Author: chogan@directresearch.com

 

Growth in daily new cases continues to slow, but only a bit.  As of Monday’s data (reported today), the U.S. stood at just over 240 new COVID-19 cases /100K population / day.

Continue reading Post #1393: COVID-19 trend to 1/10/2022

Current level and growth in infections by state.

Daily new COVID-19 infections are increasing at a rapid pace almost everywhere in the U.S. There are only ten states where the growth rate in new cases is less than 50% per week  And (see below) most of those are areas that already have a very high level of cases.

Data source for this and other graphs of new case counts:  Calculated from The New York Times. (2021). Coronavirus (Covid-19) Data in the United States. Retrieved 1/9/2022, from https://github.com/nytimes/covid-19-data.”  The NY Times U.S. tracking page may be found at https://www.nytimes.com/interactive/2020/us/coronavirus-us-cases.html.

With the exception of Rhode Island, we seem to be reaching the point where, on average, areas with the highest current level of infections are showing the lowest growth in daily new infections.  (Note the general downward slope in the scatterplot of level and growth in new infections below). That suggests were getting somewhere nearer the end of this wave.

That said, we seem to have a long way to go.  If you look in the middle of that mass of data points above, a typical U.S. state already has 200 new COVID-19 cases per 100K population per day, with new infections more than doubling in the past week.

Seems like the best we can expect is that a week from now, a lot of states will be where FL, DC, NJ, and NY are on the graph above.  Somewhere between 300 and 400 new COVID-19 cases / 100K population / day.  And a growth rate that’s not in the triple digits.

There’s not much we can do about that now.  Historically, it took about 12 days from the moment of infection, to the full reporting of that infection in the data.  What we’re seeing now are infections that occurred over the holidays.

And it sure looks like we aren’t doing much about it, in any case.  I’ve seen little or no re-imposition of (e.g.) state mask mandates or controls on public gatherings.  Nor has there been much of an increase in individuals reporting that they routinely wear a mask in public places.  We’re now at the point where we’re more cases and more hospitalizations than during last year’s winter wave.  And mask use is still nowhere close to where it was back then.

Source:  Calculations from NY Times (above) and U.S. DHHS unified hospital file.

Source:  Carnegie-Mellon University COVID Delphi project COVIDcast.

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Sorting through state-level COVID hospitalization data.

It looks like a lot of state hospital systems are going to get a major stress test this week, from COVID-19 admissions.

That leads to the obvious question, what do COVID-19 hospitalization patterns look like right now, in the U.S.

To answer that, I stepped back and took a much more systematic approach to the U.S. DHHS COVID hospitalization data.  An approach that would allow me to start looking at the hospitalization data across all 50 states.

And that’s when things started getting weird.  Because, as it turns out, beneath the single U.S. averages lies a vast amount of variation.  Some of which makes sense, some of which does not.

And so, the answer to even the most basic question about hospitalization and COVID will depend strongly on where you live.  I don’t really think the actual practice of medicine varies that much.  But testing behavior and environmental factors do.

Here are a few questions I wanted to answer:

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What’s the case hospitalization rate for new COVID-19 cases (i.e., what fraction get hospitalized).  Answer:  1% to 6.5%  This is a complete patchwork, and if there is any rationale for this variation, it’s certainly not apparent to me. 

My suspicion here is that a lot of this has to do with testing and test-seeking behavior.  And, possibly, with hospital testing practices (subject of a future post, but by that I mean, do they test every case coming in the door, and so find a lot of asymptomatic COVID cases?)

If individuals rarely seek testing, that will eliminate most of the lower-illness-severity segment of the population counted with COVID.  And of what’s left, you’ll see a high fraction hospitalized.  By contrast, locations were testing is encouraged or easily accessed, you’ll see a much broader population testing positive and a lower case-hospitalization rate.

Similarly, if hospitals test every person admitted as an inpatient, then they will find (and count) a lot of asymptomatic cases as hospital admissions with COVID.  And if they don’t test everyone, they won’t.  (An astute reader pointed me toward a press conference in the Kansas City area where hospital executives made it clear that they only test asymptomatic patients requiring general ansthesia (i.e., as a measure to prevent contamination of anesthesia equipment.)  In most other areas I have found, hospital systems advertise the fact that everyone admitted as an inpatient gets tested for COVID.

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What’s the ICU use rate for COVID cases?  (Among those hospitalized, what fraction are in the ICU).  Answer:  10% to 33%.

