TikTok Tax: The Heavy Wants a Cut

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I have a certain ambivalence toward Donald Trump, and I could go on and on about why it’s so “complicated” for me. One thing for which I’ve credited the Trump Administration is its effort to “deconstruct the administrative state”, as Steve Bannon so aptly put it shortly after the 2016 election. Of course, the progress thus far hasn’t always lived up to my hopes, but the effort to deregulate continues. And after all, the regulatory state is deeply entrenched and difficult to uproot.

Then my eyes glazed over as Trump floated an idea so bad, an intervention so awful, that I can hardly gather it in! It has to do with TikTok, the Chinese video sharing service that has gained popularity worldwide. Crazy as this might sound, it’s not so much Trump’s threat to shut down TikTok’s U.S. operations. Like most libertarians, I’d find that appalling in and of itself, except for the legitimate data security issues at stake. The company’s ties to the Chinese Communist Party (CCP) are a national security concern and an ethical blot on the company, given the CCP’s brutal treatment of Muslim Uighurs, its roughshod treatment of Hong Kong, and its threats to Taiwan. In any case, at least Trump said he’s amenable to a sale of the company’s U.S. operations to a domestic firm. Several large tech firms have expressed strong interest, including Microsoft. So, while any government imposed shutdown or forced sale makes me squirm, it’s not my main issue here.

What really stunned me was to hear Trump say the U.S. Treasury must get a cut of the deal! This is “Hall-of-Fame” statism. Where in the hell does the U.S. government get a legitimate financial claim to the value of any private business that changes hands? Well, Trump seems to think the federal government is adding value as the heavy:

But if you buy [TicTok], the United States, which is making it possible to buy, because without us they can’t do anything, should be compensated.”

Yes, the buyer would be the beneficiary of a shakedown, and the demand is another poke in the eye to the Chinese. Of course, it might well threaten the transaction, and I’m not even sure it’s in Trump’s interest politically. But that’s not even the worst of it: as Warren Meyer explains, it would be hard to think of a better way to weaponize financial regulation than having the Treasury at the bargaining table in private negotiations for corporate control:

Already there are too many regulatory hurdles to doing about anything, and Trump wants agencies to use regulatory approvals to hold up corporations for payments. And you can be sure this is a precedent the Democrats will be only too happy to latch onto — want a pipeline built, where’s our vig? Who wants [this to be] the first Trump decision AOC comes out in support of? The Republican Party sure has come a long way in my lifetime.”

The Left would certainly love to exercise this kind of coercion as a revenue source, as a cudgel of industrial policy to wield against disfavored firms and industries, and as a way to favor cronies. It’s a ready extension of Barack Obama’s deranged “You-didn’t-build-that” theme.

Is this one of trade advisor Peter Navarro‘s brainstorms? I was relieved to see Trump economic advisor Larry Kudlow cast some doubt on whether the government would follow through on Trump’s idea:

‘I don’t know if that’s a key stipulation. …. A lot of options here,’ Kudlow told ‘Varney & Co.’ on Tuesday. ‘Not sure it’s a specific concept that will be followed through.’

I think Trump would really like to kill TikTok. Maybe his grudge is driven in part by the presumptive role that TikTok played in his under-attended Tulsa rally. But there are domestic competitors to TikTok, so consumers will have alternatives. The most popular of those seems to be another Chinese app called Likee. In any case, downloads of other video sharing apps have spiked over the past few weeks. If Trump’s real aim is simply to shut down TikTok in the U.S., I’d almost rather see him do that than start making a practice of horse trading with cronies over shares of corporate booty.

COVID at Midsummer

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It’s been several weeks since I last posted on the state of the coronavirus pandemic (also see here). The charts below show seven-day moving averages of new confirmed cases and reported C19 deaths from the COVID Tracking Project as of August 3. Daily new cases began to flatten about three weeks ago and then turned down (it can take a few days for such changes to show up in a moving average). Daily C19-attributed deaths began climbing again in early July, lagging new cases by a few weeks, and they slowed just a bit over the past several days. Obviously, both are good news if those changes are maintained. The other thing to note is that deaths have remained far below their levels of April and early May.

The daily death count is that reported on each date, not when the deaths actually occurred. Each day’s report consists of deaths that were spread across several previous weeks or even a month or more. That makes the slight downturn in deaths more tenuous from a data perspective. There are sometimes large numbers of deaths from preceding weeks reported together on a single day, so reporting can be ragged and the final pattern of actual deaths is not known for some time. More on that below.

States

The increase in cases and deaths during late June and July was concentrated in four states: Arizona, California, Florida, and Texas. Here’s how those states look now in terms of cases and deaths, from the interactive COVID Time Series site:

 

New cases began to flatten or drop in these states two to three weeks ago, driving the change in the national data. Daily deaths have not turned convincingly, but again, these are reported deaths, which actually occurred over previous weeks. One more chart that is suggestive: current hospitalizations in these four states. The recent declines should bode well for the trend in reported deaths, but it remains to be seen. 

Meanwhile, other parts of the country have seen an uptrend in cases and deaths, such as Illinois, Missouri, South Carolina, and Tennessee. Here are new cases in those states:

It’s worth emphasizing that the elevated level of new cases this summer has not been associated with the rates of fatality experienced in the Northeast during the spring. There are many reasons: better patient care, new treatments, more direct summer sunlight, higher humidity, and tighter controls in nursing homes.

More On the Timing of Deaths

Back to the discrepancies in the timing of reported deaths and actual deaths. This is important because the reported totals each day and each week can be highly misleading, even to the point of frightening the public and policy makers, with consequent psychological and economic impacts.

The latest summary of provisional vs. reported deaths is shown below, courtesy of Kyle Lamb, who posts updates on his Twitter feed. This report ends with the last complete week ending August 1. It’s a little hard to read, but you might get a better look if you click on it or turn your phone sideways. Some of the key series are also graphed below. 

The table shows the actual timing of deaths in the fourth column, with dates alongside. The pattern differs from the statistics reported by the Covid Tracking Project (CTP) in the top row (shaded orange), and from the totals of actual deaths by reporting day in the third row (shaded gray). The reporting dates are always later than the dates of death. This can be seen in the chart below. The most obvious illustration is how many of the deaths from around the peak in mid-April were reported in May. In March and April, the daily reports were short of the ultimate actual death counts because so few deaths with associated dates were known by then.

 

The right-hand end of the red line shows that many deaths reported by CTP have not yet been placed at an actual date of death by the CDC.  At this point, the actual date of death has not been placed for over 10,000 deaths! Again, those will be spread over earlier weeks.

The blue line is dashed over the last four weeks because those counts are most “highly” provisional. Small changes in the actual counts are likely for dates even before that, but the last four weeks are subject to fairly substantial upward revisions. Eventually, the right end of the blue line will more closely approximate the totals shown in red.

To get an indication of trends in the actual timing of deaths, I plotted the weekly actual deaths reported for the last four reporting weeks going back in time. In the table, those are the four lowest, color-coded diagonals. In the graph below, which should include the qualifier “by recency of report week”, actual deaths in the most recent report week are represented by the blue line, the prior weekly report is red, followed by green (three weeks prior), and purple (four weeks prior… sorry, the colors are not consistent with those in the table). The lines extend farther to the right for more recent report weeks.

