Posted on 12/19/2021 8:06:58 AM PST by Hojczyk
Tell you what. You cite some sources, and we'll read them together?
Merely discarding mine is mere assertion.
You write, “The global population is not vulnerable.” Given the breakthrough news, the assertions that 5 year olds are transmitters of the virus, and more, what is “vulnerable?”
Given that China — according to my cited sources — is calculated to have deaths of about 0.0033 % of the population over almost two years, it would be lovely for you to cite your source(s).
By citing sources, we can evaluate for ourselves as for those who follow these threads as to our biases and methodologies.
Tell you what. Why don’t you explain why you don’t limit your denominator to the age group that dies? Explain why you do that according to all sources over 2 years and we can let people evaluate your methodological purity.
Why are you talking about 5 yr old transmitters after you just quoted your bizarre death stats including in them people who don’t generally die. Pregnancy rates among men are low, so lets include men in the calculation of population pregnancy rates? How can you not see your bias?
Are you saying a lot of older people in South Africa are dying of Omicron?
First, you did not offer any sources of your own. So this rachet of questions is merely a sort of avoidance to me. A parade of questions is not an answer to an earlier question. But then you know that.
1) “Why don't you explain why you don't limit your denominator to the age group that dies?”
The data collections’ brute data does not offer it. Do you have a source that does? We could follow that train of thought. But to use population as a denominator consistently in looking at various nations, data, there would be comparable results.
Please cite your data sources which break apart populations by age groups, and please define the age cutoff which you use to define “vulnerable.”
2) “Explain why you do that according to all sources over 2 years and we can let people evaluate your methodological purity.”
Such an obvious answer. The global and national death tolls are reported as an additive total, and I take the Corman-Drosten coding of the PCR test for SARS CoV2 was written in January of 2020.
What “methodological purity” is yours, other than to keep asserting and questioning? Please provide from what date you begin the total of deaths, if different than mine. Please provide URLS that we may all look at both our data sources.
3) “Why are you talking about 5 yr old transmitters after you just quoted your bizarre death stats including in them people who don't generally die.”
“...people who don't generally die” in death stats?” I confess to not understanding your assertion.
As to the remaining, 5year olds in the US are being involved in the 23-month “event” as they are being inoculated with experimental mRNA gene therapies in continuing phase three clinical trials wherein the manufacturers of these inoculations are shielded from civil liability,
Moreover in the UK infants are being experimented on. That seems horrid to me. Does it to you?
The premise for these experiments on children and infants is that they can harbor the virus, and transmit. Moreover, the “vaccinated” now when ill are described as “breakthrough,” and the justification for one booster in the US and more than one in other nations is that these “experiments” wane.
Having answered your questions, will you answer mine?
No. They are two things. 1) Absent. They fled the infected region and 2) vaxed. 60%+ of people over 50 in SA are vaxed — and since it is SA they didn’t get their vax until relatively recently, unlike the US who got vaxed ASAP in Spring/Summer. The immunity fade is not as far along there.
If everyone got their 666 boosters this would go away!
https://www-statista-com.proxy.library.upenn.edu/statistics/1191568/reported-deaths-from-covid-by-age-us/ Reasonably good presentation of COVID death by age.
I have seen elsewhere some granularity on the 50-64 age group. It’s concentrated 60+.
There are approx 56 million 65+ ppl in the US. 18ish% of the total.
The Excess Deaths page is the go to place to get age relevant visualization of what goes on state by state All Causes. Once you have examined the All Causes data and see that the graphs align with the Worldometers Covid death count graphs, it becomes obvious the source of the Excess is the virus.
https://www.cdc.gov/nchs/nvss/vsrr/covid19/excess_deaths.htm
Scroll to the Update Dashboard button, just above it to the right select weekly deaths by age. Update and scroll down. Focus more on elevation of the orange line above the gray smudge of multi year pre Covid norm — rather than a comparison to 2020.
The vaxed population is not doing so well in 2021. The vax has faded and probably more important, Delta in July.
Regardless, the elevation above gray smudge is older folks.
QED
“That absurd NY Times”
That says it all
My thoughts as well. I don’t need a test to know if I have the flu.