The moment I saw this one, I recognized the pattern.  Higher elevation means less (partial pressure of) oxygen.  For a given level of lung impairment, you’re going to see lower blood oxygen saturation at higher elevations.  And so, you find that about one-third of the state-to-state variation in  ICU use per COVID-19 patient is associated with variation in elevation.

Anywhere from 10% to 33% of COVID-19 cases end up in the ICU.  And with the exception of Maine (a high outlier in this regard), that variation largely follows variation in elevation of the states.  Which, in turn, shows how much oxygen there is in the air.  The Mountain states show up with high average ICU use per case because … they’re mountain states.

This is particularly helpful to Washington, DC and to New York City, two areas very hard-hit by Omicron.  Roughly speaking, both of these locations are at sea level.

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What fraction of COVID-19 admissions are children?  Answer:  1% to 14%.  Less than 1% of COVID-19 admissions in Maine are pediatric cases.  By contrast, 14% of admissions in Washington DC are pediatric cases.  If there is any obvious pattern to this, it escapes me.

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What fraction of ICU beds are already in use:  Answer:  44% to 91%. 

What fraction of ICU beds are occupied by COVID patients?  Answer 11% to nearly 40%

I have no idea why Texas and New Mexico are such outliers, but based on the Federal data, they both have 92% of ICU beds already occupied.  But, for Texas at least, that’s not strongly linked to COVID-19.  They are only middle-of-the-road in terms of the fraction of ICU beds occupied by COVID-19 patients.

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What is the trend in pediatric COVID hospital admissions as a share of all COVID-19 admissions?  It appears to have peaked.

This one gets so much press coverage, it’s worth putting up the national numbers, straight off the DHHS hospital file.  This is pediatric admissions with confirmed COVID as a fraction of all admissions with confirmed COVID.  The fraction of admissions that were pediatric rose from about 2.5% under Delta, to about 4.5% under Omicron.  That share now appears to have stabilized.

Certainly, the count of pediatric admissions will continue to increase.  But at the moment, that’s only in tandem with all admissions.

Cases continues to rise at 77% per week.  Which is, believe it or not, a slower rate of increase than we had one week ago.

The U.S. now stands at 206 new COVID-19 cases / 100K /day.  No area has been spared.  Just three states remain below 100 new cases / 100K / day (of all states reporting data today):   Maine, Montana, and Idaho.

At the other end of the scale, if the data are to be believed, Rhode Island now has nearly 500 cases / 100K / day (seven-day moving average basis).  At that point, it almost doesn’t that Omicron is less virulent than Delta.  With that many cases, give it a few days and you will fill your available hospital beds.

Continue reading Post #1390: COVID-19 trend to 1/7/2022

 

Looks like were getting closer to a peak in daily new COVID-19 cases.

The rate of increase is slowing, and that’s occurring across all regions.  Cases only (only?) increased 77% in the past seven days, to 187 new cases per 100K per day.  Just two days ago, we were still seeing new cases double every week.

DC appears to have peaked, but no other states have so far.

Data source for this and other graphs of new case counts:  Calculated from The New York Times. (2021). Coronavirus (Covid-19) Data in the United States. Retrieved 1/7/2022, from https://github.com/nytimes/covid-19-data.”  The NY Times U.S. tracking page may be found at https://www.nytimes.com/interactive/2020/us/coronavirus-us-cases.html.

On the bright side, there’s typically a two-week lag between new COVID-19 cases and new COVID-19 deaths.  We’re now almost three weeks into our Omicron wave (U.S. cases started to increases rapidly beginning 12/17/2021), and, so far, there’s been no uptick in COVID-19 deaths at all. 

Below, you can look back at the far-more-virulent Delta wave and see that almost-perfect two-week lag.  You can see the ramp-up in deaths followed the ramp-up in cases.  And then you can see that’s not happening with Omicron.  If there had been, you surely would have read about it by now.

Source:  CDC COVID data tracker.  Notes are mine.

That was the story in South Africa — a lot of cases, few deaths.  And that’s also how it’s unfolding in Great Britain.

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The United Kingdom Omicron wave in three charts, via Google Search.

Huge volume of new cases.  Three weeks from the upward kink in the curve (December 14 2021) to the apparent top of the curve (January 4 2022).

Big increase in hospitalizations, but not such a big increase in ICU use.

Slight uptick in deaths:

 Now here’s the crazy thing.  If the U.S. a) follows the United Kingdom’s time table for the wave, and b) I correctly dated the 1/14 (U.K.) and 1/17 (U.S.) starts of the Omicron waves, then …. tomorrow should be the peak of the U.S. Omicron wave.  That is, the day when that seven-day moving average of new cases finally levels off.

