{"id":5023,"date":"2023-02-26T21:13:54","date_gmt":"2023-02-26T21:13:54","guid":{"rendered":"https:\/\/oh17.com\/?p=5847"},"modified":"2023-02-26T21:17:07","modified_gmt":"2023-02-26T21:17:07","slug":"5847","status":"publish","type":"post","link":"https:\/\/jewelryon.com\/?p=5023","title":{"rendered":"GAME OVER: Medicare data shows the COVID vaccines increase your chance of dying"},"content":{"rendered":"

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Read Full PDF Here<\/a><\/p>\n

GAME OVER: Medicare data shows the COVID vaccines increase your chance of dying<\/h5>\n

This is why the CDC has NEVER used the Medicare data to prove the vaccines are safe. And this is why NOBODY in mainstream medicine wants you to see this data. EVER. They ALL want it hidden. FOREVER.<\/h2>\n
Steve Kirsch<\/a>18 hr ago<\/div>\n
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Nota Bene<\/h3>\n

This may well be the most important article I\u2019ll write in 2023.<\/p>\n

In this article, I publicly reveal record-level vax-death data from the \u201cgold standard\u201d <\/span>Medicare database<\/a> that proves that:<\/span><\/p>\n

    \n
  1. \n

    The vaccines are making it more likely that the elderly will die prematurely, not less likely<\/p>\n<\/li>\n

  2. \n

    The risk of death remains elevated for an unknown period of time after you get the shot (we didn\u2019t see it return to normal)<\/p>\n<\/li>\n

  3. \n

    The CDC lied to the American people about the safety of these vaccines. They had access to this data the entire time and kept it hidden and said nothing.<\/p>\n<\/li>\n<\/ol>\n

    If there is one article for you to share with your social network, this is the one.<\/p>\n

    Executive summary<\/h3>\n

    Isn\u2019t it a shame that none of the world\u2019s governments make the vaccination-death records publicly available? My claim is that if they did that, it would end the debate instantly and prove to the world that the vaccines are unsafe. So that\u2019s why they keep it locked up.<\/p>\n

    But apparently there is one whistleblower who is interested in data transparency.<\/p>\n

    Last night, I got a USB drive in my mailbox with the <\/span>Medicare data that links deaths and vaccination dates<\/a>. Finally! This is the data that nobody wants to talk or even ask about. <\/span><\/p>\n

    I was able to authenticate the data by matching it with records I already had. And the analysis that I did on the data I received matches up with other analyses I have received previously.<\/p>\n

    The nice thing about <\/span>this Medicare data<\/a> is that nobody can claim that it is \u201cunreliable.\u201d Medicare is the unassailable \u201cgold-standard\u201d database. It\u2019s the database that the CDC never wants us to see for some reason. They never even mention it. They pretend it doesn\u2019t exist. So you know it is important.<\/span><\/p>\n

    Do you want to know what it shows?<\/p>\n

    It shows that these shots increase your risk of dying and once you get shot, your risk of dying remains elevated for an unknown amount of time. And that\u2019s <\/span>in the very population it is supposed to help the most<\/em>!<\/span><\/p>\n

    Now you know why the CDC, which has always had access to the Medicare records, has <\/span>never made them publicly available for anyone to analyze to prove that the vaccines are safe<\/strong>. Because the records show the opposite. That\u2019s why they keep the data hidden from view and it\u2019s why they NEVER talk about it.<\/span><\/p>\n

    Today, in this article, you will finally get to see what nobody outside the HHS has ever seen before: the \u201cgold standard\u201d Medicare records, i.e., the truth. Y<\/span>ou can analyze it yourself.<\/strong><\/p>\n

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    \n
    \"The<\/div>
    This is a great quote. Unfortunately, the “Truth is like a lion” quotation attributed to St Augustine was never penned by him, nor by any notable philosopher, sage or theologian before the twenty first century.<\/figcaption><\/figure>\n<\/div>\n

