Monday, November 30, 2020

The Emergency Use Authorization for Covid-19 vaccines: Ignorance is Bliss

If you are an FDA official charged with approving a Covid-19 vaccine, ignorance is bliss.  FDA is being asked to approve vaccines that will be injected into many millions of people, all using new methods of vaccination that have never before been approved for human use.  That puts them at great risk of making the wrong decision.

However, the lawyers who wrote the Emergency Use Authorization (EUA) legislation understood the FDA bureaucracy and its risk aversion. They probably also worked for, or consulted with, the pandemic vaccine industry.

And so they came up with a standard that practically mandates the most minimal collection of information from clinical trials of vaccines for which emergency use authorization will be sought.  Instead of requiring specific information, the standard simply says that in order to receive an EUA, a product's known and potential benefits should outweigh its known and potential risks. So, the more its sponsor knows about adverse effects, the more trouble the vaccine is likely to have getting approved.  Accordingly, it is better for the adverse effects to be as unknown as possible.

This standard also explains what might be considered oddities in trial design:  for example, why the vaccine sponsors/developers did not collect data on whether the vaccines prevented transmission of disease. Nor were vaccine sponsors required to show statistically significant data on whether hospitalizations (severe illness) and deaths were prevented.

Basically, the FDA was given the statutory green light to approve anything it wanted to approve, with minimal actual data.  That is how Operation Warp Speed could even be conceived.

Most important, from the standpoint of FDA, it gave the agency cover.  FDA is not being asked to act as a regulator. All it needs to be able to say is that the potential for benefit exceeds the potential risks, and as long as little is actually known about the vaccines, they can say their approval was based on the best evidence available at the time. This is of course another reason for speed:  the vaccines need to be approved before meaningful safety and efficacy data accrue that could hurt them.

FDA Commissioner Stephen Hahn and CBER director Peter Marks have tiptoed around and obfuscated this.

"Look at the process that we're following," Dr. Peter Marks said. "We're going to have a very open process."

But the FDA only said it would publicly disclose reviews of the scientific data used to authorize drugs and vaccines after being criticized for hiding information. The Government Accountability Office noted that the FDA had not been sufficiently transparent in disclosing the data used to grant or revoke authorizations involving coronavirus treatments.

Dr. Hahn told USA Today "The standard that’s used for an EUA is that it must be effective..." But that is not the actual standard, which is that a vaccine's known and potential benefits should outweigh its known and potential risks.  In other words, FDA is only required to guess at its safety and effectiveness. Hahn also told USAT that the standards for EUA approval "are very similar" to the standards for issuing a license, which is far from true. 

Here is what the FDA advised Covid vaccine developers on applying for an Emergency Use Authorization:

"Based on this declaration and determination, FDA may issue an EUA after FDA has determined that the following statutory requirements are met (section 564 of the FD&C Act (21 U.S.C. 360bbb-3)) (Ref. 3):  

  • Based on the totality of scientific evidence available, including data from adequate and well- controlled trials, if available, it is reasonable to believe that the product may be effective to prevent, diagnose, or treat such serious or life-threatening disease or condition that can be caused by SARS-CoV-2.
  • The known and potential benefits of the product, when used to diagnose, prevent, or treat the identified serious or life-threatening disease or condition, outweigh the known and potential risks of the product.
  • There is no adequate, approved, and available alternative to the product for diagnosing, preventing, or treating the disease or condition."

The glue that holds this sham of a regulatory process together is the extraordinarily broad liability protection afforded to everyone with any responsibility for the medical products used under an EUA.  You don't learn about this in the EUA declarations. Instead, you must read the Prep Act and its amendments. The following "Public Readiness and Emergency Preparedness Act for Medical Countermeasures Against COVID–19," was published in the August 24, 2020 Federal Register. Everyone who has anything do with Covid vaccines has had all liability waived (with the sliver of an exception for willful misconduct).

And recently pharmacists and pharmacy interns had their liability waived for administering any recommended childhood vaccine to any child over age 3!  The justification is that since Covid-19 has reduced routine childhood vaccinations, any pharmacy employee can now administer any of those vaccinations, while facing no potential liability, via a Covid PREP Act declaration.