Well, we know Omicron can move fast.  It was upon us before we were really ready for it.  And it’ll be receding before we can get ourselves out of crisis mindset.

What am I going to do with my time once I can no longer blog about COVID?

 

As of 1/5/2022, the U.S. stands at 179 new COVID-19 cases per 100K population per day.  Washington, DC is now the only area that appears to have topped out.

That said, with this last batch of data, there was a slight — but widespread — slowing of the growth in U.S. new case counts. Cases less-than-doubled in the last seven days.

Although it probably feels like it’s been longer, today is just the the 12th day after Christmas.  That’s just about the right timing to suggest that we’re seeing the results of a slowdown in the rate of new infections over the Christmas holidays.   Continue reading Post #1388: COVID-19 trend to 1/5/2022, maybe a bit of a slowdown.

The issue

I first flagged this issue three weeks ago, in Post #1349, with a little more explanation in Post #1351.

In many data systems, a “COVID hospitalization” is a hospitalization of a person who has tested positive for COVID.  Period.  This includes:

• people who are being treated for COVID
• people who are being treated for something else entirely:
  • for whom the finding of a COVID infection is completely irrelevant or
  • for whom COVID might have exacerbated the underlying condition that got them hospitalized.

My guess is that the final category involves relatively few people.  So, crudely, this breaks down into two groups:

1:  People admitted for COVID, who wouldn’t be in the hospital if COVID hadn’t been around, and who represent a true additional burden on the hospital system caused by the pandemic.  And then,

2:  People admitted with-but-not-for-COVID, who would be in the hospital in any case, and who don’t represent any additional burden on the hospital system caused by COVID.  For the simple reason that they are sick with something else.

Any additional cost burden from that second group is truly incidental.  They have to be kept isolated, and they are capable of infecting hospital staff with COVID.  So the cost of their hospital stay may be higher than it would normally have been.  But they would have been hospitalized COVID or not.

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This is now greatly affecting U.S. COVID hospitalization counts.

In prior waves of COVID, this distinction didn’t matter much.  The overwhelming majority of patients admitted with COVID were being admitted for treatment of COVID.

In the Omicron wave, that’s no longer true.

This was first noted in South Africa, where one large hospital system there found that 76% of their patients “with COVID” were in the hospital for treatment of something else.  This was particularly true of their pediatric patients in South Africa, most of whom had an asymptomatic COVID infection that was only found after they were admitted to the hospital for the treatment of some completely unrelated condition.

Now I’m seeing reports of the same thing happening in U.S. hospitals (emphasis mine):

Roughly two-thirds of patients who have tested positive at hospitals run by the L.A. County Department of Health Services were admitted for something other than the coronavirus, according to Health Services Director Dr. Christina Ghaly.

This time around, many “may have not known they were COVID-positive ... but they’re in the hospital for something else,” Ghaly said.

Source:  LA Times, 1/4/2022.

Dr. Fritz François, chief of hospital operations at NYU Langone Health in New York City, said about 65% of patients admitted to that system with COVID-19 recently were primarily hospitalized for something else and were incidentally found to have the virus.

Source:  AP News, 1/5/2022

Compared with earlier waves, a smaller percentage need intensive care. At Hackensack Meridian Health's hospitals, 140 of 1,100 COVID patients on Tuesday, or 13%, were in ICUs, and 75 were on ventilators. That's down from about 20% requiring ICU care at the pandemic's start. 

Some patients are admitted for other illnesses, only to discover after admission testing that they have COVID. Over the last month, that portion has increased to “45% who were admitted for something else but were found to have COVID, too,” Bennett said.

Source:  NorthJersey.com, 1/5/2022

Gov. Ron DeSantis said 50% of COVID hospitalizations at Orlando Health are not being treated for the virus.

“We have found that a great majority of our hospital in-patients who have been diagnosed with COVID-19, were admitted for medical conditions not related to the virus,” Orlando Health spokeswoman Alayna Curry said.

Source:  ClickOrlando, 1/4/2022

I am pretty sure I could find more if I looked.

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Why does this matter?

Pediatric admission trend.  First, I will point out that numerous sources — including Dr. Fauci — suggest that this is particularly important issue for pediatric admissions.  I.e., that the number of children admitted for COVID in prior waves is far less than the number that has been counted (per this research).

