Category: COVID through 2022

 

As the Omicron wave recedes, I’m on a quest to determine when the risks imposed by Omicron will be no higher than the risks imposed by typical seasonal flu.  Based on what I’m reading, that “flu benchmark” has intuitive appeal to a lot of people.  If you don’t obsess about flu risks every year, that’s a reasonable starting point for discussing a return to normalcy with endemic Omicron.

Best guess?  If you’re fully vaccinated and boostered against COVID-19, once Omicron falls below 40 new cases / 100K / day, your risk of hospitalization or death from Omicron is no higher than your risk of those outcomes from flu, during an average week in a U.S. flu season.

That estimate embodies an extremely conservative assumption that COVID-19 vaccines only reduce your risk of infection, with no additional protection against hospitalization or death, if infected.  I’ve erred on the side of caution.  By contrast, if you think the vaccine plus booster provides (say) an additional 50% protection against hospitalization and death, beyond mere protection against infection, then you can more-or-less double that 40 case threshold to 80.

With six states already below 100 cases / 100K / day and falling, maybe it isn’t too soon to start thinking about a return to normalcy for those who are fully protected.

This post provides the background and outlines the calculation behind that estimate.

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Background

The overwhelming consensus of scientific opinion is that we are headed toward “endemic COVID-19”, whatever that may mean (Post #1400, part 2).

The most common model for “endemic COVID-19” seems to be seasonal flu.  It’s always present at some level in the population, almost everyone has some immunity to it, every year maybe 10 percent of the population has a symptomatic case, and small fraction of those — mostly elderly and frail — will be hospitalized or die from it.

Some seasons and some variants will be worse than others.  If you are at high risk, get your flu COVID shot every year and stay out of crowds during peak flu COVID season.

I can’t quite get my mind around that simple picture of COVID-as-flu, for two main reasons.  First, the current variant (Omicron) is ten times as contagious as typical seasonal flu.  (R-nought of 15, versus maybe 1.5 as a median value for flu in a typical year).  Second, immunity seems to fade quickly.

That combination makes it unlike any other endemic disease that we routinely deal with successfully.  A lot of childhood diseases are as infectious as Omicron, but we have long-lived vaccines for those.  If you mandate vaccination for schoolchildren you’ve more-or-less prevented large-scale epidemics.  With Omicron, by contrast, the only way I see to avoid epidemics is to mandate that everyone get vaccinated every year.  You can practically hear the squawking start at the very mention of that.

We have a while to think about it, in most places.  Based on the typical rates of decline of the Omicron wave that have been observed internationally, Omicron will still be circulating in most of  the U.S. at pandemic levels for some time yet.

In addition, in the long run, after the presumed end of the Omicron wave, there are at least three obvious unknowns that will govern how this will play out:  New variants, new vaccines, and immunity following infection.  We already have the BA.2 variant coming, reported to be 1.5 times as infectious as Omicron.  Manufacturers are slated to release at least one Omicron-specific vaccine in March 2022, of as-yet-to-be-announced efficacy.  And we still don’t know the extent to which an Omicron infection provides long-lasting immunity against a subsequent Omicron (or BA.2) infection.

Putting all those uncertainties aside, for me, it’s getting to be time to start figuring out when the world will feel “normal” again.  With the assumption that we end up with a “flu-like” endemic omicron.  Which seems to be the current consensus of where we are heading.

Last summer, when we were still facing the original (Wuhan) strain and new case counts were in the single digits, I went back to most of my normal routines.   Went back to the gym, started seeing movies again, and just generally freely used areas considered to be places with relatively high risk of COVID-19 transmission (Post #813, Qualitative rankings of activities by risk of COVID-19 infection ).  That was done with forethought, after calculating the risks (Post #1163, June 2021).

Then, when case counts went up again, I stopped doing that.

In that context, this current blog post is just another variation on that calculation. At what level of new Omicron case counts will I judge risks to be low enough to be, for all intents and purposes, I can ignore them?

 

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Prior estimate for all persons combined

In the third section of Post #1400, I did head-to-head comparison on risk of hospitalization and death from Omicron, versus typical U.S. seasonal flu.  I did that for all persons combined.  My best guess, at that time, is that once Omicron gets below 30 new cases / 100K / day, the average person faces no more risk from that, than from flu at the peak week of a typical U.S. flu season.  At 16 new cases / 100K / day, Omicron poses no more risk then flu does for the average week of the entire flu season (instead of the peak week), for the average American.  Those are the rates at which, by my calculation, the average American faces no more risk of hospitalization or death from COVID-19 than from normal seasonal flu.

The arithmetic there isn’t rocket science.  Using CDC data, I estimated that a typical week during flu season sees about 49 / 100K / day new symptomatic flu cases.  Then, again calculating from CDC data, if you catch an infection, Omicron is about three times more likely to hospitalize you or kill you, compared to flu.  So, to equalize your chances of hospitalization or death, your risk of catching Omicron has to be one-third that of flu.  And 49/3 = 16 or thereabouts.

I now want to refine that, and talk about the fully-vaccinated-and-boostered only.  That’s not only because that’s what’s relevant to me, but also because it’s pretty clear that the people won’t get vaccinated aren’t worried about the risks they impose on themselves and others.

Doing that more detailed calculation turns out to be a whole lot harder, for a wide range of reasons to be discussed below.  The results are best viewed as a refinement on my prior one-size-fits-all estimate, and not as any definitive final answer.

 

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Details and calculation, Part 1: Simplifying the issue, or, all the stuff I can’t do.

This is a tough and imprecise calculation for a variety of reasons.  I’m going to list all the things you just have to turn a blind eye to, or can’t get information for, or can only get scattered information for.  Skip to the next section of that’s of no interest.

1:  Risks vary widely by age and frailty, my target audience for analysis of risk is older adults.

The elderly have a much higher risk of hospitalization and death from Omicron or from flu, compared to other groups.  (The sole exception is a slightly elevated risk of death of flu in infants, compared to others.)

