Megalextoria

On Thursday, a Florida health official told a local news station that a young man who was listed as a COVID-19 victim had no underlying conditions.

The answer surprised reporters, who probed for additional information.

“He died in a motorcycle accident,” Dr. Raul Pino clarified. “You could actually argue that it could have been the COVID-19 that caused him to crash. I don’t know the conclusion of that one.”

The anecdote is a ridiculous example of a real controversy that has inspired some colorful memes: what should define a COVID-19 death?

While the question is important, such incidents may be just the tip of the proverbial iceberg regarding the unreliability of COVID-19 data.

In May, a public radio station in Miami broke what soon became a national story. The US Centers for Disease Control and Prevention (CDC) had been conflating antibody and viral testing, obscuring key metrics lawmakers use to determine if they should reopen their respective economies.

The story was soon picked up by NPR, who spoke to an epidemiologist who condemned the practice.

“Reporting both serology and viral tests under the same category is not appropriate, as these two types of tests are very different and tell us different things,” Dr. Jennifer Nuzzo of the Johns Hopkins Center for Health Security told NPR.

The Atlantic soon followed with an article that explained the agency was painting an inaccurate picture of the state of the pandemic. The practice, the writers said, was making it difficult to tell if more people were actually sick or had merely acquired antibodies from fighting off the virus.

Public health experts were not impressed.

“How could the CDC make that mistake? This is a mess,” said Ashish Jha, the K. T. Li Professor of Global Health at Harvard and director of the Harvard Global Health Institute.

In some ways the “mess” was no surprise. Two weeks earlier, Dr. Deborah Leah Birx, the White House’s coronavirus task force response coordinator, reportedly ripped the agency in a meeting, saying “there is nothing from the CDC that I can trust.”

Birx’s concerns about the CDC’s data did not alleviate concerns of data manipulation. The New York Times speculated that perhaps the agency had sought to “bolster the testing numbers for political purposes.” The Texas Observer wondered if the state was “inflating its COVID testing numbers by including antibody tests.”

Considering President Trump’s sometimes comically inaccurate boasts about America’s testing prowess, perhaps such questions were not unjustified. The many people who spoke to the Times said the answer was simpler, attributing the flawed system to “confusion and fatigue in overworked state and local health departments.”

If data manipulation had been the motive, the architects of the ploy were in for a rude awakening. Testing numbers did soar, but so did case numbers; the surge in late June and throughout July spawned new fears of a second wave and more lockdowns and more charges that America was botching the pandemic. (The surge was the result of both increased testing, including antibody testing, as well as a resurgence of the virus.)

Tensions between the White House and its own agency boiled over last week when the Trump Administration stripped the CDC of its role in collecting data on COVID-19 hospitalizations.

A Data Fiasco of Historical Proportions?

It’s hard to read the drama, incompetence, and confusion without thinking about Dr. John Ioannidis, the C.F. Rehnborg Chair in Disease Prevention at Stanford University.

In a March 17 STAT article, Ioannidis warned the world was looking at what could turn out to be a “once-in-a-century evidence fiasco.” He worried central planners were making sweeping and reflexive changes without sufficient data.

Locking people up without knowing the fatality risk of COVID-19 could have severe social and financial consequences that could be totally irrational, Ioannidis warned.

“It’s like an elephant being attacked by a house cat. Frustrated and trying to avoid the cat, the elephant accidentally jumps off a cliff and dies,” said Ioannidis, one of the most-cited scientists in the world.

In one sense, Ioannidis has already been proven right. The models on which lockdowns were initiated have already proven astronomically wrong. But that was hardly the only example.

Every day it seems there’s another story about reporting flaws or mixups.

Tuesday it was a lab in Connecticut where researchers said they discovered a flaw in a testing system for the virus. The flaw resulted in 90 people receiving false positives. That may not sound like many, but researchers said the test is used by labs across America.

A few days earlier, it was announced that Texas had removed 3,484 cases from its positive Covid-19 case count because the San Antonio Health Department was reporting “probable” cases. None of the people had actually tested positive for COVID-19.

We don’t know how many new cases are probable cases and not positive cases, but we know it’s a lot. That’s because in April, the CDC changed its reporting to include people who had not tested positive for the virus but might have it. (The CDC’s criteria for what qualifies as a probable case are more than a little confusing.)

As the Associated Press noted, the change was made with the understanding that “deaths could soon jump because federal health officials will now count illnesses that are not confirmed by lab testing.”

The One Thing Everyone Agrees On

COVID-19 has been far from the deadliest virus in modern history, but it has been the most divisive. The public, politicians, policy experts, and public health officials have disagreed on how deadly it is and how best to contain it.

But the one thing everyone seems to agree on is the numbers we have—fatalities and cases—are way wrong. A new CDC report estimates COVID-19 rates about 10 times higher than reported. Ioannidis put the figure even higher, estimating weeks ago that as many 300 million people had already been infected globally.

Deaths are more complicated.

The New York Times says COVID-19 deaths have been massively undercounted. Dr. Ashish Jha, speaking to Lawrence O’Donnell on MSNBC, agreed, saying most experts agreed there is a “substantial undercount.”

Others, including nearly one-third of Americans according to a recent survey, believe that the COVID-19 death toll is inflated. This includes physicians who say medical professionals are being pressured by hospital administrators to add coronavirus to death sheets.

Writing at the American Mind, Angelo Codevilla recently argued if the CDC had used the same criterion for the SARS virus as COVID-19—primarily “severe acute respiratory distress syndrome”—total COVID fatalities in the US would have been 16,000 through June.

