# New COVID Calculator with Breakout for Type 1, Type 2, and HbA1c

I found this interesting because the questionnaire includes a section that assesses risk differently for Type 1, Type, 2, and level of control, i.e., HbA1c.

The source article:

Covid’s Risk to Older Adults - The New York Times (nytimes.com)

Description from article:

A team of British researchers, led by Dr. Julia Hippisley-Cox at the University of Oxford, has conducted some of the most detailed research on Covid risks for different groups of people. The BMJ, a peer-reviewed journal, published the work, and it is available in an online calculator. The research was done before Omicron emerged and covers only residents of Britain, but it is still instructive.

Licence | QCovid™ risk calculator

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It’s curious to me that this model uses such a high glucose threshold to separate those who are well-controlled from those who are not. It basically differentiates between an A1c (IFCC) of below or above 59 mmol/mol. IFCC 59 = 7.5% or an average glucose level of 170 mg/dL. I would think that blood sugar levels much closer to normal would be more of an advantage in lowering the risk of catching or dying from Covid.

This calculator tells me that I am 1.28 times more likely to catch Covid and admitted to the hospital than someone without my risk factors and fully vaccinated. The absolute risk is 1 in 4,831.

It also indicates that I am 1.23 times more likely to catch Covid and die than someone without my risk factors and fully vaccinated. The absolute risk is 1 in 13,333.

If I test positive for Covid, my absolute risk of dying is 1 in 261.

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The biggest risk is the places you go unmasked, and how many infected people are not wearing masks!!

I checked the link, but I don’t find value in it as a risk assessment. But interesting info.

I think this is a coarse attempt to quantify the risk but there are so many factors that they don’t or can’t assess like how often you expose yourself to other people and in what kind of environment.

I find this kind of project interesting but not definitive. I never had any statistical higher education and I wonder what people who work with these kind of things, like insurance actuaries, think of a tool like this.

Terry, you are spending way too much time in the echo chamber of TuDiabetes among the exceptionally well controlled posters. It is such a contrast between the support groups in real life and TuD. In many of the support groups I help out in (past year and a half, just virtually), folks with A1C’s above 10% would just love to be able to get to 7.5%.

Among the wider adult T1 population at large (e.g. NOT TuDiabetes frequent posters), 7.5% is lower than average. Full info: https://www.liebertpub.com/doi/10.1089/dia.2018.0384

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I think the problem many people have with these are taking them literally, rather than the result being suggestive. Metaphorically, they are taking a skewer (the calculation) and pushing it through piece of meat (all the data points), The skewer is not the meat, but simply an approximation of the average path through the meat. One can ask how well the result comes close to describing the whole data set, but that is something not addressed. These are never supposed to be perfectly accurate. They are just approximations.

I don’t have a degree in statistics/mathematics, but have a mathematical bent, acing statistics in college, usually a top scorer in math-focused classes I had in B-School, and more recently - in the past 5 years - taking about 10 online classes for artificial intelligence and machine learning. My work as a software developer often requires I look at and report my users’ application usage statistically. Not an expert, but…

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For the longest time, the medical profession has used 7% as the cutoff for good versus poor control, and more recently, using time in range with a standard deviation of under 40mg/dl, or even 35mg/dl. That said, there is little proof of where the actual cutoff is, and that sub 6% is optimal. It’s not that studies do not exist, but I haven’t anything that was clearcut.

Reasonably, logically, that makes sense, but there is a paucity of evidence for tighter control, e.g., under 6%. One study I found showed higher mortality from lower HbA1c, but that could be confounded by some Type 1’s having significant medical problems.

I’m always open to new information, so if anyone has studies I haven’t seen, I’d love to read them.

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I have been looking for a tool like this —thanks, James, for posting it.

I am in full-blown pandemic fatigue.

Friends say I have to ‘live life.’ A quick scroll on IG for “passengershaming” on airplanes disproves this.

Work is pushing in office returns and, my ‘favorite’, a global all-employee 3-day conference with mandatory attendance. Omicron—just spreading like snotty-hands in pre-school.

I needed this reminder. I’m not 20 anymore. My odds of being hospitalized and dying are higher than for most people. It is what it is, so I’ve got to make the best of it.

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This era of long-term CGM, increasing knowledge of nutrition and its effects on the glucose metabolism, and the advent of automated insulin dosing systems has enabled a small percentage of the T1D population to make incredible gains in glucose management.

I don’t think that these efforts by the few should be tossed aside as statistical outliers. Instead I believe they are harbingers of better control that can spread to a wider population. The medical profession, both clinical and academic research, could help but many seem blind to that possibility.

Studies require financing and unless a study can reasonably lead to making a case for a new drug or a public agency decides to finance, it just doesn’t happen. I suspect that living with more normal blood sugar levels is worth the effort, both for long-term health and the support it gives our immune systems. Maybe our underwhelming performance as a group in managing blood glucose has dissuaded any interest.

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I believe that Tim and I have sorted out our differences over this issue using private messages.

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Bear in mind that the studies are statistical and based on the very large amount of statistics available in the UK. Those statistics are at least influenced by UK medical guidelines and that specific calculator is clearly identified as a UK calculator; you signed this, right?

This licence is governed by the laws of the England and Wales

Yeah! Not even US speeling. Then you agreed to this:

The QCovid tool may only be used in Great Britain by clinically trained professionals, for academic research and for the purpose of peer review.

So:

If you are a clinician wishing to use QCovid® to risk assess a patient, please use the NHS Digital COVID-19 Clinical Risk Assessment Tool.

Quite so. You are stating that you are competent.

I will say no more, because I am not.

I like doing these things, but then I usually ignore them because I think so many other things come into play. Diet, exercise and the area you live in for a few important ones.

I like the first 3 out of 4…but not that last number. 1 out of 992 with no risk, Mine isn’t that much different from a “normal” being 1 out of 928. I just think 1 out of 992 is still too many. And you add the BMI tops out at 25 and mine is higher so that ups the risk. I am surprised since weight plays a factor it doesn’t adjust above the recommended weight.

Absolute Risk 1.10 times more likely
Risk of catching and being admitted to the hospital 1 in 7,246 verus 1 in 9804
Risk of catching and dying from covid 1 in 43,478 versus 1 in 47,619
Risk of dying after a positive test 1 in 928 versus 1 in 992

Personally it looks like the Omnicron variant that is taking over helps reduce hospital percentages and deaths. Hospitals still might be filling because so many are getting sick so fast. But I noticed in So Africa that hospitalizations went down when Omnicron took over. And now we have some meds to help.

So hopefully that 1 out of 992 that I don’t like seeing, gets better.

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