Category Archives: Coronavirus

The Favored Cause of Death

19 Monday Oct 2020

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The CDC changed its guidelines on completion of death certificates on April 5th of this year, and only for COVID-19 (C19), just as infections and presumed C19 deaths were ramping up. The substance of the change was to broaden the definition under which death should be attributed to C19. This ran counter to CDC guidelines followed over the previous 17 years, and the change not only makes the C19 death counts suspect: it also makes comparisons of C19 deaths to other causes of death unreliable, since only C19 is subject to the new CDC guidance. That’s true for concurrent and historical comparisons. The distortions are especially bad relative to other respiratory diseases, but also relative to other conditions that are common in mortality data.

The change in the CDC guidelines was noted in a recent report prepared for the Florida House of Representatives. It was brought to my attention by a retweet by Justin Hart linked to this piece on Andrew Bostom’s site. Death certificates are divided into two parts: Part 1 provides four lines in which causes of death are listed in reverse clinical order of events leading to death. Thus, the first line is the final clinical condition precipitating death. Prior clinical events are to be listed below that. The example shown above indicates that an auto accident, listed on the fourth line, initiated the sequence of events. Part 2 of the certificate is available for physicians or examiners to list contributing factors that might have played a role in the death that were not part of the sequence of clinical events leading to death.

The CDC’s change in guidelines for C19, and C19 only, made the criteria for inclusion in Part 1 less specific, and it essentially eliminated the distinction between Parts 1 and 2. The following appears under “Vital Records Criteria”:

“A death certificate that lists COVID-19 disease or SARS-CoV-2 as a cause of death or a significant condition contributing to death.”

How much difference does this make? For one thing, it opens the door to C19-attributed deaths in cases of false-positive PCR tests. When large cohorts are subject to testing — for example, all patients admitted to hospitals — there will always be a significant number of false positives even when test specificity is as high as 98 – 99%.

The elimination of any distinction between Parts 1 and 2 causes other distortions. A review of the Florida report is illustrative. The House staff reviewed almost 14,000 certificates for C19-19 attributed deaths. Over 9% of those did not list C19 among the clinical conditions leading to death. Instead, in those cases, C19 was listed as a contributing factor. Under the CDC’s previous guidelines, those would not have been counted as C19 deaths. The Florida House report is conservative in concluding that the new CDC guidelines inflated C19 deaths by only those 9% of the records examined.

There are reasons to think that the exaggeration was much greater, however. First, the Florida House report noted that nearly 60% of the certificates contained information “recorded in a manner inconsistent with state and national guidance”. In addition, almost another 10% of the fatalities were among patients already in hospice! Do we really believe the deaths of all those patients whose diseases had reached such an advanced stage should be classified as C19 fatalities? And another 1-2% listed non-C19 conditions as the immediate and underlying causes.

Finally, more than 20% of the certificates listed C19 alone as a cause of death despite a range of other contributing conditions or co-morbidities. This in itself may have been prompted by the change in the CDC’s guidelines, as the normal standards often involve a “comorbidity” as the initial reason for hospitalization — in that case a clinical event ordinarily listed in Part 1. The high rate of errors and the fact that roughly two-thirds of the deaths reviewed occurred in the hospital, where patients are all tested and often multiple times, raises the specter that up to 20% more of the C19 deaths were either erroneous and/or misclassified due to false positives.

(An exception may have occurred in New York, where an order issued in March by Governor Andrew Cuomo to return C19-positive residents of nursing homes (including suspected C19 cases) back to those homes, The order was made before the change in CDC guidelines and wasn’t rescinded until later in April. There is reason to believe that some of the C19 deaths among nursing home residents in New York were undercounted.)

All told, in the Florida data we have potential misclassification of deaths of 9% + 9% + 2% + 20% = 40%, or inflation relative to actual C19 deaths of up to 40%/60% = 67%! I strongly doubt it’s that high, but I would not consider a range of 25% – 50% exaggeration to be unreasonable.

We know that reports of C19 deaths lag actual dates of death by anywhere from 1 to 8 weeks, sometimes even more. This is misleading when no effort is made to explain that difference, which I’ve never heard out of a single journalist. We also know that false positive tests inflate C19 deaths. The Florida report gives us a sense of how large that exaggeration might be. In addition, the Florida data show that the CDC guidelines inflate C19 deaths in other ways: as a mere contributing factor, it can now be listed as the cause of death, unlike the treatment of pneumonia as a contributing factor, for instance. The same kind of distortion occurs when patients contract C19 (or have a false positive test) while in hospice.

