Importantly, these correlation coefficients for the half year 2023-H1 are of comparable magnitude but of opposite sign to the correlation coefficients for the Covid period (2020-2022).

Therefore, generally, persistent age-and-health-status-adjusted excess all-cause mortality (P-score) into 2023 occurs in low-poverty and high-life-expectancy countries, contrary to Covid-period excess mortality prior to 2023.

Essentially the same occurs with excess all-cause mortality rate (by population) (Figure 37, Figure 38, Figure 39; and **Appendix H**).

Furthermore, persistent age-and-health-status-adjusted excess all-cause mortality (P-score) into 2023 (2023-H1) correlates to COVID-19 vaccination doses administered per population, for doses administered in 2021-2022 (r = +0.47, Figure 42), in 2021 (r = +0.46, Figure 43), and in 2022 (r = +0.37, Figure 44), contrary (of opposite sign) to the correlation coefficients for the Covid period (2020-2022) (**Appendix I**).

Generally, countries with persistent age-and-health-status-adjusted excess all-cause mortality (P-score) into 2023 (2023-H1) have relatively highly COVID-19 vaccinated populations, are not aided by being wealthier or higher-life-expectancy countries, and would typically be countries in which COVID-19 vaccination rates among the elderly are high into 2023.

Similarly, Kuhbandner and Reitner (2024) studied the 16 federal states of Germany and found that the correlation between yearly excess all-cause mortality and vaccination rate increased significantly to a positive value in going from 2021 to 2022.

Future studies should attempt to quantitatively compare late persistent excess all-cause mortality with increases (excesses on the same time periods) in mortality associated with main specific proximal causes such as respiratory conditions, cardiac failures, and cancer.
The present study (see Section 5.12) suggests that respiratory infections or conditions would continue to be the main proximal-cause association, quantitatively. However, recent epidemiological data for Japan suggests that cancer could be a significant contributor (Gibo et al., 2024).

Immediate temporal associations between COVID-19 vaccination injection and sudden onset of aggressive cancer have been reported in a human subject (Bae et al., 2023; sarcoma at the site of injection) and in a mouse (Eens et al., 2023; catastrophic lymphoma).

Wang et al. (2020) reported molecular testing in a cohort of cancer patients to suggest definitive mechanisms of interplay between cancer growth and immune manipulations, and they showed in a mouse model that molecular immune perturbations (applications of IgG4) could significantly accelerate several types of cancers.

Kyriakopoulos et al. (2024) and Valdes Angues and Peres Bustos (2023) reviewed theoretical immunology consistent with proposed mechanisms wherein COVID-19 vaccination could cause significant acceleration of cancers.

Time will tell, if critical studies are performed and allowed to be published. The results, of course, may be different in different countries having persistent age-and-health-status-adjusted excess all-cause mortality (P-score) post-Covid period. In any case, in our view, clearly at this stage countries (which tend to be wealthy countries) should stop vaccinating for COVID-19, and should have stopped years ago.
5.10 Age dependence of sex-discriminated bulk and excess all-cause mortality in European countries

Section 4.19 provides a detailed survey of the dependence of all-cause mortality and excess all-cause mortality with age in these 25 European countries: Austria, Belgium, Bulgaria, Croatia, Czechia, Denmark, Estonia, Finland, France, Greece, Hungary, Italy, Latvia, Lithuania, Netherlands, Norway, Poland, Portugal, Romania, Serbia, Slovakia, Slovenia, Spain, Sweden, and Switzerland.

The yearly rate of all-cause mortality (by population of age group) increases exponentially with age (Figure 49). The doubling times of the yearly rate are in the range 5.7—8.9 added years in age, for the years 2019 to 2022 (Figure 49, Table 3).

The yearly rate of excess all-cause mortality (by population of age group) during the Covid period (2020-2022) also increases exponentially with age (Figure 50). The doubling times of the yearly rate for excess mortality are in the range 2.5—9.7 added years in age, excluding outliers with large errors (Figure 50, Table 4).

This means that the rate of excess all-cause mortality during the Covid period has approximately the same age dependence as the normal rate of all-cause mortality itself: both increase exponentially with age, with comparable doubling increases per added age.

Whatever the specific causes of excess deaths during the Covid period, they cause death with approximately the same age dependence as with usual deaths, approximately following the same universal law of exponential increase with age (Figure 49, Figure 50) (Gompertz Law; e.g., Neilsen et al., 2024). Whatever the agent(s) of excess deaths during the Covid period in these 25 European countries, it does not (they do not) target certain age groups or one sex very differently than with the general ambient causes of overall mortality (Figure 49, Figure 50).
However, it is possible to examine this question with higher resolution by examining the excess all-cause mortality normalized by the age-group-specific baseline (or expected mortality) rather than by the age-group specific population, which is the P-score discriminated by age group. This is done in Figure 51.

Rancourt et al. (2022b) applied this method to the states of the USA, and in the pre-vaccination and vaccination periods of the Covid period (their Figures 9, 15, 17 and 19). They showed a different age structure in the pre-vaccination and vaccination periods, with the young being disproportionately impacted in the vaccination period.

We already know from the many sections above that there is large excess all-ages all-cause mortality during the Covid period (2020-2022), with large country-to-country heterogeneity, and with large temporal variations in specific countries.

Age-dependence of excess mortality gives significant additional insight. Figure 50 (rate) and Figure 51 (P-score) show the age-dependence of excess all-cause mortality in the studied 25 European countries.

As noted above, Figure 50 shows a dominant exponential variation with age of excess all-cause mortality (by population of age group), whereas one effect of the calculation used in producing Figure 51 (P-score method) is to remove the dominant exponential variation by normalization, thereby allowing one to detect and quantify any disproportionate increase or anomalous change in mortality versus age (i.e., by age group).

With such a quantity as calculated for Figure 51 (P-score by age), zero excess all-cause mortality in a given age group would give “0 %”, and non-dependence on age would be observed for a given country if the excess and baseline mortalities had exactly the same doubling times in age (same exponential variation with age). However, the actual observed age-dependent P-score mortality above 0% shows both the magnitude and
the age-targeting from the additional fatal assaults or challenges during the Covid
period, for each country and by year of the Covid period.

And we observe the following, in the 25 European countries (Figure 51).

i. At a first level of examination, the age-group-resolved P-score mortality is generally large and positive for all age groups, throughout the Covid period (2020-2022). No age group is exempt from large excess mortality relative to the natural (pre-Covid period) baseline, and the magnitudes of this relative mortality are generally comparable across all age groups. But that is only the overall general view, prior to more detailed scrutiny of yearly and age-group differences.

ii. Nine (9) of the 25 countries have large increases in P-score mortality with age in 2020 (Belgium, France, Italy, Netherlands, Poland, Portugal, Spain, Sweden, Switzerland). The elderly died preferentially in 2020 in these 9 countries, super-(baseline)-exponentially with age, suggesting an old-age-targeted assault during 2020. Of the 9 countries, seven (7) have exceedingly large March-April 2020 all-cause mortality peaks (20-3 feature, Section 4.2) (Belgium, France, Italy, Netherlands, Spain, Sweden, Switzerland), whereas the other (25 - 7) 18 countries essentially do not have the 20-3 feature. The said 20-3 feature (March-April 2020 mortality peak) has been associated with aggressive early medical interventions essentially following the declaration of a pandemic (Rancourt, 2020, 2023; Rancourt et al., 2020, 2021a, 2021b).

iii. Twelve (12) of the 25 countries have comparatively small P-score mortality in 2021 in the 85+ years age group (Austria, Belgium, Bulgaria, Czechia, Greece, Hungary, Italy, Serbia, Slovakia, Spain, Sweden, Switzerland). Of these 12 countries, five (5) (Belgium, Italy, Spain, Sweden, Switzerland) have relatively large 85+ years age group mortality in 2020, which suggests a possible so-called "dry tender effect", although it would need to carry over for one year — between 2020 and 2021 in this age group — rather than apply in the immediate following months (e.g., Rancourt et al., 2021b, their Figure 6a). Austria uniquely (of the 25 countries) has relatively small 85+ years age group P-score mortality in each of the three years (2020-2022). Several of the countries have small or near-zero
85+ years age group P-score mortality in 2020 (Denmark, Estonia, Finland, Latvia, Lithuania, Norway).

iv. Several countries of the 25 show an anomalously and uniquely large P-score mortality specifically in the age groups 75-79 years (France, Greece, Poland, Portugal, Spain) or 80-84 years (Austria, Czechia, Slovakia), in each of the Covid-period years (2020-2022). We note that (mid-Covid-year – mid-age-group-age) 2021 – 77 = 1944 and 2021 – 82 = 1939, such that the deceased would have been born essentially during the Second World War. We would hypothesize that biological stress or trauma from adversity to the pregnant mother, fetus or infant could have caused a permanent immune or other deficiency (e.g., Morey et al., 2015) making these deceased preferentially vulnerable to exceptional life-threatening assaults experienced during the Covid period.

v. Possibly in relation to this, regarding post-war societal stress, Croatia, Romania and Serbia show an anomalously heightened P-score mortality of females compared to males in the 70-74 years age group. Here: 2021 – 72 = 1949.

vi. Finally, five (5) countries of the 25 show a heightened male mortality, and an exceptionally large male-female mortality gap, in the 40-44 years age group (Austria, Belgium, Denmark, Latvia, Slovakia). Two (2) other countries show a large but inverted male-female mortality gap in the 40-44 years age group, with female mortality being the largest (Greece, Serbia). It seems that this age group (40-44 years) was at high and sex-differentiated risk in several countries during the Covid period (e.g., Rancourt et al., 2021b, their Figure 8-AB, in Canada).

Several results from the age-resolved excess mortality rate (Figure 50) and age-resolved P-score mortality (Figure 51) of the 25 European countries are difficult to reconcile with the hypothesis of a specific pathogen as the dominant cause of excess mortality during the Covid period, as follows.

* A specific presumed-widespread pathogen cannot cause a large age dependence of the P-score mortality in some and not in other (here, European) countries, in the same year (Figure 51, e.g. point-ii, above).
* A specific pathogen cannot cause one age dependence of the P-score mortality one year, and a qualitatively different (even opposite) age dependence of the P-score mortality another year, in the same country, over essentially all ages (Figure 51, e.g. point-ii, above).

* The type of specific pathogen hypothesised (viral respiratory) cannot entirely or almost entirely avoid causing P-score mortality in the most elderly (85+ years age group), not to mention only in certain countries (Figure 51, e.g. point-iii, above).

* A specific and particularly virulent widespread pathogen cannot selectively target highly specific age groups (75-79 years or 80-84 years; Figure 51, e.g. point-iv, above).

* A specific pathogen cannot cause large gaps in male-female mortality in specific age groups only in certain jurisdictions, and even inverting the male-female order in other jurisdictions (Figure 51, e.g. point-v, point-vi, above).

If all the above inconsistencies in P-score mortality by age and sex are due to pre-existing underlying lethal susceptibilities in the specific countries and age groups, then one must ask “What is the meaning and utility of a hypothetical pathogen that causes death primarily in relation to pre-existing underlying conditions?”. Similarly, “How can the hypothetical pathogen be specific in its mechanism causing death if it predominantly contributes to the mortality rate (by population) exponentially with age with a Gompertz Law-like value of the doubling age ($\tau$) (Figure 49, Figure 50), as do all the common usual causes of death that result in the Gompertz Law of mortality?”

Any largescale general regime of additional and cumulative assaults (chronic nonspecific biological stress, Selye, 1936, 1956) will cause mortality to be both primarily exponential with age (Gompertz Law) and targeted towards pre-existing underlying conditions. So, what then is the medical and epidemiological relevance of the postulated specific pathogen?
5.11 Did COVID-19 vaccine rollouts reduce all-cause mortality?

Did the unprecedented global rollouts of COVID-19 vaccines have any measurable
benefit regarding all-cause mortality? Does the data show evidence that all-cause
mortality was reduced by vaccine rollouts? The best way to answer this question — in
the absence of vaccine-status-discriminated all-cause mortality data — is to examine
the temporal evolution of all-cause mortality, before, during and after vaccine rollouts.

Watson et al. (2022) used hypothetical projections of all-cause mortality to claim that millions of lives were saved by the COVID-19 vaccines in 185 countries and territories, and their results were used to promote a Nobel Prize (see: Rancourt and Hickey, 2023). However, if the COVID-19 vaccines were effective at reducing the risk of severe illness, as is often claimed, then the impact of a known large-scale rollout during a presumed deadly pandemic should be detectable in measured all-cause mortality data directly, and its temporal dependence. Rancourt and Hickey (2023) thus argued that any purported COVID-19 vaccine efficacy is constrained by the measured mortality data itself, and demonstrated that the contrived projections of Watson et al. (2022) are necessarily false.

**Appendix B** shows all the available data in the present study of more than 100 countries, for all-cause mortality, excess all-cause mortality, and vaccine rollouts, by time 2018-2023, both by time interval (week or month) and as cumulative functions.

We find no example of a specific country for which all-cause mortality by time data
shows a benefit attributable to vaccine rollouts. We would invite readers to identify such
an example and to explain how they conclude a benefit attributable to COVID-19
vaccine rollouts.

In fact, we observe the following.
1. Large peaks of excess all-cause mortality most often continue to occur during and after COVID-19 vaccine rollouts (e.g., USA; and most cases shown in **Appendix B**).

2. The largest values of excess all-cause mortality most often occur in late 2021 (2021-H2: Table 1, Figure 16, Figure 17, **Appendix B**, **Appendix D**), after the major dose-1 and dose-2 COVID-19 vaccine rollouts of early 2021. This is shown in detail statistically (Figure 16, Figure 17).

3. In this connection, there is often a large December-2021-January-2022 peak in excess all-cause mortality (22-0 peak, Section 4.5), which is essentially synchronous with a COVID-19 vaccine (booster) rollout. Examples include: Australia, Austria, Bulgaria, Canada, Croatia, Czechia, Germany, Hungary, Latvia, Poland, Romania, and Slovakia (**Appendix B**). The case of Australia has been described in detail, and by state in Australia (Rancourt et al., 2022a). Several more cases occur in the Southern Hemisphere (Rancourt et al., 2023a), and the correlations to booster rollouts are confirmed in age-stratified data (Rancourt et al., 2023a, 2023b).

4. Many countries have no measurable excess all-cause mortality, until the vaccines are first rolled out. Examples include: Finland, Iceland, Japan, Monaco, Mongolia, Namibia, New Zealand, Norway, Singapore, South Korea, Taiwan, Thailand, and Uruguay (**Appendix B**). One can add the remarkable case of India (Rancourt, 2022).