Prep Act Quotes:

The Public Readiness and Emergency Preparedness Act (PREP Act) authorizes the Secretary of Health and Human Services (the Secretary) to issue a Declaration to provide liability immunity to certain individuals and entities (Covered Persons) against any claim of loss caused by, arising out of, relating to, or resulting from the manufacture, distribution, administration, or use of medical countermeasures (Covered Countermeasures), except for claims involving ‘‘willful misconduct’’ as defined in the PREP Act.

V.  Covered Persons 42 U.S.C. 247d–6d(i)(2), (3), (4), (6), (8)(A) and (B) Covered Persons who are afforded liability immunity under this Declaration are ‘‘manufacturers,’’ ‘‘distributors,’’ ‘‘program planners,’’ [read this as "government officials who approved the vaccines"--Nass] ‘‘qualified persons,’’ and their officials, agents, and employees, as those terms are defined in the PREP Act, and the United States. In addition, I have determined that the following additional persons are qualified persons: (a) Any person authorized in accordance with the public health and medical emergency response of the Authority Having Jurisdiction, as described in Section VII below, to prescribe, administer, deliver, distribute or dispense the Covered Countermeasures, and their officials, agents, employees, contractors and volunteers, following a Declaration of an emergency; (b) any person authorized to prescribe, administer, or dispense the Covered Countermeasures or who is otherwise authorized to perform an activity under an Emergency Use Authorization in accordance with Section 564 of the FD&C Act; (c) any person authorized to prescribe, administer, or dispense Covered Countermeasures in accordance with Section 564A of the FD&C Act; and (d) a State-licensed pharmacist who orders and administers, and pharmacy interns who administer (if the pharmacy intern acts under the supervision of such pharmacist and the pharmacy intern is licensed or registered by his or her State board of pharmacy), vaccines that the Advisory Committee on Immunization Practices (ACIP) recommends to persons ages three through 18 according to ACIP’s standard immunization schedule

Friday, November 27, 2020

A closer look at U.S. deaths due to COVID-19/ Johns Hopkins Newsletter-CENSORED

This detailed article about the research findings of a JHU professor goes against the narrative that Covid has caused a large number (2-300,000) of excess US deaths. It received a huge amount of interest, and was published and then taken down by this student-run newsletter. I must assume, given this, that the research is never likely to be published in an academic journal, and I fear the researcher may have killed her career.

I don't know if her findings are correct, but they are tantalizing.  I have not believed CDC's death numbers, since I know how CDC prefers to publish narratives rather than facts, when possible.  You will find that much (not all) data on the CDC website consists of estimates of cases of diseases or deaths, made by unknown algorithms. It is also important to be aware that a) CDC changed the guidelines for reporting deaths associated with Covid in March, in a manner that maximized Covid being reported first on death certificates, and b) payments to hospitals are much higher when Covid is diagnosed.

The JHU newletter article can be found using the waybackmachine at the following URL. I have reprinted it in full.

The article answers a sticky question about US Covid deaths:  are they deaths with Covid, or deaths because of Covid?  The answer, which has been obfuscated all year by federal public health agencies, is that in 2020 deaths coded as being caused by heart disease, cancer, etc. are way down, while deaths coded as due to Covid almost exactly fill in the gap that would have been filled in by other conditions, in any other year.

A closer look at U.S. deaths due to COVID-19


According to new data, the U.S. currently ranks first in total COVID-19 cases, new cases per day and deaths. Genevieve Briand, assistant program director of the Applied Economics master’s degree program at Hopkins, critically analyzed the effect of COVID-19 on U.S. deaths using data from the Centers for Disease Control and Prevention (CDC) in her webinar titled “COVID-19 Deaths: A Look at U.S. Data.”

From mid-March to mid-September, U.S. total deaths have reached 1.7 million, of which 200,000, or 12% of total deaths, are COVID-19-related. Instead of looking directly at COVID-19 deaths, Briand focused on total deaths per age group and per cause of death in the U.S. and used this information to shed light on the effects of COVID-19.