There is no doubt that C19 led to “excess deaths” relative to all-cause mortality. However, many of these fatalities are misclassified, and it’s likely that a large share were and are lockdown deaths as opposed to C19 deaths. That’s tragic. The CDC has done the country a massive disservice by creating “special rules” for attributing cause-of-death to C19. If reported C19 fatality rates reflected the same rules applied to other conditions, our approach to managing the pandemic surely would have inflicted far less damage to health and economic well being.

Fall Coronavirus Season

16 Friday Oct 2020

Posted by in Coronavirus, Pandemic, Uncategorized

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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!

False Positives, False Cases, False Deaths

14 Monday Sep 2020

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The tremendous increase in testing for COVID-19 (C19) this summer was associated with an increase in cases. Most of these tests were so-called PCR tests with samples collected via deep nasal swabs. More testing did not fully explain the increased case load, but false positives (FPs) still accounted for a substantial share. That’s especially true in light of the decline in positivity rates, which reflected a decline in the actual prevalence of active infections. FPs also account for a substantial share of the deaths attributed to COVID, which are obviously cases of false attribution. If a test for C19 is positive, it will be listed on the death certificate.  

COVID Case Inflation

The exaggeration of confirmed cases due to FPs is more substantial as the prevalence of active infection declines. That’s because the share of true positives in the tested population declines, while the share of false positives must rise due to the greater share of uninfected individuals in the population.

Now, as the contagion is waning in former hot spots, there is a danger that FPs create the impression of persistence in the case counts. That’s costly not just for those incorrectly diagnosed, but also in terms of medical resources, for communities subject to excessive public intervention, such as inappropriate lockdowns, and in terms of the fear promoted by these inaccuracies.

FPs are extremely disruptive when testing is relied upon in critical situations such as health care staffing, or even among sports teams. For example, at the University of Arizona, out of 25 positive tests on September 3, only 10 were confirmed as positives in later tests. The NFL has also had its share of false positives. 

Lax Testing Standards

There is evidence that testing standards under CDC guidance are so broad that a large number of inactive, non-infectious cases are being flagged as positives (see the chart above for the intuition, as well as the graphic at the bottom of this post). The tests sometimes amount to a coin flip when it comes to evaluating positives; some of the positives might even come from non-novel coronaviruses such as the common cold! This paper by Andrew N. Cohen, Bruce Kessel, & Michael G. Milgroom – CKM) questions the guidance of public health authorities on testing more generally. From the abstract (my emphasis):

“Unlike previous epidemics, in addressing COVID-19 nearly all international health organizations and national health ministries have treated a single positive result from a PCR-based test as confirmation of infection, even in asymptomatic persons without any history of exposure. …  positive results in asymptomatic individuals that haven’t been confirmed by a second test should be considered suspect.”

False Positive Math

When I wrote about “The Scourge of False Positives” in July. I noted that a test specificity of 95% implies that 5% of uninfected individuals will falsely test positive. Unfortunately, that still produces a huge number of FPs when testing is broad. That’s NOT a good reason to avoid broad testing; it just means that positive tests should be confirmed by another test. (In this case, two tests with the same specificity reduce a 5% false positive rate to 0.25%. That’s why fast, cheap tests are necessary for confirmation.

Again, exaggerated case counts due to FP’s become more severe as a contagion wanes. That’s because FPs become an increasingly large share of positive test results and overstate the persistence of the virus. If active infections fall to 1% of 750,000 daily tests, or 7,500 true cases, the 5% specificity implies 37,125 FPs: true positives would be only 17% of positive cases. Much worse than a coin flip! And again, which cases are infectious?

How Bad Are FPs, Really?

This recent research, also authored by CKM, explains the reasons why FPs are usually an issue in the real world, despite the tests’ reportedly perfect reactivity to anything other than the virus’ genetic fragments. CKM find that the median FP rate in their sample of “tests of tests” was 2.3%. That means 23 out of every 1,000 uninfected people tested will test positive.

If that seems small to you, suppose the true prevalence of active infection in a population is 4%. If 1,000,000 people are tested and there are no false negatives (unlikely), then 40,000 infected people will be identified by the test. However, another 22,000 uninfected people will also test positive ((1,000,000 – 40,000 infected) x 0.023). That means the number of positive tests will be inflated by 55%. They’ll all receive some form of treatment or ordered into quarantine. Expanded Testing and FPs This summer, the volume of daily tests increased from about 150,000 a day in early April to more than 750,000 a day in July. That’s a 400% increase, but the true prevalence of active infection in the expanded test population during the summer was almost certainly lower than in the spring. Suppose active infections fell from 10% of the test population in the spring to 5% in the summer. That means the daily number of “true positives” would have risen from 15,000 to 35,000 in the expanded test population (and again I assume no false negatives for simplicity). The number of FPs, however, would have risen from 3,105 to 16,445. Therefore, FPs would have accounted for 40% of the increase in “confirmed” cases between spring and summer.

