Excess Deaths and Avoidable Deaths

07 Monday Mar 2022

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Adverse Events, Anti-Coagulants, Avoidable Deaths, Blood Clotting, Blood Thinners, CDC, Covid-19, Death of Expertise, Deaths of Despair, Deferred Care, Emergency Use Authorization, EUA Shadow Deaths, Excess Deaths, Incidental Infections, Lockdown Deaths, Omicron Variant, Our World In Data, Post-Mortem Testing, Prime Age Deaths, Randomized Control Trials, The Ethical Skeptic, USMortality.com, Vaccine Efficacy, Vascular Integrity

Understanding the severity of the coronavirus pandemic is more straightforward when measured in terms of excess deaths, rather than total Covid deaths. We’ve had a large number of excess deaths in the U.S., but not all of them can be attributed to Covid. It’s also worth asking whether some of the deaths were avoidable, because that reflects even more profoundly on the success or failure of public policy and the health care system in dealing with the challenge. Unfortunately, while the precise number of avoidable deaths the nation has suffered is speculative, it is nevertheless significant.

Bad Metrics

A huge problem with using total Covid deaths as a measure of pandemic severity is that no one is confident in the accuracy of official statistics. There are reasons to suspect over-counting in the U.S. due to financial incentives created for hospital systems by the CARES Act. These were exacerbated by the CDC’s absurd 2020 recommendations for the completion of death certificates. Essentially, any non-primary Covid entry on a death certificate was sufficient to count the death as from Covid. No other disease is or has ever been tallied like that.

There is an important distinction between deaths “with Covid” and deaths “from Covid” that has been acknowledged only recently by health authorities. A death “with Covid” can occur when a patient tests positive for Covid after being admitted to a hospital for another primary ailment. Thus, deaths from other causes like heart failure have been improperly coded as Covid deaths under the CDC’s guidelines. Even tragedies like auto fatalities have been coded as Covid deaths.

At the same time, some public health “elites” insist that many Covid deaths in the community have gone unreported. That might have been true in the early weeks of the pandemic. However, post-mortem testing by medical examiners began to spread by April 2020, though there was a shortage of tests, and the CDC issued guidelines to encourage it late in the year.

Counting excess deaths from all causes avoids these controversies, including differences across countries in the way they record Covid deaths. It’s also possible to break down excess death into broad categories of causes, though the task is complex.

How Many?

First some simple accounting. Let’s define all-cause mortality during a period (Mort) as Covid deaths (C) plus plus all other mortality (M), or Mort = C + M. Expected mortality in the absence of a pandemic would be Exp(Mort) = Exp(M). Usually this expected value is taken as an average of deaths over several previous years. Therefore, excess mortality during the pandemic is:

EM = C + M – Exp(M)

How many excess deaths have we actually seen during the pandemic? According to Our World In Data, the figure was 950,000 as of Jan 9th. USMortality.com puts the excess at about 965,000 through the end of 2021. So these two sources are in close agreement, which says a lot given the usual difficulty of getting pandemic numbers to tie-out across sources

Through 2021, cumulative Covid deaths (by date of death) were almost 850,000. That’s less than excess deaths, so it’s obvious that other factors have contributed to the excess. Interestingly, 2021 was worse for excess deaths than 2020 for all age groups except 85+. Some have suggested the most vulnerable in this highly vulnerable age group had already succumbed to Covid in 2020, but there may have been other reasons for the difference.

Non-Covid Excesses

As noted above, some of the Covid deaths were misattributions. If we understand C to include only deaths “from Covid”, then we must acknowledge that M includes deaths from other causes but “with Covid”, as well as all deaths without Covid diagnoses. For example, because of the confounded way in which Covid deaths have been counted, a death from heart disease could end up in the official count of C, but it should be included in M instead.

The figures above imply 100,000+ excess deaths during the pandemic not associated with Covid diagnoses. If we add to those the “with Covid”, incidental total, then perhaps 300,000 – 400,000 excess deaths during the pandemic were from non-Covid primary causes!

Lockdown effects are a prime suspect in these non-Covid deaths. For example, if health care was deferred because hospitals cancelled or delayed elective procedures, or because patients feared the hospital environment, that would certainly manifest in premature deaths. Deaths of despair or neglect were also in excess, as one should expect when populations are subjected to prolonged periods of isolation.

These kinds of deaths are so-called “lockdown” deaths because they could have been avoided without such stringent policy measures and the propagation of fear by public health authorities. Those who might protest this nomenclature should note that lockdowns have been unsuccessful in mitigating the pandemic (and see here). After all, in terms of excess deaths, the Swiss approach was quite successful!

Avoidable Deaths

Many of the excess pandemic deaths were avoidable. Prolonged lockdown policies were driven by politics rather than sound public health reasoning. However, within the Covid death totals there is another category of avoidable deaths, and it is every bit as controversial. This post from The Ethical Skeptic (TES) goes into great detail on the matter. He takes a strong position, and some of his assertions and his accounting are subject to challenge. I sometimes find that TES’s posts contain ambiguities, and the graphical evidence he presents is often poorly labeled. Still, he has proven correct on other controversial issues, such as the ancestry and surprisingly early “vintage” of the Omicron variant.

Most of the “avoidable” Covid deaths (again, as distinct from the non-Covid lockdown deaths) occurred well after the primary symptoms of the infection (fever, cough, and cytokine storm) had passed. In the end, the real killers were follow-on problems induced by Covid, primarily related to blood clotting and compromised vascular integrity from endothelial dysfunction. These deadly complications were known very early in the pandemic. The following schematic from TES shows a Covid “death timeline”. The figures listed under the schematic show the large share of clotting and vascular problems involved in these deaths.

Over the past two years, not all of these patients were placed on anticoagulants or blood thinners early in the course of their infections. Indeed, many of them were told to “go home and sleep it off”. This is what happened to TES as well as a number of commenters on his Twitter account. I know several individuals who received the same advice from medical professionals. Even among the hospitalized, many were not placed on these drugs in a timely fashion, or until it was too late. TES adds the wrinkle that his physician indicated he should have been vaccinated! Short of that, tough luck, said the healer.

TES blames this medical “malfeasance” on the CDC’s Emergency Use Authorizations (EUA) for the Covid vaccines. In fact, he calls these deaths “EUA Shadow Deaths”, citing legal requirements associated with EUAs that would appear to prohibit alternatives such as therapies and even tests or studies of alternatives. That contention seems questionable given the CDC’s issuance of other EUAs for certain treatments, and there was no shortage of published experiments conducted during 2020-21.

The vaccine EUAs were not issued until late 2020, but TES claims that forces leading up to those EUAs were responsible for the failure to put patients on anticoagulants/blood thinners even earlier in 2020. The schematic says more than half of Covid deaths through the end of 2021 involved blood coagulation issues, and I have no reason to doubt those figures, which TES sources from the CDC. But He uses a value of 50% of Covid deaths to estimate that 421,000 Covid deaths were avoidable.

I’m not sure about that total, or rather, the use of the term “avoidable” in all those cases. I am sure, however, that we’ve seen a remarkable under-emphasis on therapeutics (and see here and here) relative to the emphasis on vaccines. The news media contributed to the dysfunction by condemning certain promising therapies for political reasons.

I’m also sure that there have been a meaningful number of patients who should have received anticoagulants/thinning agents but did not. Why did they not? Plausibly, the restrictions imposed by the vaccine EUAs made a difference, but clearly the medical community was not tuned into what should have been an obvious treatment regimen.

How many Covid deaths were truly avoidable? TES’s estimate of 421,000 seems too high if only because we can’t expect the dissemination of information through the medical community to be perfect. Moreover, some of these patients were undoubtedly on blood thinners already, or there might have been contraindications preventing the use of anticoagulants/thinners.

Nevertheless, a substantial number of deaths could have been avoided by more timely use of therapeutics and less stringent lockdown measures. Here is a chart from a tweet by TES showing another accounting for excess deaths:

Here, TES uses a slightly longer time frame, through about February 5, 2022, so the “EUA Shadow Death” total is somewhat larger, about 437,000, than shown in the earlier schematic. He attributes about 800,000 excess deaths, or 77%, to Covid, most of which he believes were avoidable deaths.

Lockdown deaths account for some of the additional 236,000 excess deaths reported in the chart, and probably a large share of the roughly 90,000 non-natural deaths labeled #3 (SAAAAD = “Suicide Addiction Abandonment Abuse Accident & Despair”; the two other categories in #3 relate to non-Covid illnesses acquired in-hospital or adverse reactions to medications). The Unknown/Abnormal category may include some lockdown deaths, but more on that category below.

If TES is correct about shadow deaths, the “avoidable” pandemic death total might account for well over half of all excess deaths. I suspect it might account for half, but even if less, it’s clear that avoidable deaths have been a huge part of the pandemic’s toll.

Vaccine Adverse Events

There’s been much speculation about the large number of Unknown/Abnormal deaths that have been coded during the pandemic: more than 65,000 in the chart above. One caveat is that an “unknown” cause of death usually means the cause is ambiguous: there might have been several factors contributing to the death such that the medical examiner was unable to assign a definitive cause. That status can be temporary as well. Still, the surge is noteworthy.

Unfortunately, there were an unusual number of excess deaths in younger age brackets in 2021, especially in the second half of the year after vaccinations had reached a fairly large share of the population. The pace of those deaths hasn’t yet abated in 2022. The next chart, from USMortality.com, shows excess mortality in the 25 – 44 age bracket in 2020 – early 2022.

