Jessica Rose recently highlighted an August 2021 CDC publication for Los Angeles County that defined “unvaccinated” individuals in a manner that distorted COVID hospitalization data and made it look like patients testing positive for SARS-CoV-2 were those who were never injected.

Per the report (Griffin, et al):

“Persons were considered fully vaccinated ≥14 days after receipt of the second dose in a 2-dose series (Pfizer-BioNTech or Moderna COVID-19 vaccines) or after 1 dose of the single-dose Janssen (Johnson & Johnson) COVID-19 vaccine; partially vaccinated ≥14 days after receipt of the first dose and <14 days after the second dose in a 2-dose series; and unvaccinated <14 days receipt of the first dose of a 2-dose series or 1 dose of the single-dose vaccine or if no vaccination registry data were available.”

Rose rightly characterizes these definitions of fully-, partially-, and un-vaccinated as “truly insincere and convoluted beyond belief” and is correct that the definitions distort the reported hospitalization rates (shown below).

Besides the tortuous descriptions for vaccine recipients, “COVID-19–associated hospitalizations” were defined nonsensically in the study solely by the timing of hospital admission and a positive SARS-CoV-2 test. This approach captured incidental hospitalizations, patients hospitalized for unrelated reasons who happened to test positive (which was a problem for interpreting “COVID data” prior to shot deployed as well).

Rose both understates and overstates the definitional issues when she says of the above chart, “Look! It’s the ‘unvaccinated’ who were hospitalized the most! But seeing as how these ‘unvaccinated’ individuals likely received at least 1 dose, then they were certainly qualify as being “vaccinated”, no?”

The unvaccinated group is inflated, but the definitions preclude saying that unvaccinated individuals “likely received at least one dose”. A patient was classified as unvaccinated if they fell into any of the following categories:

  1. Received first dose of a 2-dose series (Pfizer or Moderna) <14 days ago. So, the patient had dose 1 but was (presumably) hospitalized and/or tested positive within the first 13 days after the dose.
  2. Received the single-dose Johnson & Johnson shot <14 days ago. The patient wasn’t slated for a second dose but was (presumably) hospitalized and/or tested positive within the first 13 days after the J&J single-dose.
  3. No vaccination registry data is available for the patient. This would include no record of any vaccinations incomplete, mismatched, or missing records. These patients are best thought of as “unverified status”. Per the report, data were unavailable for individuals who lived in Los Angeles County but were vaccinated outside of California. This would include patients from Mexico who, in contrast to what the authors imply, may not have received any doses at all.

We can’t know how many patients classified as unvaccinated fell into group 3 and had received zero doses of a COVID shot.

Given the definitions for all categories, the study results can be dismissed out of hand. No further analysis or guesswork needed.

Chicago-Style

Like many data issues during the COVID event, the definitional problems weren’t specific to one U.S. city (or to one country, e.g., Fenton et al) and were documented by professional and armchair analysts as they were occurring. Pandemic of the unvaccinated was always a lie.

In September 2021, just after the L.A. study was published, I started to capture how the Chicago health department “breakthrough” reporting also rested on shifting, opaque definitions. (See threads here.) After obtaining raw case, hospitalization, and death data via FOIA, it became clear that partial-dose residents were routinely folded into the “unvaccinated” group (or ignored entirely) while other categories quietly disappeared and were never fully explained.

By March 2022, I had established CDPH’s vaccinated were residents who had completed 2-dose series ≥14 days before testing positive or completed J&J ≥14 days before testing positive and had no positive test in the previous 45 days. (See related article here.)

Unvaccinated was hyper-inflated and included (at various points?):

  • 0 doses
  • 1 dose of a 2-dose series
  • 2 doses but <14 days old
  • 1 dose but <14 days old
  • Anyone with a positive test in the past 45 days
  • Anyone with unverified vaccination status

The partial-dose residents became invisible and near-impossible to disentangle.

Chicago officials used the reports to shame “the unvaccinated,” coerce unnecessary shots, ban people from public places, and threaten city workers’ jobs.

No one has been held accountable for the fraud; local media never reported it properly.

Earlier Still…

The definitional issues were obvious from a CDC national “breakthrough infections” report issued in May 2021: COVID-19 Vaccine Breakthrough Infections Reported to CDC – United States, January 1-April 30 2021.

At the time, I characterized the data as “ridiculous” for reporting asymptomatic COVID hospitalizations and deaths.

