Three reviews of US and Canadian mortality quiet fears of covid-19. One of these reviews, written by one the top epidemiologists in the world, Samuel H. Preston, and his colleague, Yana Vierboom, found nearly three times as many “years of life lost” in the US to the flu in 2017 than from covid-19 in 2020, and in Canada there was no increase over the expected yearly increase.
Last updated November 18, 2021
Although this information seems impossible to most people, it is a repeat of what happened in recent epidemics such as the swine flu in 2009 and the zika virus in 2016, and exaggerated fears of infection are an integral part of human history, a topic I covered in a prior paper (Irwin 2021b). It is also a culmination of the steadily increasing survivability estimates of covid-19. I summarized this research in three previous literature reviews: first in spring 2020 (Irwin 2020a), then in summer 2020 (Irwin 2020b) and finally in fall 2020 (Irwin 2020c). These results are easily found in scientific journals and official government statistics, as well as in primary and secondary sources that are all referenced in these reviews. For example, in September 2020 the CDC estimated a mortality rate of less than 1 in 33,000 for people under the age of 20, and less than 1 in 10,000 in people under age 50 (CDC 2020). As another example, US College students had a mortality rate of less than 1 in 100,000, and the students who did die did not have symptoms consistent with a viral respiratory illness, suggesting other causes (Irwin 2020c). The flu affects younger people much more than this, which is why the measure of "years of life lost" was so much larger for the flu in the 2017-18 season compared to covid-19 in 2020, as described by Preston & Vierboom (2021).
This paper focuses on three summaries of national all-cause mortality for 2020, two from the United States (Briand 2021, Preston & Vierboom 2021), and one Canadian (Rancourt et al 2021). They will be introduced here and reviewed again in more detail at the end. All three of of them used mortality statistics directly from government databases, in the US from the Centers for Disease Control (CDC), and in Canada from Statistics Canada (StatCan).
These researchers are not alone, and tens of thousands of medical professionals and public health researchers signed an online declaration stating that the widespread social isolation protocols were causing more harm than good. The declaration called for encouraging the majority of the population to "live life as normal" and only having vulnerable populations be protected, even then in a voluntary, not mandatory fashion. This focused protection strategy also has risk of harm by isolating vulnerable populations, but was considered by over 60,000 health professionals and researchers to be a significant improvement. The declaration was drafted by three professors of epidemiology who specialize in viral epidemics, one from Harvard, one from Stanford, and one from Oxford (Kulldorff et al, 2020). Amazingly, this information was silenced and the website for the declaration was struck from search engines for several months after it was published. Most news agencies, including Wikipedia, continue to have a heavy bias against any information revealing how misguided the social isolation policies have been. However, multiple lines of evidence make clear how wildly off course these policies were, including the evidence from all-cause mortality.
Canada had no increased mortality in 2020 over the expected yearly increase, and would have had an even milder year without social isolation mandates.
Rancourt et al provide the most blunt assessment in their finding of a lack of increase in Canadian mortality: "Either a pandemic causes a significant increase in deaths, or there was not a pandemic, barring the many unscientific beliefs in public health measures for viral respiratory diseases" (Rancourt et al 2021, page 11). This is shown in a graph compiled from Canadian government all-cause mortality statistics for the past ten years (Figure 1a). Every year has a winter increase in deaths, and each year the overall number goes up slightly due to population increase, with no change in 2020 despite the extremely lare number of deaths supposedly caused by covid-19 in Canada. However, by focusing on only one year, the yearly winter increase in deaths can look alarming and be used to fuel fear-based policies and beliefs, as seen in this Figure 4, also from Rancourt et al, which zooms in on the winters of 2019-2020 and 2020-21 and does not show the prior year winter increases. For comparison here are links to the two graphs: Figure 1a, Figure 4.
Rancourt et al also emphasize that the spike for the very elderly is steeper in March and April 2020 than prior years, followed by unexpectedly lower number of deaths in the two months after that. They conclude that this was due to “response induced deaths”, especially in the elderly where vulnerability to isolation and health system disruption is high. This was made worse by the understaffing crises in elder care facilities which happened across the world, including in Canada, and a comprehensive discussion of this is found in section 4.5, “Regarding Causes of Response-Induced Deaths”, starting on Page 41. After this section they state: “We conclude that the ‘covid-peak’ was palpably induced by the pandemic response” (Page 47). “We believe that it is not a coincidence that all the “covid peaks” started their sharp and sudden surges immediately (within 1 week or so) after the WHO’s March 2020 pronouncement of a pandemic. We believe viruses did not suddenly everywhere act on cue” (Rancourt et al 2021).
Simply put, viruses do not have dramatic simultaneous effects in regions thousands of miles apart. Rancourt et al point out another anomaly, an unexpected significant increase in deaths of young males in the summer of 2020, who had increased chance of dying from drug overdoses, homicides, and suicides, all of which were already leading causes of death in this age group, stating: “Our interpretation is that the excess deaths in males of the 0-44 age group arise from the stress of the large-scale and continued societal and economic responses to the pandemic.” (Page 34). Young people are not vulnerable to dying from viruses, but they are vulnerable to dying from causes related to emotional, psychological, and spiritual distress, an area where all humans could use support. Briand (2021) also showed this in her analysis of US data, with a graph showing excess summer deaths from non-natural causes such as suicides, homicides, accidents and drug overdoses (Briand, Graph 12, page 37). This is a significant factor in the anomalous summer increases in all-cause mortality seen in most states in the US. Briand provides a mortality graph for all 50 states, and most states have no increase in all-cause mortality over prior years, but even these often have a summer increase, such as seen in the graphs for Iowa and Arkansas. Of course social isolation advocates incorrectly blame all excess deaths on covid-19.
US States with mildest social isolation mandates had no increase in peak weekly mortality.
