiovascular, cancer, and respiratory diseases correlated with higher CFR.
- CFR was positively associated with smoking-related deaths in older adults, population share over 70, GDP per capita, and democracy levels.
- Negative associations were found with deaths from lower respiratory infections and hospital bed availability.
Conclusions:
- Comorbidity and socio-economic factors play a crucial role in driving COVID-19 CFR at the population level.
- Understanding these factors is essential for developing targeted public health interventions and policies to mitigate mortality.
Related Concept Videos
00:52Pareto Chart
7.4K
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...
7.4K
01:04Pie Chart
15.4K
A pie chart (or a pie graph) is a circular graphical chart or a pictorial representation of categorical data. It is divided into slices of pie each indicating numerical proportions. It is also used to show the relative sizes of data in a single chart.
In a pie chart, the central angle, the arc length of each slice, and the area are directly proportional to the quantity or percentage it represents. Some real-world examples that can be depicted using pie charts include marks obtained by students...
In a pie chart, the central angle, the arc length of each slice, and the area are directly proportional to the quantity or percentage it represents. Some real-world examples that can be depicted using pie charts include marks obtained by students...
15.4K
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
01:20Actuarial Approach
211
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,...
211
01:20Comparing the Survival Analysis of Two or More Groups
433
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...
433
01:15Assumptions of Survival Analysis
280
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
280