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John F. McGowan, Ph.D. solves problems using mathematics and mathematical software, including developing gesture recognition for touch devices, video compression and speech recognition technologies. He has extensive experience developing software in C, C++, MATLAB, Python, Visual Basic and many other programming languages. He has been a Visiting Scholar at HP Labs developing computer vision algorithms and software for mobile devices. He has worked as a contractor at NASA Ames Research Center involved in the research and development of image and video processing algorithms and technology. He has published articles on the origin and evolution of life, the exploration of Mars (anticipating the discovery of methane on Mars), and cheap access to space. He has a Ph.D. in physics from the University of Illinois at Urbana-Champaign and a B.S. in physics from the California Institute of Technology (Caltech).
Pandemia: How Coronavirus Hysteria Took Over Our Government, Rights, and Lives
Alex Berenson
Regnery Publishing, Washington, D.C., 2021
Pandemia is a detailed, somewhat technical history of the COVID pandemic and COVID response mostly in the United States strongly critiquing much of the mainstream news coverage, the lockdown and masking policies, and many claims and policies promoted by the US CDC, Anthony Fauci, and other “public health authorities.” Alex Berenson is a former New York Times investigative reporter who covered many pharmaceutical stories and scandals while working at the Times. I first encountered him through his 2004 book The Number about manipulation and outright fraud in reported quarterly corporate earnings.
The book is written in a breezy, easy to read style with short topical chapters in a roughly chronological order. It has numerous footnotes with a footnote section at the end of the book as well as an index, all to back up his many assertions that differ from most mainstream news coverage, statements by Fauci and other “experts,” and the supposed consensus of scientists. Readers who have been following non-mainstream coverage of the pandemic and pandemic response from sources such as Berenson will not be surprised or find much new. Overall it is a well written, informative book that conveys Berenson’s positions with references to supporting information.
Some Weak Spots
I am not a Conspiracy Theorist
Berenson is adamant that he is not a “conspiracy theorist,” the dread phrase used to shut down any discussion of possible criminal conspiracy or even incompetence by the powerful (such as the “conspiracy theory” that NIH funded “gain of function” research at the Wuhan Institute of Virology produced and then accidentally released SARS-COV-2). Consequently, while he documents baffling failures, claims, and outright contradictory statements by Anthony Fauci, the CDC, and other “public health authorities” such as the dramatic reversal on the effectiveness of masks from “useless” to “highly effective” in April 2020, he stops there and rains scorn on “conspiracy theorists.”
Twitter Warrior
Berenson was eventually banned by Twitter for alleged “COVID misinformation” following what Berenson argues is pressure by the Biden administration and other political figures. He describes many provocative and sometimes offensive tweets which drew a lot of attention to himself, undoubtedly boosting sales of his Unreported Truths booklets on Amazon and now his Pandemia book. He mentions some criticism by his wife Jackie of the aggressive, snarky tone and content of many of his tweets. His substack posts continue to show a similar aggressive, snarky tone today.
Long, Long COVID (Chapter 25)
Berenson is highly skeptical of alleged “Long COVID,” linking it to a range of murky diseases such as chronic fatigue syndrome, fibromyalgia, chronic Lyme disease, and others with arguably similar symptoms which also tend to disproportionately affect women, suggesting a psychological/anxiety cause. He argues that scientists and medical doctors have so far been unable (he says) to find an underlying biological cause or correlate (more on this in a moment) whereas they supposedly found the cause of AIDS (the HIV virus) in only two years. Touting modern high tech molecular biology based science and medicine, he argues it could not be that hard to find a cause if these disease were “real.”
Blaming anxious hysterical women for phantom diseases that disproportionately affect women is not a new phenomenon. Multiple sclerosis (MS) was considered such as disease until a series of autopsies of patients showed lesions (damage) to the nerves. Today MS can be detected with magnetic resonance imaging (MRI) scans which always or frequently show lesions on the nerves in the images.
Both MS and lupus, which disproportionately affect women, continue to be misdiagnosed as psychological problems without an MRI scan or the test for the anti-nuclear antibodies (ANA). About 90 percent of people with lupus (linked article from Lupus Foundation claims 97%) have the unusual anti-nuclear antibodies while only about 5 percent of health individuals have the antibodies. Note that anti-nuclear antibodies are highly correlated with lupus but not perfectly correlated, which raises some questions whether the auto-immune reaction is the true cause of lupus. Alleged auto-immune diseases such as MS, lupus, and rheumatoid arthritis which disproportionately affect women remain mysterious.
MRI scans are unable to image damage to the critical myelin insulating sheath that enclosing nerves. Damage or loss of this sheath which occurs in carpal tunnel syndrome, ulnar and other nerve injuries can be confirmed in many but not all cases with nerve conduction tests. These tests have a several percent failure rate. Damage or loss of the myelin sheath frequently causes extreme levels of pain and in some cases atrophy of the muscles controlled by the nerve.
There are many diseases that have eluded detailed biological analysis. Berenson touts the sequencing of the human DNA as an illustration of the enormous power of modern medical biology without considering about 98% of the human DNA is purported junk DNA whose function is unknown. He extols modern molecular biology research without considering the dismal results of the “War on Cancer” nor the remarkably complex modern theory of cancer with hundreds of oncogenes, allegedly rapidly mutating cancer cells, extensive unpredictable chromosomal changes, and the mysterious anaerobic metabolism seen in many cancer cells.
Could COVID or other disease like Lyme disease occasionally produce some mysterious long lasting, possibly auto-immune syndrome disproportionately in women who disproportionately suffer from alleged auto-immune diseases such as MS, Lupus, and Rheumatoid Arthritis? Quite possibly.
Conclusion
Overall Pandemia is a well written, easy to read book, almost 400 pages of detailed discussion of the COVID pandemic and the lockdowns and other pandemic responses. Berenson makes a strong case that most of the responses have been ineffective at best and even quite harmful in some cases. He avoids speculation about possible conspiracies whether due to genuine skepticism or to avoid the dreaded “conspiracy theorist” label. He stays away from the safety and effectiveness of drugs such as hydroxychloroquine and ivermectin and says little about Vitamin D. He is highly critical of the approval process for the mRNA spike protein based vaccines and the actual real-world safety and effectiveness of the vaccines.
(C) 2022 by John F. McGowan, Ph.D.
About Me
John F. McGowan, Ph.D. solves problems using mathematics and mathematical software, including developing gesture recognition for touch devices, video compression and speech recognition technologies. He has extensive experience developing software in C, C++, MATLAB, Python, Visual Basic and many other programming languages. He has been a Visiting Scholar at HP Labs developing computer vision algorithms and software for mobile devices. He has worked as a contractor at NASA Ames Research Center involved in the research and development of image and video processing algorithms and technology. He has published articles on the origin and evolution of life, the exploration of Mars (anticipating the discovery of methane on Mars), and cheap access to space. He has a Ph.D. in physics from the University of Illinois at Urbana-Champaign and a B.S. in physics from the California Institute of Technology (Caltech).