False COVID Deaths

FPs are also inflating COVID death counts. PCR tests are routinely given at hospital admission for any cause, and even after sudden death, especially as the availability of tests increased late in the spring. This subset of the tested population will certainly have its share of FPs. If such a patient dies, regardless of underlying cause, it might well be attributed to COVID-19 as it will still appear on the death certificate. The same has occurred in the case of traffic fatalities, suicides, and other sudden deaths.

Antibody Tests

The FP problem also plagues tests of seroprevalence, which determine whether an individual has had the virus or is cross-protected against the virus by antibodies acquired via non-novel coronavirus infections. The consequences of these antibody FPs can be serious as well, because it means a positive test might not ensure immunity. As the exposed share of the population increases, however, the FP share of antibody tests is diminished.

Conclusion

As long as testing is required, dealing with FPs (and false negatives, of course) requires repeated testing, as CKM state unequivocally. And the tests must be fast to be of any use. The current testing regime must be overhauled to prevent false positives from costly impositions on the lives of uninfected patients, consuming unnecessary medical resources, making unrealistic assessments of cases and deaths, and unnecessary suspensions of normal human social activity and liberty.

Union Control, Shuttered Schools, COVID Risk

07 Monday Sep 2020

Posted by in Coronavirus, Education, Unions

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Public schools are closed in favor of virtual classrooms in some areas. Elsewhere, however, schools have physically reopened to the children of willing parents. It should be no surprise that the varying strength of teachers’ unions has a lot to do with these decisions. One cannot claim that the pattern of closures is a response to varying levels of COVID risk, as there is no geographic association between the closures and COVID cases or deaths. The shame of it is that closures compromise learning and also have destructive effects on local labor markets and the ability of parents to earn incomes.

That unions play this role, often decisively, is shown in a new paper entitled “Are School Reopening Decisions Related to Union Influence?“, by Corey DeAngelis and Christos Makridis (HT: Tyler Cowen). The authors examine the fall reopening decisions of 835 school districts and find that “… districts in locations with stronger teachers’ unions are less likely to reopen in person…“. The authors test four different measures of union strength with similar findings. They also rule out potential confounding influences like voting patterns.

Shall we defend the unions for protecting their members from excessive risk? Well, another important finding reported by the authors won’t surprise anyone having the least familiarity with data on C19 risks:

“We also do not find evidence to suggest that measures of COVID-19 risk are correlated with school reopening decisions.”

Few children catch the virus and children are not effective at transmitting C19 to their peers, teachers, and parents. Furthermore, schools closed to in-person learning are not located in areas at elevated risk relative to those remaining open.

The role of teachers’ unions in school reopening decisions is a textbook case of the inadvisability of unionized public employees. Most obviously, it is in their interests to encourage greater funding and taxes. This is but one of many dimensions of the political agendas that teachers’ unions may advance, and to which member dues are put. These are not always representative of members’ views, which is especially problematic in states without right-to-work laws.

The very nature of public service means that the work of public employees (or its absence) has profound external influences on the community at large. The unions are not shy about using this power as leverage in negotiations. Thus, teachers’ unions often act as adversaries not only to taxpayers, but to parents, children, and the business community.

Do public school administrators and elected school board members belong on the list of union adversaries as well? Perhaps: the unions have bullied school districts and have made them less attractive as educational institutions in a cost-benefit sense. In the present case, the unions have successfully lobbied for ongoing payments of income and benefits to their members despite the degraded effectiveness of on-line instruction for many K-12 students. Meanwhile, many parents are learning to exercise choice in the matter by abandoning public schools in favor of private alternatives.

Not News: Infections and Long-Term Complications

06 Sunday Sep 2020

Posted by in Coronavirus, Health Care

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At 15 years of age I was diagnosed as a Type I diabetic — 49 years ago. I had a genetic predisposition, but I’ve been told by several endocrinologists over the years that an “event” likely triggered the antibody response for which I was predisposed. The event was, in all probability, a viral or bacterial infection. The autoimmune response to that infection attacked the islet cells in my pancreas and destroyed my body’s ability to produce insulin. I’ve been dependent on external delivery of insulin ever since. Life goes on.

