Author: chogan@directresearch.com

Post #1199: This one simple hack eliminates 99% of disinformation!

I have a simple, humble request to make of our mainstream media:  If you must have comments on your stories, please randomize the order in which comments are shown.  Or, if not, do anything other than show the first-written comments first.

I was looking at a perfectly reasonable story about the current COVID-19 outbreak in Florida today, on what turned out to be the ABC News website.

And immediately following the story, in the comments section, were four pieces of what was clearly professionally-written disinformation.  Clearly working from a common pool of talking points.  Tightly written so as to hit as many hot-button issues as possible.  Obviously designed to deflect attention from the current situation in Florida, and by inference, the Republican governor of Florida.

I can only assume that whatever organization that is responsible for those comments has bots that look for newly-published on-topic articles.  They then strive to be first in line with comments.

And as a result, everyone who reads the actual news article and bothers to look at the comments is also reading that disinformation.  Lies and misdirection clearly aimed at nullifying the actual news coverage.

And so, ABC News ended up lending its entire new apparatus the forces of disinformation.  For free. 

This is really stupid, and needs to be stopped.  We have enough problems without allowing the purveyors of nonsense to piggyback on legitimate news sources.  For free.

At the minimum, if comments were shown in some random order, that would prevent organizations from hawking news articles and placing their previously-composed disinformation pieces first in the comments section.  Such a policy would not only dull the high impact of that disinformation currently has, it would reduce the incentives to publish those professionally-written comments in the first place.

Anyway, I’m just pointing this out.  My observation is that a policy of showing comments in the order written is just asking for your platform to be used by the aggressively for-profit disinformation industry.

Even if you can’t get rid of them entirely, you can certainly structure your website so as not to encourage them.  And to make it more expensive for them to keep their taking points in front of your viewer’s eyes.

In in that context, the last thing you want to do is let whoever gets first crack at the comments to get the most prominent spot in the comments.  And yet, that’s what ABC News appears to be doing.  You’re letting the enemies of information use your resources, for free.  And no matter how you slice it, that’s just plain stupid.

Post #1198: Breakthrough infections and the Provincetown outbreak revisited.

 

Last week, the CDC published research in which they found that 74% of the infections in the outbreak following July 4th celebrations in Provincetown, MA were breakthrough infections.  That is, infections of fully-vaccinated individuals.

This has been all over the news.  Needless to say, the lunatic fringe has been having a field day with this.

I wrote this up in my last post, where I took a guess as to why that rate might be so high.  My guess was that they found most of the people via contact tracing, and that individuals who refused to get vaccinated were likely to refuse to cooperate with contact tracing.  That’s all conjecture, but not unreasonable.

There turns out to be a far more straighforward explanation of the high rate of breakthrough cases in the Provincetown, MA outbreak.  And it boils down to this:  If 100% of persons are vaccinated, then 100% of infections will be breakthrough cases.  And if nearly 100% are vaccinated, then nearly 100% will be breakthrough cases.

And, to speak plainly,  nearly 100% of residents of Provincetown, MA are, in fact, fully vaccinated.  Massachusetts data (below) show that more than 95% of Provincetown residents are fully vaccinated, and that Barnstable County hasthe highest  vaccination rate of any county in Massachusetts.  That high rate of vaccination doesn’t fully explain the 74% figure, but it goes a long way toward it.

I’d like to say that I thought to check this on my own, but I didn’t.  My wife corresponded with an acquaintance of hers who lives in Provincetown.  After her acquaintance read my blog post, her immediate reaction was that “we have a very high rate of vaccination here”, or words to that effect.  Then, and only then, did I bother to look up the facts.

That said, I’m just a no-name no-readership blogger.  Shame on the U.S. CDC, and on the mainstream media, for not even bothering to look up the easily-available vaccination-rate data.  All this ammunition provided to the nut-o-verse could have been avoided if they’d done a simple bit of due diligence, and caveated their findings by pointing out the extraordinarily high rate of vaccination at the epicenter of the outbreak.

Details follow.

A naive calculation.

I will eventually get around to trying to list all the things that can affect the fraction of infections that are observed as breakthrough infections.  But let me start with the obvious one first:  The fraction of the persons involved who are vaccinated.