5. To this group, we can add many cases in which virtually the only and the largest peak in excess all-cause mortality is synchronous with or immediately preceded by (usually) the first COVID-19 vaccine rollout. Examples include: Bahamas, Cuba, French Polynesia, Gibraltar, Jamaica, Malaysia, New Caledonia, Qatar, Russia, and Suriname (**Appendix B**). More examples are described in Section 4.6.

6. There is often a large December-2022-January-2023 peak in excess all-cause mortality (23-0 peak, Section 4.4), apparently coincident with an advanced booster-dose rollout, in countries with extensive COVID-19 vaccination programs, approximately four months prior to the 5 May 2023 WHO declaration
of the end of the declared pandemic. Examples include 23 of the 87 countries
with sufficient data: Austria, Belgium, Canada, Chile, Czechia, Denmark, Finland,
France, Germany, Ireland, Japan, Latvia, Lithuania, Macao, Netherlands,
Norway, Poland, Slovakia, Slovenia, Sweden, Switzerland, United Kingdom, and
USA (Appendix B). We relate this to the late persistent excess mortality
phenomenon, in Section 5.9.

7. There is often a significant persistent excess all-cause mortality into 2023 (2023-H1*: Section 4.8), often following and beyond the 23-0 peak described above, also extending beyond the 5 May 2023 WHO declaration of the end of the declared pandemic. This occurs in 32 of the 76 countries with sufficient data: Australia, Austria, Belgium, Brazil, Canada, Denmark, Ecuador, Egypt, Finland, France, Germany, Hong Kong, Ireland, Israel, Italy, Japan, Lithuania, Netherlands, Norway, Portugal, Puerto Rico, Qatar, Russia, Singapore, South Korea, Spain, Sweden, Switzerland, Taiwan, Thailand, United Kingdom, and USA (Section 4.8, Figure 14, **Appendix A**, **Appendix B**), which includes virtually all the most COVID-19 vaccinated countries in the world (Section 5.9).

8. There are no reliable correlations between excess all-cause mortalities and COVID-19 vaccine administration, which are not confounded by correlations between vaccine administration and socio-economic variables (Section 4.14, Figure 25; Section 4.17, Figure 42 to Figure 47).

9. Finally, in most countries studied, the period with significant excess all-cause mortality is lengthy (3 years or more, **Appendix A**), which is far too long in duration to be compatible with any postulated viral respiratory disease spreading by contact and producing an epidemic or pandemic, having parameters comparable to those inferred for COVID-19 (e.g., Hickey and Rancourt, 2023a, 2023b), without additionally postulating a stream of deadly "variants" for which past postulated waves of infection provide little or no cross-immunity protection, each producing its own new pandemic, a scenario never before observed or validated and previously thought to be evolutionarily unlikely. This would require a historically unprecedented stream of new deadly variants, which abruptly ends in most countries after the 5 May 2023 WHO declaration of the end of the
declared pandemic (since excess all-cause mortality then becomes relatively small or near-zero).

Therefore, despite the long (3-year) presumed period of opportunity during a declared pandemic, the extensive COVID-19 vaccination campaigns and repeated rollouts did not measurably prevent or reduce excess all-cause mortality. It would be unreasonable for anyone to conclude that COVID-19 vaccine rollouts decreased all-cause mortality, or saved any lives sufficiently to be detectable in national data and its temporal evolution.

On the contrary, as indicated in the above numbered points and as discussed below, the COVID-19 vaccine rollout campaigns are significantly associated with excess all-cause mortality.

## 5.12 What caused excess all-cause mortality during the Covid period?

### 5.12.1 Primary cause of death versus associated diseases or conditions

> "Some diseases have specific causes, the direct actions of certain particular, disease-producing agents, such as microbes, poisons, or physical injuries. Many more diseases are not caused by any one thing in particular; they result from the body's own response to some unusual situation."

> — Hans Selye (Selye, 1956; p. 179)

Given the difficulty identified by Selye, in our analysis, we distinguish between a "primary cause of death" (hierarchical top-level circumstances or stressors or assaults that cause an early or accelerated death, irrespective of the mechanism) and "associated diseases or conditions reported at death", for example, on a clinical death
certificate (proximal or clinical causes of death in the final moments, involving organ and organ system failures or stoppages).

For example, hypothetically, infection by a specific deadly pathogen could be a primary cause of death, where the deaths, in different individuals, might be concomitantly associated with generic conditions such as “respiratory infection” and “heart failure”, and sub-categories of such conditions.

In other examples:

* A poison leads to heart failure. Then the primary cause of death is the poison.

* A poison weakens the body's defenses, leading to a massive intestinal infection and eventual respiratory and heart co-failures. Then the primary cause of death is the poison.

* Chronic biological stress causes exhaustion-phase collapse of the body's resistance to the biological stress, and death follows (Selye, 1956). Then the primary cause of death is the said chronic biological stress, which can be specific.

* Chronic psychological stress causes immunosuppression, enabling a severe respiratory infection from ambient microbes and air pollution, followed by death. Then the primary cause of death is the said chronic psychological stress, which can be specific.

* A person is seriously ill for some reason, even dying. A medical intervention accelerates the death, and the person dies prematurely. Then the primary cause of death is the medical intervention.

### 5.12.2 Inferred respiratory medical conditions in the deaths contributing to excess all-cause mortality

Prior to suggesting hypotheses about likely or possible primary causes of the deaths contributing to excess all-cause mortality, it is relevant to consider whether a dominant
syndrome, disease or condition, based on reported symptoms and conditions prior to death, can be inferred to be associated with all or most excess all-cause mortality during the Covid period.

In the high-quality databases for the USA, there is a close match between the weekly reported COVID-19 mortality and weekly excess all-cause mortality, in the entire Covid period (2020-2022), including prior to and during the vaccine rollouts (CDC, 2023).

To the degree that COVID-19 death assignation represents a serious respiratory condition at death, and given the intricate weekly temporal matching of the reported COVID-19 mortality and excess all-cause mortality for up to 3 years in the USA data, this represents strong evidence that respiratory infections were dominantly (virtually entirely) associated with excess all-cause mortality.

Rancourt et al. (2021a) showed this in detail, into 2021 (their Figures 34a through 34i). They also pointed out that more than half of the deaths assigned as COVID-19 deaths could include life-threatening co-occurring bacterial pneumonia, according to CDC tabulations of death certificates, and that prescriptions of antibiotics were significantly reduced in the same period. Rancourt et al (2021a) fervently concluded:

> "Finally, our examination of plausible mechanisms for the exceptionally large COVID-era mortality in the USA, given all our empirical observations, leads us to postulate that COVID-19 may largely be misdiagnosed bacterial pneumonia (using a faulty PCR test: Borger et al., 2021; and see Ginsburg and Klugman, 2020), that correctly assigned bacterial pneumonia itself largely goes untreated, while antibiotics (and Ivermectin) are withdrawn, in circumstances where large populations of vulnerable and susceptible residents have suppressed immune systems from chronic psychological stress induced by ("COVID response") large-scale socio-economic disruption, and that the USA has, in the COVID-era, thus recreated the conditions that produced the horrendous bacterial pneumonia epidemic of 1918 (Morens et al., 2008) (Chien et al., 2009) (Sheng et al., 2011)."
For the present purposes, these results (CDC, 2023; Rancourt et al., 2021a) imply that essentially all excess all-cause mortality during the Covid period is associated with life-threatening respiratory infections or conditions, rather than mainly associated with other conditions such as cancer, suicide, drug overdoses, homicide, delayed medical interventions, and accidents.

As emphasized above (Section 5.12.1), a dominant association to a disease or condition is separate from the question of “primary cause of death”.

Furthermore, the proposed and apparent dominant respiratory disease or condition during the Covid period relates to excess all-cause mortality, and does not preclude specific primary causes of death in individuals leading to main associated conditions at death other than respiratory infections or conditions. Such cases could produce significant mortality too small to be detected as a discernable contribution to all-cause mortality. An example might be heart failure due to COVID-19-vaccine-induced myocarditis, in which the primary cause is the vaccine. Another example could be induced immunodeficiency enabling cancer. In both examples, the increased associations at deaths could be confirmed by clinical observation or autopsy, while being too small to be recognized features in all-cause mortality data.

The proposal that respiratory infections or conditions are the dominant association with (not primary cause of) excess all-cause mortality during the Covid period is further supported by additional lines of evidence.

* (Already outlined above: Intricate weekly temporal matching of the reported COVID-19 mortality and excess all-cause mortality for up to 3 years during the Covid period in the USA data.)

* Respiratory infections are a major recognized cause of death for all ages, both historically and presently, which is consistent with the intrinsic vulnerability of the lungs and the unavoidable rate of constant breathing.
* The median ratio of all-cause mortality to reported COVID-19 mortality for the countries in the present study is 1.55 (Figure 22), which is not too different from 1.

* Many deaths occurred from early aggressive hospital treatments for respiratory conditions, such as mechanical ventilators, toxic experimental doses of drugs, and lethal palliative drug cocktails (March-April peak, "20-3" feature) (e.g., Bailey and Köhnlein, 2020; Chaillot, 2024 (their Chapter 6); Menage, 2021; Rancourt, 2020, 2023; Richardson et al., 2020; Roedl et al., 2021; Torjesen, 2021; Watts et al., 2021).

* The VAERS system of adverse effect reporting contains many post-vaccination nominally COVID-19 infections (e.g., Hickey and Rancourt, 2022, their Figures S3(a) and S4(a)).

* Post-vaccination nominally COVID-19 infections are common, and are generally more frequent and more serious following multiple COVID-19 vaccine doses (1st dose, 2nd dose, booster) (Amer et al., 2024, their Table 6).

Furthermore, from a mechanistic point of view, all forms of bacterial pneumonia have unreasonably been overlooked and left untreated during the Covid period (Rancourt et al., 2021a).

In particular, as one example, tuberculosis (TB) is a category of ancient bacterial pneumonias, which is highly prevalent and deadly, which is a leading proximal cause of death in the world, and which is clearly linked to social and economic factors, especially of household or residential living conditions.

In Section 5.4 we linked a mortality discontinuity between Eastern and Western European countries during the Covid period to the post-1990s pre-Covid-period prevalence of active TB, as an indicator of or proxy for large socio-economic differences.
Apparent transmission of TB, from infectious persons to household members, is well established (e.g., Chapman and Dyerly, 1964), such that transmission in hospital, care-home and prison settings is expected and high prevalence and incidence are demonstrated (Baussano et al., 2010; Joshi et al., 2006).

One-third of the world population is estimated to be infected with the TB pathogen, 9–14 million confirmed active cases occur every year, and there are approximately 1.2 million confirmed TB deaths per year in regular recent times (Bagcchi, 2023; Dattani et al., 2023; O’Garra et al., 2013).

It is estimated that there may be up to 10 times more active pathological TB cases than the above reviewed numbers, not confirmed by bacteriological tests (e.g., Houben et al., 2022), which would represent approximately 100 million active cases in any year.

Therefore, it appears that humanity's evolutionary and pervasive coexistence with TB represents a large (and sufficient) reservoir of potential respiratory ailments ready to enter the world stage whenever mass events suppress immunity (next section), not counting the multitude of other prevalent bacterial, fungal and co-infection pneumonias (Dietert et al., 2017; Liu et al., 2023; Torres et al., 2021).

In conclusion, it is plausible that virtually all excess all-cause mortality during the Covid period is associated with respiratory infections or conditions. This is likely to be the case in the USA, which is a large country with diverse populations of vulnerable individuals, living in diverse socio-economic, institutional, climatic and environmental conditions. In addition to confirming evidence, there is no counter evidence that this would not also be the case in other countries in the present study.

Therefore, our hypotheses about primary causes of death in excess all-cause mortality during the Covid period (next section) should be consistent with this finding of respiratory condition prevalence.
Regarding the particular microscopic pathogen to blame for the proximal respiratory conditions: You can only see what you look for (if the test is reliable), and you can't see what you don't look for.

A similar situation appears to apply to the 1918 pandemic, in which virtually all excess mortality was associated with respiratory conditions or infections (autopsy confirmed as bacterial pneumonia), whereas the primary cause of death in excess all-cause mortality would have been the conditions imposed by the First World War (Bailey et al., 2024), in the post-war socio-economic adjustment circumstances.

### 5.12.3 Primary causes of death in excess all-cause mortality during the Covid period

#### 5.12.3.1 Stress of mandates and measures

It would be difficult to overestimate the importance of biological stress (which includes psychological stress) in causing death, irrespective of the mechanism.

Selye (1956) defined biological stress and made an encyclopedic and systematic review of the many diverse stressors studied in the scientific literature up to 1976 (Selye, 1976a). Fatal physiological consequences of chronic biological stress have been known and extensively studied since their initial discovery (Selye, 1936, 1956, 1976a; Szabo et al., 2017).

While being foundational, the Selye line of research did not (Selye, 1976b) and has not generally (Szabo et al., 2017) included two paramount factors:

i. The role of social dominance hierarchy, in both human and animal societies, as a structural and leading source of complex, situational and time-dependent stressors (dominance aggression) that primarily determine an individual's (social-status-dependent) health and longevity.
ii. The dependence of biological adaptation and failure (collapse) on, not solely whether the biological stress is acute (episodic) or chronic (constant), and not solely on how the stress is experienced by the particular individual, but critically on the time sequence of the acting stressors of varying intensities; that is, on their time-type-intensity spectrum, which can have both regular and chaotic components.

More generally, as is now known, the health and survival of individuals among social animals, and primates in particular, is predominantly determined by the individual's position and role in the dominance hierarchy, in relation to the physiology and biochemistry of dominance aggression (Sapolsky, 2005), including via respiratory infections (Cohen et al., 1997a). Irrationality or randomness of the acts of dominance aggression plays an important role, via stress response mechanisms, and amplifies the harm to subordinate individuals (Silk, 2002).

Together, these studies (e.g., Selye, Sapolsky, Cohen, Silk) are clear that socially and environmentally mediated biological stress is a major determinant of death and survival. This is easily admitted for non-human animals. However, the current medical scientific literature generally shies away from admitting the prevalence of such brutality in human societies, and instead tends to focus on sanitized questions of immunology and individual psychological stress.

