Author: chogan@directresearch.com

Post #1312: COVID-19 trend to 10/29/2021: Mountain and Midwest uptick

Illustrated Recap

Circle A:  The late-summer Delta wave largely swept through Southern states.  For the U.S. as a whole, the summer Delta wave peaked on 9/1/2021.

Circle B:  U.S. average cases have been receding ever since.

Circle C:  Until this past week.  U.S. daily new case counts stopped falling this past week.

We’re waiting to see if there’s going to be a winter wave.  Last year, that started in the upper Midwest and Mountain states, let by the Dakotas.  So, naturally, we’re focused on those regions.

Circle D:  Until now, the only hint that of anything unusual was that the Mountain and (to a lesser degree) Midwest and Northeast were stuck at their Delta peaks.  New case counts fell throughout the South, but generally not in the North.

Circle E:  But now, cases are now rising in the Mountain and Midwest areas.  But only just.

Circle F:  Half the mountain states are now on an upward trajectory.  (Graph is on a log scale).

Circle G:  And maybe the Midwest reached a shallow inflection point, and is now headed upward. (Graph is on a log scale).

Comparison to last winter

This is clearly not just a repeat of last winter’s wave.  At the very least, the winter wave is late this year.  Here are the first and second pandemic years on the same graph, each year starting at April 1.  The 2021 lines are now in the process of crossing their 2020 counterparts, meaning, any uptick is later than last year.

Maybe this year’s winter wave was confounded by the ebbing of the high rates from the summer Delta wave. Maybe this year will be just-plain different, owing to the high fraction of the population with some immunity.  (Though my calculation says no, the greater immunity merely offsets the greater infectiousness of Delta compared to prior variants.)

Maybe the timing of this year’s winter wave was slowed by the generally warmer and wetter fall in the U.S. Midwest and Mountain states for 2021, compared to 2020.

Here’s 2020, generally cool (left) and dry (right) in the middle of the U.S.

 

Here’s 2021, generally warm (left) and somewhat wetter (right) in the middle of the U.S.

Summary

This week’s interesting development is that daily new COVID-19 case counts stopped falling, for the U.S. as a whole.  As was true last year, that seems to be starting with the Midwest and Mountain states.  It’s still not clear that we’ll have a winter wave, but these seem to be the first hints of it.

Post #1310: What does 30 electrical miles get you?

 

We haven’t bought gasoline since mid-August, owing to my wife’s purchase of a Prius Prime.  That’s a plug-in (PHEV) version of the Prius, with a battery that’s good for about 30 miles.

It’s not like we stumbled into that purchase.  We researched the offerings available and decided that hit the sweet spot for us.  No range anxiety, no need to rewire the garage, and no need to mortgage the house if that big battery wears out.

And, as you can guess, it’s working out well.  We’re not avoiding traveling, it’s just that most of what we do seems to fit into that 30-mile-a-day limit.  Or nearly.

Which got me to wondering: Is our experience all that unusual?

I mean, people seen to think that little 30-mile battery isn’t much.  It’s certainly no Tesla, either for distance or acceleration, for sure.  But it’s not intended to be, and, from my perspective, that smaller battery is efficient.  Most people who drive a full EV aren’t going to use the full capacity of their battery on most days.

But with this PHEV setup — where the first 30 miles is electric, then it switches to gas — just how much gas would the average American save?

More precisely, how much would total U.S. private passenger vehicle gasoline consumption decline if the first 30 miles of everybody’s driving day were done on electricity?  As if everybody had a Prius Prime, but nobody could recharge mid-day.  And with no change in behavior otherwise.

Turns out that you can’t just look that up.

You can find some glib statistics on (e.g.) the fraction of individual car trips that are short.  And yeah, sure, most car trips are for just a few miles.  I don’t think anybody’s shocked by that. But that’s not the question.

So I turned to the National Household Travel Survey (NHTS) to get an answer.  If you ever want to know anything about how Americans get from A to B, that’s the place to look.

I took their file of vehicle trips, reduced it to travel by private passenger vehicle (car, SUV, van, pickup), focused on the vehicle driver only (to avoid duplicating drivers and passengers), and summed up the total miles that each driver drove, each driving day.   That yielded about 150,000 distinct person-days of vehicle driving.  At that point, I (arithmetically) substituted up to 30 miles of that with electricity, and tabulated the results.

Source:  Calculated from 2017 NHTS trip file, weighted estimate.

And there’s your answer.  If you were to substitute the first 30 miles of everybody’s private vehicle driving-day with electrical transport, you’d reduce gasoline-powered miles by 55%.  That’s all the miles on days under 30 miles, and 41 percent of the miles on days over 30 miles.

The upshot is that with PHEV, that 30-mile battery is enough to cut average private-vehicle gasoline consumption more than in half.  All of that, without the truly huge batteries required for full EVs.  And without a whole new electrical infrastructure required to keep EVs going, at least for those of us who can recharge at home out of a standard wall socket.

So I’m back to where I ended up in my last post about electrical transport.  People seem to get all caught up in their underwear about this huge, dramatic, risky blah-blah-blah.

And it’s all nonsense.  If you have a standard outlet available, you have the option to shift most of your personal transportation to electricity.  Right now.  With absolutely no other change in your lifestyle.  And a Federal tax credit, to boot, depending on what you choose.

Well, OK, in truth, we have made a few lifestyle changes.  I buy fewer lottery tickets now.  But that’s probably a good thing.  Otherwise, except for remembering to plug it in, there’s no practical difference between our last (all-gas) car, and our current (nearly-all-electric) car.

And now, judging from the U.S. numbers, we’re probably not alone in terms of the advantages from that small PHEV battery.

Think of it as a case of diminishing returns.  Your first few miles of electric capability get the most bang-for-the-battery-buck.  Here’s the picture, same data source and analysis above, just plotted for PHEV batteries of various sizes.

Source:  Calculated from 2017 NHTS trip file, weighted estimate.

Sure, you can be a purist and insist on nothing but electrical travel.  And more power to you.  But even with zero change in behavior, and no mid-day charging, a PHEV with a modest battery size can get you a long way toward that goal.

Post #1309: Ivermectin

 

At the end of August, I got a request to look at the evidence regarding ivermectin as a treatment for COVID-19.  Before you scoff, let me be clear that you can’t just dismiss something like this out-of-hand.  That drug wasn’t chosen out of thin air.  There were sound, basic-science reasons to think that it might — emphasis might — interrupt a key part of the replication of coronavirus.

But I was stumped by what I saw.  I could not make head or tail out of the scientific literature on the topic.

That was unusual.  If you know what to look for, typically a question as straightforward as “does this drug work” has a reasonably clear answer.  Even if the answer is an unsatisfying “somewhat” or similar mediocre result.  At least the evidence will clearly show that mediocrity.

But in this case, the techniques that had served me so well in my career completely failed.  There seemed to be really compelling evidence pointing toward completely contradictory conclusions.  And in my orderly universe, that just shouldn’t happen.

Today, I finally understand why the literature on ivermectin and COVID was so abnormally confusing:  They lied.  They, being authors of the studies showing the strongest results for ivermectin as a COVID treatment. Everything from including deaths in the control group while excluding them from the ivermectin group, to just plain Xeroxing blocks of data to make a tiny study appear to have had huge numbers of participants.  You can read about it in “The Real Scandal About Ivermectin”, by James Heathers, in this month’s copy of The Atlantic.

So now, belatedly, I guess I can offer an opinion: Nope, it doesn’t work.  Plus, liver toxicity makes it downright dangerous.  If you’re of a mind to self-medicate for COVID-19, try vodka instead.  It’ll be every bit as effective as ivermectin and you’ll end up with less liver damage.