A “vaccine breakthrough infection” was defined as “the detection of SARS-CoV-2 RNA or antigen in a respiratory specimen collected from a person ≥14 days after receipt of all recommended doses of an FDA-authorized COVID-19 vaccine.”

This means that any patient who had received all recommended doses but who tested positive and was hospitalized, or was hospitalized and died before 14 days had passed, was excluded from analysis.

By this point, it was well-known that “cases” of the disease called COVID-19 were near-synonymous with testing positive for a “novel” virus named SARS-CoV-2, irrespective of symptoms. The notion of being hospitalized for or dying from something that doesn’t trigger illness or a disease-response never made sense. The language used in the May 2021 report reinforced these ideas and failed to delimit mutually-exclusive patient categories.

Author Leisha Nolan confirmed to me via email that fully-vaccinated patients who were hospitalized and incidentally tested positive for SARS-CoV-2 were not counted as “COVID” hospitalizations or deaths – a sharp contrast with how “cases” were treated in 2020, prior to shot deployment.

Nolan also admitted, “In many cases it is challenging to determine the relationship between a SARS-CoV-2 infection and the reason someone was hospitalized or passed away.”

This was always true but had not been accounted for in COVID case, hospitalization, or death data, yet suddenly became important, presumably because the shot could not afford to be shown as inefficacious, or (worse) harmful, or (even worse) never needed by anyone of any age or health condition.

Dr. Nolan never replied to my follow-up question about whether an effort was underway at CDC to apply the same parameters in their vaccine breakthrough analysis to analyzing all SARS-CoV-2 infections, versus only to “breakthrough” infections.1

Constant Reminders

The need to remind people about what was known and when it was known about the COVID event, including what was already obvious from early public data about the COVID shot, will never go away. Admittedly, we are still in a stage where “reminder” applies to the few and “first-time exposure” applies to the majority.

Not only was there never a “pandemic of the unvaccinated”, there was never a pandemic involving a sudden-spreading novel SARS virus causative of a unique disease. This makes the lies and coercion around the injections even more criminal.

Circling back to these points in late 2025 and beyond is not a waste of time and will continue to matter until more of the public grasps what the data and documentation actually showed.


  1. Explanatory visuals, appended below, illustrate the issues. ↩︎

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3 responses to “Reminders for L.A., Chicago, and Everywhere Else: “Pandemic of the Unvaccinated” was a Lie Propped Up by Bad, Data-Distorting Definitions”

  1. KW Avatar

    An interesting record on this topic (the frauds of infection classification, antibody tests, etc.,) is FDA’s May 19, 2021 statement: “Antibody Testing Is Not Currently Recommended to Assess Immunity After COVID-19 Vaccination: FDA Safety Communication” –

    “The U.S. Food and Drug Administration (FDA) is reminding the public and health care providers that results from currently authorized SARS-CoV-2 antibody tests should not be used to evaluate a person’s level of immunity or protection from COVID-19 at any time, and especially after the person received a COVID-19 vaccination. While a positive antibody test result can be used to help identify people who may have had a prior SARS-CoV-2 infection, more research is needed in people who have received a COVID-19 vaccination. Currently authorized SARS-CoV-2 antibody tests have not been evaluated to assess the level of protection provided by an immune response to COVID-19 vaccination. If antibody test results are interpreted incorrectly, there is a potential risk that people may take fewer precautions against SARS-CoV-2 exposure…”

    Available on Wayback machine,

    https://web.archive.org/web/20220322120619/https://www.fda.gov/medical-devices/safety-communications/antibody-testing-not-currently-recommended-assess-immunity-after-covid-19-vaccination-fda-safety

    along with another FDA information page:

    https://web.archive.org/web/20220319140915/https://www.fda.gov/medical-devices/coronavirus-covid-19-and-medical-devices/antibody-serology-testing-covid-19-information-patients-and-consumers which notes that

    “At this time, SARS-CoV-2 antibody tests do not tell you if:

    -You currently have COVID-19, the disease caused by the SARS-CoV-2 virus,

    -You have immunity that will prevent COVID-19,

    -You need a COVID-19 vaccine, or

    -Your COVID-19 vaccine worked.”

    The current version of this statement (last updated April 2024) is here:

    https://www.fda.gov/medical-devices/coronavirus-covid-19-and-medical-devices/antibody-serology-testing-covid-19-information-patients-and-consumers

    1. Jessica Hockett Avatar

      Thank you, will review. What I read at first glance is “SARS-CoV-2 antibody tests tell you nothing.”

      Related notes:

      David Crowe interview, 27 May 2020 (not quite verbatim):

      “Interviewer: They’ve developed an antibody test, which they claim has a 90–98% specificity. What issues do you see with these antibody tests?

