Category: COVID through 2022

All my various postings tracking the COVID-19 pandemic through the end of 2022.

Post #1401: COVID-19 trend to 1/13/2022: Maybe a week from the U.S. peak?

 

By my count, the U.S. now stands at 247 new COVID-19 cases per 100K per day, up less than 3% from yesterday, and up 33% over the past seven days.

Beneath the U.S. average is a spectrum of growth rates, from outright declines in new cases in the Northeast, to near-vertical growth in new cases in the Pacific region.  Regions are peaking in more-or-less the order that they started, which has been the norm for prior COVID-19 waves in the U.S. Continue reading Post #1401: COVID-19 trend to 1/13/2022: Maybe a week from the U.S. peak?

Post #1399: COVID-19, no trend at 1/12/2022

 

For the second day in a row, total U.S. new COVID-19 case counts did not materially increase.  We now stand at 241 new cases / 100K / day, roughly the same as two days ago.  For the past seven days, the case count rose just 35%.

Yesterday’s pause was clearly mostly an artifact of data reporting.  Today, by contrast, I can point to a handful of high-population states — starting with New York and Illinois — whose true declining new case counts contributed to the stable U.S. average.

Cases are still rising in most of the country, for sure.  But a handful of places — NY, NJ, DC and now a few others — seem to be turning the corner.

Combine this with a couple of days of non-rising new hospitalizations (Post #1398), and even if this isn’t quite the peak, it’s a nice change of pace.

We’re due for it.  This is the week that the winter wave peaked last year.  And we’re now nearly four weeks into our wave, whereas both Great Britain and Canada appear to have peaked at just over three weeks.

Edit:  It now appears that the NY Times may be suggesting that we are at or near the peak, per this reference.

Continue reading Post #1399: COVID-19, no trend at 1/12/2022

Post #1397: Is home testing suppressing COVID-19 case counts?

The issue for this post is whether widespread use of home COVID-19 tests is materially reducing the official counts of COVID-19 cases.  My wife has been asking me this question for weeks.  Yesterday I got an email from a reader with the same question.  This post is about scraping together whatever I can, to try to put some bounds on the extent to which home testing is perturbing the numbers.

Bottom line:  Between September and October 2021, the gap between the official count of cases and estimates of the “true” count (based on evidence of prior infection) grew considerably.  At that time, home COVID-19 tests were probably being sold at the rate of 100M per month.  Those sales actually exceed the roughly 45M per month PCR tests being reported to the U.S. CDC.  But test sales are not the same as test use, and nobody knows the extent to which those 100M at-home tests have been used, or are merely being held by consumers.  For sure, this issue will become more important as the Federal government aims to get at-home COVID test distribution up to 300M per month by February 2022.

Lots of details follow.

There’s already a lot of slack in the official counts of COVID-19 cases.

First, you have to realize that the official COVID-19 numbers grossly under-count total cases anyway.  I’ll demonstrate that below.  So, from the get-go, you have to take the official case counts with a large grain of salt.

You know that this degree of under-count may well vary across states, over time, and across countries.  It will vary based on both the availability of tests and population’s willingness and ability to get tested.  If there is a shortage of tests — as there was in the U.S., early in the pandemic, due to the failure of the CDC’s first test for COVID-19 — then the under-count will be higher.

Further, the degree of under-count of the true number of infections might vary across COVID-19 variants.  Plausibly, most of the under-count is persons with asymptomatic cases.  If different COVID variants generate different proportions of asymptomatic-to-symptomatic cases, that should affect the under-count.  And if COVID becomes concentrated in a population with a high proportion of asymptomatic cases (e.g., children), that will also affect the under-count.

The best you can hope for is that, for some reasonable periods of time, the degree of under-count remains relatively constant, for whatever you are trying to look at.  That way, even if it’s off, as long as it is consistently off, you can make some reasonably valid comparisons over time.

The issue with the growth of home testing is whether or not it has introduce an increasing “wedge” between the true count of infections and the official count.  If so — if the gap between actual infections and the official numbers is growing rapidly due to unreported results of home tests — that will distort metrics based on the official count of cases.  That includes not just total infections, but also measures of severity such as case hospitalization rate and case mortality rate.