The increase in actual deaths occurring in July has declined or flattened in each of the four most recent report weeks. Only the second-to-last week increased as of the August 1st report. On the whole, those changes seem favorable, but we shall see.

Closing

It’s getting trite to say, but the next few weeks will be interesting. The increase in deaths in July was a sad development, but at least the extent of it appears to have been limited. Even with a somewhat higher death count, the fatality rate continued to decline. Let’s hope any further waves of infections are even less deadly.

Dr. Fauci, RCTs, and Large Sample “Anecdotes”

Anthony Fauci insists that the only valid test of efficacy for a pharmacological treatment is a randomized control trial (RCT). Other kinds of evidence, he claims, are merely “anecdotal”. Well-designed, large sample RCTs are highly desirable, of course, but both of Dr. Fauci’s statements are balderdash. Real world RCTs often have design flaws and drawbacks, and they often produce biased results. We certainly shouldn’t invest such confidence in their universal superiority over other clinical evidence, which for years has been relied upon in the FDA’s reviews of drugs and other interventions for safety and efficacy.

An RCT is a prospective study in which subjects are randomly assigned to one or more groups who receive different treatments, one of which is a control group receiving “standard care” or a placebo. The so-called “gold standard” of trials is the double blind RCT, which means that neither the subject nor the researchers know the treatment to which the subject is assigned.

On multiple occasions, Fauci has erroneously claimed that positive findings from anything short an RCT are “anecdotal”, which, if meaningful in any way, implies that only RCTs have samples of adequate size. That’s false: traditional clinical trials (TCTs) are not at any systematic disadvantage to RCTs in terms of sample size. The difference is that individuals are not randomly assigned to different treatment groups, but rather are assigned with the researcher’s intent, by dint of opportunity, or happenstance. These groups may include a pure control, and they may be balanced according to medical history, condition, or other potentially confounding influences. TCTs might be prospective (subjects are observed over time), or retrospective (which exploit previous case files).

The idea of double-blind, random assignment to treatment groups is appealing because it prevents researchers from exerting any bias in the selection of groups that might influence the results. That’s good, but random assignment can still lead to unbalanced comparisons, and RCTs can be flawed in many other ways. This paper discusses a number of fine points of RCTs that can lead to bias, but here are a few important ones, not all of which are covered at the link:

  • The most glaring difficulty is that random assignment can result in very unbalanced characteristics across groups. The findings can be so sample-specific as to lack external validity. This is especially problematic when group sub-samples are small, as is often the case in medical research, but it is often true in samples of moderate size or even large samples. This contrasts with selecting groups with deliberate balance across key characteristics.

Contrary to frequent claims in the applied literature, randomization does not equalize everything other than the treatment in the treatment and control groups, it does not automatically deliver a precise estimate of the average treatment effect (ATE), and it does not relieve us of the need to think about (observed or unobserved) covariates. Finding out whether an estimate was generated by chance is more difficult than commonly believed.”

  • An implication of the heterogeneity across participants and random assignment of confounding attributes is that even with large treatment groups, the tests reveal differences in central tendencies, but they might not apply well to large subsets of patients. Some researchers go so far as to say all RCTs are biased in one way or another. TCT’s are also subject to bias, of course, but the point is that RCT’s are subject to significant risks of bias for reasons that TCTs often avoid.
  • Comparisons of small treatment group samples results in low-powered tests that are often statistically insignificant. This weakness is shared by all RCTs and TCTs having inadequate samples to divide between the desired number of treatment groups.
  • “Blindness” is often violated because treatment can involve a large number of  personnel and roles. This may influence outcomes, for example, if some caregivers alter standard treatment in an effort to compensate for its perceived deficiencies.
  • Recruiting for RCTs is often difficult. This leads to the small sample problems discussed above. Sometimes participation in RCTs is heavily qualified. Sometimes patients are reluctant to participate because they don’t want to be assigned to a treatment randomly. Sometimes delays are caused by the fact that RCTs require approval by an independent review board, whereas assignment in a TCT might require only treatment decisions by different physicians.
  • An RCT can be highly misleading if treatments are poorly targeted. This might take several forms: Failure to screen for conditions that might lead to treatment complications can be dangerous and counter-productive, since the general safety of the treatment might be falsely implicated. Likewise, a treatment might be effective only under certain conditions or at a certain stage of a disease, but the selection of participants might not meet those conditions. Or a treatment might be most effective in combination with other interventions, but failing to combine them will overlook the effect. Misapplications of this kind are likely to lead to erroneous conclusions.

The last bullet point has been a major bone of contention in the debate over the efficacy of hydroxychloroquine (HCQ) in the treatment of the novel coronavirus. Proponents of the drug contend it is most effective in early treatment, but a number of negative tests have studied only late treatment. Also, proponents contend that HCQ works best in combination with zinc and a Z-pak (antibiotic), but many studies have failed to use or control for those combinations.

Here are a few examples of the kinds of difficulties encountered by RCTs, as well as issues creating doubts about the results. All involve trials of HCQ .

  • NIH cancels three trials: the first trial involved only hospitalized patients, though that might not have qualified as early treatment in all subjects. The other two trials were cancelled because of recruitment problems!
  • A study of HCQ without zinc or Z-Pac antibiotic on hospitalized patients found that HCQ was associated with a greater likelihood of death and longer hospital stays, but in addition to the use of HCQ only, the study appears to have been mis-targeted at advanced cases of C19 infection.
  • This study also endeavored to investigate HCQ as a treatment, but not only did it fail to combine HCQ with zinc and a Z-pac; over 40% of the participants never tested positive for COVID-19! It’s also not clear that participants were adequately screened for complications. The following results were statistically insignificant, indicating a possible lack of statistical power, though they favored HCQ (which is not noted by the authors):

At 14 days, 24% (49 of 201) of participants receiving hydroxychloroquine had ongoing symptoms compared with 30% (59 of 194) receiving placebo (P = 0.21).  … With placebo, 10 hospitalizations occurred (2 non–COVID-19–related), including 1 hospitalized death. With hydroxychloroquine, 4 hospitalizations occurred plus 1 nonhospitalized death (P = 0.29).”

  • This study was on a relatively small sample of non-hospitalized patients. It found only a small difference favoring HCQ in terms of viral load at day 7, as well as the following statistically insignificant results otherwise favoring HCQ:

This treatment regimen did not reduce risk of hospitalization (7.1%, control vs. 5.9%, intervention; RR 0.75 [0.32;1.77]) nor shortened the time to complete resolution of symptoms (12 days, control vs. 10 days, intervention; p = 0.38).”

For a more comprehensive view of the evidence, this link contains a compendium of studies on HCQ 1) as a treatment at various stages of C19 infection, 2) as pre-exposure prophylaxis (PrEP) against infection; or 3) a post-exposure prophylaxis (PEP). It includes high-level details on many of the studies as well as links to most of the studies. A few of the studies are RCTs, but most are either prospective or retrospective TCTs; some are in vitro (lab) studies, and some are meta-analyses covering multiple prior studies; some address the safety of HCQ only.