And a lesson learned for me.  I’ve reviewed my share of scholarly publications in health services research.  I’ve seen things get published despite glaring errors.  Heck, I used to earn good money by pointing out the errors, to the people who really needed to know the underlying reality (as in this report, for example).  So in a very real sense, I profited from the lack of rigor in the standard scholarly review.

But those always seemed to be honest errors.  It was always an author who used some non-standard method, got some spectacular result, and let newsworthiness get in the way of objectivity or skepticism.  Or who didn’t understand some basic point of statistics.  In my entire career, I never ran up against results that I thought were deliberately fabricated.  Heard about it, but had never seen it.  And now I know better.

Strength-of-inference hierarchy as a way to sort through the medical literature on a drug.

Let me first narrow this down. 

In the social sciences, you get all kinds of  statistical studies that purport to provide evidence of something.  For a lot of those, broadly defined, it’s just extremely hard to say, one way or the other, whether you’re actually getting useful information or not, from any particular study.

If you want a concrete example, just think about all the seemingly-contradictory “scientific” information you’ve seen about diet.  Take any non-extreme point of view, Google for studies showing that’s good for you, and chances are, you’ll be able to find some.

There are concrete reasons why that literature, in particular, is such a mess.  I’ll just note my favorite one, which almost everyone ignores.  And that is, almost anything is healthier for you than the SAD (standard American diet).  And so, if you take a bunch of average Americans and put them on a diet — it doesn’t really matter which — sure enough, their health will improve.  Low-fat?  Low-sugar?  Vegetarian?  Paleo?  Mediterranean?  Low-salt?  High-fluid?  Anti-inflammatory?  Biblical foods only?

To a close approximation, the particular question doesn’t much matter.  The answer is “yes”.  Yes, a controlled plan of eating fill-in-the-blank is superior to uncontrolled consumption of SAD.  Feel free to fill in that blank with any non-extreme diet.

So, to be clear, in the outline below, we’re only considering a small subset, of the medical literature, dealing with the idea of purposefully consuming some drug, in order to treat a disease.

And so it boils down to a very simple question, which is, how sure can you be that taking that drug will actually improve the outcome of that disease.  What’s the strength of inference?

Finally, there’s a not-so-subtle issue that layers on top of this, in the practice of medicine.  And that’s whether people will actually use the proposed treatment, correctly, once prescribed.  I can never remember the term of art, but I think that’s the difference between “effectiveness” and “efficacy”.

Example:  Eat less and exercise more is absolutely an effective way to lose weight.  It’s just not something that most obese people will sustain.  Perfectly good advice, not not a solution for the obesity epidemic.  We’re abstracting from issues of that sort.

Without further ado, my hierarchy of strength-of-inference

Here’s my list, for all the types of studies you might commonly see that try to answer the question “will taking drug X improve outcomes in disease Y”.  I’ve listed them from lowest to highest strength of inference.

Unlike the opening section of this post, all of this assumes that the research is honestly and competently performed and presented.

  1. Computer modeling of drugs for potential effectiveness in a disease.
  2. In vitro (“test-tube”) studies, as of cell cultures.
  3. Studies in animals (mice, rats, … primates).
  4. Non-randomized trials in humans, aka “observational data”.
    1. Cross sectional.
      1. Cross-sectional without comparison group.
        1. Anecdotes (case studies).
        2. “Open-label” studies
      2. Cross-sectional with comparison group.
        1. And small/no differences in treatment vs. control behavioral.
        2. With large/obvious differences in treatment vs. control behavioral
    2. Time-series:
      1.  Pre-post comparison, no control group.
        1. Anecdotes (case studies).
        2. “Open-label” studies
      2.  Pre-post comparison with control group.
      3. Crossover designs (swap treatment and control groups over time).
  5. Small-scale randomized trials, typically not double-blind.
  6. Large, properly-designed double-blind randomized trials.

The first and most obvious question is, why don’t people just do research “right”, that is, study things with randomized trials only?  There are a lot of reasons, but foremost is cost.  I believe that the recent COVID-19 vaccine trials had an average cost of somewhere between $5,000 and $10,000 per observation.  (And so, when you read about the “30,000-person trials” of these vaccines?  Think “third-of-a-billion-dollars”.  Not to develop the vaccine, or to manufacture it.  Just to test it at that scale.  This is why drug trials often migrate to developing nations, where costs are lower.  And why statisticians who can do an accurate “power test” are key to minimizing costs, because you really don’t want to pay for one more observation than you need to, in order to prove that your drug works.)

The second — and maybe not so obvious – reason is that there are typically a lot of potential candidates.  At least at the start of research.  And so, weeding those out and/or selecting them in matters greatly, from the standpoint of both feasibility and cost.  Drug companies typically test a whole of of stuff, at some level, before they come up with one that seems to work.  They have to be pretty sure something will work before they’ll shell out the big bucks for a randomized trial.

The third reason is that all of the “observational” methods are widely available.  Any physician can feel free to write up an interesting case study (a patient or handful of patients.)  Anyone with access to electronic medical records or (in my case) abstracts of bills can do a comparison of individuals who did and did not receive a particular treatment.  And then publish the results.

It’s not that these non-randomized methods are useless.  They are a legitimate path toward identifying what works and what doesn’t.  They can provide some useful information, in some cases.  It’s just that they have low strength-of-inference.  They might give you a hint regarding some particular drug/disease combination.  But they can’t prove the point.  Which is why, for example, the FDA requires randomized controlled trials for any sort of newly-approved U.S. drugs.

A final reason is that in some cases (not necessarily this one), observational data is all you can get, and all you ever will get.  It would be difficult to do a randomized controlled trial of (say) whether air bags save lives in head-on collisions.  There, the only thing you can do is appeal to first principles, or maybe compare equivalent crashes, from equivalent models, in the pre-air-bag era.

And so, what you typically find is that research moves down this hierarchy over time.  You start with some vague notion that some class of drugs might have an effect.  You mix them up with some cultured cells, in a test tube, to see if that’s even remotely plausible.  If possible, you try them on some cheap lab animals.  You ask whether anyone is using those drugs now, and you get the observational data to see whether or not there’s any effect apparent.  And then, when you’ve got some notion that the drug actually works, you test it formally with an expensive randomized trial.

Place research on that hierarchy, then look for the signature of publication bias.

The best advice I ever came across on doing a literature review was from Light and Pillemer. They argue strongly for a quantitative review of scholarly literature.  It’s not enough just to give the usual text recitation-of-study-after-study.  You need to make some judgement about the likely accuracy or strength-of-inference of each study, and plot the results accordingly.

In the ideal case, you literally pull the key numbers from each study and plot them on a graph.  You start with the the earliest, lowest-quality, least-certain studies on the left, moving to the highest-quality strongest studies on the right.  If there is something to be found — some underlying true-and-real effect — the sorted results will converge to an answer.  Light and Pillemer present that as a “funnel plot”.  The reported answers will be all over the place among the less-reliable studies, but should narrow down as you move toward more reliable studies.  So that the resulting plot is funnel-shaped.  And if the answers don’t settle down, then chances are that there is nothing there.

In this case, if you are looking at nonsense research results, you’ll see a big break between the results at Level 4. and at Level 6. on the hierarchy above. 

And that’s my main red flag — that big about-face in the results when you finally get a randomized clinical trial.  That’s a red flag for three reasons:  shotgun research plus publication bias plus the tyranny of the t-statistic.

Shotgun research:  Up through level 4. in that hierarchy, anybody can take a shot at it.  In the computer era, it doesn’t cost much to (e.g.) re-process electronic medical records data.  But the methods are inherently error-prone, or to rephrase, at lot of people have the opportunity to take a shot at research, but they are all using guns of dubious accuracy.