Obviously, if 100% of persons in an area are vaccinated, then breakthrough infections will account for 100% of infections.   And if nearly 100% are vaccinated, then nearly 100% will be breakthrough infections.

If all other things were equal — if the vaccinated and un-vaccinated populations were otherwise completely identical in composition, behavior, risk factors, and exposure — we can calculate an “expected” fraction of all cases that ought to be breakthrough cases.   It’s just a bit of simple math in a spreadsheet.

I’ve highlighted the yellow cell to show roughly where the U.S. is right now. 

At present, about half of the entire U.S. population is fully vaccinated, per the U.S. CDC.

As an aside, I will point out that I would normally use just the adult population, or perhaps the 12-and-older population.  But when I looked at current reported COVID-19 infection rates by age, I found a startling thing:  Per capita, young children now have a higher infection rate than the elderly. 

Source:  CDC COVID data tracker.

I reviewed the true (randomized clinical trial) effectiveness of the Pfizer vaccine, against the Delta variant, in Post #1192.  You can see the original research at this link.  For the six months following vaccination, the Pfizer vaccine shows an average 91% effectiveness in preventing symptomatic infections.

Hence, for the U.S., the naive calculation puts us at the intersection of the top row (50% vaccinated) and next-to-last column (90% effective).  For the U.S. as a whole, we’d expect something like 9% of all infections to be breakthrough infections.

But note how “leveraged” these numbers are.  Once you get down to the bottom rows of the table — to the point where nearly everyone is vaccinated — you’ll frequently find that the majority of infections would be expected to be breakthrough infections.

Revisiting the Provincetown/Barnstable County, MA incident.

While I was busy taking a guess at why 74% of infections in the Provincetown July 4th outbreak were breakthrough infections, my wife did the scientific thing and actually gathered some data.  She corresponded with a friend who lives in Provincetown, MA.

Among other things, her correspondent noted that they have an extremely high vaccination rate in Provincetown. But is it true?  Does Provincetown, MA have an exceptionally high vaccination rate?

Source:  Massachusetts Department of Public Health Weekly COVID-19 Municipality Vaccination Data.

I’d say so.  If those numbers don’t jump off the page and slap you in the face, you haven’t been paying attention.  In the town that was the epicenter of the outbreak, more than 95% of almost every age group is fully vaccinated.

(It’s typical not to show exact percentages in a case like this due to privacy concerns.  In typical in health  data reporting, you don’t want to put out data that would let anyone calculate single-digit counts of individuals.  If someone could multiply the exact percentage by the population and come up with a count of less than ten,in any one cell of the table, that would raise health data privacy concerns. So they fuzzy it up a bit.)

Now let me revisit my naive table.  If the outbreak had been limited to Provincetown itself, using this naive (all-other-things-assumed-equal) calcuation, you’d have expected two-thirds of the cases to be breakthrough cases.  As shown below, in yellow.

But that hardly makes for a shocking headline, does it?  “Provincetown MA COVID-19 outbreak has slightly higher-than-expected rate of breakthrough infections.”  That’s thin gruel, as clickbait goes.

That said, Provincetown itself has a tiny resident population.  Separately, Massachusetts data show that for Barnstable County as a whole (the county in which Provincetown is located), 84% of those age 12 and up have at least one shot; 76% of that population is fully vaccinated.  (This series of celebrations was described as drawing crowds consisting largely of young men.)  Plausibly, if we restrict this to adults (which is were the infections in this outbreak largely occurred), arguably somewhere around 90% of the adult population of that county is partially or fully vaccinated.

If we then assume that cases were drawn uniformly from the county, at that vaccination rate, all other things equal, we’d only have expected half the cases to be breakthrough infections.  Versus the 74% actually observed.

Again, not exactly a startling finding, is it?  “Provincetown MA COVID-19 outbreak has 50% higher-than-expected rate of breakthrough infections.”

Of all the popular press reporting that was done about this event, and all the CDC research that was done about this event, don’t you wish that even one responsible party could have looked that up and made that known?  Instead of some no-name no-readership blogger.

