We’ve all seen statistics that most people who die of Covid-19 have at least one comorbidity. They also almost all have the particular comorbidity of age. The biggest risk, by far, is being old. The question that I don’t see being properly asked anywhere (I’d love for this post to be unnecessary because there’s a better one) is: What is your chance of death from Covid-19 if infected, conditional on which if any comorbidities you have? Which ones matter and how much? If you don’t have any, how much better off are you than your age group in general?

Thus, most people are looking at the age chart, without adjusting for their health status, unless they have an obvious big issue, in which case they adjust up. Which leads to an incorrect overall answer. That isn’t obviously a bad thing in terms of resulting behavior in practice, but that doesn’t mean we shouldn’t attempt to figure out the answer.

I used New York State’s information on deaths to get the rates of most of the major comorbidity candidates by age, and various Google-fu combined with wild mass approximation and fitting to different age groupings (not guessing, but not fully not guessing either) to get approximate population prevalence data. What matters?

A key question will always be, is this a proxy for something else, such as poverty, general poor health or obesity? Or is it the real problem? Here, we need to use some common sense and physical intuition. This isn’t attempting to be super rigorous, but rather to get an approximation.

Then, once we’ve looked at all of them, I’ll attempt to put them all together, and solve for the risk of someone in good overall health.

Spreadsheet is here, you can look at the numbers in somewhat more detail, and see some of the sources I used, on the Comorbidity tab.

In each case, the “Population X” column attempts to guess the rate at which the population has it. The morbidity column is the rate at which those in NY state that died of Covid-19 had it.

For the first few graphs I fit NY’s data to the groupings in the prevalence data I found. Later I started always using NY’s ranges instead.


Morbidity Population
Age Hypertension Hypertension
18-39 21% 8%
40-59 43% 33%
60+ 61% 63%

Hypertension in the young seems to matter. If you are 18-39, your relative chance of dying more than doubles. In your 40s and 50s combined, it’s a jump of about a third. Above age of 60, it does not matter. Should we be suspicious that this is a proxy for poor health, given that? Somewhat, definitely. It will be a recurring pattern, which we’ll need to get to make sense.


Morbidity Population
Age Diabetes Diabetes
18-44 28% 4%
45-64 40% 17%
65-74 44% 25%
75+ 34% 25%

Clearly this is a huge deal at young ages. I still don’t really believe the 4% number for the general population, but multiple sources are around there. That 4% of the population is more than a quarter of all deaths under 45 in New York. That’s a huge deal. Diabetes is a huge deal the entire way. This makes some sense, as it seems likely to correlate with slash cause direct physical problems for those with Covid-19 that can kill them. But this amount of effect is still surprisingly extreme.


Morbidity Population
Age Hyperlipidemia Hyperlipidemia
0-4 0% 7%?
5-14 0% could
15-29 4% be
30-39 5% up
40-49 9% to
50-59 16% 45%
60-69 22% in
70-79 25% adult
80-89 24% pop?
90+ 20% so
Unknown 6% meaningless

I could not get population numbers, because no one can agree on what Hyperlipidemia actually is and is not. By some definitions, almost half the adult population has it, because people like to say that we need medications and are “at risk” and turn everything into a disease. By others, it’s single digit percentages. So these morbidity numbers could be anything from scary high to same as the population, depending on the definition used, and I don’t know what that was. If anyone does know, please tell me.

But given what this physically is, any link seems more like correlation than causation, and the numbers listed seem plausibly like general population rates anyway, so I’m going to say this likely doesn’t matter.

Coronary Artery Disease

Morbidity Population
Age C. Artery D. C. Artery D.
0-4 0% 0%
5-14 0% 0%
15-29 0% 0%
30-39 0% 1%
40-49 3% 4%
50-59 6% 8%
60-69 11% 13%
70-79 14% 18%
80-89 16% 25%
90+ 13% 26%

The population rates I approximated are modestly higher across the board. Probably there’s no effect and this is a measurement error.


Morbidity Population
Age Dementia Dementia
0-4 0% 0%
5-14 0% 0%
15-29 0% 0%
30-39 0% 0%
40-49 0% 0%
50-59 1% 0%
60-69 4% 1%
70-79 10% 5%
80-89 18% 24%
90+ 28% 37%

It’s weird that this reverses at older ages, probably because of measurement, but perhaps because people with dementia have overall better health at that age due to the ones with poor health having died more often? Whereas in younger people, if you have dementia things are much more likely to be generally terrible in other ways instead?