From the perspective of the immune system, ordinary psychological stress alone significantly impacts immune response, and psychoneuroimmunology is a large field of research (Ader and Cohen, 1993; Dhabhar, 2009, 2014). Purely psychological stress alone probably causes many varied and common diseases (Cohen et al., 2007). Psychological stress and social isolation have strong associations with respiratory infections, including the common cold, acting to increase both frequency and severity of the infection (Cohen et al., 1991, 1997a, 1997b). Meanwhile, the impact of age increases vulnerability to stress (Prenderville et al., 2015). "Immunosuppressive activity increases with aging", as the immune system is "remodeled" (Salminen, 2022). Some
aspects of psychological stress in the Covid period context have been reviewed by
Peters et al. (2021).

Regarding stressors acting in the USA during the Covid period, and their social-status
dependence, Rancourt et al. (2021a) summarized this way:

> "Therefore, it is not difficult to imagine that the massive socio-economic disruptions of the COVID-era would have caused undue chronic psychological stress and amplified dominance-hierarchy stress predominantly against those who are already at the bottom of the societal dominance hierarchy, and have the least means to adjust to dramatically new circumstances. The new circumstances include: loss of sources of income, both legitimate and illegal, increased social isolation, increased hierarchical impositions, constant fear propaganda, severe mobility restrictions, closing of public and corporate-public spaces previously used, enforcement and intimidation against private or informal gatherings, mobbing against those who do not cheerfully accept the "new reality", and increased aggressions from equally stressed individuals. The missing means to adjust would include: undisturbed salary and ability to work from home, means to stay connected by Zoom (by video conferencing applications), large comfortable air-conditioned homes, means to home-school children in an adapted environment, nearby facilities for outside exercise, private facilities for physical exercise, undisturbed shopping by home delivery, undisturbed self-medication, continued access to health care, and so on."

Finally, a recent study of young mice is strikingly apropos. Li et al. (2023) found that mice subjected to a single episode of restraint and immobilization stress (single 5-20 h stress sessions of confinement) experienced severe immunosuppression, more so and differently than with other common laboratory stressors (cold, biochemical). The behaviourally stressed mice could not efficiently defend against intravenous challenge with bacteria, and also showed significant spleen macrophage cell death, among several other corroborating biochemical observations.

This study in mice (Li et al., 2023) suggests, on a mechanistic basis, that confinement and isolation in humans may have a significant negative effect on susceptibility to
infections, consistent with field observations of college students (Cohen et al., 1991, 1997b), and non-human primates (Cohen et al., 1997a).

For a human society under lockdown, the same universal enforced confinement to one's home, for example, will in-effect consist of very different confinement intensities depending on social class and financial means, for obvious reasons.

We conclude this sub-section with the hypothesis that the Covid-period mandates and measures were a primary cause (Section 5.12.1) of death significantly contributing to the measured excess all-cause mortality.

See: (Rancourt et al., 2021a, 2021b) (Rancourt et al., 2022b, their boxed figure in their Conclusion).

### 5.12.3.2 Medical interventions other than COVID-19 vaccination

There is much evidence that medical interventions (including denial of treatment) other than COVID-19 vaccination caused premature deaths during the Covid period, which would not have occurred otherwise until later (e.g., Rancourt, 2020, 2023; Rancourt et al., 2021a, 2022b, 2023a, and references therein).

In this regard, we must have the evidence-based perspective that, even in ordinary times, medicine itself is a leading cause of harm and death (Gøtzsche, 2016; Makary and Daniel, 2016; Panagioti et al., 2019; and references therein).

The said medical treatments would include:

* coordinated denial of antibiotics or Ivermectin against bacterial pneumonia

* systematic use of mechanical ventilators and their associated medications

* experimental treatment protocols (large-dose hydroxychloroquine, HCQ)

* new palliative and psychological medication protocols, overdoses (e.g., midazolam) (Marliot et al., 2020; Sy, 2024)

* isolation of vulnerable individuals in medical or institutional facilities
* denial of intensive care and disease management facilities

* denial of home and community care medical services

* aggressive-testing accidents

* accidents and infections from reduced attending staff in care homes

* encouraged voluntary or involuntary assisted dying (Marliot et al., 2020; Menage, 2021; Sy, 2024)

* increased prevalence of medical errors due to declared-pandemic conditions

The March-April 2020 peak often occurring in all-cause mortality (20-3 feature, Section 4.2), in particular, is difficult to explain absent medical interventions (Rancourt, 2020, 2023). See also: Bailey and Köhnlein, 2020; Chaillot, 2024 (their Chapter 6); Richardson et al., 2020; Roedl et al., 2021; Torjesen, 2021; Watts et al., 2021.

We conclude this sub-section with the hypothesis that medical interventions (other than COVID-19 vaccination, including denial of treatment) were a primary cause (Section 5.12.1) of death significantly contributing to the measured excess all-cause mortality.

This medical intervention primary cause of death hypothesis is not invalidated by the background of biological stress that would have characterized the Covid period, since the relevant medical interventions accelerated the deaths that would not have otherwise occurred until later.

### 5.12.3.3 COVID-19 vaccination

Rancourt (2022) and Rancourt et al. (2022a, 2023a, 2023b) have shown many examples of strong temporal associations between rapid COVID-19 vaccine rollouts and peaks in excess all-cause mortality, in more than 20 countries and states, including in age stratified data.
Evidence from the present study showing that COVID-19 vaccination is associated with increased excess all-cause mortality, rather than reduced mortality, is highlighted above in Section 5.11.

In addition, we observe the following specific associations between COVID-19 vaccine rollouts and peaks or increases in excess all-cause mortality (Appendix B):

i. 30% of countries (37 of 124) have no detected excess all-cause mortality in all of 2020, only later when vaccines are rolled out

* 124 countries have sufficient all-cause mortality data (the data for Cabo Verde is too noisy) to determine whether excess all-cause mortality started after the end of 2020, after the vaccines were introduced.

* Of these 124 countries, 37 countries (30% of countries) have no detectable excess all-cause mortality in 2020. For at least the first nine months of the declared pandemic (declared on 11 March 2020) these 37 countries had virtually no measurable excess all-cause mortality: Antigua and Barbuda, Australia, Barbados, Bermuda, Brunei, Cuba, Faroe Islands, Finland, French Guiana, French Polynesia, Gibraltar, Greenland, Hong Kong, Iceland, Jamaica, Japan, Macao, Malaysia, Martinique, Mauritius, Monaco, Mongolia, Namibia, New Caledonia, New Zealand, Norway, Philippines, Réunion, Saint Kitts and Nevis, Saint Vincent and the Grenadines, Seychelles, Singapore, South Korea, Suriname, Taiwan, Thailand, and Uruguay.

* To this list of 37 countries, we can add India (Rancourt, 2022).

* All these 37 + 1 countries have first peaks or increases in excess all-cause mortality (if present) occurring only after vaccination is initiated, or later when the bulk of injections have been administered and additional (booster) doses are rolled out.

ii. 100% of countries (110 countries with sufficient vaccination data) show varied associations between vaccine rollouts and excess mortality
* 110 countries of the 125 countries in the present study have sufficient data (both vaccination and mortality data, which is not too noisy) to allow determinations of temporal associations.

* There are significant correlations between COVID-19 vaccine rollouts and peaks or increases in excess all-cause mortality in all 110 of these countries (100% of countries) having sufficient data: Albania, Argentina, Armenia, Aruba, Australia, Austria, Azerbaijan, Bahamas, Barbados, Belgium, Belize, Bermuda, Bolivia, Bosnia, Brazil, Brunei, Bulgaria, Canada, Chile, Colombia, Costa Rica, Croatia, Cuba, Cyprus, Czechia, Denmark, Dominican Republic, Ecuador, Egypt, Estonia, Faroe Islands, Finland, France, French Guiana, French Polynesia, Georgia, Germany, Gibraltar, Greece, Guadeloupe, Guatemala, Hong Kong, Hungary, Iceland, Iran, Ireland, Israel, Italy, Jamaica, Japan, Jordan, Kazakhstan, Kuwait, Latvia, Lebanon, Liechtenstein, Lithuania, Luxembourg, Macao, Malaysia, Maldives, Malta, Mauritius, Mexico, Moldova, Monaco, Mongolia, Montenegro, Namibia, Netherlands, New Caledonia, New Zealand, Nicaragua, North Macedonia, Norway, Oman, Palestine, Paraguay, Peru, Philippines, Poland, Portugal, Puerto Rico, Qatar, Romania, Russia, Saint Kitts and Nevis, Saint Vincent and the Grenadines, Serbia, Seychelles, Singapore, Slovakia, Slovenia, South Africa, South Korea, Spain, Suriname, Sweden, Switzerland, Taiwan, Tajikistan, Thailand, Tunisia, Turkey, Ukraine, United Arab Emirates, United Kingdom, USA, Uruguay, and Uzbekistan.

iii. 97% of countries (113 of 116) show a late-2021 early-2022 peak in excess all-cause mortality temporally associated with booster rollouts

* There are 116 of the 125 countries in the present study that have sufficient and sufficient-quality data to ascertain the presence of the “22-0 feature”, a prominent or statistically evident excess mortality peak occurs within one month or so of 1 January 2022 (Section 4.5). Of these 116 countries, 113 countries have the 22-0
feature in their excess all-cause mortality data (Section 4.5). The other 3 countries do not measurably exhibit the 22-0 feature (Italy, Macao, Taiwan).

* Therefore, 113 of 116 countries (97% of countries) exhibit a peak in excess all-cause mortality within one month or so of 1 January 2022 (the 22-0 feature, Section 4.5) coincident with (immediately following) the time at which many booster doses were synchronously rolled out globally. The booster rollouts are recognized as peaks in overall (all doses) COVID-19 vaccine administration (e.g., Rancourt et al., 2023a).

* The 113 countries having discernable 22-0 features in excess all-cause mortality are: Albania, Andorra, Argentina, Armenia, Australia, Austria, Azerbaijan, Bahamas, Barbados, Belgium, Belize, Bermuda, Bolivia, Bosnia, Brazil, Brunei, Bulgaria, Cabo Verde, Canada, Chile, Colombia, Costa Rica, Croatia, Cuba, Cyprus, Czechia, Denmark, Dominican Republic, Ecuador, Egypt, Estonia, Faroe Islands, Finland, France, French Guiana, French Polynesia, Georgia, Germany, Greece, Guadeloupe, Guatemala, Hong Kong, Hungary, Iceland, Iran, Ireland, Israel, Jamaica, Japan, Jordan, Kazakhstan, Kosovo, Kuwait, Kyrgyzstan, Latvia, Lebanon, Liechtenstein, Lithuania, Luxembourg, Malaysia, Maldives, Malta, Martinique, Mauritius, Mayotte, Mexico, Moldova, Monaco, Mongolia, Montenegro, Namibia, Netherlands, New Caledonia, New Zealand, Nicaragua, North Macedonia, Norway, Oman, Palestine, Panama, Paraguay, Peru, Philippines, Poland, Portugal, Puerto Rico, Qatar, Réunion, Romania, Russia, Saint Kitts and Nevis, Saint Vincent and the Grenadines, San Marino, Serbia, Seychelles, Singapore, Slovakia, Slovenia, South Africa, South Korea, Spain, Suriname, Sweden, Switzerland, Tajikistan, Thailand, Transnistria, Tunisia, Turkey, Ukraine, United Kingdom, USA, and Uruguay.

* Among these 113 countries having discernable 22-0 features in excess all-cause mortality, some of the most striking correlations between a peak in vaccine rollout and the 20-2 feature occur for the 12 countries: Australia (and see: Rancourt et al., 2022a, 2023a, 2023b), Austria, Czechia, Hong Kong, Hungary, Poland, Qatar, Romania, Russia, Saint Vincent and the Grenadines, Slovakia, and Ukraine.
iv. 64% of countries (50 of 78) show a late-2022 early-2023 sharp peak in excess all-cause mortality temporally associated with booster rollouts

* There are 78 of the 125 countries in the present study that have sufficient and sufficient-quality data to ascertain the presence of the “23-0 feature”, a prominent or statistically evident excess mortality peak that occurs within one month or so of 1 January 2023, less than 5 months prior to the declaration of 5 May 2023 of the end of the declared pandemic (Section 4.4).

* Of these 78 countries with sufficient data, 50 countries have the 23-0 feature (Section 4.4) in their excess all-cause mortality data. These 50 countries are: Austria, Belgium, Canada, Chile, Croatia, Cyprus, Czechia, Denmark, Ecuador, Estonia, Finland, France, French Guiana, Germany, Greece, Guatemala, Hong Kong, Hungary, Iceland, Ireland, Italy, Japan, Latvia, Lithuania, Luxembourg, Macao, Moldova, Netherlands, New Zealand, Norway, Paraguay, Poland, Portugal, Puerto Rico, Qatar, Russia, Singapore, Slovakia, Slovenia, South Africa, South Korea, Spain, Sweden, Switzerland, Taiwan, Thailand, Tunisia, Turkey, United Kingdom, and USA.

* The other 28 of the 78 countries with sufficient data do not measurably exhibit the 23-0 feature (Section 4.4). These 28 countries are: Albania, Armenia, Azerbaijan, Bosnia, Colombia, Egypt, Faroe Islands, Georgia, Guadeloupe, Kazakhstan, Kosovo, Kyrgyzstan, Malaysia, Martinique, Mayotte, Mexico, Mongolia, Montenegro, North Macedonia, Oman, Philippines, Réunion, Serbia, Uruguay, Bulgaria, Mauritius, New Caledonia, and Romania.

* Therefore, 50 of 78 countries (64% of countries) exhibit a peak in excess all-cause mortality within one month or so of 1 January 2023 (the 23-0 feature, Section 4.4) coincident with (immediately following) the time at which many booster doses were synchronously rolled out globally, in the last booster rollout prior to the declaration of the end of the declared pandemic. The booster rollouts are recognized as peaks in overall (all doses) COVID-19 vaccine administration (e.g., Rancourt et al., 2023a).
* Although Bulgaria does not have the 23-0 feature (a distinct peak near 1 January 2023), it does have a broader and somewhat earlier peak structure in its excess all-cause mortality, which matches the vaccine rollout at that time (mid to end of 2022). Similar circumstances may be occurring in: Albania, Armenia, Colombia, Egypt, Georgia, Malaysia, North Macedonia, Philippines, Mauritius, and New Caledonia.

v. Particularly striking examples of vaccine-mortality associations in several specific countries

* Several countries show striking examples of vaccine-mortality associations in which the vaccine rollout is synchronous with the only exceptionally large excess all-cause mortality feature: Bahamas, Cuba, French Polynesia, Gibraltar, Jamaica, Japan, Malaysia, New Caledonia, and Suriname. Here, note that Cuba developed its own vaccine.

* Similarly striking examples include: Guadeloupe, Hong Kong, Maldives, Mauritius, Namibia, Philippines, Qatar, and Tunisia.

