el gato malo just released another analysis using table 5 which is monthly, separated by male and female and uses ONS data only which means it has the person year calculations. He also tried is best to look at <21 days post dose which is stymied by the "<3" deaths numbers for all but the older groups. A first read through, it makes a strong case although healthy or unhealthy user bias is not able to be addressed by any public data.
In terms of harms I do think disabilities are likely to prove a much more telling statistic (than deaths) and not enough attention is being paid to them.
The reasons are obvious: the sheer numbers dwarf deaths, the vax status is (relatively) incorruptible, the person is alive for verification and further investigation and the person has motivation to tell the truth about their injury (welfare programs) and the likely cause (blame, possibly litigation in the future).
Obviously at present there isn’t much salience out there in respect of the vaccines being linked to disability, but I’m sure it will come. Notwithstanding that, just looking at the sheer numbers registering as disabled looks revealing.
I computed vaccinated death rate/unvaccinated death rate using table 2 for May 2022 (the year that looked worst in egm's analysis. I got ~1.45 for 18-49, 0.9 for 50-59, 0.81 for 60-69, 0.84 for 70-79, 1.04 for 80-89 and 2.81 for 90+. I presume you would claim that I am seeing unhealthy user bias for those under 50 and healthy use bias for 50-79. But what is happening for 80-89 and 90+. They are nearly universally vaccinated and maybe the very small unvaccinated 90+ group consists of mostly the healthiest folks in that bracket. Afterall, those who weren't vaccinated because they were near end of life in early 2021 would have been long-since dead.
This does make some sense, but my question centers around the fact that the essential vaccination rate is higher in all age brackets for table 2, effectively increasing the vaxxed denominator and dereasing the unvaxxed denonimator, usually substantially as a percentage basis because it is already small.
Why should I trust one set of vaccination numbers over the other? Does table 2 represent a clear set of people who are in the country at the time and whose medical history including vaccination and death is clearly tracked even if it isn't everybody. Or are the unvaccinated undercounted for some reason. Perhaps to put it another way, does table 2 represent a clear set of individuals who were either vaccinated or not prior to that month (or who spent part of the month in each category) and who either died or did not?
"The data presented this week is the provisional proportion of living people resident in England who had received COVID-19 vaccinations. Individuals vaccinated in England who have a registered address outside of England or where their address, age, or sex is unknown have been excluded. Due to changes in GP practice lists, in order to include newly registered patients and remove those who are no longer resident, there will be slight variation to the figures to reflect those who are currently resident in England."
So it seems to be a relatively current data set based on the people currently residing in England.
So the true question becomes, "Which dataset contains a more accurate representation of the unvaccinated population?" Since the unvaccinated population is small, it is much more important to measure it accurately. I'm not convinced that the very high vaccination rates in table 2 are accurate. Although, if vaccination numbers do respresent the same population from which deaths are drawn, it is by definition and accurate number, although perhaps subject to it's own selection bias given it is a subset.
Finally, the significant change in relative death rates from January to June adds even more complexity to the issue.
Are you aware of this substack by Norman Fenton who is a maths professor from Queen Mary’s London specialising in risk assessment and statistics. He also has his own website normanfenton.com I remember him going on in the past that it’s very difficult to interpret the U.K. results as the ONS and NIMS have different numbers of population and unvaccinated. I can’t point you to a specific post but I’m sure you’d find all his analyses worthwhile.
I like your category (in the referenced post at the top), "Into the Weeds of Government Data". I used to venture into those weeds regularly, although thankfully that job ended nine years ago, never to return. It was federal and California public health data, but it seems that government data is government data. Lots of aggregated data, no de-identified raw (too sensitive to release for some reason). Having to guess the unique keys. Asking questions of the aggregate that can't be answered.
I didn't follow your analysis in great detail because it reminded me too much of working with government data, and my head started to hurt like it used to. But "Viola"? OK, the edited version says "voilà" or something close to that. But now I have MST3K to ponder. Making progress, I think. Something about "riffing"?
Jan 10, 2023·edited Jan 10, 2023Liked by Brian Mowrey
As someone who wrote several posts on mortality, I must note something that we should all be aware of.
It is not directly related to this Brian's post but I thought I can express my feelings here.
ANALYZING MORTALITY IN-DEPTH IS VERY DIFFICULT.
They invented randomized trials partly to bypass these difficulties.
For example, let's say that a fat, triple boosted guy named Bob dies a month after having his third COVID.
Having this person in mortality statistics, and having his demographic characteristics, can we answer a simple question: what killed Bob? Fatness? Covid? Vaccines? Cholesterol? Or something else?
Some people allege a healthy user bias. Some people allege unhealthy user bias. etc etc
It is not very easy. It is also not very easy to do causality properly on the population level.
This is why most respectable nations have demographics institutes, full of statisticians, MDs etc etc.
I could, for example, do a regression of "deaths" by "boosters", note a positive relationship and write about "association". Yeah, there is an association.