Publication bias:  But if, by chance, you happen to hit the bullseye, then that’s publishable.  If you do 20 studies of a particular effect, then, just by chance, one of them is going to find a result that is “statistically significant at the 5% level”.  (That’s what the 5% level means.)  And that one will get published.  Because positive findings – rejecting the null hypothesis — is the way the entire process works.

The tyranny of the t-statistic.  What’s worse, you will tend to get those chance “statistically significant” findings only when you get a really spectacular result.  For a given amont of random variation in the data, it’s the extreme results that will generate the coveted “t-statistic > 2”, and pass the standard test of statistical significance.

The upshot?  What you end up seeing published, up to level 4. in the hierarchy, is not just studies that say a drug works, but studies that say a drug works magnificently well.  By chance, this screening process tends to produce a body of literature showing absolutely phenomenal, promising results.

(Of course I have an anecdote here.  It’s about losing a client.  My client had several different small-scale observational studies of his medical device.  Each study measured outcomes along several different dimensions.  He wanted me to build a “value model” showing the benefits hospitals would get from this device, by taking all the best results, along each dimension, from among all of the studies.  (In other words, gather up all the bullseyes, and ignore the rest.   I refused, and explained why that would be misleading, using the arguments you’ve just read above.  I got fired.)

And then those spectacular results evaporate in light of a proper randomized double-blind experiment.  That complete about-face between observational data studies and randomized controlled trials is the signature of publication bias.

Don’t misinterpret this.  If a drug actually works, then (by-and-large) it may sail through those first four levels of the hierarchy as well.  That may be the only way it gets entered into a randomized trial.  But it’s only when the result don’t flip around, at the randomized trial stage, that you can be truly confident that you’re looking at a real impact, and not just artifacts of unreliable methodology and publication bias.

The canonical crash-and-burn.

This leads to what I call the canonical crash-and-burn.  This is a pattern that you will see repeated for many of the promising-but-ultimately-ineffective treatments for COVID-19.

It’s not that you see early research on some drug, and maybe it provides some modest benefit, but that benefit can’t be found in a proper randomized trial.  It’s that, for the combination of reasons outlined above, you always hear about drugs that seem to offer GAME-CHANGING RESULTS, and then those results disappear once you do a randomized trial.

And when you see that, believe the randomized trial, not the prior research.

This is where all the hard-core believers in these drugs just fail to understand the process.  To them, it’s all research.  They literally don’t grasp the hierarchy above.  So that when they see those spectacular results early on, they stick with that.  They think, well, hey, some say yes, some say no, who’s to say?  How could you have gotten those spectacular “yes” results if it didn’t work?  And so, when a proper randomized trial shows nothing, they don’t understand that the randomized trial results aren’t just another bit of evidence, the randomized trial results replace everything above them on the hierarchy.  

The key to much of this is understanding that you only get to see what makes it into publication.  Drugs have to pass those in-vitro tests before anybody will bother to test them on animals (if appropriate).  By-and-large, they have to pass those animal tests before they become available for human use.  Practically speaking, those observational studies in humans have to show results before anybody will bother to publish them.  Separately, small-scale observational studies are likely to have the required “t-statistic greater than 2.0” when they, by chance, identify some seemingly-spectacular results.

And as a result, everything about the research and academic publication industry means that results bubble up to the point where they hit the popular press if and only if the results look pretty damned good.  The gooder, the better.

And then reality intervenes, in the form of a reliable randomized double-blind controlled trial.  Which may or may not confirm the results based on the less-reliable methodologies.

And if not, then the house of cards collapses.

Except for the population that prefers to continue to live in that house.  Largely consisting of people for whom all research is more-or-less hocus-pocus.  Who have no way to differentiate research based on a strength-of-inference hierarchy.

And who are probably out there right now, shopping on-line for ivermectin with a side order of hydroxychloroquine.

Ultimately, the message is that when the facts change, you need to change your mind.  And when spectacular results appear to evaporate in the face of an actual randomized trial, that means the results were imaginary to begin with.  No matter how much you want them to have been real.

Post #1308: Washington Post article on electric vehicles.

 

Today’s Washington Post had yet another article by somebody explaining why they didn’t buy an electric car.

Am I the only one who finds that weird?  Do we see published stories about the great National Parks that the author hasn’t visited?  Detailed reviews of restaurants the author would have liked to have dined in?  Or travelogs about the wonderful luxury hotels they’ve driven by?

You get the drift.

And yet, “Why I didn’t/won’t/can’t/shan’t buy an EV” is a surprisingly robust genre.  Once you realize that it exists, you’ll soon see that it’s pretty common.

For this particular story, maybe it was the author’s high-anxiety writing style.  Maybe it was all the angst-y, over-the-top comments from the general public.

Or maybe I’d just had my fill of the unnecessary us-versus-them-ism.

Because, when you boil it down, there are two types of people in this world:  Those who divide people into two types, and those who don’t.

For whatever reason, I was motivated to leave a comment.  So here’s my comment on that WaPo article, copied in word for word.

There is a compromise: PHEV. That’s a plug-in hybrid electric vehicle.

My wife bought a Prius Prime.

We haven’t bought gas since the middle of August.

The Prius Prime is a nice balance of electric and gas. It has enough battery to do somewhere around 30 miles as a fully-capable EV. With no range anxiety. When the battery is discharged, it’s just a regular gas Prius.

It doesn’t have a huge battery. So it plugs into a regular 20 amp household circuit. And with that, it takes maybe five hours to recharge.

When you think about it, a huge battery is kind of a waste, most of the time. Most people do most of their driving pretty close to home. Give them a way to do the first 30 on electric, and you make a real dent in their gas use. Without demanding the materials needed for a 300-mile battery.

Anyway, it was the right choice for us.

We’ll probably fill the tank some time next month. Or maybe not. Depends.

I’m reading all this angsty stuff about the decision to go electric, and all I can say is, you’re making it way too hard.

Go look up what’s happened to the price of batteries over the past decade. There’s a reason that Tesla went from a rich man’s play toy to a car for the masses. It’s called a more-than-ten-fold reduction in the cost of batteries, over the last decade.

All this stuff about, Oh my God, the battery replacement will bankrupt us — that’s so last-generation. Look up the current data before you decide to stress about something that’s increasingly a non-issue.

Post #1307: More mortality rate nonsense, Part 2.

 

I need to finish the analysis started in my last post.

At the behest of a reader, I’m trying to track down the evidence behind a claim in this Fox interview.  Broadly, the claim is that COVID-19 vaccines are causing an increase in non-COVID deaths.  Plus-or-minus some strategic weasel-wording.

In my last post, I went through all the real research showing that’s not true.  There is no evidence of increased non-COVID mortality among people receiving vaccines, based on:

  • the randomized clinical trials of the vaccines.
  • a study of 11 million people in seven large health plans.
  • a study of the residents of several hundred nursing homes.

Separately, in an earlier post I noted that the rate of death among the vaccinated, as reported to the U.S. Vaccine Adverse Events Report System (VAERS), is a tiny fraction of what would be expected, based on the background mortality rate for the elderly alone.  That’s Post #1208, A funny thing about deaths in the elderly.

At this point, as you can probably guess, I’m a bit skeptical of the claim that COVID vaccines are causing non-COVID deaths.  In terms of any real research that actually looks at vaccinated individuals, there’s nothing to suggest that there’s an excess of non-COVID deaths.

In theory, one specific part of the Fox interview claim can be checked empirically.  Part of the claim is that U.S mortality statistics show excess non-COVID mortality in the U.S. age 20- (or maybe 30-)to-50 population in 2021.