    You\u2019ll soon see for yourself why the CDC will never release this data and why the mainstream press is NEVER EVER going to ask to see the data: because it would reveal they lied to people and killed over 500,000 Americans by recommending they take an unsafe \u201cvaccine.\u201d<\/p>\n

    The bottom line is this:<\/p>\n

    \n

    When there is no data transparency, there is a high chance that the government is lying to you.<\/p>\n<\/blockquote>\n

    After all, if the data supported their narrative, they\u2019d be tripping all over themselves to release the data. When it doesn\u2019t support the narrative, they simply never talk about it and pretend it doesn\u2019t exist and tell the press never to ask about it.<\/p>\n

    So you already know how this is going to end. Very badly. For Biden, the CDC, the FDA, the mainstream medical community, the mainstream press, and Congress. They all will have egg on their face because they never asked to see the data.<\/p>\n

    The \u201cmisinformation spreaders\u201d will have been proven right with the government\u2019s own \u201cgold standard\u201d database. It\u2019s payback time.<\/p>\n

    Acknowledgements<\/h3>\n

    I had Clare Craig of the HART Group look this over for any flaws. She liked it.<\/p>\n

    Professor Norman Fenton had a look as well and he didn\u2019t find anything amiss either.<\/p>\n

    This doesn\u2019t mean there aren\u2019t any flaws, but it just means that there aren\u2019t any obvious flaws. If you find a mistake, let me know in the comments.<\/p>\n

    Why this article is so important<\/h3>\n

    If nobody can explain how the \u201cslope goes the wrong way,\u201d then this should be GAME OVER for the vaccination program because we are using their own \u201cgold standard\u201d database to prove that the vaccines are not safe and that they lied to us.<\/p>\n

    Unless I made a serious error, there is no rock big enough for them to hide under on this one. No excuses. No attacks. It\u2019s basically bulletproof. The results simply cannot be explained if the vaccines are safe. And the numbers are huge. You don\u2019t need a peer reviewed study on this one.<\/p>\n

    The Medicare data that I received<\/h3>\n

    It\u2019s in Excel, there are over 114,000 records, and you can <\/span>download it here<\/a>. <\/span><\/p>\n

    While I would have liked to receive the merge of all death records and vaccination records of everyone in the US, the data I did receive, when properly analyzed, is sufficient to prove the point that the vaccines are increasing your risk of death.<\/p>\n

    LIMITATIONS<\/strong><\/p>\n

    Be sure to read the About tab for caveats about the data. It will help if you read and understand this article before you look at the records.<\/p>\n

    MEDICARE DATA NOTA BENE<\/strong><\/p>\n

    Note that the scatter plots below were produced from a <\/span>much larger<\/strong> set of Medicare records than the ones you can download. The plots from the records I received are included in the Excel spreadsheet and are consistent with the plots in this article which are the higher quality plots (and which contain dose 2 and 3 plots).<\/span><\/p>\n

    Overview of how to analyze the Medicare records<\/h3>\n

    Because we only have vax-death records of people who have died (rather <\/span>than the full set of records that any truly honest government would supply<\/a>), we have to analyze the data in a certain way to understand what is going on.<\/span><\/p>\n

    This is a new way to look at the data so let me give you the bird\u2019s eye overview first.<\/p>\n

    The main thing is that in Jan 2021 we have a double whammy of death: from COVID and seasonality (older people die more in winter).<\/p>\n

    \n
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    Figure 0.<\/strong> Days to death from Dec 15, 2020 in Medicare in Connecticut. Each bar is a 5 day period. The point of this graph is to show that the COVID outbreak exacerbated the slope since you are seeing effects of seasonality PLUS the waning part of a COVID outbreak. This is why there is a 40% drop from peak values.<\/span><\/figcaption><\/figure>\n<\/div>\n