Genevieve Briand, a professor of statistics and applied economics at Johns Hopkins University, provided an extremely thorough comparison of US weekly mortality for the past six years, with graphs of all 50 states plus New York City, Washington DC, and Puerto Rico (Briand 2021). Although overall deaths were increased in the United States, there is an incredible variability from region to region, which is not consistent with a viral pandemic. Most of the states have no increase in peak weekly all-cause mortality when compared to prior years, but many of them show the same "covid-peak" which occurred simultaneously in distant regions over a thousand miles apart just after the WHO and government proclamation of a pandemic, followed immediately by various degrees of social isolation mandates. Briand points out, just as Rancourt et al did, that viruses normally spread from region to region in stepwise fashion, and do not suddenly appear in unison in multiple distant areas as happened in March of 2020.
Many of the states with the best outcomes had the mildest social isolation measures, and yet they were often harshly criticized by the media and public health officials. For example, there were only seven states that did not issue any stay at home orders (Ballotpedia, 2021), and all of them had lower peak weekly deaths than in prior years. This is shown clearly by the graphs for each of these seven states, provided here with some comments under each one: Arkansas, Iowa, Nebraska, North Dakota, South Dakota, Utah, and Wyoming. Conversely, in an incredible example of reality reversal, many regions with the worst outcomes were praised for their strict mandates, such as Michigan. and New York City.
All-cause mortality is the most reliable measure because trying to assess a specific cause of death is difficult, and subject to many types of bias. I have signed over 3000 death certificates over the past 17 years as part of my role as a hospice medical director, and I know how difficult it can be to determine a specific cause of death, as well as how healing it can be to have good social support and quality healthcare when people are ill. I am not a Republican nor am I politically motivated, and I do not care which side of the political spectrum people are on. However, I am highly motivated to improve the care of our elderly and people with fragile health, especially since one day I will be among them. Unfortunately, the social isolation measures affect these people the most, especially the solitary confinement that comes with a covid-19 diagnosis. The negative effects of solitary confinement are more powerful in a facility or hospital, where families and friends were not allowed to visit, but these effects also impacted people in private homes. However, not every aspect of the stay-at-home mandates has been harmful. A positive effect is that it has been easier for people to care for ill family members in a private home, due to increased ability to work from home.
For younger and healthier populations time at home with family is also precious, but too much can create an imbalance. When combined with the harsh economic reality created by the societal disruptions, an expected increase in deaths from suicides, drug overdoses and homicides was the natural result, as shown in Briand's graph of US mortality from non-natural causes, which is a significant factor in the anomalous summer increases seen in many states and in Canada.
Social isolation policies have many harms, and were not effective at stopping the spread of covid-19.
Some might argue that the reason mortality in 2020 was not alarmingly high, and that over half of US States plus Canada did not have any increase over prior years, was because of the efforts to reduce spread. However, this goes against a tremendous amount of evidence to the contrary, including the CDC estimate that over one-third of the US population, about 120 million people, had already had covid-19 by May 2021 (Block 2021, Table 1, see citation in references section below). There are also a large number of deaths officially listed as being caused by covid-19 in every state. The most likely explanation for the lack of increased mortality in most states and Canada, despite official tallies of "covid-19 deaths", is that the test used to diagnose covid-19 has a greatly increased chance of being positive in anyone with a significant inflammatory condition, regardless of whether covid-19 is really the cause. This is covered below in the section "False positive PCR tests, the elephant in the room".
Such a widespread virus would cause increased mortality across the board, in every state, not only in some of them, and if mandates had any effect one would see that states with harsh social isolation mandates had better outcomes, not worse ones. The criticism of states with good outcomes and praise of states with extremely poor outcomes, which came from advocates of isolation and solitary confinement policies such as Anthony Fauci and Scott Gottlieb, suggest that this approach completely misunderstands public health. Only with an Orwellian stifling of scientific discussion and concealment of the evidence can such beliefs be maintained.
There are many other factors that likely had a significant effect on this variability, and some states with strict social isolation policies also did not have significantly increased weekly mortality. The long-term care understaffing crisis, which occurred in every country around the world and every state, was milder in some areas and reached catastrophic levels in others, such as New York City. The average age of the population would also have an impact, as would the affordability and quality of senior living and healthcare. Regarding mask mandates, there are over ten randomized studies failing to show any significant beneficial effect from wearing facemasks, including a large high quality randomized study from summer 2020 in Denmark, but numerous studies showing harm. In people with significant underlying illnesses they are more likely to increase mortality, rather than decrease it (Irwin 2021a, Kisielinski et al 2021).
In May 2021 the CDC stated that about 37% of the United States population had already had covid-19.
The CDC estimated in May 2021 that 37% of the United States population had already had covid-19, revealing the failure of the viral containment, social isolation, and solitary confinement measures adopted widely in the US and throughout the world (Block 2021, Table 1). They estimated over 120 million cumulative cases of covid-19 in the United States by May 2021. By the time of this writing a reasonable estimate is that well over half the US population has already had covid-19. This rules out the possibility that all-cause mortality was lowered by attempts to contain the virus, because quite simply the virus was not contained.
Although this may seem like a shockingly high number of positive cases, it is actually not surprising if one follows research closely, and even personal experience corroborates it given the constant stream of positive cases in neighborhoods, schools, workplaces, and athletic teams. These positive cases are only the people who were tested, but studies in multiple countries including the US showed repeatedly that many times more people were positive than the official count. A very large majority of people who were positive did not get tested due to having mild or no symptoms resulting in an extreme underestimate of how mild the illness is for most people (Bendavid 2020, Irwin 2020a).