From the 50-79 year old data I’d have said this might matter, but the prevalence rate is low there, and where the rates are high, the numbers are reversed. I don’t think dementia is doing work here.

Renal Failure

Morbidity Population
Age Renal Failure Renal Failure
18-44 6% 7%
45-64 11% 12%
65+ 11% 37%

I suppose that those with renal failure die soon thereafter, thus the 37% population rate is not going to be reflected in an alive group. It’s certainly not going to be protective. That implies there might be some real effect in the younger groups if you squint hard enough, but seems much more likely this is not a risk factor.


Morbidity Population
0-4 0% 0%
5-14 0% 0%
15-29 1% 2%
30-39 1% 3%
40-49 2% 5%
50-59 5% 7%
60-69 8% 8%
70-79 10% 10%
80-89 10% 9%
90+ 8% 8%

Very clear no effect.

Atrial Fibrulation

Morbidity Population
Age Atrial Fibrulation Atrial Fibrulation
0-4 0% 0%
5-14 0% 0%
15-29 0% 0%
30-39 1% 1%
40-49 1% 1%
50-59 2% 1%
60-69 4% 2%
70-79 8% 5%
80-89 12% 9%
90+ 14% 11%

Some effect, with minimal impact on numbers for those without the condition, and likely the effect is correlational given the physical conditions involved.


Age Cancer
0-4 0%
5-14 0%
15-29 3%
30-39 2%
40-49 2%
50-59 4%
60-69 7%
70-79 8%
80-89 9%
90+ 8%

About 0.5% of New Yorkers get newly diagnosed with Cancer every year. But translating that into a background cancer rate by age proved very difficult. It certainly can’t be what they mean by cancer here, since that means that at older ages cancer would be highly protective, so they’re clearly only counting current conditions or some other similar thing.

It seems obvious that being actively sick from cancer treatment would make Covid-19 much worse to get, but it’s not at all obvious the condition itself would matter much for many cancers, and they’re also different conditions, so it’s all very confusing. Not sure what to do here.

My guess is this is mostly ‘general poor health caused by cancer or treatment’ effects, to extent it matters.


Morbidity Population
Age Stroke Stroke
0-4 0% 0%
5-14 0% 0%
15-29 1% 0%
30-39 1% 1%
40-49 3% 1%
50-59 4% 2%
60-69 7% 5%
70-79 8% 10%
80-89 8% 15%
90+ 6% 15%

Again, those numbers on the right are largely guesses. It does seem clear that stroke is a risk factor when you are young. We once again see strangely low morbidity rates for the older age groups, pointing to a likely general difference in methodology. Probably selection effects.

That’s everything listed by New York.


When you are young, it seems like it matters a lot whether there is something relevantly seriously wrong. But it has to be something that matters. Having a health problem in an area that Covid-19 doesn’t attack does not seem to matter.

Two of the problems measured matter a lot. Hypertension and Diabetes together are about 12% of the population and constitute ~50% of the deaths up to age 40-45. The rest don’t seem to matter much at all, and you could safely ignore them.

It is safe to assume that anyone with serious trouble breathing for other reasons is going to have similar problems. Thus, asthma, obesity and so on are also (probably) serious risk factors.

One source I found was this one, which notes that 35.8% of hospital patients with Covid-19 were obese. In the city, 22% of the population is obese (and a majority are at least overweight). 43% of the invasive treatment group, which was presumably in far worse shape, was obese versus 31% for the non-invasive group, so it seems that the extra risk carries over to outcomes after hospitalization.

This source found an inverse correlation in Covid-19 patients between age and BMI, which would also make sense.

From another source at ScienceNews:

For instance, of 180 patients hospitalized from March 1 to March 30, the most prevalent underlying condition for adults ages 18 to 49 was obesity. Of 39 patients in that age range, 23, or 59 percent, were obese, researchers report in the April 17 Morbidity and Mortality Weekly Report.

