# Spatiotemporal variation of excess all-cause mortality in the world (125 countries) during the Covid period 2020-2023 regarding socio-economic factors and public-health and medical interventions

Denis G Rancourt, Joseph Hickey, Christian Linard

► To cite this version:

Denis G Rancourt, Joseph Hickey, Christian Linard. Spatiotemporal variation of excess all-cause mortality in the world (125 countries) during the Covid period 2020-2023 regarding socio-economic factors and public-health and medical interventions. Université du Québec à Trois-Rivières; CORRELATION Research in the Public Interest. 2024, pp.521. hal-05091965

HAL Id: hal-05091965

https://hal.science/hal-05091965v1

Submitted on 12 Jun 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution - ShareAlike 4.0 International License
# Spatiotemporal variation of excess all-cause mortality in the world (125 countries) during the Covid period 2020-2023 regarding socio-economic factors and public-health and medical interventions

Denis G. Rancourt*¹; Joseph Hickey¹²; Christian Linard²

¹ Correlation Research in the Public Interest (correlation-canada.org)

² Université du Québec à Trois-Rivières (http://www.uqtr.ca/PagePerso/Christian.Linard)

* denis.rancourt@gmail.com

This Correlation report is simultaneously posted on several websites, including: https://correlation-canada.org/research/ https://denisrancourt.ca/ https://ocla.ca/covid/ https://denisrancourt.substack.com/ https://depot-e.uqtr.ca/

All Correlation reports are here: https://correlation-canada.org/research/

Citation: Denis G. Rancourt, Joseph Hickey, Christian Linard. Spatiotemporal variation of excess all-cause mortality in the world (125 countries) during the Covid period 2020-2023 regarding socio-economic factors and public-health and medical interventions. CORRELATION Research in the Public Interest, Report, 19 July 2024. https://correlation-canada.org/covid-excess-mortality-125-countries

## Summary

We studied all-cause mortality in 125 countries with available all-cause mortality data by time (week or month), starting several years prior to the declared pandemic, and for up
to and more than three years of the Covid period (2020-2023). The studied countries are on six continents and comprise approximately 35 % of the global population (2.70 billion of 7.76 billion, in 2019).

The overall excess all-cause mortality rate in the 93 countries with sufficient data in the 3-year period 2020-2022 is 0.392 ± 0.002 % of 2021 population, which is comparable to the historic rate of approximately 0.97 % of population over the course of the 1918 "Spanish Flu" pandemic.

By comparison, India (which is not included in the present study) had an April-July 2021 peak in excess all-cause mortality of 3.7 million deaths for its 2021 population of approximately 1.41 billion, which corresponds to an excess death rate of 0.26 % for 2021 alone (Rancourt, 2022).

Our calculated excess mortality rate (0.392 ± 0.002 %) corresponds to 30.9 ± 0.2 million excess deaths projected to have occurred globally for the 3-year period 2020-2022, from all causes of excess mortality during this period.

We also calculate the population-wide risk of death per injection (vDFR) by dose number (1st dose, 2nd dose, boosters) (actually, by time period), and by age (in a subset of European countries). Using the median value of all-ages vDFR for 2021-2022 for the 78 countries with sufficient data gives an estimated projected global all-ages excess mortality associated with the COVID-19 vaccine rollouts up to 30 December 2022: 16.9 million COVID-19-vaccine-associated deaths.

Large differences in excess all-cause mortality rate (by population) and in age-and-health-status-adjusted (P-score) mortality are incompatible with a viral pandemic spread hypothesis and are strongly associated with the combination (product) of share of population that is elderly (60+ years) and share of population living in poverty.
There are large North-South (Canada-USA-Mexico) differences in North America, and large East-West differences in Europe, which are due to large national jurisdictional differences, or discontinuities in socio-economic and institutional conditions. Such systematic differences in mortality and underlying structure are captured by hierarchical cluster analysis using a panel of (yearly) time series, including to some extent the likelihood of persistent excess all-cause mortality into 2023.

Excluding borderline cases, 28 countries (of 79 countries with sufficient data, 35 % of countries) have a high statistical certainty of persistent and significant excess all-cause mortality into 2023, compared to the extrapolated pre-Covid historic trend, excluding excess all-cause mortality from peak residuals extending out from 2022, and excluding accidentally large values: Australia, Austria, Belgium, Brazil, Canada, Denmark, Ecuador, Egypt, Finland, Germany, Ireland, Israel, Italy, Japan, Lithuania, Netherlands, Norway, Portugal, Puerto Rico, Qatar, Singapore, South Korea, Spain, Sweden, Taiwan, Thailand, United Kingdom, and USA. More research is needed to elucidate this phenomenon.

The spatiotemporal variations in national excess all-cause mortality rates allow us to conclude that the Covid-period (2020-2023) excess all-cause mortality in the world is incompatible with a pandemic viral respiratory disease as a primary cause of death. This hypothesis, although believed to be supported by testing campaigns, should be abandoned.

Inconsistencies that disprove the hypothesis of a viral respiratory pandemic to explain excess all-cause mortality during the Covid period are seen on a global scale and include the following.

* Near-synchronicity of onset, across several continents, of surges in excess mortality occurring immediately when a pandemic is declared by the WHO (11 March 2020), and never prior to pandemic announcement in any country
* Excessively large country-to-country heterogeneity of the age-and-health-status-adjusted (P-score) mortality during the Covid period, including across shared borders between adjacent countries, and including in all time periods down to half years

* Highly time variable age-and-health-status-adjusted (P-score) mortality in individual countries during and after the Covid period, including more-than-year-long periods of zero excess mortality, long-duration plateaus or regimes of high excess mortality, single peaks versus many recurring peaks, and persistent high excess mortality after a pandemic is declared to have ended (5 May 2023)

* Strong correlations (all-country scatter plots) between excess all-cause mortality rates and socio-economic factors (esp. measures of poverty) change with time (by year and half year) during the Covid period, between diametrically opposite values (near-zero, large and positive, large and negative) of the Pearson correlation coefficient (e.g., Figure 29, first half of 2020 to first half of 2023)

One might tentatively add:

* No evidence of the large vaccine rollouts ever being associated with reductions in excess all-cause mortality, in any country (and see Rancourt and Hickey, 2023)

* Exponential increases with age in excess all-cause mortality rate (by population), consistent with age-dominant frailty rather than infection in the limit of high virulence

We describe plausible mechanisms and argue that the three primary causes of death associated with the excess all-cause mortality during (and after) the Covid period are:

(1) Biological (including psychological) stress from mandates such as lockdowns and associated socio-economic structural changes

(2) Non-COVID-19-vaccine medical interventions such as mechanical ventilators and drugs (including denial of treatment with antibiotics)
(3) COVID-19 vaccine injection rollouts, including repeated rollouts on the same populations

In all cases — for all three identified primary causes of death — a proximal or clinical cause of death associated (such as on death certificates) with the quantified excess all-cause mortality is respiratory condition or infection. Therefore, we distinguish (and define) true primary causes of death from the pervasive and accompanying proximal or clinical cause of death as respiratory.

We understand the Covid-period mortality catastrophe to be precisely what happens when governments cause global disruptions and assaults against populations. We emphasize the importance of biological stress from sudden and profound structural societal changes and of medical assaults (including denial of treatment for bacterial pneumonias, repeated vaccine injections, etc.). We estimate that such a campaign of disruptions and assaults in a modern world will produce a global all-ages mortality rate of >0.1 % of population per year, as was also the case in the 1918 mortality catastrophe.

# Table of Contents

<table><tr><td>Summary</td><td>1</td></tr><tr><td>Table of abbreviated descriptions of the figures</td><td>8</td></tr><tr><td>1 Introduction</td><td>13</td></tr><tr><td>2 Data</td><td>14</td></tr><tr><td>3 Methods</td><td>16</td></tr><tr><td>3.1 Excess all-cause mortality by time</td><td>16</td></tr><tr><td>3.2 Hierarchical clustering</td><td>17</td></tr><tr><td>4 Results and interpretive context</td><td>17</td></tr><tr><td>4.1 General overview of the mortality data</td><td>17</td></tr><tr><td>4.2 2020 March-April peak in mortality (20-3 feature)</td><td>29</td></tr></table>
<table>
  <tbody>
    <tr>
      <td>4.3</td>
      <td>2020 October-November peak in mortality (20-10 feature)</td>
      <td>30</td>
    </tr>
    <tr>
      <td>4.4</td>
      <td>2022 December excess mortality peak (23-0 feature)</td>
      <td>31</td>
    </tr>
    <tr>
      <td>4.5</td>
      <td>2021 December excess mortality peak (22-0 feature)</td>
      <td>35</td>
    </tr>
    <tr>
      <td>4.6</td>
      <td>Large sudden all-cause mortality peaks in island nations</td>
      <td>39</td>
    </tr>
    <tr>
      <td>4.7</td>
      <td>Cluster analysis of Covid-period geo-temporal patterns of all-cause mortality in the world</td>
      <td>48</td>
    </tr>
    <tr>
      <td>4.8</td>
      <td>Persistent excess all-cause mortality in 2023</td>
      <td>51</td>
    </tr>
    <tr>
      <td>4.9</td>
      <td>Quantitative excess all-cause mortality by time period and by country during the Covid period</td>
      <td>56</td>
    </tr>
    <tr>
      <td>4.10</td>
      <td>Statistics of excess all-cause mortality by time period for cluster-analysis groups of countries</td>
      <td>64</td>
    </tr>
    <tr>
      <td>4.11</td>
      <td>Spatial distribution of Covid-period excess all-cause mortality in the world</td>
      <td>71</td>
    </tr>
    <tr>
      <td>4.12</td>
      <td>Spatiotemporal variation of Covid-period excess all-cause mortality in the world</td>
      <td>75</td>
    </tr>
    <tr>
      <td>4.13</td>
      <td>Comparison to COVID-19-assigned deaths and to COVID-19-assigned cases</td>
      <td>80</td>
    </tr>
    <tr>
      <td>4.14</td>
      <td>Broad systematics of vaccine rollouts and vaccine injections per population</td>
      <td>82</td>
    </tr>
    <tr>
      <td>4.15</td>
      <td>Excess all-cause mortality (as % of expected mortality) correlations with socio-economic variables</td>
      <td>86</td>
    </tr>
    <tr>
      <td>4.16</td>
      <td>Excess all-cause mortality (as fraction of 2019 population) correlations with socio-economic variables</td>
      <td>120</td>
    </tr>
    <tr>
      <td>4.17</td>
      <td>Excess all-cause mortality correlations with vaccination by population</td>
      <td>160</td>
    </tr>
    <tr>
      <td>4.18</td>
      <td>Excess all-cause mortality correlations with products of socio-economic variables</td>
      <td>185</td>
    </tr>
    <tr>
      <td>4.19</td>
      <td>Age dependence of excess all-cause mortality in European countries during the Covid period</td>
      <td>189</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Discussion</td>
      <td>225</td>
    </tr>
    <tr>
      <td>5.1</td>
      <td>Limitations in attributing cause of death to vaccine toxicity</td>
      <td>225</td>
    </tr>
    <tr>
      <td>5.2</td>
      <td>Magnitude of the excess mortality rate during the Covid period, compared to past mortality events</td>
      <td>227</td>
    </tr>
    <tr>
      <td>5.3</td>
      <td>Large-scale jurisdictional, spatial and temporal variations of Covid-period excess all-cause mortality</td>
      <td>230</td>
    </tr>
    <tr>
      <td>5.4</td>
      <td>East-West divide in Europe</td>
      <td>232</td>
    </tr>
    <tr>
      <td>5.5</td>
      <td>Exceedingly large country-to-country heterogeneity of excess all-cause mortality</td>
      <td>235</td>
    </tr>
    <tr>
      <td>5.6</td>
      <td>Absence of spread across national boundaries</td>
      <td>237</td>
    </tr>
    <tr>
      <td>5.7</td>
      <td>Comparison of excess all-cause mortality and infection fatality rate (IFR) determinations</td>
      <td>239</td>
    </tr>
  </tbody>
</table>
<table>
  <tbody>
    <tr>
      <td>5.8 Correlations of excess all-cause mortality and socio-economic variables by country</td>
      <td>241</td>
    </tr>
    <tr>
      <td>5.9 Late and post-Covid-period persistent excess all-cause mortality</td>
      <td>246</td>
    </tr>
    <tr>
      <td>5.10 Age dependence of sex-discriminated bulk and excess all-cause mortality in European countries</td>
      <td>249</td>
    </tr>
    <tr>
      <td>5.11 Did COVID-19 vaccine rollouts reduce all-cause mortality?</td>
      <td>254</td>
    </tr>
    <tr>
      <td>5.12 What caused excess all-cause mortality during the Covid period?</td>
      <td>257</td>
    </tr>
    <tr>
      <td>5.12.1 Primary cause of death versus associated diseases or conditions</td>
      <td>257</td>
    </tr>
    <tr>
      <td>5.12.2 Inferred respiratory medical conditions in the deaths contributing to excess all-cause mortality</td>
      <td>258</td>
    </tr>
    <tr>
      <td>5.12.3 Primary causes of death in excess all-cause mortality during the Covid period</td>
      <td>263</td>
    </tr>
    <tr>
      <td>5.13 Calculated risk of death per injection from COVID-19 vaccines</td>
      <td>275</td>
    </tr>
    <tr>
      <td>5.13.1 Population-wide risk of death per injection — all ages</td>
      <td>275</td>
    </tr>
    <tr>
      <td>5.13.2 Population-wide risk of death per injection — stratified by age group</td>
      <td>290</td>
    </tr>
    <tr>
      <td>6 Conclusion</td>
      <td>316</td>
    </tr>
    <tr>
      <td>References</td>
      <td>316</td>
    </tr>
    <tr>
      <td>Appendix A: All-cause mortality and excess all-cause mortality in 125 countries, 2015-2023</td>
      <td>328</td>
    </tr>
    <tr>
      <td>Appendix B: All-cause mortality, excess all-cause mortality, and cumulative, in 125 countries, and vaccine rollouts, 2018-2023</td>
      <td>371</td>
    </tr>
    <tr>
      <td>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</td>
      <td>497</td>
    </tr>
    <tr>
      <td>Appendix D: Excess all-cause mortality for various time periods, expressed as a fraction of the 2019 population, by country</td>
      <td>500</td>
    </tr>
    <tr>
      <td>Appendix E: Temporal sequence of world maps of excess all-cause mortality by population, through the Covid period by half years</td>
      <td>504</td>
    </tr>
    <tr>
      <td>Appendix F: Scatter plots of relations between socio-economic variables</td>
      <td>510</td>
    </tr>
    <tr>
      <td>Appendix G: Table of Pearson correlation coefficients between socio-economic variables</td>
      <td>511</td>
    </tr>
    <tr>
      <td>Appendix H: Tables of Pearson correlation coefficients between excess all-cause mortality and socio-economic variables, in different time periods</td>
      <td>513</td>
    </tr>
    <tr>
      <td>Appendix I: Tables of Pearson correlation coefficients between excess all-cause mortality and number of vaccine injections by population, for different time periods</td>
      <td>516</td>
    </tr>
    <tr>
      <td>Appendix J: Correction and extension of the analysis by Ioannidis et al. (2023) — excess all-cause mortality in “less and more vulnerable” countries</td>
      <td>518</td>
    </tr>
  </tbody>
</table>
Table of abbreviated descriptions of the figures