A Plague Upon Our House: My Fight at the Trump White House to Stop COVID from Destroying America
Scott W. Atlas, M.D.
Post Hill Press, New York, 2021
A Plague Upon Our House is Scott Atlas’s personal account of his four months (“end of July” 2020 — December 1, 2020) as a special adviser to the White House on the COVID-19 pandemic and pandemic response. It also discusses some of his interactions with and advice to Florida state governor Ron De Santis. The book paints an alarming picture of key medical advisers Tony Fauci, Deborah Birx, and Robert Redfield as incompetent, highly political, dishonest career bureaucrats in the worst sense of the words and President Trump as a disengaged, indecisive, publicity obsessed leader quite far from his abrasive, tough, “You’re fired!” public image. Although Dr. Atlas avoids using names in several cases, a number of President’s Trump staff come across as young, inexperienced, perhaps well-meaning but clearly out of their depth.
Dr. Atlas and his colleagues had some success influencing the actual policies in Florida, although according to the book Governor DeSantis seems to have largely made up his own mind from reading the direct scientific literature and studying the actual data, using experts like Dr. Atlas, Harvard epidemiologist Martin Kulldorf, Oxford epidemiologist Sunetra Gupta, and his Stanford colleague Jay Battacharya as a sounding board to check his understanding. Although Dr. Atlas describes a few successes at the federal level, he had no real success at the national, federal level or with most states and state governors. While he is highly critical of the medical advisers Fauci/Birx/Redfield and even President Trump who hired him, he performs little analysis of his own failings — a trait most of us share.
Missing References and Data
The book does not have end notes or footnotes with primary references, despite Dr. Atlas’s background as an academic scholar and the highly technical nature of the issues. He presumably has a list of key references that he used as policy adviser to the White House that could have easily been incorporated into the book. He makes the point repeatedly during the book that he spent every day reviewing the latest research papers and COVID data, whereas the Fauci/Birx/Redfield “troika” seemingly did not.
The lack of primary references (or any references at all) is a serious drawback because Google and other online searches, even with the less censored DuckDuckGo search engine, often turn up hysterical mainstream news articles and “Fact Checks” rather than the primary references. Readers who have followed the technical literature and non-mainstream arguments closely will not find anything new technically in the book, but others who rely on the mainstream media likely will find contradicting claims and “information” using a search engine, even DuckDuckGo.
Dr. Atlas’s Wikipedia page (Jan 26, 2022), for example, is highly negative:
Wikipedia entry for Scott Atlas (Jan. 26, 2022)
The ineffectiveness of masks is the only technical issue out of many in the book where Atlas presents actual data in plots on pages 287-294 showing no effect from mask mandates on daily news cases in many different countries and US states (yes, theoretically mask mandates could fail although masks work and Atlas does not address this counter-argument). He does not provide primary references even for these plots.
There was and is a substantial scientific literature showing masks, especially the cloth masks, are largely or completely ineffective or even harmful. Atlas provides no references to this literature. Given the remarkable contradiction on masks and other topics between most mainstream sources such as Wikipedia and highly visible statements on cable and broadcast “news” shows by ostensible experts such as Dr. Fauci and Atlas’s claims in the book, Atlas clearly should provide primary references for incredulous readers.
Although there is much discussion of masks in the book including assertions that SARS-COV-2 is airborne, that is it floats in the air like tiny smoke or dust particles and does not drop to surfaces under gravity as larger droplets of saliva would, Dr. Atlas fails to provide references or adequately describe this key technical issue.
In particular, observers have suspected that plagues, especially respiratory illnesses, were airborne since ancient times. Mask wearing has frequently failed during historical epidemics such as the 1918 influenza epidemic. “Public health” authorities have either ignored the historical failure or rationalized it away.
In the 1950’s and 1960’s a team of researchers led by Richard Riley at Johns Hopkins University conducted an extensive series of experiments at a VA hospital showing that tuberculosis, a bacteria which is much larger and heavier than the influenza virus or the coronaviruses, is airborne. They did this by connecting tuberculosis (TB) wards at the hospital to rooms with guinea pigs via air ducts, otherwise isolating the guinea pigs from possible sources of infection, and placing ultraviolet (UV) sterilizing lights in one duct to one room of guinea pigs. The guinea pigs in the room with the UV lights in the connecting duct did not contract TB whereas the guinea pigs in the other room with no sterilizing UV light in the connecting duct did contract TB.
Although it is now “generally accepted” that TB is airborne, “public health” authorities have continued to claim all other respiratory illnesses are transmitted by large saliva droplets that fall to the ground and other surfaces quickly under gravity. This claim was used to justify the “social distancing,” mask wearing, lockdowns, and other policies that have clearly failed to contain COVID and yet continue.
Magically, perhaps as citizens began to realize that heavy droplet transmission would imply rapid efficient spread through groceries at giant stores such as Safeway, Walmart, and Target that were allowed stay open while smaller competitors serving small local regions were closed, the US CDC flipped and claimed “fomites,” meaning those saliva droplets landing on grocery products, store shelves, etc. did not spread the disease — “just kidding.” More precisely they began to claim the surface transmission was so minor that cleaning with various chemicals was no longer recommended.
Remarkably, the “public health” authorities appear to have never replicated the Johns Hopkins TB study or performed similar studies for influenza or coronaviruses despite multi-billion dollar CDC and NIH budgets and continual publicly stated concern about repeats of the 1918 “flu” pandemic.
The practical consequences of airborne transmission are profound. Masks are not expected to work as the tiny viral particles, about 1/500th the width of a human hair, will flow with the air through even microscopic holes in masks, and with the air around the masks. Confining large numbers of people to apartment complexes with interior hallways or shared ventilation is likely to rapidly spread any respiratory disease. People confined in the same house are all likely to be exposed to the virus. Herding everyone into a few small enclosed giant “Big Box” stores such as Walmart or Safeway is likely to provide an efficient route for rapid spread of the disease. Social distancing is likely to be mostly ineffective especially indoors as the viral particles diffuse through any enclosed space.
Curiously, A Plague Upon Our House, despite correctly pointing out the failure of the masks, does not delve into the key issue of airborne transmission and its implications.
President Donald Trump
Trump: Indecisive or Disingenuous?
Although most of Atlas’s criticism is directed at the seemingly incompetent “troika” of Anthony Fauci, Deborah Birx, and Robert Redfield, he is also highly critical of Trump who in fact supported the policies promoted by the troika, despite occasional tweets seemingly to the contrary. Atlas describes Trump as friendly to him and always agreeing with Atlas in private conversations. Atlas is mystified by Trump’s failure to act on his stated beliefs as shared with Atlas, blaming this both on the troika and various Trump advisers rather than Trump personally. Atlas either failed to consider Trump might be pretending to agree with him or chose not to discuss that possibility in his book.