      Crowe: Well, they’re being rolled out very quickly, and every manufacturer—whether it’s for PCR or antibody tests—claims their tests are 99 % accurate. That’s because you can’t get a test approved unless it claims to be at or near 99 % accuracy.

      But here’s the problem: the manufacturers are in charge of testing their own products. I don’t know if you remember the Volkswagen scandal in the U.S., where they were cheating on gas-mileage tests.

      Interviewer: Yes, I do.

      Crowe: Volkswagen, a major corporation, manipulated test results so they could advertise better fuel efficiency. The same principle applies here: we can’t rely solely on the manufacturers’ claims.

      One of the interesting things about antibody tests is that they’re usually detecting low prevalence—typically between 1% and 5%. These tests are meant to identify anyone who has been infected since the beginning of the outbreak. So if we tested everyone in Canada, where I live, people infected in January, February, March, April, or May should all test antibody positive by now.

      In contrast, PCR tests only indicate whether you’re currently infected—maybe today, maybe yesterday. If I test PCR-negative today, I could still test negative tomorrow. All it tells you is whether you’re positive at that moment.

      And yet, the percentage of people testing positive via PCR is often ten times higher than what’s found in antibody testing. That makes no sense.

      In places with many reported cases, you’d expect antibody positivity rates of 30, 40, 50, even up to 80%. But the numbers remain remarkably low. That’s why I don’t trust the antibody tests at all—because the results don’t align with expectations.

      Interviewer: Supposedly, these antibody tests detect antibodies specific to COVID-19. But is it possible that other viruses or medical conditions could also produce similar antibodies?

      Crowe: Yes, and that’s a major concern. Some manufacturers have tested their antibody kits against other conditions, and several found that the tests turned up positive for other viruses, bacteria, or autoimmune diseases. So we know cross-reactivity happens.

      One study in Holland even looked at old blood samples from 2018 and found that 14 % tested positive. How is that possible? Either the virus was circulating two years ago—meaning it clearly wasn’t dangerous—or the test is simply inaccurate.

      Overall, the validation work for these tests is not sufficient. We also don’t know if different tests are detecting the same thing. No one has run the same blood samples from, say, 1,000 people through all ten commercially-available antibody tests. That would reveal discrepancies—samples testing positive on one test but negative on others.

      There was even a journalist in England who believed she had COVID. She’d been sick about a month earlier and took four different antibody tests. Two came back positive, and two came back negative. That says a lot.

      So these tests haven’t been properly validated, and we really don’t know what their results mean. We don’t know if a positive result means you’re protected. More importantly, we don’t know if a negative result means you’re at risk of becoming ill. In short, we don’t know what these tests actually tell us.”

      ————

      JAH Comments:

      Regardless of what the tests are testing, Crowe is correct, using the standards of epidemiology and taking the “outbreak” at face value. He’s saying, “Look, we’ve had a lot of PCR positives. Even if you discount the false positives, shouldn’t the people who supposedly tested positive also show up positive on the antibody tests? By the PCR numbers alone, we should be seeing high antibody rates. So why aren’t we?” New York City AB testing in spring 2020 DID come back at the rates Crowe proposed (but he likely didn’t know that).

      Sunetra Gupta apparently agreed with Crowe as to how high the antibody percentages should have been. Gupta’s timeline places an evolutionary event no earlier than late summer 2019. By March 2020, she and the Oxford Evolutionary Ecology of Infectious Disease group were estimating that about half of the United Kingdom had already been infected with SARS-CoV-2, with “arrival” dated to January or even December. The Financial Times framed it this way:

      “The Oxford results would mean the country had already acquired substantial herd immunity through the unrecognised spread of Covid-19 over more than two months. If the findings are confirmed by testing, then the current restrictions could be removed much sooner than ministers have indicated.”

      What if Crowe and Gupta are “right”? The pieces can fit together without requiring a virus to be causal, or singularly causal, of illness or of any meaningful person-to-person “spread.”

      NYC’s simultaneously high % pos for PCR and sera in spring 2020 has not been explained, including by people with faith in such tests. They would have to say that it had already ‘spread’ — and did so with no impact on all-cause mortality until after “15 Days to Slow the Spread” was called.

      ——

      Journalist in England — all a part of the “we don’t have enough/enough of the right tests” plot line.

      https://www.bloomberg.com/news/articles/2020-05-05/coronavirus-antibody-tests-may-produce-contradictory-results

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