Abbreviations: ACM = all-cause mortality / xACM = excess all-cause mortality / d = day / w = week / m = month / pop = population / P-score = excess all-cause mortality as percentage of the expected (extrapolated historic trend) all-cause mortality / IFR = infection fatality rate / vaxN = number of COVID-19 vaccine injections administered / vDFR = "vaccine dose fatality rate" (xACM as % of vaxN)

<table><thead><tr><td>Figure</td><td>Section</td><td>Abbreviated description</td></tr></thead><tbody><tr><td>Figure 1</td><td>4.1</td><td>ACM by w/m by pop, 2018-2023: Australia, Brazil, Germany, Japan, South Africa, USA (each continent)</td></tr><tr><td>Figure 2</td><td>4.1</td><td>xACM by w/m as P-score, 2018-2023: Australia, Brazil, Germany, Japan, South Africa, USA (each continent)</td></tr><tr><td>Figure 3</td><td>4.1</td><td>ACM by w/m by pop, 2018-2023: Portugal, Spain, France, Germany, Poland (west-to-east Europe)</td></tr><tr><td>Figure 4</td><td>4.1</td><td>xACM by w/m as P-score, 2018-2023: Portugal, Spain, France, Germany, Poland (west-to-east Europe)</td></tr><tr><td>Figure 5</td><td>4.4</td><td>xACM by w as P-score, 2020-2023: Austria, Canada, Czechia, Germany, Sweden, USA ("23-0:23-6" feature)</td></tr><tr><td>Figure 6</td><td>4.5</td><td>xACM by w/m as P-score, 2020-2023: Australia, Brazil, Hungary, Mexico, Slovakia ("22-0" feature)</td></tr><tr><td>Figure 7</td><td>4.6</td><td>xACM by m as P-score, 2020-2023: French Polynesia and New Caledonia (two Pacific Ocean island nations)</td></tr><tr><td>Figure 8</td><td>4.6</td><td>xACM by w/m as P-score, and vaccine rollouts, 2020-2023: Antigua and Barbuda, Bahamas, Barbados, Guadeloupe, Jamaica, Martinique, and Saint Vincent and the Grenadines (seven Caribbean island countries)</td></tr><tr><td>Figure 9</td><td>4.6</td><td>xACM by m as P-score, and vaccine rollouts, 2020-2023: Bermuda, Cuba</td></tr><tr><td>Figure 10</td><td>4.6</td><td>xACM by w/m as P-score, and vaccine rollouts, 2020-2023: Maldives, Mauritius, Mayotte, Reunion (four island countries in the Indian Ocean)</td></tr><tr><td>Figure 11</td><td>4.6</td><td>xACM by w/m as P-score, and vaccine rollouts, 2020-2023: South Korea, Taiwan, Hong Kong, Macao</td></tr><tr><td>Figure 12</td><td>4.7</td><td>Hierarchical clustering dendrogram for all countries with</td></tr></tbody></table>
sufficient data (75 countries, > 1M pop)

Figure 13 4.7 World map — Three highest level clusters of countries (orange, green, red), cluster analysis

Figure 14 4.8 xACM “2023-H1* period”, as P-score or by pop, by country and by cluster (persistent xACM in 2023)

Figure 15 4.8 ACM by w, and historic trend ACM by w, and xACM by w, 2015-2023: Canada, Ireland, Netherlands (examples persistent xACM in 2023)

Figure 16 4.9 Scatter plots — xACM as P-score, for specified time period per panel (years, half-years, 2020-2023) and by cluster, by country code in alphabetical order, with statistical parameters

Figure 17 4.10 Statistical parameters (mean, median, standard deviation, coefficient of variation) of the distributions of by-country values of xACM by pop or as P-score, and by cluster, versus half-year during Covid

Figure 18 4.11 World map — xACM by pop during the Covid period (X202122, 2020-2022) (93 countries with sufficient data)

Figure 19 4.11 World map — xACM as P-score during the Covid period (X202122, 2020-2022) (93 countries with sufficient data)

Figure 20 4.11 World maps — Socio-economic variables: life expectancy at 65, share pop aged 90+, Gini coefficient, share of pop < $30/d, mean income poorest decile

Figure 21 4.12 World maps in time sequence — xACM as P-score by half-year during Covid (all countries with sufficient data)

Figure 22 4.13 Scatter plot — Ratio of xACM to reported COVID-19 mortality, for Covid period (2020-2022), by country code in alphabetical order, and by cluster (91 countries having sufficient data)

Figure 23 4.13 Scatter plot — Ratio of xACM to reported COVID-19 cases (IFR), for Covid period (2020-2022), by country code in alphabetical order, and by cluster (93 countries having sufficient data)

Figure 24 4.14 Scatter plot — Number of vaccine injections given in 2022, per pop, versus number of vaccine injections given in 2021, per pop, by country and by cluster (r = +0.42)

Figure 25 4.14 Scatter plots — Number of vaccine injections given in 2021-
2022, per pop and by cluster, versus: share pop < $30/d, GDP per capita, median income, life expectancy at 65

Figure 26 4.15 Scatter plots — xACM as P-score for different time periods during Covid (2020-2023) by country and by cluster versus GDP per capita

Figure 27 4.15 Scatter plots — xACM as P-score for different time periods during Covid (2020-2023) by country and by cluster versus Gini inequality index

Figure 28 4.15 Scatter plots — xACM as P-score for different time periods during Covid (2020-2023) by country and by cluster versus share pop < $5.50/d

Figure 29 4.15 Scatter plots — xACM as P-score for different time periods during Covid (2020-2023) by country and by cluster versus share pop < $30/d

Figure 30 4.15 Scatter plots — xACM as P-score for different time periods during Covid (2020-2023) by country and by cluster versus median income

Figure 31 4.15 Scatter plots — xACM as P-score for different time periods during Covid (2020-2023) by country and by cluster versus life expectancy at 65

Figure 32 4.15 Scatter plots — xACM as P-score for different time periods during Covid (2020-2023) by country and by cluster versus share pop 60+

Figure 33 4.15 Scatter plots — xACM as P-score for different time periods during Covid (2020-2023) by country and by cluster versus share pop 90+

Figure 34 4.16 Scatter plots — xACM by pop for different time periods during Covid (2020-2023) by country and by cluster versus GDP per capita

Figure 35 4.16 Scatter plots — xACM by pop for different time periods during Covid (2020-2023) by country and by cluster versus Gini inequality index

Figure 36 4.16 Scatter plots — xACM by pop for different time periods during Covid (2020-2023) by country and by cluster versus share pop < $5.50/d

Figure 37 4.16 Scatter plots — xACM by pop for different time periods during
Covid (2020-2023) by country and by cluster versus share pop < $30/d

Figure 38 4.16 Scatter plots — xACM by pop for different time periods during Covid (2020-2023) by country and by cluster versus median income

Figure 39 4.16 Scatter plots — xACM by pop for different time periods during Covid (2020-2023) by country and by cluster versus life expectancy at 65

Figure 40 4.16 Scatter plots — xACM by pop for different time periods during Covid (2020-2023) by country and by cluster versus share pop 60+

Figure 41 4.16 Scatter plots — xACM by pop for different time periods during Covid (2020-2023) by country and by cluster versus share pop 90+

Figure 42 4.17 Scatter plots — xACM as P-score for different time periods during Covid (2020-2023) by country and by cluster versus number of vaccine injections for 2021-2022 by country by pop

Figure 43 4.17 Scatter plots — xACM as P-score for different time periods during Covid (2020-2023) by country and by cluster versus number of vaccine injections for 2021 by country by pop

Figure 44 4.17 Scatter plots — xACM as P-score for different time periods during Covid (2020-2023) by country and by cluster versus number of vaccine injections for 2022 by country by pop

Figure 45 4.17 Scatter plots — xACM by pop for different time periods during Covid (2020-2023) by country and by cluster versus number of vaccine injections for 2021-2022 by country by pop

Figure 46 4.17 Scatter plots — xACM by pop for different time periods during Covid (2020-2023) by country and by cluster versus number of vaccine injections for 2021 by country by pop

Figure 47 4.17 Scatter plots — xACM by pop for different time periods during Covid (2020-2023) by country and by cluster versus number of vaccine injections for 2022 by country by pop

Figure 48 4.18 Scatter plots — xACM by pop for different time periods during Covid (2020-2023) by country and by cluster versus product "share pop < $30/d X share pop aged 60+"

Figure 49 4.19 Scatter plots — Yearly ACM by pop of sex and age, by sex,
versus age (group), 2019-2022 (25 European countries)

Figure 50 4.19 Scatter plots — Yearly xACM by pop of sex and age, by sex, versus age (group), 2020-2022 (25 European countries)

Figure 51 4.19 Scatter plots — Yearly xACM as P-score, by sex, versus age (group), 2020-2022 (25 European countries)

Figure 52 5.13 Scatter plot and fit — xACM versus vaxN, f or different time periods (years and half years)

Figure 53 5.13 Scatter plot and fit — xACM versus vaxN, for different time periods (years and half years) — 4-fold zoom-in

Figure 54 5.13 xACM divided by vaxN (xACM / vaxN) — by country, by cluster, for 2021-2022

Figure 55 5.13 vDFR (median of xACM/vaxN) versus half year, for each cluster and for all countries

Figure 56 5.13 vDFR (xACM/vaxN) versus age, for the half year 2022-H2, in 25 European countries, with exponential fits

Appendix A 4.1 Main Data — Actual ACM, expected ACM, xACM, by w/m, 2015-2023 (125 countries)

Appendix B 4.1 Main Data — ACM by pop by w/m, vaccine rollout by pop by w/m; xACM by pop by w/m; Cumulative xACM by pop by w/m, cumulative vaccine rollout by pop by w/m (2018-2023, 125 countries)

Appendix D 4.9 Scatter plots — xACM by pop, for specified time period per panel (years, half-years, 2020-2023) and by cluster, by country code in alphabetical order (compare Figure 16)

Appendix E 4.12 World maps in time sequence — xACM by pop by half-year during Covid (all countries with sufficient data) (compare Figure 21)

Appendix F 4.15 Scatter plots of relations between socio-economic variables

Appendix J 5.8 Correction and extension of the analysis by Ioannidis et al. (2023) — xACM in “less and more vulnerable” countries
# 1 Introduction

All-cause mortality by time and by administrative jurisdiction is arguably the most reliable data for detecting and epidemiologically characterizing events causing death, and for gauging the population-level impact of any surge or collapse in deaths from any cause. Such data can be collected by national or state jurisdiction or subdivision, by age, by sex, by location of death, and so on. It is not susceptible to reporting bias or to any bias in attributing causes of death in the mortality itself (see many references in Rancourt et al., 2023a).