Many of these prime age deaths could be a continuing hangover from deferred medical care and depression. There are claims, however, that the vaccines themselves killed a significant number of individuals. The upsurge in excess deaths suggests to some that the vaccines have had a much greater number of “adverse events” than we’ve seen reported by the CDC and the news media.

Here is how TES presents the data on excess deaths and vaccinations. The chart title is his somewhat confusing attempt to summarize the meaning of the lines plotted. The left axis measures the pace of vaccinations by week and the right access measures weekly excess non-Covid natural-cause deaths.

I have no doubt as to the efficacy of the vaccines against serious Covid outcomes in high-risk groups, though vaccine efficacy has been drastically overstated by the Biden Administration. The balance of risks for older individuals is clearly in favor of vaccination. Still, I’ve long felt that vaccination is less compelling for people in younger age brackets, and it’s possibly a bad idea. That’s both because Covid is a much smaller risk to them and because of possible vaccine risks, such as myocarditis.

To the extent that natural-cause, non-Covid excess deaths among younger age cohorts have been driven by unnecessary vaccinations, those deaths were avoidable. I’m not convinced of the significance, and it’s clear that among hospitalized Covid patients, outcomes have been better among the vaccinated. The following chart is from the link in the previous paragraph:

That sort of pattern might mean more deaths among the unvaccinated could have been avoided, on balance, had they opted for the jab. In almost all things, however, I believe we should eschew blanket mandates and instead offer protection to those seeking it in the high-risk population.

Conclusion

As many as 30% of Covid deaths to date are likely misattributions in which Covid was not really the primary cause of death. Nevertheless, excess Covid deaths “from Covid” as the primary cause are probably approaching 700,000 today.

The pandemic was certainly bad enough without a slew of bad calls by the public health and medical establishments. Of the 950,000+ excess deaths that occurred through the end of 2021, over 100,000 were not attributed to Covid. If we include deaths mis-attributed to Covid, the non-Covid total is likely in excess of 300,000 and could be as high as 400,000. It’s time to acknowledge that lockdowns and fear-mongering led to a large number of those deaths, and most of those deaths were avoidable. However, while I am skeptical, the number of deadly adverse effects from vaccines in the prime age population is an open question.

Another class of avoidable deaths was a product of the underemphasis on Covid therapies by the medical establishment. There were many cases of promising, repurposed drugs that were shouted down after so-called experts insisted that their use must be withheld until adequate randomized control trials (RCTs) had confirmed their efficacy. Not only did this ignore the long history of clinical evidence as a guide to medical practice. It also ignored the frequent real-world inadequacies that plague RCTs.

At the same time, obvious complications of the vascular system, primarily blood clotting, were not treated in a timely way or as a precautionary treatment’s, at least prior to hospitalization. Adding a conservative allowance for these deaths to the other avoidable deaths probably means that at least half of the excess deaths during the pandemic were avoidable. As of March 2022, that’s over half a million deaths! We can chalk it up to mismanagement and miscommunication by the public health establishment with a dash of ignorance, and perhaps some malfeasance, by health care practitioners. The death of expertise, indeed!

Chill-Out Advisory: Pandemic to Endemic Means Live Again

13 Sunday Feb 2022

Posted by pnoetx in Pandemic, Public Health, Uncategorized

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Acquired Immunity, Biden Administration, CDC, Child Risks, Covid-19, Covid-Like Symptoms, Covidestim.org, Delta Variant, EU Visits, HOLD2, Hope-Simpson Seasonal Pattern, Hospital Utilization, Hospitalizations, Incidental Infections, John Tierney, Lockdowns, Mask Efficacy, Natural Immunity, Omicron BA.1, Omicron BA.2, Omicron Variant, Our World In Data, Phil Kerpen, Staffed Beds, Teachers Unions, Tradeoffs, Transmissability, Vaccine Efficacy, Vaccine Risks, Virulence

We might be just be done with the coronavirus pandemic. That is, it appears to be transitioning to a more permanent endemic phase. What follows are a few details about the Omicron wave and its current status, an attempt to put the risks of Covid in perspective, and a few public policy lessons that are now gaining broad currency but should have been obvious long ago.

What’s The Status?

The Omicron variant became the dominant U.S. strain of the coronavirus in December. Omicron outcompeted Delta, which was very good news because Omicron is far less severe. The chart below (from the CDC Data Tracker site) shows Omicron’s rapid ascendance and displacement of the Delta variant. The orange bar segments represent the proportion of cases of the Delta strain, while the purple and pink segments are Omicron sub-variants known as BA.1 and BA.2, respectively. BA.2 is even more transmissible than BA.1 and is likely to become dominant over the next month or so. However, the BA.2 sub-variant appears to be far less virulent than Delta, like BA.1.

Despite a record number of infections over a period of a month or so, the Omicron wave is tapering just as rapidly as it ramped up, as the next chart demonstrates. In fact, covidestim.org shows that cases are now receding in all states, DC, and Puerto Rico. Here are new cases per million people from Our World in Data:

Whether BA.2 causes cases to plateau for a while, or even a secondary Omicron “wavelet”, is yet to be seen. That would be consistent with the normal Hope-Simpson seasonal pattern of viral prevalence in the northern hemisphere (hat tip: HOLD2):

Data problems make the Omicron wave difficult to assess, however. We don’t know the share of incidental infections for the U.S. as a whole, but more than half of hospitalized Covid patients in Massachusetts and Rhode Island are classified with incidental infections. The proportion in the UK is estimated to be rising and approaching 30% of total cases, with much higher percentages in many regions of England, as shown below.

As I’ve emphasized in the past, case numbers should not be the primary gauge of the state of the pandemic, especially with a more highly contagious but relatively mild variant like Omicron. Hospitalizations are a better measure, but only if “incidental” infections are removed from the counts. That’s been acknowledged only recently by the public health establishment, and even the Biden Administration is emphasizing it as a matter of sheer political expediency. Another measure that might be more reliable for assessing the pandemic in the community as a whole is the number of emergency room patients presenting Covid-like symptoms. From the CDC Data Tracker:

There is no doubt that incidental infections create complications in caring for patients with other ailments. That has a bearing on the utilization of hospital capacity. Generally, however, strains on hospital capacity during the pandemic have been greatly exaggerated. This is not to diminish the hard work and risks faced by health care workers, and there have been spot shortages of capacity in certain localities. However, in general, staffed beds have been more than adequate to meet needs. This chart, like a few others below, is courtesy of Phil Kerpen:

With the more highly transmissible variants we have now, it’s not at all surprising to see a high proportion of incidental cases among inpatients. Incidental infections are likely to inflate counts of Covid deaths as well, given the exceptional and odd way in which Covid deaths are being recorded. It will be some time until we see full U.S. data on cases and deaths net of incidental infections. Moreover, many of the Covid deaths in December and January were from lingering Delta infections, which might still be a factor in the February counts.

How Are Your Odds?

The mild or asymptomatic nature of most Omicron cases, the large proportion of incidental hospitalizations, and the knowledge that Omicron is not a deep respiratory threat should offer strong reassurance to healthy individuals that the variant does not pose a great risk. According to a recent CDC report, in a sample of almost 700,000 vaccinated individuals aged 65 or less without co-morbidities, there were no Covid fatalities or ICU admissions during the 10 months from December 2020 through October 2021. There was only one fatality in the sample of healthy individuals older than 65. There were just 36 fatalities across the full sample of over 1.2 million vaccinated individuals, so COVID’s fatality risk was only about 0.3%. Of those deaths, 28 were among those with four or more risk factors (including co-morbidities and > 65 years). And this was before the advent of Omicron!

I have a few doubts about the CDC’s sample selection and vagaries around certain definitions used. Nevertheless, the results are striking. However, the study did not address risks to unvaccinated adults. Another more limited CDC study found that vaccinated patients were still less likely than the unvaccinated to require critical care during the Omicron wave.

A separate CDC study found a 91% reduction in the likelihood of death for Omicron relative to Delta. A study from the UK (see summary here) found that Omicron cases were 59% less likely than Delta cases to require hospitalization and 69% less likely to result in death within 28 days of a positive test. Omicron was far less deadly among both the vaccinated and the unvaccinated, and the latter had a larger reduction in the likelihood of death. The study was stratified by age as well, with less severe outcomes for Omicron among older cohorts except in the case of death, for which there was no apparent age gradient.

Another unnecessarily contentious issue has been the risk to children during the pandemic. Based on the data, there should never have been much doubt that these risks are quite low. Apparently, however, it was advantageous for teachers’ unions to insist otherwise. Phil Kerpen soundly debunks that claim with the following chart:

Covid has been less deadly to children from infancy through 17 years than the pre-pandemic flu going back to 2012! Oh yes, but teachers FEAR transmission from the children! That claim is just as silly, since children are known to be inefficient transmitters of the virus (and see here).

Now that Omicron has relegated the Delta variant to the history books, the risks going forward seem much more manageable. Omicron is less severe, especially for the vaccinated. Levels of acquired (natural) immunity from earlier infections are now much higher against older strains, and Omicron infections seem to be protective against Delta.