Here’s how the case rates line up for flu (here, the 2017-2018 flu season), for hospitalizations and deaths per case, by age.  Just under 1 percent of children up to age 4 with symptomatic flu were hospitalized, as was about 1 percent of the 50-64 year old population.  And then the elderly had an average case hospitalization rate of about 9 percent.  Flu deaths were distributed in a roughly similar manner, except that deaths were not proportional to hospitalizations for small children.

Source: Calculated from CDC Disease Burden of Flu, 2017-18, Table 1.

You can see a similar distribution by lining up the case mortality rates for COVID-19 (for the entire pandemic, not for Omicron!) against flu, by the same age ranges.  Except for the scale, the lines are virtually identical.  The COVID-19 line is ten times the flu line because this is for the entire pandemic, not just for the later sections including Omicron.

Source: Calculated from CDC Disease Burden of Flu, 2017-18, Table 1, and data from the CDC COVID data tracker demographics section.

What’s worse, the observed rates in those tables reflect the variation in vaccination rates by age group.  And they reflect the concurrent frailties of old age.  (That is, the elderly aren’t just old, they have a lot chronic illness burden that goes along with being old.)

The upshot is that I’m going to average across all of that variation.  If you’re 30, you’ve probably never given the idea of death from flu a second though.  As you approach Medicare age, that’s more of a reality.  Roughly speaking, the average 65-year-old would have just about the mean risk rate that I’ll be talking about.So, in some sense, my target audience for this analysis is older adults.

The low cases hospitalization and mortality rates for younger age groups bring up at two other aspects of this calculation that are not covered by the simple likelihood of you, yourself, suffering these outcomes.

First, this ignores other morbidities that were associated with COVID-19, at least early on in the pandemic, that may be far more prevalent in the young.  That is, even if risk of hospitalization and death are low in certain age groups, there was also the additional risk of long COVID, temporary or long-term loss of sense of taste and smell, temporary or long-term organ damage, and the like.  It’s possible that younger people still face significant risks from those conditions, but as far as I can tell, there’s no hard data on those other morbidities under Omicron.  (Other than for myocarditis, which is its own separate topic.)

So you, personally, face health risks beyond hospitalization and death.  It’s not possible to bring those into the calculation.

Second, this also touches on the “public” part of public health in this area, which, for starters, asks you not to spread disease to others.  Even if you yourself are not particularly at risk of great harm, somebody further down a chain of infection that you helped to perpetuate might be. So a “total harm” calculation would include not just harm to self, but plausible harm caused to others by failing to (e.g.) get vaccinated or adopt agreed-upon rules of COVID-19 hygiene.

To an economist, this effect — the fact that you might cause harm to others without having to pay for it — is an “externality”.   It is, in a sense, a “missing market”, in that you don’t have to pay for the damage you cause.  Republicans are ideologically blind to externalities — in health care, in environmental policy, in areas of consumer protection, and so on — because controlling them in an economically efficient manner generally requires the government to step and and do something to try to approximate that missing market.  As a result, lots of famous Republicans publicly and proudly act as if they really, truly don’t care whom they infect.  From the standpoint of Republican orthodoxy, that’s not a bug, that’s a feature.  To an economist, it’s just inefficient.

2:  Published current illness rates commingle risk classes.

Not only are the data averaged across all ages, every population statistic you see is an average for the boostered, vaccinated, and un-vaccinated populations combined.  So when I note that the U.S. is around 170 new cases per 100K per day now, that’s a combination of a much higher rate of infection among the unvaccinated, and a much lower rate among the boostered.

This means that the first step of the process is to estimate the rates separately, for each population, using some known population proportions, some estimates of vaccine effectiveness, and some algebra.

3:  Observed rates broken out by risk classes (vaccinated versus not) do not provide reliable data on the effect of vaccination. 

You have to take your estimates of vaccine effectiveness from controlled studies of some sort.  You can’t just read them off a table of simple average rates by those who were vaccinated and not.

The longer I’m at this, the more convinced I am that most people really, truly do not understand the difference between an experiment, such as a randomized controlled trial, and “observational data”, meaning, whatever shows up in the population.

People routinely (and incorrectly!) take observational data and treat it as it were the results of a controlled experiment.  And the professional liar class that infests social media makes it a point to seek out such data, when it seems to make some point that they wish were true, and deliberately misrepresent the simple comparison of of averages as if it were the results of a controlled clinical trial.

If I randomized individuals into two groups, then vaccinate one and not the other, any difference in infection rates between the groups will be attributable to the act of vaccination.  Plus or minus a bit of statistical uncertainty, particularly if I’m only using small numbers of individuals.  That’s by design, because a) the experimenter chooses whom to vaccinate, and b) assuming the randomization is done well, all other factors affecting infection rates will be equal across the two groups.

But if I observe the infection rates of people who chose to get vaccinated or not, I’m looking at not only the impact of vaccination, but also the effect of all other differences between those two self-selected groups.

In this case, let me call those other factors the Palin Effect.  The same people who refuse to get vaccinated are likely to engage in riskier behavior across-the-board.  But they are also probably younger, on average.  They might work in a different set of careers from those who chose vaccination.  They might hang with a different peer group.  And so on.

Because of the Palin Effect — the unvaccinated really are different from the vaccinated — the averages for those groups are often vastly different from the actual vaccine impact, which is what you get from controlled clinical trials.  In Virginia, for example, I routinely see that the un-vaccinated get COVID-19 infections at ten times the rate of the vaccinated.  For the week ending 1/1/2022, it was sixteen times:

Does the observational data from Virginia mean that vaccines are actually vastly more effective against Omicron than the clinical trials demonstrated?  No, of course not.   Almost certainly, vaccination by itself provides only modest protection, and vaccination plus booster is only about 70 effective in preventing any symptomatic infection.  The difference from what you would expect at best (1/.3 = 3.33 times the infection rate) and the observational data (16.6 times the infection rate) is almost certainly a large Palin Effect.  It’s the unvaccinated and unmasked hanging out with like-minded people, going out to party over the holidays and spreading COVID-19 at a greatly heightened rate, compared to the vaccinated.

