COVID-19 and Excess Mortality

(Part 6 in a series of indeterminate length, dissecting the pandemic narrative.)

The question arises, if COVID-19 statistics are as trustworthy as penny stock projections, is there any way to assess the seriousness of the (alleged) pandemic? The answer is a qualified yes,¹ and this is by scrutinizing what is known as “excess mortality,” which is, for a specified population and a particular time period, the difference between the number of deaths from all causes, and the expected (or “usual”) number of deaths from all causes. If there is a serious pandemic, it should result in significant, positive excess mortality. If it doesn’t, unless there is some other special cause that is coincidentally reducing mortality (e.g., a cure for most cancers attaining widespread usage), then one has to question the reality of any claimed health crisis. One nice feature of excess mortality is that it’s unaffected by such mischief as recording deaths from other ailments as COVID-19 deaths. In fact, if the goal is simply to ascertain whether there’s been excess mortality, one needn’t look at COVID-19 deaths at all.

Before going further, a number of points must be understood:

1. Excess mortality is a necessary but not a sufficient condition for proving the reality of an epidemic/pandemic. Elements of the response to the alleged threat may be responsible for a jump in mortality.
2. Calculation of excess mortality will always be a guess, however educated, because one can never know exactly how many people would have died absent the pandemic.
3. There’s no single, obvious way to calculate excess mortality, in part because mortality from some conditions may be linear, others seasonal or otherwise cyclical, some exponential, etc. Another reason is that statisticians can choose a variety of different ways of depicting any deviation between expected and actual mortality. Two approaches to calculating excess mortality that are in common usage are “Z-score”² and “P-score.”³

With these understandings, the big question is, has there been excess mortality during this alleged pandemic? If one compares mortality for the period 2015-2019 to that of 2020, for most developed countries the answer is yes, definitely. For example, the website of Our World in Data, using source data from the Human Mortality Database (HMD) and the UK Office of National Statistics (ONS), has a “grapher” that calculates P-score for most European countries, most Commonwealth countries (the UK, Canada, etc.), the U.S., and some Asian countries. Recognizing that P-score can and will be negative when there are fewer deaths than normal, here are the results I obtained for several allegedly hard-hit countries (France, Italy, Sweden and Spain):

It’s quite apparent from the graph that all four of these countries,⁴ while having negative excess mortality prior to the “outbreak” and during portions of the summer, overall have had substantial excess mortality (although Sweden, a non-lockdown country, fared far better overall than the others).

Here is another such graph, for four other relatively hard-hit countries: the United States, the United Kingdom (UK), Belgium, and Scotland:

These countries each had rather different trajectories after the initial hit, but each, according to the P-score methodology, had significant excess mortality overall, especially the U.S. and Belgium, which retained substantial excess mortality through much of the remainder of the year. The UK certainly had a large peak, but it’s hard to judge its experience over the rest of the year, given that its data is unavailable after June 14 (when its P-score was 2% and trending downward).

If any reader is still clinging to the notion that there’s been no excess mortality in the COVID era, here’s a striking graph (also from Our World in Data) showing raw deaths in the United states for the years 2015-2020:

So, looking at the U.S. and the other seven countries, it’s clear that, if one accepts the P-score approach⁵ (and the total death figures from the Human Mortality Database), there has been excess mortality during the COVID-19 era. However (illustrating Point 1 on excess mortality, above), a number of researchers have scrutinized officially published excess mortality data for the current pandemic, and a consistent finding has been that there was little to no excess mortality until lockdowns and other severe responses were implemented. In fact, the spikes in excess mortality sync very well with the lockdown orders, that is, they occur immediately thereafter. Accordingly, some observers have concluded that the lockdowns killed people, just as a German government internal report had predicted.

Excess mortality may also have been exacerbated by fear mongering on the part of governmental officials (of course, abetted by media). For example, as the German report noted,⁶ people may have been so terrified of COVID-19, or (perhaps quite rightly) feared that they would not get good treatment in the hospital from overtaxed or distracted staff, that they allowed “minor” strokes or heart attacks to fester into major problems while hunkered down at home. Such a dynamic certainly could have an immediate impact on excess mortality.