Because now the numbers make a whole lot more sense.  All you need to say is that at the center of the outbreak — Provincetown itself — more than 95% of the population is fully vaccinated.  That’s it.  That’s all the caveat you would have needed to have added to have that 74% breakthrough figure make a whole lot more sense, and be a whole lot less sensational.

Let me end this here.  I contend that any of several factors could easily explain the excess rate of breakthrough infections, beyond my “naive” estimate shown above.  Just off the top of my head, the four main factors might be:

  • These individuals were found via contact tracing, and those who failed to vaccinate are probably less likely to cooperate with contact tracing, and so would not be found.  Thus the contact-traced population would overstate the fraction of infected persons who were vaccinated.
  • Persons who were vaccinated might have been more likely to attend these celebrations, given that it was, in fact, safer for them to attend than for the un-vaccinated to attend.  Thus the fully-vaccinated might have been over-represented among party attendees compared to the county resident population.
  • Contract tracing identifies both symptomatic and asymptomatic infections, and the COVID-19 vaccines are not as effective at preventing asymptomatic infections.  (I’ll expand on that point in a separate post).
  • The population of pary-goers may otherwise differ systematically from all individuals in terms of age, sex, and risk factors.

Afterword

No matter how you slice it, just that one tiny caveat — the publicly-available information on the vaccination rate of Provincetown MA — could easily have put this into context.  And easily have avoided the storm of disinformation that followed release of the CDC research.

This isn’t the first time the CDC has done something like this.  By this, I mean, fed raw meat to the lunatic fringe by doing flat-footed science instead of understanding the policy context.

I documented a similar incident nearly a year ago, in Post #793.  When the CDC began compiling COVID-19 death rate data, the fringe made a big deal out of the fact that only 6% of individuals dying with COVID-19 had COVID-19 as the sole diagnosis on the death certificate.  (As a person who has used death certificate data before, that struck me as normal, as I explained in that post.)  The nut-o-verse, starting with Fox News, went crazy about that for a while, claiming that the CDC had deliberately overstated COVID-19 deaths.

The punch line is that the CDC had, in fact, literally said that 94% of persons dying with some mention of COVID-19 had COVID-19 as the underlying cause of death.  The only problem was, they buried that in the middle of a completely separate document,  US CDC, technical notes for COVID-19 death data release.  You had to read the fine print, in the methodology document, to know that the 94% of what the CDC was counting was individuals who died from COVID-19 (i.e., for whom the physician of record listed COVID-19 as the underlying cause of death).

I am sure there are people, even now, who are convinced that the CDC grossly exaggerated COVID-19 deaths, based in part on that Fox-News-pushed line of disinformation.  All of which could have been avoided if the CDC had at least superfically looked at their research through a policy lens, and adjusted the focus accordingly.

Now we have a whole new crop of anti-vaxxers who are convinced that the COVID-19 vaccine doesn’t work.  Once again, thanks to the CDC’s inability to view their research in a policy context, and do some simple due diligence to try to explain why they found the high breakthrough rate that they found.

Post #1197: Breakthrough hysteria

Virginia updates its estimates of breakthrough and non-breakthrough COVID-19 infections on Friday.  I thought it was worth posting the data for the most recent week.

I’ll get to why I thought this was worthwhile below.

Lack of cooperation in COVID-19 contact tracing

The nice thing about Virginia’s estimate is that it’s “clean”, in the sense of being based on a match between two lists:  Persons vaccinated, and persons with a positive test.  It doesn’t require any cooperation from those who were infected, or any contact tracing, or any of that.

And that’s a good thing, because, as it turns out, a lot of people refuse to cooperate with COVID-19 contact tracing.  For example, three-quarters of persons in New Jersey routinely refused to cooperate with COVID-19 contact tracing.  Roughly half of Maryland residents ignore COVID-19 contact tracing requests  Lack of cooperation with contact tracers has been publicly noted in North Dakota.  Only about half of persons contacted in Texas would cooperate.  Similar results hold in Pennsylvania,   North Carolina, and   New York.

And in Massachusetts, more than half of residents won’t even pick up the phone when a contact tracer calls.  In fact, the Massachusetts contract tracing efforts have become so dysfunctional that the Commonwealth of Massachusetts recently cancelled its main contract for that service.  (By contrast, their track record early in the pandemic was pretty good.)