In commentary about the first CDC study discussed above, John Tierney lends perspective to the odds of death from pre-Omicron Covid:

“Those are roughly the same odds that in the course of a year you will die in a fire, or that you’ll perish by falling down stairs. Going anywhere near automobiles is a bigger risk: you’re three times more likely during a given year to be killed while riding in a car, and also three times more likely to be a pedestrian casualty. The 150,000-to-1 odds of a Covid death are even longer than the odds over your lifetime of dying in an earthquake or being killed by lightning.”

Yet with all this research confirming the low odds of death induced by Omicron, why have we seen recent deaths at levels approaching previous waves? First, many of those deaths are carried over from Delta infections. That means deaths should begin to taper rapidly as February reports roll in. And remember that daily reports do not show deaths by date of death. Deaths usually occur weeks or even months before they are reported. That also means some of the deaths reported might be “harvested” from much earlier fatalities. Second, given the high levels of incidental Omicron infections, some of those deaths are misattributed to Covid, an issue that is not new by any means. Finally, while Omicron is relatively mild for most people, the high rate of transmission means that a high number of especially vulnerable individuals may be infected with severe outcomes. We have seen much more severe consequences for the unvaccinated, of course, and for those with co-morbidities.

Things We Should Have Known

I’ll try to keep this last section brief, but as an introduction I’ll just say that it’s almost as if we’ve been allowing the lunatics to run the asylum. To paraphrase one comment I saw recently, if you wonder why there is so much dissent, you ought to consider the fact the much of what our governments have done (along with many private organizations) was to prohibit things that were demonstrably safe (e.g., going outside, using swing sets, or attending schools) and to encourage things that were demonstrably harmful (e.g., deferring medical care, or masking small children).

The following facts are only now coming into focus among those who’ve been “following the politics” rather than “the science”, despite pretensions to the latter.

Specific public health initiatives often face steep economic, emotional, social, and countervailing health tradeoffs.
Lockdowns do NOT work.
Masks do NOT work (despite the CDC’s past and recent confusion on the matter).
Children are at very low-risk from Covid.
Children do NOT present high risks to teachers.
Natural immunity is more protective than vaccines.
Vaccines do NOT “stop the spread”.
Vaccine risks might outweigh benefits for certain groups and individuals.
Vaccines should NOT be relied upon at the expense of treatments.
Don’t reject treatments based on politics.
Vaccine mandates are unethical.

Grow Up and Chill Out!

Life is full of risks, and nothing has changed to alter wisdom gained in earlier pandemics. For example, this pearl from a 2006 publication on disease mitigation measures should be heeded (hat tip: Phil Kerpen):

If there is one simple message everyone needs to hear, it is to stop allowing the virus bogeyman to rule your life. It will never go away completely, and it is likely to present risks that is are comparable to the flu going forward. In fact, it might well compete with the flu, which means we won’t be dealing with endemic Covid plus historical flu averages, but some smaller union of the two case loads.

So get out, go back to work, or go have some fun! Get back truckin’ on!

CDC Makes a Bum Lead Steer: Alternate Reality vs. The Herd

16 Sunday May 2021

Posted by pnoetx in Herd Immunity, Pandemic

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Adam Kucharski, Andy Slovitt, Anthony Fauci, CDC, Degrees of Separation, Herd Immunity, Herd Immunity Threshold, Joe Biden, Jordan Schachtel, Nathan D. Grawe, Obesity, Phil Kerpen, Pre-existing Immunity, Precautionary Principle, Reproduction Rate, Seroprevalence, Sub-Herds, Super-Spreader Events, Vaccinations, Vitamin D, Zero COVID

Jordan Schachtel enjoyed some schadenfreude last week when he tweeted:

“I am thoroughly enjoying the White House declaring COVID over and seeing the confused cultists having a nervous breakdown and demanding the continuation of COVID Mania.”

It’s quite an exaggeration to say the Biden Administration is “declaring COVID over”, however. They’re backpedaling, and while last week’s CDC announcement on masking is somewhat welcome, it reveals more idiotic thinking about almost everything COVID: the grotesquely excessive application of the precautionary principle (typical of the regulatory mindset) and the mentality of “zero COVID”. And just listen to Joe Biden’s tyrannical bluster following the CDC announcement:

“The rule is now simple: get vaccinated or wear a mask until you do.

The choice is yours.”

Is anyone really listening to this buffoon?Unfortunately, yes. But there’s no federal “rule”, unless your on federal property; it constitutes “guidance” everywhere else. I’m thankful our federalist system still receives a modicum of respect in the whole matter, and some states have chosen their own approaches (“Hooray for Florida”). Meanwhile, the state of the pandemic looks like this, courtesy of Andy Slavitt:

False Assertions

The CDC still operates under the misapprehension that kids need to wear masks, despite mountains of evidence showing children are at negligible risk and tend not to be spreaders. Here’s some evidence shared by Phil Kerpen on the risk to children:

The chart shows the fatality risk by age (deaths per 100,000), and then under the assumption of a 97% reduction in that risk due to vaccination, which is quite conservative. Given that kind of improvement, an unvaccinated 9 year-old child has about the same risk as a fully vaccinated 30 year-old!

The CDC still believes the unvaccinated must wear masks outdoors, but unless you’re packed in a tight crowd, catching the virus outdoors has about the same odds as a piano falling on your head. And the CDC insists that two shots of mRNA vaccine (Pfizer or Moderna) are necessary before going maskless, but only one shot of the Johnson and Johnson vaccine, even though J&J’s is less effective than a single mRNA jab!

Other details in the CDC announcement are worthy of ridicule, but for me the most aggravating are the agency’s implicit position that herd immunity can only be achieved through vaccination, and its “guidance” that the unvaccinated should be dealt with coercively, even if they have naturally-acquired immunity from an infection!

Tallying Immunity

Vaccination is only one of several routes to herd immunity, as I’ve noted in the past. For starters, consider that a significant share of the population has a degree of pre-existing immunity brought on by previous exposure to coronaviruses, including the common cold. That doesn’t mean they won’t catch the virus, but it does mean they’re unlikely to suffer severe symptoms or transmit a high viral load to anyone else. Others, while not strictly immune, are nevertheless unlikely to be sickened due to protections afforded by healthy vitamin D levels or because they are not obese. Children, of course, tend to be fairly impervious. Anyone who’s had a bout with the virus and survived is likely to have gained strong and long-lasting immunity, even if they were asymptomatic. And finally, there are those who’ve been vaccinated. All of these groups have little or no susceptibility to the virus for some time to come.

It’s not necessary to vaccinate everyone to achieve herd immunity, nor is it necessary to reach something like an 85% vax rate, as the fumbling Dr. Fauci has claimed. Today, almost 47% of the U.S. population has received at least one dose, or about 155 million adults. Here’s Kerpen’s vax update for May 14.

Another 33 million people have had positive diagnoses and survived, and estimates of seroprevalence would add perhaps another 30 million survivors. Some of those individuals have been vaccinated unnecessarily, however, and to avoid double counting, let’s say a total of 50 million people have survived the virus. Some 35 million children in the U.S. are under age 12. Therefore, even if we ignore pre-existing immunity, there are probably about 240 million effectively immune individuals without counting the remaining non-susceptibles. At the low end, based on a population of 330 million, U.S. immunity is now greater than 70%, and probably closer to 80%. That is more than sufficient for herd immunity, as traditionally understood.

The Herd Immunity Threshold

Here and in the following section I take a slightly deeper dive into herd immunity concepts.

Herd immunity was one of my favorite topics last year. I’m still drawn to it because it’s so misunderstood, even by public health officials with pretensions of expertise in the matter. My claim, about which I’m not alone, is that it’s unnecessary for a large majority of the population to be infected (or vaccinated) to limit the spread of a virus. That’s primarily because there is great variety in individuals’ degree of susceptibility, social connections, aerosol production, and viral load if exposed: call it heterogeneity or diversity if you like. Variation across individuals naturally limits a contagion relative to a homogeneous population.

Less than 1% of those who caught the virus died, while the others recovered and acquired immunity. The remaining subset of individuals most vulnerable to severe illness was thus reduced over time via acquired immunity or death. This is the natural dynamic that causes contagions to slow and ultimately peter out. In technical jargon, the virus reproduction rate “R” falls below a value of one. The point at which that happens is called the “herd immunity threshold” (HIT).

A population with lots of variation in susceptibility will have a lower HIT. Some have estimated a HIT in the U.S. as low as 15% -25%. Ultimately, total exposure will go much higher than the HIT, perhaps well more than doubling exposure, but the contagion recedes once the HIT is reached. So again, it’s unnecessary for anywhere near the full population to be immune to achieve herd immunity.

One wrinkle is that CIVID is now likely to have become endemic. Increased numbers of cases will re-emerge seasonally in still-susceptible individuals. That doesn’t contradict the discussion above regarding the HIT rate: subsequent waves will be quite mild by comparison with the past 14 months. But if the effectiveness of vaccines or acquired immunity wanes over time, or as healthy people age and become unhealthy, re-emergence becomes a greater risk.

Sub-Herd Immunity

A further qualification relates to so-called sub-herds. People are clustered by geographical, social, and cultural circles, so we should think of society not as a singular “herd”, but as a collection of sub-herds having limited cross-connectivity. The following charts are representations of different kinds of human networks, from Nathan D. Grawe’s review of “The Rules of Contagion, by Adam Kucharski:

Sub-herd members tend to have more degrees of separation from individuals in other sub-herds than within their own sub-herd. The most extreme example is the “broken network” (where contagions could not spread across sub-herds), but there are identifiable sub-herds in all of the examples shown above. Less average connectedness across sub-herds implies barriers to transmission and more isolated sub-herd contagions.