Therefore, there are many temporal associations between COVID-19 vaccine rollouts and increases in excess all-cause mortality, in every country with sufficient data.

It is highly unlikely that these many vaccine-mortality temporal associations — occurring at many different times for different vaccine rollouts during the two years of vaccinations and in every country having sufficient mortality and vaccination data in the present study — are coincidental.

These vaccine-mortality correlations occur even with the limitation that the contributions to all-ages excess all-cause mortality by population are exponential with age (Section 5.10) whereas the vaccine administration shown (Appendix B) is for all ages, and there are many more injections to non-elderly than to elderly, administered in different rollouts. Observed vaccine-mortality correlations are generally most distinct
with age stratified data and can be masked in all-ages data (Rancourt et al., 2023a,
2023b).

Possible mechanisms whereby COVID-19 vaccination could be a primary
(Section 5.12.1) cause of death include:

a. The injection causes death by direct toxicity. Cationic lipids are candidates for toxic components.

b. The injection causes death by inducing an immune overreaction. The resulting immune assault, analogous to an allergic reaction, is enough to accelerate and cause the death.

c. The injection and repeated injections (2nd dose and boosters) cause immunosuppression making the patient generally more susceptible to infections and less able to defend against existing infections, including respiratory infections, in turn causing death. (See Section 5.12.2 regarding Amer et al., 2024, their Table 6.)

d. A frail patient is infected or additionally infected by a person who was made more infectious by injection-induced immunosuppression — either another patient or a caregiver, for example. Their death is accelerated where it otherwise would not have been.

Regarding point-c, for example, following Selye (1956), if subjected to widespread (even
booster-repeated) presence of antigens in the blood, the body could respond by
suppressing its non-specific inflammation response, including in the respiratory tract,
thus making the individual more susceptible to varied and deeper respiratory infections
(and skin infections: Martora et al., 2023). Also, theoretical mechanisms specifically for
COVID-19 vaccine-induced immunosuppression have been suggested (Palmer et al.,
2023, ss. 3.3—3.5; Seneff et al., 2022).

These possible mechanisms (point-a through point-d) are consistent with the
observations based on age-stratified data that the effective “vaccine dose fatality rate
(vDFR)” (per injection basis) increases exponentially with age and increases with the dose number (Rancourt et al., 2023a, 2023b).

We conclude this sub-section with the hypothesis that COVID-19 vaccination was a primary (Section 5.12.1) cause of death significantly contributing to the measured excess all-cause mortality.

This COVID-19 vaccine injection primary cause of death hypothesis is not invalidated by the background of biological stress that would have characterized the Covid period, or by the assaults from other medical interventions, since the injections are taken to have accelerated the deaths that would not have otherwise occurred until later.

#### 5.12.3.4 SARS-CoV-2 as a primary cause of death

The hypothesis of a specific viral respiratory pathogen causing excess all-cause mortality during the Covid period (2020-2022) is contrary to two main direct observations:

i. It is incompatible with the large country-to-country heterogeneity of age and frailty adjusted (P-score) excess all-cause mortality rate (Section 5.5, Section 5.7).

ii. It is incompatible with the country-to-country spatiotemporal pattern of deaths, including the phenomenon of not crossing national borders. There is no evidence of spread, only local (by country) and time-specific assaults that do not geo-temporally evolve (Sections 4.11, 4.12, 5.6).

These basic incompatibilities have been previously reported (Rancourt, 2020, 2022; Rancourt et al., 2020, 2021a, 2021b, 2022a, 2022b, 2023a, 2023b). For example, in India there was no detectable excess all-cause mortality until the vaccine was first rolled out starting in March 2021 (Rancourt, 2022).
It is highly unlikely also that the frequently observed (in all countries, and repeatedly) peaks in excess all-cause mortality following the start or completion of COVID-19 vaccination rollouts, including boosters, occur from new variants of pandemic potency, for which no cross-immunity has been achieved (after more than one or two years of declared pandemic and typically more than one year of universal vaccination). For example, specific hypotheses of new variants of concern — corresponding to extraordinary coincidences following rapid vaccine rollouts — are argued to having been fabricated in the cases of India (Rancourt, 2022) and Australia (Rancourt et al., 2022a).

In addition to being incompatible with definitive and repeated observations, the SARS-CoV-2 hypothesis (or the hypothesis of any specific pandemic-causing respiratory virus) as a primary (Section 5.12.1) cause of death during the Covid period is not needed to explain any feature of the all-cause mortality in any of the countries studied.

The more immediate hypotheses of biological stress, medical interventions, and vaccination largely suffice. There was biological (including psychological) stress, induced by aggressive and life-changing mandates. There were deadly medical interventions (including denial of treatment), motivated by institutional messaging and overreaction. There was a global vaccination campaign with multiple rollouts, driven and protected by aggressive establishment and industry forces. All-cause mortality is a record of the consequences.

## 5.13 Calculated risk of death per injection from COVID-19 vaccines

### 5.13.1 Population-wide risk of death per injection — all ages

In this section we make quantitative estimates of the population-wide risk of death per COVID-19 vaccine injection, based on excess all-cause mortality and vaccine rollout data for the 100 or so countries in the present study.
We calculate the said risk as the number of excess deaths divided by the number of administered injections in a chosen time period. The chosen time periods typically capture both the excess mortality features of interest and the injections presumed to be associated with the excess mortality.

This ratio can be referred to as the vaccine dose fatality rate (vDFR). It is a population-wide risk of death per injection expressed as a percentage of the number of injections. It can be dose specific and it can be resolved by age group (Rancourt et al., 2022a, 2023a, 2023b).

Depending on the time period, the injections may include mostly first and second doses, or booster rollouts (3rd dose, 4th dose). The different vaccine manufacturers are not resolved in the present calculations.

For example, if we select the time period to be essentially the entire vaccination period (that is, if we select the 2-year period 2021-2022), then we are evaluating the combined effects of all injections within this time period without discerning different doses, and we are including all excess all-cause mortality peaks that may individually be due to distinct doses.

Furthermore, the calculation presumes that the only primary (Section 5.12.1) cause of (excess mortality) death in the time period is vaccination (Section 5.12.3.3). If other primary causes of death contribute to excess mortality in the time period then the calculated ratio will be an overestimate of the vDFR. This would produce positive outliers in a scatter plot (for all countries) of excess mortality versus number of administered injections.

Examples of such scatter plots are shown in Figure 52, for all the countries with sufficient data, and for the time periods 2021-2022, 2021, 2022, 2021-H1, 2021-H2, 2022-H1, and 2022-H2 (half years).
Figure 53 shows the same as Figure 52 (scatter plots of excess all-cause mortality versus number of COVID-19 vaccine injections, by country, by cluster, for different time periods) but with a 4-fold zoom on both x and y axes, near the origin.

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<figure><img src="image_686.png" /><figcaption>Figure 52. Scatter plots of excess all-cause mortality versus number of COVID-19 vaccine injections, by country, by cluster, for different time periods. The straight-line fit parameters (slope and intercept), the cluster colours (cluster analysis), the pearson correlation coefficient, and the country codes (right panel) are shown for each time period.</figcaption></figure>
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<figure><img src="image_699.png" /><figcaption>Figure 53. Same as the previous figure (scatter plots of excess all-cause mortality versus number of COVID-19 vaccine injections, by country, by cluster, for different time periods) but with a 4-fold zoom on both x and y axes, near the origin.</figcaption></figure>

In Figure 52 and in Figure 53, the following analytic curves are shown. These are the same for a given time period in both figures.

(1) The best straight-line fit (least squares fit) using the Y-errors in the points, assumed to be proportional to the true error (dashed black line)

(2) Upper and lower bounds on the best straight-line fit, using largest intercept and largest slope or smallest intercept and smallest slope, respectively, within the error bounds on intercept and slope (solid black lines)

(3) The standard confidence-interval limits, not using Y-errors, and therefore assuming equal and normal Y-errors for all points (curved grey lines)

For all the time periods in Figure 52 (and Figure 53), the correlations are generally strong ($r \ge 0.60$) or very strong ($r \ge 0.80$), and the intercepts are zero within error, which implies proportionality. The excess all-cause mortality in these time periods is proportional to the number of COVID-19 vaccine injections given.

The slope of the best-line fit in Figure 52 (and Figure 53) corresponds approximately to the mean all-ages all-manufacturers all-doses all-countries (in this study) population-
wide risk of death per COVID-19 vaccine injection, for the given time period, expressed
as a fraction (rather than as a percentage) in the legends for the panels.

The positive outliers (above the fitted line) in Figure 52 (and Figure 53) correspond to
countries and time periods when there are additional primary causes of death
(Section 5.12), other than the vaccine. Typically, countries that had large excess
mortalities in 2020, prior to vaccine rollouts, may be positive outliers in Figure 52. See
also Rancourt et al. (2023a).

We can also calculate the ratio of excess mortality to number of COVID-19 vaccine
injections for a given country and for a given time period. This ratio is an estimate of the
population-wide risk of death per injection (vDFR), to the degree that the injections
performed during the time period are the primary (Section 5.12.1) cause of death for the
excess mortality in the same time period in the given country.

This ratio, expressed as a percentage of the number of injections, is shown for all 78
countries (as labelled) with sufficient data for the 2-year time period 2021-2022 in Figure
54. Here, the groups of countries identified in the cluster analysis (Section 4.7) are
shown using the cluster colours on the symbols. The statistical parameters of the
corresponding distribution of values are given in the legend of the figure.

The median value for 2021-2022 is 0.127% (Figure 54). The outliers (with values
significantly above the median value) tend to be countries in the red cluster (e.g.,
Eastern European countries), which have exceptionally large excess mortality in late
2020 immediately prior to the first vaccine rollouts in early 2021.
<figure><img src="image_700.png" /><figcaption>Figure 54. Values of excess mortality divided by number of COVID-19 vaccine injections, by country (as labelled), by cluster (as per colour), for the period 2021-2022. The statistical parameters of the distribution of values are given in the legend.</figcaption></figure>

Using the median value for 2021-2022 of 0.127% for the 78 countries with sufficient data (Figure 54) gives an estimate of global all-ages excess mortality associated with the COVID-19 vaccines up to 30 December 2022 as:

$$0.00127 \times 13.3 \text{ billion doses administered up to 30 December 2022 (OWID, 2024)} = 16.9 \text{ million COVID-19-vaccine-associated deaths}$$

This is solely an estimated representative number since, as shown below, the population-wide risk of death per injection (vDFR) varies significantly by dose number (1st dose, 2nd dose, boosters) (or time period) and exponentially with age, and since the median method itself is an estimation. Table 5 and Table 6 give overviews of evaluations of time-period-dependent all-ages vDFR values.

Table 5 is a collection of estimated values of the population-wide risk of death per injection (vDFR), expressed as a percentage of the number of COVID-19 vaccine injections, in different time periods (2021-2022, 2021, 2022, and the half years in 2021 and 2022), by two different methods:

(1) from the slopes in best-line fits in Figure 52, and

(2) as the median values from figures such as Figure 54.
<table><caption>Table 5. Estimated population-wide risk of death per COVID-19 vaccine injection (vDFR), as a percentage of the number of injections, by time period, for two methods (scatter plot, median)</caption><thead><tr><th>Time period</th><th>N, countries</th><th>From slope in scatter plot (%)</th><th>From median in scatter (%)</th></tr></thead><tbody><tr><td>2021-2022</td><td>78</td><td>0.0794 ± 0.0086</td><td>0.127</td></tr><tr><td>2021</td><td>108</td><td>0.0476 ± 0.0070</td><td>0.108</td></tr><tr><td>2022</td><td>78</td><td>0.0834 ± 0.0087</td><td>0.197</td></tr><tr><td>2021-H1*</td><td>110</td><td>0.0276 ± 0.0076</td><td>0.091</td></tr><tr><td>2021-H2</td><td>108</td><td>0.0547 ± 0.0070</td><td>0.106</td></tr><tr><td>2022-H1</td><td>86</td><td>0.0593 ± 0.0064</td><td>0.125</td></tr><tr><td>2022-H2</td><td>78</td><td>0.130 ± 0.015</td><td>0.408</td></tr></tbody></table>

By comparison, previously published estimates of all-ages population-wide risk of death per COVID-19 vaccine injection (vDFR) are given in Table 6.

<table><caption>Table 6. Published values of population-wide risk of death per COVID-19 vaccine injection (vDFR)</caption><thead><tr><th>Country</th><th>vDFR (%)</th><th>Reference</th><th>Time period of excess mortality</th></tr></thead><tbody><tr><td>India</td><td>1.1</td><td>Rancourt, 2022</td><td>April-July 2021</td></tr><tr><td>high-poverty states, USA</td><td>1.</td><td>Rancourt, 2022</td><td>fall 2021</td></tr><tr><td>Australia</td><td>0.05</td><td>Rancourt et al., 2022a</td><td>January-February 2022</td></tr><tr><td>Australia</td><td>0.0515 ± 0.0014</td><td>Rancourt et al., 2023b (their Table 1)</td><td>vaccination period</td></tr></tbody></table>

<footnote>*2021-H1 is the first half year of 2021, and so forth.</footnote>
<table><tr><td>Israel</td><td>0.0527 ± 0.0030</td><td>Rancourt et al., 2023b (their Table 2)</td><td>vaccination period</td></tr><tr><td>17 countries, Southern Hemisphere</td><td>0.02 (New Zealand) — 0.20 (Uruguay)</td><td>Rancourt et al., 2023a</td><td>January-February 2022</td></tr><tr><td>17 countries, Southern Hemisphere</td><td>0.126 ± 0.004</td><td>Rancourt et al., 2023a</td><td>vaccination period</td></tr></table>

Therefore, the estimate of all-ages population-wide risk of death per injection (vDFR) made by Rancourt et al. (2023a) for 17 countries in the Southern Hemisphere during the vaccination period (0.126 ± 0.004) % is confirmed by the present study based on 78 countries with sufficient data (Table 5).

Importantly, the all-ages all-countries vaccination-period values are representative (“average”) values. Table 5 and Table 6 show that there are large variations with health status (by country), and with dose number (higher doses at later time periods). Additionally, there is an exponential variation with age (next section).