She explained that the significance of COVID-19 on U.S. deaths can be fully understood only through comparison to the number of total deaths in the United States. 

After retrieving data on the CDC website, Briand compiled a graph representing percentages of total deaths per age category from early February to early September, which includes the period from before COVID-19 was detected in the U.S. to after infection rates soared. 

Surprisingly, the deaths of older people stayed the same before and after COVID-19. Since COVID-19 mainly affects the elderly, experts expected an increase in the percentage of deaths in older age groups. However, this increase is not seen from the CDC data. In fact, the percentages of deaths among all age groups remain relatively the same. 

“The reason we have a higher number of reported COVID-19 deaths among older individuals than younger individuals is simply because every day in the U.S. older individuals die in higher numbers than younger individuals,” Briand said.

Briand also noted that 50,000 to 70,000 deaths are seen both before and after COVID-19, indicating that this number of deaths was normal long before COVID-19 emerged. Therefore, according to Briand, not only has COVID-19 had no effect on the percentage of deaths of older people, but it has also not increased the total number of deaths. 

These data analyses suggest that in contrast to most people’s assumptions, the number of deaths by COVID-19 is not alarming. In fact, it has relatively no effect on deaths in the United States.

This comes as a shock to many people. How is it that the data lie so far from our perception? 

To answer that question, Briand shifted her focus to the deaths per causes ranging from 2014 to 2020. There is a sudden increase in deaths in 2020 due to COVID-19. This is no surprise because COVID-19 emerged in the U.S. in early 2020, and thus COVID-19-related deaths increased drastically afterward.

Analysis of deaths per cause in 2018 revealed that the pattern of seasonal increase in the total number of deaths is a result of the rise in deaths by all causes, with the top three being heart disease, respiratory diseases, influenza and pneumonia.

“This is true every year. Every year in the U.S. when we observe the seasonal ups and downs, we have an increase of deaths due to all causes,” Briand pointed out.

When Briand looked at the 2020 data during that seasonal period, COVID-19-related deaths exceeded deaths from heart diseases. This was highly unusual since heart disease has always prevailed as the leading cause of deaths. However, when taking a closer look at the death numbers, she noted something strange. As Briand compared the number of deaths per cause during that period in 2020 to 2018, she noticed that instead of the expected drastic increase across all causes, there was a significant decrease in deaths due to heart disease. Even more surprising, as seen in the graph below, this sudden decline in deaths is observed for all other causes. 


Graph depicts the number of deaths per cause during that period in 2020 to 2018.

This trend is completely contrary to the pattern observed in all previous years. Interestingly, as depicted in the table below, the total decrease in deaths by other causes almost exactly equals the increase in deaths by COVID-19. This suggests, according to Briand, that the COVID-19 death toll is misleading. Briand believes that deaths due to heart diseases, respiratory diseases, influenza and pneumonia may instead be recategorized as being due to COVID-19. 


Graph depicts the total decrease in deaths by various causes, including COVID-19.  

The CDC classified all deaths that are related to COVID-19 simply as COVID-19 deaths. Even patients dying from other underlying diseases but are infected with COVID-19 count as COVID-19 deaths. This is likely the main explanation as to why COVID-19 deaths drastically increased while deaths by all other diseases experienced a significant decrease.

“All of this points to no evidence that COVID-19 created any excess deaths. Total death numbers are not above normal death numbers. We found no evidence to the contrary,” Briand concluded.

In an interview with The News-Letter, Briand addressed the question of whether COVID-19 deaths can be called misleading since the infection might have exacerbated and even led to deaths by other underlying diseases.

“If [the COVID-19 death toll] was not misleading at all, what we should have observed is an increased number of heart attacks and increased COVID-19 numbers. But a decreased number of heart attacks and all the other death causes doesn’t give us a choice but to point to some misclassification,” Briand replied.