The site includes a kind of “scorecard” at the top categorizing 66 of the studies as either positive (HCQ is effective) or negative within four categories: PrEP, PEP, early-stage infection, and late-stage infection. Studies were excluded from the scorecard for various reasons, including meta-analyses, in vitro studies, safety studies, those terminated due to inadequate recruitment, and studies that were deemed inconclusive due to data inadequacies and questions of interpretation awaiting feedback from authors.

The results for HCQ as a prophylactic were uniformly positive, as were the studies involving early-stage treatment. Results were mixed for late-stage treatment. Of special interest is the meta-analysis of 12 studies of high-risk outpatients by Harvey A. Risch, the seventh listed in the compendium referenced above. The 12 studies analyzed by Risch all showed that HCQ is highly effective. He calls out those who would insist that those studies be disregarded because they were not RCTs, including one critic who, like Dr. Fauci, abuses the term “anecdotal”:

… to distinguish from the ‘magic’ of randomized controlled trials, when government medical and scientific regulatory agencies of western countries around the world routinely use epidemiologic evidence to establish facts of causation, benefit and harm. This disingenuous argument has been discussed at length elsewhere…. Finally, in pandemic times when months and years of delay cannot be tolerated before large randomized controlled trials are completed, it is possible to quibble with apparent imperfections in almost any study. That misses the forest for the trees.”

The “elsewhere” link in the quote above includes an excellent summary of the battle waged over the efficacy of HCQ. It became a media war, which relied in part on the false assertion that only RCTs are acceptable. That was abetted by certain public health experts and researchers who might have had financial or political interests in promoting new drugs, rather than the safe, cheap alternative that had been used safely for many decades. The article notes that few media sources carried the following, which was released only days after the FDA revoked its Emergency Use Authorization for HCQ (based on faulty evidence):

TUCSON, Ariz., June 22, 2020 /PRNewswire/ — Today the Association of American Physicians & Surgeons files its motion for a preliminary injunction to compel release to the public of hydroxychloroquine by the Food & Drug Administration (FDA) and the Department of Health & Human Services (HHS), in AAPS v. HHS, No. 1:20-cv-00493-RJJ-SJB (W.D. Mich.). Nearly 100 million doses of hydroxychloroquine (HCQ) were donated to these agencies, and yet they have not released virtually any of it to the public…

‘Why does the government continue to withhold more than 60 million doses of HCQ from the public?’ asks Jane Orient, M.D., the Executive Director of AAPS. ‘This potentially life-saving medication is wasting away in government warehouses while Americans are dying from COVID-19.'”

 

 

Risk Realism, COVID Hysteria

Perhaps life in a prosperous society has sapped our ability and willingness to face risks. This tendency undermines that very prosperity, however. If we ever needed an illustration, the hysteria surrounding COVID-19 surely provides it. Do we really know how to exist in a world with risk anymore? During this episode, the media, public officials, and much of the public have completely lost their bearings with respect to the evaluation of risk, acting as if they are entitled to a zero-risk existence. Of course, COVID-19 is highly transmissible and dangerous for certain segments of the population, but it is rather benign for most people.

Perspective On C19 Risks

Just for starters, the table at the top of this post (admittedly not particularly well organized) shows calculations of odds from the CDC. These odds might well overstate the risks of both C19 and the flu, as they probably don’t account well for the huge number of asymptomatic cases of both viruses.

Another glimpse of reality is offered by a recent Swiss study showing the C19 infection mortality rate (IFR) by age, shown below. You can find a number of other charts on-line that show the same pattern: If you’re less than 50 years old, your risk of death from C19 is quite slim. Even those 50-64 years of age don’t face a substantial mortality risk, though it’s obviously higher for individuals having co-morbidities. These IFRs are lower than all-cause mortality for younger cohorts, but higher for older cohorts.

And here are a few other facts to put the risks of C19 in perspective:

  • The current pandemic is relatively benign: thus far, the U.S. has suffered a total of about 145,000 deaths, or 440 per million of population;
  • the Asian Flu of 1957-58 took 116,000, according to the CDC, or 674 per million;
  • the Spanish Flu of 1917-18 took 675,000 U.S lives, or 6,553 per million.

It should be obvious that these risks, while new and elevated for some, are not of such outrageous magnitude that they can’t be managed without bringing life to a grinding halt. That’s especially true when so-called safety measures entail substantial health risks of their own, as I have emphasized elsewhere (and here).

The Schools

Nothing illustrates our inability to assess risks better than the debate over reopening schools. This article in Wired is well-balanced on the safety issue. It emphasizes that there is little risk to teachers, students, or their families from opening schools if reasonable safety measures are taken.

Children of pre-school and elementary school age do not contract the virus readily, do not transmit the virus readily, and do not readily succumb to its effects. This German study on elementary schools demonstrates the safety of reopening. It is similar to the experience of other EU countries that have reopened schools. This article reinforces that point, but it emphasizes measures to limit any flare-ups that might arise. And while it singles out Israel as an example of poor execution, it fails to offer any evidence on the severity of infections.

Furthermore, we should not overlook the destructive effects of denying in-classroom learning to children. They simply don’t learn as well on-line, especially students who struggle. There are also the devastating social-psychological effects of the isolation experienced by many elementary school children during extended school closures. This is of a piece with the significant risks of lockdowns to well being. Perhaps not well known is that schooling is positively correlated with life expectancy: this study found that a one-year reduction in years of schooling is associated with a reduction in life expectancy of 0.6 years!

It’s true that children older than 10 might pose somewhat greater risks for C19 contagion, but those risks are manageable via hygiene, distancing, and other mitigations including hydroxychloraquine or other prophylaxes against infection for teachers who desire it. Capacity limitations might well require a temporary mix of online and in-school learning, but at least part-time attendance at brick-and-mortar schools should remain the centerpiece.

As Tyler Cowen points out, teenagers are less likely to remain isolated from others during school closures, so their behavior might be more difficult to manage. It’s quite possible they could be more heavily exposed outside of school, hanging out with friends, than in the classroom. This illustrates how our readiness to demure from absolute risk often ignores the pertinent question of relative risk.

Judging by reactions on social media, people are so frightened out of their wits that they cannot put these manageable risks in perspective. But here is a statement from the American Academy of Pediatrics. And here is a statement from the American Association of Sciences, Engineering and Medicine. They speak for themselves.

Excessive Precautionary Putzery

Our reaction to C19 amounts to a misapplication of the precautionary principle (PP), which states, quite reasonably, that precautionary measures must be invoked when faced with a risk that is not well understood. Risk must be managed! But what are those precautions and on what basis should benefits we forego via mitigation be balanced against quantifiable risks. That was one theme of my post “Precaution Forbids Your Rewards” several years back. Ralph B. Alexander discusses the PP, noting that the construct is vulnerable to political manipulation. It is, unfortunately, a wonderful devise for opportunistic interest groups and interventionist politicians. See something you don’t like? Identify a risk you can use to frighten the public. Use any anecdotal evidence you can scrape together. Start a movement and put a stop to it!