But (and credit to my wife), some, and especially the unvaccinated, may be required to test for work. Others may be hoping for a positive result as a number of employers are giving 10 days paid time off for a positive test.
Many New Yorkers are voting with their feet, heading to Red States like Florida, Missouri, and Texas.
The tests are a joke—false positives, false negatives.
They might as well do a lottery drawing and declare that the winners get some “sick time”.
;-)
I have no problem with the notion of “excess deaths” calculation per se, excepting how the data was defined and collected. But it cannot be done comparatively across whole nations or globally. I do note that your source is the CDC itself, passed through UPenn to Statista, so the calculations are the CDC’s with you as reporter for them. Ergo, as you attack my bias, your bias is for the CDC and their interpretation of the data.
One notes in the US the media age of death is about 73.8 years currently. Therefore the stats for those older fall into the question about “from” and “with.” Adding in “from” and “with” along with “assumed” — as the WHO recommended in April 2020 — one might question the data taxonomy itself. All those above 73.8 years are essentially dying. I am in that group. When my time comes, as I have seen personally with others, a death not from SARS CoV2 might get coded as a death from SARS CoV 2. Of this, I have personal experience.
I note from Statista/UPenn/CDC website that about 400,000 of the aggregate 800,000 are in the category of “over 73.8 years,” and so death was statistically expected in a COVID year as in a pre-COVID year. As likely with next year as well.
So “weekly deaths by age” falls into that interesting category whereby expected deaths can also be labeled “excess deaths.” But when expected....
https://usafacts.org/data/topics/people-society/health/longevity/average-age-of-death/
https://www.macrotrends.net/countries/USA/united-states/death-rate
As to presumption and diagnoses, you might want to review the historical creation of new codes and classification to obtain a “COVID” diagnosis. One will read “A death due to COVID-19 may not be attributed to another disease (e.g. cancer) and should be counted independently of preexisting conditions that are suspected of triggering a severe course of COVID-19.” What then follows is advice as to how to arrange the data to emphasize a COVID diagnosis. Much was newly coded, redesigned and redefined that COVID — a syndrome with remarkably flexible symptoms and also no symptoms at all — could be tallied.”
“...to arrange the data to emphasize a COVID diagnosis.”
As the WHO states: “COVID-19 should be recorded on the medical certificate of cause of death for ALL decedents where the disease caused, or is assumed to have caused, or contributed to death.”
“...or is assumed to have caused, or contributed to death.”
https://www.who.int/classifications/icd/Guidelines_Cause_of_Death_COVID-19.pdf?ua=1
One notes that your CDC page includes: “Comparing these two sets of estimates — excess deaths with and without COVID-19 — can provide insight about how many excess deaths are identified as due to COVID-19, and how many excess deaths are reported as due to other causes of death. These deaths could represent misclassified COVID-19 deaths, or potentially could be indirectly related to the COVID-19 pandemic (e.g., deaths from other causes occurring in the context of health care shortages or overburdened health care systems).”
“...could represent....”
More to my point in critiquing the CDC’s data handling as well as taxonomy is the sentence before: “As some deaths due to COVID-19 may be assigned to other causes of deaths (for example, if COVID-19 was not diagnosed or not mentioned on the death certificate), tracking all-cause mortality can provide information about whether an excess number of deaths is observed, even when COVID-19 mortality may be under counted. Additionally, deaths from all causes excluding COVID-19 were also estimated.”
“...may be undocumented?” Count as COVID. “...also estimated.”
An analysis done and then enthusiastically “debunked” was done in a seminar connected with Johns Hopkins, in which a statistical analysis showed more or less than influenza, pneumonia and other death data were recoded. Of the analysis which can be found online, one reads “This is all representative of an unproductive orthodoxy that exists around Covid-19. An orthodoxy that has a set view on how to think and how to respond to the virus. When the Great Barrington Declaration was released, a document signed by tens of thousands of medical professionals and hundreds of thousands of concerned citizens calling for the end of lockdowns, it was met with extreme vitriol. Some attacks were welcome scientific scrutiny but many others were the type of pointless slander usually reserved for the peanut gallery of politics.”
https://www.aier.org/article/the-censorship-of-dr-briand/
You write, “The vaxed population is not doing so well in 2021. The vax has faded....”
