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Allocating Vaccine Supplies: Lives or “Justice”?

29 Tuesday Dec 2020

Posted by in Pandemic, Public Health, Uncategorized, Vaccinations

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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 Testing: Cycle Thresholds and Coffee Grounds

19 Saturday Dec 2020

Posted by in Coronavirus, Public Health, Uncategorized

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Here’s some incredible data on PCR tests demonstrating a radically excessive lab practice that generates false positives. I’m almost tempted to say we’d do just as well using a thermometer and the coffee ground test. Open a coffee tin and take a sniff. Can you smell the distinct aroma of the grounds? If not, and if you have other common symptoms, there’s a decent chance you have an active COVID infection. That test is actually in use in some parts of the globe!

The data shown below on PCR tests are from the Rhode Island Department of a Health and the Rhode Island State Health Lab. They summarize over 5,000 positive COVID PCR tests (collected via deep nasal swabs) taken from late March through early July. The vertical axis in the chart measures the cycle threshold (Ct) value of each positive test. Ct is the number of times the RNA in a sample must be replicated before any COVID-19 (or COVID-like) RNA is detected. It might be from a live virus or perhaps a fragment of a dead virus. A positive test with a low Ct value indicates that the subject is likely infected with billions of live COVID-19 viruses, while a high Ct value indicates perhaps a handful or no live virus at all.

The range of red dots in the chart (< 28 Ct) indicates relatively low Ct values and active infections. The yellow range of dots, for which 28 < Ct <= 32, indicates possible infections, and the upper range of green dots, where Ct > 32, indicates that active infections were highly unlikely. It’s important to note that all of these tests were recorded as new COVID cases, so the range of Ct values suggest that testing in Rhode Island was unreasonably sensitive. That’s broadly true across the U.S. as well, which means that COVID cases are over-counted by perhaps 30% or more. And yet it is extremely difficult for subjects testing positive to learn their Ct values. You can ask, but you probably won’t get an answer, which is absurd and counterproductive.

Notice that the concentration of red dots diminished over time, and we know that the spring wave of the virus in the Northeast was waning as the summer approached. The share of positives tests with high Ct values increased over that time frame, however. This is borne out by the next chart, which shows the daily mean Ct of these positive tests. The chart shows that active infections became increasingly rare over that time frame both because positive tests decreased and the average Ct value rose. What we don’t know is whether labs bumped up the number of cycles or replications to which samples were subjected. Still, the trend is rather disturbing because most of the positive cases in May and the first half of June were more likely to be virus remnants than live viruses.

It’s also worth noting that COVID deaths declined in concert with the upward trend in Ct values. This is shown in the chart below (where the Ct scale is inverted). This demonstrates the truly benign nature of positive tests having high Ct values.

This is also demonstrated by the following data from a New York City academic hospital, which was posted by Andrew Bostom. It shows that a more favorable “clinical status” of COVID patients is associated with higher Ct values.

It’s astounding that the U.S. has relied so heavily on a diagnostic tool that gets so many subjects wrong. And it’s nearly impossible for subjects testing positive to obtain their Ct values. Instead, they are subject to self-quarantine for up to two weeks. Even worse, until recently there were delays in reporting the results of these tests of up to a week or more. That made them extremely unhelpful. On the other hand, the coffee ground test is fast and cheap, and it might enhance the credibility of a subsequent positive PCR test, if one is necessary … and especially if the lab won’t report the Ct value.

The PCR test has identified far too many false infections, but it wouldn’t have been quite so damaging if 1) a reasonably low maximum cycle threshold had been established; 2) test results had not been subject to such long delays; and 3) rapid retests had been available for confirmation. The cycle threshold issue is starting to receive more attention, quite belatedly, and more rapid tests have become available. As I’ve emphasized in the past, cheap, rapid tests exist. But having dithered in February and March in approving even the PCR test, the FDA has remained extremely grudging in approving newer tests, and it persists in creating obstacles to their use. The FDA needs to wake up and smell the coffee!

November Pandemic Perspective

18 Wednesday Nov 2020

Posted by in Coronavirus, Pandemic, Uncategorized

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I hope readers share my compulsion to see updated COVID numbers. It’s become a regular feature on this blog and will probably remain one until infections subside, vaccine or otherwise. Or maybe when people get used to the idea of living normally again in the presence of an endemic pathogen, as they have with many other pathogens and myriad risks of greater proportions, and as they should. That might require more court challenges, political changes, and plain old civil disobedience.

