Related Experiment Video

Updated: Aug 15, 2025

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses
03:53

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses

Published on: November 10, 2023

1.3K

Coronavirus disease 2019 (COVID-19) excess mortality outcomes associated with pandemic effects study (COPES): A

David Lu1, Sumeet Dhanoa1, Harleen Cheema1

  • 1Faculty of Medicine and Dentistry, Alberta Health Services, University of Alberta, Edmonton, AB, Canada.

Frontiers in Medicine
|January 2, 2023
PubMed

Insights

The COVID-19 pandemic increased all-cause mortality, including non-COVID-19 deaths, beyond what was expected from COVID-19 deaths alone. However, the evidence supporting this finding is of very low certainty.

Area of Science:

  • Public Health
  • Epidemiology
  • Healthcare Management

Background:

  • The COVID-19 pandemic has strained global healthcare systems, necessitating resource allocation for COVID-19 patients.
  • Minimizing overall excess mortality, encompassing both COVID-19 and non-COVID-19 deaths, is a critical challenge for healthcare providers.
  • Understanding the pandemic's impact on all-cause mortality is essential for effective public health strategies.

Conclusions:

  • The COVID-19 pandemic may have driven a significant increase in all-cause excess mortality.
  • This increase appears to be greater than that attributable to COVID-19 mortality alone.
  • However, the current evidence base is uncertain, highlighting the need for further high-quality research.
Abstract

Keywords:
COVID-19excess mortalitynon-COVID-19 mortalityoutcomespandemic (COVID-19)

More Related Videos

Live Imaging and Quantification of Viral Infection in K18 hACE2 Transgenic Mice Using Reporter-Expressing Recombinant SARS-CoV-208:41

Live Imaging and Quantification of Viral Infection in K18 hACE2 Transgenic Mice Using Reporter-Expressing Recombinant SARS-CoV-2

Published on: November 5, 2021

2.9K
Quantification and Whole Genome Characterization of SARS-CoV-2 RNA in Wastewater and Air Samples09:26

Quantification and Whole Genome Characterization of SARS-CoV-2 RNA in Wastewater and Air Samples

Published on: June 30, 2023

1.2K

Related Concept Videos

Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

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:  
488
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

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:
472
Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
15.5K
Causality in Epidemiology01:21

Causality in Epidemiology

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...
611
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
156
Cancer Survival Analysis01:21

Cancer Survival Analysis

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...
411