Lighter and her colleagues found that patients under 60 with a BMI over 35 were at least twice as likely to be admitted to the ICU for coronavirus than patients with healthy BMIs, the researchers report April 9 in Clinical Infectious Diseases. Those same patients were three times more likely to die from the infection than those with a lower BMI, she says.

The team tracked 3,615 people who tested positive for SARS-CoV-2, the virus that causes COVID-19, at a New York City hospital from March 4 to April 4. Of those, 1,370, or 38 percent, were obese. In patients over 60, weight did not appear to be a factor in hospital admission or the need for intensive care, she says.

Again, it seems like ‘nothing matters much if you’re old.’ But if you’re young, things do matter.

Let’s say for the time being that obesity triples your risk if you’re under 60. Obviously, this doesn’t go away the moment you turn 60, so we’ll want to do more than triple for those under 40, much less than triple for those in their 50s, and some effect probably in your early 60s.

Continuing to do approximations, if we accept the figure that 52% of Type II Diabetics were obese back in 2006, whereas Type I is actually lower than the population rate, which is now more like 40%. New York City’s seemingly terrible 22% looks positively amazing by comparison if actually the same measure.

This accounts for substantial increased risk for diabetics, but the majority clearly remains unexplained by weight. Obesity alone would have gotten us from 4% to about 10% in the young group, and we ended up at 28%.

Hypertension, on the other hand, seems mostly to be a proxy for obesity, so we can mostly ignore it.

Overall, though, obesity seems by far the most important consideration other than age, since it’s so common and has such a huge impact.

Age Alone

To adjust from a baseline we need a baseline. For New York the relative risks look like this:

%Of Deaths Population Relative Risk
0-4 0.02% 7% 0.2%
5-14 0.04% 14% 0.3%
15-29 0.3% 22% 2%
30-39 1% 16% 9%
40-49 4% 14% 26%
50-59 10% 9% 116%
60-69 20% 7% 287%
70-79 27% 5% 525%
80-89 25% 2% 1066%
90+ 12% 1%? (cuts off at 85) 1500% or so?


Then we must guess the true IFR (infection fatality rate), and adjust for the three comorbidities that we’ve found matter: Obesity and diabetes. They still correlate.

We also have to adjust for the likelihood of infection in the first place, since that changes your risk conditional on infection. This is a relatively small effect according to antibody test results, and should at least sort of be cancelled out in some ways for practical purposes, so I’m not going to worry much about it.

Most coronavirus cases are definitely not being detected by positive tests. The antibody tests show that. So the IFR is much lower than the CFR. Given death rates and antibody tests, the plausible range for IFRs is about 0.5% to 1.5%. It will also depend on conditions on the ground in various ways, of course. But as a baseline, I’m going to continue to say 1% death rate for the state. If you disagree, multiply all the numbers I get as appropriate.

Obesity gets slightly more common with age, which I’ll adjust for.

For the younger groupings, we have 4% Diabetes and (in New York) 25% Obese. Obesity dominates that group by size, but diabetes still matters. Together that’s about 27% of the population. The 4% of that that are diabetic account for 28% of the cases. The other 23% that are obese become 49% of the remaining cases, or 35% of all cases. Add that together, and that’s 63% of cases from this 27%, with the remaining 37% coming from the other 73% of the population. So if you’re healthy, with healthy defined as ‘not obese and not having diabetes’ your risk is cut roughly in half when young. There’s too many error bars all over the place, so I don’t want to try and be more exact than that.

Other conditions doubtless also matter somewhat. 10% of New York has asthma, which presumably makes a big difference, but all I could find was “may be at higher risk” repeated over and over, rather than any numbers.

This all these effects decay as you age. By age 70, there’s little or no difference.

But roughly you end up with a chart that looks something vaguely like this:

Reminder: This assumes overall infection fatality rate of ~1% and is full of guesses and approximations as all hell:

Age Risk (Healthy) Risk (Diabetes) Risk (Obesity) Risk (All Pop)
0-4 0.001% 0.013% 0.005% 0.002%
5-14 0.002% 0.016% 0.006% 0.003%
15-29 0.008% 0.078% 0.031% 0.015%
30-39 0.043% 0.26% 0.17% 0.09%
40-49 0.13% 0.53% 0.43% 0.26%
50-59 0.72% 1.8% 1.4% 1.2%
60-69 2.0% 4.0% 3.0% 2.9%
70-79 4.3% 6.5% 5.4% 5.2%
80-89 11% 11% 11% 11%
90+ 15% 15% 15% 15%


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7 comments, sorted by Click to highlight new comments since: Today at 1:01 PM

I had treatment resistant prehypertension despite exercise and healthy diet (seems to be genetic based on 23 and me). What finally really brought it down consistently was fasting for 2-4 days 2-3 times a year. Stays down for moths afterwards and only slowly rises after that. Went from average of 142 to average 127 (!).