Plus-or-minus their loose wording, that’s what I’m trying to track down here.  After I find that — if I can find it — then I can asses whether there’s any plausible to link it to COVID-19 vaccination.

Don’t lose sight of the big, obvious linkage between COVID-19 vaccination and non-COVID deaths.

I’m going to spend a lot of time going through the U.S. mortality data in excruciating detail. We are definitely on track to end up in the weeds.

But before I do that, I want to point out the elephant in the room.  So let me take one minute to state the obvious, starting from the graphs in my last post.

Here’s a graph of non-COVID deaths in 2020 and 2021, from this scholarly source.  They clearly show some significant ups and downs.  You can’t see it here, but that’s far more variation than you see in a normal year.

Here’s the same graph, with COVID deaths included.  So now, the top line on the graph is total U.S. deaths including COVID-19 deaths.

Source: Mortality Tracker:  the COVID-19 case for real time web APIs as epidemiology commons.

It’s completely clear that when COVID-19 deaths peak, there’s a small spillover to reported non-COVID deaths.  In other words, spikes in COVID deaths are driving some modest increases in non-COVID deaths. 

There’s nothing subtle about this.  Not only are the extra deaths clearly visible, but the timing tells you this is cause-and-effect.  Don’t have to appeal to any mysterious lags.  When COVID-19 deaths go up, non-COVID deaths rise to a lesser degree.

I went through three plausible mechanisms for this in my last post.  Might be simple under-coding of COVID-19, so the increases in non-COVID deaths are merely mis-classified COVID-19 deaths.  Might be that that COVID-19 is contributing to the impending deaths of otherwise frail individuals.  And it might be that the crush of COVID-19 cases is crowding other ill individuals out of hospitals, ERs, and other parts of the health care system.

In any case, the plain reading of the data is that, for whatever reason, the peaks in COVID-19 deaths are causing modest increases in reported non-COVID deaths.

OK, then:  Who is to blame for those additional non-COVID deaths, occurring underneath the COVID-19 death peaks?

Answer:  The un-vaccinated.

Why?  Because they account for the overwhelming majority of the COVID-19 deaths.  To a close approximation, the peaks in the COVID-19 deaths are peaks in the deaths of un-vaccinated individuals.

Here’s how the death count splits out in Virginia, as of the last available week:

Source:  Calculated from Virginia Department of Health COVID-19 dashboard.

And here’s how it splits out in the U.S. as a whole, for the last available month:

Source:  Calculated from CDC COVID data tracker.

No matter how you slice the pie, about 90% of all COVID-19 deaths now are occurring among the un-vaccinated.  (And that’s despite the fact that the un-vaccinated are a minority of U.S. residents, and that they are concentrated in younger age groups that have a lower risk of death.)

And so, if you want the straight story on this, connect the dots.

  • The un-vaccinated are largely to blame for COVID-19 deaths.
  • Those COVID-19 deaths appear to spill over into additional non-COVID deaths.

Therefore, to a first approximation, who is to blame for those additional non-COVID-19 deaths?  The deaths that you can see, right in front of your face, visible to the naked eye, on that chart above?  It’s the same people who are to blame for the COVID-19 deaths.  Blame un-vaccinated adults.

The obvious conclusion?  If you are genuinely worried about the potential for excess non-COVID-19 deaths, attributable to some actual cause, the first step to take is to get vaccinated and encourage others to do the same.

A more formal data analysis.

So, what was that Fox guy talking about?  Excess non-COVID-19 mortality in 2021?  And then, what’s the evidence (if any) that links that to vaccination?

First, define excess mortality.

Let me start by making sure were on the same page regarding the phrase “excess mortality.  In this context, it has a very precise meaning.

First, by “excess”, we mean “more than predicted”.   Literally, more deaths than were predicted by a sophisticated statistical model.

In theory, anybody could make a prediction.  It’s not hard.  Take average actual deaths, week-by-week, for the past decade.  Inflate for overall population growth.  And that’s a prediction.  It’s actually a fairly good prediction, as these things go.

In practice, the only prediction that matters is the prediction generated by (broadly speaking) the U.S. CDC.  They account for (e.g.) the aging of the population, the seasonality of deaths, population growth, and so on, to come up with their week-by-week prediction of total deaths.

Second, in practice, that’s often taken to mean “more than predicted, by a statistically significant amount”.  The whole point of “excess mortality” is to flag events that are unusual.  For example, to identify some year’s flu season as being a particularly deadly one.  CDC doesn’t want to flag normal year-to-year variation in mortality.  Instead, it only flags mortality as “excess” if there’s less than a 5 percent probability that such a mortality rate would be observed purely by chance, just from normal year-to-year variation.

In short, “excess mortality” has a very specific meaning.  It means that somebody (the CDC) used historical data, and a statistical model, to project how many deaths would would be expected to occur in a typical year.  And then, in practice, you only flag the mortality rate in some period as unusual if the actual, observed death rate exceeds the predicted value by some significant margin.

Because the CDC is more-or-less the arbiter of what constitutes “excess”, you really can only talk about excess mortality if the CDC identifies it for you.  In practice, it would be next-to-impossible to replicate the CDCs statistical approach to come up with a CDC-analog for (say) expected deaths by cause of death.

CDC mortality data

Near as I can tell, all of the 2021 CDC mortality data available to the public can be found from the links on the CDC web page.  For years prior to 2020, CDC published detailed (de-identified person-level) files of deaths by year.  For those, the detailed data would allow the user to tabulate the data as needed.

For current (2020, 2021) data, by contrast, CDC has produced a limited set of “provisional” data files, aggregated in various ways.  Those are the files available from links on that page.  If you want 2021 US mortality data, this is what you have to work with.  Any U.S. 2021 mortality data that you see published — including the highly-colored chart above — almost surely derives ultimately from these CDC files, or from tabulations directly provided by the U.S. CDC.

COVID-19 in a five-year perspective, from CDC data.

Source:  Calculated from CDC mortality files downloaded via this web link on  10/28/2021.

The first graph shows actual and expected (predicted) deaths.  This is, needless to say, the CDC’s prediction, not mine.  As you can see, the expected (predicted) deaths number just perks along smoothly, with modest peak each winter.  Actual deaths has a few more ups and downs, in the period prior to COVID.  And then COVID comes along in 2020.

If you ever hear people claim that COVID is really no worse than the flu, maybe show them this chart.  On the left, I’ve circled a flu season that was “the worst season we’ve had in the last decade, according to no less an authority than Dr. Anthony Fauci.  Keeping in mind that total deaths is the area under the curve, the worst U.S. flu season in the last decade isn’t even in the same ballpark as the 2020-2021 winter COVID-19 season.

Finding 2021 non-COVID excess mortality, and putting that into perspective.

The claim of interest isn’t about COVID-19 mortality, or even the flu.  It’s about an excess of non-COVID-19 deaths in 2021, following the mass COVID-19 vaccination of the U.S. public.  So now let’s put a finger on that.

Here’s the past two years, showing total deaths, predicted deaths, and COVID-19 deaths.  And then, if I merely subtract out the COVID-19 deaths, we’re looking at a comparison between non-COVID mortality and predicted mortality.  I’m going to take the difference between those two lines as “excess non-COVID mortality”.

Here’s the data as provided by CDC:

Source:  Calculated from CDC mortality files downloaded via this web link on  10/28/2021.

And here it is after subtracting the bottom line (COVID-19 deaths) from the top line (all deaths).  So the graph below is all non-COVID deaths, compared to predicted deaths.  And the difference between those lines would be unexpected non-COVID-19 deaths.