President Trump is a highly successful businessman at one point deeply involved in the rough and tumble casino industry — even taking over Resorts International, a notorious company with a scandalous past. He has a long history of close personal and business “relationships” with murky, rather iffy characters such as the late attorney Roy Cohn, the late singer Michael Jackson, and the late “hedge fund billionaire” without an actual hedge fund Jeffrey Epstein amongst others. That Trump might be something less than straightforward with Atlas or others does not seem improbable.
The actual voting base of Trump is not Pfizer or Bill Gates/Moderna or other giant companies like Walmart or Safeway, it is small business owners, farmers, and large numbers of working class Americans, many employed by small businesses. Atlas briefly notes that Trump’s FDA commissioner Scott Gottlieb took a lucrative position as a member of the board of directors of Pfizer. Atlas does say he disagreed with Gottlieb strongly, but claims he did not attribute Gottlieb’s positions to a conflict of interest with Pfizer. Preteritio?
President Trump had expressed some vaccine skepticism during the 2016 campaign, even suggesting that vaccines might play a causal role in the dramatic increase in autism in the United States over the last three decades. He apparently did reach out to Democrat vaccine skeptic Robert F. Kennedy Jr to pursue a detailed audit of the seemingly confidential public health databases used by CDC and other agencies to clarify the situation.
Robert F. Kennedy Jr. attributes the sudden reversal on this project during the transition period to a large contribution to the Trump inauguration fund by Pfizer. According to Federal Election Commission (FEC) filings, Pfizer donated $1 million dollars to the 58th Presidential Inaugural Committee on December 22, 2016 (see page 163 of the linked 510 page FEC document).
Pfizer has a long history of criminal activities, including aninternational conspiracyin the 1990s with Archer Daniels Midland and several other companies to fix the price of citric acid (a key ingredient of Coca-Cola amongst other products), lysine, and possibly other food additives.
The lockdown policies have been disastrous for these more ordinary Americans while enriching Amazon and other Big Tech companies, giant retailers such as Walmart and Safeway, and indirectly the vaccine makers like Pfizer. Unrest and opposition to the policies surfaced quickly among President Trump’s base, many of whom, contrary to the Democrat picture of mesmerized cultists, are wary of the flamboyantly sleazy casino magnate turned champion of the common man.
It is not uncommon for politicians to pay lip service to the opinions and policies preferred by their voters while in fact enacting the policies preferred by their campaign contributors and business partners.
Lack of Criticism of Operation Warp Speed
A major weakness of the book is Dr. Atlas’s unreserved enthusiasm and endorsement of Operation Warp Speed and the experimental mRNA based vaccines. Although he does not devote much space to this, it is crystal clear in reading the book. He writes approvingly about the policy to exempt the vaccine makers such as Pfizer and Moderna from liability should the vaccines prove harmful.
Dr. Atlas expresses no concern that vaccines based on the SARS-COV-2 spike protein which bonds to the ACE-2 (angiotensin converting enzyme) receptor and appears to severely disrupt the cardiovascular system, causing the often lethal blood clots reported in some COVID patients, might produce similar cardiovascular problems in vaccinated persons, although he does oppose vaccine mandates and the unscientific ignoring of natural immunity in statements by the “public health” authorities, the troika until recently, and others.
According to the book, Dr. Atlas wrote or completed writing the book in August 2021, arguably just before or as the delta wave was starting to smash through the vaccines, hospitalizing and killing large numbers of Americans if you believe the CDC’s Fluview web site and underlying data as reported.
Vaccine Failure: The COVID-19 “Delta” Wave in August 2021 (about Week 36 of 2021) (US CDC FluView Web Site)
Dr. Atlas expresses no concern about the short circuiting of numerous safety precautions that usually take years to approve a vaccine. Operation Warp Speed met its hyper-aggressive schedule as Atlas proudly claims only by disregarding established safety measures which he does not mention.
In some contexts, usually when making excuses for obvious failures or huge cost and schedule overruns which are common in R&D, scientists such as Dr. Atlas often claim an 80 to 90 percent failure rate for scientific research. For every Manhattan Project that succeeded there are dozens of tokamak fusion power programs, wars on cancer, and so on that have failed. Yet, Dr. Atlas expresses no concerns or fallback plans for the likely failure of Operation Warp Speed if the 80 to 90 percent failure rate is true.
Lack of Self Criticism
Although Dr. Atlas is highly critical of the troika and even President Trump, he does not take himself to task even though he clearly failed to achieve the policies he recommended, citing only a few small successes. This is something of relevance to all of us who seek better policies and to end the irrational hysteria about the COVID pandemic.
Dr. Atlas does claim he was naive about the political process and how politicized the COVID response was. He also is appalled by the censorship, propaganda, and extensive lying or at least false statements by the mainstream media, both “legacy” operations such as the New York Times and social media giants such as YouTube (owned by Google/Alphabet). Other than being outraged, he makes little effort in the book to analyze how and why this is happening and how to successfully combat it.
One may wonder about these claims of naivete given that Dr. Atlas is a fellow at the controversial, mostly conservative Hoover Institution at Stanford University where he has been a health policy analyst for years. The Hoover Institution as a whole is no stranger to bitter partisan political battles.
In any case, Dr. Atlas makes no attempt to understand the crazed “no lie is too big,” “no number of COVID deaths is too many” to GET TRUMP AT ANY COST mentality of the mass media and many others. Indeed this behavior is puzzling and alarming, leading naturally both to psychological explanations such as the “mass formation” theory proposed by Professor Mattias Desmet and grand “conspiracy theories” of varying degrees of seeming plausibility such as those about Klaus Schwab, the World Economic Forum, and the “Great Reset.”
The Collective Fight or Flight Response
Certainly, whether by design (conspiracy) or accident or a mixture of both, we are experiencing a collective fight or flight response on a nearly global scale, a natural response in ancient times when your tribe is attacked by another tribe, in which masks and vaccination are shibboleths to identify friend and foe, like the phrase “lollapalooza” reputedly used by American soldiers in the Pacific during World War II to separate native English speaking US soldiers from Japanese soldiers especially at night.
The collective fight or flight response is the same response that caused massacres of alleged witches, vagabonds, Jews, and others accused of poisoning wells during the Black Death. The fight or flight response is instinctual, overriding higher cognitive functions. High intelligence and formal education does not prevent it or increase one’s ability to shut it off when it is unwarranted — an overreaction for example. It is hard coded into all or nearly all human beings: rich and poor, ignorant and highly educated, dumb and super-smart.
Social conformity, hostility to dissent, censorship, and propaganda all increase markedly during historical episodes of the collective fight or flight response such as during both World Wars. Because of the demonic imagery associated with the Nazis and the Holocaust, it is easier to see the irrationality of the response in World War I than World War II. It is however an irrational, instinctual response rooted in a direct physical attack by a rival tribe in ancient times, not adapted to even modern wars let alone pandemics.
Trump as the American Hitler
Regardless of the motives of any elite conspiracy, Donald Trump’s surprise victory in 2016 caused a collective fight or flight response based on a deep seated belief in and fear of an “American Hitler,” among many Americans, disproportionately liberal Democrats, an insane, evil demagogue who would capture the votes of hillbillies and other stigmatized mostly poor rural whites and lead the US and the World to destruction.
