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Covid/Coronavirus and diabetes - the numbers


Yep, they would have a difficult time explaining that they let a 'disabled' person go in those circumstances. Now I know type 1s come under the act and some type 2s but there is protection there to stop any such person being discriminated against or treated unequally. x
 
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actually drs we’re asked to add anyone they considered extremely vulnerable not already covered by the list you quote.
 
@Max68 @KK123 @HSSS this is a very interesting discussion on the employment risks of people who consider themselves vulnerable. May I suggest that you consider opening up a separate thread with a corresponding title. In this thread these messages seem a bit off topic.
 

You are right sorry I wandered off topic. Have deleted my messages!!
 
i would have thought that the majority of the "extremely vulnerable" would be unlikely to have been able to work for some time anyway, so would not have employment problems.
 
I think you’re being somewhat selective in who you address this request to, but sure if my single sentence correcting a misconception is out of place I’ll back out. There was never an intention to derail.
 
Folks, let's keep this very interesting and informative thread on-track. The topic is:

"Covid/Coronavirus and diabetes - the numbers"

Mandering off on to other topics - interesting or not - only waters down the focus and impact of this thread.

Thank you all.
 
Hi All, I have been trying to understand the figures in the reports. Is anyone able to explain the hazard ratio .Is 1.0 the hazard ratio that a comparible individual without diabetes would have ?
 
And those for BMI are they comparing to 1.0 for diabetic with a healthy BMI or to a non diabetic healthy BMI or to a nominal population average being 1.0 for hazard ratio?
 
An important question has been posted by @Dusty911.
Dusty911 said:
Hi All, I have been trying to understand the figures in the reports. Is anyone able to explain the hazard ratio .Is 1.0 the hazard ratio that a comparible individual without diabetes would have ?
Click to expand...
As this is confusing people, let me try to answer it.
Basically hazard ratios are always calculated by comparing two population samples. One is the reference (or denominator if you are mathematically inclined). Thus you always have to carefully look at what the reference population is when interpreting a hazard ratio.

Let me illustrate this with a few examples from the two Valabhji papers, https://www.england.nhs.uk/publicat...es-and-covid-19-related-mortality-in-england/. In these papers hazard is the risk of in hospital deaths with Covid-19. In the figure on page 21 of the whole population study (paper with 24 pages) it says
female 1.
male 1.94
In this example the reference sample are females. For males the number 1.94 means that the hazard ratio of males is about twice that of females. The hazard ratio for females is of course 1, because you would compare females with themselves.
In the same figure it says
No diabetes 1.
Type 1 3.50
Type 2 2.03
Here the reference sample are people who do not have diabetes. And as discussed in this thread, the overall risk for T1s is 3.5 times that of people without diabetes and for T2s it is a factor 2.
Now let's go to the other paper which studies the population of people with diabetes, see figure on page 20 (paper with 22 pages) where it says
age < 40 0.22
age 40 to 49 0.27
age 50 to 59 0.54
age 60 to 69 1.00
age 70 to 79 1.92
age > 80 4.36
Here the reference population is people with diabetes T2 between ages of 60 and 69. We find that for people with T2 and below 50 the hazard ratio is about a factor of four lower and for people with T2 over 80 the risk is about four times higher than that for those T2s in the reference population (60 to 69). This illustrates how age is the main risk factor. After 50 it doubles every 10 years of age. On page 19 you can find the corresponding results for people with T1. Many of these have large bars through the numbers as there is limited data and the statistical error is large. However, the overall picture is the same. The average risk ratio of a T1 below 50 is at least a factor of five lower than a T1 in the reference sample of 60 to 69. In fact, the paper does not report T1 deaths below 50, so I am wondering myself how these numbers have been calculated. (This has been clarified see update below ***). The conclusions do not depend on this.

In summary when reading a number always look for the reference population where the hazard risk ratio is set to 1. And make sure you understand what the population is. Is it the full population or only people with diabetes or with T1 or T2?