In short, we know the official counts are an under-count.  The question is whether the size of that under-count is rapidly increasing due to widespread available of home COVID-19 testing.

How do we know there’s an under-count?

We know the official COVID-19 case counts are an under-count based on the CDC’s national lab seroprevalence survey.  There, they use blood drawn for other purposes (e.g., routine blood panels) and test it for antibodies to COVID-19.  Presence of antibodies demonstrates a prior or current COVID-19 infection.  You can then compare the estimated fraction of the population with antibodies, to the reported official number of COVID-19 cases, to determine some measure of the under-count in the official numbers.

Last time I checked — roughly August 2021 — there was just shy of a 50% under-count, based on the seroprevalence survey data.  At that time, there appeared to be about 1.9 actual COVID-19 infections for every one shown in the official case counts.

Seroprevalence surveys are not perfect.  Putting aside the issue of whether or not the blood samples are representative of the population, those immunoassays have limited sensitivity, and immunity fades over time.  Both of those factors suggest that, if anything, the true number of COVID-19 infections has been even higher than the seroprevalence surveys suggest.

Unfortunately, CDC changed methods in September 2021.  Per their website:

Note that in response to recent data, 23 jurisdictions in the nationwide antibody seroprevalence survey switched to an assay with increased sensitivity to detect past infection in September 2021, which could impact trends.

The upshot of that is that in comparing data prior to September 2021 to the present, I can’t tell what part of that increase is real — a true increase in the undercount of infections — and what part is due to the use of a more sensitive test in the seroprevalence survey.  That leaves me with exactly two datapoints, September and October 2021, as shown below:

Source:  CDC seroprevalence survey web page accessed 1/12/2022

I’ll point out that this was a fairly stable time for COVID-19 in the U.S.  All the cases were the Delta variant, throughout the period.  The Delta wave peaked around 9/1/2021, and the winter wave had just started prior to Thanksgiving 2021.

It’s always a risk to make a lot out of two data points.  That said, per the CDC’s analysis, the purely statistical uncertainty in their estimates is quite small.  The “95% confidence interval” for October looks to be plus-or-minus one percent of the estimate.  There may be structural (non-statistical) errors — e.g., maybe their new, more-sensitive test was not fully phased-on at the start of September.   There’s no way for the outside observer to know that.

All I can say is, taken at face value, as of October of last year, there was an increasing gap between the number of infections estimated from blood antibodies in a sample of persons, and the official count of persons who had tested positive for COVID-19.

The increasing gap between actual infections and the official count could arise from any number of sources.  We can’t rule out the rise of home testing as one of them.  Yet.

The number of OTC COVID-19 test sales is larger than the number of PCR tests reported to CDC.

Fully realizing that a test sold is not the same as a test used, I have to start with some credible estimate of the rate at which at-home COVID-19 tests are being produced and sold.  The bottom line — below — is that, right now, those home tests are probably being produced and shipped at a rate in excess of 100M tests per month.

As a benchmark, there are about 45M COVID-19 PCR tests reported to the U.S. CDC per month.  (That’s my calculation, based on data from the CDC COVID data tracker).  So that’s test that were actually performed, but would not include any results from antigen tests that might be reported to the CDC.

What I want to know, for starters, is how many cheap, quick (no lab involved), at-home, no-prescription (over-the-counter or OTC) tests get sold every month.  Fully realizing that “sold” is not the same as “used”, so that’s not strictly comparable to the number of PCR tests reported to the CDC each month.

This assumes that any test that has to be sent to a lab, or is done by a medical provider, or is done at some official testing site, should have been counted in the official statistics.  This also assumes that expensive home tests would see relatively little use.

The information is piecemeal.

As of this 1/9/2022 reporting, there were 11 at-home OTC test kits approved by the FDA. (This somewhat older reporting also lists 11 tests, but it’s a slightly different list of 11).  The list keeps expanding.  Many were only approved lately for at-home (OTC) use.