That really doesn’t help us deal with risk in a productive way. Do we understand that well being generally is enhanced by our willingness to incur and manage risks? As David Zaruk, aka, the Risk Monger, says, “our reliance of the precautionary principle has ruined our ability to manage risk.”:

“Two decades of the precautionary principle as the key policy tool for managing uncertainties has neutered risk management capacities by offering, as the only approach, the systematic removal of any exposure to any hazard. As the risk-averse precautionary mindset cements itself, more and more of us have become passive docilians waiting to be nannied. We no longer trust and are no longer trusted with risk-benefit choices as we are channelled down over-engineered preventative paths. While it is important to reduce exposure to risks, our excessively-protective risk managers have, in their zeal, removed our capacity to manage risks ourselves. Precaution over information, safety over autonomy, dictation over accountability.”

To quote Mollie Hemingway, in the case of the coronavirus, Americans are “reacting like a bunch of hysterics“.

 

 

 

 

 

 

COVID Politics and Collateral Damage

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Policymakers, public health experts, and the media responded to the coronavirus in ways that have often undermined public health and magnified the deadly consequences of the pandemic. Below I offer several examples of perverse politics and policy prescriptions, and a few really bad decisions by certain elected officials. Some of the collateral damage was intentional and motivated by an intent to inflict political damage on Donald Trump, and people of good faith should find that grotesque no matter their views on Trump’s presidency.

Politicized Treatment

The smug dismissal of hydroxychloraquine as Trumpian foolishness was a crime against humanity. We now know HCQ works as an early treatment and as a prophylactic against infection. It’s has been partly credited with stanching “hot spots” in India as well as contributing strongly to control of the contagion in Switzerland and in a number of other countries. According to epidemiologist Harvey Risch of the Yale School of Public Health, HCQ could save 75,000 to 100,000 lives if the drug is widely used. This is from Dr. Risch’s OpEd in Newsweek:

On May 27, I published an article in the American Journal of Epidemiology (AJE) entitled, ‘Early Outpatient Treatment of Symptomatic, High-Risk COVID-19 Patients that Should be Ramped-Up Immediately as Key to the Pandemic Crisis.’ That article, published in the world’s leading epidemiology journal, analyzed five studies, demonstrating clear-cut and significant benefits to treated patients, plus other very large studies that showed the medication safety.

Since [then], seven more studies have demonstrated similar benefit. In a lengthy follow-up letter, also published by AJE, I discuss these seven studies and renew my call for the immediate early use of hydroxychloroquine in high-risk patients.”

Risch is careful to couch his statements in forward-looking terms, but this also implies that tens of thousands of lives could have been saved, or patients might have recovered more readily and without lasting harm, had use of the drug not been restricted. The FDA revoked its Emergency Use Authorization for HCQ on June 15th, alleging that it is not safe and has little if any benefit. An important rationale cited in the FDA’s memo was an NIH study of late-stage C19 patients that found no benefit and potential risks to HCQ, but this is of questionable relevance because the benefit appears to be in early-stage treatment or prophylaxis. Poor research design also goes for this study and this study, while this study shared in some shortcomings (e.g., and no use of and/or controls for zinc) and a lack of statistical power. Left-wing outlets like Politico seemed almost gleeful, and blissfully ignorant, in calling those studies “nails in the coffin” for HCQ. Now, I ask: putting the outcomes of the research aside, was it really appropriate to root against a potential treatment for a serious disease, especially back in March and April when there were few treatment options, but even now?

Then we have the state governors who restricted the use of HCQ for treating C19, such as Gretchen Whitmer (MI) and Steve Sisolak (NV). Andrew Cuomo (NY) decided that HCQ could be dispensed only for hospital use, exactly the wrong approach for early stage treatment. And all of this resistance was a reaction to Donald Trump’s optimism about the promise of HCQ. Yes, there was alarm that lupus patients would be left without adequate supplies, but the medication is a very cheap, easy to produce drug, so that was never a real danger. Too much of the media and politicians have been complicit in denying a viable treatment to many thousands of C19 victims. If you were one of the snarky idiots putting it down on social media, you are either complicit or simply a poster child for banal evil.

Seeding the Nursing Homes

The governors of several states issued executive orders to force nursing homes to accept C19 patients, which was against CMS guidance issued in mid-March. The governors were Andrew Cuomo (NY), Gretchen Whitmer (MI), Gavin Newsom (CA), Tom Wolf (PA), and Phil Murphy (PA). This was a case of stupidity more than anything else. These institutions are home to the segment of the population most vulnerable to the virus, and they have accounted for about 40% of all C19 deaths. Nursing homes were ill-prepared to handle these patients, and the governors foolishly and callously ordered them to house patients who required a greater level of care and who represented an extreme hazard to other residents and staff.

Party & Protest On

Then of course we had the mayor of New York City, Bill De Blasio, who urged New Yorkers to get out on the town in early March. That was matched in its stupidity by the array of politicians and health experts who decided, having spent months proselytizing the need to “stay home”, that it was in their best interests to endorse participation in street protests that were often too crowded to maintain effective social distance. That’s not a condemnation of those who sought to protest peacefully against police brutality, but it was not a good or consistent recommendation in the domain of public health. Thankfully, the protests were outside!

Testing, Our Way Or the Highway

The FDA and CDC were guilty of regulatory overreach in preventing early testing for C19, and were responsible for many lives lost early in the pandemic. By the time the approved CDC tests revealed that the virus was ramping up drastically in March, the country was already behind in getting a handle on the spread of the virus, quarantining the infected, and tracing their contacts. There is no question that this cost lives.

Masks… Maybe, But Our Way Or the Highway

U.S. public health authorities were guilty of confused messaging on the efficacy of masks early in the pandemic. As Joel Zinberg notes in City Journal, Anthony Fauci admitted that his own minimization of the effectiveness of masks was motivated by a desire to prevent a shortage of PPE for health care workers:

In discouraging mask use, Fauci—for decades, the nation’s foremost expert on viral infectious diseases—was not acting as a neutral interpreter of settled science but as a policymaker, concerned with maximizing the utility of the limited supply of a critical item. An economist could have told him that there was no need to misinform the public. Letting market mechanisms work and relaxing counterproductive regulations would ease shortages. Masks for health-care workers would be available if we were willing to pay higher prices; those higher prices, in turn, would elicit more mask production.”

Indeed, regulators made acquisition of adequate supplies of PPE more difficult than necessary via compliance requirements, “price gouging” rules, and import controls.

Bans on Elective Surgery

Another series of unnecessary deaths was caused by various bans on elective surgeries across the U.S. (also see here), and we’re now in danger of repeating that mistake. These bans were thought to be helpful in preserving hospital capacity, but hospitals were significantly underutilized for much of the pandemic. Add to that the fright inspired by official reaction to C19, which keeps emergency rooms empty, and you have a universe of diverse public health problems to grapple with. As I said on this blog a couple of months ago:

… months of undiagnosed cardiac and stroke symptoms; no cancer screenings, putting patients months behind on the survival curve; deferred procedures of all kinds; run-of-the-mill infections gone untreated; palsy and other neurological symptoms anxiously discounted by victims at home; a hold on treatments for all sorts of other progressive diseases; and patients ordinarily requiring hospitalization sent home. And to start back up, new health problems must compete with all that deferred care. Do you dare tally the death and other worsened outcomes? Both are no doubt significant.”