In fact, one might say that refusing to cooperate with COVID-19 contact tracing is more-or-less a nation-wide phenomenon.

I wonder who the non-cooperators are?

Now, before I go further, I want to ask a question.

Consider two individuals.

Individual A got vaccinated in a timely fashion, and follows CDC guidance regarding COVID-19 hygiene.

Individual B refuses vaccination and refuses to wear a mask or otherwise engage in COVID-19 hygiene.

Which person do you think is more likely to to ignore a request for COVID-19 contact tracing?  Is it the vaccinated, mask-using Individual A?  Or the anti-vaccine, no-mask individual B?

My guess is that that it’s Individual B.  And while I can’t find any study that says that directly, that’s consistent with national survey-based results on that subject.  Less than half of those surveyed said they would be comfortable with all the required aspects of COVID-19 related contact tracing.  And — where have we seen this before — willingness to cooperate splits strongly along party lines, with Democrats being much more willing to cooperate with contact tracing than Republicans.  Much the same as  COVID-19 vaccination does.

And so, is there a practical lesson here?  I sure think so.

If you identify a cohort of individuals through contact tracing, you’ll end up with a cohort that differentially skips the un-vaccinated.  Not by design, but simply because those who won’t cooperate with a request to get vaccinated are largely the same people who won’t cooperate with a request to trace their contacts once they’ve gotten a COVID-19 infection.

Relevance to this week’s Morbidity and Mortality Weekly.

You have no doubt been seeing headlines about a July 4th outbreak in Provincetown, Massachusetts where it is claimed that three-quarter of those who were infected were fully vaccinated.  That’s based on research by CDC published in this weeks Morbidity and Mortality Weekly Report.

(Of all the people blathering about this week”s issue of the MMWR, I’d bet that I’m just about the only one who routinely read the MMWR as part of my professional life.)

The nut-o-sphere has been having a field day with this, and it inevitably morphs into the bogus claim that three-quarters of all COVID-19 cases in Massachusetts are breakthrough infections.  This, despite the fact that the Massachusetts Department of Public Health went out of its way to say otherwise, just prior to publication.

But let’s be boring and do the math, using current data for all of Massachusetts.  Based on the most recent week of reporting, reporting from Massachusetts says that 38% of new COVID-19 cases are “breakthrough” cases, that is, cases among the vaccinated.  This particular period includes the bulk of the reporting for that outbreak.  It is unclear whether they include partially-vaccinated individuals in that total.  Currently, 84% of the adult population in Massachusetts has received at least one dose, 75% of the adult population is fully vaccinated.  I’ll assume they only include the fully-vaccinated.

So, doing the math, based on the data as-reported:

Relative risk of infection, for the vaccinated, in Massachusetts, during this period = (38%/75%) / (62%/25%) = 20%.

That’s high enough to be interesting.  That’s above the 10% that keeps popping up every time I do this calculation with observational data.  It’s vastly higher than the rate observed in Virginia.

But it’s a far cry from 74%.

And so, how on earth did the CDC manage to arrive at a figure of 74% for that one outbreak?  When you get right down to it, how did the CDC know those people attended those parties, so they would know the set of people from which to calculate that 74%?

It’s not like they’ve implanted microchips in people, to track them.  As far as we know.

The only way the CDC could identify those people is if those individuals voluntarily told them, one way or the other.  And I think the key phrase for understanding this extreme estimate of breakthrough infections is this pair of seemingly-innocuous lines in the methodology section:

“During July 10–26, using travel history data from the state COVID-19 surveillance system, MA DPH identified a cluster of cases among Massachusetts residents. Additional cases were identified by local health jurisdictions through case investigation.”

In other words, what you’re looking at in this study isn’t the universe of people who attended those parties.  It’s the people who attended those parties and then cooperated fully with the subsequent contact tracing.

And, best guess, that’s how you ended up with that eye-popping 74% figure.  The sample is restricted to individuals who cooperated with contact tracing in some form.  And so the sample probably skips most of those who have refused to get vaccinated.

There is no doubt that there was quite an outbreak from what was described as a series of packed parties in bars.  Seems like every time I read about outbreaks these days, it’s from parties of some sort.