Of those, per the same reporting, the following tests do not require you to mail your sample in to a lab, and are cheap:

  • Binax Now, $20 for 2.
  • Quickvue, $23 for 2.
  • Flowflex, $10 each.
  • Ihealth, $23 for 2.
  • Intelliswab, $23 for 2.
  • On/Go, $25 for 2.
  • BD Veritor, $34 for 2.

There are in addition, a few new ones that are not yet on the market, I think:

  • Celltrion Diatrust, 10/21/2021 OTC authorization.
  • SD Biosensor, 1/5/2022 OTC authorization
  • Siemens Clinitest, 12/29/2021 OTC authorization

(The last three are from various DHHS press releases).

That may not catch them all, but it’s enough to get started.  Abbott Binax Now and Quidel Quickvue were the first two rapid test approved for OTC sales, per this 12/21/2021 reporting from Vox.

The Abbott Binax Now test was first shipped to retail outlets in April 2021, per this press release from Abbott.  Their plan at that point was to produce “tens of millions per month”.  I believe that was among the first approved for OTC use.  The Abbott test was reported to account for three-quarters of all OTC test sales, per this 12/21/2021 reporting from Vox.

The Quickvue test from Quidel test sold at a rate of greater than 20M tests per month in the 4th quarter of 2021 (Reference).

Putting those two together, the two market leaders probably sold in excess of 80M tests per month in the final quarter of 2021.  (Assuming that the 75% Abbott market share estimate is correct).

This estimate is a good match for a Federal press release from October 2021.  At that time, the Federal government was expecting to increase U.S. OTC rapid test capacity from a current 100M a month to 200M test per month by the end of 2021, rising to 300M per month by February 2022.  (Source).

That increase is part of a concerted push by Federal authorities to get rapid home test to market quickly.  At least, that’s according to those same authorities.  At the minimum, they threw $70M into a fund to streamline the authorization process.  Separately, the Federal government is issuing hundreds of millions of dollars of contracts for purchase of home tests.

In summary:

  • Each month, the CDC records the result of about 45M PCR tests.  Those are tests that were actually used.
  • Each month, recently, U.S consumers have bought on-order-of 100M OTC COVID-19 tests.

So there are certainly enough OTC tests out there to be able to perturb the official data.

The big unknowns are the rate at which consumers are using those tests, and the rate at which they are coming back with positive results.  That is, how many positives are we missing due to the presence of cheap and widely available home tests.

Is there any information on actual use of home COVID-19 tests.

There are a couple of issues here, but the bottom line is no:  As far as I can tell, nobody can tell you the fraction of those home test kits that has been used.

The only way you are going to be able to estimate this is with some sort of large-scale survey.  How else are you going to know whether or not they used the purchased tests.  The question is, has such a survey been done, and have the results been made public?  No.  Or, if so, I can’t find it.

Beyond that, I don’t think it’s worth flogging the nuances of the question.  The nuances being that your behavior toward cheap at-home testing might differ from your behavior toward more formal testing approaches.

If anything, that’s more-or-less the point of the current Federal initiative pushing at-home testing.  They want people to test more often.

Summary:

For sure, Americans are buying a lot of OTC home COVID tests.  Best guess, right now, sales of those test are two to three times the volume of PCR (DNA) tests that are being reported to CDC monthly.

For sure, the shortfall between the official counts of infections and the estimate of the true number of infections via seroprevalence testing grew in October 2021.  It’s plausible, but premature, to say that that increasing gap is due to home testing.

The big unknown is the rate at which consumers are actually using those tests and finding positive results.  Near as I can tell, the only way to know that is to ask them, via a large-scale survey.  So far, I haven’t found any evidence that anyone has conducted such a survey.

The bottom line is that it’s plausible, but not proven, that the widespread availability of cheap OTC tests is suppressing the official count of COVID-19 cases.

Post #1396: COVID-19 trend to 1/11/2022. Slower growth.

The seven-day moving average of cases fell today.  For the U.S. as a whole, and in many regions and states.  The U.S. stands at 234 new COVID-19 cases per 100K population per day, up just 39% in the last seven days.  And down from a revised 239 from yesterday.