Rancourt and collaborators have studied all-cause mortality for many jurisdictions, while developing the analytic approaches:

* several, esp. USA (Rancourt, 2020);

* France (Rancourt et al., 2020);

* India (Rancourt, 2022);

* USA (Rancourt et al., 2021a, 2022b);

* Canada (Rancourt et al., 2021b, 2022c);

* Australia (Rancourt et al., 2022a, 2023b);

* 17 countries in the Southern Hemisphere (Rancourt et al., 2023a);

* Israel (Rancourt et al., 2023b);

* world, with respect to COVID-19 vaccine efficacy (Rancourt and Hickey, 2023).

Researchers at CORRELATION and collaborators continue to be engaged in a broad research program of all-cause mortality and its associations with various factors: https://correlation-canada.org/research/

Here we study all-cause mortality in 125 countries with available all-cause mortality data by time (week or month). The studied countries are on six continents and comprise approximately 35 % of the global population (2.70 billion of 7.76 billion, in 2019).
Large countries which are notably excluded for lack of available data include China (1.41 billion in 2019), India (1.38 billion in 2029), Indonesia, Pakistan, and most countries in Africa, although India has previously been studied (Rancourt, 2022; and references therein).

See the Summary for an overview.

## 2 Data

Our main source of all-cause mortality data is the World Mortality Project (WMP) (Karlinsky and Kobak, 2021). (References for this section are immediately below.)

For Chile, we used the data from the Organisation for Economic Co-operation and Development (OECD, 2024) due to the absence of data for the year 2015 for Chile in the WMP datafile.

Additional all-cause mortality data by age group and sex for European countries are from Eurostat (Eurostat, 2024a).

COVID-19 vaccination data are from Our World in Data (Mathieu et al., 2020), the World Health Organization (WHO, 2024), and the European Centre for Disease Prevention and Control (ECDC, 2023).

Socio-economic data are from OWID's curation of the World Bank's Poverty and Inequality Platform data (Hasell and Arriagada, 2023), OWID's Population and Demography Data Explorer (OWID, 2024d) and the United Nation's World Population Prospects 2022 (United Nations, 2022).
National population data are from the World Bank (World Bank, 2023), and additional
population data by age group and sex are from Eurostat (Eurostat, 2024b).

**References for the data section:**

ECDC (2023): European Centre for Disease Prevention and Control /// Data on COVID-19 vaccination in the EU/EEA /// https://www.ecdc.europa.eu/en/publications-data/data-covid-19-vaccination-eu-eea. Downloaded on 2024-01-11.

Eurostat (2024a): Eurostat /// Deaths by week, sex and 5-year age group
/// https://ec.europa.eu/eurostat/datab browser/view/demo_r_mwk_05/default/table?lang=en.
Downloaded on 2024-01-11.

Eurostat (2024b): Eurostat /// Population on 1 January by age group and sex
/// https://ec.europa.eu/eurostat/datab browser/view/demo_pjangroup_custom_9333419/default/t
able?lang=en. Downloaded on 2024-01-13.

Hasell and Arriagada (2023): Hasell J, Arriagada P. /// Data on Poverty by Our World in Data
/// https://github.com/owid/poverty-data?tab=readme-ov. Downloaded on 2024-01-17.

Karlinsky and Kobak (2021): Karlinsky A, Kobak D. /// Tracking excess mortality across
countries during the COVID-19 pandemic with the World Mortality Dataset /// eLife
2021;10:e69336. https://doi.org/10.7554/eLife.69336. [World Mortality Project (WMP) data
downloaded on 2023-12-14 from: https://github.com/akarlinsky/world_mortality]

Mathieu et al. (2020): Mathieu E, Ritchie H, Rodés-Guirao L, Appel C, Giattino C, Hasell J,
Macdonald B, Dattani S, Beltekian D, Ortiz-Ospina E, Roser M. /// Coronavirus Pandemic
(COVID-19) [Vaccinations tab] /// https://ourworldindata.org/covid-vaccinations. Downloaded on
2023-12-17.

OECD (2024): Organisation for Economic Co-operation and Development /// OECD Data
Explorer – Archive /// https://stats.oecd.org/index.aspx?queryid=104676. Downloaded on 2024-
01-27.

OWID (2024d): Our World in Data /// Population and Demography Data Explorer
/// https://ourworldindata.org/explorers/population-and-demography. Downloaded on 2024-01-
17.

United Nations (2022): United Nations, Department of Economic and Social Affairs, Population
Division /// World Population Prospects 2022, Online Edition
/// https://population.un.org/wpp/Download/Standard/MostUsed/. Downloaded on 2023-03-21.
WHO (2024): World Health Organization /// COVID-19 Vaccination Information Hub
/// https://infohub.crd.co/. Downloaded on 2024-01-23.

World Bank (2023): World Bank /// Population, total
/// https://data.worldbank.org/indicator/SP.POP.TOTL. Downloaded on 2023-03-20.

# 3 Methods

## 3.1 Excess all-cause mortality by time

Excess all-cause mortality by time (week or month) and its one-standard-deviation uncertainty are calculated as follows. We first applied this method in Rancourt and Hickey (2023). We believe that this simple and direct method is itself a significant advance in the methodology of analyzing all-cause mortality data, which does not introduce uncertainty from arbitrary choices or tenuous extrapolation algorithms.

The excess all-cause mortality at a given time (week or month) is the difference (positive or negative) between the reported all-cause mortality for the given time and the expected all-cause mortality for the given time, which is ascertained from the historic all-cause mortality in a reference period immediately preceding the Covid period (prior to the 11 March 2020 World Health Organization declaration of a pandemic).

In practice, our reference period is 2015 through 2019, except in the few countries for which the mortality data does not extend as far back as 2015 and may begin in 2017 and 2019, for example. We least-squares fit a straight line to the same week or month in each of the five (or less) reference years as the week or month of interest, where the slope of this fitted line is constrained to always (for every week or month of interest) be equal to the slope of a least-squares fitted line to all of the all-cause mortality data (all weeks or months) in the full 5-year (or less) reference period, for each given country.
The thus obtained fitted line is used (by extrapolation) to predict the expected all-cause mortality. The one-standard-deviation (1σ) uncertainty in the expected all-cause mortality is estimated as sqrt(π/2) times the average magnitude of the 5 (or less) deviations in the 2015-2019 (or shorter) reference period, for each particular week or month of interest. This simple relation is exact in the limit of a large sampling number, for a normally distributed uncertainty.

Finally, the one-standard-deviation uncertainty of the excess mortality is the combined error that includes the 1σ uncertainty in the expected value and the independent statistical (1σ) error in the all-cause mortality (sqrt(N)).

## 3.2 Hierarchical clustering

We use the hierarchical clustering algorithms provided in the SciPy library (Python) (SciPy, 2024). The input data is an array in which the rows are country indexes and the columns are the country's total annual all-cause mortality for each of the years 2019, 2020, 2021 and 2022. The input data is thus a panel of time-series, with one time-series consisting of four data-points for each country.

We use the `scipy.cluster.hierarchy.linkage` function with options `method='complete'` and `metric='correlation'` to produce a hierarchical clustering grouping countries with similar time-series together. We then use the `scipy.cluster.hierarchy.fcluster` function with option `criterion='distance'` to obtain three clusters at a threshold distance value equal to 1 (y-axis of the dendrogram in Figure 12).

# 4 Results and interpretive context

## 4.1 General overview of the mortality data

The core data in this study is national all-cause mortality by time (week, month, year). There are large anomalous variations in this mortality data during the Covid period
(2020-2023), compared to historic seasonal and regional variations, both by time and by national jurisdiction.

Strictly speaking, the Covid period can be defined to start at the 11 March 2020 World Health Organization (WHO) declaration of a pandemic and to end at the 5 May 2023 WHO declaration of the end of the declared pandemic.

The time and jurisdictional changes during the Covid period are so large and varied that the data itself and the extracted excess all-cause mortality need to be shown for each country having available data. There is no substitute for that.

In the figure of **Appendix A**, each of the 125 countries has a panel. Each panel has two parts:

(1) Top part: Reported all-cause mortality (blue), by week or by month, and predicted (from the historic or recent pre-Covid period, see Methods) all-cause mortality (orange), 2015-2023, with shaded $1\sigma$-error ranges.

(2) Bottom part: Calculated excess all-cause mortality (green), by week or by month, 2015-2023, with shaded $1\sigma$-error range.

Here, the WHO dates of 11 March 2020 (declaration of a pandemic) and 5 May 2023 (end of declaration) are shown in each panel by vertical grey lines. Error-range shading ($1\sigma$ error, see Methods) is shown for all curves (bleu, orange, green). The statistical $1\sigma$-error for the raw all-cause mortality (blue) is generally too small to see on the figure.

**Appendix A**, therefore, shows the raw data and illustrates the extraction method to calculate the corresponding excess all-cause mortality. The linear extrapolation for the predicted mortality is based on the historic trend in the 2015-2019 period, or the available data within that period (see Methods).

Similarly, in the figure of **Appendix B**, each of the 125 countries has three panels:
(1) Top panel: Reported all-cause mortality, by week or by month, and number of COVID-19 vaccine doses administered, by week or by month, 2018-2023, both normalized by the national population in 2019.

(2) Middle panel: Calculated excess all-cause mortality, by week or by month, and number of COVID-19 vaccine doses administered, by week or by month, 2018-2023, both normalized by the national population in 2019.

(3) Bottom panel: Cumulative excess all-cause mortality, by week or by month, and cumulative number of COVID-19 vaccine doses administered, by week or by month, 2018-2023, both normalized by the national population in 2019.

Here, the 11 March 2020 date of the WHO declaration of a pandemic is shown in each panel by a vertical grey line, and up to three sources of vaccine administration data are shown, when available.

The country-to-country variations in time-dependent all-cause mortality are striking, as are differences between the recent historic record (2015-2019) and the Covid period (Appendix A, Appendix B). Some general features are as follows.

(1) There is essentially no excess mortality, in any country, prior to the 11 March 2020 WHO declaration of a pandemic.

(2) There are various large peaks and periods of excess all-cause mortality during the Covid period.

(3) Statistically significant peaks and increases of excess all-cause mortality occur in the Covid period in virtually all of the 125 countries, except one. Greenland (GRL) is the only exception, showing no detected excess mortality at any time, into 2023.

(4) No characteristic or canonical or pervasive pattern of excess all-cause mortality by time in the Covid period occurs in all countries, or in most countries, or in northern latitudes, or in southern latitudes. The country-to-country differences in excess all-cause mortality are large in both normalized magnitude and temporal evolution.
(5) Nonetheless, there are strong commonalities in excess all-cause mortality country-specific patterns within some geo-politically distinguished groups, as discussed below.

(6) Highly unusual (unprecedented) features occur in the excess and raw mortality time patterns in the Covid period, including: anomalously large amplified seasonal peaks; large non-seasonal peaks occurring in historically observed seasons of low mortality; unique, sudden, large and sharp peaks occurring in mid or late Covid period; sustained multi-seasonal long periods of high excess all-cause mortality; and synchronicity across North or South hemispheres and between North-South hemispheres or certain sharp peaks occurring in many countries.

(7) There is no systematic or statistically significant general or apparent trend that the large vaccination campaigns in 2021 and 2022 are associated with any reduction in excess all-cause mortality. Many countries have no excess mortality until the vaccines are rolled out. Several countries show temporal associations between vaccine rollouts and peaks or increases in all-cause mortality.

(8) Generally speaking, excess all-cause mortality, in the cases where data is available, often persists to the end of 2022, and most often returns to small or near-zero values in 2023.

(9) Nonetheless, there are some notable examples in which excess all-cause mortality is large in 2023, and many countries in which there is apparent moderate but sustained excess all-cause mortality into 2023. These cases are discussed below.

Some of the above-described general features can be illustrated as follows.

**Figure 1** and **Figure 2** compare all-cause mortality normalized by 2019 population and excess all-cause mortality as percentage increase relative to the historic trend value (see Methods), respectively, for a large-population country with available data from
each continent. Direct comparisons are facilitated by using the same X-scale and Y-
scale ranges for all the panels in a given figure.

Note that in the labels of axes and in legends in all the figures, and 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_2.png" /></figure>

<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>
Figure 1. All-cause mortality by week or month, normalized by national 2019 population, 2018-2023, for (as per the panel labels) Australia, Brazil, Germany, Japan, South Africa, and USA, using the same Y-scale range for all panels. Each is a large-population country with available data from each continent.
<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>
**Figure 2.** Excess all-cause mortality by week or month, expressed as a percentage of the predicted (extrapolated historic trend) all-cause mortality by week or month ("P-score"), with 1σ error bound shading, 2018-2023, for (as per the panel labels) Australia, Brazil, Germany, Japan, South Africa, and USA, using the same Y-scale range for all panels. Each is a large-population country with available data from each continent.