In other words, the effect of COVID-19 on deaths in the U.S. is considered problematic only when it increases the total number of deaths or the true death burden by a significant amount in addition to the expected deaths by other causes. Since the crude number of total deaths by all causes before and after COVID-19 has stayed the same, one can hardly say, in Briand’s view, that COVID-19 deaths are concerning.

Briand also mentioned that more research and data are needed to truly decipher the effect of COVID-19 on deaths in the United States.

Throughout the talk, Briand constantly emphasized that although COVID-19 is a serious national and global problem, she also stressed that society should never lose focus of the bigger picture — death in general. 

The death of a loved one, from COVID-19 or from other causes, is always tragic, Briand explained. Each life is equally important and we should be reminded that even during a global pandemic we should not forget about the tragic loss of lives from other causes.

According to Briand, the over-exaggeration of the COVID-19 death number may be due to the constant emphasis on COVID-19-related deaths and the habitual overlooking of deaths by other natural causes in society. 

During an interview with The News-Letter after the event, Poorna Dharmasena, a master’s candidate in Applied Economics, expressed his opinion about Briand’s concluding remarks.

“At the end of the day, it’s still a deadly virus. And over-exaggeration or not, to a certain degree, is irrelevant,” Dharmasena said.

When asked whether the public should be informed about this exaggeration in death numbers, Dharmasena stated that people have a right to know the truth. However, COVID-19 should still continuously be treated as a deadly disease to safeguard the vulnerable population.

Peter Doshi: Pfizer and Moderna’s “95% effective” vaccines—let’s be cautious and first see the full data/ BMJ

Only full transparency and rigorous scrutiny of the data will allow for informed decision making, argues Peter Doshi

In the United States, all eyes are on Pfizer and Moderna. The topline efficacy results from their experimental covid-19 vaccine trials are astounding at first glance. Pfizer says it recorded 170 covid-19 cases (in 44,000 volunteers), with a remarkable split: 162 in the placebo group versus 8 in the vaccine group. Meanwhile Moderna says 95 of 30,000 volunteers in its ongoing trial got covid-19: 90 on placebo versus 5 receiving the vaccine, leading both companies to claim around 95% efficacy.

Let’s put this in perspective. First, a relative risk reduction is being reported, not absolute risk reduction, which appears to be less than 1%. Second, these results refer to the trials’ primary endpoint of covid-19 of essentially any severity, and importantly not the vaccine’s ability to save lives, nor the ability to prevent infection, nor the efficacy in important subgroups (e.g. frail elderly). Those still remain unknown. Third, these results reflect a time point relatively soon after vaccination, and we know nothing about vaccine performance at 3, 6, or 12 months, so cannot compare these efficacy numbers against other vaccines like influenza vaccines (which are judged over a season). Fourth, children, adolescents, and immunocompromised individuals were largely excluded from the trials, so we still lack any data on these important populations.

I previously argued that the trials are studying the wrong endpoint, and for an urgent need to correct course and study more important endpoints like prevention of severe disease and transmission in high risk people. Yet, despite the existence of regulatory mechanisms for ensuring vaccine access while keeping the authorization bar high (which would allow placebo-controlled trials to continue long enough to answer the important question), it’s hard to avoid the impression that sponsors are claiming victory and wrapping up their trials (Pfizer has already sent trial participants a letter discussing “crossing over” from placebo to vaccine), and the FDA will now be under enormous pressure to rapidly authorize the vaccines.

But as conversation shifts to vaccine distribution, let’s not lose sight of the evidence. Independent scrutiny of the underlying trial data will increase trust and credibility of the results. There also might be important limitations to the trial findings we need to be aware of.

Most crucially, we need data-driven assurances that the studies were not inadvertently unblinded, by which I mean investigators or volunteers could make reasonable guesses as to which group they were in. Blinding is most important when measuring subjective endpoints like symptomatic covid-19, and differences in post-injection side-effects between vaccine and placebo might have allowed for educated guessing. Past placebo-controlled trials of influenza vaccine were not able to fully maintain blinding of vaccine status, and the recent “half dose” mishap in the Oxford covid-19 vaccine trial was apparently only noticed because of milder-than-expected side-effects. (And that is just one of many concerns with the Oxford trial.)