Lockdowns

The lockdowns were unnecessary and ineffectual in their ability to control the spread of the virus. A study of 50 countries published by The Lancet last week found the following:

Increasing COVID-19 caseloads were associated with countries with higher obesity … median population age … and longer time to border closures from the first reported case…. Increased mortality per million was significantly associated with higher obesity prevalence … and per capita gross domestic product (GDP) …. Reduced income dispersion reduced mortality … and the number of critical cases …. Rapid border closures, full lockdowns, and wide-spread testing were not associated with COVID-19 mortality per million people.”

That should have been obvious for a virus that holds little danger for prime working-age cohorts who are most impacted by economic lockdowns.

Like the moratoria on elective surgeries, lockdowns did more harm than good. Livelihoods disappeared, business were ruined, savings were destroyed, dreams were shattered, isolation set in, and it continues today. These kinds of problems are strongly associated with health troubles, family dysfunction, drug and alcohol abuse, and even suicide. It’s ironic that those charged with advising on matters pertaining to public health should focus exclusively on a single risk, recommending solutions that carry great risk themselves without a second thought. After all, the protocol in reviewing new treatments sets the first hurdle as patient safety, but apparently that didn’t apply in the case of shutdowns.

Even as efforts were made to reopen, faulty epidemiological models were used to predict calamitous outcomes. While case counts have risen in many states in the U.S. in June and July, deaths remain far below model predictions and far below deaths recorded during the spring in the northeast.

One last note: I almost titled this post “Attack of the Killer Morons”, but as a concession to what is surely a vain hope, I decided not to alienate certain readers right from the start.

 

 

COVID Trends and the Scourge of False Positives

Positive COVID-19 tests continue to mount, which is scary, but the more I learn about the processes generating the data, the more skeptically I regard the numbers. And whether the data is junk or otherwise, it’s often misinterpreted or misused by the media. Here I’ll focus mainly on issues related to testing and cause-of-death. What’s striking is the likelihood of upward bias in the reported case counts based on one set of tests, even while the incidence of antibodies to the virus appear to be more widespread.

Testing

Almost all of the C19 test results included in the case counts we’ve seen are from polymerase chain reaction (PCR) tests, the kind involving samples collected with “brain scrambling” nasal swabs. These tests detect whether the subject is shedding any viral particles. The other kind of test is for antibodies, called a serological test, which focuses on whether a subject has HAD the virus, not on whether they have it currently. The latter test, however, might catch some active cases in addition to resolved infections.

The first problem is that some states have combined results from these two kinds of tests. That’s likely to inflate the case count because they would capture those who are infected, and those who were infected but aren’t any longer.

A second problem is the faulty reporting of test results we’ve seen in states like Florida, where some labs have been reporting an implausible 100% positivity rate over certain periods. That might or might not imply an exaggerated count of positives, but it certainly inflates the positivity rate. There are other practices that systematically inflate the positive test count, however, such as counting all members of a household as “probable positives”, and counting multiple positive tests on the same patient as multiple cases.

Test reliability

This is the third problem and it’s really a biggie. It’s also more complicated because there is more than one kind of accuracy on which tests are evaluated.

1) The PCR tests are said to have a sensitivity of anywhere from 66%-80%, depending on testing and lab conditions. That means about one of every three or four tests on infected people will miss the actual infection: that’s a false negative and a horrible mistake. An article in The New England Journal of Medicine puts sensitivity at 70%. These levels of sensitivity are poor, so there is good reason for repeat testing, or to develop and implement more sensitive tests!

2) The other kind of accuracy is called specificity. It indicates the percentage of uninfected subjects who actually test negative. If it’s 90%, then one out of every ten tests identifies an infection that really isn’t there. That’s a false positive. It’s extremely hard to find estimates of specificity for PCR tests outside of perfect lab conditions. We know there are false positives in the real world, however, and I’ll get to that evidence below. But we know, for example, that individuals will continue to shed virus for a short time even after the virus is dead, and that reduces specificity. False positives can also result from poor testing or lab conditions.

So here’s an example: let’s be generous and assume that test sensitivity is 80%, and we’ll give the benefit of the doubt to test specificity and say it’s 95%. Further suppose that 2% of the population is currently infected. Out of 1,000 tests, 20 involve infected subjects. The sensitivity implies that we’ll correctly identify 16 of them (80%) and we’ll miss four. The other 980 tests subjects are virus-free, but 95% specificity implies that about 49 of those tests will come back positive (49/980 = 5%). All together, that yields a whopping positivity rate of:

(49 + 16)/1000, or 6.5%, well above the true infection rate of 2%.

So it’s very easy for a test having inadequate specificity to inflate the number of positives. That’s less problematic when prevalence is high, since fewer virus-free subjects are available to misidentify. Unfortunately, it becomes a larger concern when testing is broad and less focused on symptoms, since that implies lower prevalence in the tested population. The U.S. has increased testing over the past two months, roughly quintupling the number of daily tests over a span of three months. The tested population has therefore broadened to include many more subjects who are either asymptomatic, freaked out about their allergy symptoms, or have been routinely tested on admittance to hospitals for other illnesses or procedures.

Discussion

It’s absolutely necessary for society to have testing capacity for those with symptoms and those likely to be exposed to the virus, such as first responders. But rolling out the test to the broader population means the case data are much less accurate unless positive diagnoses are based on repeated tests. Unfortunately, the bulk of the testing we’ve seen thus far has been so lacking in specificity as to inflate the number of cases as testing became more widespread.

Evidence for this claim is offered by a paper just published by a Connecticut epidemiologist. He used a more robust technique to re-examine ten positive and ten negative tests provided by the CT Department of Public Health. He found that nine of the 20 cases were true infections, but two of those came from the ten negative tests! So, in fact, there were three false positives and two false negatives among the 20 tests. Therefore, the tests overestimated the number of actual cases by around 11% in the sample, net of both kinds of errors. Granted, this was a small sample, and we don’t know the true prevalence of the virus in the full population of test subjects, but if we assume the positive tests and negative tests were representative, a prevalence of 5% would imply, after weighting, a rather drastic inflation of the positivity rate to 32.5%!

0.95(3/10) + 0.05(8/10)

That’s just outrageous!

The U.S. positivity rate by the end of April was about 12%, when testing was still limited; it’s been running at about 8% recently. The decline almost certainly reflects both a broadening of the test population and a decline in prevalence among the tested population. That, in turn, implies that a positive test has less predictive value, for even though the test captures the same percentage of true positives, a larger percentage of all positive tests will be false negatives. It might seem paradoxical, but it’s likely that the 12% positivity rate early in the pandemic had a smaller upward bias than the 8% we see currently, but that is due to the composition of the population tested. Under current testing, the specificity percentage is applied to a larger proportion of uninfected subjects, so the number of false positives overwhelms the test’s ability to identify true positives.