We stress that the illustrated large-population country in each continent is not representative of all or most of the countries in the continent (see **Appendix A** and **Appendix B**).

Next, Figure 3 and Figure 4 compare all-cause mortality normalized by 2019 population and excess all-cause mortality as percentage increase relative to the historic trend value (see Methods), respectively, for five European countries in a geographically contiguous series from West to East: Portugal, Spain, France, Germany, and Poland. Direct comparisons are facilitated by using the same X-scale and Y-scale ranges for all the panels in a given figure.
<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /></figure>

<figure><img src="image_16.png" /></figure>

<figure><img src="image_17.png" /></figure>

<figure><img src="image_18.png" /></figure>
Figure 3. All-cause mortality by week, normalized by national 2019 population, 2018-2023, for five European countries in a geographically contiguous series from West to East (as per the panel labels, top to bottom): Portugal, Spain, France, Germany, and Poland, using the same Y-scale range for all panels.
<figure><img src="image_19.png" /></figure>

<figure><img src="image_20.png" /></figure>

<figure><img src="image_21.png" /></figure>

<figure><img src="image_22.png" /></figure>

<figure><img src="image_23.png" /></figure>
Figure 4. Excess all-cause mortality by week, expressed as a percentage of the predicted (extrapolated historic trend) all-cause mortality by week ("P-score"), with 1σ error bound shading, 2018-2023, for five European countries in a geographically contiguous series from West to East (as per the panel labels, top to bottom): Portugal, Spain, France, Germany, and Poland, using the same Y-scale range for all panels.

## 4.2 2020 March-April peak in mortality (20-3 feature)

Some understanding of the commonalities and differences in country-to-country mortality behaviours can be achieved by examining specific features or groups of features in the mortality by time curves. Examples are as follows, first in relation to the above figures.

A prominent and sharp peak in excess all-cause mortality occurs in March-April 2020, extending into May 2020, immediately following the 11 March 2020 WHO declaration of a pandemic, in Spain and France (Figure 3, Figure 4). Hereafter, we nominally refer to this peak as "20-3", for the year 2020 and the third month. 20-3 is also prominent in the USA data (Figure 1, Figure 2), and is present as a weaker near-absent feature in the data of Portugal and Germany. 20-3 is entirely absent in the data of Australia, Japan, and South Africa. Where it does occur, it is synchronous (or it would not be "20-3").

20-3 occurs prominently in the data of 26 (of 125) countries in the present study: Andorra, Belarus, Belgium, Brazil, Canada, Chile, Ecuador, Egypt, France, Ireland, Italy, Kuwait, Mexico, Moldova, Netherlands, Nicaragua, Peru, Qatar, San Marino, Spain, Sweden, Switzerland, Tajikistan, United Arab Emirates, United Kingdom, and USA.

20-3 occurs as a weaker near-absent feature in the data of 11 (of 125) countries in the present study: Austria, Bermuda, Colombia, Denmark, Germany, Iran, Luxembourg, Malta, Mayotte, Norway, and Portugal.
20-3 is entirely absent (undetected) in the remaining 88 (of 125) countries in the present study: Albania, Algeria, Antigua and Barbuda, Argentina, Armenia, Aruba, Australia, Azerbaijan, Bahamas, Barbados, Belize, Bolivia, Bosnia, Brunei, Bulgaria, Cabo Verde, Costa Rica, Croatia, Cuba, Cyprus, Czechia, Dominican Republic, El Salvador, Estonia, Faroe Islands, Finland, French Guiana, French Polynesia, Georgia, Gibraltar, Greece, Greenland, Guadeloupe, Guatemala, Hong Kong, Hungary, Iceland, Israel, Jamaica, Japan, Jordan, Kazakhstan, Kosovo, Kyrgyzstan, Latvia, Lebanon, Liechtenstein, Lithuania, Macao, Malaysia, Maldives, Martinique, Mauritius, Monaco, Mongolia, Montenegro, Namibia, New Caledonia, New Zealand, North Macedonia, Oman, Palestine, Panama, Paraguay, Philippines, Poland, Puerto Rico, Reunion, Romania, Russia, Saint Kitts and Nevis, Saint Vincent and the Grenadines, Serbia, Seychelles, Singapore, Slovakia, Slovenia, South Africa, South Korea, Suriname, Taiwan, Thailand, Transnistria, Tunisia, Turkey, Ukraine, Uruguay, and Uzbekistan.

Similarly, many such distinctive peaks or local increases in excess all-cause mortality are found, which are essentially synchronous when they occur, but that occur in patchy distributions around the world. In analogy with 20-3, for convenience, we nominally assign 11 more such features as: 20-6, 20-10, 21-0, 21-3, 21-6, 21-10, 22-0, 22-3, 22-6, 23-0, and 23-6. Some of these are truly distinctive features, while others are merely broader regions (in time) of excess mortality. Not all distinct features are captured by this tentative nomenclature. Here, 21-0 means a peak that straddles December 2020 and January 2021, and so forth for 22-0 and 23-0.

We further apply this nomenclature to look for definitive patterns occurring in several countries, as follows.

## 4.3 2020 October-November peak in mortality (20-10 feature)

The features 20-3, 20-6 and 20-10 occur prior to vaccination rollouts (Appendix B), and are massive in many countries.
One distinctive pattern involving 20-3, 20-6 and 20-10 can be defined as:

$$20-3 : 20-6 : 20-10 = \text{absent} : \text{absent} : \text{prominent} \quad (1)$$

Here (Equation 1), a "prominent 20-10" corresponds to a particularly deadly early Northern-Hemisphere winter of excess all-cause mortality, in the absence of prior Covid-period excess all-cause mortality. This pattern (Equation 1) captures 17 of the 124 countries with sufficient data: Belize, Bulgaria, Croatia, Czechia, Georgia, Greece, Hungary, Jordan, Liechtenstein, Lithuania, Montenegro, Poland, Romania, Russia, Transnistria, Tunisia, and Ukraine.

An associated pattern is:

$$20-3 : 20-6 : 20-10 = \text{absent} : \text{near-absent} : \text{prominent} \quad (2)$$

This pattern (Equation 2) captures 7 of the 124 countries with sufficient data: Albania, Armenia, Bosnia, Cabo Verde, Lebanon, North Macedonia, and Serbia.

We note that these similar patterns (Equations 1 and 2) capture most Eastern European countries, which all had particularly deadly early Northern-Hemisphere winters of excess all-cause mortality, prior to COVID-19 vaccination rollouts.

## 4.4 2022 December excess mortality peak (23-0 feature)

Another distinctive feature can be defined as:

$$23-0 : 23-6 = \text{prominent} : \text{absent or near-absent} \quad (3)$$

In the data, this corresponds to a large excess mortality peak which occurs approximately four months prior to the 5 May 2023 WHO declaration of the end of the declared pandemic, after the great majority of the COVID-19 vaccine injections have
been administered, followed by an abrupt step-wise drop in excess all-cause mortality into 2023.

This pattern (Equation 3) captures 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, including many of the most vaccinated countries in the world.

An associated pattern is of the same shape as the pattern of Equation 3, but of lesser magnitude:

$$23-0 : 23-6 = \text{near-absent (or moderate)} : \text{absent (or near-absent)} \quad (4)$$

This pattern (Equation 4) captures 15 of the 87 countries with sufficient data: Australia, Chile, Croatia, Ecuador, Estonia, Hong Kong, Hungary, Iceland, Italy, Luxembourg, Mexico, Portugal, Russia, South Korea, and Spain.

In these 38 (23 + 15) of 87 countries, the prominent or near-absent 23-0 excess all-cause mortality peak drops suddenly in a step-wise manner to smaller (near-absent or absent) values at the end of 2022 and very start of 2023, moving into 2023 (Appendix B). This is illustrated for six countries in Figure 5.
<figure><img src="image_24.png" /></figure>

<figure><img src="image_25.png" /></figure>

<figure><img src="image_26.png" /></figure>
<figure><img src="image_27.png" /></figure>

<figure><img src="image_28.png" /></figure>

<figure><img src="image_29.png" /></figure>
Figure 5. Excess all-cause mortality by week, expressed as a percentage of the predicted (extrapolated historic trend) all-cause mortality by week ("P-score"), with 1σ error bound shading, 2020-2023, for six select countries exhibiting the "23-0:23-6" feature described in the text (as per the panel labels): Austria, Canada, Czechia, Germany, Sweden, and USA. The vaccine rollout data from different sources by week is also shown (thin black lines).

We note that the 23-0 mortality peaks in the 38 (23 + 15) of 87 countries discussed above are preceded by and thus temporally associated with synchronous apparently booster rollout peaks (Appendix B).

4.5 2021 December excess mortality peak (22-0 feature)

Similarly, a common 22-0 prominent or statistically evident excess mortality peak occurs within one month or so of 1 January 2022 in 113 of the 121 countries having sufficient mortality data (4 countries have no data at the relevant dates). This nominally 22-0 peak in excess all-cause mortality is temporally associated with a synchronous or preceding increase in vaccine administration (Appendix B). These synchronous or preceding increases in vaccine administration, associated with the 22-0 excess mortality peaks, are most often apparently boosters, which were rolled out in unison around the world at those dates. Nominally 22-0 excess mortality peaks are pervasive, and occur both in the Northern Hemisphere winter and in the Southern Hemisphere summer (Appendix B).

The 113 countries in question, exhibiting nominally 22-0 excess mortality peaks, are (Appendix B): Albania, Andorra, Antigua and Barbuda (earlier peak), Argentina, Armenia, Aruba (earlier peak), Australia, Austria, Azerbaijan, Bahamas (earlier peak), Barbados, Belgium, Belize, Bermuda, Bolivia, Bosnia, Brazil, Brunei, Bulgaria, Cabo Verde (earlier peak), Canada, Chile, Colombia, Costa Rica, Croatia, Cuba (home-country vaccine), Cyprus, Czechia, Denmark, Dominican Republic, Ecuador, Egypt, Estonia, Faroe Islands, Finland, France, French Guiana, French Polynesia (earlier peak), Georgia, Germany, Gibraltar (first vaccine dose), Greece, Guadeloupe, Guatemala, Hong Kong, Hungary, Iceland, Iran, Israel, Jamaica (earlier peak), Japan, Jordan, Kazakhstan, Kosovo (no vaccine data), Kuwait, Latvia, Lebanon, Liechtenstein,
Lithuania, Luxembourg, Malaysia, Maldives, Malta, Martinique (earlier large peak, small peak, no vaccine data), Mauritius, Mayotte (no vaccine data), Mexico, Moldova, Monaco, Mongolia, Montenegro, Namibia, Netherlands, New Caledonia (earlier large peak, small peak), Nicaragua (earlier peak, precedes main vaccination surge), North Macedonia, Norway, Oman, Palestine (earlier peak), Paraguay, Peru, Philippines, Poland, Portugal, Puerto Rico, Qatar, Reunion, Romania, Russia, Saint Kitts and Nevis, Saint Vincent and the Grenadines (earlier peak), San Marino, Serbia, Seychelles, Singapore, Slovakia, Slovenia, South Africa, South Korea, Suriname, Sweden, Switzerland, Taiwan (later peak), Tajikistan (earlier peak, first dose rollout), Thailand, Transnistria (no vaccine data), Tunisia, Turkey, Ukraine, United Arab Emirates (earlier peak), USA, Uruguay, and Uzbekistan (earlier and later peaks).

The excess mortality and vaccine administration data for five select examples of these 113 countries (Australia, Brazil, Hungary, Mexico, Slovakia) are shown in Figure 6. Six more examples of the 113 countries (Austria, Canada, Czechia, Germany, Sweden, USA) exhibiting a nominally 22-0 excess mortality peak are shown in Figure 5. Here, Sweden is a rare limiting case in which the nominally 22-0 excess mortality peak is not so pronounced as a distinctive peak.
<figure><img src="image_30.png" /></figure>

<figure><img src="image_31.png" /></figure>

<figure><img src="image_32.png" /></figure>
<figure><img src="image_33.png" /><figcaption>**Figure 6.** Excess all-cause mortality by week or month, expressed as a percentage of the predicted (extrapolated historic trend) all-cause mortality by week or month ("P-score"), with 1σ error bound shading, 2020-2023, for five select countries exhibiting the "22-0" feature described in the text (as per the panel labels, top to bottom): Australia, Brazil, Hungary, Mexico, and Slovakia. The vaccine rollout data from different sources by week is also shown (thin black lines).</figcaption></figure>

Somewhat related to the case of Sweden, 8 of the 121 countries with sufficient mortality data have no definitive or detected nominally 22-0 excess all-cause mortality peak (Appendix B): Greenland (no measurable excess mortality at any time in the Covid period), Ireland, Italy, Kyrgyzstan, Macao, New Zealand, Spain, and United Kingdom. Among these — like the case of Sweden — Ireland, Italy, Spain and UK have relatively
large 20-3 (2020 March-April) excess mortality peaks (comparatively massive in Spain) and relatively large near-21-0 (2020 December, 2021 January) excess mortality peaks. Also, Italy has an unusual sharp and large 2022 mid-summer (22-6) excess mortality peak, and Ireland (like Sweden) has a relatively large 23-0 peak (Appendix B).