Most disturbingly, it appears that governmental and institutional policies regarding the handling of elderly and nursing home populations may have been responsible for most excess mortality. In the U.S., for example, six states (New York, Massachusetts, Pennsylvania, Michigan, New Jersey, and Illinois) forced nursing homes to accept COVID-19 patients, with disastrous results. The Commonwealth countries, at least Canada and the UK, seem to have specifically targeted the elderly and nursing populations for destruction: In Canada, extremely loose definitions of COVID-19 cases and epidemics were established for nursing homes, policies were put in place to ration care in a way that severely disadvantaged the elderly, and the authorities centralized control of death certification, taking care to encourage expeditious cremation so that postmortems would be impossible; in the UK, many nursing home (they call them care homes) residents – with family members unable to observe what was going on thanks to COVID rules – were put on end-of-life “pathways” (similar to the infamous, discredited “Liverpool” pathway), and the National Health System (NHS) engaged in mass imposition of Do Not Attempt to Resuscitate (DNAR) orders on the elderly, often by administrative fiat, without any patient or family consent.

Denis Rancourt, a researcher with the Ontario Civil Liberties Association, in a very thoughtful and disturbing study (admittedly a “working paper” not submitted for publication, but in my opinion, very compelling), draws attention to a number of anomalous features of the initial surge (or spike) in mortality associated with the current “pandemic”:

• The lateness in the infectious-season cycle of the onset of the surge, occurring after week-11 of 2020, “which is unprecedented for any large, sharp-peak feature” (such peaks almost invariably peak in early-to-mid January);
• Its synchronicity, across continents, on the heels of the WHO declaration of the pandemic;
• Its narrowness, “with a full-width at half-maximum of only approximately 4 weeks” (the excess mortality graphs above, for Spain, the UK and Belgium, provide good examples); and
• In the U.S. its “state-to-state absence or presence for the same viral ecology on the same territory, being correlated with nursing home events and government actions rather than any known viral strain.” (I think what Rancourt means here is that the U.S mortality spike in a given state seemed to correlate better with actions taken in response to the “crisis” rather than the spread of the virus.)

Rancourt, who admittedly is not an epidemiologist (his background is in Physics and Environmental Science), goes on to explain his understanding of how viruses spread and under what conditions (drawing primarily on studies of influenza epidemics), and why “winter-burden mortality in mid-latitude nations is robustly regular” (drawing on the work of a number of researchers, he concludes that the low absolute humidity characterizing the winter months allows tiny aerosolized viral particles to remain floating in the air for much longer, and thus available to be inhaled, before they are brought to earth by moisture in the air, a process called “gravititational sedimentation.”) With this in mind, and in light of the above anomolies, Rancourt boldly concludes:

These “COVID peak” characteristics, and a review of the epidemiological history, and of relevant knowledge about viral respiratory diseases, lead me to postulate that the “COVID peak” results from an accelerated mass homicide of immune-vulnerable individuals, and individuals made more immune-vulnerable, by government and institutional actions, rather than being an epidemiological signature of a novel virus, irrespective of the degree to which the virus is novel from the perspective of viral speciation. [emphasis mine]

Rancourt, Denis. “All-cause mortality during COVID-19: No plague and a likely signature of mass homicide by government response,” Research Gate, 2 June 2020, https://www.researchgate.net/publication/341832637_All-cause_mortality_during_ COVID-19_No_plague_and_a_likely_signature_of_mass_homicide_by_government_ response. PDF Download.

Wow. I don’t think his argument is tight enough to categorically make such a statement, but I appreciate that Rancourt has the courage to state his conviction so forcefully. And in favor of his conclusion (that the carnage is due to the response), it certainly does seem odd that a virus that can barely kill anyone absent serious preexisting conditions (in most countries, it’s been reported that over 90% of COVID-19 deaths involve comorbidities), would be capable of causing such a dramatic spike in mortality at what seems an unlikely time of the year.