It’s absolutely true that the breakthrough infection rate currently being reported in Massachusetts is higher than I would expect, even accounting for the fact that this is observational data.

And, apparently, this is the research that convinced the CDC that fully-vaccinated individuals are perfectly capable of spreading COVID-19.

But it’s not 74% of all new infections.  Breakthrough infections accounted for 74% of the individuals that the CDC identified, via contact tracing, as having attended those parties.  In effect, based on those who would voluntarily report the whereabouts of themselves and their friends.

What the rate for the actual universe of party-goers is is not known.  Short of locking up the entire town and interrogating then under torture, that’s simply not knowable.

Post #1196: COVID-19 hysteresis

 

Hysteresis, not hysteria.

Webster’s Dictionary defines hysteresis as “a retardation of an effect when the forces acting upon a body are changed …”.  Wikipedia offers a different take on it, that “Hysteresis is the dependence of the state of a system on its history.” 

No matter which way you look at it, a system with hysteresis is one that clings to its recent past, and does not change fully to reflect current conditions. Continue reading Post #1196: COVID-19 hysteresis

Post #G21-044: Stealth mildew and a farewell to squash

Squash vine borer, and an observation.

Source:  U Wisconsin Vegetable Entomology.

As was the case last year, the squash vine borer season has been slowly tapering off.  My last sighting of a squash vine borer was a lone female about six days ago, or circa 7/23/2021.

I’m calling that the end of the season, unless I see another one. Continue reading Post #G21-044: Stealth mildew and a farewell to squash

Post #1192: Randomized clinical trial results demonstrate that COVID-19 vaccines remain effective for at least six months.

I recent posts I’ve discussed results released by the Israeli Ministry of Health last week.  These got a lot of press because they appear to show a rapid decline in immunity from COVID-19 vaccines.  The Israelis inferred that immunity to infection was almost gone after six months.

That was an extraordinary result, and got a lot of attention because if it were true, it would have serious implications for health policy.

As I discussed those results, I hope I made it clear that the methods used in the Israeli study were weak.  That wasn’t a controlled trial, it was “observational data”, contrasting cohorts of Israelis based on what month they had been vaccinated.  Those cohorts differed not just in terms of how long ago they were vaccinated, but also in terms of health risk, age, and occupational mix.

Earlier today, I went looking for any evidence of that rapid dropoff in immunity in Virginia’s data.  I couldn’t find it. Near as I can tell, there’s been no uptick in breakthrough infections in Virginia, despite the onset of the Delta wave of COVID-19.

We now have direct evidence from a randomized, controlled clinical trial that immunity from the Pfizer vaccine remains high for at least six months.  That’s based on research that was reported today.  You can see the original research at this link.

Here’s the key table, below.  Yes, the effectiveness of the vaccination falls somewhat over time.  But no, it does not plummet.  If falls off at a fairly modest rate, comparable to other vaccines.  The authors of the study characterize it as declining roughly 6 percent every two months.  This is what I would call a perfectly normal result for a vaccine:

Source:  Six Month Safety and Efficacy of the BNT162b2 mRNA COVID-19 Vaccine, doi: https://doi.org/10.1101/2021.07.28.21261159

Once you develop full immunity, the efficacy of the vaccine in preventing a symptomatic COVID-19 infection is:

  • 96% effective in months 1 and 2.
  • 90% effective in months 3, and 4.
  • 84% effective in months 5 and 6.

Over the entire six-month period, the vaccine had an average effectiveness of 91% in preventing symptomatic disease, and 97% effectiveness in preventing severe disease.  (There was no testing to see the extent to which it prevented asymptomatic infections).

The method used here is the gold standard.  It’s a double-blind randomized trial with placebo.

This randomized clinical trial is a far more reliable way to estimate the effectiveness of the vaccine than the “observational data” studies from Israel and other places.  Here, we can be certain the vaccinated and unvaccinated groups are otherwise identical (to within sampling error), because individuals were randomly assigned to one group or the other.  By contrast, in the Israeli study, the groups vaccinated in January and June were vastly different in terms of age, risk, and occupational mix.  The resulting differences in breakthrough infection rate for those two groups (one with vaccination just one month old, one with vaccination six months old) reflected not just the age of the vaccination, but also any effects of the large difference in risk, age, and occupational mix between those two groups.