## 4.6 Large sudden all-cause mortality peaks in island nations

Sixteen island states and four small coastal countries in the present study have unusual all-cause mortality by time (week or month) behaviours exhibiting zero or relatively small excess mortality at all times in the Covid period with available data, except for a single dominant and sharp peak (Appendix A, Appendix B).

The sixteen island countries in question are: Antigua and Barbuda (Caribbean Sea), Bahamas (Caribbean Sea), Barbados (Caribbean Sea), Bermuda (Atlantic Ocean, north of Caribbean Sea), Cuba (Caribbean Sea), French Polynesia (Pacific Ocean), Guadeloupe (Caribbean Sea), Jamaica (Caribbean Sea), Maldives (Indian Ocean), Martinique (Caribbean Sea), Mauritius (Indian Ocean), Mayotte (Indian Ocean), New Caledonia (Pacific Ocean), Reunion (Indian Ocean), Saint Vincent and the Grenadines (Caribbean Sea), and Taiwan (East China Sea, South China Sea)

The four small coastal (or largely coastal) states in question are: Brunei (border with Malaysia), Hong Kong (city-state, special administrative region of China), Macao (city-state, special administrative region of China), and South Korea (Sea of Japan, East China Sea, border with North Korea).

The two Pacific Ocean island countries, French Polynesia and New Caledonia, have sudden columnar peaks in monthly all-cause mortality in August-September 2021 and September-October 2021, respectively. Each said columnar peak in mortality coincides exactly with a large burst in vaccines injections administered in the same months. This is shown in Figure 7.
<figure><img src="image_34.png" /><figcaption>Figure 7. Excess all-cause mortality by month, expressed as a percentage of the predicted (extrapolated historic trend) all-cause mortality by month ("P-score") (blue), with 1σ error bound shading, and vaccine rollout data from different sources by month (thin black lines), 2020-2023, for the two Pacific Ocean island nations, French Polynesia and New Caledonia.</figcaption></figure>

A similar behaviour is seen in the seven Caribbean island countries: Antigua and Barbuda, Bahamas, Barbados, Guadeloupe, Jamaica, Martinique, and Saint Vincent and the Grenadines. This is shown in Figure 8. The behaviour also occurs in Cuba (see below).
Here, the columnar 2-month peaks in mortality always occur in July through October 2021, and are generally temporally associated with known vaccine rollout bursts, except that the peak in mortality in Barbados occurs in March 2022 and is associated with a fourth-dose vaccine rollout, and vaccination data was not available for Martinique.

<figure><img src="image_35.png" /></figure>

<figure><img src="image_36.png" /></figure>
<figure><img src="image_37.png" /></figure>

<figure><img src="image_38.png" /></figure>

<figure><img src="image_39.png" /></figure>
<figure><img src="image_40.png" /><figcaption>**Figure 8.** Excess all-cause mortality by week or by month, expressed as a percentage of the predicted (extrapolated historic trend) all-cause mortality by week or by month ("P-score") (blue), with 1σ error bound shading, and available vaccine rollout data from different sources by month (thin black lines), 2020-2023, for the seven Caribbean island countries, Antigua and Barbuda, Bahamas, Barbados, Guadeloupe, Jamaica, Martinique, and Saint Vincent and the Grenadines.</figcaption></figure>

Bernuda, which is in the Atlantic Ocean more than 1,000 km north of the Caribbean Sea, also shows a columnar mortality peak in September 2021, but which follows a large vaccination rollout period rather than being synchronous with it. Back to the Caribbean Sea, Cuba also has a large columnar mortality peak in August-September
2021, which is essentially synchronous with the rapid rollout of its own manufactured vaccine. The cases of Bermuda and Cuba are shown in Figure 9.

<figure><img src="image_41.png" /><figcaption>**Figure 9.** Excess all-cause mortality by month, expressed as a percentage of the predicted (extrapolated historic trend) all-cause mortality by month ("P-score") (blue), with 1σ error bound shading, and available vaccine rollout data from different sources by month (thin black lines), 2020-2023, for Bermuda and Cuba.</figcaption></figure>

The dates (in 2021 and 2022) of the columnar mortality peaks are more variable in the island countries of the Indian Ocean in the present study: Maldives, Mauritius, Mayotte, and Reunion. These cases are shown in Figure 10. The case of Maldives is suggestive of an association between preceding vaccine rollouts and peaks in all-cause mortality.
<figure><img src="image_42.png" /></figure>

<figure><img src="image_43.png" /></figure>
<figure><img src="image_44.png" /><figcaption>**Figure 10.** Excess all-cause mortality by week or by month, expressed as a percentage of the predicted (extrapolated historic trend) all-cause mortality by week or by month ("P-score") (blue), with 1σ error bound shading, and available vaccine rollout data from different sources by month (thin black lines), 2020-2023, for four island countries in the Indian Ocean, Maldives, Mauritius, Mayotte, and Reunion.</figcaption></figure>

The coastal country of South Korea and the large island of Taiwan both have their dominant columnar mortality peaks in early 2022, following the bulk of vaccine rollouts in these countries. Two coastal city special administrative regions of China, Hong Kong and Macao, have their dominant columnar mortality peaks in 2022, also following the bulk of vaccine rollouts in these countries, but temporally associated (immediately
following and synchronous with) late-dose or booster-dose vaccine rollouts. These
cases of South Korea, Taiwan, Hong Kong, and Macao are shown in Figure 11.

<figure><img src="image_45.png" /></figure>
<figure><img src="image_46.png" /><figcaption>Figure 11. Excess all-cause mortality by week or by month, expressed as a percentage of the predicted (extrapolated historic trend) all-cause mortality by week or by month (“P-score”) (blue), with 1σ error bound shading, and available vaccine rollout data from different sources by week or by month (thin black lines), 2020-2023, for South Korea, Taiwan, Hong Kong, and Macao.</figcaption></figure>

## 4.7 Cluster analysis of Covid-period geo-temporal patterns of all-cause mortality in the world

In addition to the many specific features described in the above subsections, a cluster analysis immediately discerns distinct categories of coarse-grain features of mortality by time data, on continental and sub-continental scales, for example, as follows.
Simply using the time series of raw all-cause mortality integrated over the calendar years 2019, 2020, 2021 and 2022 (4-point time series), three clusters immediately emerge, without any adjustable parameters (see Methods), as shown in Figure 12.

<figure><img src="image_47.png" /><figcaption>Figure 12. Hierarchical clustering dendrogram result using the 4-point time series of integrated all-cause mortality for the calendar years 2019, 2020, 2021 and 2022, for all countries with sufficient data. The three highest level clusters are coloured orange, green and red, for easy reference.</figcaption></figure>

Here, 75 of the 125 countries have sufficient data (through 2022) and 2019 populations more than 1 million, and divide into the three highest level clusters as follows:

* ORANGE (22 countries): Armenia, Austria, Azerbaijan, Belgium, Chile, Ecuador, France, Italy, Kosovo, Kuwait, Kyrgyzstan, Mexico, Netherlands, Portugal, Qatar, Slovenia, Spain, Sweden, Switzerland, United Kingdom, USA, Uzbekistan

* GREEN (14 countries): Australia, Canada, Denmark, Finland, Germany, Hong Kong, Japan, New Zealand, Norway, Puerto Rico, Singapore, South Korea, Taiwan, Thailand

* RED (39 countries): Albania, Bosnia, Brazil, Bulgaria, Colombia, Costa Rica, Croatia, Cuba, Cyprus, Czechia, Egypt, Estonia, Georgia, Greece, Guatemala,
Hungary, Ireland, Israel, Jordan, Kazakhstan, Latvia, Lithuania, Malaysia, Mauritius, Moldova, Mongolia, North Macedonia, Oman, Paraguay, Philippines, Poland, Romania, Russia, Serbia, Slovakia, South Africa, Tunisia, Turkey, Uruguay

These three main clusters are shown on a world map in Figure 13.

<figure><img src="image_48.png" /><figcaption>Figure 13. World map of the three (orange, green and red) highest level clusters of countries, from the cluster analysis described in the text.</figcaption></figure>

Note that the strong East-West divide highlighted in the above analysis of the 20-10 peak in excess all-cause mortality is captured in the cluster analysis, as an orange-cluster/red-cluster divide (Figure 13).

Also note that the green-cluster (14 countries) is almost entirely populated by countries that have certain persistent excess all-cause mortality in 2023 (29 countries described above), except Hong Kong (borderline excess mortality in 2023) and New Zealand, which is 12 of 29 countries, even though the time series input into the cluster analysis does not include 2023. The said 12 of 29 countries are essentially those in the 29
countries that have small or zero excess mortality in the first year or more of the declared pandemic.

## 4.8 Persistent excess all-cause mortality in 2023

Generally, in the majority of cases, excess all-cause mortality decreases to relatively small values — compared to large values at times in the Covid period — virtually immediately in January 2023, up to and beyond the 5 May 2023 WHO announcement of an end to a declared pandemic (**Appendix A, Appendix B**).

Seventy nine (79) of the 125 countries in the present study have all-cause mortality data up to or beyond March 2023, which allows an evaluation of excess mortality into 2023. Seventy six (76) of the 125 countries in the present study have all-cause mortality data up to at least 30 June 2023, which allows reliable quantification of excess mortality into 2023. The magnitude and statistical reliability of inferred excess all-cause mortality into 2023 is best assessed using the analysis on the mortality by time data itself, illustrated in the figure of **Appendix A**.

Of the 76 countries allowing reliable quantification, 32 countries have a high statistical certainty of having persistent and significant excess all-cause mortality in the available data for 2023, excluding excess mortality from peak residuals extending out from 2022 (by selecting an integration start date a few weeks or more later than 1 January 2023).

The quantification result is shown — for the integration period 2023-01-16 to 2023-06-30 (weekly data) or 2023-02-01 to 2023-06-30 (monthly data) (the 2023-H1\* period) — in Figure 14, which is interpreted in conjunction with the figure in **Appendix A**.
<figure><img src="image_49.png" /><figcaption>**Figure 14.** Excess all-cause mortality for the period 2023-01-16 to 2023-06-30 (weekly data) or 2023-02-01 to 2023-06-30 (monthly data) (the 2023-H1* period), expressed as a percentage of the predicted mortality for the same period (upper panel) or as a fraction of the 2019 population (lower panel), with $1\sigma$ error bars, by country (as labelled). The colours correspond to the three clusters from the cluster analysis. Each panel shows the values of the mean, median, standard deviation, coefficient of variation (standard deviation / mean), and skewness (Fisher-Pearson coefficient of skewness).</figcaption></figure>

Therefore, the 32 countries having a high statistical certainty of persistent and significant excess all-cause mortality into 2023, compared to the extrapolated pre-Covid historic trend (Appendix A), excluding excess all-cause mortality from peak residuals extending out from 2022, and excluding accidentally large values, are: 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. Of these 32 countries, the four that are most borderline (subjectively least certain) would be France, Hong Kong, Russia and Switzerland.
The corresponding values of reliable persistent excess all-cause into 2023 for the 32 countries in question typically are in the range 5-15% of expected historical baseline mortality, or (2-20 x 10⁻⁴) % of 2019 population per week (Figure 14).

The three examples (from **Appendix A**) of Canada, Ireland and Netherlands are shown in Figure 15.
<figure><img src="image_50.png" /></figure>

<figure><img src="image_51.png" /></figure>

<figure><img src="image_52.png" /></figure>
**Figure 15.** All-cause mortality by week (bleu) and historic trend all-cause mortality by week (orange) (top curves) and excess all-cause mortality by week (bottom curves), with 1σ error bounds, 2015-2023, for Canada, Ireland and Netherlands, from Appendix A. 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).

The other 44 of 76 countries show near zero or negative excess all-cause mortality into 2023, and no statistically reliable positive value (Figure 14 and **Appendix A**). This means that 58% of the countries with sufficient data in the present study returned to normal or non-excess all-cause mortality in 2023. Three countries with less data appear to also have normal mortality, at least at the start of 2023. These 3 countries are Montenegro, Philippines and Uruguay (**Appendix A**).