In contrast to a normal saline placebo, early phase trials suggested that systemic and local adverse events are common in those receiving vaccine. In one Pfizer trial, for example, more than half of the vaccinated participants experienced headache, muscle pain and chills—but the early phase trials were small, with large margins of error around the data. Few details from the large phase 3 studies have been released thus far. Moderna’s press release states that 9% experienced grade 3 myalgia and 10% grade 3 fatigue; Pfizer’s statement reported 3.8% experienced grade 3 fatigue and 2% grade 3 headache. Grade 3 adverse events are considered severe, defined as preventing daily activity. Mild and moderate severity reactions are bound to be far more common.

One way the trial’s raw data could facilitate an informed judgment as to whether any potential unblinding might have affected the results is by analyzing how often people with symptoms of covid-19 were referred for confirmatory SARS-CoV-2 testing. Without a referral for testing, a suspected covid-19 case could not become a confirmed covid-19 case, and thus is a crucial step in order to be counted as a primary event: lab-confirmed, symptomatic covid-19. Because some of the adverse reactions to the vaccine are themselves also symptoms of covid-19 (e.g. fever, muscle pain), one might expect a far larger proportion of people receiving vaccine to have been swabbed and tested for SARS-CoV-2 than those receiving placebo.

This assumes all people with symptoms would be tested, as one might expect would be the case. However the trial protocols for Moderna and Pfizer’s studies contain explicit language instructing investigators to use their clinical judgment to decide whether to refer people for testing. Moderna puts it this way:

It is important to note that some of the symptoms of COVID-19 overlap with solicited systemic ARs that are expected after vaccination with mRNA-1273 (eg, myalgia, headache, fever, and chills). During the first 7 days after vaccination, when these solicited ARs are common, Investigators should use their clinical judgement to decide if an NP swab should be collected.

This amounts to asking investigators to make guesses as to which intervention group patients were in. But when the disease and the vaccine side-effects overlap, how is a clinician to judge the cause without a test? And why were they asked, anyway?

Importantly, the instructions only refer to the first seven days following vaccination, leaving unclear what role clinician judgment could play in the key days afterward, when cases of covid-19 could begin counting towards the primary endpoint. (For Pfizer, 7 days after the 2nd dose. For Moderna, 14 days.)

In a proper trial, all cases of covid-19 should have been recorded, no matter which arm of the trial the case occurred in. (In epidemiology terms, there should be no ascertainment bias, or differential measurement error). It’s even become common sense in the Covid era: “test, test, test.” But if referrals for testing were not provided to all individuals with symptoms of covid-19—for example because an assumption was made that the symptoms were due to side-effects of the vaccine—cases could go uncounted.

Data on pain and fever reducing medicines also deserve scrutiny. Symptoms resulting from a SARS-CoV-2 infection (e.g. fever or body aches) can be suppressed by pain and fever reducing medicines. If people in the vaccine arm took such medicines prophylactically, more often, or for a longer duration of time than those in the placebo arm, this could have led to greater suppression of covid-19 symptoms following SARS-CoV-2 infection in the vaccine arm, translating into a reduced likelihood of being suspected for covid-19, reduced likelihood of testing, and therefore reduced likelihood of meeting the primary endpoint. But in such a scenario, the effect was driven by the medicines, not the vaccine.

Neither Moderna nor Pfizer have released any samples of written materials provided to patients, so it is unclear what, if any, instructions patients were given regarding the use of medicines to treat side effects following vaccination, but the informed consent form for Johnson and Johnson’s vaccine trial provides such a recommendation:

“Following administration of Ad26.COV2.S, fever, muscle aches and headache appear to be more common in younger adults and can be severe. For this reason, we recommend you take a fever reducer or pain reliever if symptoms appear after receiving the vaccination, or upon your study doctor’s recommendation.”

There may be much more complexity to the “95% effective” announcement than meets the eye—or perhaps not. Only full transparency and rigorous scrutiny of the data will allow for informed decision making. The data must be made public.

