Author: chogan@directresearch.com

Post #1314: William and Mary COVID-19 trend to 10-5-2021, 7 new cases this week.

 

I’m continuing to track this just because you can’t easily recover the history of case counts off the William and Mary COVID-19 dashboard.

I would classify this week’s seven new cases as no different from new case rate for the past six weeks.  In other words, I wouldn’t read anything into it.

Source:  Calculated from William and Mary COVID-19 dashboard, last accessed 11/5/2021.

If you read any of the rest of my blog — and there’s no reason you should — you can see that I’m still waiting for any sign of a U.S. winter COVID-19 wave.

So far, there’s nothing.  Just a little flattening of the trend in the past two weeks.  Same thing appears to be happening in Canada.

But in the U.S. South, and Virginia in particular, the COVID-19 new case rate continues on a modest downward trend.

If that changes, I’ll surely note it in these William and Mary posts.

For now, no news is good news.

Post #G21-058: Nuts, peppers, and storing up for winter. Part 2: Peppers

 

This is the gardening post I started to write yesterday.  We’ve finally hit full fall conditions here in Northern Virginia, with frost or near-frost conditions each night.  So this is a post about a few final things I learned in this year’s gardening.

In a nutshell:

  • If you are planting sweet potatoes, plant lots of slips, rather than counting on the spreading of the vines to fill your beds.
  • Radiant barrier works well to extend the fall season of low-to-the-ground crops such as lettuce.
  • Might as well plant what survives well, rather than struggle to keep ill-suited crops alive.

Sweet potato nuances:  In times of famine …

“In times of famine, we’d be glad to have that.”  That’s the polite phrase my wife uses when I pull some undesirable bit of produce out of the garden.

It’s far nicer than “who in their right mind would eat that”, yet makes the same point.  It can be said equally of the undesirable (e.g., eggplant), the ludicrously undersized (e.g., pinky-sized carrots), and the only-partially-edible (e.g., spade-marked potatoes).

But before I diss the sweet potato as mere famine-food, let me sing its praises.  As far as I can tell, it needs absolutely no care whatsoever, other than keeping it watered until it gets established.  It grows like a weed, covering its beds and shading out any actual weeds.  It puts out lovely little morning-glory-type flowers (as it is in the same family as morning glory).  It produces a lot of calories per square foot.  You can plant it beneath taller plants (such as sunflowers or peppers) and it’ll cover the ground beneath and produce tubers.  And harvest is easy — peel back the vines, scrape the soil, and you’ll see the tops of the sweet potatoes, ready to be pulled.

The yield of calories per square foot is only slightly lower than potatoes (per this reference).  If I’ve done the metric-to-ridiculous conversion correctly, that works out to just about 100 edible calories per square foot for either of them.

I learned one important thing about sweet potato cultivation this year:  Plant lots of slips.

This year, I grew them on a lark.  I had a few store-bought sweet potatoes that had gotten moldy, and I decided to try to grow slips from them rather than just toss them.  One out of three moldy potatoes yielded slips. But I figured it wouldn’t matter, as they would spread, and could be easily propagated by cutting the ends of vines and re-planting them.

So I started with just a handful of slips, and I let those spread to fill out the allotted portions of the beds.  I had heard that the vines would put out sweet potatoes wherever they set down roots, as they spread out.  I figured that I’d end up with a bed full of sweet potatoes, despite starting with just a few plants.

That was a mistake.  Sure, the vines will put out additional sweet potatoes as they spread.  But each vine only puts out big sweet potatoes at the original rooting spot for that vine.  As it went along, it produced additional sweet potatoes at various nodes along the vine.  But all of those “secondary” sweet potatoes were much smaller.  

Here’s my harvest, from about 50 square feet of raised beds.  (The hammer is  there to give a sense of scale.)

By weight, I ended up with a roughly 60/40 mix of sweet potatoes of the size you’d see in the store, and sweet potatoes of the “in times of famine” variety.  Large enough that they’re probably worth the effort of peeling and eating.  But only just.

The moral of the story?  In my climate (Zone 7), plant lots of slips.  You can grow them the lazy way, by planting a few slips and letting the vines run to cover the allotted bed space.  But you don’t want to.  That gives you a few good-sized sweet potatoes, and a whole lot of undersized ones.  I’d have done far better to have had three times as many slips, and kept the vines one-third as long.

Would I plant these again?  You bet.  I’m just going to plant them a little smarter next year.  Stick them in the ground in the spring.  Come back in the fall and harvest a significant amount of food.  That’s pretty hard to argue with.

Radiant barrier for late lettuce.

In April (Post #G21-018), I tested the idea of using a radiant barrier to keep raised beds warm at night.  And by tested, I mean tested.  I used data loggers to track temperatures overnight in beds with and without a radiant barrier cover.  The cover raised the bed temperatures by about 10F.