Much of the country-wise (by cluster) and dose-wise (by half year) variability is captured in Figure 55. Here, the median-value determination of the population-wide risk of death per COVID-19 vaccine injection (vDFR) is shown versus half-year time period in 2021 and 2022, for the different clusters of countries (as colour coded, cluster analysis, Section 4.7) and for all countries (blue).
Excess deaths / Num vax doses administered

<figure><img src="image_701.png" /><figcaption>Figure 55. Population-wide risk of death per COVID-19 vaccine injection (vDFR), as a percentage of the number of injections, versus half year, obtained from the median value, by cluster of countries (cluster analysis) and for all countries (as per legend).</figcaption></figure>

There is generally an increase with higher doses (at later times) (Figure 55), which has been reported previously from age-stratified and dose-discriminated data (Rancourt et al., 2023a, 2023b).

There is also generally an upward shift of the values towards clusters of countries with lower health status (green < orange < all-countries:blue = undetermined:black < red) (Figure 55).

These observations (Table 5, Table 6, Figure 55) are consistent with the above described hypothesis in which the vaccine acts to suppress immune response (and more so with more doses to a given individual), thus making already frail and sick vaccine-injected individuals more likely to succumb via fatal respiratory conditions (Section 5.12, Section 5.12.3.3).
5.13.2 Population-wide risk of death per injection — stratified by age group

We have shown above that excess all-cause mortality increases exponentially with age, in 25 European countries with sufficient data (Section 5.10, Section 4.19, Figure 50).

The population-wide risk of death per COVID-19 vaccine injection (vDFR) also increases exponentially with age (Rancourt et al., 2023a, 2023b).

This is shown in the present study for the time period 2022-H2 (second half year of 2022, when boosters are administered) for 25 countries with sufficient data in Figure 56.

Here (Figure 56), the 25 countries are: Austria, Belgium, Bulgaria, Croatia, Cyprus, Czechia, Denmark, Finland, France, Greece, Hungary, Iceland, Italy, Latvia, Lithuania, Malta, Netherlands, Norway, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, and Sweden. The all-ages value of vDFR is shown in the panel of each figure as a horizontal line with 1σ error shading. The parameters of each exponential fit are given in the legend for the panel for the specific country.
Austria (AT)

Period (excess deaths): 01-Jul-2022 to 31-Dec-2022

Period (vax doses): 01-Jul-2022 to 31-Dec-2022

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Belgium (BE)

Period (excess deaths): 01-Jul-2022 to 31-Dec-2022

Period (vax doses): 01-Jul-2022 to 31-Dec-2022

<figure><img src="image_703.png" /></figure>
Bulgaria (BG)

Period (excess deaths): 01-Jul-2022 to 31-Dec-2022

Period (vax doses): 01-Jul-2022 to 31-Dec-2022

<figure><img src="image_704.png" /></figure>
Cyprus (CY)

Period (excess deaths): 01-Jul-2022 to 31-Dec-2022

Period (vax doses): 01-Jul-2022 to 31-Dec-2022

<figure><img src="image_705.png" /></figure>
Czechia (CZ)

Period (excess deaths): 01-Jul-2022 to 31-Dec-2022

Period (vax doses): 01-Jul-2022 to 31-Dec-2022

<figure><img src="image_706.png" /></figure>
Denmark (DK)

Period (excess deaths): 01-Jul-2022 to 31-Dec-2022

Period (vax doses): 01-Jul-2022 to 31-Dec-2022

<figure><img src="image_707.png" /></figure>
Estonia (EE)

Period (excess deaths): 01-Jul-2022 to 31-Dec-2022

Period (vax doses): 01-Jul-2022 to 31-Dec-2022

<figure><img src="image_708.png" /></figure>
Greece (EL)

Period (excess deaths): 01-Jul-2022 to 31-Dec-2022

Period (vax doses): 01-Jul-2022 to 31-Dec-2022

<figure><img src="image_709.png" /></figure>
Spain (ES)

Period (excess deaths): 01-Jul-2022 to 31-Dec-2022

Period (vax doses): 01-Jul-2022 to 31-Dec-2022

<figure><img src="image_710.png" /></figure>
Finland (FI)

Period (excess deaths): 01-Jul-2022 to 31-Dec-2022

Period (vax doses): 01-Jul-2022 to 31-Dec-2022

<figure><img src="image_711.png" /></figure>
France (FR)

Period (excess deaths): 01-Jul-2022 to 31-Dec-2022

Period (vax doses): 01-Jul-2022 to 31-Dec-2022

<figure><img src="image_712.png" /></figure>
Croatia (HR)

Period (excess deaths): 01-Jul-2022 to 31-Dec-2022

Period (vax doses): 01-Jul-2022 to 31-Dec-2022

<figure><img src="image_713.png" /></figure>
Hungary (HU)

Period (excess deaths): 01-Jul-2022 to 31-Dec-2022

Period (vax doses): 01-Jul-2022 to 31-Dec-2022

<figure><img src="image_714.png" /></figure>
Iceland (IS)

Period (excess deaths): 01-Jul-2022 to 31-Dec-2022

Period (vax doses): 01-Jul-2022 to 31-Dec-2022

<figure><img src="image_715.png" /></figure>
Italy (IT)

Period (excess deaths): 01-Jul-2022 to 31-Dec-2022

Period (vax doses): 01-Jul-2022 to 31-Dec-2022

<figure><img src="image_716.png" /></figure>
Lithuania (LT)

Period (excess deaths): 01-Jul-2022 to 31-Dec-2022

Period (vax doses): 01-Jul-2022 to 31-Dec-2022

<figure><img src="image_717.png" /></figure>
Latvia (LV)

Period (excess deaths): 01-Jul-2022 to 31-Dec-2022

Period (vax doses): 01-Jul-2022 to 31-Dec-2022

<figure><img src="image_718.png" /></figure>
Malta (MT)

Period (excess deaths): 01-Jul-2022 to 31-Dec-2022

Period (vax doses): 01-Jul-2022 to 31-Dec-2022

<figure><img src="image_719.png" /></figure>
Netherlands (NL)

Period (excess deaths): 01-Jul-2022 to 31-Dec-2022

Period (vax doses): 01-Jul-2022 to 31-Dec-2022

<figure><img src="image_720.png" /></figure>
Norway (NO)

Period (excess deaths): 01-Jul-2022 to 31-Dec-2022

Period (vax doses): 01-Jul-2022 to 31-Dec-2022

<figure><img src="image_721.png" /></figure>
Poland (PL)

Period (excess deaths): 01-Jul-2022 to 31-Dec-2022

Period (vax doses): 01-Jul-2022 to 31-Dec-2022

<figure><img src="image_722.png" /></figure>
Portugal (PT)

Period (excess deaths): 01-Jul-2022 to 31-Dec-2022

Period (vax doses): 01-Jul-2022 to 31-Dec-2022

<figure><img src="image_723.png" /></figure>
Sweden (SE)

Period (excess deaths): 01-Jul-2022 to 31-Dec-2022

Period (vax doses): 01-Jul-2022 to 31-Dec-2022

<figure><img src="image_724.png" /></figure>
Slovenia (SI)

Period (excess deaths): 01-Jul-2022 to 31-Dec-2022

Period (vax doses): 01-Jul-2022 to 31-Dec-2022

<figure><img src="image_725.png" /></figure>
Slovakia (SK)

Period (excess deaths): 01-Jul-2022 to 31-Dec-2022

Period (vax doses): 01-Jul-2022 to 31-Dec-2022

<figure><img src="image_726.png" /><figcaption>Figure 56. Population-wide risk of death per COVID-19 vaccine injection (vDFR) versus age, for the half year 2022-H2, in 25 European countries with sufficient data, with exponential fits, and all-ages value as a horizontal line. Fit parameters are given in the legend.</figcaption></figure>

The exponential increase with age of the population-wide risk of death per COVID-19 vaccine injection (vDFR) is consistent with the hypothesis that the injection has a greater lethality in the most frail individuals already most susceptible to life threatening respiratory conditions (Section 5.12.3.3).
# 6 Conclusion

There is an overview in the Summary.

We are compelled to state that the public health establishment and its agents fundamentally caused all the excess mortality in the Covid period, via assaults on populations, harmful medical interventions and COVID-19 vaccine rollouts.

We conclude that nothing special would have occurred in terms of mortality had a pandemic not been declared and had the declaration not been acted upon.

(see appendixes after references)

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# Appendix A: All-cause mortality and excess all-cause mortality in 125 countries, 2015-2023

The figure in this Appendix has 125 consecutive panels, in alphabetical order of country name.

All-cause mortality by week or by month (bleu) and historic trend all-cause mortality by week or by month (orange) (top curves) and excess all-cause mortality by week or by month (bottom curves), with 1σ error bounds, for 125 countries (as labelled).

The vertical grey lines indicate the dates of 11 March 2020 (declared start of a pandemic) and 5 May 2023 (declared end of the declared pandemic).

In the labels of axes and in legends in all figures, throughout the present article, "1e-4" or "1E-4" means "times 10⁻⁴", and so forth (following scientific and computer-programming notation).
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Antigua and Barbuda (ATG)

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Argentina (ARG)

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Armenia (ARM)

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French Guiana (GUF)

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French Polynesia (PYF)

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Georgia (GEO)

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Maldives (MDV)

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Malta (MLT)

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Martinique (MTQ)

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Montenegro (MNE)

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Namibia (NAM)

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Netherlands (NLD)

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Réunion (REU)

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Romania (ROU)

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Russia (RUS)

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Saint Kitts and Nevis (KNA)

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Saint Vincent and the Grenadines (VCT)

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San Marino (SMR)

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Slovakia (SVK)

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Slovenia (SVN)

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South Africa (ZAF)

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Tajikistan (TJK)

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Thailand (THA)

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Transnistria (TRA)

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United Arab Emirates (ARE)

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United Kingdom (GBR)

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United States (USA)

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End of Appendix A
# Appendix B: All-cause mortality, excess all-cause mortality, and cumulative, in 125 countries, and vaccine rollouts, 2018-2023

The figure in this Appendix has 125 consecutive three-part panels, in alphabetical order of country name. Each panel, for a given country, has three subpanels, as follows.

**Top subpanel:** Reported all-cause mortality by week or by month (blue line), and COVID-19 vaccine doses administered by week or by month (from different sources, as labelled), 2018-2023, both normalized by the national population in 2019.

**Middle subpanel:** Calculated excess all-cause mortality, by week or by month (blue line), with 1σ error (shaded blue), and COVID-19 vaccine doses administered by week or by month (from different sources, as labelled), 2018-2023, both normalized by the national population in 2019.

**Bottom subpanel:** Cumulative calculated excess all-cause mortality (blue line), by week or by month, with 1σ error (shaded blue), and cumulative COVID-19 vaccine doses administered by week or by month (from different sources, as labelled), 2018-2023, both normalized by the national population in 2019.

In the labels of axes and in legends in all figures, throughout the present article, “1e-4” or “1E-4” means “times 10⁻⁴”, and so forth (following scientific and computer-programming notation).
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Algeria (DZA)

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Antigua and Barbuda (ATG)

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Azerbaijan (AZE)

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Barbados (BRB)

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Bolivia (BOL)

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<figure><img src="image_884.png" /><figcaption>Brunei (BRN)</figcaption></figure>
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Ecuador (ECU)

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<figure><img src="image_909.png" /></figure>

<figure><img src="image_910.png" /></figure>
<figure><img src="image_911.png" /></figure>
<figure><img src="image_912.png" /></figure>
<figure><img src="image_913.png" /></figure>
<figure><img src="image_914.png" /></figure>
Guadeloupe (GLP)

<figure><img src="image_915.png" /></figure>

<figure><img src="image_916.png" /></figure>

<figure><img src="image_917.png" /></figure>
<figure><img src="image_918.png" /></figure>
<figure><img src="image_919.png" /><figcaption>Hong Kong (HKG)</figcaption></figure>

<figure><img src="image_920.png" /></figure>

<figure><img src="image_921.png" /></figure>
<figure><img src="image_922.png" /></figure>
<figure><img src="image_923.png" /></figure>
<figure><img src="image_924.png" /></figure>
<figure><img src="image_925.png" /></figure>
<figure><img src="image_926.png" /></figure>

<figure><img src="image_927.png" /></figure>

<figure><img src="image_928.png" /></figure>
<figure><img src="image_929.png" /></figure>
<figure><img src="image_930.png" /><figcaption>Jamaica (JAM)</figcaption></figure>

<figure><img src="image_931.png" /></figure>

<figure><img src="image_932.png" /></figure>
<figure><img src="image_933.png" /><figcaption>Japan (JPN)</figcaption></figure>
<figure><img src="image_934.png" /></figure>
<figure><img src="image_935.png" /><figcaption>Kazakhstan (KAZ)</figcaption></figure>
<figure><img src="image_936.png" /></figure>
<figure><img src="image_937.png" /><figcaption>Kuwait (KWT)</figcaption></figure>

<figure><img src="image_938.png" /></figure>

<figure><img src="image_939.png" /></figure>
<figure><img src="image_940.png" /></figure>
<figure><img src="image_941.png" /></figure>
<figure><img src="image_942.png" /></figure>
<figure><img src="image_943.png" /></figure>
<figure><img src="image_944.png" /></figure>
<figure><img src="image_945.png" /></figure>
<figure><img src="image_946.png" /></figure>
<figure><img src="image_947.png" /></figure>
<figure><img src="image_948.png" /></figure>
<figure><img src="image_949.png" /></figure>
<figure><img src="image_950.png" /></figure>
Mauritius (MUS)

<figure><img src="image_951.png" /></figure>

<figure><img src="image_952.png" /></figure>

<figure><img src="image_953.png" /></figure>
<figure><img src="image_954.png" /></figure>
<figure><img src="image_955.png" /></figure>
<figure><img src="image_956.png" /></figure>

<figure><img src="image_957.png" /></figure>

<figure><img src="image_958.png" /></figure>
<figure><img src="image_959.png" /></figure>
<figure><img src="image_960.png" /></figure>
<figure><img src="image_961.png" /></figure>
<figure><img src="image_962.png" /></figure>
<figure><img src="image_963.png" /></figure>
<figure><img src="image_964.png" /></figure>
<figure><img src="image_965.png" /></figure>
Nicaragua (NIC)

<figure><img src="image_966.png" /></figure>

<figure><img src="image_967.png" /></figure>

<figure><img src="image_968.png" /></figure>
North Macedonia (MKD)

<figure><img src="image_969.png" /></figure>

<figure><img src="image_970.png" /></figure>

<figure><img src="image_971.png" /></figure>
<figure><img src="image_972.png" /></figure>
Oman (OMN)

<figure><img src="image_973.png" /></figure>

<figure><img src="image_974.png" /></figure>

<figure><img src="image_975.png" /></figure>
<figure><img src="image_976.png" /></figure>
<figure><img src="image_977.png" /></figure>
Paraguay (PRY)