The only uncertainty left is whether there is something unique about the Delta variant that would invalidate these results.  The prevalent strains in the locations and time that this study took place did not include Delta.  But I think it’s not plausible to suggest that these results held for all of the strains in circulation at the time, but that, uniquely, there would be a big dropoff in immunity for Delta (and only Delta).  That is especially given that he vaccines are known to be effective against Delta, just not quite as effective as they were against the native strain of COVID-19.  There’s no reason grounded in basic science to think that such a thing was possible, let alone likely.

I think this provides the definitive answer to the question “Do you need a booster shot at six months”.  The answer is no.  Protection against symptomatic disease remains good, protection against severe disease remains even better.  That’s what the controlled clinical trial now shows.

And, for sure, with these results, the U.S. is not going to approve booster shots at six months.  Not only do you not need it, but it’s not going to be possible to obtain it legally in the U.S.

It may be a coincidence that this research come out today in preprint (no-peer-reviewed) form.  But it may well be that this was hustled into preprint in response to the Israeli Ministry of Health findings that were released last week.  That would have been the right thing to do, to make it clear that the huge dropoff in immunity observed in the Israeli results was an artifact of methods, and was not a real effect.

With that cleared up, I will return to my task of calculating the odds of infection and harm, for the fully-vaccinated population, in this U.S. Delta wave.

Post #1191: Breakthrough infections in Virginia suggest little loss of vaccine-created immunity over time.

Background:  Why breakthrough infections suddenly matter.

Two posts ago (Post #1189), I went into the new findings from Israel regarding breakthrough infections of the Delta variant.  Their data suggest that by six months after the time of vaccination, the Pfizer vaccine has almost completely lost its ability to prevent infections with Delta.  It still does a good job of preventing hospitalization and death, just not infections.

I’m not sure if that’s a real result, or just an artifact of the way in which Israel went about vaccinating people.  Their sample size was small, and their results were odd in that younger people appeared to lose immunity at a much higher rate than the elderly.

The Israeli results aren’t from a clinical trial.  They come from comparing the current infection rates of cohorts of Israelis who were vaccinated in January, February, and so on.  The members of those cohorts aren’t randomly selected, but differ systematically.  The earliest cohorts (the persons vaccinated first) focused on high-risk individuals, the elderly, and health care workers.

The upshot is that by contrasting cohorts of individuals based on month of vaccination, you aren’t looking solely at the effect of time-since-vaccination.  You are also looking at the effect of being elderly, being at high risk, and working in the health care system.  Plausibly, some of those other factors would influence your odds of being exposed to Delta and picking up a breakthrough infection.

Some aspects of their results suggest that at least some of what they observe is an artifact of who was selected for those cohorts.  In particular, they found that immunity fades to a much greater degree among the non-elderly, which is the exact opposite of what you would expect, given the generally weaker immune response of the elderly.   (That weaker response is why there are annual flu shots specifically formulated for the elderly, with an enhanced dose designed to stimulate those aging immune systems).

That said, the finding is out there.  And if it’s true — if what the Israelis are seeing is in fact an indication that the vaccine’s protective effects fade profoundly within half a year — that has major implications for individuals and for our public health strategy.

But is it true, or just an artifact of their methods? 

Excellence in public data:  Virginia

Faced with something like this — some hazy finding, showing a huge and important effect, from a small number of cases, in a distant land, that nobody has seen before, using non-randomized data  — you get the drift — my first reaction is to see if anybody else says anything even remotely similar.  And I want to see that based on data that I understand and trust.

It seems to me that tracking breakthrough infections ought to be a piece of cake for U.S. states.  As I understand it, state health departments know which individuals have been vaccinated.  (There’s a caveat here for vaccines that flowed through various Federal programs, including the armed forces, Veterans’ Administration, and the U.S. Indian Health Service.  But states distributed the vast majority.)  For sure, state health departments know which individuals have had a positive test.  I’d be shocked if both lists didn’t contain the Social Security Number (SSN).  And even if not, name/gender/age/address is good enough to match up 99+% of those entries absent a unique identifier such as SSN.  (I speak from experience there, because figuring out how to make such “soft” merges between data files used to be part of my job.)