This frightening archetype is common in US popular culture and serious scholarship, dating back at least to the fears of and historical reactions to William Jennings Bryan and most importantly the crusade against Louisiana Senator Huey Long culminating in Long’s assassination in September of 1935, painting Long as the US equivalent of Hitler.
If you believe Trump is the American Hitler, a secular liberal progressive equivalent of the Antichrist figure of the Book of Revelations, then anything is justified to stop him: lying, cheating, stealing, even advocating policies that will in fact increase deaths during a pandemic to undermine him, particularly given the spectre of global thermonuclear war even though the “American Hitler,” Trump, shows a marked antipathy to global thermonuclear war.
Completely irrational behavior such as trying to stop efforts to make deals with Russia and North Korea becomes justifiable if Trump is the American Hitler. Even loopy ideas like provoking a conflict with nuclear armed super-power Russia over Ukraine (currently in the news) to rally the public and prevent Trump or a Trump successor from returning in the mid-term elections may seem reasonable to otherwise intelligent people. Keep in mind if the Trump Republicans were to gain a super-majority in both houses in the 2022 election, they could in principle invalidate the contested 2020 election and restore Trump in 2022 instead of 2024.
Regardless of how unjustified and perhaps unhinged such beliefs about Trump, potential Trump replacements such as Governor De Santis or Senator Rand Paul, or Trump voters actually are, Dr. Atlas, Trump himself, and others have failed to allay them. Once a group of people or a single person is in the instinctual fight or flight response, reason usually fails until they calm down. It is incumbent upon Dr. Atlas to assess why he failed and how to succeed. This is a major weakness of the book.
Conclusion
A Plague Upon Our House paints an alarming portrait of the US government, President Trump and especially the “troika” of Anthony Fauci, Deborah Birx, and Robert Redfield and their many allies — something of continuing concern given Fauci’s current role in the Biden administration. It is filled with anecdotes suggesting Fauci is either incompetent or something worse.
The book would be stronger and more helpful in the continuing crisis if Dr. Atlas carefully evaluated the reasons for his failure to stop the disastrous policies, inability to reach the public and allay the concerns of frightened “never Trumpers,” and how to succeed now in the continuing crisis.
(C) 2022 by John F. McGowan, Ph.D.
About Me
John F. McGowan, Ph.D. solves problems using mathematics and mathematical software, including developing gesture recognition for touch devices, video compression and speech recognition technologies. He has extensive experience developing software in C, C++, MATLAB, Python, Visual Basic and many other programming languages. He has been a Visiting Scholar at HP Labs developing computer vision algorithms and software for mobile devices. He has worked as a contractor at NASA Ames Research Center involved in the research and development of image and video processing algorithms and technology. He has published articles on the origin and evolution of life, the exploration of Mars (anticipating the discovery of methane on Mars), and cheap access to space. He has a Ph.D. in physics from the University of Illinois at Urbana-Champaign and a B.S. in physics from the California Institute of Technology (Caltech).
This is the legal disclaimer that appears when starting the US Centers for Disease Control (CDC’s) Fluview Interactive application which purports to report the percentage of deaths per week “due to” pneumonia and influenza (P&I) prior to March 2020 and pneumonia, influenza, and COVID-19 (PIC) since March 2020. (URL: http://gis.cdc.gov/grasp/fluview/mortality.html)
Emphasis is added to key phrases. The NOTES explain the definition and meaning of several technical terms used in the disclaimer.
The disclaimer essentially says, in plain English, the data — the COVID-19 death counts — which is presented with no estimates of statistical or systematic errors is provisional and could be entirely wrong. Two sentences in one paragraph appear to contradict one another.
National Center for Health Statistics Mortality Surveillance System-
NOTE: The National Center for Health Statistics (NCHS) is a division of the US Centers for Disease Control and Prevention (CDC).
The National Center for Health Statistics (NCHS) collects and disseminates the Nation’s official vital statistics. NCHS collects death certificate data from state vital statistics offices for all deaths occurring in the United States. Pneumonia and/or influenza (P&I) deaths and pneumonia, influenza and/or COVID-19 (PIC) deaths are identified based on ICD-10 multiple cause of death codes.
NOTE: ICD-10 is the International Classification of Diseases 10th Edition, a medical classification list by the World Health Organization (WHO). “ICD-10 multiple cause of death codes” refers to multiple “causes of death” listed on death certificates. Many death certificates have many causes of death such as emphysema, a degenerative eventually terminal condition, and pneumonia. One cause of the death is singled out as the “underlying cause of death” or UCOD. One cause of death is singled out as the “immediate cause of death.” The immediate cause of death is often not the underlying cause of death. For example, emphysema may be the underlying cause of death and pneumonia, the influenza virus, or the “common cold” may be the immediate cause of death.
NCHS Mortality Surveillance System data are presented by the week the death occurred at the national, state, and HHS Region levels, based on the state of residence of the decedent. Data on the percentage of deaths due to P&I or PIC are released one week after the week of death to allow for collection of enough data to produce a stable percentage. States and HHS regions with less than 20% of the expected total deaths (average number of total deaths reported by week during 2008-2012) will be marked as having insufficient data. Not all deaths are reported within a week of death therefore data for earlier weeks are continually revised and the proportion of deaths due to P&I or PIC may increase or decrease as new and updated death certificate data are received by NCHS.
NOTE: Notice the conflictbetween “to allow for collection of enough data to produce a stable percentage” and “the proportion of deaths due to P&I or PIC may increase or decrease as new and updated death certificate data are received by NCHS.” Percentage is a way of expressing the proportion: for example, fifty percent (a percentage) versus one half (another way of expressing the same percentage). “Stable” usually means “not changing or fluctuating” (Merriam Webster) when used in this way.
The COVID-19 death counts reported by NCHS and presented here are provisional and will not match counts in other sources, such as media reports or numbers from county health departments. COVID-19 deaths may be classified or defined differently in various reporting and surveillance systems. Death counts reported by NCHS include deaths that have COVID-19 listed as a cause of death and may include laboratory confirmed COVID-19 deaths and clinically confirmed COVID-19 deaths. Provisional death counts reported by NCHS track approximately 1-2 weeks behind other published data sources on the number of COVID-19 deaths in the U.S. These reasons may partly account for differences between NCHS reported death counts and death counts reported in other sources.
NOTE: The language “a cause of death” likely means that COVID-19 (or pneumonia or influenza in pre-2020 figures) is one of the causes of death listed on the death certificate, not necessarily the underlying cause of death (UCOD). Remember, many death certificates have multiple causes of death, one of which is identified as the underlying cause of death. (UCOD). Note also that the disclaimer specifically states that NCHS numbers “will not match..numbers from county health departments.” County health departments are presumably official, primary sources of death data with qualified staff — medical examiners and others.