Source:  Calculated from CDC mortality files downloaded via this web link on  10/28/2021.

And there it is, circled above.  That gap, between those lines, is 2021 excess non-COVID deaths.   That’s the thing we’ve been looking for.  That’s what the Fox interview was about.  The claim is, that was caused by COVID-19 vaccination.

Source:  Calculated from CDC mortality files downloaded via this web link on  10/28/2021.

But in terms of attributing this to vaccines, we hit an immediate and obvious snag.  There were plenty of gaps between those two lines, as larger or larger than the last one, occurring well before vaccines were even available.  In fact, in the past two years, for the majority of the time, there have been big gaps between the curves for predicted deaths and total non-COVID deaths.

Finding a gap between actual and predicted deaths isn’t unusual.  Ever since COVID-19 hit, that’s been the norm.  There’s been a gap that size more often than not.

Source:  Calculated from CDC mortality files downloaded via this web link on  10/28/2021.

In fact, if you now pull back to the five-year perspective above, you see that differences between those two curves occur all the time.  Not just prior to COVID-19 vaccines, but prior to COVID-19.  Differences that are every bit as large as the late-2021 excess non-COVID-19 deaths.

To be clear:  If that late-2021 event had been something new and unique, then there would be reason to look further.  If a gap appeared between those lines, where none had ever been before, then that would surely be something worth looking at.  But the reality is that gaps appear between those two lines all the time.  It’s just business as usual.  To take that last gap and say, well, that must be due to vaccines, that’s clearly nonsense.  That’s no evidence at all that the last (of very many) differences between actual and predicted U.S. deaths is somehow linked to vaccines.

At some point, I may want to return to this and drill down into that late 2021 excess mortality.  Possibly there is some useful lesson there.  I’ve heard a lot of chatter are about drug deaths being up and such, it might bear looking into.

But given how frequently actual and predicted mortality diverge, it doesn’t seem like there’s any pressing need to look.  Mainly, nobody has any clue what’s driving that excess mortality in that particular instance.  Because it’s just one of a long string of such gaps that have been occurring for at least the past half-decade.  Given that, taking time to lay out the details of it seems like wasted effort.

When you pull back and look at it, this is just a particular variation of the semi-attached figure.  That’s a standard trick explained in How to Lie With Statistics.  Yep, there is excess non-COVID mortality in late 2021.  Yes, there were vaccines given earlier in that year.  But nope, there’s no reason to think that one of them has anything to do with the other.  Episodes of such “excess mortality” occur all the time, vaccine or no vaccine, COVID or no COVID.

In fact, this one is a subset of the semi-attached figure that I refer to as a “stopped-clock methodology”.  So let me go on to explain that, as a sort of extras for experts.

Extras for Experts:  The uses of the stopped-clock methodology

“When your client is the target, your job is to fuzzy up the bullseye”.  I got that advice from a co-worker, back when I worked as a consultant in the area of Federal health care policy.

In that sort of environment — where people will purposefully offer misleading information to achieve their goals — if you want to separate fact from fiction, you have to get good at spotting the fuzzy.  By that I mean all the pseudo-science, “How to Lie With Statistics“, truthy-sounding stuff that turns out to be nonsense if you look at it in detail.

It’s not as easy as you think.  There’s big money in the artful lie.  In any given situation, it was a pretty good guess that the guys paid to fuzzy up the picture were making a lot more dough than I was.

But after a while, I came to realize that the Industrial-Fuzzy Complex uses a more-or-less standard set of tools.  Foremost of which is the stopped-clock methodology.

Define a “stopped clock methodology” as a statistical analysis that always gives you the same answer, no matter what you apply it to.  If the question is something like “is there a problem here?”, it’s a method that is guaranteed to give you the answer “yes, there is”.  All the time.  (Or, maybe, “no there isn’t”, all the time).  Either way, it’s a sciencey-looking method that always returns the same answer, no matter what the particular situation may be.

Useless, you say?  Then you’ve never worked with lobbyists.  Because if it’s your job to defend the indefensible, a stopped clock methodology is your friend.  It’s your sure-fire go-to defense.  It doesn’t have to be right, it doesn’t have to be good.  It just has to have enough face validity to provide cover for your duly-purchased elected members of the Congress to vote your way.

So let me begin this analysis by praising the logic behind this latest nonsense about the COVID-19 vaccines.   

Put yourself in the shoes of some foreign power, say, who wanted to discourage Americans from getting vaccinated, and so prolong the COVID-19 drag on American society and the American economy.  How would you scare them away from getting vaccinated?

Well, you can’t talk about vaccines and COVID-19 deaths.  At this point, unless you’ve been living in a cave somewhere, you realize that the cat’s out of the bag on that one.  Everybody who looks at the data concludes that the vaccines are quite effective at preventing COVID-19 deaths.

Here, for example, are the last four weeks of data from Virginia, showing how much more likely you are to die from COVID-19 if you are un-vaccinated.

Source:  Virginia Department of Health COVID-19 dashboard accessed 10/27/2021.

That’s just the raw observational data.  It doesn’t even account for the much younger age (and far lower risk of death) of the average un-vaccinated individual remaining in Virginia.  The true apples-to-apples impact of vaccines on COVID-19 deaths is almost certainly larger than what is shown above.  But this is good enough to get the point across with no statistical hocus-pocus.

Not to worry.  You’ll just have to focus on non-COVID deaths instead.

But you can’t directly compare non-COVID deaths among the vaccinated and un-vaccinated populations, either.  As I showed in yesterday’s post, if you do the obvious thing — track individuals who were and were not vaccinated, and compare the death rates — you end up with nothing.  No excess mortality in the randomized controlled trials, in a study of hundreds of nursing homes, or in a study of 11 million persons in seven large health care plans.

So your last chance of scaring people, on the risk-of-death front, is to fuzzy things up as much as possible.  The only thing left for you to do is to talk about  non-COVID deaths, and avoid directly comparing the the vaccinated and un-vaccinated populations.

But that’s OK, because if there’s enough stuff to look at, you can always find something to point to.  Surely, somewhere, for some population, for some cause of death, for some time period, some mortality rate must have gone up after COVID-19 vaccination started.  All you have to do is point to that and call it your smoking gun.  It doesn’t matter if no serious analyst would accept that as evidence because, let’s face it, the people who you’re aiming this at aren’t exactly the sharpest knives in the drawer.  All you need is something that’s adequate for your target population.  Pretty much any claim will do when your audience is sufficiently credulous.

And so we arrive at the current bit of Fox-pushed nonsense.  This nonsense being that, somehow, indirectly, in some countries, for some age groups, for some time periods — but not for others, and with no direct link to the level of COVID-19 vaccinations — the COVID-19 vaccine has caused large increases in non-COVID deaths that are visible as excess non-COVID mortality in national mortality statistics.

Which I now hope you can recognize as a classic stopped-clock methodology.  Give me enough cells to look at — cause of death x age x time period x county — and I’m fairly sure I can find one that went up after vaccines were introduced.  Point to that one, ignore all the others, and you’re good to go.  There’s your answer.

So long as some estimate, somewhere, says that some population had excess non-COVID mortality in 2021, that’s all you need.  Find the clock that’s stopped at the right place, then point to it, wave your hands, and tell people that it means what you say it means.

Post #1306: COVID-19, are we done being stupid yet? More mortality rate nonsense, part 1.

 

It’s a year and a half now that the U.S. COVID-19 pandemic has been grinding on.

So, are we done being stupid yet?

Ah, heck no.

A reader alerted me to the latest nonsense going around in the Fox alternative universe.  It’s the assertion that COVID-19 vaccines are killing so many people that it’s raising non-COVID, all-(other)-cause mortality numbers.