    So if the vaccine does absolutely nothing, we\u2019ll see the slope of the histogram of the deaths per day curve go dramatically down in the first quarter as COVID and seasonality effects diminish. Then it will flatline for a time until seasonality picks up again in winter or there is another big COVID outbreak. The drop could be as much as 40% from the peak value (e.g., from 536 to 324) in Figure 0.<\/p>\n

    If the vaccine is PERFECT, we\u2019ll see the same slope go down, but not as much because we\u2019ll just see seasonality effects going down (since nobody is dying from COVID). It will then remain perfectly flat until it picks up again in winter. See Figure 1 below for what the \u201cdeaths per week\u201d curve should look like for a perfect vaccine.<\/p>\n

    The main point is this: if the vaccine isn\u2019t causing harm, the slope will go down and remain flat.<\/p>\n

    What I will be doing below is calculating the days until death from shot #1 if and only if shot #1 was given in Q1 of 2021. So that histogram should look very similar to Figure 1. It\u2019s going to be smoothed somewhat since the shot was given over a quarter (rather than on a single day), but since most of the vaccine in Q1 was delivered in the first half of January, the curve will be pretty similar to Figure 1, but it will start to flatline a couple of weeks sooner.<\/p>\n

    Once you understand these concepts, you are ready for the details.<\/p>\n

    For people in Medicare, there is a strong seasonality effect on the death rate<\/h3>\n

    For the elderly, there is a strong seasonality of deaths. They are high in the winter and low in the summer. The difference between peaks and troughs is around 20%. This data is from the CDC for ages 65-84:<\/p>\n

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    Figure 1<\/strong>. This is the weekly death counts from 2015-2019 summed over all US states for ages 65-84. This was created using a<\/span> visualization on the CDC website using this dataset<\/a>. Epidemiologists are very familiar with this effect. There are no surprises here. The peak is 256K, the trough is 213, so there is a 17% seasonality drop in deaths from the peak.<\/span><\/figcaption><\/figure>\n<\/div>\n

    What this means is if you got the shot in Q1 of 2021, and you look at the days until death, <\/span>if the vaccines are safe<\/strong>, you should find that it will go lower in time and then turn upwards. <\/span><\/p>\n

    But what we find is the opposite.<\/p>\n

    The control group for 2021<\/h3>\n

    Figure 2 shows the deaths by week in 2021 for all states ages 65-84. Note that the rates drop for the first 11 weeks and stabilize.<\/p>\n

    In 2021, there is a steeper drop than normal because of COVID adding to the drop:<\/p>\n

    \n
    \n
    \"\"<\/div>
    Figure 2<\/strong>. This is the weekly death counts summed over all US states for 2021. This is essentially the control graph. This was created using a<\/span>visualization on the CDC website using this dataset<\/a>. Epidemiologists are very familiar with this effect. There are no surprises here. The deaths drop for the first 11 weeks of the year then stabilize. The peak is 81K, the trough is 50K so there is a 39% combined drop from peak to trough.<\/span><\/figcaption><\/figure>\n<\/div>\n

    The vaccine program was initiated on Dec 14, 2020, and peaked in the third week of Jan 2021 for people in this age group:<\/p>\n

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    Figure 3<\/strong>. Connecticut vax rollout schedule for <80 Medicare participants peaked in weeks 3 and 4 of 2021. Each bar is a week<\/span><\/figcaption><\/figure>\n<\/div>\n

    This means that if we limit our \u201cdays from shot #1 to death\u201d analysis to people who got their first vaccine in Q1 of 2021, if the shot is harmless, we should see the rate of deaths dropping for at least 9 weeks after the shot, and then remaining flat for the next 15 weeks before turning upward. This is because about half the shots got delivered before week #3 (11-2=9)<\/p>\n

    The charts show the slope goes up instead of down<\/h3>\n

    As we noted in the previous section, if the first shot is given in Q1, the number of days after the shot until you die should go down for at least 9 weeks and then stabilize for the next 15 weeks per the seasonality described in the previous section. So a safe vaccine would look like Figure 2<\/p>\n