In previous seasons, the NCHS surveillance data were used to calculate the percent of all deaths occurring each week that had pneumonia and/or influenza (P&I) listed as a cause of death. Because of the ongoing COVID-19 pandemic, COVID-19 coded deaths were added to P&I to create the PIC (pneumonia, influenza, and/or COVID-19) classification. PIC includes all deaths with pneumonia, influenza, and/or COVID-19 listed on the death certificate. Because many influenza deaths and many COVID-19 deaths have pneumonia included on the death certificate, P&I no longer measures the impact of influenza in the same way that it has in the past. This is because the proportion of pneumonia deaths associated with influenza is now influenced by COVID-19-related pneumonia. The PIC percentage and the number of influenza and number of COVID-19 deaths will be presented in order to help better understand the impact of these viruses on mortality and the relative contribution of each virus to PIC mortality.
The PIC percentages are compared to a seasonal baseline of P&I deaths that is calculated using a periodic regression model that incorporates a robust regression procedure applied to data from the previous five years. An increase of 1.645 standard deviations above the seasonal baseline of P&I deaths is considered the “epidemic threshold,” i.e., the point at which the observed proportion of deaths is significantly higher than would be expected at that time of the year in the absence of substantial influenza, and now COVID-related mortality. Baselines and thresholds are calculated at the national and regional level and by age groups.
* The 10 U.S. Department of Health and Human Services regions include the following jurisdictions. Region 1: Connecticut, Main, Massachusetts, New Hampshire, Rhode Island, and Vermont; Region 2: New Jersey, New York, and New York City; Region 3: Delaware, District of Columbia, Maryland, Pennsylvania, Virginia, and West Virginia; Region 4: Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South Carolina, and Tennessee; Region 5: Illinois, Indiana, Michigan, Minnesota, Ohio, and Wisconsin; Region 6: Arkansas, Louisiana, New Mexico, Oklahoma, and Texas; Region 7: Iowa, Kansas, Missouri, and Nebraska; Region 8: Colorado, Montana, North Dakota, South Dakota, Utah, and Wyoming; Region 9: Arizona, California, Hawaii, and Nevada; Region 10: Alaska, Idaho, Oregon, and Washington.
(C) 2021 by John F. McGowan, Ph.D.
About Me
John F. McGowan, Ph.D. solves problems using mathematics and mathematical software, including developing gesture recognition for touch devices, video compression and speech recognition technologies. He has extensive experience developing software in C, C++, MATLAB, Python, Visual Basic and many other programming languages. He has been a Visiting Scholar at HP Labs developing computer vision algorithms and software for mobile devices. He has worked as a contractor at NASA Ames Research Center involved in the research and development of image and video processing algorithms and technology. He has published articles on the origin and evolution of life, the exploration of Mars (anticipating the discovery of methane on Mars), and cheap access to space. He has a Ph.D. in physics from the University of Illinois at Urbana-Champaign and a B.S. in physics from the California Institute of Technology (Caltech).
This is a summary (below) of our lengthy (about 13,000 word) paper on the many issues with the CDC’s pneumonia, influenza, and COVID-19 (PIC) death numbers. It is about 1,000 words in length (5-10 minute read) and summarizes most of our key findings.
John F. McGowan, Ph.D., Tam Hunt, Josh Mitteldorf, PhD. Improving CDC Data Practices Recommendations for Improving the United States Centers for Disease Control (CDC) Data Practices for Pneumonia, Influenza, and COVID-19 (v 1.1). Authorea. November 29, 2021. DOI:10.22541/au.163822197.79126460/v1 (https://doi.org/10.22541/au.163822197.79126460/v1)
A number of CDC data presentation and statistical practices since the start of the COVID-19 pandemic in early 2020 have not followed common scientific and engineering practice. Several problems with data presentation and analyses for pneumonia and influenza death numbers – which have been merged with COVID-19 death numbers in the FluView web site ‒ predate the pandemic.
Before the pandemic (March 2020), the non-standard data presentation and statistical practices appear to increase the number of deaths attributed to the influenza virus and imply the death counts are certain whereas substantial uncertainty exists due to uncertainty in the assignment of the cause of death and other reasons. Since the pandemic, these practices appear to do the same for SARS-COV-2 and COVID-19.
Remarkably, the CDC had at least three (3) different numbers for deaths attributed to pneumonia and influenza before 2020: the leading causes of death report count with about two (2) percent of deaths (about 55,000) per year attributed to influenza and pneumonia, the influenza virus deaths model with about 55,000 deaths per year attributed specifically to the influenza virus, and the FluView web site count with about 6-8 percent of deaths (about 188,000) per year attributed to pneumonia and influenza.
The FluView number differs from the other two death numbers by a factor of OVER THREE. The probable reason for this difference is that — according to the FluView technical notes — FluView counts deaths where pneumonia or influenza is listed as “a cause of death” whereas the leading cause of death report — according to the technical notes — counts only deaths where pneumonia or influenza is listed as “the underlying cause of death.” This probably reflects a large uncertainty in the assignment of the cause of death in respiratory illness cases; indeed the underlying cause of death may be ill-defined in many cases.
The CDC’s excess deaths estimates on their excess deaths web site does not report any standard goodness of fit statistics, notably the coefficient of determination often known as “R squared” and the “chi squared” goodness of fit statistic. Our analysis shows that different models with the same goodness of fit statistics give different estimates of the number of excess deaths, varying by up to 200,000 deaths in 2020. The CDC web site does not report this systematic modeling error.
The CDC appears to have chosen a set of parameters for the Noufaily/Farrington algorithm used to estimate excess deaths by the CDC that gives a lower “R Squared” value for goodness of fit than other choices and a HIGHER ESTIMATE of excess deaths — whereas common scientific and engineering practice would be to use the models with the best goodness of fit statistics, the “R Squared” closest to 1.0.
The Noufailly/Farrington algorithm is an empirical trend detection and extrapolation model theoretically incapable of accurately modeling the aging “baby boom” population which would be expected to produce “excess deaths” in recent years — nor is it able to explain the puzzling near stop of the increase in deaths per year reported in the immediate pre-pandemic years 2017-2019 despite the aging population.
The CDC does not publish (as of Dec 2021) years of life lost (YLL) estimates which include increases in suicides, homicides, and other adverse effects of the lockdowns, nor systematic modeling errors on the YLL estimates. YLL can illustrate the difference between a disease that largely kills those nearing death anyway versus a disease that easily kills the healthy.
The CDC issued a COVID death certificate guidance document in April of 2020 that appears to change the standards for assigning the underlying cause of death (UCOD) from the pre-pandemic practice for assigning the underlying cause of death for pneumonia and influenza, making COVID-19 the underlying cause of death in the many cases where the person who died had serious pre-existing conditions such as chronic bronchitis, emphysema, heart failure etc.. — the deaths counted in FluView but not in the leading causes of death report. There does not appear to have been any public comment on this guidance document to date.
In general the CDC does not report statistical errors, systematic errors, or estimates of biases in pneumonia, influenza, and COVID-19 death numbers. They do not report any monitoring of the effect of their guidance documents or other directives on the assignment of the cause of death by doctors, medical examiners, and others.