How do you square COPD having no effect with

It is safe to assume that anyone with serious trouble breathing for other reasons is going to have similar problems. Thus, asthma, obesity and so on are also (probably) serious risk factors.

COPD having no effect is very confusing to me. I really wish we had numbers for asthma.

It looks like there's actually some evidence that asthma isn't that bad either. I suspect the reason is that a lot of the deaths among young people aren't due to respiratory distress but rather blood clotting issues, which squares well with comorbidities like hypertension and obesity rather than COPD and asthma being the most dangerous.

I looked at covid and BMI in a post linked here. Using data from UK critical care units, it seems that BMI from 30-40 is only a small risk, though the risk from BMI>40 is substantial. This is after adjusting for age and sex.

A more recent UK study here found increased risk of death, after adjusting for age and sex, for obesity (BMI>30), and a substantially higher risk for BMI>40. These risks went down a bit when also adjusting for other factors. (Though depending on your purpose, this may not be valid, since some of these factors might be on the causal path from obesity to risk of death.)

You're saying that obesity matters mostly if you're younger than 60, which is a question not directly addressed by these results (which come up with a single risk ratio, on the assumption that it's the same for all ages). The study here which you link to gives a scatterplot of BMI versus age in covid patients, showing a negative correlation. This is not meaningful information without a corresponding scatterplot for the general population from which these patients came, which they oddly do not show (or even speculate about, if data is hard to find). In that scatterplot, patients with really extreme obesity (BMI>50) seem to play a large role.

In my post, I present evidence that low BMI may be a risk factor, perhaps not only for BMI below the official "underweight" threshold of BMI<18.5 but also for around BMI<20. Frustratingly, many studies do not present the data that would allow one to investigate this. For the recent UK study I linked above, they use "not obese" as their reference class, grouping everyone with BMI<30 together, though their raw, unadjusted, data indicates that there is a substantial firsk for BMI<18.5 (and even in that data, they group everyone with BMI from 18.5 to 24.9 together, even though past studies show that there may be substantial differences within this supposedly "normal" range).

When looking at varying effects by age, one possibility to consider is that high BMI is a risk factor for younger people, and that low BMI is a risk factor for older people (as opposed to BMI not mattering for older people, as you sort of conclude).

Could someone look at whether the underlying explanation for this could be SIRT-1 pathway, which involves zinc, nicotinic acid/niacin/B3 and D

Here is a list of studies* I collated which seem to support that possibility and point to effective low-cost actions.

BMI does have an impact on D3 requirement. More sunlight exposure is required to get adequate B3 serum levels in older people, and of course this exposure is down due to skin cancer advice, lockdowns and urbanisation.

Re Diabetes: it appears that covid infection may profoundly impact ketone levels in diabetes, so some endocrinologists are advising people to monitor ketone levels more than usual, and this might be part of what's going on.

*NB please ignore all the studies and reviews which focus on osteoporosis, which is a rather different issue - here we're looking at prophylaxis of respiratory viruses

Hi Zvi,

A couple of months ago I wrote a covid-19 risk calculator that's gotten some press, and even translated into Spanish. Here's the link:


I've updated the calculations to leverage your table for age & preconditions, which were better than what I had. You can check the code for the calculator by clicking on the link near the top of the page. I've also put a link in that code to your article here.

Note that I'm trying to keep the interface ultra-simple. I get a stream of suggestions (e.g. can you add a separate slider for condition x), which if all implemented, will have little effect on the overall outcome, but will overcomplicate the interface, and make the calculator lose its appeal.



What about male vs female?

Might be important in whatever the mechanism is, in relation to hormones / steroids / cholesterol / SiRT-one pathway and D3.