And it’s easy enough to convince yourself of this.  Just look at some very different state, such as Washington.  There, the raw difference in infection rates between vaccinated and un-vaccinated is typically about 4-to-1.  Same disease, same vaccine.  The fact that the observational number is all over the map should clue you into the fact that it’s not showing you the impact of vaccination in isolation.

Scientifically, there’s also a murky middle ground between proper randomized trials and just taking average rates by group.  These go by a wide range of names (“case-control study”, “propensity score analysis”, “regression analysis”, “pre-post with control comparison”, “instrumental variables”, “simultaneous equations”, “natural experiment”, “cross-sectional analysis”, …), but they all boil down to using statistical techniques to try to separate out the effect of (say) vaccination from the effect all those other factors (covariates).  That gray area is where I lived all my professional life, for the simple reason that there are no controlled experiments in economics.  Much of what you read in the newspapers consists of results of studies like that.  They can be well done and provide useful information.  They can be poorly done and be completely misleading.  Typically, the researchers themselves don’t really know which, and for sure, newspaper reporters have no way of knowing the difference.  Hence we end up with a rich and diverse array of bad science that gets public notice.  Of course, the more bizarre the results, the more attention they will gain.

4:  Other stuff I’m just going to ignore.

4.`1:  Risk aversion and assumption of risk-neutral persons.  I’m going to treat people as if they are risk-neutral, in the way economists use that term.  The crux of that is that I’m only going to pay attention to the average rate of bad outcomes, not the distribution.

For example, I’m going to say flu and Omicron generate equal risk if (say) you’re half as likely to catch COVID as flu, but each COVID cases is twice as likely to land you in the hospital.  The fact that I’m valuing those two scenarios as equivalent to one another is saying that I’m risk-neutral.  But people can (and will) rationally value those two scenarios differently.  You might rationally fear Omicron more, because if you catch it, there’s a much higher risk of hospitalization.  That’s not irrational, that’s just typical risk-averse behavior.  I’m just ignoring that.

4.2  There are numerically important quirks and differences in how we estimate infection rates and case rates for COVID and flu.  I went over those in Post 1400, Part 3, and I won’t repeat them here.

4.3  The data on effectiveness of booster shots at preventing hospitalization and death from Omicron, above and beyond their ability to prevent infection in the first place, is scant.  Due to the small number of events, most of what I’ve seen is observational data based on relatively few observations, or just straight-up anecdote.

There are good theoretical reasons to expect vaccination or prior infection to be more effective at preventing the worst Omicron outcomes, compared to preventing infection alone.  Preventing any infection relies on the rapid-response part of the immune system (antibodies), and Omicron has found ways to get around existing antibodies (and/or antibody levels fade).  But other parts of the immune system remain primed to fight COVID even if antibodies fail, and these parts react more rapidly to a new COVID infection than would occur in an un-vaccinated, un-infected individual.

That said, I struggle to find a consensus on just how large that effect is.

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Details and calculation, Part 2: Calculation and results.

I’m only going to give the barest outline here.  Really, I’m going to show you the assumptions I made, and then you’ve got to trust that I did the arithmetic correctly.

Step 1 of the calculation estimates the number of COVID-19 infections and flu infections you would expect in the vaccinated and un-vaccinated portions of the population, all other things held equal.  This is the part where I break the published total into what I would expect to see, if the only difference between the vaccinated and unvaccinated populations was vaccine.  (I.e., absent any Palin Effect).

• Start with the known average number of cases / 100K / day.
• Factor in the experimental estimates of vaccine efficacy at preventing infection.
• Work in the fraction of the population that is vaccinated.
• Solve for the infection rates in each segment of the population that, when averaged together, would give you the observed rate.

I’m omitting the details of the calculation, but the upshot is that, absent any Palin Effect, even if you are vaccinated and boosted, your likelihood of getting an Omicron infection per day is higher than your likelihood of getting a flu infection, per day, for a typical flu season.

You will not be able to follow the calculation from the table above because I’ve omitted numerous intermediate columns.  You’re going to trust that I’ve done it right.

I’m also not going to explain where all the assumed numbers come from.  The basic numbers on flu prevalence and such are from CDC:

The rest of it is my summary of the literature.  Flu vaccine is only about 40% effective, on average, at preventing symptomatic infection.  COVID vaccine and booster is about 70% effective against Omicron.  And so on.

Step 2:  Once you have that, do the same trick again, but this time solving for the hospitalization case rates by population segment, and converting that to the risk of hospitalization per day per 100K persons.

This time, you have to start with the expected number of infections per 100 population.  As before, you can’t follow the calculation from the numbers shown.  But I’m showing you that I assumed vaccines provided no additional protection against hospitalization, above and beyond the reduction in getting infected in the first place.

The upshot is that, right now, for equivalent populations, those with vaccine and booster for COVID are about five times as likely to be hospitalized for Omicron, per day, than they would be hospitalized for flu, in a typical flu season.  That’s the result of much higher case rates for Omicron right now (relative to flu), offset by a somewhat more effective vaccine, but also factoring in the much higher average case hospitalization rate for Omicron compared to flu.

Step three is to go back and change the current Omicron case rate to something lower, until those final rates are equalized.  In this case, those final hospitalization rates would be equal if there were just 40 new cases of Omicron / 100K / day.

I can do the exact same calculation with mortality rates, using my most recent case mortality rate estimate.   And I find roughly the same thing.  If Omicron were to fall to 40 cases / 100K / day for the entire population, the expected mortality rate from Omicron, for the fully vaccinated and boostered, would match that for flu on a typical day of flu season, for a person who has had flu vaccine.  (Which I estimate to be about 0.05 deaths / 100K / day).

Does that all hang together?  At a population average of 40 cases per day, the fully vaccinated and boostered population would see a theoretical average of just 18 cases per day.  I have assumed no additional protection against hospitalization or death.  The estimated case mortality rate for Omicron is about 0.3%.  And, sure enough 18 x .003 = .05.