A viral pandemic with such incredibly high penetration should affect all regions, and not be limited to specific areas. However, data reviewed below will show that many areas have been unaffected, with half of US states having lower peak deaths than they had in the 2017-18 season. New York City and New Jersey had extremely high numbers of excess deaths, and when these were removed the entire rest of the US (the other 48 states plus upstate New York) also did not have a significant increase over 2017-18. This is shown by a graph in the thorough review of US weekly deaths by Briand, with the yearly winter rise being compared for the past six years (Briand 2021, Graph 70). These results should be very comforting, but in another Orwellian reversal they are made to look frightening by focusing in on only one year which amplifies the standard yearly winter rise in mortality and makes it look unique. It is only when each year is compared to prior years that one can put things in perspective.
A likely explanation for at least some of the extreme variance from one region to another is that most excess deaths were caused by the social isolation-based pandemic response. This affected vulnerable populations the most, with an understaffing crisis in long-term care facilities, disruption of the normal social support safety nets, and interference with healthcare delivery. Some of this interference comes directly from the quarantine of people diagnosed with covid-19 whose healthcare quality was sacrificed in a failed attempt to contain the virus.
Rancourt et al discuss evidence for "response-caused deaths" in detail, and explain that they tend to occur in an extremely varied way depending on the local factors, unlike viral epidemics. This is seen clearly in the mortality data that they review. It is likely that well over half of excess deaths in the US could have been prevented if people had received normal high quality care instead of social isolation, health system disruption, and solitary confinement of people who were ill. If normal care had been available, the mortality increase in 2020 would likely have been more like a milder winter season such as 2018-19, shown in Briand's Graph 70.
Mortality data is routinely misrepresented and made to look alarming when it is actually quite reassuring regarding the risk from covid-19.
The mortality increase in the year 2020 has been misrepresented with incredible consistency, and made to look alarming by focusing only on the year 2020 as if it were unique. There is also an incredible tendency to criticize the very states that had the best outcomes, with no increased mortality over prior years, because they often also had the mildest social isolation mandates. These milder approaches drew harsh criticisms from "national health experts" who advocated harsher solitary confinement and viral isolation protocols.
The misrepresentation of mortality is not new, and actually began in February 2020 with studies all around the world, as described in my first paper (Irwin 2020a). It is clearly demonstrated by examining three mortality graphs from the review article by Rancourt et al (2021). The graph for Canadian 2020 mortality looks alarming, with a spike in weekly deaths which is usually claimed to caused by covid-19 (Figure 4). However, when one takes a step back to see Canadian mortality patterns for the past ten years, as they do in Figure 1a and Figure 2, one sees that the winter increase is a yearly pattern, and 2020 looks very similar to all the other years. Figure 1a shows that the yearly winter spike back in 2017-18 was actually higher than the winter peak in winter 2020-21, a shocking finding if one believes that the extremely large number of covid-19 cases were causing increased deaths. Based on this data, Rancourt et al state simply, “We conclude there was no covid-19 pandemic in Canada. It would be difficult to conclude otherwise” (2021, page 11).
In the US the situation was much more varied than in Canada, with some areas having significant numbers of excess deaths, but a very similar exaggeration of the risk from covid-19 is still evident. Preston and Vierboom (2021) address this very directly in the beginning of their Discussion section: "Because it has captured a great deal of national attention, the number of deaths from the COVID-19 epidemic in 2020 forms a timely basis of comparison. On 20 February 2021, the Centers for Disease Control and Prevention reported that 376,504 deaths ascribed to COVID-19 had occurred in the United States in calendar year 2020. That figure is similar to but below the estimated total number of excess deaths of 401,000 in the United States in 2017 (mostly from a bad influenza season - Table 1)." They accept that there were increased deaths, but point out that it was a much less significant increase than what occurred in winter 2017-18 because younger people are affected by the flu much more severely. The covid-19 pandemic is not the main focus of their paper, which is perhaps why they do not address increased deaths from the overly aggressive pandemic response measures. However, both Briand (2021) and Rancourt et al (2021) highlight this issue quite clearly in multiple areas of their analyses.
Most US states had higher peak weekly mortality rates in the 2017-18 season than they did in 2020. Many states with the best results were harshly criticized due to their having much milder social isolation mandates.
Briand (2021) emphasized the extreme variability in different regions of the US, and how inconsistent this is with a respiratory viral pandemic. Graphs 17-69 (on pages 60-112) show mortality in 53 regions, including each US state, for the past six years. Shockingly, fully twenty five of them had a peak weekly number of deaths the same or lower than occurred in a prior year, and five others only had a normal increase consistent with the standard yearly increase that occurs every year as population grows. Seven examples of this were provided previously, and contrasted with two regions with high mortality, Michigan. and New York City. Michigan's particularly harsh lockdown measures were praised by Anthony Fauci saying they had "a really good governor". He also praised New York City initially, but went silent when it developed the worst increase in mortality of any region in the US, caused by one of the world's most severe nursing home crises (Jones 2021). Although most people blame covid-19 for the deaths in nursing homes around the world, it was actually understaffing and increased time in solitary confinement that shortened prognosis that these elderly and fragile populations experienced.
In contrast to the praise for Michigan, despite its rather poor mortality data, Iowa and South Dakota were attacked by the major media and public health "experts" for having milder social isolation measures, but they had no increased mortality (Iowa, South Dakota), similar to many other states. The severity of the attacks is indicated by this Washington Post headline, "Welcome to Iowa, a state that doesn't care if you live or die" (Lenz 2021). South Dakota was also singled out for harsh criticism by Scott Gottlieb, a board member of Pfizer who is also a former commissioner of the FDA. He stated in an interview that it had allowed covid-19 to "travel largely unfettered" and claimed high mortality for covid-19 in South Dakota, using biased disease-specific criteria instead of all-cause mortality. It might benefit media sources to avoid using board members of Pfizer as their "national health expert" (Mayer 2021). Fauci also criticized South Dakota, saying that "the numbers don't lie", but the numbers he cited were very biased disease specific criteria and "increased caseloads", ignoring the elephant in the room of no increase in all-cause mortality clearly shown in the CDC weekly mortality graphs provided by Briand for South Dakota (Czachor 2021).