In Virginia, 10F should add about a month to the growing season.  In Vienna, VA, over the past 30 years, the median date at which nighttime temperatures reached 22F or lower was roughly December 8.  Compared to an expected first-frost date in the first week of November.

So, this fall, I’m putting that to use.  Beneath the radiant barrier above is a small patch of lettuce.  So far, practice validates theory.  My lettuce is still alive despite a couple of frosts so far this week.  I hope to grow that lettuce — albeit slowly (Post #G21-055) — into December.

In the end, I’m not sure this is any less effort than a hoop-house style greenhouse, set atop the bed.  (PVC pipes bent into semi-circles, anchored to the ground, and covered with clear plastic sheet.)  But I already own the pieces of radiant barrier, cut to size.  So radiant barrier it is.  It works.

Final harvest before winter:  Peppers and other stragglers.

With frost coming, I did that garden ritual of picking absolutely everything that was left in the garden.  That yielded the artfully arranged jumble you see above.  Or the more orderly view of the same pile, below.

This year, overwhelmingly, what was left was peppers.  Green to the left, banana to the right, cayenne at the top.  (The cayennes are green, but in theory they will turn red now that they’ve been picked.)

I’m ambivalent about peppers.  They don’t produce a lot of calories.  But they pickle well, they’re OK in salads, and they have the outstanding advantage of taking care of themselves.  Nothing around here seems to bother them much.

The lesson learned here is that I didn’t start out to have a pepper-heavy garden.  With the exception of the eggplant and beans, these are the long-term survivors of what I planted back in the spring/early summer.  With the lesson being that if I’m aiming for the best yield per unit of effort, maybe I need to change my attitude toward a family of produce that manages to last the whole year with no effort on my part.

Concluding remarks for the 2021 gardening year.

At the end of 2021, the only things left growing are some lettuce, and some garlic that I planted for harvest next year.  So now’s a good time to recap and tentatively plan for what I’ll grow next year.

Non-food crops:  Sunflowers, marigolds, zinnias.  These are nice for taking up the odd corners of the garden and attracting bees.  Zero upkeep other than watering the sunflowers in the driest part of the year.  The sunflowers require serious deer deterrents.  But they look nice, they feed the bird and the bees.  So why not grow them again.

Low-maintenance starchy root crops:  Potatoes, sweet potatoes.  Those are both a definite yes for next year.  So far those have been zero maintenance with good yield.  Fresh potatoes tasted particularly good.  I won’t bother with fingerling potatoes (turned out way too small).  I’ll plan to fill a bed with sweet potato slips, rather than count on the spread of the vines to fill the bed.

Tomatoes:  Yes, but.  I will continue to “follow the rules” as I did this year, including staking and trimming.  But I need to stagger the plantings by month so that I have them coming in all year.  I have a least-effort process for making small batches of tomato sauce down cold (Post #G21-046).  But if I’m going to end up making sauce, I should just go ahead and plant Romas or similar, as that should be much more energy-efficient (Post #G21-046).  The home-dried tomatoes were a big hit, so I will definitely do that again next year.  Given that, it’s well worth working out a practical way to do that with solar energy, in my humid climate (Post #G21-050).

Cucumbers and summer squash.  I’m going to give those a pass next year.  I expected these to be mainstays of my garden.  Instead, after one year of bliss, they turned out to be nothing but trouble.  I how have a garden area infested with cucumber beetles and targeted by squash vine borers.  I may consider growing parthenocarpic (self-fertile, no-bees-needed) cucumbers under netting.  But honestly, once you reach that point, it’s like Mother Nature is telling you to grow something else.

Butternut squash.  Those are a definite yes.  They seem to grow well, produce a reasonable yield of calories per square foot, and keep well once harvested.  And they’re tasty.  I can even keep the powdery mildew off them if I’m willing to put in the effort (Post #G20).  The traditional Waltham variety has beaten all others that I’ve tried.  And they all taste the same.  So I see no reason to plant anything but that.

Green beans.  Despite early failures, those are definitely on the list.  For some reason, my first two plantings got hit by common bean mosaic.  Only the last planting had a significant yield.  They are labor-intensive to pick, but when they grow, they produce a nice steady yield.

Peas.  Of course, peas.  No work, some yield.  Every year, I am tempted into growing “bush type” peas, figuring they need no support.  Every year, I regret that when I end up with a tangle of peas that is difficult to harvest and impossible to weed.  So my pledge is never to grow peas without support again.  No matter what.

