Here is a government study that claims COVID-19 was a greater health problem than influenza.
In the study, the authors state in their conclusions:
“Our paper highlighted the greater population-level impact of COVID-19 compared with influenza in terms of adult hospitalizations, especially among those unvaccinated. However, influenza had greater impact than COVID-19 among <18 regardless of vaccine status or the circulating variant.”
Let’s check to see if their own results reveal something different:
To decipher this confusing graph, first ignore all the age groups under 18 (because the authors give all sorts of explanations/excuses that seem to defy logic).
The first thing to notice is that for all ages 18 and up, vaccinated patients who tested positive for COVID 19 are compared against patients who were hospitalized for influenza in 2009/10, 2015/16, and 2016/17. All patients who tested COVID positive up to two weeks prior to hospitalization were counted in the beige/red bars, even if their hospital stay was for another reason.
That’s right! Patients could test positive for COVID-19, go in for a scheduled hip surgery 2 WEEKS later and then be counted as a “Covid Hospitalization”. Seriously.
Did these authors finish high school?
(Even elementary school kids know the difference between grandma going into hospital for a cough and grandma going into hospital because her hip hurts.) It would make sense to only compare COVID illnesses with Influenza illnesses.
But before we jump to conclusions, let’s look a little more closely .
What’s with the COVID bars being half beige, and half red?
It looks like the beige bars are the authors trying to separate vaccinated patients who were in hospital for another reason from patients who were in hospital for COVID-19 illness. Seems like a good idea. How did they do that?
(Maybe the authors aren’t so bad after all…)
It looks like a 600 patient hospital chart review was how they “estimated” how many COVID-19 positive patients were hospitalized for “another reason”. The non COVID but COVID positive became the beige half of the bar, and the red half is supposed to be those in hospital because of COVID. A sample of hospital charts to see how many who tested positive for COVID in the past 2 weeks (or two days after admission, which might be for something non covid like appendicitis) were actually there because of a COVID illness looks reasonable.
Extrapolating that estimate to all the patients seems a stretch but not entirely unreasonable either.
During the influenza seasons they cited (2009/10, 2015/16, and 2016/17), there was no mass testing for influenza. (I know because I was there and working through all 3 flu seasons.) Before COVID, patients would usually only get tested for influenza if they were being admitted for a respiratory illness. Even then, not all patients admitted with respiratory symptoms were tested for influenza. If an influenza diagnosis would change treatment, only then would many physicians go through the extra time and expense of the test. If the patient’s respiratory isolation status in hospital and their treatments are the same regardless of what virus they had, many physicians like me would wait, only checking for influenza if the patient failed to get better.
In 2020, the panic over COVID-19 changed all that, and suddenly there was mass testing for COVID-19 regardless of whether the patient was in the hospital for respiratory illness or other reasons like depression.
Far more people were tested for COVID than any other influenza.
This excessive testing is reflected in the high numbers of hospitalized patients who were COVID-19 positive but NOT in hospital for COVID.
So looking at their graph, if we wanted to compare COVID to Influenza, we should only look at the dark red portions of the bars.
For the first 8 weeks of 2022, during the author’s claimed peak of the Omicron wave, they estimated that less than half the hospitalizations in the “fully vaccinated” population who tested positive for COVID-19 were because of a COVID-19 illness.
Now, let’s look at their first graph from March 2020-February2021!
(mostly unvaccinated, because vaccine first became available in December 2020)
The red bars in this graph have 9 months of unvaccinated patients (March-November 2021); and 3 months of possibly vaccinated patients (December 2020-February 2021). The authors compare influenza hospitalizations with hospitalizations for people who just tested positive for COVID.
The hospitalization rate for double vaccinated people who tested positive at the peak of Omicron was 200 per 100,000 for people aged 70+; and about 100 per 100,000 population for ages 60-69. (Graph with the beige bars)
The hospitalization rate before COVID “vaccines” just over 200 per 100,000 for people aged 70+; and about 100 per 100,000 population for ages 60-69. (Graph with the all red bars)
Not much difference pre “vaccine” and post “vaccine” eh?
Why is it that when most patients are unvaccinated, the authors don’t make any beige bars in their graph? The bars are all red!!!
Did these authors not try to differentiate between those in hospital who just tested positive for COVID-19 (up to 2 weeks prior) in hospital for other reasons from those who were in hospital because of a COVID-19 illness?
Why separate the 2 groups for double “vaccinated” but not for mostly “unvaccinated”?
Why did they not perform a 600 chart review to see how many “Covid positive” patients were in hospital for “other reasons” in the 2020 group?
Were they trying to say that COVID-19 is worse than other severe influenza seasons because so many people tested positive are in hospital? (Hey look at all those scary red bars that are so much taller than the blue influenza bars?)
Were they trying to say that after mass vaccination, “Look at how much smaller those scary red bars are now that everyone’s been double “vaccinated”!”? (But you can ignore the beige part of the bar because they were not in hospital for COVID)
Why are there no BEIGE bars in the pre “vaccination” graph?
Trying to make sense of a non-sense study
The author’s own 600 chart review showed that less than half of COVID positive patients in hospital are actually there because of COVID.