We’ve seen isolated spikes in cases in different geographies, and there have been spikes within geographies among sub-herds of individuals sharing commonalities such as race, religious affiliation, industry affiliation, school, or other cultural affiliation. Furthermore, transmission of COVID has been dominated by “super-spreader” events, which tend to occur within sub-herds. In fact, sub-herds are likely to be more homogeneous than the whole of society, and that means their HIT will be higher than we might naively calculate based on higher levels of aggregation.

We have seen local, state, or regional contagions peak and turn down when estimates of total incidence of infections reach the range of 15 – 25%. That appears to have been enough to reach the HIT in those geographically isolated cases. However, if those geographical contagions were also concentrated within social sub-herds, those sub-herds might have experienced much higher than 25% incidence by the time new infections peaked. Again, the HIT for sub-herds is likely to be greater than the aggregate population estimates implied, The upshot is that some sub-herds might have achieved herd immunity last year but others did not, which explains the spikes in new geographic areas and even the recurrence of spikes within geographic areas.

Conclusion

It’s unnecessary for 100% of the population to be vaccinated or to have pre-existing immunity. Likewise, herd immunity does not imply that no one catches the virus or that no one dies from the virus. There will be seasonal waves, though muted by the large immune share of the population. This is not something that government should try to stanch, as that would require the kind of coercion and scare tactics we’ve already seen overplayed during the pandemic. People face risks in almost everything they do, and they usually feel competent to evaluate those risks themselves. That is, until a large segment of the population allows themselves to be infantalized by public health authorities.

CDC Wags Finger; Diners Should Wag One Back

09 Tuesday Mar 2021

Posted by pnoetx in Coronavirus, Public Health

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Biden Administration, Causality, CDC, COVID Relief Bill, Covid-19, Dining Restrictions, Hope-Simpson, Karl Dierenbach, Lockdowns, Mask Mandates, Masks, Non-Pharmaceutical interventions, NPIs, Seasonality, Spurious Correlation, Vaccinations, Zero COVID

The CDC’s new study on dining out and mask mandates is a sham. On its face, the effects reported are small. And while it’s true most of the reported effects are statistically significant, the CDC acknowledges a number of factors that might well have confounded the results. This study should remind us of the infinite number of spurious and “significant” correlations in the world. Here, the timing of the mandates (or their removal) relative to purported effects and seasonal waves is highly suspicious, and as always, attributing causality on the basis of correlation is problematic.

On one hand, the CDC’s results are contrary to plentiful evidence that mandates are ineffective; on the other hand, the results are contrary to earlier CDC “guidance” that masks and limits on indoor dining are “highly effective”. Nevertheless, the latest report has massive propaganda value to the CDC. The media lapped up the story and provided cover for Democrats eager to pass the COVID (C19) relief package. Likewise, the Biden Administration is apparently committed to the narrative of an ongoing crisis as cover for continued attempts to shame political opponents in states that have elected to “reopen” or remain open.

Right off the bat, the study’s authors assert that the primary mode of transmission of C19 is from respiratory droplets. This is false. We know that aerosols are the main culprit in transmission, against which cloth masks are largely ineffective.

Be that as it may, let’s first consider the findings on dining. There was no statistically significant effect on the growth rate of cases or deaths up to 40 days after restrictions were lifted, according to the report. In fact, case growth declined slightly. There was, however, a small but statistically significant increase after 40 days. The fact that deaths seemed to “respond” faster and with greater magnitude than cases makes no sense and suggests that the results might be spurious.

The CDC offers possible explanations the long delay in the purported impact, such as the time required by restaurants to resume operations and early caution on the part of diners. These are speculative, of course. More pertinent is the fact that the data did not distinguish between indoor and outdoor dining, nor did it account for other differences in regulation such as rules on physical distancing, intra-county variation in local government mandates, and compliance levels.

Finally, the measurement of effects covered 100 days after the policy change, but this window spans different stages of the pandemic. There were three waves of infections during 2020, which correspond to the classic Hope-Simpson pattern of virus seasonality. One was near year-end, but as each of the first two waves tapered (April-May, August-September), it should be no surprise that many restrictions were lifted. Within two months, however, new waves had begun. Karl Dierenbach notes that most of the reopenings occurred in May. Here’s how he explains the pattern:

“The map on the left shows counties where there was no on-premises dining (pink) in restaurants as of the beginning of May (4/30). … The map on the right shows that by the end of May, almost the entire country moved to allow some on-premises dining (green).”

“In the 100 days after May 1, cases nationwide fell slightly, then began to rise, and then plateaued.”

“And what did the CDC find happened after restaurants were allowed (changing mostly in May) to have on-premises dining? … Surprise! The CDC found that cases fell slightly, then began to rise, and then plateaued.”

The summer “mini-wave” is typical of mid- and tropical-latitude seasonality. Thus, the CDC’s findings with respect to dining restrictions are likely an artifact of the strong seasonality of the virus, rather than having anything to do with the lifting of restrictions between waves.

What about the imposition of mask mandates? The CDC’s findings show a much faster response in this case, with statistically significant changes in growth during the first 20 days. Another indicator of spurious correlation is that the growth response of deaths did not lag that of cases, but in fact deaths have reliably lagged cases by over 18 days during the pandemic. Again, the CDC’s caveats apply equally to its findings on masks. A large share of individuals adopted mask use voluntarily before mandates were imposed, so it’s not even clear that the mandates contributed much to the practice.

It’s a stretch to believe that mask mandates would have had an immediate, incremental effect on the growth of cases and deaths, given probable lags in compliance, exposure, and onset of symptoms. Moreover, a number of mask mandates in 2020 were imposed near the very peak of the seasonal waves. Little wonder that the growth rates of cases and deaths declined shortly thereafter.

We’ve known for a long time that masks do little to stop the spread of viral particles. They become airborne as aerosols which easily penetrate the kind of cloth masks worn by most members of the public, to say nothing of making contact with their eyes. The table below contains citations to research over the past 10 years uniformly rejecting the hypothesis of a significant protective effect against influenza from masks. There is no reason to believe that they would be more effective in preventing C19 infections.

The CDC’s report on dining restrictions and mask mandates is a weak analysis. They wish to emphasize their faith in non-pharmaceutical interventions (NPIs) to minimize risks. They do so at a time when the vaccinated share of the most vulnerable population, the elderly, has climbed above 50% and is increasing steadily. Thus, risks are falling dramatically, so it’s past time to weigh the costs and benefits of NPIs more realistically. The timing of the report also seemed suspicious, coming as it did in the heat of the battle over the $1.9 trillion COVID relief bill, which subsequently passed.

It’s also a good time to note that zero risk, including “Zero COVID”, is not a realistic or worthwhile goal under any reasonable comparison of costs and benefits. Furthermore, NPIs have proven weak generally (also see here); claims to the contrary should always make us wary.

CDC Flubs COVID Impact on Life Expectancy

03 Wednesday Mar 2021

Posted by pnoetx in Coronavirus, Public Health

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Acquired Immunity, Cause of Desth, CDC, Covid-19, Death Certificates, Deferred Care, Excess Deaths, Influenza, Kyle Smith, Life Expectancy, Mortality Rates, Overdoses, Peter B.Bach, STAT News, Suicide, Vaccinations, Zero Hedge

The CDC choked on a new analysis estimating COVID-19’s impact on U.S. life expectancy as of year-end 2020: they reported a decline of a full year, which is ridiculous on its face! As explained by Peter B. Bach in STAT News, the agency assumed that excess deaths attributed to COVID in 2020 would continue as a permanent addition to deaths going forward. Please forgive my skepticism, but isn’t this too basic to qualify as an analytical error by an agency that subjects its reports to thorough vetting? Or might this have been a deliberate manipulation intended to convince the public that COVID will be an ongoing public health crisis. Of course the media has picked it up; even Zero Hedge reported it uncritically!

Bach does a quick calculation based on 400,000 excess deaths attributed to COVID in 2020 and 12 life-years lost by the average victim. I believe the first assumption is on the high side, and I say “attributed to COVID” as a reminder that the CDC’s guidance for completing death certificates was altered in the spring of 2020 specifically for COVID and not other causes of death. Furthermore, if our objective is to assess the impact of the virus itself, under no circumstances should excess deaths induced by misguided lockdown policies enter the calculation (though Bach entertains the possibility). Bach arrives at a reduction in average life of 5.3 days! Of course, that’s not intended to be a projection, but it is a reasonable estimate of COVID’s impact on average lives in 2020.

The CDC’s projection essentially freezes death rates at each age at their 2020 values. We will certainly see more COVID deaths in 2021, and the virus is likely to become endemic. Even with higher levels of acquired immunity and widespread vaccinations, there will almost certainly be some ongoing deaths attributable to COVID, but they are likely to be at levels that will blend into a resumption of the long decline in mortality rates, especially if COVID continues to displace the flu in its “ecological niche”. I include the chart at the top to emphasize the long-term improvement in mortality (though the chart shows only a partial year for 2020, and there has been some flattening or slight backsliding over the past five years or so). As Bach says:

“Researchers have regularly demonstrated that life expectancy projections are overly sensitive to evanescent events like pandemics and wars, resulting in considerably overestimated declines. … And yet the CDC published a result that, if anything, would convey to the public an exaggerated toll that Covid-19 took on longevity in 2020. That’s a problem.”