Immunizations immunize. These “vaccines” are defined as such because the definitions were changed to fit the “event.” Even “pandemic” was redefinded by the WHO. As Oleich of Bayer observed, the inoculations are in fact experimental mRNA gene therapies, which the public would have resisted if marketed as such. A “vaxed” population which becomes taxonomically “unvaxxed” as inoculations wane, and a “vaxed population” which requires continual mRNA therapy every six months or less is not a “vaxed population.” It is a population being repeatedly injected with what are still “experimental” drugs.
You and I will disagree. Your bias is for the CDC and mine is against.
My suppositions still stand, based on “official data.” My suppositions are as they are based of so many wiggle words such as “assumed” used by the WHO and beyond.
If the world's deaths as related to world's population and any nation's deaths related to its population are of any value in being collected, presented and analyzed — as the JHU Coronavirus Resource Center and Worldometer are and do — then some brute facts emerge.
Comparative values for the world (0.0675 %), China (0.0033 %) and the US (0.23 %) are starkly different. These calculated values suggest that — among all the nations of the world — the WORST at handling the “pandemic” has been the United States, its response being led by Fauci, Collins, Walensky, the CDC and the FDA.
You might object to including China, so as a methodological control, one looks at Germany from only last month.
( 101,794 German dead officially from SARS CoV2 / 83,783,942 population of Germany ) / 100 = about 0.12%. Less than half the US toll.
Other nations as methodological control:
Ireland through 23 months — ( 5,609 deaths since beginning of pandemic / 4,937,786 Irish) x 100 = 0.114 %.
Greece since the pandemic — ( 17,168 / 10,423,054 ) x 100 = 0.165 %.
Netherlands with its hysterical lockdown for Christmas: ( 19,344 deaths / 17,134,872 population of the Netherlands ) x 100 = circa 0.113 %.
My use of available data does not rely on the CDC, as do you and the UPenn site whose data you cite. Your methodology seems to take the CDC taxonomy, data collection and analyses at face value.
And yet, the Fauci, Collins and Walensky team along with two federal administrations may be said to be the WORST in the world in handling the “pandemic.”
“The U.S. has the worst coronavirus outbreak in the world: ‘The numbers don't lie,’ Dr. Fauci says.”
If the worst outbreak in the world, and numbers don't lie, one need explain why. Is the US population — vulnerable” or not — different than the people of the world. Yes, some populations are younger, and some more frail and some more robust, etc. Even so, the “worst” in the world refers to the disease and deaths. attributed to it.
But the WHO instructed “to arrange the data to emphasize a COVID diagnosis” and the CDC has been operating on “assume” for some of its data, as above.
And one recalls: “Dr. Birx: Unlike Some Countries, ‘If Someone Dies With COVID-19 We Are Counting That As A COVID-19 Death’.”
“Dr. Deborah Birx said ‘there is nothing from the CDC that I can trust’ in a White House coronavirus task force meeting”
https://www.businessinsider.com/deborah-birx-cdc-comments-coronavirus-task-force-meeting-2020-5?op=1
You trust the CDC. I don't. I look to available data and do simple arithmetic.
We differ and therein is the tale. Regards.
As of today there are 6179 hospitals, with 776,546 hospital beds in America reporting to HHS. Of those beds, 605,544 are in use total. Of those in use, 68,208 are beds with Covid patients. Our hospitals nationwide are at 77.98% capacity. Of that percent 8.89% are Covid beds in use nationwide.
As of Aug. 11, HHS data indicated that nationwide, about 76.4% inpatient beds in the U.S. were in use — and 10.5% of those beds were in use for Covid.
Nationally, 75.4% of ICU beds in the U.S. are in use, and 22.2% of those beds are in use for COVID-19.
Source: Inpatient Bed Utilization by State
https://protect-public.hhs.gov/pages/hospital-utilization
Scroll to bed utilization by state and you can look at any state in top right corner drop down, or main page shows Nationwide.
No, actually I don’t worship at the altar of the CDC, but there is no other compiler of Excess Deaths for the US that I know of. I once found a UK site quoting them, but probably from the CDC. More important, the UK numbers look a lot like the US.