Peter Doshi, associate editor, The BMJ.

Competing interests: I have been pursuing the public release of vaccine trial protocols, and have co-signed open letters calling for independence and transparency in covid-19 vaccine related decision making.

Thursday, November 26, 2020

Too Much Caution Is Killing Covid Patients/ WSJ Nov 24

    Doctors should follow the evidence for promising therapies. Instead they demand certainty.

    Wednesday, November 25, 2020

    FDA Citizen's Petition and Request for Stay of Decision on Covid vaccines--until accurate tests are used to determine actual number of cases and non-cases in Pfizer vaccine clinical trial

    Dr. Sin Hang Lee and the Informed Consent Action Network (ICAN), through attorneys Aaron Siri and Elizabeth Brehm, have requested that FDA require accurate counts of Covid cases in the Pfizer/BioNTech Covid-19 mRNA vaccine trial and other Covid vaccine trials. 

    Furthermore, until an accurate number of Covid-19 cases in the vaccinated and placebo groups has been determined, FDA is asked to withhold an Emergency Use Authorization for this and other vaccines.

    The Pfizer Phase 2/3 clinical trial was designed for haste:  in other words, to accumulate as many "cases" as possible in the shortest time, in order to speed approval and use.

    To do so, only one symptom was required for a "case," and almost any  nonspecific symptom, such as a sore throat, fever, a cough or an episode of vomiting or diarrhea would do.  Patients with a nonspecific symptom were then given a PCR test.  Most PCR tests used the Cepheid XpertXpress system, which is known to have a high false positive rate. Cycle thresholds of 35 and over were used, even though Anthony Fauci and the NY Times have both noted such high cycle  thresholds lead to false positives.

    Thus, both required elements of the case definition (a nonspecific symptom and an inaccurate PCR test result) lacked precision, and allowed for the inclusion of false positives.

    It would have been easy to establish that PCR positive subjects were truly positive by using the gold standard of Sanger sequencing. This verifies whether the PCR product really represents the SARS-Cov-2 virus. It is a standard procedure in many labs. Sequencing would probably have slowed the collection of Covid cases, thereby slowing FDA approval... and it was not performed.

    FDA is being asked in the Petition to assure that PCR positive products are sequenced, so that accurate data on efficacy of the vaccine become available.  Because Pfizer asked FDA to issue an Emergency Use Authorization for the vaccine using much less safety and efficacy data than are required to apply for a full license, imposing a requirement to assure accuracy of the limited data available is a no-brainer. 

    FDA is also being asked to withhold its decision on the EUA until such time as confirmed case numbers become available.

    The Petition was filed on Nov 23, and the request for a Stay was filed on November 25.  The 2 documents are similar.

    Tuesday, November 24, 2020

    Anthrax Vaccine manufacturer, despite shameful history, to produce 3 Covid vaccines for US population

    My recent talk for the National Vaccine Information Center's Fifth International Conference goes into the sorry history of this very sketchy company, from its founding in 1998 to the present.

    It could also be titled:  How Taxpayers Made the Anthrax Vaccine Manufacturer Very, Very Rich from Unused Biowarfare Vaccines and Drug Overdoses. What a business model!

    Now this very same company, having sold 3 failed products, will be the actual manufacturer of three--yes 3--Covid-19 vaccines: the Astra-Zeneca (Oxford) vaccine, the Janssen/Johnson and Johnson vaccine, and the Novavax vaccine for the US market.

    It is a 33 minute talk, here.

    Monday, November 23, 2020

    More Than 2/3rds Of Americans Oppose Mandatory COVID-19 Vaccinations/ Zero Hedg

    By "Tyler Durden" (ZeroHedge's anon.)

    Ever since it burst out of Wuhan, China roughly one year ago, the coronavirus has created what one economist described as a "trilemma" - that is, the struggle to balance the inevitable tradeoffs between safeguarding public health, the economy and personal freedom.

    In the US (and in many spots around Europe as well), some have pointed to skepticism surrounding the accelerated development process for the myriad COVID vaccine projects as a potential obstacle to achieving herd immunity, since a lack of public confidence might force some governments to try and unduly pressure citizens to accept the vaccine.