This extremely harsh criticism of states and governors with the best outcomes, combined with praise of states with high mortality, is a Stalinesque reversal that reminds one of propaganda from totalitarian states. Another similarity is that there have been concerted efforts to silence people providing this information, including some of the top epidemiologists and health statisticians in the world. However, the facts are available for anyone to examine: Michigan's very high mortality compared to Iowa and South Dakota can be easily found in the CDC's weekly all-cause mortality reports. Briand's graphs of each state's mortality can be printed out (graphs 17-69), and she also informs people how they can look up the data themselves on the CDC website. For comparison, here again are the links to graphs for Michigan, South Dakota, and Iowa.
Even states that did well usually had at least a slight increase in weekly deaths immediately after the official declaration of the pandemic in March, and some states with strict social isolation policies also had no increases in mortality. There are many possible factors to consider. South Dakota, which had very mild social isolation mandates and excellent outcomes, also was ranked #1 in affordable senior living and healthcare, has a fairly low average age, and a fairly low population density (Wong 2016). The graph for weekly deaths in South Dakota shows no change over previous years, without any appreciable spike in March 2020, unlike most other states (South Dakota).
An example of a state with stricter social isolation policies that still did well is Oregon. It is also highly ranked,in affordable senior living and healthcare, at #4, and has a very low population density. These factors may explain why the mortality graph for Oregon in 2020 did not have any appreciable increase over prior years (Oregon). Even states with mild social isolation policies often had increases in mortality starting immediately after the declaration pf a pandemic, such as Florida, which kept schools open for in person learning and limited mask mandates. However, Florida still had lower increases than states like New jersey, Rhode Island, and New York that all had extremely strict social isolation mandates. Also, Florida has one of the highest concentrations of people with advanced age and fragile health in the entire country, so would be expected to have higher mortality than many other states. Finally, although they kept schools open in Florida, long-term care facilities still had strict social isolation mandates for most of 2020, and a similar long-term care understaffing crisis, just as happened all across the world including countries like Sweden. For comparison, here are the graphs for New York, Rhode Island, and Florida.
The precise timing of the spike, combined with the extreme variability, suggests that the fear-response and its effect on long term care and general healthcare delivery was a much more likely cause of the sudden increase in deaths in some regions such as New York City. After the schools and businesses were closed, most people stayed isolated at home, including many health care workers and long-term care facility staff. Briand accounted for the extreme variability by removing the deaths from the two worst regions, New York City and New Jersey, which had particularly severe long-term care crises. The graph of mortality for the other 49 states including Washington DC and upstate New York, did not show a significant increase in mortality in 2020 (Briand 2021, Graph 70, page 50).
New York City became tragically famous for the catastrophic long-term care crisis there, as indicated by graph 50 on page 93, with weekly deaths rising as high as 8 times as high as normal, starting the week after the pandemic was declared. The graph tells the story through mortality statistics, but the story was told through primary and secondary sources in my first paper, written in spring 2020 (Irwin 2020a). Here is a quote with link to an original article discussing a specific nursing home in Brooklyn, NY: "Condon et al. (2020) focus on a nursing home in New York which lost a third of its work force, making it nearly impossible to give adequate care, with a typically high death count of 55, even though no one was actually tested for covid-19 and all the diagnoses were “presumed”. Presumed cases are also counted as covid-19 deaths, and this adds to the death tally that is focused on by the media and used when computing fatality rates from covid-19... In the article by Condon et al, seven other elder care facilities are briefly mentioned which each had 40 or more fatalities and were also badly understaffed." (Irwin 2020a page 8, Condon et al 2020)
This same situation in various forms occured in thousands of long term care facilities around the world and it was common that understaffing was so severe that residents had to be evacuated. Here is an example of a nursing home in Canada where this occurred, also from my initial paper:
"Bilefsky (2020) focused on cases with even more severe crises, starting with a nursing home in Canada where the health department was called in to relocate residents. Thirty-one residents died, but only five of them were confirmed positive for covid-19. The following quote shows the severe situation:
'They found dehydrated residents lying listless in bed, unfed for days.... ‘I’d never seen anything like it in my 32-year nursing career,’ said Loredana Mule, a nurse on the team. ‘It was horrific — there wasn’t enough food to feed
people, the stench could’ve killed a horse.’ After she left the home, she said, she collapsed in her car and wept. A
skeleton staff of two nurses had been left to care for a residence with nearly 150 beds. The remaining staff had fled
amid the outbreak of the coronavirus, leaving patients, some paralyzed or with other chronic illnesses, to fend for
themselves' (Bilefsky, 2020).
The same article describes similar examples occurring in several other countries, including the United States and Europe: “The phenomenon has been seen across Europe as well. In Spain, soldiers sent to disinfect nursing homes found people abandoned, or even dead, in their beds” (Irwin, 2020a page 8-9, Bilefsky, 2020).
While some might blame the managers and workers at the nursing home for this tragedy, it is actually the extremely exaggerated fears that are to blame, fuelled by the stream of inaccurate claims about the danger of covid-19. It is a normal human reaction to avoid a place considered life threatening, especially if it is believed that this threat can be brought back home invisibly to affect one's family and friends. This basic fact of humanity appears to have been ignored by social isolation advocates who made extremely inaccurate claims about mortality rates, including Anthony Fauci in testimony to the US congress in March 2020.