Wow!

And nobody is talking about it!!  Double wow — that means there’s a conspiracy of silence!!!

Or, alternatively, it means that it’s nonsense!!!!  But apparently that obvious explanation didn’t seem to cross the minds of the people spreading this!!!!!

So, to summarize, the allegation, from this Fox interview, is that COVID-19 vaccination is:

  • Killing people, but only in some countries.  Great Britain, Germany, and the U.S.  But not in other countries with equal or higher vaccination rates.
  • Killing people in some age groups, but not other, more-highly-vaccinated age groups.  In the U.S., it’s only killing the 20- (or 30-) to 50 age group.  Elsewhere, it’s killing everybody.
  • Killing people during some time periods, but not others, and those time periods vary across countries and populations, with a long and variable lag between vaccination and death.

For those of you work in the sciences — or simply have common sense — you’ve probably already spotted what’s wrong with the “logic” of this argument.

It’s called post hoc propter hoc.  Dressed, in this case, with a side-order of cherry-picking.  The argument is that if (non-COVID) excess mortality went up anywhere, for anyone, in 2021, well, that must be due to vaccination.

Because, of course, nothing else has happened in the past couple of years that might affect the health status of the world’s population.  So it has to be vaccination!!!!!!

It almost goes without saying that these allegations of mass vaccine-related deaths, in this Fox interview, are being made by a person with no background in (e.g.) vaccines, mortality data, or health statistics.  Instead, this is, of course, yet another allegation about COVID-19 from a right-wing commentator with not the faintest grasp of what he’s talking about.

Yet, by request, I guess I have to look into this in detail.

Let me break this into two parts.  The key checkable fact is that there is significant non-COVID-19 excess mortality, in the U.S., in 2021, for the population aged 20 to 50 (or 30 to 50, depending on which sentence you listen to, in this Fox interview).  Then, separately, there is the attribution of that excess mortality to a specific cause — COVID-19 vaccination.  The interview also contains allegations about two (but only two) European countries, but checking those out correctly requires so much work to understand country-specific deaths data that, for now, I’m sticking to the U.S.A.

So, for my task, this boils down to:

  • What’s happened to the U.S. mortality rate for 20 (or 30-) to 50-year olds in 2021?
  • Can that plausibly be attributed to COVID-19 vaccination?

Spoiler:  I’m just going to start on it today.  This post is already too long.  I’ll finish it tomorrow.

Some hard-earned advice

There’s a phrase to keep in mind whenever anybody alleges something of this magnitude:  Extraordinary claims require extraordinary proof.

At root, this is a claim that mass deaths are occurring as a result of some government-sponsored intervention.  (And, secondarily, that there’s a coverup, because nobody is talking about it.)

I learned the hard way, from years of working with Medicare data, any claim about unusual death rates is an extraordinary claim.  PBut, as I show below, there’s nothing to back up this claim.  It’s the opposite of extraordinary proof.

People in responsible roles in the government take any claims about excess deaths extremely seriously.  If you are serious about it, you really need think twice, and check your work, before shooting your mouth off about any claim regarding deaths.  In particular, a claim that mass deaths are occurring as a result of some government-sponsored intervention.

But if all you want is attention, the last thing you do is bother to check your facts.

That said, let me now walk through this logically.  And check my facts.  Because everybody makes mistakes sometimes.

A little background.

These days, most people who talk about U.S. mortality rates have no understanding, at all, about the subject.  They don’t know how deaths are reported, or how death certificates are filled out.  They don’t understand how cause-of-death is assigned.  And they certainly have no understanding of how stable the U.S. mortality rate typically is from year-to-year, the factors that may affect the year-to-year variation in U.S. deaths, or how various arms of the U.S. government publish and use those data.

(Or, for that matter, how difficult it is to cast votes in the names of dead people, due in large part to Social Security’s Death Master File.  In the U.S., we may not take care of you from cradle to grave, but we sure as hell track you from one to the other, if only to minimize the amount of Social Security fraud that occurs.)

Just for the record, I’m not one of those people.  I spent much of my career in the analysis of data from the Medicare program.  Roughly one-quarter of all Medicare spending is for individuals in the last year of life.  That’s not “the high cost of dying”, that’s largely the high cost of living with severe illness.  But given the importance of last-year-of-life spending, I had to learn about death and dying in the U.S. merely to do my job competently.  In the last last two decades of my career I had a focus on the cost of Medicare end-of-life care (Reference, Reference).

So, if you care to be among the informed, you can look at a few prior posts here and try to get up to speed.

If you want some basic understanding of what a death certificate looks like, and how those are filed out, go to Post #793, August 2020.  That was in response to people who knew nothing about death certificate data distributing disinformation about COVID-19 deaths.

If you want a quick tutorial on how CDC tracks adverse events following vaccines, you can try Post #1208, A Funny Thing About Deaths in the Elderly.  That was in response to people who knew nothing about the CDC’s adverse event tracking system, spreading disinformation about deaths and COVID-19 vaccines.

I think I’m seeing a pattern here.

In any case, this post is about U.S. mortality data.  And about the people who know nothing about it, who are now spreading disinformation about deaths and COVID-19 vaccines.  Again.

Existing research.

I addressed the last round of nonsense regarding COVID-19 vaccination and deaths in “A funny thing about deaths in the elderly (Post #1208, August 10, 2021).  That time, the allegation was that the U.S. Vaccine Adverse Events Reporting System (VAERS) proved (proved!!!!) that there were mass deaths from COVID-19 vaccines.

That allegation was, needless to say, incorrect.  I walked through that, in that posting.

(And yet, as is the way in the alternative Republican universe, everyone seems to have forgotten that.  And so, we’re on to yet a different reason why vaccines are evil.  Which we wouldn’t have to touch, if the last one had been right.

You might think that people would eventually tire of being mislead.  But they never remember it.  So we get a kind of policy Alzheimer’s.  Every day is a blank slate, and each new wacky conspiracy is taken afresh, with no bothersome historical context to get in the way of belief.)

In any case, now we have somebody saying that the CDC excess mortality data clearly maybe sort of possible shows that the COVID-19 vaccine is causing mass deaths!!!  But there’s a conspiracy of silence about it!!!

And so, I’m once again in the business of tracking through something that I’m almost certain will be yet another bogus analysis, by people who have no clue what they’re looking at.

Why am I so sure this will turn out to be nonsense?  “This”, meaning that  somehow, unexplained deaths are occurring at a scale large enough to perturb U.S. national mortality data?  All linked to COVID-19 vaccination?  And yet, nobody has noticed?

To answer that, let me just lay out a just a few of pieces of actual research relevant to the topic.  Because facts always exist in a context.  And if you don’t know the context of the existing research, you probably aren’t competent to make any judgement about some newly-claimed facts.

0:  First, keep this in mind

All of the discussion below is about the side-effects of the vaccine, and only the side-effects.  All of it ignores the main effect of vaccination, which is to prevent death from COVID-19 itself.  None of the research below provides any assessment of the net benefits of COVID-19 vaccination. It’s an analysis of potential side-effects, only.

1:  No excess deaths in the large-scale vaccine clinical trials

All COVID-19 vaccines went through lengthy, multi-phase randomized clinical trials.  In those trials, Phase 1 is purely a test of safety.  The first thing they do is test the vaccines to make sure they won’t (e.g.) kill you.  Then, if they pass Phase 1, Phase 2 tests whether or not the vaccine works.  And Phase 3 — if there is a Phase 3 — re-tests that and fine-tunes things like timing and dosage.  For example, the analysis of booster shots is formally part of Phase 3 of the vaccine clinical trials.