The question of the day is: Are these turnarounds in the trend lines real (and so expected to continue), or just an artifact of holiday reporting (and so, just a one-off jiggle in the line)?

Unfortunately, my answer is that this one-day decline is mostly a data reporting artifact.  New case counts may be slowing down, but there has been no true decline in new cases yet.

All this means is that the actual rate of new case growth is lower than what has been shown for the past few days.  But new cases are (probably) still increasing, and will continue to increase for a few days yet.

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 1/8/2022, 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.

Drill-down

I would love to belabor this, but the issue is simple. Every day, I show you the seven-day moving average.  Every day, that seven-day “window” slides forward.   The oldest day drops out, the newest day drops in, and you get a new average.

States sometimes skip data reporting for a few days.  Most do, on the weekends.  Most will on holidays.  Following those non-reporting days, you’ll get a big “lump” of data as they catch up with the backlog of cases that haven’t been reported.

Source:  Google.  Notes are mine.

As long as those “lumps” come at the same time every week, that’s not a problem.  But if you end up with two lumps in a single seven-day period, then the average will jump up.  In effect, the state will have crammed 9 days’ worth of cases into a seven-day window.

And so, the question is, did we see a decline today because some great “lump” of data dropped out of the seven-day window?  That’s what I would call a data reporting artifact.  Or did we see it because we added relatively few new cases today?

For the U.S. as a whole, the data reporting does not look that unusual.  The block that passed out of the seven-day moving average today (Tuesday 1/4/2022) was not unremarkable.  And the day that got added today (Tuesday 1/11/2022) was a bit lower.  And so the average fell.

But if you look at some individual states, you can see that there is a data reporting artifact for several of them.  And, unfortunately, there’s a whopping great one for Florida, which is one of our most populous states.

I spotted one for Rhode Island yesterday.  They had clearly delayed their “lump” by one day last week, and so gave an absurdly high new case rate for the seven days ending 1/10/2022.  That number has now dropped down.  Like so:

Here’s how it looks day-by-day, below.   They skipped reporting an extra day last week, and that dumped a lot of cases into yesterday’s average.  Yesterday’s seven-day window contained not one but two lumps of data.  But not today’s average.

Source:  Google

Rhode Island is too small to matter much in the U.S. average.  But, unfortunately, Florida does matter.  And Florida did the same thing.  Like so:

Source:  Google

And then, if I look a little harder at all the downturns, I find the same for Illinois, Georgia, South Carolina, and several others.  More than enough to have had an impact on the U.S. averages.

The upshot is that the 39% increase over the past seven days is more-or-less correct.  The endpoint of the line is now in the right place, and case growth is slowing.   But the finding of an absolute decline in cases today is an artifact of many states taking a reporting holiday on Monday 1/3/2022, with no matching data reporting holiday on Monday 1/10/2022.

If I had to sketch in my best estimate of the true trend, by eye, here it is.  We’ve had a significant slowing in the rate of growth, but by eye, we’re not at the peak yet.

 

Post #1395: COVID-19 hospitalizations flattening out?

 

To answer the question in the title, um, yeah.  That’s how it looks.  We probably need another few days to be completely sure.  Let me now work through that.

The data source for this analysis is what the US CDC refers to as the Unified Hospital Dataset.  The actual file I’m using can be seen and downloaded at this U.S. DHHS link.  More-or-less every acute care hospital in the U.S. is required to submit data on a daily basis.  Data fields include not only COVID case counts, bed and ICU occupancy rates and such, but also include (e.g.) self-reported critical staffing shortages, the amount of PPE is on hand, how many doses of key medicines are in stock, and so on.

The file has a few quirks and takes a bit of getting used to.  It’s a process of sorting out how things work, and which pieces of of data actually provide any information.

For example, you may have recently seen that umpty-percent of hospitals are reporting a critical shortage of staff.  What you don’t see reported is that it’s been that way for a long time, well before Omicron hit the scene.  So, it may well be true and it certainly could be a significant problem for some hospitals.  But it’s not some brand-new crisis caused by Omicron. 

Source:  Calculated from Unified Hospital Dataset, cited above.