There were excess deaths from other causes in 2020, which Bach acknowledges. Perhaps 100,000 or more could be attributed to lockdowns and their consequences like economically-induced stress, depression, suicide, overdoses, and medical care deferred or never sought. The Zero Hedge article mentioned above discusses findings that lockdowns and their consequences, such as unemployment spells and lost education, will have ongoing negative effects on health and mortality for many years. The net effect on life expectancy might be as large as 11 to 12 days. Again, however, I draw a distinction between deaths caused by the disease and deaths caused by policy mistakes.

The CDC’s estimate should not be taken seriously when, as Kyle Smith says, there is every indication that the battle against COVID is coming to a successful conclusion. Public health experts have not acquitted themselves well during the pandemic, and the CDC’s life expectancy number only reinforces that impression. Here is Smith:

“We have learned a lot about how the virus works, and how it doesn’t: Outdoor transmission, for the most part, hardly ever happens. Kids are at very low risk, especially younger children. Baseball games, barbecues, and summer camps should be fine. Some pre-COVID activities now carry a different risk profile — notably anything that packs crowds together indoors, so Broadway theater, rock concerts, and the like will be just about the last category of activity to return to normal.”

But return to normal we should, and yet the CDC seems determined to poop on the victory party!

COVID Now: Turning Points, Vaccines, and Mutations

20 Wednesday Jan 2021

Posted by pnoetx in Coronavirus, Pandemic, Vaccinations

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Alex Tabarrok, Case Fatality Rate, CDC, CLI, Convalescent Plasma, Covid-19, COVID-Like Illness, Date of Death, Herd Immunity, Herd Immunity Threshold, Infection Fatality Rate, Ivermectin, Johns Hopkins, Monoclonal Antibodies, Phil Kerpen, Provisional Deaths, South African Strain, UK Strain, Vaccinations, Youyang Gu

The pandemic outlook remains mixed, primarily due to the slow rollout of the vaccines and the appearance of new strains of the virus. Nationwide, cases and COVID deaths rose through December. Now, however, there are several good reasons for optimism.

The fall wave of the coronavirus receded in many states beginning in November, but the wave started a bit later in the eastern states, in the southern tier of states, and in California. It appears to have crested in many of those states in January, even after a post-holiday bump in new diagnoses. As of today, Johns Hopkins reports only two states with increasing trends of new cases over the past two weeks: NH and VA, while CT and WY were flat. States shaded darker green have had larger declines in new cases.

A more detailed look at WY shows something like a blip in January after the large decline that began in November. Trends in new cases have clearly improved across the nation, though somewhat later than hoped.

While the fall wave has taken many lives, we can take some solace in the continuing decline in the case fatality rate. (This is not the same as the infection mortality rate (IFR), which has also declined. The IFR is much lower, but more difficult to measure). The CFR fell by more than half from its level in the late summer. In other words, without that decline, deaths today would be running twice as high.

Some of the CFR’s decline was surely due to higher testing levels. However, better treatments are reducing the length of hospital stays for many patients, as well as ICU admittance and deaths relative to cases. Monoclonal antibodies and convalescent plasma have been effective for many patients, and now Ivermectin is showing great promise as a treatment, with a 75% reduction in mortality according to the meta-analysis at the link.

Reported or “announced” deaths remain high, but those reports are not an accurate guide to the level or trend in actual deaths as they occur. The CDC’s provisional death reports give the count of deaths by date of death (DOD), shown below. The most recent three to four weeks are very incomplete, but it appears that actual deaths by DOD may have peaked as early as mid-December, as I speculated they might last month. Another noteworthy point: by the totals we have thus far, actual deaths peaked at about 17,000 a week, or just over 2,400 a day. This is substantially less than the “announced” deaths of 4,000 or more a day we keep hearing. The key distinction is that those announced deaths were actually spread out over many prior weeks.

A useful leading indicator of actual deaths has been the percentage of ER patients presenting COVID-like illness (CLI). The purple dots in the next CDC chart show a pronounced decline in CLI over the past three weeks. This series has been subject to revisions, which makes it much less trustworthy. A less striking decline in late November subsequently disappeared. At the time, however, it seemed to foretell a decline in actual deaths by mid-December. That might actually have been the case. We shall see, but if so, it’s possible that better therapeutics are causing the apparent CLI-deaths linkage to break down.

A more recent concern is the appearance of several new virus strains around the world, particularly in the UK and South Africa. The UK strain has reached other countries and is now said to have made appearances in the U.S. The bad news is that these strains seem to be more highly transmissible. In fact, there are some predictions that they’ll account for 30% of new cases by the beginning of March. The South African strain is said to be fairly resistant to antibodies from prior infections. Thus, there is a strong possibility that these cases will be additive, and they might or might not speedily replace the established strains. The good news is that the new strains do not appear to be more lethal. The vaccines are expected to be effective against the UK strain. It’s not yet clear whether new versions of the vaccines will be required against the South African strain by next fall.

Vaccinations have been underway now for just over a month. I had hoped that by now they’d start to make a dent in the death counts, and maybe they have, but the truth is the rollout has been frustratingly slow. The first two weeks were awful, but as of today, the number of doses administered was over 14 million, or almost 46% of the doses that have been delivered. Believe it or not, that’s an huge improvement!

About 4.3% of the population had received at least one dose as of today, according to the CDC. I have no doubt that heavier reliance on the private sector will speed the “jab rate”, but rollouts in many states have been a study in ineptitude. Even worse, now a month after vaccinations began, the most vulnerable segment of the population, the elderly, has received far less than half of the doses in most states. The following table is from Phil Kerpen. Not all states are reporting vaccinations by age group, which might indicate a failure to prioritize those at the greatest risk.

It might not be fair to draw strong conclusions, but it appears WV, FL, IN, AK, and MS are performing well relative to other states in getting doses to those most at risk.

Even with the recent increase in volume, the U.S. is running far behind the usual pace of annual flu vaccinations. Each fall, those average about 50 million doses administered per month, according to Alex Tabarrok. He quotes Youyang Gu, an AI forecaster with a pretty good track record thus far, on the prospects for herd immunity and an end to the pandemic. However, he uses the term “herd immunity” as the ending share of post-infected plus vaccinated individuals in the population, which is different than the herd immunity threshold at which new cases begin to decline. Nevertheless, in Tabarrok’s words:

“… the United States will have reached herd immunity by July, with about half of the immunity coming from vaccinations and half from infections. Long before we reach herd immunity, however, the infection and death rates will fall. Gu is projecting that by March infections will be half what they are now and by May about one-tenth the current rate. The drop will catch people by surprise just like the increase. We are not good at exponentials. The economy will boom in Q2 as infections decline.”

That sounds good, but Tabarrok also quotes a CDC projection of another 100,000 deaths by February. That’s on top of the provisional death count of 340,000 thus far, which runs 3-4 weeks behind. If we have six weeks of provisionals to go before February, with actual deaths at their peak of about 17,000 per week, we’ll get to 100,000 more actual deaths by then. For what it’s worth, I think that’s pessimistic. The favorable turns already seen in cases and actual deaths, which I believe are likely to persist, should hold fatalities below that level, and the vaccinations we’ve seen thus far will help somewhat.

Allocating Vaccine Supplies: Lives or “Justice”?

29 Tuesday Dec 2020

Posted by pnoetx in Pandemic, Public Health, Uncategorized, Vaccinations

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Alex Tabarrok, CDC, Chicago, Co-Morbidities, Covid-19, Emma Woodhouse, Essential Workers, Historical Inequities, Infection Fatality Rate, Long-Term Care, Megan McArdle, Super-Spreaders, Transmission, Vaccinations, Vaccine Allocation, Vaccine Passports

There are currently two vaccines in limited distribution across the U.S. from Pfizer and Moderna, but the number and variety of different vaccines will grow as we move through the winter. For now, the vaccine is in short supply, but that’s even more a matter of administering doses in a timely way as it is the quantity on hand. There are competing theories about how best to allocate the available doses, which is the subject of this post. I won’t debate the merits of refusing to take a vaccine except to say that I support anyone’s right to refuse it without coercion by public authorities. I also note that certain forms of discrimination on that basis are not necessarily unreasonable.

The vaccines in play all seem to be highly effective (> 90%, which is incredible by existing standards). There have been a few reports of side effects — certainly not in large numbers — but it remains to be seen whether the vaccines will have any long-term side effects. I’m optimistic, but I won’t dismiss the possibility.

Despite competing doctrines about how the available supplies of vaccine should be allocated, there is widespread acceptance that health care workers should go first. I have some reservations about this because, like Emma Woodhouse, I believe staff and residents at long-term care facilities should have at least equal priority. Yet they do not in the City of Chicago and probably in other areas. I have to wonder whether unionized health care workers there are the beneficiaries of political favoritism.

Beyond that question, we have the following competing priorities: 1) the vulnerable in care homes and other elderly individuals (75+, while younger individuals with co-morbidities come later); 2) “essential” workers of all ages (from police to grocery store clerks — decidedly arbitrary); and 3) basically the same as #2 with priority given to groups who have suffered historical inequities.