As a result, there is meticulously-maintained record of adverse reactions and deaths, in a true randomized trial, for a total population that exceeds 50,000 vaccinated persons.  That’s for the clinical trials of the three vaccines used in the U.S.

(For those of you who don’t understand why double-blind randomized trials are superior to any other form of analysis, it’s simple.  The randomization step — randomly selecting who gets the treatment and who gets the placebo — gets rid of all the other factors that might influence the outcomes, such as difference in health behaviors, lifestyle, and so on.  By contrast, “observational data” — comparing people who chose to get vaccinated or not — always commingles differences in those populations with the actual impact of the vaccine.  The double-blind  aspect — neither the subjects nor the doctors know who got vaccine versus placebo — means that nobody’s subjective feelings can influence the results. Again by contrast, in “observational data”, you will find that (e.g.) people who know they got the vaccine are much more likely to attribute vague side-effects to the vaccine. 

Whenever you see some goofy-assed “research” results reported in health care, it’s almost a sure bet that they were based on “observational data”, and not on a proper double-blind randomized clinical trial.  And if you don’t realize that, the constant stream of dog-bites-man nonsense that is reported in the popular press probably leaves you with the feeling that all research is nonsense.  Which is as unfortunate as it is common.)

Let’s take a peek at the actual reported results of the Pfizer vaccine trial, as Pfizer is the most-commonly-provided COVID-19 vaccine in the U.S.  These are literally the data that convinced the FDA to approve its use.  Here, I refer to the the formal writeup as published in the New England Journal of Medicine.

In this trial, after randomization into treatment and control groups, more than 22,000 individuals were given the Pfizer vaccine, and an equal number got a  placebo.  Adverse events were tracked pro-actively for two months, while any adverse event (including death) could be reported for up to six months after injection.

Of that 22,000+ population of vaccinated individuals, and other 22,000 who got the placebo, how many died during the study period?  (Where “BNT162b2” is the Pfizer vaccine).

“Two BNT162b2 recipients died (one from arteriosclerosis, one from cardiac arrest), as did four placebo recipients (two from unknown causes, one from hemorrhagic stroke, and one from myocardial infarction). No deaths were considered by the investigators to be related to the vaccine or placebo.”

To be crystal clear, they looked hard and long at a population of tens of thousands of individuals who got that vaccine.  And they found no evidence of any excess deaths.

If you look around, you can find popular reporting of some longer follow-up period, when more deaths were reported.  You still find no excess deaths in the vaccinated population.

2:  No excess deaths in analysis of large-scale observation data.

Maybe you just don’t like those extremely-high-quality double-blind controlled clinical trials.  Maybe looking at a mere 22,000 people for half a year isn’t enough to satisfy you that the COVID-19 vaccines don’t generate massive additional deaths.

If that’s your bent, then how about an observational data study of 11 million people?  Would that be enough to satisfy you?  Some careful tracking of health care, death, and vaccination records for 3 percent of the entire U.S. population?

If that’s more to your taste, then read on.

For U.S. deaths data, I of course turn to the CDC’s Morbidity and Mortality Weekly Report (MMWR).  As one does.  And there, today, featured front-and-center on their website, is this piece of analysis, just published:

Source:  Xu S, Huang R, Sy LS, et al. COVID-19 Vaccination and Non–COVID-19 Mortality Risk — Seven Integrated Health Care Organizations, United States, December 14, 2020–July 31, 2021. MMWR Morb Mortal Wkly Rep. ePub: 22 October 2021. DOI: http://dx.doi.org/10.15585/mmwr.mm7043e2.

It’s not hard to spot their main conclusion.  I have underlined it in red below.

Source:  Same as above.

Admittedly, this is “observational data”.  So it lacks the guarantees of double-blind randomized trials.  But for me, having done work like this all my life, this one ticked all the boxes that say “carefully done analysis”.  It’s done about as well as it can be done short of a controlled clinical trial.

Just a few highlights:

  • They tracked 11 million people, in seven large health care organizations.  These were split about 60/40 between those who chose to be vaccinated, and those who did not.  So there’s no doubt that the sample size is more than adequate.
  • Obviously, the vaccine protects against COVID-19 deaths, so they removed all COVID-19 deaths to get an apples-to-apples comparison between the vaccinated and unvaccinated.  They didn’t merely toss out deaths that listed COVID-19 as cause of death.  They tossed out any death occurring within 30 days of any diagnosis of COVID-19 or any positive COVID-19 test.  They excluded all deaths even remotely plausibly related to COVID-19.
  • They fully accounted for the fact that you have to be alive in order to get vaccinated.  (Which sounds dumb, but it’s a necessary step, and it was good to see that they did that with an industry-standard “pseudo-event date” method.  That is, they took the distribution of actual vaccination dates, and imposed that set of dates on the un-vaccinated comparison group.  This ensured that individuals in both samples were known to be alive as of that vaccination or equivalent pseudo-vaccination date.)
  • They accounted for differences in preventive health behavior in general by drawing the population without COVID-19 vaccines from those who had gotten a flu shot in the last couple of years.  So the entire study population consists of people who are willing to be vaccinated.  It’s just that some didn’t get the COVID-19 vaccine. 
  • They accounted for differences in the age/sex/race mix of the vaccinated and un-vaccinated populations.
  • And, in the end, the COVID-19-vaccinated population had a much lower mortality rate.

To be clear, this sort of observational data research is not the gold standard.  The most accurate thing you can say is that if vaccines did cause mass deaths, they were too small to be observed against the background of pre-existing health status differences between those choosing to be COVID-19 vaccinated and not.

And if you’re interested in a higher-mortality population, this study of nursing home residents found no short-term increase in mortality following vaccination, compared to an un-vaccinated control group.  So if the vaccine kills old people, it most assuredly doesn’t kill them very quickly.

3:  But wait, there’s more …

Hilariously enough, in looking at research, I found a serious study showing an increase in non-COVID deaths in the younger (age 25-44) population, for the U.S., for a few months.  Just exactly as that Fox interview alleges.

What’s the catch?  That was a study of 2020 — the year before the vaccines were introduced.  This is from a Research Letter in the Journal of the American Medical Association Network, so I’m not sure how tightly that was reviewed.  That said, the JAMA imprimatur means it was probably credibly well done.  FWIW, their takeaway is that COVID-19 was probably under-diagnosed, at that time, in that generally healthy young population.

Clearly, the only possible explanation is that those vaccines are so damned dangerous, they were killing off young people in the year before they were administered. (That’s sarcasm, in case that wasn’t crystal clear.)

But seriously, this, by itself, means that you can’t make much of it, even if you can identify a 2021 period of excess mortality for that population.  For all I know, apparent excess mortality in that population is a regular thing, owing perhaps to above-average instability in the measured death rate, because the death rate for that population is miniscule.

Sometimes, in research, you just can’t make up stuff that’s half as good as the real thing.  In this case, it sure looks like there was excess non-COVID mortality in the young, occurring at the time of the initial U.S. COVID-19 outbreak … well before vaccines were even on the horizon.

4:  To summarize the existing research

Serious researchers have looked for any evidence of excess non-COVID-19 mortality related to COVID-19 vaccination.  The conclusion so far is that such excess mortality is:

  • Not in the randomized clinical trials of the vaccines.
  • Not in an 11-million-person study of private health care plans.
  • Not in a study of hundreds of nursing homes.

But, you do seem to see something, for a few months, at the height of the first COVID-19 wave, in the younger population, in the year before the vaccines were in use.  The researchers who spotted that attributed it to under-diagnosis of COVID-19 in that otherwise healthy population.  Given how hard it was to get tested at that time, I think that’s at least plausible.