So here, then, is an update on the U.S. COVID numbers released over the past few days. The charts below are attributable to Don Wolt (@tlowdon on Twitter).

First, reported deaths began to creep up again in the latter half of October and have escalated in November. They’ve now reached the highs of the mid-summer wave in the south, but this time the outbreak is concentrated in the midwest and especially the upper midwest.

Reported deaths are the basis of claims that we are seeing 1,500 people dying every day, which is an obvious exaggeration. There have been recent days when reported deaths exceeded that level, but the weekly average of reported deaths is now between 1,100 and 1,200 a day.

It’s important to understand that deaths reported in a given week actually occurred earlier, sometimes eight or more weeks before the week in which they are reported. Most occur within three weeks of reporting, but sometimes the numbers added from four-plus weeks earlier are significant.

The following chart reproduces weekly reported deaths from above using blue bars, ending with the week of November 14th. Deaths by actual date-of-death (DOD) are shown by the orange bars. The most recent three-plus weeks always show less than complete counts of deaths by DOD. But going back to mid-October, actual weekly deaths were running below reported deaths. If the pattern were to follow the upswings of the first two waves of infections, then actual weekly deaths would exceed reported deaths by perhaps the end of October. However, it’s doubtful that will occur, in part because we’ve made substantial progress since the spring and summer in treating the disease.

To reinforce the last point, it’s helpful to view deaths relative to COVID case counts. Deaths by DOD are plotted below by the orange line using the scale on the right-hand vertical axis. New positive tests are represented by the solid blue line, using the left-hand axis, along with COVID hospitalizations. There is no question that the relationship between cases, hospitalizations, and deaths has weakened over time. My suspicions were aroused somewhat by the noticeable compression of the right axis for deaths relative to the two charts above, but on reviewing the actual patterns (peak relative to troughs) in those charts, I’m satisfied that the relationships have indeed “decoupled”, as Wolt puts it.

Cases are going through the roof, but there is strong evidence that a large share of these cases are false positives. COVID hospitalizations are up as well, but their apparent co-movement with new cases appears to be dampening with successive waves of the virus. That’s at least partly a consequence of the low number of tests early in the pandemic.

So where is this going? The next chart again shows COVID deaths by DOD using orange bars. Wolt has concluded, and I have reported here, that the single-best short-term predictor of COVID deaths by DOD is the percentage of emergency room visits at which patients presented symptoms of either COVID-like illness (CLI) or influenza-like illness (ILI). The sum of these percentages, CLI + ILI, is shown below by the dark blue line, but the values are shifted forward by three weeks to better align with deaths. This suggests that actual COVID deaths by DOD will be somewhere around 7,000 a week by the end of November, or about 1,000 a day. Beyond that time, the path will depend on a number of factors, including the weather, prevalence and immunity levels, and changes in mobility.

I am highly skeptical that lockdowns have any independent effect in knocking down the virus, though interventionists will try to take credit if the wave happens to subside soon for any other reason. They won’t take credit for the grim lockdown deaths reaped by their policies.

Despite the bleak prospect of 1,000 or more COVID-attributed deaths a day by the end of November, the way in which these deaths are counted is suspect. Early in the pandemic, the CDC significantly altered guidelines for the completion of death certificates for COVID such that deaths are often improperly attributed to the virus. Some COVID deaths stem from false-positive PCR tests, and again, almost since the beginning of the pandemic, hospitals were given a financial incentive to classify inpatients as COVID-infected.

It’s also important to remember that while any true COVID fatality is premature, they are generally not even close to the prematurity of lockdown deaths. That’s a simple consequence of the age profile of COVID deaths, which indicate relatively few life-years lost, and the preponderance of co-morbidities among COVID fatalities.

Again, COVId deaths are bad enough, but we are seeing an unacceptable and ongoing level of lockdown deaths. This is now to the point where they may account for almost all of the continuing excess deaths, even with the fall COVID wave. It’s probable that public health would be better served with reduced emphasis on COVID-mitigation for the general population and more intense focus on protecting the vulnerable, including the distribution of vaccines.