I relate this information to emphasize that it is not “novel” for a virus to trigger long-term “complications”. Recently, certain media factions have been shrieking about the long-term complications that might be triggered by the coronavirus (C19) even in those with otherwise light symptoms. Those are unfortunate, but again, this aspect of viral and bacterial infection is not uncommon.

We know, for example, that bacterial and viral infections often trigger autoimmune diseases like diabetes. Other examples are chronic fatigue syndrome, fibromyalgia, rheumatoid conditions, celiac disease, Graves’ disease, Guillain-Barré syndrome, Sjogren’s Syndrome, multiple sclerosis, and many others.

One condition that’s been cited as an especially dangerous complication of C19 is myocarditis, or inflammation of the heart muscle. This has been invoked as a reason to cancel sports competitions, for example. (See here for a denial of one rather hyperbolic claim regarding this condition.) Myocarditis has a long history as a side effect of influenza. Most people recover with no long-term complications, and others manage to live with it and remain productive. While C19 is “novel”, infection-induced myocarditis is not.

If you catch a virus or a bacterial infection, you might experience other complications with varying severity. Get used to the idea. It’s an unfortunate fact of life.

COVID Immunity, Herd By Herd

01 Tuesday Sep 2020

Posted by in Coronavirus, Herd Immunity

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Too many public health authorities remain in denial, but epidemiologists are increasingly convinced that heterogeneity implies a coronavirus herd immunity threshold (HIT) that is greatly reduced from traditional models and estimates. HIT is the share of the population that must be infected before the contagion begins to recede (and the transmission ratio R falls below one). Traditional models, based on three classes of individuals (Susceptibles, Infectives, and Recovered – SIR), predict a HIT of 60% or more. However, models that incorporate variation in susceptibility, transmissibility, and occupational or social behavior reduce the HIT substantially. Many of these more nuanced models show that the HIT could be in a range of just 15% to 25%. If that is the case, many regions are already there!

For background, I refer you to the first post I wrote about heterogeneity in late March, more detailed thoughts from early May, examples and more information on the literature later in May. I’ve referenced it repeatedly in other posts since then. And now, more than five months later, even the slow kids at the New York Times have noticed. The gist of it: if not everyone is equally susceptible, for example, a smaller share of the population needs to be “immunized via infection” to taper the spread of the virus.

Some supporting evidence appears in the charts below, courtesy of Kyle Lamb on Twitter. The first chart shows a seven-day average of C19 cases per million of population for ten states that reached an estimated 10% antibodies. These antibodies confer at least short-term immunity against C19. Most of these states saw cases/m climb at least through the day when the 10% level was reached, though Rhode Island appears to have been an exception.

The second chart shows the seven-day average of cases/m in the same states starting seven days after the 10% immunity level was reached. I’d prefer to see the days in the interim as well, but the changes in trend are still noteworthy. All of these states except Louisiana had a downturn in the seven-day average of new cases within a few weeks of breaching the 10% infection level (Louisiana had distinct and non-coincident outbreaks in different parts of the state). These striking similarities suggest that things turned as the infection level reached 15% or more, consistent with many of the epidemiological models incorporating heterogeneity.

Next, take a look at the states in which C19 surged most severely this summer. The new cases are not moving averages, so the charts are not quite comparable to those above. However, the peaks seem to occur prior to the breach of the 15% infection level.

Speculation about early herd immunity has been going on for several months with respect to various countries and even more “micro” settings such as cruise ships and military vessels, where populations are completely isolated. Early on, this “early” herd immunity was discussed under the aegis of “immunological dark matter”, but we know now that T-cell immunity has played an important role. In any case, anti-body expression (or seroprevalence) at around 20% has been linked to reversals in C19 cases and deaths in several countries. As Yinon Weiss notes, New York City and Stockholm were both C19 hotspots in the spring, both have seen deaths decline to low levels, and they have little in common in terms of public health policy. London as well. The one thing they share are similar levels of seroprevalence.

An important qualification is that herd immunity is not relevant at high levels of aggregation. That is, herd immunity won’t be achieved simultaneously in all regions. The New York City metro area might have reached its HIT in April, but Florida (or perhaps only Miami) might have reached a HIT in July. Many areas of the Midwest probably still aren’t there.

In the absence of a new mutation of C19, the final proof of herd immunity in many of the former hotspots will be in the fall and winter. We should expect at least a few cases in those areas, but if there are more intense contagions, they should be confined to areas that have not yet seen a level of seroprevalence near 15%.