Now let’s take an initial look at the U.S. mortality data.

US Mortality data:  First, use your eyes.

Let’s do a little reality check first.  I can easily get U.S. mortality data by cause, graphed, from this scholarly source.  So let’s start there.

The story we are investigating is that, somehow, there’s an increase in 2021 non-COVID deaths that is linked to COVID-19 vaccination.  So let’s start with the vaccinations, from CDC, displayed over the same time period as the subsequent mortality data.  And contrast that, by eye, with U.S. non-COVID deaths over the same period.

The top graph is newly vaccinated individuals, from the U.S CDC COVID data tracker.  The second is U.S. deaths for all causes other than COVID-19, from the scholarly source above.  Matching time periods for both graphs.

 

If you see some cause-and-effect relationship between those two curves, you have a much more vivid imagination that I have.  Among other things:

  • There was a small peak in non-COVID-19 deaths in April 2020, before vaccines were even available.
  • The peak rate of vaccination was April 2021.  At that time, non-COVID-19 deaths were gently declining.
  • In fact, U.S. non-COVID-19 deaths peaked in the winter of 2020, and were declining throughout the entire period during which the population was getting intensively vaccinated.

And yet, there was more month-to-month variation in the death rate shown above than is typical or normal for the U.S.  In a typical year, that U.S. mortality data is pretty much a flat line, other than for a little seasonal rise during the cold weather.  So there certainly was some unusual variation in non-COVID-19 deaths in 2020 and 2021.  But it clearly bore no relationship to COVID-19 vaccinations.  At least, not in any way that is obvious to the eye, or related in terms of timing.

I wonder what could possibly explain those odd rises in U.S. non-COVID-19 deaths in April 2020, in the winter of 2020/2021, and then again in August 2021?

Here’s the same graph of U.S. deaths, but this time including COVID-19 deaths as the top segment.

Notice any correlation?  At this point, the explanation for the variation in non-COVID-19 deaths is pretty clear.  They coincide with the peaks in COVID deaths. There’s clearly something about COVID-19 deaths that is spilling over into other causes of death.  The big spikes in COVID-19 deaths have been creating secondary peaks in non-COVID deaths.

All this graph establishes is the fact of that, not the reason why.  But it’s easy enough to point to a few plausible mechanisms. 

First, those could simply be miscoded COVID-19 deaths, done deliberately or otherwise.  In some parts of the country, or some social circles, there’s probably quite a stigma associated with dying from a non-existent disease that was made up by liberals.  And so, in the same way that other stigmatized diseases have been under-reported on death certificates (such as dementia), COVID-19 may have been deliberately under-reported, at the family’s behest.

Second, those could be direct medical spillovers from COVID-19 onto other diseases of the frail.  Of all persons who die with COVID-19 mentioned on the death certificate, six percent are said to have died from other causes, with COVID-19 as a secondary diagnosis.  (I documented that back in one of my earlier analyses of COVID-19 deaths, cited above).  For some frail individuals, COVID-19 may have hastened an otherwise-impending death from other causes, and thus caused a coincident peak in non-COVID deaths, broadly speaking.

Third is the potential for crowd-out of health care access.  In some areas, COVID-19 patients have crowded the hospitals and other facilities to the extent that persons with non-COVID diagnoses had difficulty obtaining care.  I think it’s at the edge of plausibility that some of the increase in non-COVID deaths might have been caused by that.  But I doubt that it’s much.  I would expect it to be quite rare that an individual ill enough to be nearing death would have been denied live-saving inpatient care under almost any circumstances.

Summary to this point.

Everything in the serious medical or epidemiological literature says that there’s no excess non-COVID-19 mortality associated with getting vaccinated.  The clinical trials show nothing.  The observational data shows lower mortality among the vaccinated (almost certainly an artifact of better health status or health habits).

By eye, the U.S. mortality data show no linkage whatsoever to the rate of COVID-19 vaccinations. 

Instead, it’s crystal clear that something about the peaks in COVID-19 deaths did, in fact, spill over onto non-COVID deaths.  Whether that’s mere mis-reporting of cause of death, or has some actual medical basis (COVID-19 hastened death in some frail people reported as having died from other causes, or COVID-19 caused additional deaths via hospital crowding), there is no way to tell from a simple graph.

Tomorrow, I’ll try to find whatever-it-was that prompted somebody to say that there were “excess non-COVID-19 deaths” in the 20- (or 30-) to 50 year old population.

Spoiler alert:  Near as I can tell, CDC only defines excess deaths for total deaths, not deaths by cause.  And CDC is the entity that defines any sort of official U.S. excess deaths measure, using a reasonably complex algorithm to do so.  What I’m trying to say is that, as far as I can tell so far, there is no such thing as excess non-COVID-19 deaths, defined by the U.S. CDC.  So where that particular bit of mis-information came from, I cannot yet say.  I’ll try to track down what I can.

Post #G21-056: First frost date trend and an outdated farmers’ market law in Vienna VA

 

Over the past two-and-a-half decades, our fall first-frost date has been getting later.

That’s not really a surprise.  Global warming and all that.  Temperatures are rising slightly in most of North America.  Among other things, the USDA hardiness zones have been shifting consistently northward.

The surprise here is the rate at which our first-frost date is changing.  In Fairfax County, it’s been getting later at the rate of about one day per year.  That may not not sound like much, but it means that our typical first-frost date is more than three weeks later than it was back in the 1990s.

I found that to be a surprisingly rapid change, so I thought I’d post it.

And then, maybe if I’m still feeling the math, I’ll work up the likelihood that this year will have the latest first-frost data on record for Fairfax County, VA.  But muse of math seems to have abandoned me, so that will have to be a separate Part II of this post. Continue reading Post #G21-056: First frost date trend and an outdated farmers’ market law in Vienna VA

Post #1305: William and Mary COVID-19 update to 10/22/2021

Six new cases this week, compared to five last week.

Source:  Calculated from William and Mary COVID-19 dashboard accessed 10/23/2021.

As with the national numbers (just-prior post), things seem to have settled into an equilibrium.  No evidence of a coming winter wave of COVID.  No evidence that it’s going away any time soon, either.

Post #1304: COVID-19 trend, last daily post

 

It has now been almost exactly one month since I had anything noteworthy to say about the U.S. COVID-19 trend.  Rather than continue to repeat the same story (average new case counts are falling, we’re still looking for any sign of a winter wave, vaccinations are flat), I’m going to stop these daily postings on COVID-19 trends.

Maybe I’ll do a weekly update, until such time as there is any material change.

Let me wrap up where things stand in the U.S. as of 10/22/2021.

In a nutshell, the entire U.S. COVID-19 scene is stagnant.  Seems like we’ve reached an equilibrium, for the time being.

Our daily new case rate is stuck at a high level in many northern states.  Nothing devastating, outside of a few excess deaths from lack of hospital capacity in a few areas.  Not going up, but not coming down either.

Our vaccination rate is stuck, with new vaccinations having slowed to a trickle.  People aren’t even getting booster shots very much now, after an initial flurry of interest.

In short, as of now, to me, this looks like the new normal.  Keep your antibodies up-to-date — or not, depending on your tolerance for needless risk — and, barring bad luck, the worst you’ll get will be something like a bad case of the flu.

And what of the one-to-two percent of formally-diagnosed new cases who end up dying, the eight  percent or so who end up hospitalized, and the unknown percent with long-term effects?  You’ll just have to hope that’s somebody else’s problem.

I’ll keep tracking it, and if I see any material change I’ll surely post about it.  But it’s a waste of everybody’s time to keep posting the same story day after day.

Continue reading Post #1304: COVID-19 trend, last daily post