ArticleDisaster medicine and public health preparedness2020
Public Health Lessons Learned From Biases in Coronavirus Mortality Overestimation.
Article in Disaster medicine and public health preparedness, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed.
- Reducing risk of bias in interventional studies during their design and conduct: a scoping review.BMC medical research methodology · 2025Article
- A Situational Analysis of the Impact of the COVID-19 Pandemic on Digital Health Research Initiatives in South Asia.Cureus · 2023Review
- Article
- The effect of COVID-19 on public confidence in the World Health Organization: a natural experiment among 40 countries.Globalization and health · 2022Article
- The pharmaceutical industry is dangerous to health. Further proof with COVID-19.Surgical neurology international · 2022Review
- The day after tomorrow: financial repercussions of COVID-19 on systemic risk.Review of evolutionary political economy · 2022Article
- COVID-19 and the Political Economy of Mass Hysteria.International journal of environmental research and public health · 2021Article
- COVID-19 Severity Index: A predictive score for hospitalized patients.Medicina intensiva · 2020Article
- From COVID-19 to COVID-20: One Virus, Two Diseases.Disaster medicine and public health preparedness · 2020Article
- Article
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In testimony before US Congress on March 11, 2020, members of the House Oversight and Reform Committee were informed that estimated mortality for the novel coronavirus was 10-times higher than for seasonal influenza. Additional evidence, however, suggests the validity of this estimation could benefit from vetting for biases and miscalculations. The main objective of this article is to critically appraise the coronavirus mortality estimation presented to Congress. Informational texts from the World Health Organization and the Centers for Disease Control and Prevention are compared with coronavirus mortality calculations in Congressional testimony. Results of this critical appraisal reveal information bias and selection bias in coronavirus mortality overestimation, most likely caused by misclassifying an influenza infection fatality rate as a case fatality rate. Public health lessons learned for future infectious disease pandemics include: safeguarding against research biases that may underestimate or overestimate an associated risk of disease and mortality; reassessing the ethics of fear-based public health campaigns; and providing full public disclosure of adverse effects from severe mitigation measures to contain viral transmission.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.