These issues are sometimes shared with other government agencies such as the US Social Security Administration (SSA) and US Census Bureau that work closely with the CDC.
Death counts for both individual causes and “all cause” deaths are frequently reported as precise to the last digit without any statistical or systematic errors, despite both known and unknown uncertainties in counting deaths, such as missing persons, unreported deaths due to deceased payee fraud, the ~1,000 living Americans incorrectly added to the government Deaths Master File (DMF), each month, for unknown reasons, and considerable uncertainties in assigning the underlying cause of death (UCOD) by coroners and doctors.
Similarly, raw counts, adjusted counts, and estimates – often based on incompletely documented computer mathematical models – are often not clearly identified as such. The Deaths Master File, with names and dates of death of deceased persons is exempt from the Freedom of Information Act (FOIA) and unavailable to the general public, independent researchers, and even other government agencies such as the IRS. This confidentiality of data makes independent verification of many CDC numbers, such as the excess deaths numbers tracked during the COVID-19 pandemic, all but impossible.
This omission of common scientific and engineering practices raises questions about the accuracy of the CDC’s data, conclusions, and public health policies in a number of important areas, including the COVID-19 pandemic.
The non-standard data presentation and statistical practices appear to increase the number of deaths attributed to the influenza virus and imply the death counts are certain whereas substantial uncertainty exists due to uncertainty in the assignment of the cause of death and other causes. Since the pandemic, these practices appear to do the same for SARS-COV-2 and COVID-19.
END OF SUMMARY
(C) 2021 by John F. McGowan, Ph.D.
About Me
John F. McGowan, Ph.D. solves problems using mathematics and mathematical software, including developing gesture recognition for touch devices, video compression and speech recognition technologies. He has extensive experience developing software in C, C++, MATLAB, Python, Visual Basic and many other programming languages. He has been a Visiting Scholar at HP Labs developing computer vision algorithms and software for mobile devices. He has worked as a contractor at NASA Ames Research Center involved in the research and development of image and video processing algorithms and technology. He has published articles on the origin and evolution of life, the exploration of Mars (anticipating the discovery of methane on Mars), and cheap access to space. He has a Ph.D. in physics from the University of Illinois at Urbana-Champaign and a B.S. in physics from the California Institute of Technology (Caltech).
Posted an updated version of our paper on fixing the US Centers for Disease Control (CDC)’s problematic pneumonia, influenza, and COVID-19 (PIC) death numbers.
John F. McGowan, Ph.D., Tam Hunt, Josh Mitteldorf, PhD. Improving CDC Data Practices Recommendations for Improving the United States Centers for Disease Control (CDC) Data Practices for Pneumonia, Influenza, and COVID-19 (v 1.1). Authorea. November 29, 2021. DOI: 10.22541/au.163822197.79126460/v1
Key points are:
o The CDC has at least three different estimates/counts of deaths from influenza and pneumonia before March 2020, two of which (FluView and the leading causes of death report) differ by a factor of over three. FluView attributes about 6-8 percent of deaths to pneumonia and influenza each year and the leading causes of death report attributes about 2 percent of deaths each year to pneumonia and influenza. The FluView numbers have been expanded to incorporate COVID deaths since March 2020.
o The likely cause of the discrepancy between the FluView and leading causes of death numbers is that the FluView numbers, based on the technical notes, count any death where pneumonia and/or influenza is listed as “a cause of death” whereas the leading causes of death report counts only deaths where pneumonia and/or influenza is listed as the “underlying cause of death.” Except for the fine print in the technical notes, the language, labels and titles on graphs etc., on both FluView and the leading cause of death reports state the deaths are caused by (“due to”) pneumonia and influenza.
o What does this mean for COVID? The CDC appears to have changed the criterion for assignment of underlying cause of death for COVID in their April 2020 COVID death certificate guidance to always assign COVID as the underlying cause of death even in cases of chronic lower respiratory disease and COPD (a subset of chronic lower respiratory disease usually meaning chronic bronchitis or emphysema). Hence the CDC’s deaths “from COVID” are probably comparable to the larger FluView deaths — about 188,000 deaths per year before March 2020 — or an even larger number due to the attribution of deaths to COVID that would be called heart attack or stroke deaths absent a positive COVID test or diagnosis. This supports but does not prove the hypothesis that COVID is largely a threat to a subset of vulnerable persons with preexisting serious health problems such as COPD and largely or entirely not a threat to the general healthy population; we do not state this in the paper but it is an obvious implication.
o Extensive failures to follow common scientific and engineering practice and use of confusing terminology. Lack of statistical and systematic errors, especially with respect to the assignment of a/the cause of death which many studies show is substantially uncertain — as appears illustrated by the discrepancy between the leading causes of death report and the FluView numbers. Frequent confusion between what is a model/estimate and what is a count.
(C) 2021 by John F. McGowan, Ph.D.
About Me
John F. McGowan, Ph.D. solves problems using mathematics and mathematical software, including developing gesture recognition for touch devices, video compression and speech recognition technologies. He has extensive experience developing software in C, C++, MATLAB, Python, Visual Basic and many other programming languages. He has been a Visiting Scholar at HP Labs developing computer vision algorithms and software for mobile devices. He has worked as a contractor at NASA Ames Research Center involved in the research and development of image and video processing algorithms and technology. He has published articles on the origin and evolution of life, the exploration of Mars (anticipating the discovery of methane on Mars), and cheap access to space. He has a Ph.D. in physics from the University of Illinois at Urbana-Champaign and a B.S. in physics from the California Institute of Technology (Caltech).
In fact, based on available data from the summer, only one of out of every 160,000 fully vaccinated Americans was hospitalized for COVID per day.
This actually means that over 1,000 fully vaccinated American per day were hospitalized, around 90,000 over the three months of summer if this refers to new hospitalizations per day rather than number of people in a hospital on each day, suggesting around 4-9 million fully vaccinated Americans were infected with the virus during the summer when respiratory viruses are at a seasonal low.
The US federal government currently (Sep 10, 2021) claims about 53 percent of American are fully vaccinated. There are about 330 million people in the United States. This means about 175 million Americans are fully vaccinated. If one (1) in 160,000 fully vaccinated Americans was hospitalized for COVID per day, this works out to (175 million/160,000) about 1,093 American hospitalized per day.
COVID has an infection fatality rate (IFR) of around 0.3 percent (not the case fatality rate or CFR which is often confused with the IFR, which is usually the fractions of deaths among symptomatic cases reported by doctors or hospitals — not people who stay home and recover or simply have no or negligible symptoms). Probably a few percent of people infected with COVID actually are hospitalized.
For example in the recent Provincetown, Cape Cod (Barnstable County, Massachusetts) outbreak four (4) fully vaccinated people out of 346 fully vaccinated persons identified by the CDC and Massachusetts state public health contact tracing were hospitalized, giving a hospitalization rate of about (4/346) 1.16 percent of the fully vaccinated. The hospitalization rate for the unvaccinated in the Provincetown outbreak was statistically the same as the hospitalization rate for the fully vaccinated — about one (1) percent.