The first priority of testing is to reliably identify true cases. The current PCR tests fail in that objective due to low sensitivity. But inspecific test are costly too. First, they waste medical resources on uninflected subjects. Second, a major set of worries and inconveniences are imposed on false positives, which have a real cost. Third, inspecific tests can be costly because of the even higher likelihood of false positives over several rounds of tests. For example, it will be extremely difficult for sports teams to establish continuity, or even to maintain a full roster, because so many players are likely to become victims of false-positivity under repeated testing.

Death tolls

Anything that inflates the C19 case count tends to inflate C19-attributed deaths. For example, almost all hospital admissions are now tested. A high number of false positives leads to more deaths being wrongly attributed to C19. Other issues related to counting deaths go beyond the vagaries of test accuracy. Hospitals have a perverse incentive to boost their C19 cases and deaths via more generous Medicare reimbursements. Deaths are also attributed to C19 in a variety of other circumstances, some quite suspicious, but we are constantly told without evidence that C19 deaths are undercounted and so these additions must be reasonable.

The argument that deaths of C19 patients with comorbidities are rightfully attributed to C19 is likewise flawed for some of the reasons discussed above. False positives are all too common. Furthermore, patients might be admitted to a hospital with advanced or terminal conditions and die having caught C19 coincidentally at the hospital. And one can certainly quibble with the notion that the deaths of otherwise terminal patients should be attributed to C19. There is a significant grey area.

Finally, as I discussed in a previous post, the deaths reported each week are at odds with the actual timing of those deaths. There are occasionally large additions to the CDC’s provisional deaths counts many weeks in the past. It’s bad enough that those deaths are reported so late and treated by the media as if they just occurred. Possibly worse is the potential for manipulating death counts for political purposes, which is enabled by the large backlog of deaths lacking attributed causes over the course of weeks and months.

Serological tests and false positives

The first serological tests for C19 antibodies, back in April, yielded surprisingly high estimates of individuals with acquired immunity to the virus, often 10 or more times the number of infections based on case counts (also see here and here). The earliest antibody test results were criticized because their specificity and the prevalence of antibodies in the general population were thought to be low. That made it relatively easy for critics to rationalize the high estimates as a consequence of false positives. We now know, however, that serological tests have higher specificity than the PCR tests for active infections, and those tests have consistently shown a larger than expected share of individuals having acquired immunity. But how does that square with the argument that case counts based on PCR tests are inflated? How can so many have developed antibodies if the case counts are so exaggerated?

To rephrase: how can the population with antibodies, those who have HAD the virus, accumulate to a level several times the case count? Keep in mind that a high proportion of the serological tests have been conducted in relative hot spots, where there are likely to be many undetected cases. There is also some question about the real timing of the pandemic in the U.S. Some believe it was spreading prior to March, so the true number of cases, diagnosed and undiagnosed, might have mounted more quickly early in the pandemic than later case diagnoses suggested. Moreover, serological testing has not been conducted on a random sample of the population. In fact, those tests are more often administered when patients go to labs for other blood work, so there is reason to believe that prevalence in this group might exceed that of the general population. It’s also possible that the serological tests are picking up antibodies developed in response to other forms of the coronavirus, which might in fact be protective. Finally, the serological tests are still subject to a level of false positives. So the antibody findings from serological tests are not necessarily inconsistent with the notion that case counts and death counts are inflated now.

Summary

We truly need better, quicker tests, and many talented people are now working to improve them. My point is not to degrade the effort to conduct testing, but to note that our current testing regime has many flaws, one of which is to raise alarm about extremely high case and death counts. I do not doubt that the number of actual infections has grown in June and July. However, the positivity rate remains lower than early in the pandemic with a much larger, less focused selection of test subjects. Many of the cases identified by PCR tests are false positives. As disappointing as it is to someone who loves to work with data, C19 case counts and mortality look unreliable.

 

 

Some Cheery COVID Research Tidbits

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Here’s a short list of new or newish research developments, some related to the quest to find COVID treatments. Most of it is good news; some of it is very exciting!

Long-lasting T-cell immunity: this paper in Nature shows that prior exposure to coronaviruses like severe acute respiratory syndrome (SARS) and even the common cold prompt an immune reaction via so-called T-cells that have long memories and are reactive to certain proteins in COVID-19 (SARS-CoV-2). The T-cells were detected in both C19-infected and uninfected patients. This comes after discouraging reports that anti-body responses to C19 are short-lived, but T-cells are a different form of acquired immunity. Derek Lowe says the following:

This makes one think, as many have been wondering, that T-cell driven immunity is perhaps the way to reconcile the apparent paradox between (1) antibody responses that seem to be dropping week by week in convalescent patients but (2) few (if any) reliable reports of actual re-infection. That would be good news indeed.”

The herd immunity threshold (HIT) is much lower than you think: I’ve written about the effect of heterogeneity on the HIT before, here and here. This new paper, by three Oxford zoologists, shows that the existence of a cohort having some form of prior immunity, innate or acquired, reduces the number of infections required to achieve the HIT. For example, if initial transmissibility (R0) is 2.5 and 40% of the population has prior immunity (both reasonable assumptions for many areas), the HIT is as low as 20%, according to the authors’ calculations. That’s when the contagion begins to recede, though the final infected share of the population would be higher. This might explain why new cases and deaths have already plunged in places like Italy, Sweden, and New York, and why protests in NYC did not lead to a new wave of infections, while those in the south appear to have done so.

Seasonal effects: viral loads might be decreasing. From the abstract:

Severity of COVID-19 in Europe decreased significantly between March and May and the seasonality of COVID-19 is the most likely explanation. Mucosal barrier and mucociliary clearance can significantly decrease viral load and disease progression, and their inactivation by low relative humidity of indoor air might significantly contribute to severity of the disease.”

The BCG vaccine appears to be protective: this is the bacillus Calmette-Guérin tuberculosis vaccine administered in some countries, This finding is not based on clinical trials, so more work is needed.

Is there no margin in plasma? No subsidy? This is the only “bad news” item on my list. It’s widely agreed that blood plasma from recovered C19 patients can be incorporated into an immune globulin drug to inoculate people against the virus. It’s proven safe, but for various reasons no one seems interested. Not the government. Not private companies. Did Trump happen to mention it or something?

C19 doesn’t spread in schoolsthis German study demonstrates that there is little risk in reopening schools. One of the researchers says:

Children act more as a brake on infection. Not every infection that reaches them is passed on…. This means that the degree of immunization in the group of study participants is well below 1 per cent and much lower then we expected. This suggests schools have not developed into hotspots.”

Also worth emphasis is that remote learning leaves much to be desired, as acknowledged by the National Academies of Science, Engineering and Medicine, which has recommended that schools reopen for younger children and those with special needs.

Can angiotensin drugs (ACE Inhibitors/ARBs) reduce mortalityThis meta-analysis of nine studies finds that these drugs reduce C19 mortality among patients with hypertension. The drugs were also associated with a reduction in severity but not with statistical significance. These results run contrary to initial suspicions, because ACEI/ARB drugs actually “up-regulate” ACE-2 receptors, to which C19 binds. Researchers say the drugs might be working through some other protective channel. This is not a treatment per se, but this should be reassuring if you already take one of these medications.