Nobody knows the true count, of course. But the one thing left and right seem to agree on is the data we have are junk. And yet the lesson we keep hearing is “trust the experts.”

“Follow the science. Listen to the experts. Do what they tell you,” Joe Biden said in April.

But thinkers as diverse as Matthew Yglesias at Vox to author Matt Ridley have pointed out the dangers of blindly following “the experts,” especially when they’ve shown themselves to be spectacularly wrong from the very beginning on the COVID-19 pandemic.

“It’s dangerous to rely too much on models (which lead politicians to) lock down society and destroy people’s livelihood,” Ridley recently told John Stossel. “Danger lies both ways.”

Ridley has a point. The experts can’t agree on their own numbers or even clearly answer if a man who died in a motorcycle accident while infected should be labeled as a COVID-19 death.

In light of this, perhaps it’s time for the experts to exercise some humility and begin offering guidance to individuals instead of advocating collective blunt force.

This article was originally published on FEE.org. Read the original article.

Dr. John Ioannidis became a world-leading scientist by exposing bad science. But the COVID-19 pandemic could prove to be his biggest challenge yet.

Ioannidis, the C.F. Rehnborg Chair in Disease Prevention at Stanford University, has come under fire in recent months for his opposition to state-ordered lockdowns, which he says could cause social harms well beyond their presumed benefits. But he doesn’t appear to be backing down.

In a wide-ranging interview with Greek Reporter published over the weekend, Ioannidis said emerging data support his prediction that lockdowns would have wide-ranging social consequences and that the mathematical models on which the lockdowns were based were horribly flawed.

Ioannidis also said a comprehensive review of the medical literature suggests that COVID-19 is far more widespread than most people realize.

“There are already more than 50 studies that have presented results on how many people in different countries and locations have developed antibodies to the virus,” Ioannidis, a Greek-American physician, told Greek Reporter. “Of course none of these studies are perfect, but cumulatively they provide useful composite evidence. A very crude estimate might suggest that about 150-300 million or more people have already been infected around the world, far more than the 10 million documented cases.”

Ioannidis said medical data suggest the fatality risk is far lower than earlier estimates had led policymakers to believe and “is almost 0%” for individuals under 45 years old. The median fatality rate is roughly 0.25 percent, however, because the risk “escalates substantially” for individuals over 85 and can be as high as 25 percent for debilitated people in nursing homes.

“The death rate in a given country depends a lot on the age-structure, who are the people infected, and how they are managed,” Ioannidis said. “For people younger than 45, the infection fatality rate is almost 0%. For 45 to 70, it is probably about 0.05-0.3%. For those above 70, it escalates substantially…”

Because of this, Ioannidis sees mass lockdowns of entire populations as a mistake, though he says they may have made sense when experts believed the fatality rate of COVID-19 was as high as 3-5 percent.

In March, in a widely read STAT article, Ioannidis said it was uncertain how long lockdowns could be maintained without serious consequences.

“One of the bottom lines is that we don’t know how long social distancing measures and lockdowns can be maintained without major consequences to the economy, society, and mental health,” Ioannidis wrote. “Unpredictable evolutions may ensue, including financial crisis, unrest, civil strife, war, and a meltdown of the social fabric.”

Nearly three months after that interview, the world has seen unemployment levels unseen since the Great Depression, mass business closures, spikes in suicide and drug overdose, and social unrest on a scale not seen in the US since the 1960s.

“I feel extremely sad that my predictions were verified,” Ioannidis said. He continued:

“Major consequences on the economy, society and mental health” have already occurred. I hope they are reversible, and this depends to a large extent on whether we can avoid prolonging the draconian lockdowns and manage to deal with COVID-19 in a smart, precision-risk targeted approach, rather than blindly shutting down everything. Similarly, we have already started to see the consequences of “financial crisis, unrest, and civil strife.” I hope it is not followed by “war and meltdown of the social fabric.” Globally, the lockdown measures have increased the number of people at risk of starvation to 1.1 billion, and they are putting at risk millions of lives, with the potential resurgence of tuberculosis, childhood diseases like measles where vaccination programs are disrupted, and malaria. I hope that policymakers look at the big picture of all the potential problems and not only on the very important, but relatively thin slice of evidence that is COVID-19.”

Ioannidis did not spare modelers who predicted as many as 40 million people would die, or those who claimed the US healthcare system would be overrun.

“The predictions of most mathematical models in terms of how many beds and how many ICU beds would be required were astronomically wrong,” Ioannidis said. “Indeed, the health system was not overrun in any location in the USA, although several hospitals were stressed.”

Conversely, he added, these actions had detrimental effects on the US health care system, which was “severely damaged” because of measures taken.

Only time will tell if Ioannidis is proven correct in his assessments. But if he’s even half right, it would suggest that the experts did indeed fail again.

There’s little question that the lockdowns have caused widespread economic, social, and emotional carnage. Evidence that US states that locked down fared better than states that did not is hard to find.

Though not yet certain, the COVID-19 pandemic may well turn out to be another example of central planning gone wrong.

As I previously noted, it’s a sad irony that many of the greatest disasters in modern history—from Stalin’s “kolkhoz” collective farming system to Mao’s Great Leap Forward and beyond—are the result of central planners trying to improve the lot of humanity through coercive action.

During the coronavirus pandemic, experts may have unintentionally brought about one of the most serious human disasters in modern history by removing choice from individuals with superior local knowledge.

“This is not a dispute about whether planning is to be done or not,” Hayek wrote in The Use of Knowledge in Society. “It is a dispute as to whether planning is to be done centrally, by one authority for the whole economic system, or is to be divided among many individuals.”

This article was originally published on FEE.org. Read the original article.