In Post #1391, I showed a lot of the oddness of patterns of COVID-19 hospitalization across the states.  In particular, COVID-19 case hospitalization rates varied seven-fold across states.  Among other things, this means that what we observe as “the national rate” is going to depend, to a large degree, on where those cases are showing up.

Source:  Calculated from Unified Hospital Dataset, cited above.

But of all the oddities of the file, the most important one here is a weekly pattern of under-reporting and catch-up.  It appears that many states allow smaller hospitals to skip reporting over weekends, and just book those new cases every Monday.  (Or something — the exact timing on this file still baffles me).  (Separately, the file also tails off in the last two days of reporting, but that doesn’t matter materially for this analysis because I stop before the last day of reported data.)

Anyway, the raw count of new hospitalizations looks like this:

Source:  Calculated from Unified Hospital Dataset, cited above.

I smoothed that out by ( … does anybody really care? … ) estimating and apply a percentage adjustment based on day of the week.  Not rocket science.  I calculated the percentage amount that things seemed to be up or down on each day of the week, historically, on average.  Then applied the reverse of that to the data.  blah blah de blahdity blah blah.  And blah blah blahblah Bob Loblaw’s Law Blog.  And blah.  This seems to remove any obvious weekly pattern, which is exactly what it should do, leaving this:

Source:  Calculated from Unified Hospital Dataset, cited above.

And when you lay that curve up against the count of daily new US COVID-19 cases, you can see that hospitalizations are not keeping pace with new cases.

Source:  Calculated from Unified Hospital Dataset, cited above.

And if you do the long division, you find that the case hospitalization rate continues to fall.  Under Delta, 6.5% (and up) of new COVID-19 cases ended up in the hospital.  Under Omicron, it’s now down to 2.6% and still falling.  Which is good news, for a change, I think.

Source:  Calculated from Unified Hospital Dataset, cited above.

Why the COVID case hospitalization rate continues to fall, I could not tell you.  Maybe cases are simply shifting to lower-hospitalization-rate states, such as Florida.  Maybe there really was some residual of the higher-hospitalization Delta hanging around.  (CDC says no to that, with a most recent estimate that 98.3% of cases were Omicron for the week ending 1/8/2022).  Maybe the age mix is continuing to shift toward younger, less hospitalization-prone populations.

But if I had to guess, I’d guess that physicians are slowly re-calibrating their criteria for hospital admission.  On the one hand, it’s generally frowned upon to send somebody away from the hospital and have them die.  On the other hand, it’s also frowned upon to admit a lot of patients with two- or three-day stays.  At the very least, that will eventually attract the attention of Medicare’s auditors, who will not only question why you have all those short-stay cases, they may very well say that Medicare isn’t going to pay for them as inpatient stays, forcing the hospital to re-file the bills as much-less-well-paid outpatient observation stays.

It’s a balancing act.  For a disease this new and this mutable, surely a lot of it is rule-of-thumb.  It would not surprise me if we started the Omicron wave with Delta-based standards for who should and should not require inpatient care.  And as the fact of lower mean severity of illness gets discovered, those standards might reasonably change.

In any case, for sure, as you can see from the graphic earlier in this post, the hospitalization rates are, in fact, all over the map.  It certainly appears that some practices — either data reporting, or standards for hospital admission, or both — are varying across the states.

The upshot of this is that, as best I can tell, COVID-19 hospitalization rates are not keep up with new cases.  Instead, they appear to be leveling off.  Given the ongoing increase in daily new cases, that’s a very good and very lucky thing.

Post #1394: The U.S. CDC: Argh.

I saw this headline in today’s Washington Post.  It appears that the U.S. CDC is almost ready to maybe sort of recommend that you wear a good mask, not just any mask.

I guess, as pictured above, they’re looking back on the entire history of the pandemic, assessing where we now sit, and asking whether or not they might, possibly, at this stage, as a last resort, recommend an easy, cheap, and effective method for radically reducing the population’s exposure to COVID-19.

Hmmm.

If you read this blog, you know I’ve been strongly in favor of use of high-filtration masks for a long time.  Since before the CDC even recommended wearing masks.  Just search the “mask” category and you’ll see what I mean

With this latest near-pronouncement from the U.S. CDC, I hardly even know where to start.  In the interest of saving time, I’ll skip the rant, and remind you of a few useful things.