#1 is clearly the way to save the most lives, at least in the short-run. Over 40% of the deaths in the U.S. have been in elder-care settings, and COVID infection fatality rates mount exponentially with age:

To derive the implications of #1 and #2, it’s more convenient to look at the share of deaths within each age cohort, since it incorporates the differences in infection rates and fatality rates across age groups (the number of “other” deaths is much larger than COVID deaths, of course, despite similar death shares):

The 75+ age group has accounted for about 58% of all COVID deaths in the U.S., and ages 25 – 64 accounted for about 20% (an approximate age range for essential workers). This implies that nearly three times as many lives can be saved by prioritizing the elderly, at least if deaths among so-called essential workers mimic deaths in the 25 – 64 age cohorts. However, the gap would be smaller and perhaps reversed in terms of life-years saved.

Furthermore, this is a short-run calculation. Over a longer time frame, if essential workers are responsible for more transmission across all ages than the elderly, then it might throw the advantage to prioritizing essential workers over the elderly, but it would take a number of transmission cycles for the differential to play out. Yes, essential workers are more likely to be “super-spreaders” than work-at-home, corporate employees, or even the unemployed, but identifying true super-spreaders would require considerable luck. Moreover, care homes generally house a substantial number of elderly individuals and staff in a confined environment, where spread is likely to be rampant. So the transmission argument for #2 over #1 is questionable.

The over-riding problem is that of available supply. Suppose enough vaccine is available for all elderly individuals within a particular time frame. That’s about 6.6% of the total U.S. population. The same supply would cover only about 13% of the younger age group identified above. Essential workers are a subset of that group, but the same supply would fall far short of vaccinating all of them; lives saved under #2 would then fall far short of the lives saved under #1. Quantities of the vaccine are likely to increase over the course of a few months, but limited supplies at the outset force us to focus the allocation decision on the short-term, making #1 the clear winner.

Now let’s talk about #3, minority populations, historical inequities, and the logic of allocating vaccine on that basis. Minority populations have suffered disproportionately from COVID, so this is really a matter of objective risk, not historical inequities… unless the idea is to treat vaccine allocations as a form of reparation. Don’t laugh — that might not be far from the intent, and it won’t count as a credit toward the next demand for “justice”.

For the sake of argument, let’s assume that minorities have 3x the fatality rate of whites from COVID (a little high). Roughly 40% of the U.S. population is non-white or Hispanic. That’s more than six times the size of the full 75+ population. If all of the available doses were delivered to essential workers in that group, it would cover less than half of them and save perhaps 30% of minority COVID deaths over a few months. In contrast, minorities might account for up to two-thirds of the deaths among the elderly. Therefore, vaccinating all of the elderly would save 58% of elderly COVID deaths and about 39% of minority deaths overall!

The COVID mortality risk to the average white individual in the elderly population is far greater than that faced by the average minority individual in the working age population. Therefore, no part of #3 is sensible from a purely mathematical perspective. Race/ethnicity overlaps significantly with various co-morbidities and the number of co-morbidities with which individuals are afflicted. Further analysis might reveal whether there is more to be gained by prioritizing by co-morbidities rather than race/ethnicity.

Megan McArdle has an interesting column on the CDC’s vaccination guidelines issued in November, which emphasized equity, like #3 above. But the CDC walked back that decision in December. The initial November decision was merely the latest of the the agency’s fumbles on COVID policy. In her column, McArdle notes that the public has understood that the priority was to save lives since the very start of the pandemic. Ideally, if objective measures show that identifiable characteristics are associated with greater vulnerability, then those should be considered in prioritizing individuals who desire vaccinations. This includes age, co-morbidities, race/ethnicity, and elements of occupational risk. But lesser associations with risk should not take precedence over greater associations with risk unless an advantage can be demonstrated in terms of lives saved, historical inequities or otherwise.

The priorities for the early rounds of vaccinations may differ by state or jurisdiction, but they are all heavily influenced by the CDC’s guidelines. Some states pay lip service to equity considerations (if they simply said race/ethnicity, they’d be forced to operationalize it), while others might actually prioritize doses by race/ethnicity to some degree. Once the initial phase of vaccinations is complete, there are likely to be more granular prioritizations based on different co-morbidities, for example, as well as race/ethnicity. Thankfully, the most severe risk gradient, advanced age, will have been addressed by then.

One last point: the Pfizer and Moderna vaccines both require two doses. Alex Tabarrok points out that first doses appear to be highly effective on their own. In his opinion, while supplies are short, the second dose should be delayed until all groups at substantially elevated risk can be vaccinated…. doubling the supply of initial doses! The idea has merit, but it is unlikely to receive much consideration in the U.S. except to the extent that supply chain problems make it unavoidable, and they might.

COVID Interventions: Costly, Deadly, and Ineffective

14 Monday Dec 2020

Posted by pnoetx in Coronavirus, Liberty, Lockdowns, Public Health

≈ 1 Comment

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AJ Kay, Andrew Cuomo, CDC, Contact Tracing, Covid-19, David Kay, Do-Somethingism, Eric Garcetti, Essential Businesses, Fairfax County Schools, Federalism, Friedrich Hayek, Human Rights Watch, J.D. Tucille, Justin Hart, Kelsey Munro, Knowledge Problem, Lemoine, Life Value, Nature, Non-Prescriptive Interventions, Philippe Lemoine, Public Health, Scott Sumner, Seth Flaxman, Stringency Index, University of Oxford, World Health Organization

What does it take to shake people out of their statist stupor? Evidently, the sweet “logic” of universal confinement is very appealing to the prescriptive mindset of busybodies everywhere, who anxiously wag their fingers at those whom they view as insufficiently frightened. As difficult as it is for these shrieking, authoritarian curs to fathom, measures like lockdowns, restrictions on business activity, school closures, and mandates on behavior have at best a limited impact on the spread of the coronavirus, and they are enormously costly in terms of economic well-being and many dimensions of public health. Yet the storm of propaganda to the contrary continues. Media outlets routinely run scare stories, dwelling on rising case numbers but ignoring them when they fall; they emphasize inflated measures of pandemic severity; certain researchers and so-called health experts can’t learn the lessons that are plain in the data; and too many public officials feel compelled to assert presumed but unconstitutional powers. At least the World Health Organization has managed to see things clearly, but many don’t want to listen.

I’ll be the first to say I thought the federalist approach to COVID policy was commendable: allow states and local governments to craft policies appropriate to local conditions and political preferences, rather than have the federal government dictate a one-size-fits-all policy. I haven’t wavered in that assessment, but let’s just say I expected more variety. What I failed to appreciate was the extent to which state and local leaders are captive to provincial busybodies, mavens of precautionary excess, and fraudulent claims to scientific wisdom.

Of course, it should be obvious that the “knowledge problem” articulated by Friedrich Hayek is just as dangerous at low-levels of government as it is in a central Leviathan. And it’s not just a knowledge problem, but a political problem: officials become panicked because they fear bad outcomes will spell doom for their careers. Politicians are particularly prone to the hazards of “do-somethingism”, especially if they have willing, status-seeking “experts” to back them up. But as Scott Sumner says:

“When issues strongly impact society, the science no longer ‘speaks for itself’.

Well, the science is not quite as clear as the “follow-the-science” crowd would have you believe. And unfortunately, public officials have little interest in sober assessments of the unintended effects of lockdown policy.

In my last post, I presented a simple framework for thinking about the benefits and costs of lockdown measures, or non-pharmaceutical interventions (NPIs). I also emphasized the knowledge problem: even if there is some point at which NPI stringencies are “optimized”, government does not possess the knowledge to find that point. It lacks detailed information on both the costs and benefits of NPIs, but individual actors know their own tolerance for risk, and they surely have some sense of the risks they pose to others in their normal course of affairs. While voluntary precautions might be imperfect, they accomplish much of what interventionists hope will be gained via coercion. But, in an effort to “sell” NPIs to constituents and assert their authority, officials vastly over-estimate benefits of NPIs and under-estimate the costs.

NPI Stringency and COVID Outcomes

Let’s take a look at a measure of the strength of NPIs by state — the University of Oxford Stringency Index — and compare those to CDC all-cause excess deaths in each state. If it’s hard to read, try clicking on the image or turn your phone sideways. This plot covers outcomes through mid-November:

The chart doesn’t suggest any benefit to the imposition of greater restrictions, or more stringent NPIs. In fact, the truth is that people will do most of the work on their own based on perceptions of risk. That’s partly because government restrictions add little risk mitigation to what can be accomplished by voluntary social distancing and other precautions.

Here’s a similar chart with cross-country comparisons, though the data here ended in early October (I apologize for the fuzzy image):

But what about reverse causality? Maybe the imposition of stringency was a response to more severe contagions. Now that the virus has swept most of the U.S and Europe in three distinct waves, and given the variety and timing of NPIs that have been tried, it’s harder to make that argument. States like South Dakota have done fairly well with low stringency, while states like New Jersey with high stringency have fared poorly. The charts above provide multiple pair-wise examples and counter-examples of states or countries having faced hard waves with different results.

But let’s look at a few specific situations.

The countries shown above have converged somewhat over the past month: Sweden’s daily deaths have risen while the others have declined to greater or lesser degrees, but the implications for mask usage are unaltered.