    But it doesn\u2019t. It goes up! That\u2019s the problem.<\/p>\n

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    Figure 4<\/strong>. This shows days until death from Shot #1 where shot #1 was given in Q1 2021 to Medicare recipients under 80. What is supposed to happen is the line is supposed to slope DOWNWARD due to seasonality. The slope goes the wrong way. Note that the increase in risk is still present after 2 years from the initial value at day 50, but at least it\u2019s not getting any worse over time. NB: The graph drops off starting at 660 days out because we run out of months to die (since the shot is given in Q1 and the person must die before Feb 1, 2023). <\/span><\/figcaption><\/figure>\n<\/div>\n

    Similarly, if we restrict our analysis to the first shot given in Q2 (most of which would have been given in April), we see the same problem. The slope should be flat for around the first 15 weeks after the shot is given (we are starting in a flat period (week 13) and we have about 15 weeks of flat deaths after that. Yet the slope is going up when it is supposed to be flat.<\/p>\n

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    Figure 5<\/strong>. Same as Fig. 4 except we restrict shot #1 to be given in Q2. Not that the peak shifts since seasonality does not move. The drop off is now starting at 570 since we are now giving the shot a quarter later.<\/span><\/figcaption><\/figure>\n<\/div>\n

    The same wrong slope happens with shot #2<\/h3>\n

    The same problem happens with the second shot. About 75% of the people in Medicare were injected with shot #2 prior to April 15, 2021.<\/p>\n

    Here\u2019s what the shot #2 injection schedule looked like in Connecticut:<\/p>\n

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    Figure 6.<\/strong> Shots 1 and 2 were quickly rolled out to the Medicare community with most everyone getting fully vaccinated in Q1 of 2021. This is from Medicare data from Connecticut.<\/span><\/figcaption><\/figure>\n<\/div>\n

    Therefore, we should have seen a downward slope in the beginning and we are seeing the opposite again.<\/p>\n

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    Figure 7<\/strong>. This chart is days till death from Shot #2 given that shot #2 was delivered in 2021. Since most of the shot #2 were delivered in Q1 2021, you should see a strong downward slope here as well. You don\u2019t. The slope goes the wrong way for shot #2 too. That\u2019s inexplicable.<\/span><\/figcaption><\/figure>\n<\/div>\n

    The same wrong slope happens with shot #3 too<\/h3>\n

    Most people in Medicare got shot #3 in October, 2021. So we should see an upward trend for about 60 days (due to seasonality and another COVID wave), and <\/span>then it should fall dramatically<\/strong>.<\/span><\/p>\n

    It doesn\u2019t. It remains flat. That\u2019s problematic. It suggests that if you lived until shot #3, it will still increase your risk of dying, just not as much as the earlier shots.<\/p>\n

    This chart would have been more useful had the Dose 3 vax window been narrowly restricted. Stay tuned\u2026<\/p>\n

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    Figure 8. <\/strong>Shot #3 delivered in 2021. Most people in Medicare got their booster in October 2021, so we\u2019d expect the slope to go down after 60 days. That doesn\u2019t happen. The slop remains flat which is problematic.<\/span><\/figcaption><\/figure>\n<\/div>\n

    <\/h3>\n

    This is the most damaging data I\u2019ve seen<\/h3>\n
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    Figure 9.<\/strong> Number of days died after dose #2 if you just got dose #2. So there is a rapid fall off at Day 200 which is people opting for Dose #3 and beyond. But I realized later than fewer than 50% opted for >2 shots. So we can raise the baseline by 2X and get a conservative estimate of steady state. This allows us to clearly see that the shots elevated your risk of death by around 50% for at least the first 200 days after the shot. This is a DISASTER and it\u2019s also going to be impossible for the CDC to explain away. <\/span><\/figcaption><\/figure>\n<\/div>\n