I hope that helps understanding these numbers. And my usual warning at the end. These are averages, each person is an individual and our risk could vary substantially from these depending on their natural variations which is usually much larger than hazard ratios close to 1 and in particular if there are other factors.

*** Update: they don't give the numbers of T1 deaths below 50 but @Draco16 inferred from the available information in the paper that these are small but non-zero.
 
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Lupf said:
In fact, the paper does not report any T1 deaths below 50, so I am wondering myself how these numbers have been calculated. To me they should be upper limits of risk ratios, but the conclusions do not depend on this.
Click to expand...

Hi @Lupf the paper (2) does state total T1 deaths on page 2 at 418 deaths, then from the table on page 17 you can see the breakdown by age groups 50+ summing to 396 deaths; the data for under 50s is obfuscated for confidentiality reasons, but the balance is 22 deaths for T1s under 50 (a further check is that the deaths for T1s over 50 in that table only sum to 94.6%).

So 22 deaths for T1 aged under 50 (though these can't be broken down further to under 40 and 40-49 groups).

Thank you for this thread, it is good to have this detailed discussion.
 

Thank you that helps hugely
 

Thanks a lot for pointing this out, unfortunately the table goes over 2 pages and I did not see the explanation of the symbol *. I have added an update to my post.
 
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That is a massive difference between male and female. People over 70 are told to shield, but maybe it should be women over 70 and men over 60 (or men over 65 and women over 75)? Given the massive influences of age and sex, I don't think a diabetes diagnosis on its own should be enough to be told to self isolate. Plug in your age and sex and then decide whether you need to panic....

Stay safe and well everyone.
 
Can someone (@Lupf maybe?) confirm the correct way to multiple several risk factors for this latest data. Just want to make sure I have the process straight in my mind.

Eg type 2 is x2 baseline risk. how do you do the maths if you are also older, male, obese and have hypertension
Or mixing + and - factors type 2, younger than baseline and obese?
 
How do you take into account multiple risks?

The short answer is that unless you know what you are doing it might be best not to do this.
Combining more than one risk factor needs do be done very carefully.
1) Define clearly the question you want to answer.
2) Make sure you understand what your reference population is.
3) Try to consider correlations, usually without having the necessary information.
4) Don't over-interpret the results.

Let's try to do an example: What is the risk of the 70 to 79 year old with T2, who also are obese with a BMI >40?
What do you use as reference population? This is important as your answer will depend on this, The papers from the Valabhji group take 60 to 69 year olds as reference sample, others use 50 to 59 age bracket. If you want to panic, take the less than 40 year olds as reference population. Compared to them, the risk ratio of the 70 to 79 year old is a huge factor of 263, but using the number in this way does not any make sense. All it says that the risk for young and healthy people is very, very small and less than a percent of the 70+.

Let's take 60 to 69 year olds as reference population. The two Valabhji group papers look at two different populations, all and with diabetes. Taking the risk factor of 2.63 for 70 to 79 year old compared to the 60 to 69 in the overall population and multiplying this by 2.03 (for having T2) you obtain 2.63 x 2.03 = 5.34. Now lets try to add in obesity. If your BMI is over 40 than the risk factor compared to a T2 with a BMI of 25 to 29 is 1.64. So you could multiply 5.34 x 1.64 = 8.8. So you end up with a risk factor of almost 9 which is getting scary. However, this is most likely an overestimate. You could have done the calculation by taking the risk ratio of 1.93 between the 70 to 79 year old with T2 and the 60 to 69 year old with T2 which would yield an overall risk ratio of 1.93 x 2.03 x 1.64 = 6.4. This difference is due to correlations between the different variables. There is not enough information given to find the exact value. In addition the factor 1.64 is an overestimate as the risk ratio due to BMI > 40 compares to the 30% of T2 with a BMI of 25 to 29 which is lower than the average of all T2s. I quickly (without warranty) estimated that a risk factor ratio of 1.30 due the large BMI is probably more correct. Thus depending on the actual correlations the correct answer for the combined risk ratio could be as low as a factor of 5.