Beets, rutabagas, turnips, radishes.  Maybe.  I’m taking radishes off the list.  Even if they grow to size, I just don’t like them enough to bother to grow them.  Beets have been a total failure due to failure-to-sprout.  But I now know this is a common problem in heavy soils, and I’ll try something new next year.  Rutabagas and turnips were a near-total-failure this year, for reasons unknown.  But the turnip varieties that grew were tasty — not at all like the turnips of my youth.  So these remain on the list, if only because, in theory, you can get an early spring crop of them.  I’m not going to bother with a fall crop because, unlike the spring crop, the fall-planted ones were devastated by insect or insects unknown.

Lettuce:  Yes.  I never had any luck at all in the past, but this year, the lettuce seemed to thrive with no intervention on my part.  Zero calories, but nice in salads.  I’ll go for both a spring and a fall crop again.

Peppers.  Well, I guess so.  I mean, they are edible, they produce a nice steady crop, and (this year, at least) they seem to grow with no intervention on my part.  They make a nice lacto-fermented pickle when there are more than can be eaten at once.  Now that I know that I can grow them, rather than pick up the first seed pack I see at the hardware store, I’ll do a little more research on sweet pepper varieties.

Others.  I’ll probably try okra again, but only if I can get my hands on some of the high-yield varieties.  Four mature Clemson Spineless never gave me enough pods at one time to do anything with.  Eggplant, I may try for a late-spring planting.  A planting for fall harvest yielded a lot of leaves and little in the way of anything edible.  I have a few herbs that may overwinter, and I have garlic started for harvest next year.  I may try walking onions next year.  Not that I’m particularly fond of them, but every other variety of onion I have tried has failed.  I’m still undecided on pumpkins, if only because they need a lot of space and a lot of time to mature.  If I plant them again, they are going in early, in the back corners of the yard.  And then if they survive, great, and if they don’t, so be it.

That’s it for this garden year.  I don’t anticipate posting anything about gardening until next year.  If then.

Post #1313: Nuts, peppers, and storing up for winter. Part 1: Nuts.

 

With the onset of cold weather, I’m now waiting to see what this year’s crop of nuts is going to look like.  I am of course referring to events of two days ago, here in Virginia.  I’m hoping that our newly elected leadership will be in the mold of traditional (that is, rational) Virginia Republicans.  But ever since the last Republican administration here, which I will describe briefly below, that assumption of rationality has been questionable.

Anyway, this started off as a post on gardening, but quickly morphed into a post on politics.

Let me start with the interesting fact that you probably haven’t realized, first.  And then get into it.

Continue reading Post #1313: Nuts, peppers, and storing up for winter. Part 1: Nuts.

Post #G21-057: First frost, fall garden fail, COVID winter prep

 

Depending on exactly which forecast you believe, we should have our first frost in Vienna VA sometime in the next few days, possibly as early as tonight.  The National Weather Service is showing lows of 33F for the next few nights at Dulles Airport.  Other forecasts show lows of 31F.

A first frost date in the next few days puts us more-or-less exactly on the recent upward trend line.  This is the National Weather Service data for Dulles, VA. for the past few decades.

Not unexpected.

At this point, I can evaluate my “fall garden” as more-or-less a complete failure.  In theory, you can plant crops late in the summer, for fall harvest.  In practice, as far as I can tell, plants grow so slowly in the reduced temperatures and sunlight of the fall (Post #055) that the harvest is hardly worth the effort.

Plants that were already well-established continued to produce at reduced levels.  E.g., I got a few more peppers off the pepper plants.  But the plants that I put in at the end of August have produced more-or-less nothing.  A few eggplant, a few lettuce leaves.  Not worth the bother.

In hindsight, I note that a lot of the sites that I referenced said that you can plant certain crops late in the year.  And that was true.  I planted them, and, in theory, I got them in before the days-to-maturity exceeded the likely first frost date.  I did, in fact, successfully grow them.

I think I’ve learned the difference between “can” and “should” in this case.  I can direct-sow crops in late summer for a fall harvest.  But I’m not convinced that I should.  This year, that seems to have been a near-total waste of time. Either I have to start my fall garden in the heat of late July, or start the plants indoors for planting outside in late August.  Or just skip it.

Finally, with first frost, we are now starting the season of low indoor relative humidity.  As I have noted in many prior posts, I think that low relative humidity increases the spread of respiratory illness.   I believe that national heating and cooling experts say the same:

As of today, there’s scant indication that there will be any resurgence of COVID-19 this winter.  That said, I’m sticking to the plan.  I have a couple of hygrometers placed around my home.  (Why not?  They’re cheap.)  When indoor relative humidity dips below 40 percent, I’m going to drag my humidifiers out of the closet and get them running.  As with wearing a mask, or getting vaccinated, it’s just another harmless bit of cheap insurance against airborne illness.

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.