Covid/Coronavirus and diabetes - the numbers

Brunneria

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Significant outbreak in a market in Beijing according to Beeb.
D.

Thanks. Very new info.
Various media sources have been trying to muster ‘second wave in China’ drama since the middle of May.
I wonder if this is the actual start of one.
Hope it isn’t, but the risk is there, isn’t it?
 

lindisfel

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Well I hope they stop it dead and then the clowns in Britain take notice and learn a lesson this time from countries that take positive action.

I see some scientists in Britain are no longer going to take the blame for political leaders advisors using unproved theories.
D.
 

Pipp

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Lupf

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Begley S
(2012) In cancer science, many “discoveries” don’t hold up. Reuters Science News. Available at www.reuters.com/article/us-science-cancer-idUSBRE82R12P20120328. Accessed July 5, 2017.

https://www.pnas.org/content/115/11/2563#xref-ref-46-1
You made a statement about scientists fiddling their data, which is dishonest and needs to be exposed, But it happens less often then headlines claim, in particular on social media where these are unchecked. Your example is not about cheating, but something different.

That said the newspaper article that you quote is a good illustration of what is actually going on. In drug studies a lot of claims don't hold up and cannot be repeated. Why is this happening? One reason is: "Never underestimate incompetence" by which I mean sloppy work, bad methods, bias for positive results (unconscious or not). Another one is - medical studies are difficult. It is very hard to find a true causal relation which, in reality, is not a correlation. This is why I always look if a result has been corroborated by at least one other study
 

JohnEGreen

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Lupf

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Pipp

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You made a statement about scientists fiddling their data, which is dishonest and needs to be exposed, But it happens less often then headlines claim, in particular on social media where these are unchecked. Your example is not about cheating, but something different.

That said the newspaper article that you quote is a good illustration of what is actually going on. In drug studies a lot of claims don't hold up and cannot be repeated. Why is this happening? One reason is: "Never underestimate incompetence" by which I mean sloppy work, bad methods, bias for positive results (unconscious or not). Another one is - medical studies are difficult. It is very hard to find a true causal relation which, in reality, is not a correlation. This is why I always look if a result has been corroborated by at least one other study
I don’t intend to derail this thread with further discussion on this issue. Apart from to say that JohnEGreen has illustrated with his reference what I meant.
Thank you @JohnEGreen
 

DCUKMod

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I discovered this page, via Professor Karol Sikora's Twitter trail.

upload_2020-6-17_9-12-9.png


This seems to give a truer picture of the trends, rather than looking at dates reported, when weekends and public holidays have impacted, as well as plain old delays in loved ones registering deaths and so on.

Whilst these are not his figures, or his data, in my view Professor Sikora talks a lot of sense. He has been speaking out of the collateral implications of COVID for a long time.

https://www.ons.gov.uk/peoplepopula...nglandandwalesprovisional/weekending5june2020
 

JohnEGreen

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Off topic I know but it would seem latest advice coming from China I believe is when flushing the loo after a poo is to keep the toilet lid down as particulate matter from uncontrolled loo flushing can travel upto four feet or more into the air and could contribute to spreading of the virus. :meh:
 

Lupf

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Screenshot_2020-06-17 Deaths registered weekly in England and Wales.png
excess-deaths-100620.jpg
View attachment 42162 View attachment 42161 View attachment 42160 View attachment 42161
I discovered this page, via Professor Karol Sikora's Twitter trail.

View attachment 42137

This seems to give a truer picture of the trends, rather than looking at dates reported, when weekends and public holidays have impacted, as well as plain old delays in loved ones registering deaths and so on.

Whilst these are not his figures, or his data, in my view Professor Sikora talks a lot of sense. He has been speaking out of the collateral implications of COVID for a long time.

https://www.ons.gov.uk/peoplepopula...nglandandwalesprovisional/weekending5june2020
On this page you can also find the chart the deaths as a function of time for England and Wales until 5. June, see attached.
There are 53,848 excess deaths compared with the the five-year average.