In summary my best estimate is that for 70 to 79 year old with T2 and a BMI >40 when compared to the overall population of 60 to 69 has a risk factor of 5 to 9 and likely closer to the lower value. Note however, that this is a very simplistic estimate which ignores the correlations between age, diabetes and BMI as well as all other factors.

While I did this estimate since @HSSS and others before have asked for it, I would like to caution against doing these calculations. In particular if you consider the risk for an individual then the variation of your risk factors will vary within their natural variation from the averages over population samples reported in these studies.

In my view, it is better and definitely safer to consider each risk separately.
1) Being T2 has a risk factor of about 2. If you have it well controlled this risk factor will be lower, the paper I quoted on the first post of this thread gives a risk factor of 1.5 for diabetics with Hb1Ac < 58 mmol/mol.
2) Similar the age risk is such that after 50 the risk of T2s doubles every 10 years when comparing within the T2 population. For the general population the risk after 50 triples every 10 years. These risk ratios are large, thus if you are below 50 or even 40 your risk of dying from Covid-19 will be very low - independent of being a T2 or T1 diabetic or not.
3) Whenever you see risk factor ratios of 1.1 or 1.2 it is probably best to ignore these as the variance for individuals is likely going to be larger.
 
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@Lupf thank you. I hear your words of caution loud and clear and fully appreciate they are population risks more so than individual. And I was indeed referring to the recent dual paper release with it myriad of data. It is quite muddled with all the different reference populations to multiply risks and keep it clear exactly what you are comparing against.
 
Now here is an issue for the Numbers guys!
https://www.sciencedirect.com/science/article/pii/S0378378220302929?via=ihub#s0015

Get your finger out

The stats and math is all in the scientific paper from Swansea Uni above but for ease of reading I link the Daily Mail article below.
We panic about 2x or 3 1/2 times the risk.
We also do know the high relevance of being Male to our risk of dying with COVID - in some populations perhaps even higher than D.
But can we simply see the extent of that risk in our Fingers?

https://www.dailymail.co.uk/news/ar...g-fingers-face-lower-risk-dying-Covid-19.html
 

Thanks to @Bill_St for this challenge. How do I explain the 2D/4D ratio to non scientists?

Well if I come across a new term and want to learn what it is about, I go to Wikipedia. And in this case it contains actually very useful info and all you probably need to know, see https://en.wikipedia.org/wiki/Digit_ratio. Here is the Wikipedia definition: "The digit ratio is the ratio of the lengths of different digits or fingers. The 2D:4D ratio is the most studied digit ratio and is calculated by dividing the length of the index finger of a given hand by the length of the ring finger of the same hand."

But why would one measure this ratio? It turns out that the 2D:4D ratio is affected by the exposure to testosterone while humans develop in the uterus of their mother. Crudely speaking the higher the levels of prenatal testosterone the lower will be the 2D:4D ratio. Males have higher levels of testosteron already before birth and therefore a smaller 2D:4D ratio than females. Correlations have been observed between this ratio and many physical, psychological and personality traits of humans. Wikipedia lists a table with lots of results in the section "Correlation with traits". You probably already know that increase levels of testosteron correlates with aggression and indeed "Finger length ratio (2D:4D) correlates with physical agression in men." as reported in https://www.sciencedirect.com/science/article/pii/S0301051104001048?via=ihub. Another example is women athletes. "The highest achieved level of participation in any sport was significantly negatively associated with lower 2D:4D ratio", and is reported in https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2577466/. You can find many other examples in this list, including the answer to the question of @JohnEGreen.

And now you can understand why this could be relevant to Covid-19. We would like to understand why some people have no symptoms when infected while others fall severely ill and about 1% die. The paper quoted by @Bill_St studies the correlation between case fatality rates - the rate of a group dying from Covid-19 - versus the 2D:4D ratio. And they find for males that the higher the 2D:4D ratio the higher is also the case fatality rate. Hence the conclusion of the paper is that "Low prenatal testosterone may be a risk factor for COVID-19 in males."

I hope this helps.
 
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