<figure><img src="image_978.png" /></figure>

<figure><img src="image_979.png" /></figure>

<figure><img src="image_980.png" /></figure>
<figure><img src="image_981.png" /></figure>
Philippines (PHL)

<figure><img src="image_982.png" /></figure>

<figure><img src="image_983.png" /></figure>

<figure><img src="image_984.png" /></figure>
Poland (POL)

<figure><img src="image_985.png" /></figure>

<figure><img src="image_986.png" /></figure>

<figure><img src="image_987.png" /></figure>
<figure><img src="image_988.png" /></figure>
<figure><img src="image_989.png" /></figure>
<figure><img src="image_990.png" /></figure>
<figure><img src="image_991.png" /></figure>
<figure><img src="image_992.png" /></figure>
<figure><img src="image_993.png" /></figure>

<figure><img src="image_994.png" /></figure>

<figure><img src="image_995.png" /></figure>
Saint Kitts and Nevis (KNA)

<figure><img src="image_996.png" /></figure>

<figure><img src="image_997.png" /></figure>

<figure><img src="image_998.png" /></figure>
<figure><img src="image_999.png" /></figure>
<figure><img src="image_1000.png" /></figure>
<figure><img src="image_1001.png" /></figure>
<figure><img src="image_1002.png" /></figure>
<figure><img src="image_1003.png" /></figure>
<figure><img src="image_1004.png" /></figure>
Slovenia (SVN)

<figure><img src="image_1005.png" /></figure>

<figure><img src="image_1006.png" /></figure>

<figure><img src="image_1007.png" /></figure>
South Africa (ZAF)

<figure><img src="image_1008.png" /></figure>

<figure><img src="image_1009.png" /></figure>

<figure><img src="image_1010.png" /></figure>
<figure><img src="image_1011.png" /></figure>
<figure><img src="image_1012.png" /></figure>
<figure><img src="image_1013.png" /></figure>
<figure><img src="image_1014.png" /></figure>
<figure><img src="image_1015.png" /></figure>
<figure><img src="image_1016.png" /></figure>
Tajikistan (TJK)

<figure><img src="image_1017.png" /></figure>

<figure><img src="image_1018.png" /></figure>

<figure><img src="image_1019.png" /></figure>
<figure><img src="image_1020.png" /></figure>
<figure><img src="image_1021.png" /></figure>
Tunisia (TUN)

<figure><img src="image_1022.png" /></figure>

<figure><img src="image_1023.png" /></figure>

<figure><img src="image_1024.png" /></figure>
<figure><img src="image_1025.png" /></figure>
<figure><img src="image_1026.png" /></figure>
United Arab Emirates (ARE)

<figure><img src="image_1027.png" /></figure>

<figure><img src="image_1028.png" /></figure>

<figure><img src="image_1029.png" /></figure>
<figure><img src="image_1030.png" /></figure>
<figure><img src="image_1031.png" /></figure>
<figure><img src="image_1032.png" /></figure>
<figure><img src="image_1033.png" /></figure>

End of Appendix B
# Appendix C: Table of excess all-cause mortality 2020-2022, as rate per 2019 population, and as P-score, for the 93 countries with sufficient data, in decreasing order of population

Mortality data columns: xACM 2020-2022 (X202122) -- its error -- baseline historic ACM 2020-2022 (H202122) -- its error -- xACM 2020-2022 rate by 2019 population (X/pop) -- its error -- P-score 2020-2022 -- its error -- (see Section 4.9)