The 44 of 76 countries showing near zero or negative excess all-cause mortality into 2023, or no statistically reliable positive value, are: Albania, Azerbaijan, Bosnia, Bulgaria, Czechia, Estonia, French Guiana, Georgia, Greece, Greenland, Guadeloupe, Hungary, Kazakhstan, Kosovo, Kyrgyzstan, Latvia, Luxembourg, Macao, Mayotte, Mexico, New Caledonia, New Zealand, Oman, Poland, Reunion, Romania, San Marino, Serbia, Slovakia, Slovenia, and South Africa (with high statistical certainty of being ≤ 0); and Armenia, Barbados, Chile, Croatia (with low statistical certainty of being ≥0), Cyprus, Iceland, Malaysia, Malta, Martinique, Mongolia, North Macedonia, Paraguay, and Uzbekistan (with low statistical certainty of being ≤ 0).

Here, Uzbekistan is a special case in which the all-cause mortality strikingly shows large excess mortality peaks systematically occurring in mid-summer 2020, 2021, 2022 and 2023.

Note that countries with high post-declared-pandemic excess all-cause mortality are generally Western countries whereas Eastern European countries, for example, generally have no measurable post-declared-pandemic excess all-cause mortality.

Of the group of 32 countries having significant excess mortalities into 2023, nine (9) had virtually no excess all-cause mortality for more than one year into the declared
pandemic, and had excesses occurring solely after the commencement or near-
completion of COVID-19 vaccination. These 9 countries are: Australia, Denmark,
Finland, Japan, Norway, Singapore, South Korea, Taiwan, and Thailand (Appendix A,
Appendix B).

All nine of these countries are among the 14 countries of the green cluster (cluster 2) from the above cluster analysis. And, thirteen (13) of the 14 green-cluster (cluster 2) countries (Figure 12, Figure 13, Figure 14) are among the 32 countries having significant excess mortalities into 2023. The only exception is green-cluster New Zealand. The 13 green-cluster countries in question are: Australia, Canada, Denmark, Finland, Germany, Hong Kong, Japan, Norway, Puerto Rico, Singapore, South Korea, Taiwan, and Thailand.

4.9 Quantitative excess all-cause mortality by time period and by country during the Covid period

A summary of excess all-cause mortality (and rate and P-score) data for the Covid
period (2020-2022) is given in the table in **Appendix C** which has:

* 93 countries with data up to the end of 2022, in decreasing order of population

* The country names and country-name codes

* National population for 2019, by country

* Total excess all-cause mortality for the years 2020-2022 ("X202122"), with associated error, by country, for the 93 countries with sufficiently complete data

* Total predicted mortality for the years 2020-2022 deduced from the pre-Covid historic trend ("H202122"), with associated error, by country, for the 93 countries with sufficiently complete data

* Total excess all-cause mortality for the years 2020-2022 ("X202122"), expressed as a percentage ("X/pop%") of national population in 2019, with associated error, by country, for the 93 countries with sufficiently complete data

* Total excess all-cause mortality for the years 2020-2022 ("X202122"), expressed as a percentage ("X/hist%") of the predicted mortality for the same period (2020-
2022) deduced from the pre-Covid historic trend (“H202122”), with associated error, by country, for the 93 countries with sufficiently complete data

Here, X202122 is essentially the excess all-cause mortality during the Covid period, excluding the dates 1 January 2023 through 5 May 2023 of the declared pandemic.

This excess all-cause mortality during the Covid period (X202122), expressed as a percentage of the national population in 2019, has a broad range of values such as (rank-country, percentage, 1σ error in parenthesis): 1-Bulgaria, 1.11(0.03)%; 2-Serbia, 1.01(0.05)%; ... 4-Russia, 0.93(0.02)%; ... 17-Mexico, 0.567(0.005)%; ... 21-Italy, 0.51(0.02)%; ... 30-USA, 0.424(0.005)%; ... 36-Brazil, 0.398(0.006)%; ... 61-Sweden, 0.21(0.01)%; ... 63-Canada, 0.196(0.006)%; ... 75-Japan, 0.135(0.009)%; ... 86-Australia, 0.070(0.005)%; ... 93-Greenland, -0.1(0.1)%. See table in **Appendix C**.

The 93 countries having sufficient data comprise a population of 2.363 billion in 2019. The mean excess all-cause mortality during the Covid period (X202122), expressed as a percentage of the national population in 2019, taking the 93 countries with sufficient data as a single jurisdiction, is: $X202122/2019pop = (0.3844 \pm 0.0021)\%$.

Taking this mean fraction to be representative of the world, and adjusting to the world population to 2021 (mid-Covid-period), this implies a total excess all-cause mortality during the Covid period equal to

* $(29.8 \pm 0.2)$ million deaths, or

* $(0.377 \pm 0.002)\%$ of 2021 World population, excluding the excess all-cause mortality from 1 January 2023 through 5 May 2023 of the declared pandemic.

Note that this number (29.8M) is greater than the total number of COVID-19 deaths in the world, reported to the WHO up to 11 February 2024 (7.03M). It is more than 4.2 times greater. However, the 93 countries in question may not be representative of the other countries, in terms of Covid-period excess all-cause mortality, and the reporting
and assignation of "COVID-19 deaths" may be highly incomplete and variably reliable or
consistent from country to country.

Similarly, the total excess all-cause mortality in the period 2020-2022 for the 93 countries having sufficient data is 9.086 million, for a total historic baseline value on the same period of 56.737 million (Appendix C), which gives an excess-to-baseline ratio, for all 93 countries together, of 16.01 %, whereas the by-country median value is 14.1 % (see below).

Figure 16 shows excess all-cause mortality for each of the eleven periods 2020-2022, 2020, 2021, 2022, and the half calendar years 2020-H1 through 2023-H1, expressed as a percentage of the predicted (extrapolated historical trend, see Methods) mortality for the same period, with 1σ error bars, by country (as labelled, with country codes in alphabetical order).

Each panel in Figure 16 shows a legend with the corresponding values, for the given time period, of the mean, median, standard deviation, coefficient of variation (standard deviation / mean), and skewness (Fisher-Pearson coefficient of skewness).
<figure><img src="image_53.png" /></figure>

<figure><img src="image_54.png" /></figure>

<figure><img src="image_55.png" /></figure>

<figure><img src="image_56.png" /></figure>
<figure><img src="image_57.png" /></figure>

<figure><img src="image_58.png" /></figure>

<figure><img src="image_59.png" /></figure>

<figure><img src="image_60.png" /></figure>

mean = 27.0

median = 22.7

stdev = 18.5

cv = 0.7

skewness = 0.5
<figure><img src="image_61.png" /><figcaption>**Figure 16.** Excess all-cause mortality for the periods (as labelled for each panel) 2020-2022, 2020, 2021, 2022, and each half calendar year 2020-H1 through 2023-H1, expressed as a percentage of the predicted mortality for the same period ("P-score"), with 1σ error bars, by country (as labelled, with country codes in alphabetical order). The colours correspond to the three clusters from the cluster analysis. Each panel shows the values of the mean, median, standard deviation, coefficient of variation (standard deviation / mean), and skewness (Fisher-Pearson coefficient of skewness).</figcaption></figure>

The figure in **Appendix D** shows the same results for excess all-cause mortality as illustrated in Figure 16 but calculated as fractions of 2019 population, rather than as percentages of the predicted mortalities for the same periods.
Table 1 lists the statistical parameters of the country-wise distributions of excess all-cause mortality values, for each time period, and for both as percentage of the predicted mortality for the same period (from Figure 16) and as fraction of 2019 population (from Appendix D). The listed statistical parameters are: number of countries (N), mean, median, standard deviation, coefficient of variation (cv = standard deviation / mean), and skewness (Fisher-Pearson coefficient of skewness).

<caption>**Table 1: Statistical parameters of the country-wise distributions of excess all-cause mortality values, for various time periods**</caption>

*As percentage of the predicted mortality for the same period (from Figure 16) *

<table><thead><tr><th>period</th><th>N</th><th>mean (%)</th><th>median (%)</th><th>stdev (%)</th><th>cv</th><th>skewness</th></tr></thead><tbody><tr><td>2020-2022</td><td>93</td><td>14.7</td><td>14.1</td><td>8.1</td><td>0.6</td><td>+0.2</td></tr><tr><td>2020</td><td>124</td><td>11.5</td><td>10.3</td><td>13.3</td><td>1.2</td><td>+0.7</td></tr><tr><td>2021</td><td>119</td><td>23.3</td><td>22.2</td><td>15.0</td><td>0.6</td><td>+0.1</td></tr><tr><td>2022</td><td>93</td><td>11.4</td><td>12.3</td><td>6.5</td><td>0.6</td><td>-0.2</td></tr><tr><td>2020-H1</td><td>125</td><td>4.4</td><td>0.7</td><td>13.2</td><td>3.0</td><td>+2.2</td></tr><tr><td>2020-H2</td><td>124</td><td>19.0</td><td>16.0</td><td>19.4</td><td>1.0</td><td>+0.6</td></tr><tr><td>2021-H1</td><td>121</td><td>19.9</td><td>15.2</td><td>21.5</td><td>1.1</td><td>+1.5</td></tr><tr><td>2021-H2</td><td>119</td><td>27.0</td><td>22.7</td><td>18.5</td><td>0.7</td><td>+0.5</td></tr><tr><td>2022-H1</td><td>99</td><td>12.8</td><td>12.3</td><td>8.8</td><td>0.7</td><td>+0.7</td></tr><tr><td>2022-H2</td><td>93</td><td>10.2</td><td>9.5</td><td>8.6</td><td>0.8</td><td>+1.2</td></tr><tr><td>2023-H1</td><td>76</td><td>4.2</td><td>3.6</td><td>8.6</td><td>2.0</td><td>+2.6</td></tr></tbody></table>

*As fraction of 2019 population (from Appendix D) *

<table><thead><tr><th>period</th><th>N</th><th>mean (10<sup>-3</sup>)</th><th>median (10<sup>-3</sup>)</th><th>stdev (10<sup>-3</sup>)</th><th>cv</th><th>skewness</th></tr></thead><tbody><tr><td>2020-2022</td><td>93</td><td>3.5</td><td>3.1</td><td>2.5</td><td>0.7</td><td>+0.8</td></tr><tr><td>2020</td><td>124</td><td>0.79</td><td>0.65</td><td>0.94</td><td>1.2</td><td>+0.4</td></tr><tr><td>2021</td><td>119</td><td>1.7</td><td>1.3</td><td>1.4</td><td>0.8</td><td>+0.9</td></tr><tr><td>2022</td><td>93</td><td>0.92</td><td>0.92</td><td>0.60</td><td>0.6</td><td>+0.1</td></tr></tbody></table>
<table><tr><td>2020-H1</td><td>125</td><td>0.11</td><td>0.026</td><td>0.37</td><td style="background-color: #00a9e0;">3.4</td><td>+1.2</td></tr><tr><td>2020-H2</td><td>124</td><td>0.67</td><td>0.49</td><td>0.77</td><td>1.1</td><td>+0.9</td></tr><tr><td>2021-H1</td><td>121</td><td>0.70</td><td>0.47</td><td>0.75</td><td>1.1</td><td>+0.9</td></tr><tr><td>2021-H2</td><td>119</td><td style="background-color: #00a9e0;">1.0</td><td style="background-color: #00a9e0;">0.74</td><td style="background-color: #00a9e0;">0.91</td><td>0.9</td><td style="background-color: #00a9e0;">+1.3</td></tr><tr><td>2022-H1</td><td>99</td><td>0.52</td><td>0.47</td><td>0.40</td><td>0.8</td><td>+0.5</td></tr><tr><td>2022-H2</td><td>93</td><td>0.40</td><td>0.37</td><td>0.33</td><td>0.8</td><td>+0.5</td></tr><tr><td>2023-H1</td><td>76</td><td>0.11</td><td>0.12</td><td>0.22</td><td>2.1</td><td>-0.2</td></tr></table>

* The orange and blue colours are to highlight largest values, for years and half years, respectively.

From Table 1, note that:

* The excess all-cause mortality, relative to the extrapolated historical trend baseline, during the declared pandemic (during the Covid period) has a country-to-country median magnitude, for the countries in the present study, equal to 4.7% on average per year of expected historical baseline, or 1.0 per 1000 of 2019 population on average per year, during 2020-2022.

* If every country experienced the country-to-country median excess mortality observed in the present study (93 countries having data up to 2022) during the nominal 2020-2022 Covid period, then using the 2021 estimated global population (7.9 billion), this would correspond to $0.31\% \times 7.9 \text{ billion} = 24 \text{ million}$ deaths, which is smaller than the above estimate of $(29.8 \pm 0.2) \text{ million deaths}$, based on overall average mortality (total for countries in the study). The difference between these two estimates is due to many small countries having relatively smaller excess mortalities: the country-to-country median is not representative of large countries in which most excess deaths occur.