    With all this in mind, policymakers and economists are struggling to pinpoint an acceptable trade-off between public health, economic health and personal freedom. Some analysts have taken to calling these conflicting priorities the coronavirus "trilemma". However these conflicts are resolved will be critical to the economic outlook in 2021, and with Wall Street increasingly expecting the US economy to slide back into contraction during Q4, speculation about the timing and pace of the rebound has been pushed out to next year, and 2022.

    To be sure, the timing of the COVID-19 vaccine rollout will be critical in deciding how all of this plays out. But there's another issue, more closely related to the "personal freedom" leg of the "trilemma", that epidemiologists and policymakers may have underestimated. And that's the question of public confidence in the vaccine.

    Several Wall Street research shops have published insights on the subject, using data gleaned from YouGov and Gallup opinion polls, along with other sources. The other day, Deutsche Bank published a chart breaking down eagerness to receive the vaccine, along with opinions about whether it should be "mandatory".

    Opinion polling is clear: In the US, more than 2/3rds of the population feels receiving a COVID-19 vaccine should be voluntary. What's more, only a small percentage of Americans would push to be vaccinated within the first month of a vaccine being widely available. Most appear content to hang back, presumably more concerned about when the economy will be allowed to reopen than when they might be able to get vaccinated.

    Of course, opinions could shift substantially if something unexpected happens between now and when the FDA is due to begin reviewing the Pfizer (and, eventually, Moderna) applications for emergency-use approval. As Bloomberg's John Authers reminds us, it's not a done deal.

    It’s also conceivable that something goes wrong with vaccine safety or the manufacturing process. Most precariously, there is what is known as “vaccine-hesitancy.” Across the world, many are reluctant to take one. These are the results of surveys conducted in the U.S. and western Europe for Deutsche Bank AG. They suggest that politicians may be forced to make vaccinations mandatory, which could make the politics of 2021 very dangerous:

    Authers shared another chart from DB showing that attitudes about vaccines across Europe are mostly the same as in the US, with the UK seeing generally higher acceptance of vaccines (perhaps there might be a correlation between acceptance and levels of public hysteria driven by notably higher mortality rates?).

    Over the past week, Dr. Fauci has upped the rhetoric about vaccine skeptics, labeling them as a "serious threat" to public health, while millions of Americans prepare to ignore the CDC's guidelines and travel to see family and friends despite the situation.  Could it be that, nearly a year into the worst pandemic in a century, the public's attitudes about the threat posed by COVID-19 are notably more lax than Dr. Fauci's?

    Thursday, November 19, 2020

    Best explanation of why the Covid PCR tests result in large numbers of false positives

    No lab test is 100% accurate.  Hopefully most have about 90-99% sensitivity and specificity.  Doctors know that some results will be wrong, so we are always weighing all our evidence, and not relying entirely on a lab result to make a diagnosis.  

    We test sick patients.  Health insurance does not pay for screening tests in healthy patients, with certain notable exceptions.  The combination of symptoms and lab results allows us to be mostly right.

    When you start to screen healthy patients, who don't have symptoms, and you are trying to make a diagnosis based exclusively on a lab test, there are numerous possible pitfalls.  Below is an excellent discussion of how baked-in test limitations can lead you wildly astray.

    Taken verbatim from Sebastian Rushworth, MD's blog:

    "As mentioned, the sensitivity of the PCR test seems to be around 88% . A good value for the specificity is harder to determine, but it’s somewhere between 88% and 100%, so if we assume a specificity of 94% (halfway between the two values) we’re probably not far off.

    Let’s say the disease is spreading rampantly through the population, and one in ten people are infected at the same time. If we test 1,000 people at random, that will mean 100 of those people actually have covid, while 900 don’t. Of the 100 who have covid, the test will successfully pick up 88. Of the 900 who don’t have covid, the test will correctly tell 846 that they don’t have it, but it will also tell 54 healthy people that they do have covid. So, in total 142 people out of 1,000 are told that they have covid. Of those 142 people, 62% actually have the disease, and 38% don’t.