In short, all a state needs to do is match up the list of the vaccinated and the list of the infected.  The people who are on both lists constitute your breakthrough infections.  You’ll miss a few — individuals who moved into or out of state, individuals whose cases were dealt with by Federal rather than state systems — but in most states, those exceptions should be a trivial fraction of the population.

And so, for months now, I’ve wondered why states haven’t done that.

Turns out, Virginia has.  Virginia now has a web page devoted to tracking breakthrough infections.  It’s titled “Cases by Vaccination Status”, but that’s breakthrough infections.  And I sure wish other states would follow suit.

I’m going to take one paragraph to put in a plug for the Virginia Department of Health.  I’ve been using Federal, state, and sometimes local government data sources for more than a year now, tracking the pandemic.  Virginia’s public-facing data is head and shoulders above the rest.  A lot of times when I’ve wanted to discuss a national issue, I “illustrate” it with data from Virginia.  That’s because Virginia was the only place I could find the data files, publicly available, that would allow me to do it.  When you see that — when the data meet the analytical needs — you know that the people creating the data are almost certainly the same as the people who are using the data.  That’s how they end up providing usable data files.

In light of the Israeli findings, I would love to see Virginia’s data tabulated by month of vaccination.  Even though those monthly cohorts were not randomly selected, I’d at least like to see whether or not the crude finding that appears in the Israeli data — that breakthrough infections become common by six months after vaccination — before considering the Israeli results further.

But let me try to do the next best thing.  Let me at least look to see of those breakthrough infections are rising, as they plausibly should as the vaccinations age.  If the Israeli findings are true and not spurious.

In any case, by looking at the Virginia data for the past couple of weeks, we can be reasonably sure that, so far, as of about a week ago, breakthrough infections were uncommon here in Virginia.  This, despite a reasonably high fraction of the population being vaccinated.

Below, the breakthrough cases would be 1 minus the percentage shown.  So, in this case, (1 – 98.54% = ) ~ 1.5% of infections were breakthrough cases for fully-vaccinated individuals.  The remainder (98.54%) were among the un-vaccinated.

To interpret that, you need to realize that there’s considerable uncertainty around these numbers.  It’s not “statistical uncertainty”, because this is a census of cases, not a sample.  It’s more like “natural variation”, when small numbers of infections occur within a very large population pool.  Each number is a bit shaky, so to speak, but not because we’ve drawn a sample.  They are shaky just because there are so few of them and they may fluctuate from day to day.

In addition, you need to know that there is a strong age-related correlation in vaccination rate, hospitalization rate, and mortality rate.  So you can’t just take this raw count and infer that (e.g.) the vaccines are better at preventing infection than they are at preventing hospitalization.  That’s not true.  Arguably, the reason you’re seeing breakthrough cases as a higher fraction of hospitalizations than of infections is that hospitalization is strongly concentrated in the elderly, who have a very high rate of vaccination.  I’d have to age-adjust the infection and hospitalization numbers separately if I wanted to get a true apples-to-apples comparison of impact on infection versus impact on hospitalization.

What’s at issue with the Israeli findings is the infection rate.  So let me just state this plainly, and do a bit of math.  Almost all these infections are in adults, so let me focus on the adult population.

As of this most recent two-week period available, the fully-vaccinated population accounted for:

  • 64% of the adult population.
  • 1.5% of the infections.

Doing the math, that means that the observed effectiveness of the vaccines, against COVID-19, in Virginia, over this period, is:

(1.5/64) / (98.5/36) = <1%

(Ah, well, what I really mean to say is that the effectiveness is >99%.  The chance of getting infected is <1% of the chance for a non-vaccinated individual.)

In Virginia, during this most recent time period, if you were vaccinated, your chance of having a COVID-19 infection was less than one percent of the chance faced by an un-vaccinated person.

That’s substantially better than the clinical trials found.  So, no doubt there’s a behavioral aspect to number.  The vaccinated aren’t chosen at random, but instead are drawn from the rational population possessed of common sense.  The unvaccinated, by contrast, are largely a mix of the irrational and those ideologically-driven to reject the vaccine.  Almost without a doubt, the unvaccinated are also the ones who reject COVID-19 hygiene.

