For 18 months, the U.S. and much of the West have based their responses to COVID-19 on garbage data. First, we used “cases.” Politicians and policymakers used positive tests as a measure to justify lockdowns, masks, school closures, and a host of other measures that public health agencies had never used before. Millions of tests given to asymptomatic people inflated these numbers, impairing our ability to assess the risk of COVID-19 causing symptomatic illness for the general population.
The case counts ran on cable news channels, along with a running tally of deaths. With no context, these numbers were alarming. If media outlets had told you that three-quarters of the deaths were occurring in Americans over 70, the mitigation actions would have made little sense. Nursing home deaths far outstripped the percentage of the population that resides there. These trends were evident from experience in Italy and very early in the pandemic in the United States. One early retrospective study found that nearly 90% of hospitalized patients who died with COVID-19 had a Do Not Resuscitate order in place before testing positive for the virus.
Related: Study Finds 89% of Patients Who Died From COVID-19 Had a Do-Not-Resuscitate Order
Since the arrival of the Delta variant, health agencies and the media still publish case counts. Thankfully the real-time counters are gone. Now, hospitalizations are the metric central to the panic. As it turns out, even this metric is not a good guide. Testing all patients for COVID-19 and then counting them as a COVID-19 hospitalization is misleading and overstates how the virus impacts our healthcare system. One study from the VA showed that as many as 50% of patients listed as a COVID hospitalization were admitted for another reason or experienced mild illness. Many of the positive tests were incidental because all inpatients received one.
Now the NHS in the United Kingdom has published a similar finding. Looking at the various regions, somewhere between 17% and 38% of patients listed as COVID-19 hospitalizations were admitted and receiving treatment for another illness. NHS started tracking incidental cases picked up by mandatory testing in June of 2021. According to the Daily Mail:
Leaked figures in July suggested more than half (56 per cent) of these were patients who only tested positive after admission.
This trend is largely consistent with separate data published every fortnight by Public Health England, which shows four in 10 Covid admissions in patients with the Delta variant are ‘incidental’.
At best, experts say the data suggests that hospital figures reported on the Government’s Covid dashboard are ‘misleading’.
Prime Minister Boris Johnson’s Winter Plan would put the nation on “Plan B” if the NHS comes under unsustainable pressure due to COVID-19 hospitalizations. Plan B triggers mandatory COVID passports and masks in specific settings. It seems, at minimum, that British citizens deserve accurate information about the number of hospitalizations for COVID-19 before being asked to submit to masking and a “papers please” society.
So do Americans. At a minimum, hospital capacity monitoring by the CDC needs to distinguish between patients admitted for COVID-19—with symptoms of the illness—and those who test positive incidentally after being admitted for something else. To put a finer point on the data, the CDC should adopt the NIH definition of severe COVID-19 and provide a separate count of those whose blood oxygen falls under 94% and require supplemental oxygen.
Related: A Review of COVID-19 Deaths in Two California Counties Drops the Total by Nearly 25%
It is astonishing that nearly two years after the arrival of COVID-19, the U.S. is still using misleading and manipulative data. Two studies, along with CDC breakthrough infection data, show that current death tracking overcounts the incidence by as much as 25%. It is time to limit the counts only to patients who died after suffering from severe COVID-19 symptoms. In an era of vaccine mandates and mandatory masking, our agencies owe Americans accurate data.
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