Tricor appears to clear lung tissue of C19: this research focused on C19’s preference for an environment rich in cholesterol and other fatty acids:

What they found is that the novel coronavirus prevents the routine burning of carbohydrates, which results in large amounts of fat accumulating inside lung cells – a condition the virus needs to reproduce.”

Tricor reduces those fats, and the researchers claim it is capable of clearing lung tissue of C19 in a matter of days. This was not a clinical trial, however, so more work is needed. Tricor is an FDA approved drug, so it is safe and could be administered “off label” immediately. Tricor is a fibrate; the news with respect to statins and C19 severity is pretty good too! These are not treatments per se, but this should be reassuring if you already take one of these medications.

Hydroxychloroquine works: despite months of carping from media and leftist know-it-all’s dismissing the mere possibility of HCQ as a potential C19 treatment, evidence is accumulating that it is effective in treating early-stage infections after all. The large study conducted by the Henry Ford Health System found that treatment with HCQ early after hospitalization, and with careful monitoring of heart function, cut the death rate in half relative to a control group. Here’s another: an Indian study found that four-plus maintenance doses of HCQ acted as a prophylactic against C19 infection among health care workers, reducing the odds of infection by more than half. An additional piece of evidence is provided by this analysis of a 14-day Swiss ban on the use of HCQ in late May and early June. The ban was associated with a huge leap in the C19 deaths after a lag of less than two weeks. Resumption of HCQ treatment brought C19 deaths down sharply after a similar lag.

Meanwhile, a study in Lancet purporting to show that HCQ was ineffective and posed significant risks to heart health was retracted based on the poor quality of the data.

Remdesivir also cuts death rate: by 62% in a smaller controlled study by the drug maker Gilead Sciences.

Pet ownership might confer some immunity: this one is a little off-beat, and perhaps the research is under-developed, but it is interesting nonetheless!

I owe Instapundit and Marginal Revolution hat tips for several of these items.

Case Fatality, Stale Ratios and Exaggerated Loss

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I hope someday I won’t feel compelled to write or worry about the coronavirus. However, as the pandemic wears on, it seems to take only a few days for issues to pile up, and I just can’t resist comment. Today I have a couple of beefs with uses of data and concomitant statements I’ve seen posted of late.

People are still quoting case fatality rates (CFRs) as if those cumulative numbers are relevant to the number of deaths we can expect going forward. They are not. Just as hair-brained are applications of cumulative hospitalization and ICU admittance rates to produce “rough and ready” estimates of what to expect going forward. Or, I’ve seen people express hospitalizations as ratios to CFR, as if those ratios will be the same going forward. Again, they are not. Let me try to explain.

The chart below shows the course of the U.S. CFR since the start of the pandemic. It’s taken from the interactive Covid Time Series site. My apologies if you have to click on the chart for decent viewing (or you can visit the site). The CFR at any date is the cumulative number of deaths to-date divided by the cumulative number of confirmed cases. It is a summary of past history, but it is not well-suited to making predictions about death rates in the future. The CFR began to taper a little before Memorial Day, and it is now at about 4% (as of July 13).

Out of curiosity, I also generated CFRs for AZ, CA, FL, GA, and TX, which now average about half of the national CFR. There’s an obvious lesson: if you must use CFRs, understand that they vary from place to place.

Again, CFRs are cumulative. Their changes over time can tell us something about recent trends, but even then they are flawed. For example, case counts have risen dramatically with more widespread testing. Those testing positive more recently are concentrated in younger age cohorts, for whom infections are much less severe. Treatment has improved dramatically as well, so there is little reason to expect the CFR’s of recently diagnosed cases to be as high as the latest CFRs shown above.

There is no easy way to calculate an unflawed “marginal” CFR for a recent period, though an effort to do so might improve the predictive value. Deaths lag behind case counts because the progression from early symptoms to death can take several weeks. Even more vexing for constructing a valid, recent fatality rate is that reporting of deaths is itself delayed, as I explained in my last post. Each day’s report of deaths captures deaths that may have occurred over a period of several weeks in the past, and sometimes many more.

Finally no CFR can capture the true mortality rate of the virus without ongoing, ubiquitous testing. As the state of testing stands, the true mortality rate must reflect undiagnosed cases in the denominator. The CDC’s latest “best” estimate of the true mortality rate is just 0.3%, and 0.05% for those aged 50 years or less. Those figures are based on serological tests for the presence of antibodies to C19 in more random samples of the population. Those findings reflect the extent of undiagnosed and/or asymptomatic cases.

The point is one shouldn’t be too blithe about throwing numbers around like 4% mortality based on the CFR, or even 1% mortality as a “nice, round number”, without heavy qualification. Those numbers are gross exaggerations of what we are likely to see going forward.

The same criticisms can be leveled at claims that hospitalizations will proceed at some fixed ratio relative to diagnosed cases, or some fixed ratio relative to deaths. Again, new cases tend to be less severe, so hospitalizations are likely to be a much lower ratio to cases than what is reflected in cumulative totals. Because of improved treatment, the ratio of deaths to hospitalizations will be much lower in the future as well.

CFRs are not a useful guide to future COVID deaths. The true mortality rate is a much better baseline, particularly for subsets of the population matching the current case load. Finally, and this is the only disclaimer I’ll bother to provide today, we all know that suffering is not confined to terminal cases, and it is not confined to the hospitalized subset. But don’t exaggerate the extent of your preferred interpretation of suffering by applying inappropriate cumulative calculations.

 

Reported and “Actual” COVID Deaths

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I was updating my post from twelve days ago on the upward trend in new coronavirus cases when I came across a great tabular summary of a phenomenon that’s been underway since early April: significant delays in reporting deaths from COVID-19 (C19). Before I get to that, a quick word on what’s happened over the past 12 days. New coronavirus cases keep climbing in a number of states, and it’s been a grisly waiting game to see whether the severity and lethality of infections will follow the case counts upward. The following chart provides a very preliminary answer. It’s taken from Our World In Data, and it shows the seven-day moving average of C19 deaths in the U.S.

There has indeed been an upturn in reported deaths over the past week. Just prior to that, a temporary plateau in late June was caused by a set of “reclassifications” of earlier deaths in New Jersey (the “plateau effect is caused by seven-day averaging). These kinds of changes in reporting make it rather difficult to interpret trends accurately. Unfortunately, the reporting of deaths has been subject to continuing distortions that are even more difficult to discern than New Jersey’s spike.

Kyle Lamb provides the interesting table below, which might be difficult to read without either clicking on it or going to the link at Twitter. Here is another link to an annotated version of the table. The top row labeled “CTP Total” is the C19 death toll reported each week by the COVID Tracking Project. This is generally what the public sees. These reports show that deaths reached their highest levels during the weeks of April 11th through May 9th. However, the second column shows C19 deaths by their actual week of occurrence. This series shows a more distinct peak on April 18th with steady declines thereafter.