And of course we have this gem, predicated on the mental gymnastics lockdown enthusiasts are fond of performing:

But seriously, it’s been a typical pattern: cases rise to a point at which officials muster the political will to impose restrictions, often well after the “exponential” phase of the wave or even the peak has passed. For the sake of argument, if we were to stipulate that lockdowns save lives, it would take time for these measures to mitigate new infections, time for some of the infected individuals to become symptomatic, and more time for diagnosis. For the lockdown arguments to be persuasive, the implementation of NPIs would have to precede the point at which the growth of cases begins to decline by a few weeks. That’s something we’ve seldom observed, but officials always seem to take credit for the inevitable decline in cases.

More informed lockdown proponents have been hanging their hats on this paper in Nature by Seth Flaxman, et al, published in July. As Philippe LeMoine has shown, however, Flaxman and his coauthors essentially assumed their result. After a fairly exhaustive analysis, Lemoine, a man who understands sophisticated mathematics, offers these damning comments:

“Their paper is a prime example of propaganda masquerading as science that weaponizes complicated mathematics to promote questionable policies. Complicated mathematics always impresses people because they don’t understand it and it makes the analysis look scientific, but often it’s used to launder totally implausible assumptions, which anyone could recognize as such if they were stated in plain language. I think it’s exactly what happened with Flaxman et al.’s paper, which has been used as a cudgel to defend lockdowns, even though it has no practical relevance whatsoever.”

The Economic Costs of Stringency

So the benefits of stringent lockdowns in terms of averting sickness and death from COVID are speculative at best. What about the costs of lockdowns? We can start with their negative impact on economic activity:

That’s a pretty bad reflection on NPI stringency. In the U.S, a 10% decline in GDP in 2020 amounts to about $2.1 trillion in lost goods and services. That’s just for starters. The many destroyed businesses and livelihoods carry an ongoing cost that could take years to fade, as this graphic on permanent business closures shows:

If you’re wondering about the distributional effects of lockdowns, here’s more bad news:

It’s possible to do many high-paying jobs from home. Not so for blue-collar workers. And distributional effects by size of enterprise are also heavily-skewed in favor of big companies. Within the retail industry, big-box stores are often designated as “essential”, while small shops and restaurants are not. The restaurant industry has been destroyed in many areas, inflicting a huge blow to owners and workers. This despite evidence from contact tracing showing that restaurants and bars account for a very small share of transmission. To add insult to injury, many restaurants invested heavily in safety measures and equipment to facilitate new, safer ways of doing business, only to be double-crossed by officials like Andrew Cuomo and Eric Garcetti, who later shut them down.

Public Health Costs of Stringency

Lives are lost due to lockdowns, but here’s a little exercise for the sake of argument: The life value implied by individual willingness-to-pay for risk reduction comes in at less than $4 million. Even if the supposed 300,000 COVID deaths had all been saved by lockdowns, that would have amounted to a value of $1.2 trillion, about half of the GDP loss indicated above. Of course, it would be outrageously generous to concede that lives saved by NPI’s have approached 300,000, so lockdowns fall far short at the very outset of any cost-benefit comparison, even if we value individual lives at far more than $4 million.

As AJ Kay says, the social and human costs go far beyond economic losses:

I cited specific examples of losses in many of these categories in an earlier post. But for the moment, instead of focusing on causes of death, take a look at this table provided by Justin Hart showing a measure of non-COVID excess deaths by age group in the far right-hand column:

The numbers here are derived by averaging deaths by age group over the previous five years and subtracting COVID deaths in each group. I believe Hart’s numbers go through November. Of greatest interest here is the fact that younger age groups, having far less risk of death from COVID than older age groups, have suffered large numbers of excess deaths NOT attributed to COVID. As Hart notes later in his thread:

These deaths are a tragic consequence of lockdowns.

Educational Costs of Stringency

Many schools have been closed to in-person instruction during the pandemic, leading to severe disruptions to the education f children. This report from the Fairfax County, VA School District is indicative, and it is extremely disheartening. The report includes the following table:

Note the deterioration for disabled students, English learners, and the economically disadvantaged. The surfeit of failing grades is especially damaging to groups already struggling in school relative to their peers, such as blacks and Hispanics. Not only has the disruption to in-person instruction been disastrous to many students and their futures; it has also yielded little benefit in mitigating the contagion. A recent study in The Lancet confirms once again that transmission is low in educational settings. Also see here and here for more evidence on that point.

Conclusion

It’s clear that the “follow-the-science” mantra as a rationale for stringent NPIs was always a fraud, as was the knee-jerk response from those who conflated lockdowns with “leadership”. Such was the wrongheaded and ultimately deadly pressure to “do something”. We can be thankful that pressure was resisted at the federal level by President Trump. The extraordinary damage inflicted by ongoing NPIs was quite foreseeable, but there is one more very ominous implication. I’ll allow J.D. Tucille to sum that up with some of the pointed quotes he provides:

“‘The first global pandemic of the digital age has accelerated the international adoption of surveillance and public security technologies, normalising new forms of widespread, overt state surveillance,’ warned Kelsey Munro and Danielle Cave of the Australian Strategic Policy Institute’s Cyber Policy Centre last month.

‘Numerous governments have used the COVID-pandemic to repress expression in violation of their obligations under human rights law,’ United Nations Special Rapporteur on Freedom of Expression David Kaye noted in July.

‘For authoritarian-minded leaders, the coronavirus crisis is offering a convenient pretext to silence critics and consolidate power,’ Human Rights Watch warned back in April.

There’s widespread agreement, then, that government officials around the world are exploiting the pandemic to expand their power and to suppress opposition. That’s the case not only among the usual suspects where authorities don’t pretend to take elections and civil liberties seriously, but also in countries that are traditionally considered ‘free.’ … It’s wildly optimistic to expect that newly acquired surveillance tools and enforcement powers will simply evaporate once COVID-19 is sent on its way. The post-pandemic new normal is almost certain to be more authoritarian than what went before.”

Most Hospitals Have Ample Capacity

05 Saturday Dec 2020

Posted by pnoetx in Coronavirus, Health Care

≈ 1 Comment

Tags

AJ Kay, CARES Act, CDC, CLI, COVID, COVID-Like Illness, Don Wolt, Emergency Use Authorization, FAIR Health, False Positives, FDA, HealthData.gov, Hospital Utiluzation, Houston Methodist Hospital, ICU Utilization, ILI, Influenza-Like Illness, Intensive Care, Length of Stay, Marc Boom, Observation Beds, PCR Tests, Phil Kerpen, Remdesivir, Staffed Beds, Statista

Let’s get one thing straight: when you read that “hospitalizations have hit record highs”, as the Wall Street Journal headline blared Friday morning, they aren’t talking about total hospitalizations. They reference a far more limited set of patients: those admitted either “for” or “with” COVID. And yes, COVID admissions have increased this fall nationwide, and especially in certain hot spots (though some of those are now coming down). Admissions for respiratory illness tend to be highest in the winter months. However, overall hospital capacity utilization has been stable this fall. The same contrast holds for ICU utilization: more COVID patients, but overall occupancy rates have been fairly stable. Several factors account for these differing trends.

Admissions and Utilization

First, take a look at total staffed beds, beds occupied, and beds occupied by COVID patients (admitted “for” or “with” COVID), courtesy of Don Wolt. Notice that COVID patients occupied about 14% of all staffed beds over the past week or so, and total beds occupied are at about 70% of all staffed beds.

Is this unusual? Utilization is a little high based on the following annual averages of staffed-bed occupancy from Statista (which end in 2017, unfortunately). I don’t have a comparable utilization average for the November 30 date in recent years. However, the medical director interviewed at this link believes there is a consensus that the “optimal” capacity utilization rate for hospitals is as high as 85%! On that basis, we’re fine in the aggregate!

The chart below shows that about 21% of staffed Intensive Care Unit (ICU) beds are occupied by patients having COVID infections, and 74% of all ICU beds are occupied.

Here’s some information on the regional variation in ICU occupancy rates by COVID patients, which pretty much mirror the intensity of total beds occupied by COVID patients. Fortunately, new cases have declined recently in most of the states with high ICU occupancies.

Resolving an Apparent Contradiction

There are several factors that account for the upward trend in COVID admissions with stable total occupancy. Several links below are courtesy of AJ Kay:

The flu season has been remarkably light, though outpatients with symptoms of influenza-like illness (ILI) have ticked-up a bit in the past couple of weeks. Still, thus far, the light flu season has freed up hospital resources for COVID patients. Take a look at the low CDC numbers through the first nine weeks of the current flu season (from Phil Kerpen):

There is always flexibility in the number of staffed beds both in ICUs and otherwise. Hospitals adjust staffing levels, and beds are sometimes reassigned to ICUs or from outpatient use to inpatient use. More extreme adjustments are possible as well, as when hallways or tents are deployed for temporary beds. This tends to stabilize total bed utilization.
The panic about the fall wave of the virus sowed by media and public officials has no doubt “spooked” individuals into deferring care and elective procedures that might require hospitalization. This has been an unfortunate hallmark of the pandemic with terrible medical implications, but it has almost surely freed-up capacity.
COVID beds occupied are inflated by a failure to distinguish between patients admitted “for” COVID-like illness (CLI) and patients admitted for other reasons but who happen to test positive for COVID — patients “with” COVID (and all admissions are tested).
Case inflation from other kinds of admissions is amplified by false positives, which are rife. This leads to a direct reallocation of patients from “beds occupied” to “COVID beds occupied”.
In early October, the CDC changed its guidelines for bed counts. Out-patients presenting CLI symptoms or a positive test, and who are assigned to a bed for observation for more than eight hours, were henceforth to be included in COVID-occupied beds.
Also in October, the FDA approve an Emergency Use Authorization for Remdesivir as a first line treatment for COVID. That requires hospitalization, so it probably inflated COVID admissions.
The CDC also announced severe penalties in October for facilities which fail to meet its rather inclusive COVID reporting requirements, creating another incentive to capture any suspected COVID case in its reports.