And so, currently, pediatric admissions are increasing as a fraction of all COVID admissions.  Does that mean that Omicron differentially attacks children? Or does this just mean that these “incident” cases matter more in this group, where the number of admissions for COVID-19 is quite small?  Nobody can give you an answer to that.

True comparisons of illness severity.  One of the main ways everyone compares Omicron to Delta is by the likelihood that it will put you in the hospital, if you catch it.  But if a larger share of Omicron hospitalizations are persons for whom COVID-19 is an incidental finding, it makes Omicron appear to have higher severity than it actually does.

True estimates of hospital stress.  If half of the Omicron cases in the hospital are people who would have been hospitalized anyway, regardless of Omicron, then the raw hospitalization count overstates the true degree of hospital stress because caused by Omicron.

Finally, it’s not even clear what hospitals are reporting to the Federal government.  For the stock of patients in the hospital, it’s clear that the definition includes anyone with a positive COVID-19 test.  For “new admissions”, the guidance says to count patients ” … who had confirmed COVID-19 at the time of admission … ” .  If the hospital gives every incoming patient a rapid test for COVID-19, then, aside from the high false-negative rate of those tests, presumably, they are going to count every patient with detectable COVID at time of admission.  (I went through all of this in Post #1394).

My contention is that everybody talks about that new admissions number, but it’s not crystal clear what it actually represents.  Plausibly, it does include most of the “incidental” COVID-19 cases, if they are found by antigen testing during the hospital admission process.

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A big problem with the math.

Here’s my main problem with this.  There’s no plausible set of circumstances that I can come up with that would generate that many incidental COVID-19 cases.  I.e., that many people who were hospitalized for something else, and just happened to have COVID discovered once admitted to the hospital.

It’s easy enough to understand how these incidental COVID-19 cases get into the hospital.  In the U.S., in 2018, per the AHRQ HCUP hospital database, 55% of hospital admissions occurred via the emergency room.  (And so were presumed to be emergency admissions).  Those patients are going to have to be admitted no matter what. Another chunk would be “urgent” admissions that cannot be postponed indefinitely.  Those people would likely be admitted regardless of COVID-19 status.  (And somewhere around 20% are “elective” or “scheduled” admission where, presumably, anybody testing positive for COVID would not be admitted.)

And so, presumably, these “incidental” COVID cases are just randomly distributed across all emergency (and urgent) hospitalizations, based on the prevalence of COVID in the community.  For emergency and urgent care, those persons end up hospitalized.  And they would carry any existing COVID infection to the hospital with them.

In other words, if 1 percent of the people in your community are walking around with an active COVID-19 infection, then, to a first approximation, about 1 percent of those showing up to be admitted to the hospital for any random condition will have a COVID-19 infection.

The problem is that a) yeah, somewhere around 1 percent of the population probably is walking around with an active COVID infection right now, but b) 1 percent of ongoing hospital admissions wouldn’t come close to explaining the high number of cases admitted to the hospital with COVID as an incidental finding.

Here’s the math.

There’s nothing tricky here.

• First, the U.S. has about 30 hospitalizations per 100K population per day (in a normal year).
• Second, based on the number of daily new COVID-19 cases, I estimate that just over 1% of the U.S. population is currently walking around with COVID.  (I’ve gone through my factor-of-six rule of thumb in prior posts).
• Multiply those two together, and you get an estimated 0.4 “incidental” COVID-19 admissions per 100K per day.
• But right now, we’re getting 5.5 COVID admissions per 100K per day.
• So … those incidental admission would only amount to 7% of the total.  Nothing close to the 50% to 66% being reported anecdotally.

This has been puzzling me for weeks, ever since the South African finding came out.  And it boils down to “something most be wrong with the assumptions behind that calculation”.  But I have yet to figure out what.

One possibility is that there are vastly more asymptomatic Omicron cases than anyone has estimated.  There is a tiny amount of evidence to suggest that this might be plausible.   (I.e., this one research study).  But you would need so many cases that I don’t think you could account for this with asymptomatic infections alone.

That is, to bring that 7% figure up to 50%, you’d need to have seven times as many infections present in the population.  I think that, at that rate, the entire population would have been infected with Omicron within a couple of weeks.  It just requires too many people to have an active infection.

A second possibility is that persons who need hospitalization for some other condition have a vastly higher rate of COVID-19 infections.  But recall that most of these individuals are entering the hospital with asymptomatic COVID.  So it’s not like the finding that certain comorbodities increase risk of hospitalization for COVID-19.  Here, you’d have to believe that comorbidities likely to get you hospitalized also vastly increase your likelihood of asymptomatic COVID infection.  I can’t quite get my mind around that.

