I am back in Minnesota, the world changed while I was in Florida–the spotlight moved off the epidemic and onto the destruction of a country and the wanton slaughter of its population. All of a sudden CV-19 isn’t that dangerous and we didn’t need all those restrictions. A reader asked me if I felt vindicated. Not really, I mostly feel angry. Angry that people who pointed out the obvious for two years were ignored and pilloried as Grandma killers. Angry that a generation of our children had their eductations and lives ruined. Angry the many people died because they were terrorized into not seeking health care they needed. Angry because we wasted trillions of dollars and jeopardized our financial well-being. So, no, I don’t feel any satisfaction at being right about what the appropriate response would have been. And I feel even worse because I don’t think any lessons will be learned.
Dave Dixon is a tremendous data analyst. I always knew what I wanted to do with the data and I knew what it said, but I lack the technical skills or the time to really dig into the data and to communicate it in an easy to understand fashion. Dave volunteering to help was the best thing that happened to the site. He is dogged in accruing all the data as it is released and in comparing various data sources. He finds and illuminates the most meaningful data. And his charts and tables always tell the story at a glance.
Dave noted to me a couple of weeks ago that some of the per capita rates DOH puts out comparing vaxed and unvaxed groups seemed to be changing dramatically. We had wondered about those rates since they were first published because they appeared very inconsistent with what other data was saying and they implied much greater effectiveness of the vaccines than any other research was showing. Dave, as I noted above, is astoundingly persistent in keeping copies of every data sheet as it is released. He has therefore been able to compare the per capita rates published over time and he has found an extremely large change in those published rates. I will add a chart to this post later, but wanted to get it out now.
I will just quote verbatim from Dave’s emails to me in a second, but first a brief explanation. Per capita rates obviously have a numerator–cases, hospitalizations or deaths; and a denominator–the population in each sub-group, in this case those who are vaxed or those who are unvaxed. There are cumulative rates at any point in time, but there also are rates published as of a specific date. Looking at those rates as of a specific date, we have seen very large revisions that really make no sense. You can understand some numerator changes. All cases are initially reported and basically treated as in the unvaxed, because DOH uses a separate process to match cases to their vax records (which by the way are incomplete). There is always at least a two week lag in that process because of the definition of being fully vaxed. So the numerator as of a certain data can change, but Dave tracks those changes and they aren’t typically large.
What is harder to understand are big changes in the denominator. The state constantly publishes the number of people who are totally vaxed. Pretty simple to subtract that number from the total state population and know the size of the sub-groups of vaxed and unvaxed on any given day. But we have seen massive changes in the size of the denominator, which have dramatically lowered the per capita rates, as detailed by Dave below. Now this could just be typical government incompetence, someone forgets to build an automatic data feed to update numbers or pulls the wrong numbers. But the fact that the state continually harped on these differences in per capita rates makes me suspicious that it was more than that; that perhaps the DOH was holding back on updating or just plain fiddling with the rates to try to make the vax look more effective than they are. We are trying to get an explanation, and DOH needs to give the public one.
Dave is continuing to work on this, and as noted, we will add some graphs and Dave is going to do his own per capita calculations. Dave’s comments, in the order of receipt:
This is interesting. In the graphic, and the underlying data, they have reduced the unvaxed per capita rates for cases, hospitalizations, and deaths by roughly 20% for all weeks going back to the very beginning. There are no corresponding changes to the VBT Counts. This is true for the 50-64 and 65+ age groups. At the same time there were minor increases in the corresponding vaxed rates, but increases did not go back to the first week like the unvaxed rate changes did. No notes on the web page to explain it.
I went back and checked, and after their population size error they had lowered all of the unvaccinated rates starting for the week of 12/27/2021. Those reductions were about 40%. This set of reductions of about 10-20% means that many of their case rates for the unvaccinated are less than half their initial published rates. For example. 65+ unvaccinated, for the week of 11/14/2021. The first published case rate was 3302.8 per 100k on 12/20/21. This was lowered to 1802 on 12/27/21, drifted lower to 1717 on 2/21/22, and then Monday further lowered to 1320 per 100k. For the unvaccinated 65+ age group the week of 11/14/21, hosp. were initially 797 per 100k, and are now 320.8. Similarly, deaths the week of 11/14/21, deaths were initially 237 per 100k, and are now 116 per 100k.