In addition to the above, let’s not forget: early on, hospitals were given an incentive to diagnose patients with COVID, whether tested or merely “suspected”. The CARES Act authorized $175 billion dollars for hospitals for the care of COVID patients. In the spring and even now, hospitals have lost revenue due to the cancellation of many elective procedures, so the law helped replace those losses (though the distribution was highly uneven). The point is that incentives were and still are in place to diagnose COVID to the extent possible under the law (with a major assist from false-positive PCR tests).

Improved Treatment and Treatment

While more COVID patients are using beds, they are surviving their infections at a much higher rate than in the spring, according to data from FAIR Health. Moreover, the average length of their hospital stay has fallen by more than half, from 10.5 to 4.6 days. That means beds turn over more quickly, so more patients can be admitted over a week or month while maintaining a given level of hospital occupancy.

The CDC just published a report on “under-reported” hospitalization, but as AJ Kay notes, it can only be described as terrible research. Okay, propaganda is probably a better word! Biased research would be okay as well. The basic idea is to say that all non-hospitalized, symptomatic COVID patients should be counted as “under-counted” hospitalizations. We’ve entered the theater of the absurd! It’s certainly true that maxed-out hospitals must prioritize admissions based on the severity of cases. Some patients might be diverted to other facilities or sent home. Those decisions depend on professional judgement and sometimes on the basis of patient preference. But let’s not confuse beds that are unoccupied with beds that “should be occupied” if only every symptomatic COVID patient were admitted.

Regional Differences

Finally, here’s a little more information on regional variation in bed utilization from the HealthData.gov web site. The table below lists the top 25 states by staffed bed utilization at the end of November. A few states are highlighted based on my loose awareness of their status as “COVID “hot spots” this fall (and I’m sure I have overlooked a couple. Only two states were above 80% occupancy, however.

The next table shows the 25 states with the largest increase in staffed bed utilization during November. Only a handful would appear to be at all alarming based on these increases, but Missouri, for example, at the top of the list, still had 27% of beds unoccupied on November 30. Also, 21 states had decreases in bed utilization during November. Importantly, it is not unusual for hospitals to operate with this much headroom or less, which many administrators would actually prefer.

Of course, certain local markets and individual hospitals face greater capacity pressures at this point. Often, the most crimped situations are in small hospitals in underserved communities. This is exacerbated by more limited availability of staff members with school-age children at home due to school closures. Nevertheless, overall needs for beds look quite manageable, especially in view of some of the factors inflating COVID occupancy.

Conclusion

Marc Boom, President and CEO of Houston Methodist Hospital, had some enlightening comments in this article:

“Hospital capacity is incredibly fluid, as Boom explained on the call, with shifting beds and staffing adjustments an ongoing affair. He also noted that as a rule, hospitals actually try to operate as near to capacity as possible in order to maximize resources and minimize cost burdens. Boom said numbers from one year ago, June 25, 2019, show that capacity was at 95%.”

So there are ample beds available at most hospitals. A few are pinched, but resources can and should be devoted to diverting serious COVID cases to other facilities. But on the whole, the panic over hospital capacity for COVID patients is unwarranted.

The Pernicious COVID PCR Test: Ditch It or Fix It

02 Wednesday Dec 2020

Posted by pnoetx in Coronavirus, Public Health

≈ 2 Comments

Tags

Active Infections, Amplification Cycles, Andrew Bostom, Anthony Fauci, Antigen Tests, Asymptomatic. Minimally Infectious, Brown University, CDC, Coronavirus, Covid-19, Cycle Threshold, DNA, Elon Musk, Eurosurveillence, False Positives, Molecular Tests, New York Times, PCR Tests, Portugal, Replication Cycles, RNA, SARS-CoV-2

We have a false-positive problem and even the New York Times noticed! The number of active COVID cases has been vastly exaggerated and still is, but there is more than one fix.

COVID PCR tests, which are designed to detect coronavirus RNA from a nasal swab, have a “specificity” of about 97%, and perhaps much less in the field. That means at least 3% of tests on uninfected subjects are falsely positive. But the total number of false positive tests can be as large or larger than the total number of true positives identified. Let’s say 3% of the tested population is truly infected. Then out of every 100 individuals tested, three individuals are actively infected and 97 are not. Yet about 3 of those 97 will test positive anyway! So in this example, for every true infection identified, the test also falsely flags an uninfected individual. The number of active infections is exaggerated by 100%.

But again, it’s suspected to be much worse than that. The specificity of PCR tests depends on the number of DNA replications, or amplification cycles, to which a test sample is subjected. That process is illustrated through three cycles in the graphic above. It’s generally thought that 20 – 30 cycles is sufficient to pick-up DNA from a live virus infection. If a sample is subjected to more than 30 cycles, the likelihood that the test will detect insignificant dead fragments of the virus is increased. More than 35 cycles prompts real concern about the test’s reliability. But in the U.S., PCR tests are regularly subjected to upwards of 35 and even 40-plus cycles of amplification. This means the number of active cases is exaggerated, perhaps by several times. If you don’t believe me, just ask the great Dr. Anthony Fauci:

“It’s very frustrating for the patients as well as for the physicians … somebody comes in, and they repeat their PCR, and it’s like [a] 37 cycle threshold, but you almost never can culture virus from a 37 threshold cycle. So, I think if somebody does come in with 37, 38, even 36, you got to say, you know, it’s just dead nucleotides, period.“

Remember, the purpose of the test is to find active infections, but the window during which most COVID infections are active is fairly narrow, only for 10 – 15 days after the onset of symptoms, and often less; those individuals are infectious to others only up to about 10 days, and most tests lag behind the onset of symptoms. In fact, infected but asymptomatic individuals — a third or more of all those truly infected at any given time — are minimally infectious, if at all. So the window over which the test should be sensitive is fairly narrow, and many active infections are not infectious at all.

PCR tests are subject to a variety of other criticisms. Many of those are discussed in this external peer-review report on an early 2020 publication favorable to the tests. In addition to the many practical shortfalls of the test, the authors of the original paper are cited for conflicts of interest. And the original paper was accepted within 24 hours of submission to the journal Eurosurveillance (what a name!), which should raise eyebrows to anyone familiar with a typical journal review process.

The most obvious implication of all the false positives is that the COVID case numbers are exaggerated. The media and even public health officials have been very slow to catch onto this fact. As a result, their reaction has sown a panic among the public that active case numbers are spiraling out of control. In addition, false positives lead directly to mis-attribution of death: the CDC changed it’s guidelines in early April for attributing death to COVID (and only for COVID, not other causes of death). This, along with the vast increase in testing, means that false positives have led to an exaggeration of COVID as a cause of death. Even worse, false positives absorb scarce medical resources, as patients diagnosed with COVID require a high level of staffing and precaution, and the staff often requires isolation themselves.

Many have heard that Elon Musk tested positive twice in one day, and tested negative twice in the same day! The uncomfortable reality of a faulty test was recently recognized by an Appeals Court in Portugal, and we may see more litigation of this kind. The Court ruled in favor of four German tourists who were quarantined all summer after one of them tested positive. The Court said:

“In view of current scientific evidence, this test shows itself to be unable to determine beyond reasonable doubt that such positivity corresponds, in fact, to the infection of a person by the SARS-CoV-2 virus.”

I don’t believe testing is a bad thing. The existence of diagnostic tests cannot be a bad thing. In fact, I have advocated for fast, cheap tests, even at the sacrifice of accuracy, so that individuals can test themselves at home repeatedly, if necessary. And fast, cheap tests exist, if only they would be approved by the FDA. Positive tests should always be followed-up immediately by additional testing, whether those are additional PCR tests, other molecular tests, or antigen tests. And as Brown University epidemiologist Andrew Bostom says, you should always ask for the cycle threshold used when you receive a positive result on a PCR test. If it’s above 30 and you feel okay, the test is probably not meaningful.

PCR tests are not ideal because repeat testing is time consuming and expensive, but PCR tests could be much better if the number of replication cycles was reduced to somewhere between 20 and 30. Like most flu and SARS viruses, COVID-19 is very dangerous to the aged and sick, so our resources should be focused on their safety. However, exaggerated case counts are a cause of unnecessary hysteria and cost, especially for a virus that is rather benign to most people.

Sacred Cow Chips

Tag Archives: CDC

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CDC Makes a Bum Lead Steer: Alternate Reality vs. The Herd

CDC Wags Finger; Diners Should Wag One Back

CDC Flubs COVID Impact on Life Expectancy

COVID Now: Turning Points, Vaccines, and Mutations

Allocating Vaccine Supplies: Lives or “Justice”?

COVID Interventions: Costly, Deadly, and Ineffective

Most Hospitals Have Ample Capacity

The Pernicious COVID PCR Test: Ditch It or Fix It