1:  An N95 isn’t just better than a standard blue procedure mask, it’s vastly better.

2:  If you insist on wearing a cheap blue procedure mask, at least learn the “tucked and tied” technique.

3:  Leave the KN95s on the shelf.

1:  An N95 isn’t just better than a standard blue procedure mask, it’s vastly better.

Here, I’m just repeating a part of Post #938, from almost exactly one year ago.

Here’s a simple question.  Even if you think you really, truly understand masks, take 15 seconds to see if you can get the correct answer.

Question:  An N95 respirator (mask) filters out 95% of airborne particles.  A procedure mask with ear loops filters out about 30% of airborne particles.  (That’s based on an actual test of those masks as published more than a year ago in JAMA).   Let me loosely call that an “N30” mask.  Roughly speaking, how much better is an N95 mask, compared to an N30 ear-loop procedure mask?

  1. Obviously, it’s about three times better, because 30 x 3 = 90, which is close to 95.
  2. Obviously, it’s about 14 times better, because (100 -30)/(100 – 95) = 70 / 5 = 14.
  3. Obviously, this must be a trick question.

The answer is B, it’s 14 times better.  Why?  The mask rating (N30, N95) shows you what the mask keeps out.  But the viral load you inhale isn’t about what the mask keeps out.  It’s about what the mask lets through.  It’s about 1-minus-the-mask-rating.  And in any given situation, the ear-loop surgical mask will let through and expose you to 70% of what’s floating around.  While the N95 exposes you to 5%.  And 70/5 = 14.

In case you still don’t quite get it, let me do the math the other way.  How much better is that N30 ear-loop surgical mask, compared to wearing no mask at all?

Question 2:  Assume that you need to inhale 100 copies of COVID-19, at a sitting, in order to get infected.  Assume that you are going to inhale one cubic meter of air, at a sitting.  How dense can the COVID-19 particles in the air be, before you inhale enough to get infected, based on wearing:

  • No mask.
  • N30 mask (ear-loop surgical mask, worn loosely)
  • N95 respirator.

Answer:

Question 2, same math, but rephrased.  Suppose there’s a room filled with COVID-19 aerosol.  Suppose that, without a mask, you can sit in that room for no more than 10 minutes before you get infected.  How much more time does your cheap, blue ear-loop surgical mask buy you?  That is, how long could you sit in that room and remain uninfected, wearing an ear-loop procedure mask? And then, how long wearing an N95 respirator?

Answer:

  • No mask — 10 minutes.
  • N30 mask (ear-loop surgical mask, worn loosely) – 14 minutes (10/.70)
  • N95 respirator — 200 minutes (10/.05).

That cheap blue mask buys you a whopping four additional minutes of time, before you get infected.  Which not only makes my point, but which shows you why you want to stay away from close, crowded situations, mask or no mask.

Sure, a loosely-fitting ear-loop surgical mask is better than no mask at all.  But not by a whole lot, in the overall scheme of things.

I hope you now get why I’m so persnickety about masks.  The difference between a good mask and a poor mask isn’t a little bit.  It’s a lot. It’s an order-of-magnitude difference in performance.

Tucked-and-tied.

Still wearing those 20-cent blue procedure masks that you bought a year ago?  Can’t bring yourself to pay a whopping 89 cents each for genuine 3M N95 respirators, even though the 3Ms are good for hundreds of hours of normal use before the filter material clogs? Or maybe just just plain don’t like N95s of any sort, despite the wide variety available?

Then you should at least learn the tucked-and-tied technique.  By itself, this improves the filtration ability of the typical surgical style mask from roughly an N30 to roughly an N60.

Takes a few seconds to do.  Costs you nothing.  Doubles the effectiveness of the mask.  What’s not to like?

Or watch that directly in YouTube.

In the U.S., KN95 is a style of mask, not a legally-enforceable filtration standard.

The CDC will be doing nobody any favors if they recommend using an N95 or KN95 mask.  I’ll go so far as to say that adding KN95 to the recommendation is simply an incompetent mistake.