    This is a chart of people who just got two shots and no more. At first, I dismissed it because if you got 3 or more shots, you\u2019d leave the group so the flat part starting at day 400 isn\u2019t a valid steady state number because the size of the cohort changes due to the \u201cno other shots\u201d criteria.<\/p>\n

    But then I did a calculation using the Connecticut data and found that when there were 23,259 deaths from Dose #2, there were only 10,557 deaths from Doses #3 onwards. So this suggests to me that fewer than half the people in Medicare opted for the jabs.<\/p>\n

    Then I confirmed in <\/span>USA FACTS that fewer than half the people who got shot #2 got any of the boosters (68% vs. 33%)<\/a>.<\/span><\/p>\n

    So if we simply take our 200 deaths per day flatline number from the chart above and adjust it for the people who left the cohort, we can see that the first 200 days, we had a 50% increase in the rate of death vs. the rate after 1 year (which itself might be elevated from normal).<\/p>\n

    This is a complete disaster no matter how you look at it.<\/p>\n

    The good news here is that it shows if you stop the shots, it appears your risk lowers after a year.<\/p>\n

    As you can see from this chart, if you keep on with the shots, as half the people did, your risk of death remains elevated!<\/p>\n

    \n
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    Figure 10. <\/strong>This is the same as Figure 9, but here we do NOT have the restriction that you didn\u2019t get any more shots. The number of deaths remains elevated due to the fact that half the people opted for subsequent shots. If nobody opted for any more shots after shot #2, we would have expected the curve to flatline at around 400 deaths \/ day.<\/span><\/figcaption><\/figure>\n<\/div>\n

    Even more Medicare data: cardiac events after vaccination<\/h3>\n

    Below is a graph of people with an ICD10 code of I2 to I5, showing the number of days from the date of the COVID vax to the time of the cardiac event.<\/p>\n

    This is NOT normal. This should be a flat line. There is no way they can explain this way.<\/p>\n

    More importantly, why isn\u2019t the CDC releasing this data? It\u2019s in Medicare and they can easily pull it. What is wrong with them? It seems as if they are protecting the vaccine instead of the American people, doesn\u2019t it?<\/p>\n

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    \"\"<\/div>\n<\/figure>\n<\/div>\n

    The Tableau visualizer<\/h3>\n

    You can <\/span>play with the data here thanks to Albert <\/a>Benavides<\/a>.<\/span><\/p>\n

    Additional confirmation the vaccines are deadly<\/h3>\n

    See my <\/span>newly updated article on the UK data<\/a>, which now includes US Mortality\u2019s latest analysis:<\/span><\/p>\n

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    \n
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    Basically, even the flawed UK data still has a huge signal they couldn\u2019t hide: <\/span>there is a bigger killer than COVID and NOBODY can figure out what it is<\/strong>! Isn\u2019t that odd?<\/span><\/p>\n

    Joel Smalley\u2019s analysis of the UK data<\/a> is superb as well. Even with the flaws relative to the unvaccinated, by focusing on the vaccinated, he can show they are dying at a disproportionately high rate.<\/span><\/p>\n

    Furthermore, Ed Dowd\u2019s data, beautifully presented in his book \u201c<\/span>Cause Unknown<\/a>,\u201d is also hard for anyone to refute. How are working people 18-64 suddenly dying at a higher rate than non-working people in America right after the vaccine mandates hit? Nobody can explain that one.<\/span><\/p>\n

    Ed\u2019s conclusions are the same as mine. So now you have two very powerful, but completely different datasets that are easy to explain if the vaccines are dangerous and impossible to explain using any other hypothesis.<\/p>\n

    And of course my favorite example is the VAERS excess deaths. How can there possibly be over 16,000 reported in VAERS if nothing is going on? The only vaccine with excess deaths is the COVID vaccine. All the other vaccines show the same number of excess deaths as in prior years. The argument that the COVID vaccines were rolled out to 100X more people than a normal vaccine is ridiculous. For example, the flu vaccine was given to at least 33% of the Medicare recipients so maybe you can argue a factor of 3X at most. So there is no way to explain the excess deaths which are effectively over 640,000 for a 41 underreporting factor.<\/p>\n