What I’m trying to say is that although the many assumptions may be questionable, I think I’ve done the arithmetic right.  At 40 new Omicron cases / 100K / day for the population as a whole, under these assumptions, the fully boostered population would face:

• 18 cases of Omicron / 100K / day.
• 0.54 hospitalizations for Omicron / 100K / day.
• 0.05 deaths from Omicron / 100K / day.

That 40 / 100K / day level of new Omicron cases (for the entire population) would give those fully vaccinated and boostered individuals a much lower chance of catching Omicron COVID than of catching flu on a typical U.S. flu season day.  And that would give roughly the same chance of hospitalization or death, per day, as those individuals incur in a normal flu season, assuming they get their flu shot each year.

FYI, if you believe that vaccination plus booster provides even more protection against hospitalization and death than it does against mere infection, then “flu-equivalent” rate of Omicron infections would go up.  If you think it cuts your case rate of hospitalization or death in half, then the “flu-equivalent” rate of daily new COVID-19 cases for the entire population would rise to about 80.

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Epilog

All I’m shooting for here is a rough guideline for when we can reasonably expect a return to normalcy.  Personally, I’m sketching out the new Omicron case rate at which I’ll be going back to the gym, going to the movies, and so on.

What’s your alternative?  You can sit around until the CDC tells you it’s OK.  If they ever do so.  You can depends on some random internet source.  You can try to go with the herd.

For me, I like to figure the odds.

I’ve built a few safety factors into this estimate.  So it’s fairly conservative.  But my best guess is that when the overall population case rate drops below 40 / 100K / day in my area, then pretty much all COVID-19 hygiene becomes optional.  For the simple reason that I don’t sweat the risk of hospitalization or death from flu.  And I face that every year, mask- and restriction-free.

YMMV.  If you can find a better guide to where “normal” starts, use it.

At any rate, this finally ends post #1400.  This is my estimate of the case rate at which the fully-vaccinated-and-boostered population can start to ignore COVID-19.  This is my best guess for the psychological point at which endemic Omicron starts.

I think I’m going to reserve the right to re-write this one.  It was quite a chore to crank it out, and I’m not sure I’ve been very clear.

Bottom line:  When I see a daily case rate of 40 / 100K or lower, here in Fairfax County VA, I’m just going to stop worrying about my COVID risk.  I’ve never worried much about flu risk.  Below that level, it would be irrational of me to worry about risk from Omicron.

I might still wear as mask where convenient.  Because, why not?  I already own what I hope is a more-than-lifetime supply of N95s.  And, honestly, sometimes wearing that mask is just an act of politeness, if the people your with are more worried about COVID than you are.  Nothing wrong with that. Otherwise, on or about that time, it’ll be back to business-as-usual for me.

 

In the U.S. the decline in new cases continues to accelerate.  New cases fell 25 percent in the past seven days, to just under 170 new COVID-19 cases per 100K population per day.

Fewer than ten states saw increases over the past week.  It’s hard to say, exactly, as several states dumped large numbers of old cases into their data.  I have corrected that were I can (LA, WA), but in other cases (e.g., MN) the state is so vague about what they did that no correction is possible.

Arguably, the only state that is still appears to be experiencing rapid new case growth this point is Montana. Continue reading Post #1418: COVID-19 trend to 1/28/2022: U.S. decline picks up speed, U.K. stalls, BA.2 variant 1.5x as infectious as Omicron

 

Now that all the regions (and most states within each region) are on a downward trend, the decline in the U.S. new COVID-19 cases is accelerating.  Cases are down 20% in the past seven days, and we’re now more than 25% below the peak of the Omicron wave, at just over 180 new cases per 100K population per day.

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 1/28/2022, 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

Enough individual states have peaked that we can now clearly see the “arch” shape characteristic of a peak.

By historical standards, the number of cases is still astronomical.

That said, the average infection under Omicron is nowhere near as risky as it was under Delta.  Here are my latest estimates for the case hospitalization rate and case mortality rate under Delta and Omicron.  (Where the “case rate” is the number of events per formally diagnosed case.)  I calculate a crude mortality rate by comparing current deaths to new cases from two weeks ago, to account for the mean lag between infection and death for decedents.

Source:  Calculated from CDC COVID data tracker, counts of cases, hospitalizations and deaths.   

If I focus narrowly on just the risk of hospitalization and death, then for the U.S. population as a whole, an Omicron infection is somewhere between (0.3/1.3 = ) 23% as risky (for death) to (3.0/7.5 = ) 40% as risky (for hospitalization), compare to Delta.

Let me just slur over that difference and say that, for the U.S. population as a whole, an Omicron infection is about one-third as risky as a Delta infection.  By the “population as a whole” I mean not just the demographics of the U.S. population, but also the current mix of unvaccinated, vaccinated, and boostered individuals, plus those who have and have not survived a prior COVID-19 infection.

Here’s a fun fact:  The case hospitalization and mortality rates in the 2020-2021 winter wave — before vaccines — were just about equal to the rates of the Delta wave — after vaccines.  Delta was far more virulent than the native (Wuhan) strain.  But that was (purely by chance) offset on average by the impact of a roughly 65% vaccination rate, yielding roughly the same population-average case rates.

The upshot is that for the population as a whole, each Omicron cases bears about one-third the risk of serious adverse events that the U.S. population faced from any of the prior strains.  That’s either the native (Wuhan) strain with no vaccinations, or the Delta strain with about a 65% vaccination rate.

So if I “risk adjust” the case numbers across the strains, accounting for the much lower risk that each Omicron case has, and factor in the rate at which Omicron took over from Delta, I get a chart that looks like this:

The bottom line is that the overall risk of hospitalization and death from COVID-19 are about the same this winter as they were last winter, for the average U.S. resident.  That, despite the vastly higher number of cases.

Somewhat higher risk of hospitalization:

Source:  CDC COVID data tracker, accessed 1/28/2022

Somewhat lower risk of death:

Source:  CDC COVID data tracker, accessed 1/28/2022

That’s the result of offsetting effects.  Much higher case count.  Much lower risk per case.