False positive PCR tests: the elephant in the room
The most reasonable explanation for the lack of increased mortality in most states and Canada, despite often quoted high numbers of "covid-19 deaths" in every region, is that the test used to diagnose covid-19 tends to test positive in anyone with a significant inflammatory condition, regardless of whether covid-19 is really the cause. The test also often gives positive results in people with mild symptoms similar to the common cold, and in people with no symptoms. This allows plentiful numbers of frightening anecdotes to be presented where covid-19 is blamed and other more significant issues are ignored, and also allows huge numbers of positive "cases". Unfortunately, the very measures used to prevent spread of the virus, isolation, quarantine, and solitary confinement of people who test positive, tend to shorten people's prognosis, especially in people with fragile health.
I wrote a paper about false positive PCR tests twenty years ago, which reviews research into false positive PCR tests in great detail (Irwin 2001). False positives come in many ways, and are much more likely in people with severe symptoms than in someone with mild or no symptoms. One type of false positive is when the virus is present, but not a significant cause of the symptoms. Another is when RNA from other viruses and bacteria cross-react with the PCR probe, triggering a positive result when no covid-19 virus is present. Perhaps the most common type is when strands of RNA from people’s own human cells, which vastly outnumber viral RNA, cause the test to turn positive.
A person’s own healing system makes cells and proteins that perfectly match viral protein sequences, allowing it to regulate the virus and adapt to its presence. In this process it creates large amounts of RNA that matches the viral RNA that the test is seeking. When a very large inflammatory response occurs, as in severe illnesses or hyperactive allergic and autoimmune states, the sheer quantity of RNA has a high chance of triggering a positive test. Similarly, shortly after vaccination the immune system stimulation can cause a positive test result. All this adds up to a test that cannot be relied upon for any significant health decisions, and which should not be used to frighten people who test positive. For people who do have significant symptoms, the quarantine makes it harder to help them recover instead of helping them. This is a major argument in several class action lawsuits currently in process around the world (Donn 2020, Principia Scientific International 2021).
Resistance to change and attempts to silence communication: a normal human response
When a paradigm is shown to be imbalanced, experts in the area being rebalanced are often more resistant to new information than non-experts, even when the information comes from the highest quality research available. This is a normal reaction which all humans share, and the uncovering of such misinformation can also be very stressful for people with less investment in the outcome. Much of humanity’s suffering comes from such ego defenses, on a personal as well as a societal level, and it historically takes decades for the new information to be widely accepted and adopted. The strong beliefs and reputations being defended often result in angry and personal attacks to be used in response, as well as efforts to prevent information from being communicated freely, often referred to as a “cover-up”. However, eventually even the experts come to accept the new perspective.
In the case of covid-19 some common ways to silence and censor information are to claim that the research is politically motivated, that it comes from a “fringe group”, “denialists”, “conspiracy theorists”, or simply that the information is “dangerous”, and to demand an apology. However, many of the top epidemiologists in the world, from the most respected research institutions and universities, have published about extremely high survivability of covid-19 and the excessive harm from excessive viral containment measures. These have included professors of epidemiology and statistics at Universities like Stanford, Harvard, Oxford, University of North Carolina, Johns Hopkins, University of Pennsylvania, and several German Universities, to name but a few (Bendavid et al 2020, Briand 2021, Halperin 2020, Kisielinsky et al 2021, Rancourt, 2020, Rancourt et al 2021, Towey 2020). This is quite different from prior epidemics and reveals that the paradigm shift is progressing relatively rapidly.
New Paradigm: supporting immune systems instead of isolating or killing germs
The danger of infection is real but routinely exaggerated in our current germ theory based model of health. Very often this exaggeration reaches incredibly high levels. However, this belief is difficult to maintain when one considers that humans live in co-existence with about 39 trillion microbes which are part of a healthy human “microbiome” including bacteria, viruses and fungi (Lynch 2016, Mun-Keat 2020). The harm caused by isolating and quarantining humans and animals deemed infectious is also under-appreciated.
A healthier paradigm would emphasize living with and adapting to nature, instead of trying to combat problems through complete eradication and viral isolation. Immune systems can handle all these microbes quite well when they are well-balanced. Finding the best way to support them to get through the illness will result in better health for the individual and the larger population. This also applies to animals with illnesses such as hoof and mouth disease or the “bird flu”.
The infectious belief system is so strong that it often persists for decades even after the risk of infection from a particular illness has been proven to be mild or even completely non-existent. This is exactly what happened with many illnesses now known to be non-infectious, such as the vitamin deficiency diseases scurvy, pellagra, and Beri-Beri (Duesberg 1995, Irwin 2021b). This pattern has continued most recently with covid-19, where a continuous stream of research has shown extremely high survivability. When carefully considering the detailed analyses of all-cause mortality from Briand (2021), Preston & Vierboom (2021), and Rancourt et al (2021), only one conclusion makes sense: the virus is just another virus like the trillions that we normally encounter during our lives.
The covid-19 experience has revealed clearly that helping our systems work better is a wiser approach than trying to protect people through viral containment, social isolation, and solitary confinement. The extra strain and obstacles that isolation places on people giving care affects the entire health care system, whether the caregivers are professionals, family, or friends. It also undermines people’s immune systems directly, and these effects combine to make it more difficult for people to recover from illness. A better public health policy would place primary emphasis on things that help us get through illnesses more easily: healthy nutrition, balanced exercise programs, supportive social connections, healthy stress management, and spiritual health. Put simply, it would encourage things that help and support people’s own healing systems.
About the author
As stated previously, I am not a Republican and I do not care what political party people support. Nor am I a denialist, especially about people dying. I have been a hospice and family medicine doctor since 2004, and I know that nearly 4 million people die every day around the world. I have visited thousands of people in their homes who have advanced life-limiting illnesses, and have signed over 3000 death certificates. My motivation for writing is not political, but it is at least partly for my own self-interest. I know that I will eventually find myself in the same shoes as my hospice patients, and that my life experience will be helped if the lives of the people around me improve.