The weekly totals in the second column are not final, however. Take a look at the last reporting week in the far right column (July 11th). The CTP reported 4,286 deaths, an increase over the prior week consistent with the upturn in the first chart above. But the table shows that over half of that week’s reported deaths actually occurred in late April and early May! So the upturn in deaths is something of a mirage.

We won’t have a reasonable approximation of the death totals for the past several weeks (or how they compare) for at least several more weeks. In fact, one can argue that it might be a matter of months before we have a reasonable approximation of those deaths, but it’s worth noting that the vast bulk of “actual” C19 deaths tend to be reported within four weeks of the initial reporting week, and the additions or revisions to the two weeks in late April and early May in the last column were exceptionally large. Chances are we won’t see many more that big…. Or will we?

Aspects of this process hint at the ease with which the C19 death totals could be manipulated. The reported totals for all-cause mortality in the first column are incomplete; more recent weeks, especially, are not fully settled as to causes of death. Some of those fatalities are certain to be attributed to C19. Others might be reclassified as C19. And here is the scary part: the all-cause totals are certain to include a significant number of lockdown-related or COVID-phobic deaths: individuals who were unable or unwilling to seek medical care for urgent needs due to lockdowns or fears of rampant spread of C19 infections within hospital environments. To anyone with an interest in manipulating the C19 death toll, whether hospital officials seeking higher reimbursements, local or state officials seeking federal funds, or public officials at any level seeking to promote pandemic fears and/or political discord, these “extra” deaths might be tempting marks for reclassification.

I’m fairly confident that the uptrend of new cases will be far less severe than early in the pandemic. I believe much of the alarm I see on social and mainstream media is misplaced. More on that in a subsequent post, but for now I’ll simply note that those testing positive are concentrated in much lower ranges of the age distribution, and treatment has improved in a variety of ways. The table above shows that the downtrend in actual weekly C19 deaths is intact as of the admittedly incomplete July 11th reporting week. We won’t know the “actual” pattern of early-July C19 fatalities for another month or more. Even then, one might harbor suspicions that the totals are manipulated for economic or political reasons, but we can hope the reporting authorities are exercising the utmost objectivity in assigning cause of death.

Unfortunate COVID Follies

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This post is devoted to a few coronavirus policies and positions that trouble me. 

Counting Deaths: People have the general impression that counting COVID-19 cases and deaths is straightforward. The facts are more reminiscent of the following exchange in the film Arsenic and Old Lace, when Jonathan Brewster angrily insists he has offed more souls than his sweet little aunties have poisoned with elderberry wine:

Dr. Einstein: You cannot count the one in South Bend. He died of pneumonia!
Jonathan Brewster: He wouldn’t have died of pneumonia if I hadn’t shot him! 

Here, Dr. Einstein wears the shoes of public health authorities who claim that C19 deaths are undercounted. But lives counted as lost from C19, in many cases, are individuals who also had the flu, pneumonia, stroke, kidney failure, and a variety of other co-morbidities. Yes, other causes of death might be induced by the coronavirus, but like Johnny’s victim in South Bend, many would not have died from C19 if they hadn’t had a prior health event. In addition, otherwise unexplained deaths are often attributed to C19 with little justification.

In fact, the C19 death toll has been distorted by a perverse federal hospital reimbursement policy that rewards hospitals for COVID patients. Death certificates seem to list C19 as the cause for almost anyone who dies in or out of a hospital during the pandemic, whether they’ve been tested or not. In fact, deaths have been attributed to C19 despite negative test results when officials decided, for one reason or another, that the test must have been unreliable!

Lockdowns: almost all of the “curve flattening” in late March and April was accomplished by voluntary action, which I’ve covered before here. The lockdowns imposed by state and local governments were highly arbitrary and tragic for many workers and business owners who could have continued to operate as safely as many so-called “essential” businesses. Lockdowns in certain areas were also blatant violations of religious rights. There is little to no evidence that lockdowns themselves led to any actual abatement of the virus. And of course, people are fed up

The Beach: Right now I’m at a wonderful beach condo in Florida for a week. There are other people on the beach, mostly families and a few groups of friends, but there is plenty of open space. You will not catch the coronavirus on a beach like this. And there is almost zero chance you’ll catch it on any beach. In fact, the chance you’ll catch it anywhere outside is minuscule unless you’re jammed so tightly among hundreds of protesters that you can’t even turn around. Yet government officials have closed beaches in many parts of the country while allowing the protests to go on. Oh sure, they think people will CROWD onto beaches as if they’re at a BLM protest… except they’re not. Ah, then it must be banned! That takes a special kind of dumbass.     

Waiting for Results: How could we have spent trillions of dollars as a nation on economic stimulus, much of it skimmed off by grifters, but we can’t seem to get sufficient resources to make calls to those awaiting test results? This is a case of misplaced priorities. Even now, people are waiting more than a week for their results, and many are wandering around in the community without knowing their status. Wouldn’t you think we’d get that done? We can conduct well over a half million tests a day, but can’t we find a few bucks to deliver results via phone, email, or text within 24 hours of processing results. This is truly absurd. 

Vaccine Candidates: A similar point can be made about vaccine development: We are spending $5 billion on Operation Warp Speed to build capacity in advance for five promising vaccine candidates. These will be identified over the next few months, and it looks as if all five will come from established pharmaceutical majors. There are many more vaccine candidates, however, some being developed by smaller players using inventive new techniques. The OWS expenditure looks pretty meager when you compare it to the trillions in funds the federal government is spending on economic stimulus, especially when finding an effective vaccine would obviate much of the stimulus. 

TreatmentHydroxycloroquine has been found to lower the death rate from COVID-19 in a large controlled trial. Congratulations, morons, for trashing HCQ as a potential treatment, solely because Trump mentioned it. Way to go, dumbasses, for banning the use of a potential treatment that could have saved many thousands of lives. 

Air Conditioning: I’m shocked that public health experts haven’t been more vocal about the potentially dangerous effects of running air conditioners at high levels in public buildings. The virus is known to thrive in cool, dry environments, which is exactly what AC creates, yet this seems to have been almost completely ignored.   

Vitamin D: Likewise, I think public health experts have been far too reticent about the connection between Vitamin D deficiencies and the severity of C19 (also see here and here). The accumulating evidence about this association offers an explanation for the disturbingly high severity of cases among Black, Asian and Minority Ethnics (BAME), not to mention a possible role in C19 deaths among the generally D-deficient nursing home population. For the love of God, get the word out to the community that Vitamin D supplements might help, and they won’t hurt, and otherwise, tell people to get some sun!

Masks: I’m not in favor of strict mask mandates, but I have trouble understanding the aversion to masks among certain friends. Of course, there’s been way too much mixed messaging on the benefits of masks, and it didn’t all come from politicians! Scientists, the CDC, and the World Health Organization seemingly did everything possible to squander their credibility on this and other issues. However, a consensus now seems to have developed that masks protect others from the wearer and seem to protect the wearer from others as well. It should be obvious that masks offer a middle ground on which the economy can be restarted while mitigating the risks of further contagion. But even if you don’t believe masks protect the wearer, but only protect others from an infected wearer, donning a mask inside buildings, and when social distancing is impossible, still qualifies as a mannerly thing to do.