The kicker is that last year there were no vaccines, and we were more-or-less all in the same boat.  The average risk from last year was the average for everybody.  By contrast, this year, vaccination plus booster greatly reduces risk of infection, hospitalization, or death under Omicron.  The average boostered person actually faces considerably lower risk in the Omicron wave than they faced in the winter 2020/21 wave.

 

This post asks whether the January 2022 return-to-school has boosted the number of new Omicron cases in Virginia schoolchildren.  As far as I can tell, return to the classroom in January 2022 has had no material impact.  This is consistent with the fall 2021 return to school, where the resumption of in-person classes had no observable impact on the rate of new COVID-19 infections in Virginia children.

 

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Last fall

Last fall, the return to in-person schooling in Virginia appeared to have no impact on the spread of COVID-19.  At that time, I tracked cases by age, and used the staggered start dates of the school districts as a type of “natural experiment”.  The idea was that if COVID-19 was spreading in the schools, you’d see the proportion of cases among school-age children rise, and you’d see that first where schools re-opened first.

But nothing happened.  At least, nothing that I could see.  Here’s the final graph from that analysis, Post #1280, 10/7/2021.

Last fall’s school reopening analysis:  No impact

Source:  Analysis of data from Virginia Department of Health, and school calendars from Virginia Department of Education.  This embodies a crosswalk of school district to Virginia health district.

That was almost entirely under the Delta variant.  Omicron only came on the scene as schools were finishing the fall semester.

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A look at Spring 2022 using alternative data sources

Now schools are opening for the spring 2022 term under Omicron.  Omicron, recall, is about three times as infectious as Delta, and neither prior infection nor two-shot vaccination provides much protection against it.

In short, it’s a whole new ballgame.

I figured I should redo some version of that prior analysis.  But I can’t.  The data file I used for that no longer functions.  Virginia stopped updating the underlying file of cases-by-age last week, and now I know why.  Virginia apparently built up a huge backlog of cases with unknown age.  Last week, they corrected that and dumped the age-corrected cases into the file.  As a result, there’s no way I can see to calculate recent trends in cases by age, from Virginia’s current data.

There are, however, alternative data sources that provide some information on COVID-19 infections in the school population of Virginia.

Count of outbreaks

Outbreaks are defined as three or more related COVID-19 cases.  Virginia tracks outbreaks in a variety of high-risk settings including nursing homes, prisons, schools, and similar.

This isn’t a very quantitative measure.  It’s just the count of events that have been reported to the Virginia state government.  There are vastly more cases in schools than would be covered by the “outbreak” definition.

Source:  Calculated from Virginia Department of Health data.

That said, the number of daily outbreaks in Virginia K-12 schools is well within the historical level.  That’s actually a bit surprising, given how much the Omicron case level exceeds that of prior variants.  In any case, FWIW, the official count of outbreaks in schools is showing no red flags so far.

Pediatric share of Virginia hospital admissions.

Again, no indication of an uptick following resumption of classes in January 2022.

Source: Calculated from U.S. DHHS unified hospital dataset.

FCPS counts of infections

Fairfax County has the largest school system in Virginia.  Based on my analysis last year, Fairfax County Public Schools (FCPS) manages to count about 60% of all cases in school-age children (compared to the Virginia Department of Health data).  Arguably, then, Fairfax County is a pretty good bellwether for what’s happening in Virginia schools, even though snow delayed the start of school by about a week.

FCPS does not put out any raw counts, only pretty graphics.  All you can do, then, is eyeball their graph of cases in schools (top), against a similarly-scaled graph of all cases in Virginia (bottom).

There’s not a lot of precision there, and you have to ignore the one outlier day in the school counts.  But by eye, the case counts in the FCPS schools do not seem disproportionate to the counts for the Virginia population as a whole.

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Conclusion

As far as I can tell, so far, from the available data, the January return to school has had no material impact on pediatric COVID-19 cases in Virginia.  At the very least, there’s been no large increase in any of the measures examined here.

New U.S. COVID-19 cases fell 17% in the past seven days, to just over 190 / 100K population  / day.   The rate of decline for the U.S. as a whole seems to be accelerating as more states pass their peaks.  As of today, three-quarters of all states saw a decline in cases over the past seven days.  There are no longer any states with 400 or more new cases per 100K per day.  Continue reading Post #1414: COVID-19 trend to 1/26/2022

Things are going as expected.  Now that all the data reporting artifacts of the King Day holiday are gone, the U.S. is down to about 200 new COVID-19 cases per 100K per day.  No real change from yesterday.

Beyond that, I would like to update my analysis of the impact of school re-opening.  I can’t, though, as irginia seems to have stopped updating the relevant data file (along with several others), for the time being.  I hope that’s temporary, and not a consequence of the change in the Governorship.  I really was kidding when I asked why Virginia couldn’t be more like Florida (Post #1403).  Continue reading Post #1413: COVID-19 trend to 1/25/2022

 

In brief:  Probably the only interesting thing you will learn in this post is that the U.S. has an injury compensation program covering COVID-19 vaccines.  If you’ve suffered significant harm as a result of a COVID-19 vaccine, you can file a claim and ask for monetary compensation.

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The issue?  Is there an issue?

I got an email from a reader the other day, asking me to look into the safety and efficacy of COVID-19 boosters for young men.  I was floored by the request, because I didn’t think there was anything to discuss.

Among the known risks of the COVID-19 vaccine, including booster doses, is myocarditis or pericarditis, inflammation of the heart muscle or the sac around the heart.  That’s not that most common serious condition that can result from vaccination.  (I believe that anaphylactic shock is #`1.)  But that’s a risk that is much higher in young men, as is the risk of myocarditis in general.

This, apparently, has spawned yet another social media industry to feed disinformation to those who don’t want to get vaccinated.

Near as I can tell, every credible source still says that risks associated with vaccination or booster shots are vastly lower than the risks associated with not getting vaccinated or boostered.  Even now, with Omicron having a lower average severity but vastly higher incidence than prior variants. Even for young men.