In the U.S., N95 is a U.S. standard maintained by the U.S. National Institute for Occupational Safety and Health (NIOSH).  A NIOSH-certified N95 respirator must fit tightly to the face, using straps that pass behind the head (never ear loops), and, when properly fitted, filter out at least 95% of of the hardest-to-filter particles (0.3 micron).

Masks may then be further certified for medical use by the FDA.  Masks certified for medical use must meet additional standards, including resistance to splashes.  It is completely possible to have a NIOSH-certified N95 that is not suited for medical use.  Most or all NIOSH-certified N95s sold for industrial use — such as the ones you can easily purchase at your local Home Depot or other hardware store — filter to the N95 standard, but are not certified for medical use.

In the U.S., KN95 means nothing.  It’s a Chinese standard, and has no legal meaning in the U.S.  Anybody can make a mask and sell it as a “KN95” mask.

Practically speaking, in the U.S., KN95 refers to a style of mask, not to a guaranteed level of filtration.  A mask that will fold flat, unfold into some sort of cone shape, and use ear loops rather than behind-the-head straps.

I have tried several KN95 masks over the course of the pandemic, and none of them worked well enough to use.  They all fit too loosely, allowed too much air to leak around the face seal, allowed my glasses to fog, and were generally insecure due to loose-fitting ear loops.

My point is, the things you can buy in the drug store labeled “KN95” are in no way a substitute for a NIOSH-certified N95 respirator. Not even close.  I sincerely hope that some CDC bureaucrats will get out from behind their desks, walk into a few hardware and drug stores, buy a few packs of what are routinely sold as “KN95” masks in the U.S., and assess them for air-tightness and likely filtration ability.  And come to the realization that, as I just said, the typical KN95 in America is not even in the same league as a NIOSH-certified N95.

In theory, the FDA had, at one time, a list of certified Chinese manufacturers whose masks could be used in U.S. hospitals under an emergency use authorization.  The FDA has long-since cancelled that EUA, and so, technically speaking, there are no KN95 masks certified for medical use in the U.S.

The bottom line is that, for the average consumer, you have no idea what you are buying when you purchase a KN95 mask. For myself, at least, every one I tried failed due to obvious air leaks.  And that doesn’t even begin to address the actual filtration ability of the cloth itself, which you have no way of testing, and which was never tested or certified by an U.S. agency.

Maybe if you’ve never worn a properly-fitted N95, you wouldn’t know the difference.  But once you’ve worn an N95, and realize that absolutely no air is supposed to leak around the mask, you will instantly reject any hardware-store KN95s on the basis of lack of air-tight fit.

If you must use an ear-loop mask, I’d recommend a made-in-Korea KF94, such as the LG Airwasher.  (KF94 is a filtration standard more-or-less equivalent to N95 in terms of particulate filtration.)  If it’s genuinely made in Korea, that provides a known filtration ability, and the ear loops are adjustable for tight fit.  Of all the masks that I asked my daughter to try, that was by far the most preferred (Post #1246, What mask should I wear?  We have a winner).

And at the end of the day, it’s all about wearing the best mask that you are willing to wear.

Post #1392: A funny thing happened on the way to COVID vaccine ineffectiveness …

 

The funny thing is that it’s not happening.  Not yet, anyway.

Here’s why I’m writing this.  Based on the research I looked at a couple of weeks ago, I thought that standard two-shot vaccination  did more-or-less nothing to prevent an Omicron infection.  (Ditto for prior COVID infection.)  And so, I’ve been checking the numbers on infections broken out by vaccination status.  I’ve been expecting to see infection rates level up, between the vaccinated and unvaccinated populations, under Omicron.

But that’s not happening.  At least, not yet.

As-observed, in the population, vaccinated individuals are still vastly less likely to get infected, get hospitalized, or die from COVID-19.   Even now.  Even with Omicron. Continue reading Post #1392: A funny thing happened on the way to COVID vaccine ineffectiveness …

Post #1391: Making sense of U.S. COVID-19 hospitalization data

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.

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:

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.

 

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.

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.

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.

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.