    That’s not great. Four in ten people getting a positive test result don’t actually have covid, even in a situation where the disease is so common that 10% of people being tested really do have the disease.

    Unfortunately, it gets worse. let’s assume the disease is starting to wane, and now only one in a hundred people being tested actually has covid. If we test 1,000 people, that will mean ten will really have covid, while 990 won’t. Of the ten who have covid, nine will be correctly told that they have it. Of the 990 who don’t have it, 931 will be correctly told that they don’t have it, while 59 will be incorrectly told that they do have the disease. So, in total, 68 people will be told that they have covid. But only 9 out of 68 will actually have the disease. To put it another way, in a situation where only 1% of the population being tested has the disease, 87% of positive results will be false positives.

    There is another thing about this that I think is worth paying attention to. When one in ten people being tested has the disease, you get 142 positive results per 1000 people tested. But when one in a hundred has the disease, you get 68 positive results. So, even though the actual prevalence of the disease has decreased by a factor of ten, the prevalence of PCR positive results has only decreased by half. So if you’re only looking at PCR results, and consider that to be an accurate reflection of how prevalent the disease is in the population, then you will be fooled, because the disease will seem to be much more prevalent than it is.

    Let’s do one final thought experiment to illustrate this. Say the disease is now very rare, and only one in a thousand tested people actually has covid. If you test 1,000 people, you will get back 61 positive results. Of those, one will be a true positive, and 60 will be false positives. So, even though the prevalence of true disease has again decreased by a factor of ten, the number of positive results has only decreased slightly, from 68 to 61 (of which 60 are false positives!). So by looking just at positive PCR tests, you can easily be convinced that the disease is continuing to be roughly as prevalent in the population, even as it goes from being present in one in a hundred people to only being present in one in a thousand. The rarer the disease becomes in reality, the less likely you are to notice any difference in the number of tests returning positive results.

    I want to restate this again, in a slightly different way, to make sure the message sinks in. As the disease drops enormously, by a factor of 100, from affecting one in ten to one in a thousand tested people, there is little more than a halving in PCR positive results, from 142 to 61. So a huge reduction in real infections only causes a small reduction in PCR confirmed “cases”. In fact, the disease could vanish from the face of the Earth, and you would still be getting 60 positive results for every 1,000 tests carried out!

    The same trend is seen even if the PCR test were to have a much better specificity than we are estimating here, of say 99% . Here’s a quick illustration, since I don’t want to tire you with too many more numbers. If one in ten has the disease and you test 1,000 people, you will get back 97 positive results, of which 88 will be true positives and 9 will be false positives. If one in 100 has the disease, you will get back 19 positive results, of which 9 will be true positives and ten will be false positives. If one in 1,000 has the disease, you will get back 12 positive results, of which 11 will be false positives.

    So, even if the test has a very high specificity of 99%, when the virus stops being present at pandemic levels in the population and starts to decrease to more endemic levels, you quickly get to a point where most positive results are false positives, and where the disease seems to be much more prevalent than it really is.

    As you can see, the less prevalent the disease is in reality, the more likely the test is to generate a false positive result, and the less useful the test is as a method for figuring out who actually has covid. And the less prevalent the disease is, the more prevalent it will seem to be in relation to reality. If decisions about covid continue to be made largely based on what PCR tests show, we might never be able to call off the pandemic!

    And that, ladies and gentlemen, is why PCR positive cases are a very poor indicator of how prevalent covid is in the population, and why we should instead be basing decisions on the rates of hospitalization, ICU admission, and death. If we just look at the PCR tests, we will continue to believe that the disease is widespread in the population indefinitely, even as it becomes less and less common in reality." 

    Now, I (Meryl Nass) must add that currently, real cases ARE going up in the US, based on hospitalizations, ICU admissions, deaths.  I don't for a second deny that.  

    But high total case counts are partly due to the problems inherent in the test process.  The US is testing 1.5 million people a day, and about 160,000 people a day are testing positive.  How many thousands are false positives?