Fall Coronavirus Season

16 Friday Oct 2020

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We’ve known for some times that COVID-19 (C19) follows seasonal patterns typical of the flu, though without the flu’s frequent antigenic drift. Now that we’re moving well into autumn, we’ve seen a surge in new C19 case counts in Europe and in a number of U.S. states, especially along the northern tier of the country.

The new case surge began in early to mid-September, depending on the state, and it’s been coincident with another surge in tests. From late July through early October, we had a near doubling in the number of tests per positive in the U.S. An increase in tests also accompanied the previous surge during the summer, which claimed far fewer lives than the initial wave in the early spring. In the summer, infections were much more prevalent among younger people than in the spring. Vitamin D levels were almost certainly higher during the summer months, our ability to treat the virus had also improved, and immunities imparted by prior infections left fewer susceptible individuals in the population. We have many of those advantages now, but D levels will fade as the fall progresses.

As for the new surge in cases, another qualification is that false positives are still a major testing problem; they inflate both case counts and C19-attributed deaths. In the absence of any improvement in test specificity, of which there is no evidence, the exaggeration caused by false positives grows larger as testing increases and positivity rates fall. So take all the numbers with that as a caveat.

How deadly will the virus be this fall? So far in Europe, the trends look very promising. Kyle Lamb provided the following charts from WHO on Twitter yesterday. (We should all be grateful that Twitter hasn’t censored Kyle yet, because he’s been a force in exposing alarmism in the mainstream media and among the public health establishment.) Take a look at these charts, and note particularly the lag between the first wave of infections and deaths, as well as the low counts of deaths now:

If the lag between diagnosis and death is similar now to the spring, Europe should have seen a strong upward trend in deaths by now, yet it’s hardly discernible in most of those countries. The fatality rates are low as well:

As Lamb notes, the IFRs in the last column look about like the flu, though again, the reporting of deaths and their causes are often subject to lags.

What about the U.S.? Nationwide, C19 cases and attributed death reports declined after July. See the chart below. More recently, reported deaths have stabilized at under 700 per day. Note again the relatively short lags between turns in cases and deaths in both the spring and summer waves.

Clearly, there has been no acceleration in C19 deaths corresponding to the recent trend in new cases. Northeastern states that had elevated death rates in the spring saw no resurgence in the summer; southern states that experienced a surge in the summer have now enjoyed taperings of both cases and deaths. But with each season, the virus seems to roll to regions that have been relatively unscathed to that point. Now, cases are surging in the upper Midwest and upper mountain states, though some of these states are lightly populated and their data are thin.

A few state charts are shown below, but trends in deaths are very difficult to tease out in some cases. First, here are new cases and reported deaths in Michigan, Wisconsin, and Minnesota. There is a clear uptrend in cases in these states along with a very slight rise in deaths, but reported deaths are very low.

Next are Idaho, Montana, North Dakota, and South Dakota. A slight uptrend in cases began as early as August. Idaho and Montana have had few deaths, so they are not plotted in the second chart. The Dakotas have had days with higher reported deaths, and while the data are thin and volatile, the visual impression is definitely of an uptrend in deaths.

The following states are somewhat more central in latitude: Colorado, Illinois, and Ohio. There is a slight upward trend in new cases, but not deaths. Illinois is experiencing its own second wave in cases.

Out of curiosity, I also plotted Massachusetts, Pennsylvania, and New Jersey, all of which suffered in the first wave during the spring. They are now experiencing uptrends in cases, especially Massachusetts, but deaths have been restrained thus far.

The upshot is that states having little previous exposure to the virus are seeing an uptrend in deaths this fall. The same does not seem to be happening in states with significant prior exposure, at least not yet.

There are major questions about the reasons for the lingering death counts in the U.S.. But consider the following: first, the infection fatality rate (IFR) keeps falling, despite the stubborn level of daily reported deaths. Second, deaths reported have increasingly been pulled forward from deaths that actually occurred in the more distant past. This sort of “laundering” lends the appearance of greater persistence in deaths than is real. Third, again, false positives exaggerate not just cases, but also C19 deaths. Hospitals test everyone admitted, and patients who test positive for C19 are reimbursed at higher rates under the CARES Act; Medicare reimburses at a higher rates for C19 patients as well.

