Washington Post article - the cancer spike is caused by covid, not the vaxx \...

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Interesting discussion

Here is my favorite comment by Peter Todd : Washington Post article - the cancer spike is caused by covid, not the vaxx \ stacker news ~conspiracy

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The same immune system fights both viruses and cancer.

Immunologists have been warning about this for quite a while:
Screenshot 2024-06-12 at 6.20.47 AM

If you overload the immune system with a nasty ACE2-binding viruses, you’ll get cancers.

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If virus is the cause rather than “vaxx”, then where are the cancer clusters which would surely follow outbreaks of at least some of the other coronaviruses ( the common cold) have been present forever. The most unusual thing which happened was the “vaxx”, not the viral infection.

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Most coronaviruses aren’t nearly as severe as SARS-nCoV-2. It’s pretty well-known that viruses cause cancer, often directly. The above mechanism gives you an indirect cause (with overloaded immune system).

The data is lagging, but you can compare cancer rates between Dem (high vax, low infection) vs Rep (low vax, high infection) states in the US to see the differentials in rates.

For example, low-vax Republicans are dying at a considerably higher clip than high-vax Democrats:
Screenshot 2024-06-12 at 10.46.46 AM

I’ll rest my case.

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What is the source of that data? There may be confounding factors. For example, Republican voters tend to be older than Democrat voters – and it is clearly established that older people are at higher risk of dying, whether from the experimental gene therapy or from many other causes.

As every statistician says – correlation is not causation.

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That’s why they’re using ‘excess mortality’ – among other things this addresses the age and some other matters (not sure about SES / access to healthcare).

But you should also be looking at the lack of the discrepancy prior to vax.

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Back to the question – What is the source of that data?

When people analyze deduced “excess mortality” rather than actual observed mortality, the potential for honest (or deliberate) mis-representation increases exponentially. What assumptions are in there? Who determined that Republican voters were more likely to resist the immense pressure to get injected than Democrat voters?

Some statisticians have asserted that the majority of peer-reviewed scientific papers on a wide range of topics misuse statistics. Maff is hard! When it comes to statistical analysis on controversial politically-driven topics, we should all be from Missouri.

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The show me state

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There are additional potential confounders, such as the pull-in for deaths occurring earlier in one population vs another.

The ethical skeptic on twitter has been producing a long series of fairly transparently analyzed charts that show that when controlling for administrative adjustments the magnitude of the excess deaths following the mRNA shots exceed prior trends.

It won’t be surprising to at least this reader if the sentiment across left leaning readers of that analysis (or in general) would be schadenfreude at the assumed pest that befell their political opponents. But how quickly does one forget the sincere concerns the lefties expressed as operation warp speed was succeeding in the fall of 2020. So yes, pretty clear that separating politics from data analysis would turn out to be much harder than one would think.

Where are the fearless members of the fourth estate that would never tire in their ceaseless effort to get to the truth? Perhaps the weight of their assumed deontological obligations is slowing them down to almost complete paralysis?

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Have you tried clicking on the chart? It will lead you to data with all your questions answered.

You mean this fellow Hidden in Plain Sight ? I’ve seen some terrible analysis of C19 data that have been debunked repeatedly.

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Thanks for unlocking the key to the “data”, Eggspurt. Bitter experience has taught me to be reluctant to click on items.

Lots of questions about that analysis.
The median age at death was 78 years” – Would that we could all live that long!

Democratic voters had significantly higher excess death rates compared with Republican voters for the age group 65 to 74 years.” Isn’t that the age group where Democrats would be expected to be more vaccinated than Republicans?

To the authors’ credit, they point out several of the more important weaknesses in their study

"Our study has several limitations.

First, there are plausible alternative explanations for the difference in excess death rates by political party affiliation beyond the explanatory role of vaccines discussed herein.

Second, our mortality data, although detailed and recent, only included approximately 83.5% of deaths in the US and did not include cause of death. … it is possible that the deaths that our study data did not include may disproportionately occur among individuals registered with a particular political party, potentially biasing our results. In addition, the completeness of our mortality data may vary across states or time, potentially biasing our estimates of excess death rates.