My own position is shaped by having read Arthur Firstenberg’s extraordinary book, The Invisible Rainbow,⁷ in which he makes a very strong case that influenza is not an infectious disease. I’ll let that sink in … okay, now I’ll go through Firstenberg’s logic: First, he gives a history lesson on influenza pandemics, noting that hundreds of years ago, they used to be few and far between, and always associated with solar maxima (when sunspots are at a high level). For example, during what is called the “Maunder Minimum,” when sun spots were rare and the polar lights (auroras) were dormant, there were no influenza pandemics. In the 19th century, influenza took on a different character, wherein it was apparently associated with the rollout of new electromagnetic technologies (the telegraph, the telephone, electric lights, etc.), finally becoming an annual phenomenon with the advent of mass residential electrification in the late 1880s. Major pandemics continued to accompany new global exposures, such as transcontinental radio, radar, satellite communications, Wi-Fi, cell phone systems, etc. Second, Firstenberg asserts that epidemiological and medical research has never proven, or even provided robust evidence, that influenza is contagious. He relates the astonishing experiments done during the 1918 “Spanish” Flu outbreak, by the U.S. Public Health Service, in which researchers made increasingly fanatical attempts to prove that this killer flu was transmissible (including transferring mucous from feverish patients to various orifices of what must have been extremely tolerant or well-compensated “volunteers”), but met with utter failure.

This background knowledge makes influenza a dubious analog of COVID-19. Moreover, the original SARS “mini” pandemic peaked in late May (2003), so it’s not necessarily a bizarre anomaly that COVID-19 peaked in April rather than January. Yet, Rancourt’s conclusion (and he’s not alone) that COVID-19 amounts to a form of mass homicide may well still be true. For one thing, we need to keep in mind the revelations (above) about lethal nursing home policies, mass imposition of DNAR orders, etc. For another, as Rancourt also mentions in his paper, there is abundant evidence that mass use of ventilators as the go-to treatment is responsible for a huge proportion of the fatalities (and I will get into this topic, in some detail, later). Furthermore, if we are dealing with a nasty virus, how come many European countries – including Denmark, Estonia, Finland, Germany, Greece, and Norway – have not seen any significant excess mortality, overall, during this pandemic (see graph below – note Spain is included to give some perspective on how well the other countries have fared, at least comparatively)? In the face of these counterexamples, in an age when people travel so far, often and freely, it’s pretty hard not to suspect that something more than just the natural course of a pandemic is responsible for the disparity:

Maybe the countermeasures were all super-effective in these countries, and maybe the German experts were overly pessimistic about the risks of the response. But, looking at Sweden’s excess mortality curve, which was only significantly elevated for a couple of months (at least until what’s, so far, a relatively modest bump during the “second wave”), and not nearly so elevated as that of Belgium, the UK, Spain, France, or the Netherlands, it’s pretty hard to say that excess mortality data provides a slam dunk case in favor of lockdowns.

So, we’ve established that, contrary to what many in the alternative media are claiming, there has been substantial excess mortality in most countries,⁸ at least most developed countries. The questions that arise, then, are how do we explain all these abnormal deaths, and how do we know the difference between COVID-19 mortality and deaths caused by the response? We’ve begun to answer the second question, as disturbing as the answer is. The third question is more difficult to deal with, given (purposely) extremely “generous” COVID-19 accounting, vague symptoms, and even an uncertainty whether the virus exists. I’ll spend more time on the meaning of COVID-19 excess mortality in a later post.

[In my next post, having established that there is an apparent establishment bias toward inflating COVID-19 numbers, but that this probably can’t explain the excess mortality figures we’re seeing (because such numbers would be very hard to fabricate across multiple levels of government), I will take a closer look at the issue of reclassifying non-COVID deaths as COVID. This may help in ascertaining the difference between “normal” deaths from other causes mislabeled as COVID deaths; “excess” deaths caused by lockdowns or treatment, yet attributed to COVID; and actual COVID deaths (if there really are any). It may be difficult to make such distinctions accurately, but I need to try.]

¹ The issue with excess mortality is that, like any statistics coming out of governments known to lie at the drop of a hat, the underlying data may be fabricated. I will say, however, that in the case of the U.S., at least, the underlying data is coming from state and local governments, and it seems to me that it would be very difficult to coordinate a data fraud on that scale.

2 Here is how Our World in Data describes their sourcing of the chart data, including a brief explanation of P-score:

Data from the Human Mortality Database (HMD) Short-term Mortality Fluctuations project for all countries except the UK (HMD has data for England & Wales, Scotland, and N. Ireland but not the UK as a whole). UK data sourced from the UK Office for National Statistics (ONS).