* In every time period during the declared pandemic (2020-2022, 2020, 2021, 2022, and every half year up to and including 2023-H1) there is a large country-to-country variability in excess all-cause mortality, quantified as coefficients of variation (cv = standard deviation / mean) in the range 0.6 to 3.4. These are exceptionally large country-to-country coefficients of variability of excess all-cause mortality.
* By far, the largest coefficient of variability (cv = 3.0 or 3.4, by percent or by population, respectively) occurred in the first half year of 2020, following when the pandemic was declared on 11 March 2020. The disparity in excess mortality from country to country is exceptionally large in this period, possibly unprecedented for human populations in any large-scale regional mortality-causing event (pandemic, war, economic crash, ecological collapse). Even the lesser later values of cv are likewise exceptional. Likewise, skewness of the distribution is typically largest for this half year (2020-H1, Table 1), arising from large-value outliers (Figure 16).

* The year of largest excess all-cause mortality, in the present study, is unquestionably 2021, rather than 2020 or 2022.

* The half year of largest excess all-cause mortality is the second half of 2021 (2021-H2, Table 1), with a country-to-country median magnitude, for the countries in the present study, equal to 45.4% on a per year basis of expected historical baseline, or 1.5 per 1000 of 2019 population on a per year basis, during 2021-H2.

An equivalent analysis is made for each separate cluster-analysis group of countries (Section 4.7) and these results are presented in the following Section 4.10.

## 4.10 Statistics of excess all-cause mortality by time period for cluster-analysis groups of countries

The results presented in Table 1 are shown graphically versus integration-time period, and for each of the cluster-analysis groups of countries identified in Section 4.7, as follows.

Figure 17 shows the mean, median, standard deviation, and coefficient of variation (cv = standard deviation / mean) values of the excess all-cause mortality (normalized either by population or by historic baseline all-cause mortality) for all countries (blue) in the present study and for countries in each of the identified clusters of countries
(Section 4.7: cluster 1, orange; cluster 2, green; cluster 3, red; no cluster, black, 2019 population ≤ 1 million, or data ends prior to end of 2022), and for each of several different time periods of integration (2020-2022, 2020-2023-H1, and each half year 2020-H1 through 2023-H1).
% increase mortality

<figure><img src="image_62.png" /></figure>

Excess deaths per pop

<figure><img src="image_63.png" /></figure>
<figure><img src="image_64.png" /></figure>

<figure><img src="image_65.png" /></figure>
% increase mortality

<figure><img src="image_66.png" /></figure>

Excess deaths per pop

<figure><img src="image_67.png" /></figure>
% increase mortality

<figure><img src="image_68.png" /></figure>

Excess deaths per pop

<figure><img src="image_69.png" /></figure>
<figure><img src="image_70.png" /></figure>

<figure><img src="image_71.png" /><figcaption>Figure 17. Statistical parameters of the distributions of values of excess all-cause mortality (mean, median, standard deviation, and coefficient of variation, as per labelled y-axes), for normalization by historic baseline all-cause mortality (top panel for each parameter) and by 2019 population (bottom panel for each parameter), versus half year 2020-H1 through 2023-H1. Each panel has five datasets, for: cluster 1 countries (orange),</figcaption></figure>
cluster 2 countries (green), cluster 3 countries (red), no cluster countries (black), and all countries (blue). Solid horizontal lines (same colours) show the values for time period 2020-2022, dashed horizontal lines for 2020-2023-H1.

## 4.11 Spatial distribution of Covid-period excess all-cause mortality in the world

The values of excess all-cause mortality during the Covid period (actually, 2020-2022; X202122), expressed as a percentage of the national 2019 population, for the 93 countries with sufficient data, are shown on a world map in Figure 18.

<figure><img src="image_72.png" /><figcaption>Figure 18. World map of excess all-cause mortality during the Covid period (X202122), by country, expressed as a percentage of the national population in 2019, for the 93 countries with sufficient data.</figcaption></figure>

The excess all-cause mortality during the Covid period (X202122) can also be expressed as a percentage of the predicted mortality for the same period (2020-2022) deduced from the pre-Covid historic trend, for the 93 countries with sufficient data. This accounts for the varying country-specific pre-Covid mortality rates. These values are shown on a world map in Figure 19.
<figure><img src="image_73.png" /><figcaption>Figure 19. World map of excess all-cause mortality during the Covid period (X202122), expressed as a percentage of the predicted mortality for the same period (2020-2022) deduced from the pre-Covid historic trend (“P-score”), for the 93 countries with sufficient data.</figcaption></figure>

Note the following general features shown in Figure 18 and Figure 19:

* There is an East-West divide in Europe, with Eastern European nations having significantly larger Covid-period mortality, and among the largest in the world

* There is a large north-south gradient in North America, with significantly increasing Covid-period mortality in going from Canada to USA to Mexico

* Australia, New Zealand and Japan have relatively small (near-zero for New Zealand) Covid-period mortality

* Major and populous continental-scale regions of the world have no data on these maps

The same general features (East-West divide in Europe; north-south gradient in North America; Australia-New Zealand-Japan) are apparent in the world maps of several socio-economic variables. Notably, with life expectancy at age 65, share of population aged 90+, income or consumption inequality (Gini coefficient), share of population living
on less than $30 per day, and mean income or consumption per day within the poorest decile (tenth of the population) (Intl-$), as illustrated in the five panels of Figure 20.
Here, the socio-economic data are from the sources specified in Section 2.

<figure><img src="image_74.png" /><figcaption>Life expectancy at age 65</figcaption></figure>

<figure><img src="image_75.png" /></figure>
Income or consumption inequality (Gini coefficient)

<figure><img src="image_76.png" /></figure>

Share of population living on less than $30 per day

<figure><img src="image_77.png" /></figure>
Mean income or consumption per day within the poorest decile (tenth of the population) (Intl-$)

<figure><img src="image_78.png" /><figcaption>Figure 20. World maps of select socio-economic variables (as labelled): life expectancy at age 65, share of population aged 90+, income or consumption inequality (Gini coefficient), share of population living on less than $30 per day, and mean income or consumption per day within the poorest decile (tenth of the population) (Intl-$).</figcaption></figure>

## 4.12 Spatiotemporal variation of Covid-period excess all-cause mortality in the world

As is clear from Appendix A and Appendix B, excess all-cause mortality by country is far from uniform in time during the Covid period. Therefore, it is far more informative to examine the world map of excess mortality by half year through the Covid period (2019-2023), rather than for the near-entirety of the Covid period (X202122).

The 9 panels of Figure 21 show the time sequence of world maps of excess all-cause mortality by half year ("H1", "H2") through the Covid period (2019-2023: 2019-H1, 2019-H2, 2020-H1, ... 2023-H1), expressed as a percentage of the predicted mortality for the given half year deduced from the pre-Covid historic trend, for the countries with sufficient data for each half year. Here, the colour coding is capped at 50 (%) for all the maps to aid visualization.
% increase in mortality (2019-01-01 to 2019-06-30)

<figure><img src="image_79.png" /></figure>

% increase in mortality (2019-07-01 to 2019-12-31)

<figure><img src="image_80.png" /></figure>
% increase in mortality (2020-01-01 to 2020-06-30)

<figure><img src="image_81.png" /></figure>

% increase in mortality (2020-07-01 to 2020-12-31)

<figure><img src="image_82.png" /></figure>
% increase in mortality (2021-01-01 to 2021-06-30)

<figure><img src="image_83.png" /></figure>

% increase in mortality (2021-07-01 to 2021-12-31)

<figure><img src="image_84.png" /></figure>
% increase in mortality (2022-01-01 to 2022-06-30)

<figure><img src="image_85.png" /></figure>

% increase in mortality (2022-07-01 to 2022-12-31)

<figure><img src="image_86.png" /></figure>
% increase in mortality (2023-01-01 to 2023-06-30)

<figure><img src="image_87.png" /><figcaption>**Figure 21.** Time sequence of world maps of excess all-cause mortality by half year through the Covid period (top to bottom: 2019-H1, 2019-H2, 2020-H1, ... 2023-H1), expressed as a percentage of the predicted mortality for the given half year deduced from the pre-Covid historic trend ("P-score"), for the countries with sufficient data for each half year. The colour coding is capped at 50 (%) for all the maps to aid visualization.</figcaption></figure>

The equivalent is shown, but on a per-population basis rather than on the predicted mortality basis, in the 9-panel figure of **Appendix E**, where the colour coding is capped at 25 excess deaths per 10k population for all the maps to aid visualization.

## 4.13 Comparison to COVID-19-assigned deaths and to COVID-19-assigned cases

As noted above, the projected Covid-period excess all-cause mortality for the world (29.8 ± 0.2 million) is significantly larger than the world COVID-19-assigned-death mortality up to 11 February 2024 reported by WHO (7.03 million).

This disparity is smaller when we use the country-specific Covid-period COVID-19-assigned-death mortality reported to WHO, for the same 93 countries having mortality data through 2022 in the present study, and which were used to project the said Covid-period excess all-cause mortality for the world.
Figure 22 shows the ratio of Covid-period excess all-cause mortality (excess all-cause mortality 2020-2022, X202122) to Covid-period COVID-19-assigned-death mortality reported to WHO, for the 91 countries having both sufficient all-cause mortality data in the present study and available COVID-19-assigned-death mortality data, excluding three outliers from the figure (Uzbekistan, +2,070 %; Egypt, +1,450 %; Greenland, -330 %).

Including the outliers, the resulting ratio has a country-wise average of 217 %, a median value of 155 %, and a standard deviation of 273 %, as per the figure legend. Here, the Covid-period COVID-19-assigned-death mortality data is from Worldometer (2024).

<figure><img src="image_88.png" /><figcaption>**Figure 22.** Ratio of Covid-period excess all-cause mortality (2020-2022, X202122) to Covid-period COVID-19-assigned-death mortality reported to WHO, expressed as percentages, with error bars from the $1\sigma$ uncertainty in X202122, for the 91 countries having both sufficient all-cause mortality data in the present study and available COVID-19-assigned-death mortality data, excluding three outliers from the figure (Uzbekistan, +2,070 %; Egypt, +1,450 %; Greenland, -330 %). The colours correspond to the three clusters from the cluster analysis.</figcaption></figure>

Likewise, Figure 23 shows the ratio of Covid-period excess all-cause mortality (X202122) to Covid-period COVID-19 cases reported to WHO, for the 91 countries having both sufficient all-cause mortality data in the present study and available COVID-19 cases data, excluding two outliers (Egypt, +69.0 %; Uzbekistan, +13.4 %).
Including the outliers, the resulting ratio (or presumed case fatality rate, CFR) has a country-wise average of 2.8 %, a median value of 1.1 %, and a standard deviation of 7.4 %, as per the figure legend. These would be extraordinarily large all-ages (overall population) actual CFR values. Here, the Covid-period COVID-19 cases data is from Worldometer (2024).

<figure><img src="image_89.png" /><figcaption>**Figure 23.** Ratio of Covid-period excess all-cause mortality (2020-2022, X202122) to Covid-period COVID-19 cases reported to WHO, expressed as percentages, with error bars from the 1σ uncertainty in X202122, for the 91 countries having sufficient all-cause mortality data in the present study, as labelled by country code in alphabetical order. The colours correspond to the three clusters from the cluster analysis.</figcaption></figure>

Calculating CFR values as ratios of Covid-period COVID-19-assigned-death mortality reported to WHO to Covid-period COVID-19 cases reported to WHO, expressed as percentages, both from the Worldometer (2024) database, for the 91 countries having sufficient all-cause mortality data in the present study, gives a country-wise average of 1.0 %, a median value of 0.71 %, and no obvious outliers.

## 4.14 Broad systematics of vaccine rollouts and vaccine injections per population

One of the socio-economic variables we use in the present study is number of vaccine injections per population, which is representative of so-called vaccine coverage. Therefore, we next examine the general features of this variable for the countries in the present study.
Figure 24 shows number of vaccine injections per population in 2022 versus number of vaccine injections per population in 2021, for each country as labelled, using the WHO data on vaccination. Here, the Pearson correlation coefficient (r) is +0.42.

<figure><img src="image_90.png" /><figcaption>**Figure 24.** Number of vaccine injections given in 2022, per 2019 population, versus number of vaccine injections given in 2021, per 2019 population, as points (left panel) and for each country as labelled (right panel), using the WHO data on vaccination. The colours correspond to the three clusters from the cluster analysis. The Pearson correlation coefficient is r = +0.42.</figcaption></figure>

The median values of number of vaccine injections per population are approximately 0.4 in 2022 and 1.5 in 2021, or 1.9 overall, and there are large country-to-country differences, from approximately 0.4 to approximately 3.0, on the two-year period.