So if we were to try and make sense of this, it would be reasonable that each of the tall red bars in the pre“vaccination” graph should be half beige just like in the post “vaccination” graph.
If the two graphs looked similar, that would make COVID-19 illnesses requiring hospital stays less than or equivalent to influenza illnesses requiring hospital stays (because the overall hospitalizations were about 200 per 100,000 pop for ages 70+; and about 100 per 100,000 pop for ages 60-69 in both the first wave and Omicron “peaks”).
A researcher with a high school diploma, er a “university” degree wouldn’t forget to Separate grandparents in hospital for a hip replacement from grandparents who have trouble breathing isn’t something right?
Is it credible that the authors compared COVID 2020, with Influenza 2009, 2015 and 2016, without knowing to differentiate between people hospitalized because of COVID-19 illness from people who just tested positive sometime in the 2 weeks prior?
Is it reasonable that the authors suddenly started differentiating between people hospitalized because of COVID from people who only tested positive because they were double “vaccinated”?
Is it reasonable for this to be published in BMJ Open Respiratory Research and called “science”?
If this was a high school book report what grade would you give it?
In conclusion the authors (Solmaz Setayeshgar, James Wilton, Hind Sbihi, Moe Zandy, Naveed Z Janjua, Alexandra Choi, Kate Smolina) say:
“Our paper highlighted the greater population-level impact of COVID-19 compared with influenza in terms of adult hospitalizations, especially among those unvaccinated.”
What their data actually shows:
Hospitalization rates with COVID-19 positivity did not appreciably change after vaccination. (Still around 200/100,000 pop for 70+ and 100/100,000 pop for 60-69)
Based on their own estimations that less than half of people who tested positive for COVID-19 are actually in hospital because of COVID-19, the March 2020 - February 2021 hospitalization rates FOR COVID-19 are less than or equivalent to other pandemic flu seasons. (they performed the chart review on a 600 patient sample between December 2021 and January 2022 in BC)
The COVID-19 vaccine did not reduce the COVID-19 positive rates in hospital. (Basically, proof the vaccine did not prevent transmission.)
Is this an academic “mistake” or is this a lie?
The authors clearly knew that testing positive for COVID-19 did not mean that COVID-19 was the reason for being in hospital. They even tried to compensate for this in their “Omicron peak” Graph.
The authors may have known that mass testing for a respiratory virus had never been done in previous flu seasons or influenza pandemics. If they knew, then the authors probably knew that mass testing would lead to high case counts.
YET they still compared COVID test positives with Hospitalized FOR influenza anyway.
If the authors were able to differentiate the reasons for hospitalization in double “vaccinated”, did they just forget to do the same when the population was mostly UNVACCINATED?
Oversight = Mistake.
Intentional = Lie.
(I’ve reproduced their figures using the claim they make in their paper:
“Dissemination to participants and related patient and public communities
We will disseminate the findings to members of the public through press releases, knowledge translation products on institutional websites, as well as personal communication and social communication platforms. “)
BONUSES STRAIGHT FROM THE AUTHORS:
Solmaz Setayeshgar, James Wilton, Hind Sbihi, Moe Zandy, Naveed Z Janjua, Alexandra Choi, Kate Smolina,
Near the end of their paper they have a section called “Transparency”. In this section they write:
The lead author and the senior author (the manuscript’s guarantors) affirm that this manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.”
References to their figures:
Here is their official text associated with each of their graphs. (It appears they take liberties with the meaning of the word “For”)
“Annual hospitalization rates for patients testing positive for COVID-19 (2020/21) compared with patients hospitalized for influenza 2009/10 (H1N1 pandemic), 2015/16 (severe for children), and 2016/17 (severe for adults), by age group, British Columbia, Canada Note: For the annual COVID-19 cohort we included all patients hospitalized for COVID-19 up to 14 days after or 2 days prior to specimen collection date from March 2020 to February 2021. For influenza, we selected three 12-month periods from September to August with distinct severity: 2009/10=H1N1 pandemic, 2015/16=mild influenza with higher severity in children, 2016/17=severe influenza with higher severity in adults.”
“Hospitalization rates in patients testing positive for COVID-19 (excluding unvaccinated 18+) during the peak of Omicron variant (week 1-8 of 2022) compared with hospitalized patients for influenza in the peak of 2009/10 (H1N1 pandemic), 2015/16 (severe for children), and 2016/17 (severe for adults) season, by age group and COVID-19 vaccine status, British Columbia, Canada Note: For the peak COVID-19 cohort we included all patients hospitalized for COVID-19 up to 14 days after or 2 days prior to specimen collection date during the first 8 weeks of 2022 when Omicron was dominant and >90% of adults in British Columbia were vaccinated with at least 2 doses. We selected the 8-week peak of three seasons with distinct severity for influenza: 2009/10=H1N1 pandemic, 2015/16=mild with higher severity in children, 2016/17=severe with higher severity in adults. We applied the following estimates for proportion of hospitalizations likely primarily due to COVID-19: unvaccinated <50 years old (50%), unvaccinated >=50 years old (70%), vaccinated <50 years old (20%) and vaccinated >=50 years old (40%).”
So many statistical and loose definition tricks at play from the 'experts' -- such 'studies' are more marketing for certain products than anything scientific.
Excellent post. Thank you.