Another trait I share with humanity and other sentient beings, is a deep fear of death. This is built into our DNA, and not something we can control. However, it manifests in different ways. In my case, I am confident that increasing healthy social relationships and human contact helps us live longer, and reducing them makes our lives shorter, as well as reducing quality of life. This has been validated in social science research (Tan &Wang 2019). Spiritual experiences that often occur as people near end of life can help reduce this fear, as described by University of Virginia professor of psychiatry, Bruce Greyson, in his book, After (2021) which describes decades of research into this area.
While many relationships are not ideal, they are better than isolation, and improving the quality of all relationships is a valid public health goal. While solitary time can be valuable as an opportunity for reflection, study, prayer, and meditation, too much isolation creates an imbalance, albeit one that can often be easily improved with some effort.
Reviews of mortality statistics for 2020: Yearly all-cause mortality increase in 2020 in Canada is consistent with the standard increase which happens every year, and does not show evidence of a pandemic.
In April 2021 all-cause mortality data for 2020 was presented in the journal, Proceedings of the National Academy of Sciences, by Preston & Vierboom (2021). Through analysis of the CDC’s own data, they found that the increase in mortality in 2020 from covid-19 was not significantly different from the increase in 2017 due to influenza. Although there were more excess deaths in 2020 overall, they were mainly in very elderly people who suffer most from the pandemic response related isolation protocols. I documented the harms of this isolation based response in my first paper, written in spring of 2020 (Irwin 2020a). In 2017 more young people died which resulted in more “years of life lost” for 2017 than 2020. This is a startling and shocking finding for most people, but not for those who followed the increasing survivability estimates or the elder care crisis that swept across the world due to fear and isolation.
The research showing extremely high survivability from covid-19 started appearing in the very beginning, as early as January 2020, as reviewed in my prior papers (Irwin 2020a, 2020b, 2020c). The high survival rate became more and more obvious as data came in, and the mortality data for the year 2020 was just a logical extension of this. Two other detailed statistical analyses of all-cause mortality for 2020 had similar findings. The first was done by a professor of statistics at Johns Hopkins University, Genevieve Briand (2021), also using United States CDC data, and then in August 2021 a review of Canadian mortality was published by Rancourt et al (2021).
Rancourt et al also provide detailed information about specific age groups, suggesting that an increase in deaths in people over 85 and in young males did not fit any viral explanation, and are most likely “response caused deaths”. They state: “We showed strong evidence that the pandemic response was so aggressive and ill-advised as to have large negative health consequences.” (page 47), a statement that few have been willing to make so clearly. The harms caused by policies of viral containment, social isolation, and solitary confinement, are more obvious in people with fragile underlying health, but they also significantly impact younger and healthier people. As mentioned previously, a similar excess was seen in most US states as demonstrated by Briand in her graph of deaths from non-natural causes including suicides, homicides, and drug overdoses.
The article by Samuel Preston and Yana Vierboom from April 2021 is perhaps the most significant because of the source. Professor Preston is one of the most distinguished demographers and health statisticians in the world. He and his colleagues have been analyzing mortality statistics for decades. Their finding that years if life lost was worse in the US in 2017, attributed to a strong influenza season, than in 2020, is therefore the first paper to be reviewed below (Preston & Vierboom, 2021).
1: Preston S & Vierboom Y (April, 2021). Excess mortality in the United States in the 21st century. Proceedings of the National Academy of Sciences 118 (16) e2024850118; DOI: 10.1073/pnas.2024850118
This article’s lead author is one of the world’s leading experts in population health and statistics, Samuel H. Preston, and it was published in a highly respected medical journal. Despite this, absolutely no one seems to be paying attention. According to Wikipedia, Preston “is one of the leading demographers in the United States… a Professor Emeritus at the University of Pennsylvania in Philadelphia, PA, …the former Dean of the School of Arts and Sciences at Penn as well as a member of the National Academy of Sciences since 1987. The Preston curve is named after him. Preston's major research interest is in the health of populations. He has written primarily about mortality trends and patterns in large aggregates, including twentieth century mortality transitions and black/white differentials in the United States.”
Below is a quote from the discussion section of their article, comparing mortality in 2017 and 2020.
“Because it has captured a great deal of national attention, the number of deaths from the COVID-19 epidemic in 2020 forms a timely basis of comparison. On 20 February 2021, the Centers for Disease Control and Prevention reported that 376,504 deaths ascribed to COVID-19 had occurred in the United States in calendar year 2020. That figure is similar to but below the estimated total number of excess deaths of 401,000 in the United States in 2017 (Table 1).
The comparison is more striking when years of life lost is the measure used. Goldstein and Lee estimate that the mean loss of life years for a person dying from COVID-19 in the United States is 11.7 y. Multiplying 377,000 decedents by 11.7 y lost per decedent gives a total of 4.41 million life years lost to COVID-19 in 2020, only a third of the 13.02 million life years lost to excess mortality in the United States in 2017 (Table 1).”
In other words, they estimate about three times as many years of life lost in 2017 because the flu affected younger people much more than covid-19, which primarily affects the elderly. Below are excerpts from an article about the Preston and Vierboom study by a news organization that has consistently argued against the viral containment, social isolation, and solitary confinement policies surrounding covid-19.
“In February, the CDC reported it attributed 376,504 deaths in 2020 to COVID-19. Each death is regrettable, but to put that number in perspective, the COVID deaths in 2020 were actually lower than the 401,000 excess deaths in 2017 — a bad flu year. This finding mirrors excess death data from other countries, where excess deaths were also higher in 2017 than in 2020. A recent research paper in the prestigious journal, The Proceedings of the National Academy of Sciences, points out these statistics and then an even more surprising difference in years of life lost”.