I’ve been looking at the changes in the case rate, hosp, rate, and death rate data again this morning, and nothing about it is consistent or makes sense.
VBTCounts did not change all the way back to 5/2/21, but the cases, hospitalization, and deaths rates mainly did change back to 5/2, especially the older age groups. Bucket shifting from unvaxed to vaxed may be a partial explanation, but there are plenty of weeks where the unvaxed event rate went down without a corresponding rise in the vaxed rate. I’m starting to wonder if what we are seeing is either some errors that got introduced into their charts, or they fixed older errors, superimposed on bucket shifting. The note about including 5-11 year olds last week is still suspicious, but this wouldn’t explain the changes to the older group rates.
Another nagging doubt I have is that it isn’t clear how they are handling reinfections. I think reinfections should be excluded from the vax/unvax rate analysis, and calculated separately, because we don’t know whether the reinfection occurred in a vax or unvax person. This could be another source of error. I also have reinfections by age and date so I can calculate reinfection rates separately, but I don’t have reinfection hospitalizations or deaths by age, only cases.
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Wow. Just wow.
“This set of reductions of about 10-20% means that many of their case rates for the unvaccinated are less than half their initial published rates.”
I’ve followed the California statistics most closely along with daily Los Angeles reports and we know the problems of “with-COVID” versus “from-COVID” – and hasn’t the CDC promised to say something about that, some weeks ago? Anyway. I’ve always looked for further consistency between various statistics, and never found any, but things have gotten much, much worse since Omicron. I’ve just drawn big, upwards-trending “Bogosity!” arrows on my major graphs. The testing protocols, the reporting protocols, the test technologies – have all changed drastically in the last 90 days, and who knows how often the CDC issues updates and what they say, they were somewhat open about that in 2020 but it’s all turned hush-hush since then. Anyway everyone now seems to be changing the protocols to make things look better, rather than worse. If you catch some retcon’ing of old numbers, I wouldn’t be a bit surprised.
Kevin thank you for your and your team’s dogged determination. Somewhere here there’s a wonderful book that shows just how inept or corrupt the people in charge actually were.
I have kept a tally of cases and deaths in Indiana, and could tell things were off from the official story. Your blog has helped solidify my intuition with facts.
Thank you, and I hope you can keep up the good work, despite, well, everything.
Covid data has always been an unreliable cesspool.
Home testing and omicron create more uncertainty. Neither are reported consistently. Why bother. If you think you have covid your doctor will just tell you not to come to their office. Just stay home. And who reports home test results? No one.
If I were telling you how I really feel it would involve ropes and lampposts. But that would be uncivilized
A State of Fear: How the UK government weaponised fear during the Covid-19 pandemic Paperback – May 16, 2021 by Laura Dodsworth (Author)
Regarding the ‘book’ comment, it’s out there already. It would be great for a US version to be written.
The data cesspools were created/orchestrated to cover up the fraud of event. Now that their political asses are on the line, it’s time to yet again orchestrate another fraud called the cover up. It’s not fair to be critical of the yeoman’s work KR & DD have undertaken, but they are working within the ‘cesspool’. As with with the election fraud events that are now coming out as reality, a couple years from now this great work, as well as others, will be the backbone of the C19 fraud validation.
It’s time to look at every global event and ask what role the WEF and their global acolytes are playing. It’s time to publicly expose every one of their minions who’ve sold their souls to the twisted ideologies of the ‘Great Reset’ so there can be a concerted effort to remove them from all public offices. They are the ‘Wizards of Oz’, behind the curtain, pulling the strings and levers that are resulting in today’s world.
We’ve gone from Tinfoil Hats to well fitting Fedoras over the last 2 years !
My my, the apparent effectiveness of the vaccines going down dramatically, again?
Another shocking development from Minnesota!
In a post tomorrow, I will reveal Dave’s findings that the state just screwed up its data feed, and the rates they publish, according to their data, are accurate. But there are other issues with those rates–missing vaxed persons, confounding by reinfection case overlap, huge lags on breakthru identification.