According to multiple reports in the mainstream media, the United States has now seen 100,000 deaths attributed to COVID-19. CNN’s sensationalist headline was typical of the mainstream coverage.
“Coronavirus has killed more than 100,000 people in the US in less than four months,” reads the May 28 CNN headline that comes directly from the “if it bleeds it leads” school of fearmongering, sensationalistic “journalism.”
In case people weren’t fearful enough, the Washington Post, bleated: “Experts say coronavirus might never go away as U.S. death toll reaches 100,000.”
{modulepos inner_text_ad}
All of these deaths are tragic and are to be mourned. But from a public policy standpoint, it is imperative to get beyond the fear and try to understand the numbers by putting them into a broader context. Failure to do that is to allow public manipulation through fear, enabling creation of public policies that are improperly aligned with reality, dangerous to social and economic well-being, and corrosive of individual liberty and the rule of law.
Even if not exact, comparisons are necessary. It is also necessary to note that the following comparisons do not suggest equivalency. Influenza is not the same as COVID-19. But, that being said, the parallels are instructive from the point of view of creating perspective.
With these provisos in mind we can look at flu statistics and compare them to COVID-19 statistics.
For the current flu season, the CDC estimates that there have been between 39,000,000 to 56,000,000 flu illnesses. The agency says that from this number, between 18,000,000 to 26,000,000 people visited medical care facility because of the flu. Of these, they estimate that between 410,000 to 740,000 people were hospitalized as a result of the flu. Finally, in this flu season, the CDC estimates that between 24,000 and 62,000 people died from the flu.
How does this compare with COVID-19 to date? As of May 27, the CDC reports that there have been 1,678,843 cases of COVID-19 in the United States. Total deaths, the CDC says, stand at 99,031 as of May 27.
In both cases, these seem to be large numbers of deaths, and they are close enough to be roughly equivalent. How do they stand as a percent of the population?
As of May 27, the U.S. Census Bureau puts the total population of the United States at 329,704,059. For the flu, that means 0.018 percent of the population of the United States succumbed to the flu, if the CDC’s high estimate of deaths is considered. For comparison 0.03 percent of the U.S. population died of COVID-19.
These numbers, of course, depend on the accuracy of the reported statistics. For influenza, questions have long circulated about the potential inaccuracy of the data collected and reported.
In his 2018 book Influenza, Dr. Jeremy Brown, who is the director of the Office of Emergency Care Research at the National Institutes of Health, described the process of collecting and reporting influenza statistics:
Every week…nurses, physicians, and their assistants…fill out a form that tells the CDC how many patients they’ve seen with an influenza-like illness. It’s a time-consuming but valuable report from the front line of the battle against influenza, but it has obvious limitations in terms of the quality of data it produces. Remember that one doctor might report “influenza” while another doctor seeing similar symptoms might report “fever,” or “gastroenteritis” or “viral syndrome,” all ILIs. When it comes time to aggregate the numbers and report ILI activity to the CDC, the electronic medical record might include some, all, or none of these diagnoses.
The CDC also relies on hospital labs to report how many tests for influenza they run, and how many of these are positive. You might think that these data would be more accurate than reviewing the electronic medical record, but here, too, the true incidence of influenza might vary, depending on which patients were swabbed and on the locations of the clinics and hospitals. You might have a doctor who swabs only when she is treating a patient who is very ill, or one who has cancer, HIV, or another complicating condition. In this case, the pool of all patients swabbed is limited but the number of positive cases is high. Or you could have the inverse: another doctor — even in the same hospital — whose practice is to swab many patients, not just those with a chronic illness. In this case, the sample size would be quite large and the number of positive cases of influenza comparatively small. And these numbers, in either situation, include only those who choose to see a doctor, and those doctors who choose to swab their patients. This imperfect, sometimes contradictory information is what the CDC has to work with.
It is easy to imagine that these limitations, as described by Dr. Brown, likely also impact collection of COVID-19 statistics.
Layered on top of that, of course, are the admissions that COVID-19 stats are inflated. One of these admissions comes from Dr. Ngozi Ezike, director of Illinois Department of Public Health, who said, ““The case definition is very simplistic,” for COVID-19. “It means, at the time of death, it was a COVID positive diagnosis. That means, that if you were in hospice and had already been given a few weeks to live, and then you also were found to have COVID, that would be counted as a COVID death. It means, technically even if you died of clear alternative cause, but you had COVID at the same time, it’s still listed as a COVID death.”
Clearly, this practice will lead to an inflated number of deaths attributed to COVID-19.
John Lott, one of the world’s better analysts and scholars and president of the Crime Prevention Research Center, was joined by Dr. Timothy Craig Allen, chair of the Department of Pathology at the University of Mississippi Medical Center and a Governor of the College of American Pathologists, in writing a piece for TownHall.com examining the over-counting of COVID-19 deaths.
Lott and Allen note: “New York is classifying cases as Coronavirus deaths even when postmortem tests have been negative. Despite negative tests, classifications are based on symptoms, even though the symptoms are often very similar to those of the seasonal flu. The Centers for Disease Control guidance explicitly acknowledges the uncertainty that doctors can face. When Coronavirus cases are ‘suspected,’ they advise doctors that “it is acceptable to report COVID-19 on a death certificate.”
They further point out that bad data is informing and shaping bad policy.
”Erroneous data unduly scare people about the risks of the disease,” they write. “It keeps the country locked down longer than necessary, which destroys peoples’ lives and livelihoods in many other ways. Exaggerated fears of the virus endanger lives by keeping people from obtaining treatment for other medical problems. It also makes it impossible to accurately compare policies across countries.”
Their conclusion?
“It is hard to believe that we are basing such crucial decisions on such flawed data.”
In fact, constructing a proper response to any public health crisis requires clear understanding of the nature and scale of the problem at hand. Policies based on incomplete, flawed, or manipulated data generally are ineffective at best and considerably damaging at worst.
With COVID-19, we’ve seen ample evidence of the latter, considering the catastrophic damage done to people’s lives, careers, and liberties by the tyrannical measures favored by petty dictators around the United States and the world.
Photo: asiandelight/iStock/Getty Images Plus