But how much proof do we have? I tried hard to find "proof" beyond correlations and sometimes, I believe I found other convincing statistical things, like having similar slopes in UK deprivation quartiles and worldwide country level statistics. That increases our strong suspicions about vaccines.
But proof "beyond reasonable doubt" may remain elusive. So many confounders can be alleged.
We can only prove something by analyzing individual level data on millions of people, and hopefully doing some targeted interventions.
The data is not available, the authorities are ignoring excess mortality and discontinuing their reports.
This leaves us wondering, naturally, if something nefarious is going on.
And something nefarious IS going on because ignoring excess mortality IS nefarious by definition.
This commentary is sadly above my pay grade:). But it concerns me (and at the same time makes me hopeful) that you are calling out EGM for confirming my biases which are most depressing...his reach seems huge...and if we’re repeating false info that’s concerning. For me it’s easy...NO MORE MANDATES EVER!! Have you reached out to him for clarification?
I guess the reason I am not dead yet despite being unvaxxed is that I am not obese. After all, the fake President told me that as an unvaxxed person I was going to die last winter or and the latest this winter (although, this winter is not over yet.)
Maybe he skipped over the bit on his teleprompter where it said "you fat fucks are gonna die this winter if you are not vaxxed!"
Darn, I was writing a long response and somehow just lost it :(. I guess basically I wanted to say that how can we track much from the UK when they used midazolam at such a high frequency and failed to give antibiotics for secondary pneumonia creating a crazy pull forward effect and their vaccination schedule started with AZ, then J and J, then Pfizer and finally Moderna leading to a jumble of information?
I’m defense of EGM, and myself, we’ve spent months trying to get clean all-cause mortality data from the US and UK bureaucracies and neither are willing to share their taxpayer-financed info with taxpayers. I’d be curious if anyone has a theory as to why.
I confess that at face value you speak over my head, but I want to know the REAL facts, whether they support my bias or not. Belief doesn’t create truth, truth should shape belief. So, would you say that the data seem to say that the injected appear to be overall “doing better” than the non? And are the data credible? (i.e, statistics for dummies)
el gato malo just released another analysis using table 5 which is monthly, separated by male and female and uses ONS data only which means it has the person year calculations. He also tried is best to look at <21 days post dose which is stymied by the "<3" deaths numbers for all but the older groups. A first read through, it makes a strong case although healthy or unhealthy user bias is not able to be addressed by any public data.
https://boriquagato.substack.com/p/another-look-at-uk-all-cause-mortality
In terms of harms I do think disabilities are likely to prove a much more telling statistic (than deaths) and not enough attention is being paid to them.
The reasons are obvious: the sheer numbers dwarf deaths, the vax status is (relatively) incorruptible, the person is alive for verification and further investigation and the person has motivation to tell the truth about their injury (welfare programs) and the likely cause (blame, possibly litigation in the future).
Obviously at present there isn’t much salience out there in respect of the vaccines being linked to disability, but I’m sure it will come. Notwithstanding that, just looking at the sheer numbers registering as disabled looks revealing.
http://www.phinancetechnologies.com/HumanityProjects/US%20Disabilities%20-%20Part1.htm
I computed vaccinated death rate/unvaccinated death rate using table 2 for May 2022 (the year that looked worst in egm's analysis. I got ~1.45 for 18-49, 0.9 for 50-59, 0.81 for 60-69, 0.84 for 70-79, 1.04 for 80-89 and 2.81 for 90+. I presume you would claim that I am seeing unhealthy user bias for those under 50 and healthy use bias for 50-79. But what is happening for 80-89 and 90+. They are nearly universally vaccinated and maybe the very small unvaccinated 90+ group consists of mostly the healthiest folks in that bracket. Afterall, those who weren't vaccinated because they were near end of life in early 2021 would have been long-since dead.
This does make some sense, but my question centers around the fact that the essential vaccination rate is higher in all age brackets for table 2, effectively increasing the vaxxed denominator and dereasing the unvaxxed denonimator, usually substantially as a percentage basis because it is already small.
Why should I trust one set of vaccination numbers over the other? Does table 2 represent a clear set of people who are in the country at the time and whose medical history including vaccination and death is clearly tracked even if it isn't everybody. Or are the unvaccinated undercounted for some reason. Perhaps to put it another way, does table 2 represent a clear set of individuals who were either vaccinated or not prior to that month (or who spent part of the month in each category) and who either died or did not?
With table 8, there clearly are more deaths tracked, implying a larger group of people. But where do the vaccinated numbers used by EGM (which can be downloaded here: https://www.ons.gov.uk/peoplepopulationandcommunity/healthandsocialcare/conditionsanddiseases/articles/coronaviruscovid19latestinsights/vaccines#vaccination-rates
Information on how the values are calculated can be found on page 80 of this report:
https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1127554/Weekly_Flu_and_COVID-19_report_w1.pdf
Here is the nugget:
"The data presented this week is the provisional proportion of living people resident in England who had received COVID-19 vaccinations. Individuals vaccinated in England who have a registered address outside of England or where their address, age, or sex is unknown have been excluded. Due to changes in GP practice lists, in order to include newly registered patients and remove those who are no longer resident, there will be slight variation to the figures to reflect those who are currently resident in England."