A third possibility is that maybe tests are turning up a lot of false positives on old COVID-19 infections.  So that, as these persons enter the hospital for some other reason, and get tested for COVID, we’re picking up not just those currently infected, but a significant fraction of those who have been infected sometime in (say) the past 90 days.

But, while such false positives do occur with PCR tests, I don’t think anybody says that they are common.  (The possibility of false-positives in that timeframe is why you are not supposed to get routinely re-tested for COVID within 90 days of a positive test.)  And nobody, so far, suggests that such false positives are more common with Omicron than with prior variants.

None of these explanations seems plausible to me, given the number of persons that would have to be involved.

Let me just spin the numbers the other way, and you can see what I’m talking about, in terms of not being able to get the math to work out.

On any given day, per 100,000 population, these days the U.S. would see

• about 30 hospitalizations unrelated to COVID-19
• about 6 COVID-19 hospitalizations.

If half of those COVID hospitalizations are incidental ones (where the person is admitted for something else, but has an asymptomatic case of COVID), that means you need to see 3 “incidental” COVID admissions coming out of the 30 individuals who were admitted for other causes.

In other words, if half the COVID admissions are “incidental”, that requires that 10 percent of all regular hospital admissions have an asymptomatic COVID infection upon entering the hospital.  (Or manage to trigger a positive on a COVID-19 test for some other reason.)

Which, to a first approximation, requires that, at any given time, 10 percent of the population would have to have an asymptomatic COVID infection?

Which I think is not even remotely plausible.

And that’s where I give up.  I cannot gin up any plausible scenario under which half the current set of COVID-19 admissions are “incidental” COVID cases.  That would require something like 10% of all regular hospital admissions have asymptomatic COVID.  Or somehow, certain diseases and conditions attract asymptomatic COVID infections.  Or Omicron results in a huge number of false positives, based on having had a prior (not active) infection.

None of those seems plausible.  But without something along those lines, the numbers just don’t make sense.  At current rates, if half of COVID-19 admissions are “incidental”, that equates to 10% of all admissions for treatment of anything but COVID walking in the door with an active asymptomatic COVID infection.

 

 

New York and Washington, DC still appear to have plateaued.  Otherwise, for the U.S. as a whole, new infections are still doubling every seven days.  With the exception of NY, DC, and Maine, new cases are rising rapidly everywhere.  The U.S. now stands just shy of 170 new COVID-19 infections / 100K / day. Continue reading Post #1386: COVID-19 trend to 1/4/2021, new infections still doubling every seven days.

The CDC issued another weeks’ worth of estimates of Omicron as a fraction of all cases. 

For the week ending 1/1/2022, they estimate that 95% of cases were Omicron. Estimates for prior weeks were revised as well.  Below, the current estimates are in the first column.  And you can see how those numbers have bounced around from week to week by reading across the additional columns.

Source:  CDC COVID data tracker, accessed on various dates ending 4/1/2022.

Assuming this latest estimate is more-or-less correct, this has implications for a lot of things.  Some good, some not so good, some neither/nor.

The worst of which is that the U.S. case hospitalization rate for Omicron is nowhere near as low as one-third that of Delta.  It’s now looking like it’s half, or more. Continue reading Post #1385: Revised CDC estimate of Omicron incidence, and the Omicron case hospitalization rate.

 

Today, this headline caught my eye, from this source.

It caught my eye because it’s that’s not even remotely close to being true.  I just did the numbers this morning.

If you read far enough down into the story, you can see this:

These guys can’t possibly be so stupid as to fail to understand this.  Or, if they are, they shouldn’t be reporting on it.

If a state doesn’t report data for (e.g.) three days, what you see when they finally do report a new case number is four days’ worth of cases.

Just taking a simple average across the states, today’s raw count reflects nearly three days’ worth of cases, on average.

So they just took today’s raw total.  Fully realizing that it reflects cases accumulated over several days.  Then misleadingly added the word “daily” to their headline.  Just to make sure you misunderstood what you were being shown.  And ran with that as their headline.  As if the U.S. had seen a million new COVID-19 cases today.

Isn’t the actual Omicron outbreak scary enough as-is?  Exaggeration would seem to be unnecessary.  But if fear is your main product, rather than information, then any amount of exaggeration you can plausibly justify appears fair game.

It boils down to this:  They aren’t so stupid as to misunderstand what that raw number is.  But they’re hoping that you are.  Hey, they got me to click on it.  I guess that’s all that counts.