<table><thead><tr><th>Country Name</th><th>Code</th><th>Population (2019)</th><th>X202122</th><th>err (X202122)</th><th>H202122</th><th>err (H202122)</th><th>X/pop (%)</th><th>err (X/pop%)</th><th>X/hist (%) P-score</th><th>err (X/hist%)</th></tr></thead><tbody><tr><td>United States</td><td>USA</td><td>328,329,953</td><td>1,392,962</td><td>14,855</td><td>8,636,491</td><td>14,514</td><td>0.4243</td><td>0.0045</td><td>16.13</td><td>0.20</td></tr><tr><td>Brazil</td><td>BRA</td><td>211,782,878</td><td>843,047</td><td>13,064</td><td>4,150,043</td><td>12,872</td><td>0.3981</td><td>0.0062</td><td>20.31</td><td>0.38</td></tr><tr><td>Russia</td><td>RUS</td><td>144,406,261</td><td>1,340,131</td><td>24,155</td><td>5,138,692</td><td>24,022</td><td>0.9280</td><td>0.0167</td><td>26.08</td><td>0.59</td></tr><tr><td>Japan</td><td>JPN</td><td>126,633,000</td><td>170,685</td><td>11,852</td><td>4,248,180</td><td>11,665</td><td>0.1348</td><td>0.0094</td><td>4.02</td><td>0.29</td></tr><tr><td>Mexico</td><td>MEX</td><td>125,085,311</td><td>709,033</td><td>6,327</td><td>2,271,473</td><td>6,087</td><td>0.5668</td><td>0.0051</td><td>31.21</td><td>0.36</td></tr><tr><td>Philippines</td><td>PHL</td><td>110,380,804</td><td>253,877</td><td>7,895</td><td>1,919,253</td><td>7,756</td><td>0.2300</td><td>0.0072</td><td>13.23</td><td>0.46</td></tr><tr><td>Egypt</td><td>EGY</td><td>105,618,671</td><td>356,011</td><td>16,769</td><td>1,657,368</td><td>16,710</td><td>0.3371</td><td>0.0159</td><td>21.48</td><td>1.23</td></tr><tr><td>Turkey</td><td>TUR</td><td>83,481,684</td><td>252,688</td><td>10,155</td><td>1,327,683</td><td>10,078</td><td>0.3027</td><td>0.0122</td><td>19.03</td><td>0.91</td></tr><tr><td>Germany</td><td>DEU</td><td>83,092,962</td><td>264,713</td><td>13,404</td><td>2,775,578</td><td>13,290</td><td>0.3186</td><td>0.0161</td><td>9.54</td><td>0.53</td></tr><tr><td>Thailand</td><td>THA</td><td>71,307,763</td><td>157,351</td><td>11,902</td><td>1,503,701</td><td>11,832</td><td>0.2207</td><td>0.0167</td><td>10.46</td><td>0.87</td></tr><tr><td>France</td><td>FRA</td><td>67,388,001</td><td>166,250</td><td>7,569</td><td>1,769,150</td><td>7,440</td><td>0.2467</td><td>0.0112</td><td>9.40</td><td>0.47</td></tr><tr><td>United Kingdom</td><td>GBR</td><td>66,836,327</td><td>244,415</td><td>8,888</td><td>1,749,595</td><td>8,775</td><td>0.3657</td><td>0.0133</td><td>13.97</td><td>0.58</td></tr><tr><td>Italy</td><td>ITA</td><td>59,729,081</td><td>303,446</td><td>9,278</td><td>1,841,103</td><td>9,162</td><td>0.5080</td><td>0.0155</td><td>16.48</td><td>0.59</td></tr><tr><td>South Africa</td><td>ZAF</td><td>58,087,055</td><td>248,980</td><td>4,470</td><td>1,569,515</td><td>4,262</td><td>0.4286</td><td>0.0077</td><td>15.86</td><td>0.33</td></tr><tr><td>South Korea</td><td>KOR</td><td>51,764,822</td><td>77,877</td><td>2,939</td><td>907,896</td><td>2,767</td><td>0.1504</td><td>0.0057</td><td>8.58</td><td>0.35</td></tr><tr><td>Colombia</td><td>COL</td><td>50,187,406</td><td>190,870</td><td>1,780</td><td>748,901</td><td>1,493</td><td>0.3803</td><td>0.0035</td><td>25.49</td><td>0.28</td></tr><tr><td>Spain</td><td>ESP</td><td>47,134,837</td><td>188,935</td><td>5,806</td><td>1,202,206</td><td>5,685</td><td>0.4008</td><td>0.0123</td><td>15.72</td><td>0.56</td></tr><tr><td>Poland</td><td>POL</td><td>37,965,475</td><td>207,038</td><td>5,337</td><td>1,224,080</td><td>5,202</td><td>0.5453</td><td>0.0141</td><td>16.91</td><td>0.51</td></tr><tr><td>Canada</td><td>CAN</td><td>37,601,230</td><td>73,568</td><td>2,275</td><td>873,591</td><td>2,057</td><td>0.1957</td><td>0.0061</td><td>8.42</td><td>0.28</td></tr><tr><td>Uzbekistan</td><td>UZB</td><td>33,580,350</td><td>33,881</td><td>4,342</td><td>488,370</td><td>4,282</td><td>0.1009</td><td>0.0129</td><td>6.94</td><td>0.95</td></tr><tr><td>Malaysia</td><td>MYS</td><td>32,804,020</td><td>42,953</td><td>2,168</td><td>551,194</td><td>2,027</td><td>0.1309</td><td>0.0066</td><td>7.79</td><td>0.42</td></tr><tr><td>Australia</td><td>AUS</td><td>25,340,217</td><td>17,620</td><td>1,371</td><td>503,282</td><td>1,166</td><td>0.0695</td><td>0.0054</td><td>3.50</td><td>0.28</td></tr><tr><td>Taiwan</td><td>TWN</td><td>23,600,000</td><td>29,706</td><td>5,378</td><td>534,411</td><td>5,326</td><td>0.1259</td><td>0.0228</td><td>5.56</td><td>1.06</td></tr></tbody></table>
<table>
  <tr>
    <td>Romania</td>
    <td>ROU</td>
    <td>19,371,648</td>
    <td>134,276</td>
    <td>3,766</td>
    <td>759,100</td>
    <td>3,645</td>
    <td>0.6932</td>
    <td>0.0194</td>
    <td>17.69</td>
    <td>0.58</td>
  </tr>
  <tr>
    <td>Chile</td>
    <td>CHL</td>
    <td>19,039,485</td>
    <td>59,544</td>
    <td>1,166</td>
    <td>337,317</td>
    <td>982</td>
    <td>0.3127</td>
    <td>0.0061</td>
    <td>17.65</td>
    <td>0.39</td>
  </tr>
  <tr>
    <td>Kazakhstan</td>
    <td>KAZ</td>
    <td>18,513,673</td>
    <td>86,870</td>
    <td>3,543</td>
    <td>393,808</td>
    <td>3,475</td>
    <td>0.4692</td>
    <td>0.0191</td>
    <td>22.06</td>
    <td>1.09</td>
  </tr>
  <tr>
    <td>Netherlands</td>
    <td>NLD</td>
    <td>17,344,874</td>
    <td>53,443</td>
    <td>1,874</td>
    <td>449,963</td>
    <td>1,735</td>
    <td>0.3081</td>
    <td>0.0108</td>
    <td>11.88</td>
    <td>0.46</td>
  </tr>
  <tr>
    <td>Ecuador</td>
    <td>ECU</td>
    <td>17,343,740</td>
    <td>81,814</td>
    <td>1,040</td>
    <td>227,961</td>
    <td>879</td>
    <td>0.4717</td>
    <td>0.0060</td>
    <td>35.89</td>
    <td>0.58</td>
  </tr>
  <tr>
    <td>Guatemala</td>
    <td>GTM</td>
    <td>16,604,026</td>
    <td>53,197</td>
    <td>1,037</td>
    <td>253,360</td>
    <td>878</td>
    <td>0.3204</td>
    <td>0.0062</td>
    <td>21.00</td>
    <td>0.47</td>
  </tr>
  <tr>
    <td>Tunisia</td>
    <td>TUN</td>
    <td>12,049,314</td>
    <td>39,913</td>
    <td>1,881</td>
    <td>212,677</td>
    <td>1,813</td>
    <td>0.3312</td>
    <td>0.0156</td>
    <td>18.77</td>
    <td>1.04</td>
  </tr>
  <tr>
    <td>Belgium</td>
    <td>BEL</td>
    <td>11,488,980</td>
    <td>38,278</td>
    <td>1,784</td>
    <td>313,572</td>
    <td>1,684</td>
    <td>0.3332</td>
    <td>0.0155</td>
    <td>12.21</td>
    <td>0.63</td>
  </tr>
  <tr>
    <td>Cuba</td>
    <td>CUB</td>
    <td>11,316,697</td>
    <td>57,381</td>
    <td>2,613</td>
    <td>342,810</td>
    <td>2,536</td>
    <td>0.5070</td>
    <td>0.0231</td>
    <td>16.74</td>
    <td>0.88</td>
  </tr>
  <tr>
    <td>Greece</td>
    <td>GRC</td>
    <td>10,721,582</td>
    <td>49,920</td>
    <td>2,074</td>
    <td>361,572</td>
    <td>1,973</td>
    <td>0.4656</td>
    <td>0.0193</td>
    <td>13.81</td>
    <td>0.65</td>
  </tr>
  <tr>
    <td>Jordan</td>
    <td>JOR</td>
    <td>10,698,683</td>
    <td>17,093</td>
    <td>1,097</td>
    <td>86,135</td>
    <td>1,049</td>
    <td>0.1598</td>
    <td>0.0103</td>
    <td>19.84</td>
    <td>1.51</td>
  </tr>
  <tr>
    <td>Czechia</td>
    <td>CZE</td>
    <td>10,671,870</td>
    <td>54,481</td>
    <td>1,821</td>
    <td>330,209</td>
    <td>1,713</td>
    <td>0.5105</td>
    <td>0.0171</td>
    <td>16.50</td>
    <td>0.63</td>
  </tr>
  <tr>
    <td>Portugal</td>
    <td>PRT</td>
    <td>10,286,263</td>
    <td>44,902</td>
    <td>2,076</td>
    <td>324,327</td>
    <td>1,985</td>
    <td>0.4365</td>
    <td>0.0202</td>
    <td>13.84</td>
    <td>0.72</td>
  </tr>
  <tr>
    <td>Sweden</td>
    <td>SWE</td>
    <td>10,278,887</td>
    <td>21,398</td>
    <td>1,129</td>
    <td>259,934</td>
    <td>997</td>
    <td>0.2082</td>
    <td>0.0110</td>
    <td>8.23</td>
    <td>0.46</td>
  </tr>
  <tr>
    <td>Azerbaijan</td>
    <td>AZE</td>
    <td>10,024,283</td>
    <td>42,605</td>
    <td>1,581</td>
    <td>170,729</td>
    <td>1,513</td>
    <td>0.4250</td>
    <td>0.0158</td>
    <td>24.95</td>
    <td>1.14</td>
  </tr>
  <tr>
    <td>Hungary</td>
    <td>HUN</td>
    <td>9,771,141</td>
    <td>53,933</td>
    <td>2,337</td>
    <td>374,209</td>
    <td>2,244</td>
    <td>0.5520</td>
    <td>0.0239</td>
    <td>14.41</td>
    <td>0.71</td>
  </tr>
  <tr>
    <td>Israel</td>
    <td>ISR</td>
    <td>9,054,000</td>
    <td>16,475</td>
    <td>656</td>
    <td>133,046</td>
    <td>531</td>
    <td>0.1820</td>
    <td>0.0072</td>
    <td>12.38</td>
    <td>0.53</td>
  </tr>
  <tr>
    <td>Austria</td>
    <td>AUT</td>
    <td>8,879,920</td>
    <td>29,869</td>
    <td>1,348</td>
    <td>238,839</td>
    <td>1,245</td>
    <td>0.3364</td>
    <td>0.0152</td>
    <td>12.51</td>
    <td>0.63</td>
  </tr>
  <tr>
    <td>Switzerland</td>
    <td>CHE</td>
    <td>8,575,280</td>
    <td>22,741</td>
    <td>1,115</td>
    <td>196,328</td>
    <td>1,013</td>
    <td>0.2652</td>
    <td>0.0130</td>
    <td>11.58</td>
    <td>0.62</td>
  </tr>
  <tr>
    <td>Hong Kong</td>
    <td>HKG</td>
    <td>7,507,900</td>
    <td>18,932</td>
    <td>1,278</td>
    <td>145,143</td>
    <td>1,212</td>
    <td>0.2522</td>
    <td>0.0170</td>
    <td>13.04</td>
    <td>0.98</td>
  </tr>
  <tr>
    <td>Bulgaria</td>
    <td>BGR</td>
    <td>6,975,761</td>
    <td>77,376</td>
    <td>1,769</td>
    <td>310,377</td>
    <td>1,657</td>
    <td>1.1092</td>
    <td>0.0254</td>
    <td>24.93</td>
    <td>0.70</td>
  </tr>
  <tr>
    <td>Serbia</td>
    <td>SRB</td>
    <td>6,945,235</td>
    <td>69,860</td>
    <td>3,304</td>
    <td>293,729</td>
    <td>3,249</td>
    <td>1.0059</td>
    <td>0.0476</td>
    <td>23.78</td>
    <td>1.38</td>
  </tr>
  <tr>
    <td>Paraguay</td>
    <td>PRY</td>
    <td>6,530,026</td>
    <td>27,151</td>
    <td>917</td>
    <td>104,577</td>
    <td>843</td>
    <td>0.4158</td>
    <td>0.0140</td>
    <td>25.96</td>
    <td>1.07</td>
  </tr>
  <tr>
    <td>Kyrgyzstan</td>
    <td>KGZ</td>
    <td>6,456,200</td>
    <td>13,678</td>
    <td>763</td>
    <td>96,892</td>
    <td>687</td>
    <td>0.2119</td>
    <td>0.0118</td>
    <td>14.12</td>
    <td>0.88</td>
  </tr>
  <tr>
    <td>Denmark</td>
    <td>DNK</td>
    <td>5,814,422</td>
    <td>7,830</td>
    <td>693</td>
    <td>161,587</td>
    <td>558</td>
    <td>0.1347</td>
    <td>0.0119</td>
    <td>4.85</td>
    <td>0.44</td>
  </tr>
  <tr>
    <td>Singapore</td>
    <td>SGP</td>
    <td>5,703,569</td>
    <td>6,076</td>
    <td>495</td>
    <td>67,160</td>
    <td>415</td>
    <td>0.1065</td>
    <td>0.0087</td>
    <td>9.05</td>
    <td>0.78</td>
  </tr>
  <tr>
    <td>Finland</td>
    <td>FIN</td>
    <td>5,521,606</td>
    <td>13,795</td>
    <td>751</td>
    <td>160,843</td>
    <td>625</td>
    <td>0.2498</td>
    <td>0.0136</td>
    <td>8.58</td>
    <td>0.50</td>
  </tr>
  <tr>
    <td>Slovakia</td>
    <td>SVK</td>
    <td>5,454,147</td>
    <td>33,652</td>
    <td>962</td>
    <td>155,558</td>
    <td>859</td>
    <td>0.6170</td>
    <td>0.0176</td>
    <td>21.63</td>
    <td>0.73</td>
  </tr>
  <tr>
    <td>Norway</td>
    <td>NOR</td>
    <td>5,347,896</td>
    <td>9,157</td>
    <td>611</td>
    <td>117,863</td>
    <td>496</td>
    <td>0.1712</td>
    <td>0.0114</td>
    <td>7.77</td>
    <td>0.55</td>
  </tr>
  <tr>
    <td>Costa Rica</td>
    <td>CRI</td>
    <td>5,084,532</td>
    <td>7,332</td>
    <td>570</td>
    <td>78,900</td>
    <td>489</td>
    <td>0.1442</td>
    <td>0.0112</td>
    <td>9.29</td>
    <td>0.77</td>
  </tr>
  <tr>
    <td>New Zealand</td>
    <td>NZL</td>
    <td>4,979,200</td>
    <td>-1,542</td>
    <td>506</td>
    <td>106,753</td>
    <td>389</td>
    <td>-0.0310</td>
    <td>0.0102</td>
    <td>-1.44</td>
    <td>0.47</td>
  </tr>
  <tr>
    <td>Ireland</td>
    <td>IRL</td>
    <td>4,934,340</td>
    <td>10,396</td>
    <td>555</td>
    <td>91,468</td>
    <td>454</td>
    <td>0.2107</td>
    <td>0.0112</td>
    <td>11.37</td>
    <td>0.65</td>
  </tr>
  <tr>
    <td>Oman</td>
    <td>OMN</td>
    <td>4,602,768</td>
    <td>4,757</td>
    <td>376</td>
    <td>28,130</td>
    <td>329</td>
    <td>0.1034</td>
    <td>0.0082</td>
    <td>16.91</td>
    <td>1.51</td>
  </tr>
  <tr>
    <td>Kuwait</td>
    <td>KWT</td>
    <td>4,441,100</td>
    <td>7,052</td>
    <td>250</td>
    <td>22,495</td>
    <td>182</td>
    <td>0.1588</td>
    <td>0.0056</td>
    <td>31.35</td>
    <td>1.31</td>
  </tr>
  <tr>
    <td>Croatia</td>
    <td>HRV</td>
    <td>4,065,253</td>
    <td>27,860</td>
    <td>1,012</td>
    <td>147,105</td>
    <td>922</td>
    <td>0.6853</td>
    <td>0.0249</td>
    <td>18.94</td>
    <td>0.80</td>
  </tr>
  <tr>
    <td>Georgia</td>
    <td>GEO</td>
    <td>3,720,161</td>
    <td>28,787</td>
    <td>1,336</td>
    <td>130,773</td>
    <td>1,276</td>
    <td>0.7738</td>
    <td>0.0359</td>
    <td>22.01</td>
    <td>1.23</td>
  </tr>
  <tr>
    <td>Uruguay</td>
    <td>URY</td>
    <td>3,428,409</td>
    <td>5,336</td>
    <td>974</td>
    <td>107,791</td>
    <td>914</td>
    <td>0.1556</td>
    <td>0.0284</td>
    <td>4.95</td>
    <td>0.94</td>
  </tr>
  <tr>
    <td>Bosnia</td>
    <td>BIH</td>
    <td>3,360,711</td>
    <td>25,716</td>
    <td>1,060</td>
    <td>105,254</td>
    <td>996</td>
    <td>0.7652</td>
    <td>0.0315</td>
    <td>24.43</td>
    <td>1.23</td>
  </tr>
  <tr>
    <td>Mongolia</td>
    <td>MNG</td>
    <td>3,232,430</td>
    <td>664</td>
    <td>509</td>
    <td>53,169</td>
    <td>453</td>
    <td>0.0205</td>
    <td>0.0157</td>
    <td>1.25</td>
    <td>0.97</td>
  </tr>
  <tr>
    <td>Puerto Rico</td>
    <td>PRI</td>
    <td>3,193,694</td>
    <td>10,055</td>
    <td>606</td>
    <td>89,136</td>
    <td>519</td>
    <td>0.3148</td>
    <td>0.0190</td>
    <td>11.28</td>
    <td>0.74</td>
  </tr>
  <tr>
    <td>Albania</td>
    <td>ALB</td>
    <td>2,854,191</td>
    <td>18,117</td>
    <td>850</td>
    <td>62,967</td>
    <td>801</td>
    <td>0.6348</td>
    <td>0.0298</td>
    <td>28.77</td>
    <td>1.70</td>
  </tr>
  <tr>
    <td>Armenia</td>
    <td>ARM</td>
    <td>2,820,602</td>
    <td>24,095</td>
    <td>903</td>
    <td>72,605</td>
    <td>848</td>
    <td>0.8543</td>
    <td>0.0320</td>
    <td>33.19</td>
    <td>1.61</td>
  </tr>
  <tr>
    <td>Qatar</td>
    <td>QAT</td>
    <td>2,807,235</td>
    <td>1,349</td>
    <td>114</td>
    <td>6,740</td>
    <td>70</td>
    <td>0.0481</td>
    <td>0.0041</td>
    <td>20.01</td>
    <td>1.83</td>
  </tr>
</table>
<table><tr><td>Lithuania</td><td>LTU</td><td>2,794,137</td><td>26,248</td><td>668</td><td>105,761</td><td>562</td><td>0.9394</td><td>0.0239</td><td>24.82</td><td>0.75</td></tr><tr><td>Moldova</td><td>MDA</td><td>2,664,224</td><td>21,083</td><td>999</td><td>101,572</td><td>936</td><td>0.7913</td><td>0.0375</td><td>20.76</td><td>1.16</td></tr><tr><td>Slovenia</td><td>SVN</td><td>2,088,385</td><td>8,107</td><td>449</td><td>60,747</td><td>365</td><td>0.3882</td><td>0.0215</td><td>13.35</td><td>0.81</td></tr><tr><td>North Macedonia</td><td>MKD</td><td>2,076,694</td><td>17,952</td><td>747</td><td>58,662</td><td>694</td><td>0.8645</td><td>0.0360</td><td>30.60</td><td>1.62</td></tr><tr><td>Latvia</td><td>LVA</td><td>1,913,822</td><td>11,937</td><td>504</td><td>80,782</td><td>402</td><td>0.6237</td><td>0.0263</td><td>14.78</td><td>0.68</td></tr><tr><td>Kosovo</td><td>KOS</td><td>1,788,878</td><td>5,609</td><td>272</td><td>29,533</td><td>198</td><td>0.3135</td><td>0.0152</td><td>18.99</td><td>1.02</td></tr><tr><td>Estonia</td><td>EST</td><td>1,326,898</td><td>5,617</td><td>356</td><td>45,394</td><td>276</td><td>0.4233</td><td>0.0268</td><td>12.37</td><td>0.84</td></tr><tr><td>Mauritius</td><td>MUS</td><td>1,265,711</td><td>1,913</td><td>303</td><td>35,358</td><td>234</td><td>0.1511</td><td>0.0239</td><td>5.41</td><td>0.89</td></tr><tr><td>Cyprus</td><td>CYP</td><td>1,228,836</td><td>2,878</td><td>215</td><td>17,877</td><td>160</td><td>0.2342</td><td>0.0175</td><td>16.10</td><td>1.32</td></tr><tr><td>Réunion</td><td>REU</td><td>949,419</td><td>1,182</td><td>175</td><td>16,065</td><td>117</td><td>0.1245</td><td>0.0184</td><td>7.36</td><td>1.13</td></tr><tr><td>Macao</td><td>MAC</td><td>663,653</td><td>765</td><td>143</td><td>6,788</td><td>114</td><td>0.1153</td><td>0.0215</td><td>11.27</td><td>2.26</td></tr><tr><td>Montenegro</td><td>MNE</td><td>622,028</td><td>3,762</td><td>235</td><td>19,464</td><td>180</td><td>0.6048</td><td>0.0378</td><td>19.33</td><td>1.35</td></tr><tr><td>Luxembourg</td><td>LUX</td><td>620,001</td><td>236</td><td>173</td><td>13,206</td><td>129</td><td>0.0381</td><td>0.0279</td><td>1.79</td><td>1.32</td></tr><tr><td>Malta</td><td>MLT</td><td>504,062</td><td>1,417</td><td>164</td><td>10,886</td><td>121</td><td>0.2811</td><td>0.0325</td><td>13.02</td><td>1.62</td></tr><tr><td>Brunei</td><td>BRN</td><td>438,048</td><td>509</td><td>119</td><td>5,429</td><td>91</td><td>0.1162</td><td>0.0272</td><td>9.38</td><td>2.32</td></tr><tr><td>Guadeloupe</td><td>GLP</td><td>384,239</td><td>1,537</td><td>154</td><td>10,566</td><td>108</td><td>0.4000</td><td>0.0401</td><td>14.55</td><td>1.57</td></tr><tr><td>Martinique</td><td>MTQ</td><td>364,508</td><td>1,521</td><td>148</td><td>10,807</td><td>98</td><td>0.4173</td><td>0.0406</td><td>14.07</td><td>1.46</td></tr><tr><td>Iceland</td><td>ISL</td><td>360,563</td><td>535</td><td>117</td><td>6,733</td><td>81</td><td>0.1484</td><td>0.0324</td><td>7.95</td><td>1.82</td></tr><tr><td>French Guiana</td><td>GUF</td><td>290,932</td><td>316</td><td>80</td><td>3,104</td><td>55</td><td>0.1086</td><td>0.0275</td><td>10.18</td><td>2.71</td></tr><tr><td>Barbados</td><td>BRB</td><td>280,180</td><td>462</td><td>206</td><td>8,499</td><td>183</td><td>0.1649</td><td>0.0735</td><td>5.44</td><td>2.53</td></tr><tr><td>New Caledonia</td><td>NCL</td><td>271,230</td><td>416</td><td>111</td><td>5,008</td><td>84</td><td>0.1534</td><td>0.0409</td><td>8.31</td><td>2.33</td></tr><tr><td>Mayotte</td><td>MYT</td><td>270,372</td><td>545</td><td>70</td><td>2,401</td><td>45</td><td>0.2016</td><td>0.0259</td><td>22.70</td><td>3.23</td></tr><tr><td>Seychelles</td><td>SYC</td><td>97,625</td><td>-69</td><td>71</td><td>2,603</td><td>51</td><td>-0.0707</td><td>0.0727</td><td>-2.65</td><td>2.71</td></tr><tr><td>Greenland</td><td>GRL</td><td>56,225</td><td>-70</td><td>58</td><td>1,648</td><td>43</td><td>-0.1245</td><td>0.1032</td><td>-4.25</td><td>3.48</td></tr><tr><td>Faroe Islands</td><td>FRO</td><td>51,681</td><td>22</td><td>46</td><td>1,260</td><td>29</td><td>0.0426</td><td>0.0890</td><td>1.75</td><td>3.70</td></tr><tr><td>Monaco</td><td>MCO</td><td>37,034</td><td>223</td><td>60</td><td>1,445</td><td>44</td><td>0.6021</td><td>0.1620</td><td>15.43</td><td>4.52</td></tr><tr><td>San Marino</td><td>SMR</td><td>34,178</td><td>150</td><td>42</td><td>767</td><td>30</td><td>0.4389</td><td>0.1229</td><td>19.56</td><td>6.08</td></tr></table>
# Appendix D: Excess all-cause mortality for various time periods, expressed as a fraction of the 2019 population, by country

The figure in this Appendix has 11 consecutive panels, in order of the time periods considered.