But the assertion in this latest round is that all the mainstream experts are wrong, that boosters don’t actually do anything, and that the myocarditis risk in young men is so high that you actually increase risk of harm by requiring boosters for young men.  I a nutshell, it’s the same debunked claim that pro-vaccine zeal is killing people.  Only this time, it’s killing young men.

Near as I can tell, everything about that assertion is hooey, except for the fact that there is an excess myocarditis risk from COVID-19 vaccination for  young men.  I’ll get around to putting down the numbers to back that up in Part  2 of this post.

To be honest, it’s almost … well, boring … to have to grind through yet another one of these false claims.  The same techniques come up again and again.  Ignore what scientists say about their own data.  Ignore multiple warnings that you can’t infer cause and effect from certain observational data sets.  And when the numbers, by chance, fall your way, hop on that, ignore all the warnings, and make a big deal of it.   And when they don’t, say nothing.  And the credulous will believe that you’ve discovered some deep, dark secret that’s being purposefully ignored by mainstream experts.

Before I do the numbers, I want to put a little context around this issue.  Plus, this post has gotten ridiculously long.  So I’m going to do this in two part.

The current part is the context.  Why does this issue matter, what do we already do to track and compensate individuals with adverse events from COVID-19 vaccination?

The next part will be the exact numbers, as close as I can get them, on the risks of getting a booster shot , and not getting a booster shot, for young men most at risk for myocarditis and pericarditis.

 

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College and University COVID-19 booster mandates.

I’m not keen on appeal-to-authority arguments.  E.g., I have a Ph.D., but I don’t bring it up on this website.  Arguments should rise or fall based on their merits, not on the credentials of the person offering them.

On the other hand, I’m very much in favor of paying attention to the smartest person in the room, on any given issue.  So let me just briefly summarize where U.S. colleges and universities stand on the issue of COVID booster mandates. A large fraction of their student population consists of the young men who are at elevated risk of myocarditis from a COVID-19 booster.  So if this is an issue, I’d expect them to have considered it.

In the United States, near as I can tell, virtually all colleges and universities (other than those legally barred form doing so) have required students to get COVID booster shots in order to return to campus for winter 2022 classes.   Those mandates are almost certainly why this issue is now circulating on social media.

Let me just list a few, so you know I’m not kidding.  In each case, I link to their instructions for their mandatory COVID-19 booster policy.

• Harvard
• Columbia
• Yale
• Princeton.
• M.I.T.
• Cal Tech
• Stanford
• University of Chicago
• Penn
• Duke

Here in Virginia, just to pick a few at random:

• University of Virginia
• Virginia Tech
• William and Mary

All of those colleges and universities above mandate a COVID-19 booster for their students.

I want to point out a few things.

First, nobody is making them do this.  These colleges and universities are themselves making the call that boosters are mandatory.   CDC Guidance for Institutions of Higher Learning (accessed 1/23/2022) merely recommends that colleges promote vaccination, and that they make it easy for students to get vaccinated.  CDC’s guidance never even mentions boosters, nor does it discuss the merits of having a vaccine mandate.

In Virginia, the official guidance from the state government is to do what CDC says. And far from promoting vaccine via mandate, Virginia state universities can no longer require that employees get vaccinated, thanks to our new Governor.  Under Executive Directive 2, it’s no longer legal even to ask a state employee if they’ve gotten vaccinated or not.

Second, that brief list represents one whole hell of a lot of intellectual firepower.  That’s the ten top-rated universities in the U.S., in 2022 U.S. News and World report list of the best universities in the U.S.

And, I repeat, all of them mandate a COVID booster.  Those decisions were made by smart people, running educational institutions with multi-billion-dollar annual budgets, most of whom have access to staff with a deep, deep understanding of the issues involved.  And all of them, independently, decided that a mandatory booster policy was a good idea.

And so, here’s my one-and-only appeal-to-authority argument for this post:

To believe this latest disinformation circulating on social media, you have to be able to look at that bulleted list above and say, nah, those jokers don’t know the real story.  None of ’em.  They’re all too dumb to admit what the real story is about boosters.  But I know the truth, because I found it on the internet.

If you have enough ego to do that, I can only guess that nothing I can say is going to sway your opinion.  If you lack the common sense to question your opinion, when it clearly conflicts with the opinions of the best and brightest in the U.S., ditto.  If this paragraph describes you, there’s no point in reading the rest of this.

Finally, some states bar any vaccination requirements, so that, for example, University of Texas cannot require vaccination, nor can University of Florida, based on laws or orders from their respective governors or legislators.  But even with a state-wide ban on vaccine mandates, both of those schools strongly endorse and recommend vaccination and boosters for their students. 

You really have to look around to find a plausibly legitimate institution of higher learning in the U.S. that doesn’t at least encourage booster shots for their students.  Based on my small search, those appear limited to religiously-affiliated institutions.  Liberty University is one, where they merely ask that you let the university know if you’ve been vaccinated.   (If you are familiar with their abysmal track record on COVID, that’s no surprise.)   Southern Methodist stops short of recommending vaccines, but makes them available on campus.  (As a Texas school, I believe they could not mandate them anyway.)  But not all religiously-affiliated universities seem indifferent to COVID vaccination.  Notre Dame, for example, has a COVID booster requirement for students, despite some controversy within the American Catholic church.

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A little illustration of why colleges are doing this.

I just took a look at the College of William and Mary COVID-19 dashboard.  I’ve been tracking their COVID situation pretty closely for the past year and a half.

This semester, William and Mary mandated pre-arrival testing.  (They’ve been off-and-on about that from the start of the pandemic, but all for logical reasons.)

The results?  Even ignoring the significant potential for false negatives, six percent of the Williamsburg student body tested positive for COVID-19 and had to delay their return.

Source:  William and Mary COVID-19 dashboard, updated 1/21/2022, all marks in red and calculations are mine.

By contrast, when they came back for in January 2021, in the middle of the then-largest COVID-19 wave to date, they had a total of 66 pre-arrival positives.  The upshot is that the COVID case load within the student body is five times higher than it was at this time last year.  (That’s not unexpected — the COVID case load for the U.S. is five times higher than it was at the peak of last year’s winter wave.)