The Economist has a model for global: https://www.economist.com/graphic-detail/coronavirus-excess-deaths-tracker
Attribution during one stimulus package did indeed reward hospitals that coded Covid, but that item disappeared from PPP in a later stimulus package. There IS a requirement that all patients get a COVID test now, so docs and nurses know how what protection they need treating that patient.
This requirement likely does elevate case count, but not likely death count because if a patient is dying over a period of time, his test will make clear why.
Probably the most compelling issue in the world of attribution is the elderly person living alone who is aware that the vast majority of people recover. So they start coughing at age 73 and try to tough it out. They die. Alone. Smell brings an ambulance a few weeks later, and presto a death occurs that should have been but is not attributed to Covid, because never tested. This is a hugely common scenario. It is why all community police forces now have a “wellness check” process on the books, from a neighbor call.
Overall, the reason to largely believe attribution is how the death curves align to All causes EDs. In March/April last year, before lockdowns or media splash, the northeast spiked deaths on the attributed graph and it looked just like EDs. Nothing else was happening in March/April. No meteor hit NYC. Just old people were dropping like flies, and Covid declarations aligned.
You say, "..there is no other compiler of Excess Deaths for the US that I know of. I once found a UK site quoting them, but probably from the CDC." Correct, as the Economist estimates from the CDC, so gathering data is seen but estimating is also seen. As to data, the taxonomy and coding with precede excess death calculations still are interesting as I had mentioned before. With changing definitions, coding and data acquisition, much remains a question.
Still amusing was from mid 2020 -- "WHAT'S UP, DOC? Dr Deborah Birx ‘doesn't trust the CDC’ as experts fear coronavirus cases have been INFLATED."
https://www.thesun.co.uk/news/11599541/dr-deborah-birx-doesnt-trust-coronavirus-cdc-data-inflated/
Johns Hopkins today reports 5,353,969 global deaths, and US (as 4.25 percent of the world population) deaths at 806,439.
As a small share of the global population and assuming that the virus affects humans somewhat equally, the US' deaths should be about that percentage of the global deaths. It is not. 227,544 deaths would be that number. The number collected from their data set is currently the larger 806,439 as of today. Almost three times the global toll. But....
India with its 1.4 billion is half the death toll of the US, at 477,158 as of today.
Your Economist site states, "In India, for example, our estimates suggest that perhaps 2.3m people had died from covid-19 by the start of May 2021, compared with about 200,000 official deaths." Which data are correct? That collected or that estimated?
Their "estimate" of 2.3 million is far larger than the JHU' tabulation of 477,158 reported today, which is larger than a normative estimate. More than four times as many as that counted and reported. So the data and the Eocnomist-admitted "estimates" vary hugely.
All the excess death calculations do not explain such data discrepancies. This is because they tabulate with their data collection and estimated excess deaths. Before the data is collected is the issue.
"From" and "with" have been conflated. "Including the "assumed" in reports has been the WHO methodology since April of 2020. The Economist "corrects" a value for India, as above, with an estimate wildly outside any data set I have reviewed.
So my skepticism after looking at your sources actually has been reinforced.
The live-alone old codger scenario is very powerful. It leads to grows undercounting of Covid deaths.
India is safely ignored. Focus on things like this from the data and only that particular data leads to errors that could be avoided with some extra awareness — specifically, India has an absurdly low life expectancy. They have a magnificent space program doing interplanetary exploration and a huge proportion of their population has no toilets.
So . . . India LE is actually about 70 yrs old. Men sub 70 as I recall. And for purposes of Covid this says exactly what we would think — of course Covid deaths are not properly proportional to the total population. The prime Covid targets died long before they could be hit.
China is a different matter. I originally thought their bionerds had discovered a subtle difference of Asian lung cells vs others and this explained low death rates in 2020 of ALL OF ASIA, not merely China. But . . . then the variants arrived and smacked Asia, more than China.
I shot my own theory down by discovering US citizens and UK citizens of Asian descent were dying at the same rates as other ethnicities. So . . . killed my own theory.
You have not convinced me, and obviously I have not convinced you.
With no common science on which to agree, what seems to remain as the distance between us is political.
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