Each panel shows excess all-cause mortality versus country code (by country, as labelled, with country codes in alphabetical order), for a given time period (as labelled for each panel: 2020-2022, 2020, 2021, 2022, and each half calendar year 2020-H1 through 2023-H1), expressed as a fraction (not percentage) of 2019 population, with 1σ error bars, with the points colour coded by hierarchical cluster.

Each panel shows a legend with the values of the mean, median, standard deviation, coefficient of variation (standard deviation / mean), and skewness (Fisher-Pearson coefficient of skewness), for the data in the panel. The number (N) of countries is given in parentheses at the bottom of each panel, for each time period.

In the labels of axes and in legends in all figures, throughout the present article, "1e-4" or "1E-4" means "times 10⁻⁴", and so forth (following scientific and computer-programming notation).
<figure><img src="image_1034.png" /></figure>

<figure><img src="image_1035.png" /></figure>

<figure><img src="image_1036.png" /></figure>

<figure><img src="image_1037.png" /></figure>
<figure><img src="image_1038.png" /><figcaption>Country codes (125)</figcaption></figure>

<figure><img src="image_1039.png" /><figcaption>Country codes (124)</figcaption></figure>

<figure><img src="image_1040.png" /><figcaption>Country codes (121)</figcaption></figure>

<figure><img src="image_1041.png" /><figcaption>Country codes (119)</figcaption></figure>
<figure><img src="image_1042.png" /></figure>

<figure><img src="image_1043.png" /></figure>

<figure><img src="image_1044.png" /></figure>

End of Appendix D
# Appendix E: Temporal sequence of world maps of excess all-cause mortality by population, through the Covid period by half years

The figure in this appendix has 9 panels (9 world maps), for the half years 2019-H1 through 2023-H1, in chronological order.

The colour coding is capped at 25 excess deaths per 10k population for all the maps to aid visualization.
Excess deaths per 10k persons (2019-01-01 to 2019-06-30)

<figure><img src="image_1045.png" /></figure>

Excess deaths per 10k persons (2019-07-01 to 2019-12-31)

<figure><img src="image_1046.png" /></figure>
Excess deaths per 10k persons (2020-01-01 to 2020-06-30)

<figure><img src="image_1047.png" /></figure>

Excess deaths per 10k persons (2020-07-01 to 2020-12-31)

<figure><img src="image_1048.png" /></figure>
Excess deaths per 10k persons (2021-01-01 to 2021-06-30)

<figure><img src="image_1049.png" /></figure>

Excess deaths per 10k persons (2021-07-01 to 2021-12-31)

<figure><img src="image_1050.png" /></figure>
Excess deaths per 10k persons (2022-01-01 to 2022-06-30)

<figure><img src="image_1051.png" /></figure>

Excess deaths per 10k persons (2022-07-01 to 2022-12-31)

<figure><img src="image_1052.png" /></figure>
Excess deaths per 10k persons (2023-01-01 to 2023-06-30)

<figure><img src="image_1053.png" /></figure>

End of Appendix E
Appendix F: Scatter plots of relations between socio-economic variables

<figure><img src="image_1054.png" /></figure>
## Appendix G: Table of Pearson correlation coefficients between socio-economic variables

<table><thead><tr><th>SOCIO-ECONOMIC VARIABLE</th><th>% of population living on less than $5.50 per day</th><th>% of population living on less than $30 per day</th><th>GDP per capita in 2021 ($100k USD)</th><th>Median income or expenditure per day ($)</th><th>Income or consumption inequality (Gini coefficient)</th><th>% of the population aged 60+</th><th>Life expectancy at age 65</th></tr></thead><tbody><tr><td>% of population living on less than $5.50 per day</td><td>1</td><td>0.69</td><td>-0.56</td><td>-0.7</td><td>0.48</td><td>-0.68</td><td>-0.63</td></tr><tr><td>% of population living on less than $30 per day</td><td>0.69</td><td>1</td><td>-0.87</td><td>-0.98</td><td>0.38</td><td>-0.59</td><td>-0.8</td></tr><tr><td>GDP per capita in 2021 ($100k USD)</td><td>-0.56</td><td>-0.87</td><td>1</td><td>0.91</td><td>-0.32</td><td>0.48</td><td>0.63</td></tr><tr><td>Median income or expenditure per day ($)</td><td>-0.7</td><td>-0.98</td><td>0.91</td><td>1</td><td>-0.41</td><td>0.55</td><td>0.76</td></tr></tbody></table>
<table><tr><td>Income or consumption inequality (Gini coefficient)</td><td>0.48</td><td>0.38</td><td>-0.32</td><td>-0.41</td><td>1</td><td>-0.52</td><td>-0.18</td></tr><tr><td>% of the population aged 60+</td><td>-0.68</td><td>-0.59</td><td>0.48</td><td>0.55</td><td>-0.52</td><td>1</td><td>0.56</td></tr><tr><td>Life expectancy at age 65</td><td>-0.63</td><td>-0.8</td><td>0.63</td><td>0.76</td><td>-0.18</td><td>0.56</td><td>1</td></tr></table>
# Appendix H: Tables of Pearson correlation coefficients between excess all-cause mortality and socio-economic variables, in different time periods

## 1 of 2: Using excess all-cause mortalities normalized by baseline historic mortality (P-score)

<table><thead><tr><th></th><th>2020-2022</th><th>2020-H1</th><th>2020-H2</th><th>2021-H1</th><th>2021-H2</th><th>2022-H1</th><th>2022-H2</th><th>2023-H1</th><th>2020-2023-H1</th></tr></thead><tbody><tr><td>GDP per capita in 2021 ($100k USD)</td><td>-0.368</td><td>-0.013</td><td>-0.238</td><td>-0.300</td><td>-0.362</td><td>-0.100</td><td>0.297</td><td>0.225</td><td>-0.511</td></tr><tr><td>Income or consumption inequality (Gini coeff.)</td><td>0.227</td><td>0.154</td><td>-0.007</td><td>0.394</td><td>0.039</td><td>0.169</td><td>-0.327</td><td>0.061</td><td>0.271</td></tr><tr><td>% of population living on less than $5.50 / day</td><td>0.394</td><td>0.178</td><td>0.258</td><td>0.310</td><td>0.345</td><td>0.008</td><td>-0.540</td><td>-0.216</td><td>0.398</td></tr><tr><td>% of population living on less than $30 / day</td><td>0.544</td><td>0.035</td><td>0.394</td><td>0.500</td><td>0.573</td><td>0.204</td><td>-0.661</td><td>-0.419</td><td>0.634</td></tr><tr><td>Median income or expenditure per day ($)</td><td>-0.533</td><td>-0.028</td><td>-0.366</td><td>-0.484</td><td>-0.533</td><td>-0.225</td><td>0.607</td><td>0.347</td><td>-0.618</td></tr></tbody></table>
<table><tr><td>Life expectancy at age 65</td><td>-0.326</td><td>0.060</td><td>-0.315</td><td>-0.325</td><td>-0.446</td><td>0.032</td><td>0.516</td><td>0.440</td><td>-0.345</td></tr><tr><td>% of the population aged 60+</td><td>-0.153</td><td>-0.136</td><td>-0.096</td><td>-0.314</td><td>-0.162</td><td>0.054</td><td>0.384</td><td>0.074</td><td>-0.131</td></tr><tr><td>% of the population aged 90+</td><td>-0.146</td><td>-0.032</td><td>-0.158</td><td>-0.156</td><td>-0.229</td><td>0.006</td><td>0.218</td><td>0.154</td><td>-0.241</td></tr></table>

## 2 of 2: Using excess all-cause mortality rates (normalized by 2019 population)

<table><thead><tr><th></th><th>2020-2022</th><th>2020-H1</th><th>2020-H2</th><th>2021-H1</th><th>2021-H2</th><th>2022-H1</th><th>2022-H2</th><th>2023-H1</th><th>2023-H1</th></tr></thead><tbody><tr><td>GDP per capita in 2021 ($100k USD)</td><td>-0.256</td><td>0.045</td><td>-0.174</td><td>-0.228</td><td>-0.244</td><td>-0.111</td><td>0.256</td><td>0.186</td><td>-0.493</td></tr><tr><td>Income or consumption inequality (Gini coeff.)</td><td>-0.020</td><td>0.057</td><td>-0.180</td><td>0.226</td><td>-0.079</td><td>-0.053</td><td>-0.375</td><td>-0.011</td><td>0.092</td></tr><tr><td>% of population living on less than $5.50 / day</td><td>0.113</td><td>0.074</td><td>0.057</td><td>0.175</td><td>0.098</td><td>-0.135</td><td>-0.524</td><td>-0.238</td><td>0.152</td></tr><tr><td>% of population living on less than $30 per day</td><td>0.391</td><td>-</td><td>0.291</td><td>0.493</td><td>0.380</td><td>0.155</td><td>-0.555</td><td>-0.449</td><td>0.494</td></tr><tr><td>Median income or expenditure per day ($)</td><td>-0.372</td><td>0.060</td><td>-0.258</td><td>-0.488</td><td>-0.352</td><td>-0.158</td><td>0.510</td><td>0.377</td><td>-0.476</td></tr></tbody></table>
<table><tr><td>Life expectancy at age 65</td><td>-0.275</td><td>0.145</td><td>-0.279</td><td>-0.315</td><td>-0.336</td><td>-0.017</td><td>0.423</td><td>0.458</td><td>-0.339</td></tr><tr><td>% of the population aged 60+</td><td>0.375</td><td>0.068</td><td>0.249</td><td>0.124</td><td>0.243</td><td>0.437</td><td>0.702</td><td>0.240</td><td>0.366</td></tr><tr><td>% of the population aged 90+</td><td>0.130</td><td>0.107</td><td>0.010</td><td>0.106</td><td>0.000</td><td>0.209</td><td>0.412</td><td>0.344</td><td>0.080</td></tr></table>
# Appendix I: Tables of Pearson correlation coefficients between excess all-cause mortality and number of vaccine injections by population, for different time periods

## 1 of 2: Using excess all-cause mortalities normalized by baseline historic mortality (P-score)

<table><thead><tr><th></th><th>2020-2022</th><th>2020-H1</th><th>2020-H2</th><th>2021-H1</th><th>2021-H2</th><th>2022-H1</th><th>2022-H2</th><th>2023-H1</th></tr></thead><tbody><tr><td>vaxN 2021-2022 (WHO) per 2019 pop</td><td>-0.473</td><td>0.225</td><td>-0.324</td><td>-0.166</td><td>-0.533</td><td>-0.098</td><td>0.433</td><td>0.472</td></tr><tr><td>vaxN 2021 (WHO) per 2019 pop</td><td>-0.479</td><td>0.162</td><td>-0.422</td><td>-0.219</td><td>-0.481</td><td>-0.110</td><td>0.454</td><td>0.461</td></tr><tr><td>vaxN 2022 (WHO) per 2019 pop</td><td>-0.325</td><td>0.349</td><td>-0.101</td><td>-0.024</td><td>-0.397</td><td>-0.098</td><td>0.261</td><td>0.371</td></tr></tbody></table>
2 of 2: Using excess all-cause mortality rates (normalized by 2019 population)

<table><thead><tr><th></th><th>Excess deaths per pop (2020-2022)</th><th>Excess deaths per pop (2020-2023-H1)</th><th>Excess deaths per pop (2020-H1)</th><th>Excess deaths per pop (2020-H2)</th><th>Excess deaths per pop (2021-H1)</th><th>Excess deaths per pop (2021-H2)</th><th>Excess deaths per pop (2022-H1)</th><th>Excess deaths per pop (2022-H2)</th><th>Excess deaths per pop (2023-H1)</th></tr></thead><tbody><tr><td>vaxN 2021-2022 (WHO) per 2019 pop</td><td>-0.554</td><td>-0.554</td><td>0.213</td><td>-0.396</td><td>-0.309</td><td>-0.511</td><td>-0.322</td><td>0.218</td><td>0.409</td></tr><tr><td>vaxN 2021 (WHO) per 2019 pop</td><td>-0.467</td><td>-0.480</td><td>0.156</td><td>-0.414</td><td>-0.312</td><td>-0.409</td><td>-0.251</td><td>0.284</td><td>0.428</td></tr><tr><td>vaxN 2022 (WHO) per 2019 pop</td><td>-0.509</td><td>-0.511</td><td>0.320</td><td>-0.233</td><td>-0.234</td><td>-0.478</td><td>-0.364</td><td>0.042</td><td>0.301</td></tr></tbody></table>
# Appendix J: Correction and extension of the analysis by Ioannidis et al. (2023) — excess all-cause mortality in “less and more vulnerable” countries

This appendix has 3 composite panels. Each composite panel presents and compares the countries used in the study by Ioannidis et al. (2023), alone or with more countries, and using our excess all-cause mortality quantification method (rather than the method used by Ioannidis et al., 2023).

All subpanels are in left-right pairs, with the country codes shown in the partner on the right.

Each composite panel shows P-score for the entire Covid period (half years 2020-H1 through 2023-H1) (y-axis) versus each of the three socio-economic parameters used by Ioannidis et al. (2023) (x-axis: GDP, top; Gini coefficient, middle; share population living with <$5.50/day, bottom). Pearson correlation coefficients are indicated for all plots (top-right).

**First composite panel:** Only counties included by Ioannidis et al. (2023), labelled by them as “vulnerable” (red) and “less vulnerable” (green).

**Second composite panel:** Same, adding countries with presently available data not considered by Ioannidia et al. (2023) (in black).

**Third composition panel:** All countries discerned by colour, as labelled in the legend of the top-left subpanel.
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