We’re definitely seeing a seasonal upswing in C19 infections in the US., now going on five weeks. In Europe, the surge in cases began slightly earlier. However, in both Europe and the U.S., these new cases have not yet been associated with a meaningful surge in deaths. The exceptions in the U.S. are the low-density upper mountain states, which have had little prior exposure to the virus. The lag between cases and deaths in the spring and summer was just two to three weeks, and while it’s too early to draw conclusions, the absence of a surge in deaths thus far bodes well for the IFR going forward. If we’re so fortunate, we can thank a combination of factors: a younger set of infecteds, earlier detection, better treatment and therapeutics, lower viral loads, and a subset of individuals who have already gained immunity.

Lockdowns Subvert Public Health and Life Itself

15 Thursday Oct 2020

Posted by in Coronavirus, Lockdowns, Public Health, Uncategorized

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Acceptance of risk is a necessary part of a good life, and extreme efforts to avoid it are your own business. Government has no power to guarantee absolute safety, nor should we presume to have such a right. Ongoing COVID lockdowns are an implicit assertion of exactly that kind of government power, despite the impotence of those efforts, and they constitute a rejection of more fundamental rights.

Lockdowns have had destructive effects on health and economic well being while conferring little if any benefit in mitigating harm from the virus. The lockdowns were originally sold as a way to “flatten the curve”, that is, to avoid a spike in cases and an overburdened health care system. However, this arguably well-qualified rationale later expanded in scope to encompass the mitigation of smaller and much less deadly outbreaks among younger cohorts, and then to the very idea of extinguishing the virus altogether. It’s become painfully obvious that such measures are not capable of achieving those goals.

In the U.S., the ongoing lockdowns have been a cause célèbre largely on the interventionist Left, and they have been prolonged mainly by Democrats at various levels of government. In a way, this is not unlike many other policies championed by the Left, often ostensibly designed to help members of the underclasses: instead, those policies often destroy or wrongly obviate incentives and promote dependency on the state. In this case, the plunge into dependency is a reality the Left would very much like to ignore, or to blame on someone else. You know who.

The lockdowns have been largely unsuccessful in mitigating the spread of the virus. At the same time, they have been used as a pretext to deny constitutional rights such as the free practice of religion, assembly, and a broad range of unenumerated rights under the “penumbra” of the Bill of Rights and the Ninth Amendment. What’s more, the severity of the economic blow caused by lockdowns has been borne disproportionately by the working poor and the small businesses who employ so many of them.

Lockdowns are deadly. It’s not clear that they’ve saved any lives, but they have massively disrupted the operation of the health care system with major consequences for those with chronic and undiagnosed conditions. The lockdowns have also led to spikes in mental health issues, alcoholism, drug abuse, and deaths of despair. A recent study found that over 26% of the excess deaths during the pandemic were non-COVID deaths. Those deaths were avoidable or accelerated, whereas the lockdowns have failed to meaningfully curtail COVID deaths. Don’t tell me about reduced traffic fatalities: that reduction is relatively small relative to the increase in non-COVID excess deaths (see below).

What proof do we have that lockdowns cause excess deaths? See this study in The Lancet on cancer deaths due to lockdown-induced delays in diagnoses. See this study on UK school closures. See this Oxfam International report on lockdown-induced starvation. Other reports from the UK suggests that lockdown deaths are widespread, having taken nearly 2,800 per week early in the pandemic, and many other deaths yet to occur have been made inevitable by lockdowns. Doctors in the U.S. have warned that lockdowns are a “mass casualty incident”, and a German government study warned of the same.

The Ethical Skeptic (TES) on Twitter has been tracking a measure of lockdown deaths for some time now. The following graphic provides a breakdown of excess non-COVID deaths since the start of the pandemic. The total “pie” shows almost 320,000 excess deaths through September 26th (avoiding less complete counts in recent weeks), as reported by the CDC. COVID accounted for 202,000 of those deaths, based on state-level reporting. Of the remaining 117,000 excess deaths, TES uses CDC data to allocate roughly 85,000 to various causes, the largest (more than half) being “Suicide, Addiction, Abandonment, and Abuse”. Other large categories include Cardio/Diabetes, Stroke, premature Alzheimers/Dementia death, and Cancer Access. Nearly 32,000 excess deaths remain as a “backlog”, not yet reported with a cause by states.