Using a one percent hospitalization rate among the fully vaccinated means that nine (9) million fully vaccinated American were infected with COVID-19 during the summer of 2021, despite the vaccines and the seasonal low in respiratory viral diseases. A more conservative hospitalization rate of two (2) percent gives 4.5 million Americans infected.
Even if President Biden’s number refers to the number of fully vaccinated persons in a hospital each day rather than new hospital admissions with COVID, assuming a typical stay of two weeks in a hospital with COVID, this gives about 6,000 hospitalizations over a three month (six two-week periods) summer, meaning 300,000 to 600,000 fully vaccinated persons infected with COVID during the seasonal summer low in respiratory viral diseases.
What Does This Mean?
As with the official CDC report on the Provincetown, Cape Cod, Massachusetts outbreak and disappointing result in Israel, President Biden’s highly misleading number strongly suggests the COVID vaccines are failing to prevent infection by COVID and the spread of the disease on a large scale.
(C) 2021 by John F. McGowan, Ph.D.
About Me
John F. McGowan, Ph.D. solves problems using mathematics and mathematical software, including developing gesture recognition for touch devices, video compression and speech recognition technologies. He has extensive experience developing software in C, C++, MATLAB, Python, Visual Basic and many other programming languages. He has been a Visiting Scholar at HP Labs developing computer vision algorithms and software for mobile devices. He has worked as a contractor at NASA Ames Research Center involved in the research and development of image and video processing algorithms and technology. He has published articles on the origin and evolution of life, the exploration of Mars (anticipating the discovery of methane on Mars), and cheap access to space. He has a Ph.D. in physics from the University of Illinois at Urbana-Champaign and a B.S. in physics from the California Institute of Technology (Caltech).
The Provincetown, Massachusetts (Cape Cod, Barnstable County) COVID-19 outbreak does NOT prove that the COVID-19 vaccines are working as widely claimed in the mainstream media. (See the CDC Report “Outbreak of SARS-CoV-2 Infections, Including COVID-19 Vaccine Breakthrough Infections, Associated with Large Public Gatherings — Barnstable County, Massachusetts,” https://www.cdc.gov/mmwr/volumes/70/wr/pdfs/mm7031e2-H.pdf)
Subscribe to our free Weekly Newsletter for articles and videos on practical mathematics, Internet Censorship, ways to fight back against censorship, and other topics by sending an email to: subscribe [at] mathematical-software.com
John F. McGowan, Ph.D. solves problems using mathematics and mathematical software, including developing gesture recognition for touch devices, video compression and speech recognition technologies. He has extensive experience developing software in C, C++, MATLAB, Python, Visual Basic and many other programming languages. He has been a Visiting Scholar at HP Labs developing computer vision algorithms and software for mobile devices. He has worked as a contractor at NASA Ames Research Center involved in the research and development of image and video processing algorithms and technology. He has published articles on the origin and evolution of life, the exploration of Mars (anticipating the discovery of methane on Mars), and cheap access to space. He has a Ph.D. in physics from the University of Illinois at Urbana-Champaign and a B.S. in physics from the California Institute of Technology (Caltech).
The July 30, 2021 CDC report on an outbreak of COVID-19 (delta variant) in Barnstable County, Massachusetts (July 6-25, 2021) used to justify guidance for vaccinated persons to wear masks indoors appears to indicate the COVID vaccines failed to reduce either the rate of infection with COVID in the vaccinated or the hospitalization rate if infected, contrary to widespread statements in the mass media by public health authorities and others. Although there are some possible if improbable explanations, no explanation for this seeming contradiction appears to be present in the report.
Outbreak of SARS-CoV-2 Infections, Including COVID-19 Vaccine Breakthrough Infections, Associated with Large Public Gatherings — Barnstable County, Massachusetts, July 2021 Brown et al Morbidity and Mortality Weekly Report Early Release / Vol. 70 July 30, 2021 U.S. Department of Health and Human Services Centers for Disease Control and Prevention https://www.cdc.gov/mmwr/volumes/70/wr/pdfs/mm7031e2-H.pdf
Relevant Numbers from Report:
cases of COVID-19 associated with multiple summer events and large public gatherings in a town in Barnstable County, Massachusetts (July 6-25, 2021) 469 vaccination coverage among eligible Massachusetts residents 69.00% cases in fully vaccinated 346 73.77% cases in unvaccinated, which may include partially vaccinated and recently vaccinated (less than 14 days since final shot) 123 26.23% number of vaccinated hospitalized 4 number of unvaccinated hospitalized 1
hospitalization rate of vaccinated infected with COVID-19 1.16% hospitalization rate of unvaccinated infected with COVID-19 0.81%
Number of cases sequenced 133 Number of cases with B.1.617.2 (Delta) variant 120 90.23%
Number of symptomatic vaccinated cases 274 Number of symptomatic unvaccinated cases ?
Subscribe to our free Weekly Newsletter for articles and videos on practical mathematics, Internet Censorship, ways to fight back against censorship, and other topics by sending an email to: subscribe [at] mathematical-software.com
John F. McGowan, Ph.D. solves problems using mathematics and mathematical software, including developing gesture recognition for touch devices, video compression and speech recognition technologies. He has extensive experience developing software in C, C++, MATLAB, Python, Visual Basic and many other programming languages. He has been a Visiting Scholar at HP Labs developing computer vision algorithms and software for mobile devices. He has worked as a contractor at NASA Ames Research Center involved in the research and development of image and video processing algorithms and technology. He has published articles on the origin and evolution of life, the exploration of Mars (anticipating the discovery of methane on Mars), and cheap access to space. He has a Ph.D. in physics from the University of Illinois at Urbana-Champaign and a B.S. in physics from the California Institute of Technology (Caltech).
This is a preprint of a new academic paper written by Tam Hunt, Josh Mitteldorf, Ph.D. and myself on the US Centers for Disease Control (CDC)’s data practices during the COVID-19 pandemic and for pneumonia and influenza prior to the pandemic. I am the corresponding author.
Abstract
During the pandemic, millions of Americans have become acquainted with the CDC because its reports and the data it collects affect their day-today lives. But the methodology used and even some of the data collected by CDC remain opaque to the public and to the community of academic epidemiology. In this paper, we highlight areas in which CDC methodology might be improved and where greater transparency might lead to broad collaboration. (1) “Excess” deaths are routinely reported, but not “years of life lost”, an easily-computed datum that is important for public policy. (2) What counts as an “excess death”? The method for computing the number of excess deaths does not include error bars and we show a substantial range of estimates is possible. (3) Pneumonia and influenza death data on different CDC pages is grossly contradictory. (4) The methodology for computing influenza deaths is not described in sufficient detail that an outside analyst might pursue the source of the discrepancy. (5) Guidelines for filling out death certificates have changed during the COVID-19 pandemic, preventing the comparison of 2020-21 death profiles with any previous year. We conclude with a series of explicit recommendations for greater consistency and transparency, and ultimately to make CDC data more useful to outside epidemiologists.