“Some might claim COVID deaths would have been much worse without lockdowns and mask mandates. There is increasing evidence, however, that these non-pharmaceutical interventions had little or no effect on COVID mortality. Good examples are South and North Dakota, two neighboring states with similar populations and almost identical COVID death curves, even though North Dakota instituted a state-wide mask mandate and restrictions on indoor activities last fall, while South Dakota did not. A similar comparison exists between Florida and California, where Florida actually fared better after lifting state-wide restriction in September 2020, while California continued with strict lockdowns.”
“In Europe, Sweden provides a counterpoint to the countries that chose hard lockdowns but had worse mortality outcomes. The excess mortality numbers and COVID case numbers call into question the soundness of the public health response to the pandemic. The long-term consequences of the lockdowns have been catastrophic, in both economic and health outcomes. Lockdowns put elderly people at increased risk in the long run, and deprive younger people of freedom without significant benefit.” (Children’s Health Defense, 2021).
2: Briand G (2021) COVID-19 Deaths A Look at U.S. Data, February 2021, Working paper. DOI:10.13140/RG.2.2.15125.86242 https://www.researchgate.net/publication/349925425_COVID-19_Deaths_A_Look_at_US_Data_FEB_2021_WORKING_PAPER_Genevieve_Briand
Genevieve Briand, a professor of statistics in the Johns Hopkins University department of applied economics, published a detailed summary of 2020 mortality data with very similar findings to Preston and Vierboom (Briand, 2021). She also showed how the CDC data demonstrated an unexpected drop in 2020 in the number of deaths from all other causes, such as heart disease, cancer, and other respiratory infections. This drop is easily explained by an erroneous reclassification of deaths from other causes as being caused by covid-19. This is at least partly because the covid-19 PCR test tends to test positive in anyone with inflammatory symptoms such as people with allergic reactions, active autoimmune conditions, or other viral illnesses. It is also because once a positive covid-19 test is found the search for other causes usually stops, partly because hospitals and other providers receive significantly more money if a covid-19 diagnosis is used (Daly, 2021).
Briand states in her introduction: “The objective of this analysis is not to produce an ‘excess deaths number estimate’, but to assess whether the total deaths number the U.S. experienced in 2020 was unexpected or alarming.” (page 3). She goes on to analyze in detail the death statistics, and show how other estimates of excess deaths misrepresented the data by not using the all-cause and other mortality data from the CDC, and by not comparing it to prior years.
In the conclusions section of her article she makes a simple statement about the excess deaths in 2020 not being alarmingly different from prior years: “The CDC data on U.S. deaths used in this analysis are the best available data… U.S. total deaths for 2020, or season 2019-2020, are normal death numbers.” (page 53)
She then comments on how deaths due to other causes such as pneumonia and influenza had unrealistically low numbers in 2020, and how they were most likely reclassified as covid-19 deaths:
“The historically low levels of deaths due to these old respiratory diseases points to a reclassification of deaths into a newly and specifically introduced respiratory disease category for COVID-19. Levels of death increasing from their historical levels, such as deaths due to non-natural causes (suicides)… points to mitigation efforts (isolation, lockdowns) being the cause of death, rather than COVID-19.” (page 53, parentheses in original)
3: Rancourt et al (2021). Analysis of all-cause mortality by week in Canada 2010-2021, by province, age and sex: There was no COVID-19 pandemic, and there is strong evidence of response-caused deaths in the most elderly and in young males. August 6, 2021. Denis G. Rancourt, Marine Baudin, Jérémie Mercier
Rancourt et al have the most terse and blunt assessment of the lack of increased mortality saying simply, “We conclude that a pandemic did not occur” (page 1). They found this by analyzing government data for Canadian all-cause mortality. Three of their graphs show how statistics can be used to mislead or educate, depending how they are presented, as discussed previously in the introduction to this paper. Figure 4 looks alarming – the increased mortality in winter 2020 (spike C) and winter 2021 (Spike 2) appear to be frightening increases. However, figure 1a and figure 2 show that this spike is actually a yearly trend, and when the past ten years are all shown together, the spike does not look at all alarming or out of the ordinary. The yearly increase is caused by the constantly growing population, and Figure 2 shows that the increase is nearly linear right through 2020, with no increase in the slope of the line.
Rancourt et al also emphasize that the spike was steeper in March and April 2020 for people over age 85 than in prior years, followed by unexpectedly lower number of deaths in the two months after that, suggesting that something unrelated to a virus caused it (Figure 6a, page 25). They conclude that this was due to “response induced deaths”, especially in the elderly where vulnerability to isolation and health system disruption is high, and discuss this in detail (Section 4.5, pages 41-46).
They also point out an unexpected significant increase in deaths of young males aged 0-44, who had increased chance of dying from drug overdoses and suicides, all of which were already leading causes of death in this age group (Irwin, 2020b, 2020c). They state that this is most likely due to the negative effects of an overly aggressive public health response: “Our interpretation is that excess deaths in males of the 0–44 years age group arise from the stress of the large scale and continued societal and economic responses to the declared pandemic. and that the experienced stress in young men is lesser in Quebec because of significant cultural differences with Anglophone provinces.” (page 34)
Later in their paper they address this again: “There is an anomalous mass mortality of young males in Canada… in summer 2020 and into the fall. This ignored and silent epidemic is most likely not due to any viral respiratory disease, and merits an independent investigation.” (page 41).
