This is not medical advice. See full disclaimer at the bottom.

Having coined the phrase “Pandemic of PCR-Negative COVID-Like Illness” on January 27 (republished on Substack February 19), I was overjoyed to just find a paper published on February 13 entitled “RT-PCR negative COVID-19.” As the title suggests, it found that PCR-negative COVID-like illness is often…

drumroll…

COVID!

The study was done by researchers from Rutgers New Jersey Medical School using samples from University Hospital in Newark. They used samples from April through October, 2020, before the vaccines were rolled out and while the population seroprevalence (the commonness of serum antibodies against the COVID virus) was low.

They separated patients according to their medical records into four groups:

PCR-Confirmed COVID patients had the clinical signs of COVID confirmed by a positive PCR test.
“COVID Probables” had the clinical signs of COVID, no alternative diagnosis, and at least two negative PCR tests (although three patients in this group were not tested at all and referred on clinical suspicion).
“COVID Suspects” had the clinical signs of COVID and at least two negative PCR tests, but also had an alternative diagnosis that was in competition with the potential COVID diagnosis.
“COVID Non-Suspects” were collected from the last two months of the period, September and October, who had a negative PCR test and did not have clinical symptoms of COVID.

Alternative diagnoses in the “Suspects” included exacerbation of heart failure or COPD, and bacterial pneumonia.

They initially found 314 PCR-Confirmed, 58 Probables, 46 Suspects, and 280 Non-Suspects. We should keep in mind this is before the vaccine rollout, so there was no systematic way of giving people with COVID-like illness a negative test yet. This probably explains why there were so many more PCR-Confirmed than Probables. They whittled these numbers down to those who had leftover blood samples in the hematology lab within four to six days of being drawn, so that antibody levels could be measured. Then they whittled down the PCR-Confirmed further by finding those best matched to the Probables by age, sex, symptom duration, and disease severity. They did the same with the Non-Suspects, only matching them by age, sex, BMI, and co-morbidities.

After all the whittling, there were 40 PCR-Confirmed, 20 Probables, 15 Suspects, and 43 Non-Suspects.

The IgG antibodies, suggesting COVID infection at any time in the past, were positive in 80% of the PCR-Confirmed and 60% of the Probables (P=0.236). The IgM antibodies, suggesting recent infection, were positive in 72.5% of the PCR-Confirmed and 50% of the Probables (P=0.096). While antibody positivity doesn’t show definitively the patients are hospitalized because of the infection, the IgM are particularly suggestive. It would be rather bizarre if patients had recently gotten over COVID and then soon after landed in the hospital for something that looked just like COVID but wasn’t.

The differences between the two groups did not reach statistical significance, but they are consistent with a positive PCR test making it slightly more likely that a case of COVID-like illness is COVID.

The bigger point, though, is this: some half to two-thirds of PCR-negative COVID-like illness, in the absence of a compelling alternative diagnosis, is COVID.

In fact, if we assume that all of the PCR-Positives are actual COVID cases and we adjust the Probables for relative prevalence of IgM antibodies, we arrive at the conclusion that 69% of PCR-negative COVID-like illness without a compelling alternative diagnosis is COVID.

In contrast to the first two groups, the Suspects and Non-Suspects were less than 15% positive for IgG and less than 5% positive for IgM. Thus, the high prevalence of antibodies in the Probables was not driven by a high seroprevalence in the background.

Since the PCR-Confirmed and Probables were matched by symptom duration and severity, it is not possible to directly compare the severity between the two groups.

To review some key points from Explaining the Hospitalization Paradox:

In the Pfizer trial, at the two-month mark the vaccine brought COVID-like illness from 18% to 16.3%. Despite this 9.4% relative reduction in illness, serious illnesses (3 per group) and hospitalizations (2 per group) were perfectly evenly distributed between the two groups. However, the vaccine was 95% effective at making the ill people test negative on a PCR nasal swab. If PCR-negative COVID-like illness is almost as likely to be COVID as PCR-positive COVID-like illness, this 95% reduction in a positive test is meaningless.
We do not have this data from the six-month results of the Pfizer trial. It was never published in the peer-reviewed trial report of the two-month data. It was only leaked in the FDA advisory board’s briefing notes. Perhaps the six-month data will be found in the 55,000 pages of documents dumping on March 1. Presumably, this data will look even worse for the vaccine since it covers the period of waning efficacy between 3 and 6 months.
We do not have this data for any of the other vaccines.
According to CDC data, among those hospitalized between late August 2021 and early January 2022, 79% of those hospitalized with COVID-like illness test negative and 57% of those hospitalized with COVID-like illness are vaccinated. The booster shots are 90-94% effective at earning those hospitalized with COVID-like illness a negative test. If PCR-negative COVID-like illness is almost as likely to be COVID as PCR-positive COVID-like illness, this 90-94% reduction in a positive test is meaningless.
None of this data separates those with and without a compelling alternative diagnosis, as the current study did.

While this study is too small to make broad, sweeping conclusions such as “69% of all hospitalizations for PCR-negative COVID-like illness are actually COVID,” it provides proof of principle that a large percentage of them are. Whether that is 50% or 99% is besides the point.

The point is that it is now entirely plausible that a huge proportion of PCR-negative COVID-like illness is COVID, and that this now has evidence from IgM antibodies backing it up rather than mere speculation.

Notably, this study was done before the vaccine rollout. If the vaccines actually reduce the incidence of COVID, then this study might be overestimating how much PCR-negative COVID-like illness is actually COVID in vaccinated people. On the other hand, if the vaccines just reduce the incidence of a positive test, this study could be dramatically underestimating how much is actually COVID. Since both of these are equally plausible, the only worthwhile data is data designed to distinguish between them. Data that assumes a negative test means no COVID is built on a false premise.

This deeply questions the efficacy estimates of the vaccines. We now can say that much of the COVID-like illness is probably COVID, so we aren’t just talking about viral competition, other types of disease substitution, vaccine side effects, spike protein toxicity, or coincidentally high rates of unrelated diseases. We are talking about vaccines generating negative COVID tests for actual COVID, even COVID worthy of hospitalization.

In addition to demanding the long-term disability, ICU admissions, and mortality of total COVID-like illness separated by PCR status, vaccination status, age, and comorbidities, we should now demand from the CDC separation according to presence or absence of alternative diagnosis, as this may aid in separating out likely cases of “true COVID” from other forms of COVID-like illness.

More to the point, the entire premise of the vaccine efficacy estimates — that we can ignore those who are just as ill but test negative — is crumbling.

We must demand in response to any analysis of “severe COVID-19,” “COVID-19-associated hospitalizations,” and “COVID-19 mortality” that those who are just as sick, just as hospitalized, and just as dead — but who test negative — be given a voice in the data.

Disclaimer

I am not a medical doctor and this is not medical advice. My goal is to empower you with information. I will not take a position on whether you should or should not get vaccinated. Please make this decision yourself, consulting sources you trust, including a caring health care professional.

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