Even with Omicron having a lower average severity of illness, the sheer number of cases is a problem.  It’s a problem not just for the potential morbidity and mortality of all those cases, but it’s a problem for disrupting campus life due to the requirement to isolate or quarantine after known exposure to COVID-19.

Start with congregate housing.  Add in a disease that is about as contagious as any disease ever measured.  Toss in the fact that six percent of the incoming population is infected with it — that you know of.  Now realize that boosters are the only known way to provide significant immunity to this disease.

Of course they’re going to require boosters.  It’s the  most reasonable and safest course of action.

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Vaccination involves risks, Part 1:  A simple orders-of-magnitude lesson from the National Vaccine Injury Compensation Program.

Vaccination involves risks.  There’s nothing new about that.  The important thing to keep in mind is the risk incurred without vaccines.

For more than a third of a century, the U.S. has run the National Vaccine Injury Compensation Program (VICP), covering injuries claimed from a specific list of (mostly childhood) vaccines.  Funded by a 75-cent tax on those vaccines, the VICP was primarily a way to keep these injury claims out of the courts, and shelter vaccine makers from lawsuit.  It more-or-less standardized and streamlined the process for making a claim for injury for injury attributable to the most common known risks of childhood vaccines.

Arguably the most telling aspect of this program is the number of awards made.  In FY 2021, this fund made a total of 719 awards, with average compensation of about $300,000.  (Calculated from the report listed on this US DHHS web page).  Keep in mind that the CDC recommends something like 40 different vaccine doses between birth and age 18, excluding annual flu shots (roughly estimated from this table).  If I were roughly to estimate 85% compliance with every recommended shot (based on this study of infants), then with 73M persons age 18 or younger in the U.S., roughly 1 in every 200,000 childhood vaccinations results in some claim compensated through this fund.

Not all vaccine injury claims are paid through that fund.  That said, the 719 awards for FY 2021 represents a large fraction of all awards to compensate for  childhood vaccine-related injuries in the U.S.

Let me take just one single childhood disease to try to put those 719 awards in FY 2021 into perspective.   Pertussis (whooping cough) used to be a common and serious disease of childhood.  Prior to the widespread use of vaccines, the U.S. routinely saw 9,000 deaths a year attributed to whooping cough alone, though the true number of likely higher due to non-reporting of the disease (reference, reference).  (Currently, there are about 10 deaths per year in the U.S. from pertussis.)

Now assess the relative risks.  On the one hand, you’d have 9,000 deaths per year with no U.S. use of the vaccine, from just one of the diseases covered.  On the other hand, you have 719 claims for compensation, for all childhood vaccines combined.

Childhood vaccine are recommended, despite the risks, because the estimated benefits outweigh the risks, to the U.S. population as a whole, by an extremely wide margin.

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Vaccination involves risks, Part 2:  The U.S. has an injury compensation program for COVID-19 vaccines.

COVID-19 vaccination involves risks.  There’s nothing new about that, either.  What I found new, in looking at this, is that the U.S. already has a fund in place to compensate people injured by COVID-19 vaccines.

The U.S. Countermeasures Injury Compensation Program (CICP) was authorized by the U.S. Congress in 2005 (reference), as a way to deal with injuries that may arise from vaccines and other countermeasures specifically in the context of epidemics and pandemics.  It has covered injuries related to COVID-19 vaccine since March 2020 (same reference).

As with most Federal programs, they have to report periodically on what they are doing.  So you can get statistics on claims filed, adjudicated, and paid, at this U.S. DHHS web page.

The U.S. has now delivered more than 500 million COVID-19 vaccine doses (per CDC COVID data tracker).  Given the large numbers, involved, it’s worth taking a look at the number of claims filed and paid under the CIPC, for alleged serious injury relating to COVID-19 vaccines.

As with the VICP (childhood vaccines), not every serious injury will generate a claim.  For example, if all costs were covered by third parties (insurers, say), there’s likely no cause to pay a claim under CIPC.

And, as with the VICP, it looks like the processing of typical claim takes several years, so there is no hard data yet on the number of claims that will eventually be paid.  To date, just 29 claims have been paid, for injury from all countermeasures (for all relevant diseases, and all relevant countermeasures, vaccines, testing, and other). 

Of note, none of the 29 claims paid so for is for COVID-19 vaccine.  Most are for H1N1 flu vaccine, and smallpox vaccine.  Also of note, the one myocarditis claim paid so far was for smallpox vaccine, which apparently has a known risk of myocarditis among young men.

Even though there’s a huge backlog of un-examined claims, it’s well worth looking at total filings — payable or not — to get some handle on the extent to which those 500M vaccine shots are claimed to have generated some significant injury.

As of December 1, 2021, they show the following (about half of which relate to COVID-19 vaccines, half to other countermeasures):

• 6032 eligible claims
• 5630 in review or pending review
•   402 reviewed
•   362 denied
•     40 eligible for compensation

So far, it looks like about 10 percent of claims are judged eligible for compensation.  Pro-rating the entire set of 6032, then, we would eventually expect about 600 claims to be eligible for compensation.  Of which, about half of claims were for COVID-19 vaccination.

Or, if I’ve done the math right, best guess, about 1 in 400,000 COVID-19 vaccinations will eventually result in a some claim compensated through this fund.  Which, given the approximations involved, is not hugely different from the rate of compensation for the childhood vaccination fund (VICP).

In other words, to a first approximation, risk of serious, compensable illness or death from COVID-19 vaccines appears to be about the same as all other vaccines.

It’s worthwhile, I think, to stop and illustrate just how small that is, compared to the risk incurred from not being vaccinated.  Taking that same population base of 400,000, the last few weeks of data from Virginia suggest that we’d see an average of seven excess deaths per week, from Omicron, among the unvaccinated.  (That is, death rate for the unvaccinated less death rate for the vaccinated).  If the Omicron wave last a couple of months in total, that will be excess 56 Omicron-related deaths in the unvaccinated population, for the current wave alone.  Compare that one projected claim for vaccine-related injury. 