    The 640,000 number for the first two years of the vax rollout was validated in <\/span>Mark Skidmore\u2019s paper<\/a> (which was published in a peer-reviewed journal) along with personal communications with Mark. Mark used polling and found a large number of deaths in 2021. Note that people are trying to get Mark\u2019s paper retracted because they said it is unethical to ask people about vaccine deaths. Apparently, it\u2019s OK to ask about COVID deaths, but it\u2019s unethical to ask the exact same question about vaccine deaths. Also, they objected to the statement about who funded the study and wanted a complete bio of the funder. Mark has written over 70 papers published in the scientific literature and he\u2019s never seen anything like these objections. The paper could easily note these, but they seem more interested in having the paper retracted because they don\u2019t like the result. This is how science works. You <\/span>can watch my interview with Mark Skidmore here<\/a> so you can see first hand how science is manipulated with ridiculous objections when they don\u2019t like what you find. I just learned that his university is now also investigating him. His crime? He reported survey results that go against the narrative.<\/span><\/p>\n

    Could there be an error in the queries?<\/h3>\n

    No. I replicated the shot #1 charts myself and you can see them yourself in the <\/span>Excel charts<\/a> (which are drawn from the record-level data).<\/span><\/p>\n

    Is there any other way to explain away these results?<\/h3>\n

    Not that I\u2019m aware of.<\/p>\n

    I\u2019d like to see someone try though. It would be fun to see the attempts.<\/p>\n

    Of course, you could interpret the upward slope as \u201cSee, the vaccine is saving COVID lives in the short term, that\u2019s why the slope goes up over time as it wears off\u201d but that <\/span>is simply preposterous<\/strong>. <\/span><\/p>\n

    Nobody has ever claimed the vaccine reduces all cause mortality below baseline. There is no clinical trial showing that and there is no known mechanism of action whereby introducing a pathogen into your body will reduce all-cause mortality.<\/p>\n

    The only claim they make now is that the vaccine reduces COVID deaths. Fine. Let\u2019s say that the vax is perfect and reduces every single COVID death, then the slope must still be downwards due to seasonality as we said before. But it\u2019s not.<\/p>\n

    That is why all these pro-vaccine people are upset about this data: because they can\u2019t explain it. So they will have to ignore it and hope that nobody reads my article.<\/p>\n

    So if you share this article, you won\u2019t let them get away with it.<\/p>\n

    UPenn Professor Jeffrey Morris tries to attack my piece: Epic fail<\/h3>\n

    Jeffrey Morris wrote \u201ctemporal HVE\u201d on Twitter:<\/p>\n

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    But this is simply a hand-waving dismissal of all this work with no evidentiary support whatsoever. HVE refers to the \u201chealthy vaccinee effect.\u201d His \u201ctheory\u201d is that the healthiest people get the vaccine first and since those people aren\u2019t likely to die soon, it causes the slope to go upwards. The second part of the effect is that if you are dying from terminal cancer and will be dead in 3 days, you\u2019re unlikely to want to get a COVID shot to protect you from dying from COVID. So people \u201cself-select\u201d out of the vax program if they know they are going to die.<\/p>\n

    But in our case, there was a mass vaccination effort for all Medicare patients and they were all vaccinated ASAP come December.<\/p>\n

    What Jeffrey can\u2019t explain is why the slope is even more distinct for people who got their shots in March 2021. Those would be the \u201cstragglers\u201d and thus less healthy, yet the upward slope is even <\/span>more <\/strong>pronounced than in January. So his \u201cexplanation\u201d just doesn\u2019t fit the data. Nice try, no cigar.<\/span><\/p>\n

    Furthermore, here are the days to death numbers for the flu and pneumococcal shots in Medicare patients. Nobody has ever seen these before either.<\/p>\n