We used the raw weekly death data from HMD to calculate P-scores. The P-score is the percentage difference between the number of weekly deaths in 2020 and the average number of deaths in the same week over the years 2015–2019. For the UK P-scores were calculated by the ONS.

We do not show the most recent weeks of countries’ data series. The decision about how many weeks to exclude is made individually for each country based on when the reported number of deaths in a given week changes by less than ~3% relative to the number previously reported for that week, implying that the reports have reached a high level of completeness. The exclusion of data based on this threshold varies from zero weeks (for countries that quickly reach a high level of reporting completeness) to four weeks. For a detailed list of the data we exclude for each country see this spreadsheet: https://docs.google.com/spreadsheets/d/1Z_mnVOvI9GVLiJRG1_3ond-Vs1GTseHVv1w-pF2o6Bs/edit?usp=sharing.

For a more detailed description of the HMD data, including reporting week date definitions, the coverage (of individuals, locations, and time), whether dates are for death occurrence or registration, the original national source information, and important caveats, see the HMD metadata file at https://www.mortality.org/Public/STMF_DOC/STMFmetadata.pdf.

“Sources: Excess mortality P-scores, all ages” Our World in Data, Dec., 2020,
https://ourworldindata.org/grapher/excess-mortality-p-scores?tab=chart&stackMode=absolute&country=SWE~USA&region=World [select “Sources” tab]

³ Here is how EuroMOMO’s website describes this statistic:

Z-scores are used to standardize series and enable comparison mortality pattern between different populations or between different time periods. The standard deviation is the unit of measurement of the z-score. It allows comparison of observations from different normal distributions.

In general, Z-score = (x-mean of the population)/Standard deviation of the population, which could be approximated in our context by S-score = (number of deaths – baseline) / Standard deviation of the residuals (variation of the number of deaths around the baseline) on the part of the series used to fit the model, used as the standard unit.

Z-score are computed on the de-trended and de-seasonalized series, after a 2/3 powers transformation according to the method described in Farrington et al. 1996. This enables the computation of Z-scores for series that are originally Poisson distributed.

“What is a z-score?” EuroMOMO, Statens Serum Institut, 2020,
https://www.euromomo.eu/how-it-works/what-is-a-z-score/

At first blush, it seems to me that this method might exaggerate fluctuations in mortality for large countries relative to smaller countries, because it uses the absolute difference between the historical and target period deaths, rather than a percentage. I’m no expert on the matter, however.

⁴ It should be noted that, in calculating expected mortality as the average over the period 2015-2019, this methodology makes fast-growing countries like Sweden, the U.S. and France look worse than they are (that is, since their populations are growing rapidly, there’s likely to be significantly greater mortality than the average of the earlier period).

⁵ I did go so far as to calculate P-score myself, for a few weeks of data from the CDC’s Weekly_Counts_of_Deaths_by_State_and_Select_Causes_2014-2020 file (Dec. 9 update), for the U.S., and verified that the values on the Our World in Data grapher were correct.

⁶ Using online translator apps – the report is in German – I found the following paragraph, specifically referring to heart attacks and strokes:

Over the past years and decades, integrated concepts have been developed that have successfully influenced morbidity and mortality and are based on providing care as early (in the course of the disease), as quickly (time to care) and as competently as possible. These intersectoral/disciplinary chains are damaged in many ways (outpatient care, withdrawal of resources) and also suffer at most from the fact that, due to one-sided and exaggerated information policies, those affected unjustifiably fear corona more than these diseases and suppress warning signs and also fear that they will not be treated well with these diseases in the current corona fixation in hospital. As a consequence, many patients do not consult a doctor or do so too late, which means increased morbidity, deteriorated rehabilitation and increased mortality.

Federal Republic of Germany. KM4. “Analyse des Krisenmanagements (Kurzfassung).” Web, https://ichbinanderermeinung.de/Dokument93.pdf, accessed 8 Jun, 2020.

⁷ Firstenberg, A. (2020). The Invisible Rainbow: A History of Electricity and Life. Chelsea Green.

⁸ At least according to figures that would be hard to manipulate synchronously at multiple levels of government; for example, county and state, in addition to federal (in the U.S.).