Third, all excess death models rely on fundamentally untestable assumptions to construct the baseline number of deaths we would expect in the absence of the COVID-19 pandemic.

Fourth, because we did not have information on individual vaccination status, analyses of the association between vaccination rates and excess deaths relied on county-level vaccination rates.

Fifth, our study was based on data from [only 2 possibly untypical] states with readily obtainable historical voter registration information (Florida and Ohio); hence, our results may not generalize to other states.

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I didn’t read this part of the study, but was going to respond that trying to develop an excess death model on a small population is very prone to error. Even for large populations, developing a model for excess deaths is prone to error. At a county level, it has to be horrendous. You need to capture at least 80% of the variability which you cannot do at low populations.

My point was going to be, given you have vaccine status associated with individual deaths, why would you introduce error by developing a model. I guess they didn’t. That was not obvious to me in the meat of the report.

I wonder why they don’t have access to individual vaccine status. The government records all vaccine information and it didn’t seem they were too concerned about medical privacy when they wanted everyone to have a vaccine card to prove they had been vaccinated before being allowed in a public space.

It also answers my other question. I was wondering why anyone would do such an blatantly political study? Isn’t the question and the only legitimate question for research whether or not the vaccine is safe and effective? We already know there are differences between Republicans and Democrats. Democrats and Republicans are different in ways that impact life expectancy. The demographics of race, sex, education, income, etc. are not uniformly distributed between parties.

I question the ethics of anyone that would do this type of study. When you do a scientific study and your groups are political parties, you introduced several factors that add potential error without any obvious research reason.

For the tremendous amount of money our government spends, it shouldn’t be a big deal to be able to do anonymous studies that have all the relevant information such as vaccine status, sex, death. Is there something wrong with using database ID?

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There is no perfect statistical study. The way to argue is to provide evidence for a counter-point.

In this case, can you provide evidence for a statement that vaccination has hurt the health prospects of the population.

I’ll then respond with a critique of that evidence, and we’ll compare the two.

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Come on! That is an old debating trick – Divert from discussing the weaknesses in your position by trying to put any other position under the microscope.

Personally, my view on the CovidScam was formed by the Diamond Princess incident in Japan. Nearly 4,000 human beings trapped in a cruise ship with “Covid” running rampant – younger crew, heavily senior citizen passengers. Only a handful of people died, and they were all very old and had serious pre-existing medical conditions. That real-world data demonstrated “Covid” was comparable to the flu – which the human race has lived with for millenia – and we were being subjected (for undeclared reasons) to a health scare scam.

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You’re changing the topic. My question was to ask you to support the statement that vaccines led to excess mortality.

You’ve produced a story where C19 infection resulted in 2% mortality.

We know that New Zealand used mRNA vaccines to vaccinate 86.1% of the population, 8,020,529. 2% mortality would result in 160,410 deaths. In 2021, there were 34,997 deaths.

Seems like your point has been refuted?

Now, New Zealand relied on vaccination and opened its borders in 2022 to the virus. As a result, the number of deaths registered during 2022 was 38,574, up 3,642 (10.4 percent) from the previous year. This speaks to the danger of the virus, not of the vaccine.

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I don’t want to be argumentative, but about 10 people (all old, sick) dying out of a shipload of about 4,000 people (passengers & crew) is about 0.3% mortality – about an order of magnitude less than the figure you quoted.

As for New Zealand – they vaccinated 86% of the population and then opened their borders to an influx of virus in 2022 – and deaths increased by 10%. That suggests vaccination (more precisely, injection with an untested bio-active agent) did not accomplish much. Or I am misunderstanding something?

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You need to look at IFR. They limited infection: they locked down, distributed N95 masks and established quarantine. So they actively limited the spread infection. Moreover, this was in Japan with a top-tier medical system and considerable experience with SARS.

Sweden in 2020 saw 98124 deaths vs 88766 = 10.5% - with the recommendations around isolation.

Just two data points, but yeah, the reliance on vaccines (vaccine maximalism) isn’t helpful. It would be interesting to see the increase in mortality in China after they reopened.

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