    See how the lines are all FLAT for the same study on these vaccines??<\/p>\n

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    \"\"<\/div>\n<\/figure>\n<\/div>\n
    \n
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    \"\"<\/div>\n<\/figure>\n<\/div>\n

    If you look closely, you can see that there is a slight rise in the slope for a few days after the shot only. That\u2019s the HVE effect. It\u2019s small and short lived. It is NOTHING like what we see for the COVID vaccines.<\/p>\n

    Also note that anyone taking these shots isn\u2019t planning on dying the day of the shot (why take the shot if you are going to die?). Yet they do on the same day of the shot, in massive numbers. Why is that? Because these \u201csafe vaccines\u201d kill people; that\u2019s why there is a huge spike on Day 0.<\/p>\n

    This is another reason why the CDC never shows you the Medicare data: it would reveal that other vaccines are deadly as well (and kill more than 1 person per million which is the threshold for safety).<\/p>\n

    <\/h3>\n

    If the CDC wants to prove I\u2019m wrong, it\u2019s easy: release the data!<\/h3>\n

    We need to stop holding the data hostage.<\/p>\n

    If the CDC wants to prove I\u2019m wrong, the best way to do that is to <\/span>publicly release all the data as specified in this article<\/a>. That would be in the public interest. <\/span><\/p>\n

    Will they do that? No way. Never. They will come up with excuse after excuse why they can\u2019t do this.<\/p>\n

    And that tells you EVERYTHING you need to know.<\/p>\n

    Summary<\/h3>\n

    The record-level vax-death <\/span>Medicare data<\/a> I received is now publicly available. Now, for the very first time, you can analyze it yourself.<\/span><\/p>\n

    It shows the vaccines increase the risk of death for the elderly and that these risks appear to remain persistently elevated. It\u2019s anyone\u2019s guess for how long.<\/p>\n

    So now you know why the CDC never showed us the <\/span>Medicare data<\/a>. And now you know why the medical community and mainstream media never asked to see it and never will. They had it the whole time and kept it from public view so they wouldn\u2019t create \u201cvaccine hesitancy.\u201d<\/span><\/p>\n

    If you think public health officials don\u2019t hide the data, you should read this <\/span>tweet from Chris Martenson<\/a> where the Australian health authorities <\/span>admit that they covered up vaccine deaths because they \u201cdidn\u2019t want to undermine public confidence\u201d in the vaccine<\/strong>. Get it?<\/span><\/p>\n

    If you think public health officials in the US want to see all the safety data even for just themselves, you should watch <\/span>my video of Stanford Professor Grace Lee calling the Palo Alto Police on me when I tried to ask her if she wanted to see the safety data from the Israeli Ministry of Health<\/a>. Basically, the health authorities in the US run the other way when you try to confront them with data showing they are wrong.<\/span><\/p>\n

    Finally, if your doctor still tells you to take the shot, ask her to first explain to you why the slope in the <\/span>Medicare data<\/a>goes the wrong way before you get the shot. And let us all know what they say in the comments.<\/span><\/p>\n

    Share<\/span><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

    === Read Full PDF Here GAME OVER: Medicare data shows the COVID vaccines increase your chance of dying This is why the CDC has NEVER used the Medicare data to prove the vaccines are safe. And this is why NOBODY in mainstream medicine wants you to see this data. EVER. They ALL want it hidden.…<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-5023","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/jewelryon.com\/index.php?rest_route=\/wp\/v2\/posts\/5023","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/jewelryon.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/jewelryon.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/jewelryon.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/jewelryon.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=5023"}],"version-history":[{"count":0,"href":"https:\/\/jewelryon.com\/index.php?rest_route=\/wp\/v2\/posts\/5023\/revisions"}],"wp:attachment":[{"href":"https:\/\/jewelryon.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=5023"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/jewelryon.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=5023"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/jewelryon.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=5023"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}