John F. McGowan, Ph.D., Tam Hunt, Josh Mitteldorf. Improving CDC Data Practices Recommendations for Improving the United States Centers for Disease Control (CDC) Data Practices for Pneumonia, Influenza, and COVID-19. Authorea. July 19, 2021. DOI: 10.22541/au.162671168.86830026/v1
Here are the key recommendations from the paper:
Recommendations
In light of the previous discussion, we make a number of recommendations to improve CDC’s data practices, including improved observance of common scientific and engineering practice – such as use of significant figures and reporting of statistical and systematic errors. Common scientific and engineering practice is designed to prevent serious errors and should be followed rigorously in a crisis such as the COVID-19 pandemic.
Note that some of these recommendations may require changes in federal or state laws, federal or state regulations, or renegotiation of contracts between the federal government and states. This is probably the case for making the Deaths Master File (DMF), with names and dates of death of persons reported as deceased to the states and federal government, freely available to the public and other government agencies.
All CDC numbers, where possible, should be clearly identified as estimates, adjusted counts, or raw counts, with statistical errors and systematic errors given, using consistent clear standard language in all documents. The errors should be provided as both ninety-five percent (95%) confidence level intervals and the standard deviation – at least for the statistical errors.
In the case of adjusted counts, the raw count should be explicitly listed immediately following the adjusted count as well as a brief description of the adjustment and a reference for the adjustment methodology. For example, if the adjusted number of deaths in the United States in 2020 is 3.4 million but the raw count of deaths was 3.3 million with 100,000 deaths added to adjust for unreported deaths of undocumented immigrants, the web pages and reports would say:
Total deaths (2020): 3.4 million (adjusted, raw count 3.3 million, unreported deaths of undocumented immigrants, adjustment methodology citation: Smith et al, MMWR Volume X, Number Y)
The distinction between the leading causes of death report “pneumonia and influenza” deaths, ~55,000 per year pre-pandemic, and the FluView website “pneumonia and influenza” deaths, ~188,000 per year pre-pandemic, should be clarified in the labels and legends for the graphics and prominently in the table of leading causes of death or immediately adjacent text. Statistical and systematic errors on these numbers should be provided in graphs and tables.
In general, where grossly different raw counts, adjusted counts, or estimates are presented in CDC documents and websites with the same name, semantically equivalent or nearly equivalent names such as “pneumonia and influenza” and “influenza and pneumonia,” clearly distinct names should be used instead, or the reasons for the gross difference in the values should be prominently listed in the graphs and tables or immediately adjacent text. It should be easy for the public, busy health professionals, policy makers and others to recognize and understand the differences.
CDC should provide results for different models for the same data with similar R2 values – coefficient of determination – to give the audience a quick sense of the systematic modeling errors – since there is no generally accepted methodology for estimating the 95% confidence level for the systematic modeling errors. See Figure 7 above for an example.
All mathematical models should be free and open source with associated data provided using commonly used free open-source scientific programming languages such as Python or R, made available on the CDC website, GitHub, and other popular sources. The models and data should be provided in a package form such that anyone with access to a standard MS Windows, Mac OS X, or Linux/Unix computer can easily download and run the analysis – similar to the package structure used by the GNU project, for example.
Specifically, the influenza virus deaths model should be provided to the public as code and data. The justification for the increase in the number of deaths attributed to influenza (~6,000 to ~55,000) should be presented in clear language with supporting numbers, such as the false positive and negative rates for the laboratory influenza deaths and general diagnosis of influenza in the absence of a positive lab test as well as in the code and data.
With respect to excess deaths tracking, include all major select causes of death, rather than just the thirteen (13) in the cause-specific excess deaths that CDC tracks, which currently account for about 2/3 of all deaths.
Include a Years of Lives Lost (YLL) display for COVID-19 deathsi and non-COVID-19 deaths, as well as excess deaths analysis, due to the higher granularity of YLL analysis when compared to excess deaths analysis. Explain the pros and cons of both analytical tools. Do the same for any future pandemics or health crises.
Adopt or develop a different algorithm or algorithms for tracking excess deaths which are mostly attributed to non-infectious causes such as heart attacks, cancer, and strokes. The Farrington/Noufaily algorithms were specifically developed as an early warning for often non-lethal infectious disease outbreaks such as salmonella. A medically-based model or models that incorporates population demographics such as the aging “baby boom” and evolving death rates broken down by age, sex, and possibly other factors where known is probably a better practice rather than simple empirical trend models such as the Noufaily algorithm.
Eliminate the zeroing procedure in calculating excess deaths, in which negative excess deaths in some categories are set to zero, rather than being added to the full excess deaths sum over all categories.
The anonymized data with causes of death as close to the actual data as possible, e.g. the actual death certificates, should be available on the CDC website in a simple accessible widely used format such as CSV (comma separated values) files. The code used to aggregate the data into summary data such as the FluView website data files should also be public.
The full Deaths Master File (DMF) including the actual names of the deceased persons and dates of death should be made available to the general public, independent researchers, and others. This is critical to independent verification of many numbers from the CDC, SSA, and US Census.
COVID-19-related deaths figures should be tracked based on year-specific age of death, rather than 10-year age ranges, as is currently the case.
CDC frequently changes the structure and layout of the CSV files/spreadsheets on their websites. The CDC should either (1) not do this or (2) provide easy conversion between different file formats with each new format so it is trivial for third parties to quickly adapt to the changes without writing additional code. CDC should provide a program or program in a free and open source language like R to convert between the formats.
The CDC and other agencies should be required to announce and solicit public comment for changes to case definitions, data collection rules, etc. for key public policy data such as the COVID-19 case definitions, death certification guidelines, and coding rules. Other government agencies have significantly more public participation than CDC, which is appropriate in a modern democracy.
Any practices and policies imposed in a public emergency, such as case definitions, definitions of measured quantities, data reporting practices, etc. imposed without public comment and review, should have an expiration date (e.g. sixty days) beyond which they must be subject to public review. Public comment, reviews, and cost/benefit analyses should happen during this emergency period.
Enacting these reforms should reduce the risk of serious errors, increase the quality and accuracy of CDC data and analyses, as well as any policies or CDC guidelines based on the data and analysis, and strengthen public confidence in the CDC and public health policies.
(C) 2021 by John F. McGowan, Ph.D.
About Me
John F. McGowan, Ph.D. solves problems using mathematics and mathematical software, including developing gesture recognition for touch devices, video compression and speech recognition technologies. He has extensive experience developing software in C, C++, MATLAB, Python, Visual Basic and many other programming languages. He has been a Visiting Scholar at HP Labs developing computer vision algorithms and software for mobile devices. He has worked as a contractor at NASA Ames Research Center involved in the research and development of image and video processing algorithms and technology. He has published articles on the origin and evolution of life, the exploration of Mars (anticipating the discovery of methane on Mars), and cheap access to space. He has a Ph.D. in physics from the University of Illinois at Urbana-Champaign and a B.S. in physics from the California Institute of Technology (Caltech).