Also of interest in the graphic are estimates of life-years lost. The vast bulk of COVID victims are elderly, of course, which means that any estimate of lost years per victim must be relatively low. On the other hand, most non-COVID, lockdown-related deaths are among younger victims, with correspondingly greater life-years lost. TES’s aggregate estimate is that lockdown-related excess deaths involve double the life-years lost of COVID deaths. Of course, that is an estimate, but even granting some latitude for error, the reality is horrifying!

John Tierney in City Journal cites several recent studies concluding that lockdowns have been largely ineffective in Europe and in the U.S. While Tierney doesn’t rule out the possibility that lockdowns have produced some benefits, they have carried excessive costs and risks to public health going forward, such as lingering issues for those having deferred important health care decisions as well as disruption in future economic prospects. Ultimately, lockdowns don’t accomplish anything:

“While the economic and social costs have been enormous, it’s not clear that the lockdowns have brought significant health benefits beyond what was achieved by people’s voluntary social distancing and other actions.”

Tierney also discusses the costs and benefits of lockdowns in terms of life years: quality-adjusted life-years (QALY), which is a widely-used measure for evaluating of the use of health care resources:

“By the QALY measure, the lockdowns must be the most costly—and cost-ineffective—medical intervention in history because most of the beneficiaries are so near the end of life. Covid-19 disproportionately affects people over 65, who have accounted for nearly 80 percent of the deaths in the United States. The vast majority suffered from other ailments, and more than 40 percent of the victims were living in nursing homes, where the median life expectancy after admission is just five months. In Britain, a study led by the Imperial College economist David Miles concluded that even if you gave the lockdown full credit for averting the most unrealistic worst-case scenario (the projection of 500,000 British deaths, more than ten times the current toll), it would still flunk even the most lenient QALY cost-benefit test.”

We can now count the World Health Organization among the detractors of lockdowns. According to WHO’s Dr. David Nabarro:

“Lockdowns just have one consequence that you must never ever belittle, and that is making poor people an awful lot poorer…. Look what’s happened to smallholder farmers all over the world. … Look what’s happening to poverty levels. It seems that we may well have a doubling of world poverty by next year. We may well have at least a doubling of child malnutrition.”

In another condemnation of the public health consequences of lockdowns, number of distinguished epidemiologists have signed off on a statement known as The Great Barrington Declaration. The declaration advocates a focused approach of protecting the most vulnerable from the virus, while allowing those at low risk to proceed with their lives in whatever way they deem acceptable. Those at low risk of severe disease can acquire immunity, which ultimately inures to the benefit of the most vulnerable. With few, brief, and local exceptions, this is how we have always dealt with pandemics in the past. That’s real life!

Teachers Face Low-to-Moderate COVID Risk

29 Saturday Aug 2020

Posted by in Education, Pandemic, School Choice, Uncategorized

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A quick follow-up to my recent post “COVID Hysteria and School Reform“: the graphic above is from an occupational risk study recently conducted by Swedish health authorities. The horizontal axis is obscured by the lower banner from Twitter (my fault), but the average risk of infection across all occupations was slightly less than 1%, and the highest-risk occupations were in the 4 – 5% range. Keep in mind, the data was collected while the virus was still raging in Sweden, while schools remained open. The virus hasn’t completely vanished in Sweden since then, but it has largely abated.

The study found that teachers had roughly average or below average risk, especially for pre-school and upper secondary (so-called “gymnasium”) teachers. The results demonstrate the lack of merit to claims by teachers unions that their members are somehow at greater risk of contracting coronavirus than other “essential” workers. We already know that children have extremely low susceptibility to COVID-19 and that they do not readily transmit the virus.

The health benefits of closing schools or taking them on-line do not compensate for the loss of educational effectiveness and detrimental health effects of preventing children from attending schools. The digital divide between children from disadvantaged households and their peers is likely to grow more severe if online learning is their only option. They should have choices, including functioning public schools.

To the last point, however, read this link for the sort of thing one teachers union supports. If the members are okay with that insanity then they shouldn’t be teaching your kids.

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

01 Saturday Aug 2020

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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

29 Wednesday Jul 2020

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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 Trends and the Scourge of False Positives

20 Monday Jul 2020

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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

16 Thursday Jul 2020

Posted by in Pandemic, Public Health, Uncategorized

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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 schools: this 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 mortality? This 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.