Rancourt ask the question as to whether the excessive pandemic response resulted in more deaths than would have otherwise occurred, and state “We conclude the answer is ‘yes’. The ‘covid-peak’ was palpably induced by the pandemic response… It was followed by an anomalously small mortality for the 85+ age group, showing that deaths were accelerated in this age group. Likewise, the mortality of young males has a large increase in the summer-2020 and into the fall, a phenomenon never-before seen, which cannot be due to a viral respiratory pathogen.” (page 47),
They also question the standard paradigm that increased deaths in the winter months every year is due to a yearly flu virus, or any other specific cause, saying “the pandemic paradigm is a beautiful theory… But it is not supported by hard epidemiological data, and has great potential to cloud public health thinking by directing focus on a presumed pathogen specific disease rather than identifying and addressing all the important aspects of health…”(page 36)
As stated previously, good public health policy includes focusing on ways to improve health, not just efforts to frighten people away from unhealthy behaviors. These approaches have many negative consequences, especially because frightening people from microbes also frightens them away from fellow humans, who are coated with them. Humans depend on one another and benefit from healthy interactions which are best encouraging along with other healthy behaviors.
Below is the complete abstract from the Rancourt et al paper, followed by references. After the references are the three figures from Rancourt et al which illustrate the erroneous picture created by focusing on only one year (Figure 4) instead of looking at the pattern across multiple years (Figures 1a and 2). Finally, the table from Block (2021) is provided, showing the CDC’s estimation that over 120 million people in the United States, about 37% of the population had already had a case of covid-19 by May 2021.
We analyzed all-cause mortality by week (ACM/w) for Canada, and for the Canadian provinces, and by age group and sex, from January 2010 through March 2021; in comparison with data for other countries and their regions or counties.
We find that there is no extraordinary surge in yearly or seasonal mortality in Canada, which can be ascribed to a COVID-19 pandemic; and that several prominent features in the all-cause mortality in the COVID-19 period exhibit anomalous province-to-province heterogeneity that is irreconcilable with the known behaviour of epidemics of viral respiratory diseases (VRDs). We conclude that a pandemic did not occur.
In addition, our analysis of the all-cause mortality by week, by province, age and sex, allows us to highlight anomalies, occurring during the COVID-19 period, which provide strong evidence that:
• Among the most elderly (85+ years), many died from the immediate response to the pandemic that was announced by the WHO on 11 March 2020.
• Predominantly young males (0-44 years, and also 45-64 years) probably indirectly died from the sustained pandemic response, in the summer months of 2020, and into the fall and winter, starting in May 2020, especially in Alberta, significantly in Ontario and British Columbia, whereas not in Quebec.
Ballotpedia (2021). Retrieved from https://ballotpedia.org/States_that_did_not_issue_stay-at-home_orders_in_response_to_the_coronavirus_(COVID-19)_pandemic,_2020
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Block J. (2021). Vaccinating people who have had covid-19: why doesn’t natural immunity count in the US? BMJ 2021; 374n2021 doi: https://doi.org/10.1136/bmj.n2101 (Published 13 September 2021)
Briand G (2021) COVID-19 Deaths A Look at U.S. Data, February 2021, Working paper. DOI:10.13140/RG.2.2.15125.86242 https://www.researchgate.net/publication/349925425_COVID-19_Deaths_A_Look_at_US_Data_FEB_2021_WORKING_PAPER_Genevieve_Briand
CDC (2020). COVID-19 Pandemic Planning Scenarios, September 9, 2020. Accessed 10-12-2021.
The link below is to the CDC webpage which lists mortality by age for covid-19. For people under 20 the death rate is less than 1 in 33,000, cited as a fatality ratio of 0.00003 under the “Scenario 5: Current best estimate” column of table 1, at the bottom of page 5. The fatality ratio for people between 20 and 50 in the table is listed as 1 in 5000 (0.0002). Combining these two age groups, the death rate for people under 50 would be about 1 in 10,000. This is five times lower than what was reported by the CDC in May 2020. At that time their table said the death rate for people under 50 was about 1 in 2000, which was already extremely low compared what was predicted in February and March when the first alarms were broadcast around the world, with Anthony Fauci saying to congress that the death rate "could be as high as 3%" resulting in worldwide school and business closures and massively harmful social isolation policies. Without these policies, and if normal care was given instead of solitary confinement and quarantine for covid-19 positive people, more than half of the excess deaths in 2020 would have been avoided.
Children's Health Defense (2021). COVID Deaths — Putting the Numbers in Perspective, The Defender, May 14, 2021. https://childrenshealthdefense.org/defender/covid-deaths-numbers-in-perspective/?utm_source=salsa&eType=EmailBlastContent&eId=5dc56d69-401b-4657-ac7c-da2caef074e2
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The graphs with links below are from Rancourt’s review of Canadian all-cause mortality (2021). Figure 4 appears alarming, with a spike in mortality in March and April 2020. However, when the pattern for the past ten years is shown, as in Figure 1a and Figure 2, one sees that this is a yearly pattern and it no longer appears alarming or even unusual. Very similar data for United States all-cause mortality is provided by Briand (2021) and Preston and Vierboom (2021).
See original paper by Rancourt for Figure 1a (page 8), Figure 2 (page 11) and Figure 4 (Page 17).
Table 1 from CDC data as presented in Block (2021). “Estimated total infections in the United States between February 2020 and May 2021”. The last row shows that when all ages are combined an estimated 120.3 million people out of a population of 328 million had a covid-19 infection as of May 2021.
Age group (years) No of infections (millions) Total Population in 2019 (millions) % previously infected
0-17 26.8 (22 to 33.1) 73 37 (30 to 45)
18-49 60.5 (50.4 to 73.2) 138 44% (36 to 53)
50-64 20.4 (17.0 to 24.6) 63 32% (27 to 39)
65+ 12.3 (9.9 to 15.5) 54 23% (18 to 29)
All ages 120.3 (103.3 to 140.9) 328 37% (31 to 43)
- * Sources: CDC (estimated infections) and US Census (2019 estimated population