So it seems to be a relatively current data set based on the people currently residing in England.
So the true question becomes, "Which dataset contains a more accurate representation of the unvaccinated population?" Since the unvaccinated population is small, it is much more important to measure it accurately. I'm not convinced that the very high vaccination rates in table 2 are accurate. Although, if vaccination numbers do respresent the same population from which deaths are drawn, it is by definition and accurate number, although perhaps subject to it's own selection bias given it is a subset.
Finally, the significant change in relative death rates from January to June adds even more complexity to the issue.
thought experiment:
https://woodhouse.substack.com/p/thought-experiments
https://wherearethenumbers.substack.com/archive?sort=new
Are you aware of this substack by Norman Fenton who is a maths professor from Queen Mary’s London specialising in risk assessment and statistics. He also has his own website normanfenton.com I remember him going on in the past that it’s very difficult to interpret the U.K. results as the ONS and NIMS have different numbers of population and unvaccinated. I can’t point you to a specific post but I’m sure you’d find all his analyses worthwhile.
I saw this in my email and said oh good, another substack discussion with Brian weighing in. Thanks, I always appreciate your perspective. 👍🏽💕
I like your category (in the referenced post at the top), "Into the Weeds of Government Data". I used to venture into those weeds regularly, although thankfully that job ended nine years ago, never to return. It was federal and California public health data, but it seems that government data is government data. Lots of aggregated data, no de-identified raw (too sensitive to release for some reason). Having to guess the unique keys. Asking questions of the aggregate that can't be answered.
I didn't follow your analysis in great detail because it reminded me too much of working with government data, and my head started to hurt like it used to. But "Viola"? OK, the edited version says "voilà" or something close to that. But now I have MST3K to ponder. Making progress, I think. Something about "riffing"?
Cello.
As someone who wrote several posts on mortality, I must note something that we should all be aware of.
It is not directly related to this Brian's post but I thought I can express my feelings here.
ANALYZING MORTALITY IN-DEPTH IS VERY DIFFICULT.
They invented randomized trials partly to bypass these difficulties.
For example, let's say that a fat, triple boosted guy named Bob dies a month after having his third COVID.
Having this person in mortality statistics, and having his demographic characteristics, can we answer a simple question: what killed Bob? Fatness? Covid? Vaccines? Cholesterol? Or something else?
Some people allege a healthy user bias. Some people allege unhealthy user bias. etc etc
It is not very easy. It is also not very easy to do causality properly on the population level.
This is why most respectable nations have demographics institutes, full of statisticians, MDs etc etc.
I could, for example, do a regression of "deaths" by "boosters", note a positive relationship and write about "association". Yeah, there is an association.
But how much proof do we have? I tried hard to find "proof" beyond correlations and sometimes, I believe I found other convincing statistical things, like having similar slopes in UK deprivation quartiles and worldwide country level statistics. That increases our strong suspicions about vaccines.
But proof "beyond reasonable doubt" may remain elusive. So many confounders can be alleged.
We can only prove something by analyzing individual level data on millions of people, and hopefully doing some targeted interventions.
The data is not available, the authorities are ignoring excess mortality and discontinuing their reports.
This leaves us wondering, naturally, if something nefarious is going on.
And something nefarious IS going on because ignoring excess mortality IS nefarious by definition.
This commentary is sadly above my pay grade:). But it concerns me (and at the same time makes me hopeful) that you are calling out EGM for confirming my biases which are most depressing...his reach seems huge...and if we’re repeating false info that’s concerning. For me it’s easy...NO MORE MANDATES EVER!! Have you reached out to him for clarification?
I dunno why you are wrestling with these probably faulty data-sets when Ron Unz tells us that the problem is Obesity!
https://www.unz.com/runz/obesity-and-the-end-of-the-vaxxing-debate/
I guess the reason I am not dead yet despite being unvaxxed is that I am not obese. After all, the fake President told me that as an unvaxxed person I was going to die last winter or and the latest this winter (although, this winter is not over yet.)
Maybe he skipped over the bit on his teleprompter where it said "you fat fucks are gonna die this winter if you are not vaxxed!"
It would be good to know.
Darn, I was writing a long response and somehow just lost it :(. I guess basically I wanted to say that how can we track much from the UK when they used midazolam at such a high frequency and failed to give antibiotics for secondary pneumonia creating a crazy pull forward effect and their vaccination schedule started with AZ, then J and J, then Pfizer and finally Moderna leading to a jumble of information?
I’m defense of EGM, and myself, we’ve spent months trying to get clean all-cause mortality data from the US and UK bureaucracies and neither are willing to share their taxpayer-financed info with taxpayers. I’d be curious if anyone has a theory as to why.
I confess that at face value you speak over my head, but I want to know the REAL facts, whether they support my bias or not. Belief doesn’t create truth, truth should shape belief. So, would you say that the data seem to say that the injected appear to be overall “doing better” than the non? And are the data credible? (i.e, statistics for dummies)