Public Health Lessons Learned From Biases in Coronavirus Mortality Overestimation

Abstract

In testimony before U.S. Congress on March 11, 2020, members of the House Oversight and Reform Committee were informed that estimated mortality for the novel coronavirus was ten-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.

COVID-19 and the Political Economy of Mass Hysteria

“…The infection fatality rate is normally much lower than the case fatality rate. In the congressional hearing on 11 March 2020, the infection fatality rate of the seasonal influenza was compared to the estimated case fatality rate of the coronavirus, leading to the alarming statement that the coronavirus would be ten times more deadly than the seasonal influenza [ 95 ]. This false statement coming out of the Congress of the United States and with its authority greatly contributed to generate anxiety and panic.…”

Coronavirus (COVID‐19), pandemic psychology and the fractured society: a sociological case for critique, foresight and action

“…Subsequent estimates suggest the infection fatality rate and perhaps more importantly for public policy, the population fatality rate , could be much lower than 0.1 per cent and the costs of mass lockdowns could outweigh the benefits (Dingwall, 2020a; Morgan, 2020; Sayers, 2020). Most recently, Brown (2020, p. 15) has critically appraised ‘fear‐based public health measures’ with reference to how ‘[s]ampling bias in coronavirus mortality calculations led to a ten‐fold increased mortality overestimation in March 11, 2020 U.S. Congressional testimony’. As per Strong’s (1990) model on the ‘emotional maelstrom’ (p. 249) surrounding epidemics, such controversies are unsurprising.…”

COVID-19 Severity Index: A predictive score for hospitalized patients

“…The way in which outbreaks affect countries depends on multiple factors and its impact is difficult to foresee 1 . However, the numbers of infected people and casualties are evidence that despite attempts to plan, the global healthcare systems remain unprepared 2 .…”