This sort of lopsided, orders-of-magnitude cost/benefit ratio occurs again and again when you look at vaccines.  COVID-19 vaccines appear to be no different in that regard.  And that’s why every responsible organization strongly recommends (and sometimes mandates) COVID-19 vaccination.

It’s really not magic, or self-delusion, or ignorance.  It’s that the case for vaccination is that strong.  Particularly here, for COVID-19, where there is so much of it in circulation that you don’t even have to appeal to the “public” part of public health.  But also for many other disease that were once scourges of mankind, and are now merely slight risks in the background of first-world existence.

Maybe if you still don’t get it, you ought to take a peek at Post #1247: Harking back to a more dangerous and less foolish era of public health in the U.S.A.

If I summarize the list of conditions for which compensation has been sought, separating out the pericarditis/myocarditis risk, it looks like this:

Source:  HRSA, calculation of rate per million is mine.

The only point here is that out of roughly 500M COVID-19 vaccine doses so far, there are 95 claims of significant injury from myocarditis and pericarditis.  Not all of those will be found to have merit.  But that raw claim rate — basically, the count of persons who believe they were significantly harmed by vaccine-induced myocarditis and pericarditis — works out to one per every half-million doses of vaccine.

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A quick cut of the VAERS data.

This next bit uses the Vaccine Adverse Event Reporting System data maintained by U.S. DHHS.  You can find the background in Post #1208, A funny thing about deaths in the elderly.

In case you can’t be bothered to read that post, the important point is that VAERS asks people to report any adverse event that followed COVID-19 vaccination.  There’s no guarantee of cause-and-effect here.  And in the case of death, I looked in detail, and most deaths in the period following COVID-19 vaccination were directly attributed to other causes, by the individuals reporting those deaths. (That’s in Post #1208 above).

You should also be aware the VAERS is a voluntary self-reporting system, with all the data integrity and quality that implies.

I just want to check the number of people who died, with some mention of myocarditis or pericarditis, at some point following COVID-19 vaccination.  Without trying to guess whether the heart condition or the death was due to the vaccination or not.

To be clear, I’m checking all the symptom keyword fields for “DEATH” and any mention of “MYOCARDITIS” or “PERCARDITIS”.

There were a total of 17 reports that included both death and myo/pericarditis.  Of these, one (1) was a male under age 40.  The median age of all 17 was 60 years.  Roughly half were men, half were women.

I think that, by itself, this suggests that all the talk about large numbers of excess deaths resulting from a mandatory booster policy is pretty clearly bullshit.  While VAERS is not perfect, if there had been a material risk of death from this cause, in this population, for this vaccine, we’d likely have seen more than one reported so far.

That is not to dismiss the risk of myocarditis, only the fear-mongering.

More generally, there were 1854 records in VAERS with mention of myocarditis/pericardits at some point after COVID-19 vaccination.  Of those where age was reported, 55% were men under age 40.  Interestingly, the apparent highest-risk group is high-school age, not college age.

Source:  Analysis of VAERS 2021 file, downloaded 1/25/2022 from this website.

In short, there is a real and fairly well-known risk of myocarditis / pericarditis.  Using Federal data sources, I can document over 1800 cases with some mention of it, the majority of which were in young men.  Without doing the formal math, I think that’s above the background rate at which you would expect to see myocarditis in this population.

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Summary

There’s a claim afoot that the potential harm from booster shots outweighs the benefits, for young men.  This is attributed to the risk of myocarditis from the vaccine.

I can only assume this latest claim is gaining currency now because  universities are mandating boosters in the face of Omicron.  They are doing this because the benefits grossly outweigh the risks.  (Quantifying that is the aim of my next post in this series).

More to the point, if we look at the top ten universities in the country, all of them are mandating booster shots.  To believe that risks outweigh benefits from booster shots is to believe that all of these extremely bright people are wrong.  And that some random internet source has it right.

For sure, there is a myocarditis risk from COVID-19 vaccines.  That’s been fairly well established.  Myocarditis risk from vaccination is not a new idea and is not unique to COVID-19 vaccines).

Looking at Federal data sources, there have been over 1800 instances where myocarditis/endocarditis was reported some time after COVID-19 vaccination.  There have been nearly 100 persons who have submitted a claim for compensation for vaccine-induced harm from it.  There are 17 cases where the VAERS record mentions this condition and death (though the median age for those records was 60). And there is one record showing that a young man died, with mention of myocarditis, some time after COVID-19 vaccination.

But the real point is that this risk is small, and has been well-recognized and well-examined.   My secondary point is that hyping this as some great wave of deaths resulting from misguided pro-vaccination zeal is just the worst sort of fear-mongering.  That argument — large numbers of excess deaths — is clearly not borne out by the facts.

Once upon a time, I did a lot of professional work on end-of-life care in the United States.  (e.g., this reference).  People in the health care field take claims about excess deaths with the utmost seriousness.  As a consequence, if you make some extraordinary claim (young men dying) you’d better have some extraordinary proof to back that up.  It’s only the amateurs who are willing to mouth off about something as serious as that without bother to check their facts first.

I’ll look in detail at the estimates risks of booster and non-booster status tomorrow.  That’s the only way to put the myocarditis risk into perspective.  But I wouldn’t expect any surprises.

I’m guessing that Harvard, Columbia, Yale at al. have made the right decision.

And I’ll tell you one thing for damned sure.  I wouldn’t contradict all of that reasoned judgment unless I had the overwhelming and undisputed facts in hand.  Extraordinary claims require extraordinary proof.  Except on social media.

 

Today we get the final data reporting artifact from the King Day holiday.  What appeared as a sharp, temporary dip last week now re-appears as a sharp, temporary increase.

My best guess for the true trend puts us at about 200 new cases / 100K / day as of 1/25/2022.  That’s down 20 percent in the nine days since the peak of the U.S. Omicron wave, which makes our rate of decline much slower than average for countries on the downslope of their Omicron waves. Continue reading Post #1410: COVID-19 trend to 1/24/2022, no change other than for data glitches.