The COVID-19 epidemic has shattered the idol of data-driven, evidence-based medical and public health practices that emerged in the 1990s.
Had we stuck to an evidence-based model, we would have never ventured beyond three understandable, sustainable, fiscally sensible tactics that apply to all respiratory infections:
- Stay home when you are sick — even with a common cold or influenza. Don’t go out until you are mostly free of symptoms, one to three weeks depending on what you have.
- Keep your hands clean, even to the point of obsession, and cough or sneeze into your elbow.
- Stay away from hospitals, nursing homes, retirement centers, and other places with vulnerable populations.
The bigger problem now, however, is how the medical and public health establishments, along with their media enablers, twist the data about the epidemic.
For example, the media routinely call positive tests “cases,” painting a frightening picture of two million very sick people. That is clearly untrue. Inadequate random sampling leaves us still unable to estimate how many people were exposed and not infected, or infected but asymptomatic. The first researchers who have courageously showed that COVID-19 is far more widespread and less deadly than claimed were shouted down. Today, however, this fact is much more widely acknowledged.
Another example: Hospital admissions are rising in some states, but we lack appropriate stratification of the data. Each hospital knows the age, race, gender, insurance coverage, and comorbidities of every person admitted to the facility. Why don’t we have these data? It is a relevant fact if those being hospitalized now are more likely to recover than those hospitalized in the initial wave.
Brown University’s new public health school dean, Ashish Jha, projects that by Sept. 30 about 200,000 Americans will have died from COVID-19. The unanswered question is how this compares to or affects the secular death rate. On average, 7,708 Americans die every day, and 65% of them are over age 70. Even though the 740 excess daily deaths this would add due to the coronavirus implies a 10% increase, they are very likely to overlap with other deaths, overwhelmingly concentrated among those who would die from any new clinical stressor — the old and those with life-shortening comorbidities.
If big data had lived up to its billing, we would know quickly, precisely, and transparently the demographics of people who test positive for COVID-19 (age, race, gender, zip code+4), whether their test results are for antibodies or for the virus itself, and what symptoms they had when tested. Instead, we have almost none of this.
And this failure to produce data is having consequences. A finding that hospitalizations and deaths are clearly concentrated in one or two particular group(s) would refute the wisdom of the shutdown strategy. The entire scam is even more striking when you consider that people such as Jha believe an excess of 740 deaths per day is “catastrophic,” but the loss of more than $ 1.3 trillion in gross domestic product and more than 20 million American jobs is acceptable. This is an easy position to take for an extravagantly compensated Ivy League dean whose livelihood is in no danger.
Americans should defy unreasonable activity restrictions and show the world what civilized civil disobedience and peaceful redress of grievances really looks like. Turn on the lights. Open your businesses. Help your children’s teams start back up again. Gather in your churches, homes, and neighborhoods. And yes, do it from behind a mask, but still, go back to work. Live your life. Dare your local dictators to push back on your legitimate claim to a place in the public square.
When the data are in, we will likely conclude that the virtual shutdown was never evidence-based. I wonder if Jha will one day consider it catastrophic that, with the COVID-19 calamity, the American medical and public health establishments lost their credibility, becoming disreputable caricatures of themselves.
Vik Khanna is a retired health policy consultant who earned his graduate degree in health policy and management at Johns Hopkins.