And so, this is probably best interpreted as saying that if you’re vaccinated and adopt common-sense COVID-19 hygiene measures, your risk of getting infected is less than one percent of the risk faced by those who can’t be bothered to do either.

In Virginia.  As of a couple of weeks back.

And so, whatever is driving those Israeli findings does not appear to have started happening here yet.

Now I need to ask a couple of more questions.

First, does this reflect the Delta variant?  I’d say yes.  I can’t find any direct measure of that, because CDC didn’t sequence enough samples to provide a state-level estimate for Virginia.  But I can infer it from the fact that this period is squarely in the middle of the current upsurge in cases in Virginia.

The pale blue lines mark the start and end dates used in the breakthrough calculation above.  Those increases didn’t really get going until Delta dominated, and Virginia is right in line with the rest of the South Atlantic states.  The CDC shows that, during this period, about two-thirds of cases in this region were the Delta variant.  Between those two pieces of evidence, I’m fairly confident in saying that the breakthrough rate above is largely reflective of Delta infections.

The next question to ask is, has this changed over time?  That’s easy enough to answer.  Let me set the dates to span an equivalent period on the downslope of the curve above, and see what the Commonwealth says the breakthrough infection rate was.

And the short answer is that breakthrough infection, as a percent of total, was actually higher at the start of June than it is now.  That’s a time period when the Alpha variant was still dominant.  Those accounted for 4.5 percent of total infections — in line with the clinical trials data — compared to less than one percent in the most recent period.

Here’s the kicker:  If you download yet another one of Virginia’s data files, you can readily calculate that 24% of Virginia’s vaccine doses were administered before March 1, 2021.

In other words, somewhere around one-quarter of Virginia’s vaccinated individuals fall into the categories that should be suffering a massive loss of immunity to COVID-19 now, if the Israeli results are true.  And yet, we are seeing no uptick in breakthrough infections.  To the contrary, based on the two time periods I looked at, those breakthrough infections actually fell.

My conclusion, based on publicly-available data from Virginia, is that whatever is happening in Israel surely does not seem to be happening in Virginia.  The Israeli findings shows a massive reduction in immunity for those whose immunizations were several months old.  If true, given that almost a quarter of Virginia COVID-19 immunizations are five months old or older, given the estimated effect from Israel, that really should have started boosting the rate of breakthrough infections by now.  And no such thing has happened.

This analysis could be done more cleanly by tabulating by date of vaccination, but that would require the person-level data that only the Commonwealth possesses.  I hope they’ll take a quick cut at that and make the results now.  Otherwise, these Israeli results would seem to through a monkeywrench into any planning for the pandemic.

Afterthought

I want to be clear that I think Israel’s Ministry of Health did the right thing in releasing their statistical analysis.  In fact, I’d say they were ethically compelled to do so.  And, based on the news reporting, the accompanying text (in Hebrew, which I cannot read) did mention all the relevant caveats, in that the monthly cohorts of the vaccinated were not randomly chosen.

It’s a tough call.

On the one hand, Israel was a couple of months ahead of most other countries.  If this result were real, they’d be the first to have it show up in their national data. And if it were real, they really would be compelled to offer a warning to other countries that might be subject to the same loss of immunity within a couple of months.

On the other hand, you don’t want to make health care policy based on spurious results.  (Though this would hardly be the first time that happened).  Consider the the expense and hassle of providing booster shots on a semi-yearly basis to the entire vaccine-accepting population.  Now consider the risk of doing that for no reason, if the Israeli result are spurious.  (And I note that Israel itself has not yet decided to do that, based on their own results.)

So it’s a tough call.  Alerting other public health agencies to this possibility seems like the right thing to do.  The US CDC and FDA aren’t going to make any sort of snap decision on booster shots.  They are going to gather the evidence, and make up their own minds.  And, based on what I can see in Virginia, they are going to find that the Israeli results are not replicated in other places.  That will tell them that the correlation observed there is an artifact of something about the Israeli experience and not a failure of vaccine-generated immunity.  And the scientific method will have done the right thing in filtering out fact from fiction.