John Burn-Murdoch from the Financial Times also compiles the number of deaths
which he regularly posts on twitter,
https://twitter.com/jburnmurdoch/status/1270735582742839296
According to this there were 64,200 excess deaths in the whole of the UK,
or an increase of 57%. Most of these will be due to Covid-19.
The attached graph from the FT plots the excess deaths for 20 countries, which shows how bad the UK and the US are doing.
On JBM's twitter you can also find a plot for the different region in the UK.
 

Lupf

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The latest CDC data of co-morbitity deaths with diabetes in the top three.

https://www.cdc.gov/mmwr/volumes/69/wr/mm6924e2.htm?s_cid=mm6924e2_w
This is a CDC study from the US. Having had a quick look it is not quite straightforward to compare it with the studies by the group of Jonathan Valabhji, which I've discussed earlier in this thread. The papers are available at https://www.england.nhs.uk/publicat...es-and-covid-19-related-mortality-in-england/.
One of the main result of the CDC study is: "the most common underlying health conditions were cardiovascular disease (32%), diabetes (30%), and chronic lung disease (18%). Hospitalizations were six times higher and deaths 12 times higher among those with reported underlying conditions compared with those with none reported."
Where as the main result of the Valabhji paper include
1) the odds of dying for people with T1 was 3.5 times that of people without diabetes.
2) the odds of dying for people with T2 was 2.0 times that of people without diabetes.
The CDC study lumps all conditions together and comes up with a risk of death being 12 times higher. You naively might say that the death risk for all diabetes people in the US is 30% of 12 times that of healthy people, i.e. scaling by fraction of the people with diabetes compared to all conditions. However you should not do this as this assumes that the increase in risk is the same for all conditions, which is clearly not the case. For example if people have more than one condition the risk increases. For a comparision would also nee to take into account differences in the populations.

In conclusion I can't find the information in the CDC study to make a 1:1 comparison on covid death risk for diabetics with the Valabhji study.
 
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JohnEGreen

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This is a new study out today.

"
It looked at nearly 35,000 Covid-19 patients in 260 hospitals across England, Scotland and Wales up until the middle of May.
"South Asians are definitely more likely to die from Covid-19 in hospital, but we don't see a strong effect in the black group," Prof Ewen Harrison, from the University of Edinburgh, told the BBC.
People from South Asian backgrounds were 20% more likely to die than white people. Other minority ethnic groups did not have a higher death rate.
The study, the largest of its type in the world, shows:
290 die out of every 1,000 white people needing hospital treatment for Covid-19
350 die out of every 1,000 South Asian people needing hospital treatment for Covid-19
The study also reveals profound differences in who is needing hospital care based on ethnicity.
"The South Asian population in hospital looks completely different to the white population," Prof Harrison said.
He added: "They're 12 years younger on average, that's a massive difference, and they tend not to have dementia, obesity or lung disease, but very high levels of diabetes."
Around 40% of South Asian patients had either type 1 or type 2 diabetes compared with 25% of white groups."

https://www.bbc.co.uk/news/health-53097676

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3618215
 

Jamie H

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This is a CDC study from the US. Having had a quick look it is not quite straightforward to compare it with the studies by the group of Jonathan Valabhji, which I've discussed earlier in this thread. The papers are available at https://www.england.nhs.uk/publicat...es-and-covid-19-related-mortality-in-england/.
One of the main result of the CDC study is: "the most common underlying health conditions were cardiovascular disease (32%), diabetes (30%), and chronic lung disease (18%). Hospitalizations were six times higher and deaths 12 times higher among those with reported underlying conditions compared with those with none reported."
Where as the main result of the Valabhji paper include
1) the odds of dying for people with T1 was 3.5 times that of people without diabetes.
2) the odds of dying for people with T2 was 2.0 times that of people without diabetes.
The CDC study lumps all conditions together and comes up with a risk of death being 12 times higher. You naively might say that the death risk for all diabetes people in the US is 30% of 12 times that of healthy people, i.e. scaling by fraction of the people with diabetes compared to all conditions. However you should not do this as this assumes that the increase in risk is the same for all conditions, which is clearly not the case. For example if people have more than one condition the risk increases. For a comparision would also nee to take into account differences in the populations.

In conclusion I can't find the information in the CDC study to make a 1:1 comparison on covid death risk for diabetics with the Valabhji study.
Well said. We have a great study taken from our own population that is specifically focused on diabetes. This should be our main point of reference.