The number of vaccine injections per population, 2021-2022 (WHO data), by country correlates with various socio-economic variables. This is illustrated in the four panels of Figure 25 for: share of population living on less than $30 per day (r = -0.54), GDP per capita (r = +0.39), median income per day (r = +0.51), and life expectancy at age 65 (r = +0.57), as labelled.
<figure><img src="image_91.png" /></figure>

<figure><img src="image_92.png" /></figure>

<figure><img src="image_93.png" /></figure>

<figure><img src="image_94.png" /></figure>
<figure><img src="image_95.png" /><figcaption>**Figure 25.** Number of vaccine injections given in 2021-2022 (WHO data), per 2019 population, as points (left panel) and for each country as labelled (right panel) versus: share of population living on less than $30 per day (r = -0.54), GDP per capita (r = +0.39), median income per day (r = +0.51), and life expectancy at age 65 (r = +0.60), as labelled in the separate twin panels. The colours correspond to the three clusters from the cluster analysis.</figcaption></figure>

Therefore, corresponding due care must be taken in interpreting correlations between
excess all-cause mortality and degree of vaccination.
4.15 Excess all-cause mortality (as % of expected mortality) correlations with socio-economic variables

In examining possible relations or correlations between excess all-cause mortality and socio-economic variables (by country), two considerations, in particular, are important: First, the time period of integration of the excess all-cause mortality, since the correlation can vary significantly depending on the chosen time period; and second, the possible correlations between the socio-economic variables themselves, since these are not necessarily (or usually) independent variables.

The dependent variables (outcomes) are taken to be the excess all-cause mortality, by country, for the time periods of integration: Covid period (2020 through 2022 or 2020 through 2023-H1), and the half years 2020-H1 to 2023-H1.

The socio-economic variables of the countries are from any convenient and reliable year near 2020, usually 2019.

An examination of inter-socio-economic variable relations is presented in the figure of **Appendix F**. Likewise; a table of the corresponding Pearson correlation coefficients is given in **Appendix G**. We see, therefore, unsurprisingly, for example, that all the socio-economic variable that quantify poverty or wealth of residents are all highly correlated between themselves, to different degrees, and with different functional dependencies on each other. The correlations between poverty/wealth indicators and age structure of the population or income inequality (Gini) are not as strong, suggesting quasi-independence or relational complexity between these types of variables (poverty/wealth vs age structure; and poverty/wealth vs income inequality).

The main results of the correlations between excess all-cause mortality during the Covid period and socio-economic variables are shown in the figures below, for GDP per capita, Gini inequality index, share of population living on less than $5.50 per day, share of population living on less than $30 per day, median income per day, life expectancy at
age 65, share of population aged 60+, and share of population aged 90+. In each case, the excess all-cause mortality is for the Covid period itself (2020-2022) and for each half year in the Covid period, 2020-H1 to 2023-H1, and is expressed as a percentage of the predicted (extrapolated historic trend, see Methods) mortality for the same period (this percentage has been referred to as the “P-score”, Aron et al., 2020, Msemburi et al., 2023). Here, 2023-H1 is for the actual calendar half year, unlike 2023-H1*. The nominal Covid period 2020-2022 and the different half years include different numbers of points (countries), depending on availability of mortality data for each half year. A table of the corresponding Pearson correlation coefficients is in **Appendix H**.
<figure><img src="image_96.png" /></figure>

<figure><img src="image_97.png" /></figure>

<figure><img src="image_98.png" /></figure>

<figure><img src="image_99.png" /></figure>
<figure><img src="image_100.png" /></figure>

<figure><img src="image_101.png" /></figure>

<figure><img src="image_102.png" /></figure>

<figure><img src="image_103.png" /></figure>
<figure><img src="image_104.png" /></figure>

<figure><img src="image_105.png" /></figure>

<figure><img src="image_106.png" /></figure>

<figure><img src="image_107.png" /></figure>
<figure><img src="image_108.png" /><figcaption>**Figure 26.** Excess all-cause mortality for the Covid period (2020-2022) and for each half year 2020-H1 to 2023-H1, as labelled, top to bottom twin panels, expressed as a percentage of the predicted (extrapolated historic trend, see Methods) mortality for the same period ("P-score"), versus GDP per capita, with 1σ error bars (left panels) and country codes (right panels). The colours correspond to the three clusters from the cluster analysis. The corresponding Pearson correlation coefficient is given in each twin panel.</figcaption></figure>
<figure><img src="image_109.png" /></figure>

<figure><img src="image_110.png" /></figure>

<figure><img src="image_111.png" /></figure>

<figure><img src="image_112.png" /></figure>
<figure><img src="image_113.png" /></figure>

<figure><img src="image_114.png" /></figure>

<figure><img src="image_115.png" /></figure>

<figure><img src="image_116.png" /></figure>
<figure><img src="image_117.png" /></figure>

<figure><img src="image_118.png" /></figure>

<figure><img src="image_119.png" /></figure>

<figure><img src="image_120.png" /></figure>
<figure><img src="image_121.png" /><figcaption>**Figure 27.** Excess all-cause mortality for the Covid period (2020-2022) and for each half year 2020-H1 to 2023-H1, as labelled, top to bottom twin panels, expressed as a percentage of the predicted (extrapolated historic trend, see Methods) mortality for the same period ("P-score"), versus Gini inequality index, with $1\sigma$ error bars (left panels) and country codes (right panels). The colours correspond to the three clusters from the cluster analysis. The corresponding Pearson correlation coefficient is given in each twin panel.</figcaption></figure>
<figure><img src="image_122.png" /></figure>

<figure><img src="image_123.png" /></figure>

<figure><img src="image_124.png" /></figure>

<figure><img src="image_125.png" /></figure>
<figure><img src="image_126.png" /></figure>

<figure><img src="image_127.png" /></figure>

<figure><img src="image_128.png" /></figure>

<figure><img src="image_129.png" /></figure>
<figure><img src="image_130.png" /></figure>

<figure><img src="image_131.png" /></figure>

<figure><img src="image_132.png" /></figure>

<figure><img src="image_133.png" /></figure>
<figure><img src="image_134.png" /><figcaption>**Figure 28.** Excess all-cause mortality for the Covid period (2020-2022) and for each half year 2020-H1 to 2023-H1, as labelled, top to bottom twin panels, expressed as a percentage of the predicted (extrapolated historic trend, see Methods) mortality for the same period ("P-score"), versus share of population living with less than $5.50 per day, with 1σ error bars (left panels) and country codes (right panels). The colours correspond to the three clusters from the cluster analysis. The corresponding Pearson correlation coefficient is given in each twin panel.</figcaption></figure>
<figure><img src="image_135.png" /></figure>

<figure><img src="image_136.png" /></figure>

<figure><img src="image_137.png" /></figure>

<figure><img src="image_138.png" /></figure>
<figure><img src="image_139.png" /></figure>

<figure><img src="image_140.png" /></figure>

<figure><img src="image_141.png" /></figure>

<figure><img src="image_142.png" /></figure>
<figure><img src="image_143.png" /></figure>

<figure><img src="image_144.png" /></figure>

<figure><img src="image_145.png" /></figure>

<figure><img src="image_146.png" /></figure>
<figure><img src="image_147.png" /><figcaption>**Figure 29.** Excess all-cause mortality for the Covid period (2020-2022) and for each half year 2020-H1 to 2023-H1, as labelled, top to bottom twin panels, expressed as a percentage of the predicted (extrapolated historic trend, see Methods) mortality for the same period ("P-score"), versus share of population living with less than $30 per day, with 1σ error bars (left panels) and country codes (right panels). The colours correspond to the three clusters from the cluster analysis. The corresponding Pearson correlation coefficient is given in each twin panel.</figcaption></figure>
<figure><img src="image_148.png" /></figure>

<figure><img src="image_149.png" /></figure>

<figure><img src="image_150.png" /></figure>

<figure><img src="image_151.png" /></figure>
<figure><img src="image_152.png" /></figure>

<figure><img src="image_153.png" /></figure>

<figure><img src="image_154.png" /></figure>

<figure><img src="image_155.png" /></figure>
<figure><img src="image_156.png" /></figure>

<figure><img src="image_157.png" /></figure>

<figure><img src="image_158.png" /></figure>

<figure><img src="image_159.png" /></figure>
<figure><img src="image_160.png" /><figcaption>**Figure 30.** Excess all-cause mortality for the Covid period (2020-2022) and for each half year 2020-H1 to 2023-H1, as labelled, top to bottom twin panels, expressed as a percentage of the predicted (extrapolated historic trend, see Methods) mortality for the same period ("P-score"), versus median income per day, with 1σ error bars (left panels) and country codes (right panels). The colours correspond to the three clusters from the cluster analysis. The corresponding Pearson correlation coefficient is given in each twin panel.</figcaption></figure>
<figure><img src="image_161.png" /></figure>

<figure><img src="image_162.png" /></figure>

<figure><img src="image_163.png" /></figure>

<figure><img src="image_164.png" /></figure>
<figure><img src="image_165.png" /></figure>

<figure><img src="image_166.png" /></figure>

<figure><img src="image_167.png" /></figure>

<figure><img src="image_168.png" /></figure>
<figure><img src="image_169.png" /></figure>

<figure><img src="image_170.png" /></figure>

<figure><img src="image_171.png" /></figure>

<figure><img src="image_172.png" /></figure>
<figure><img src="image_173.png" /><figcaption>**Figure 31.** Excess all-cause mortality for the Covid period (2020-2022) and for each half year 2020-H1 to 2023-H1, as labelled, top to bottom twin panels, expressed as a percentage of the predicted (extrapolated historic trend, see Methods) mortality for the same period ("P-score"), versus life expectancy at age 65, with 1σ error bars (left panels) and country codes (right panels). The colours correspond to the three clusters from the cluster analysis. The corresponding Pearson correlation coefficient is given in each twin panel.</figcaption></figure>
<figure><img src="image_174.png" /></figure>

<figure><img src="image_175.png" /></figure>

<figure><img src="image_176.png" /></figure>

<figure><img src="image_177.png" /></figure>
<figure><img src="image_178.png" /></figure>

<figure><img src="image_179.png" /></figure>

<figure><img src="image_180.png" /></figure>

<figure><img src="image_181.png" /></figure>
<figure><img src="image_182.png" /></figure>

<figure><img src="image_183.png" /></figure>

<figure><img src="image_184.png" /></figure>

<figure><img src="image_185.png" /></figure>
<figure><img src="image_186.png" /><figcaption>**Figure 32.** Excess all-cause mortality for the Covid period (2020-2022) and for each half year 2020-H1 to 2023-H1, as labelled, top to bottom twin panels, expressed as a percentage of the predicted (extrapolated historic trend, see Methods) mortality for the same period ("P-score"), versus share of population aged 60+, with 1σ error bars (left panels) and country codes (right panels). The colours correspond to the three clusters from the cluster analysis. The corresponding Pearson correlation coefficient is given in each twin panel.</figcaption></figure>
<figure><img src="image_187.png" /></figure>

<figure><img src="image_188.png" /></figure>

<figure><img src="image_189.png" /></figure>

<figure><img src="image_190.png" /></figure>
<figure><img src="image_191.png" /></figure>

<figure><img src="image_192.png" /></figure>

<figure><img src="image_193.png" /></figure>

<figure><img src="image_194.png" /></figure>
<figure><img src="image_195.png" /></figure>

<figure><img src="image_196.png" /></figure>

<figure><img src="image_197.png" /></figure>

<figure><img src="image_198.png" /></figure>
<figure><img src="image_199.png" /><figcaption>Figure 33. Excess all-cause mortality for the Covid period (2020-2022) and for each half year 2020-H1 to 2023-H1, as labelled, top to bottom twin panels, expressed as a percentage of the predicted (extrapolated historic trend, see Methods) mortality for the same period (“P-score”), versus share of population aged 90+, with 1σ error bars (left panels) and country codes (right panels). The colours correspond to the three clusters from the cluster analysis. The corresponding Pearson correlation coefficient is given in each twin panel.</figcaption></figure>
## 4.16 Excess all-cause mortality (as fraction of 2019 population) correlations with socio-economic variables

Raw excess mortality needs to be normalized in such a way as to allow valid country-to-country comparisons.

Each of the above figures (Figure 26 through Figure 33) is for excess all-cause mortality expressed as a percentage of the predicted (extrapolated historic trend, see Methods) mortality for the same period (equivalently, expressed as a “P-score”).

When excess mortality is expressed in this way (“P-score”), it is measured in comparison to the intrinsic baseline mortality rate (by time period) in the society or jurisdiction, and the latter includes all the factors affecting mortality, such as age structure and baseline health status.

If we prefer to measure the excess mortality directly on a per population basis, irrespective of the health status and age structure of the population, then we express the excess all-cause mortality as a fraction of national population for a recent year. We use the year 2019.

The two excess-mortality normalizations (“P-score” or by population) produce different numbers, having their distinct meanings, and in general produce different correlations and correlation coefficients for a given socio-economic variable.

The correlations between excess all-cause mortality expressed on a per population basis during the Covid period and socio-economic variables are shown in the figures below (Figure 34 through Figure 41), for GDP per capita, Gini inequality index, share of population living on less than $5.50 per day, share of population living on less than $30 per day, median income per day, life expectancy at age 65, share of population aged 60+, and share of population aged 90+. In each case, the thus normalized excess all-cause mortality is for the Covid period itself (2020-2022 and 2020-2023-H1) and for
each half year in the Covid period, 2020-H1 to 2023-H1. Here, the nominal Covid
periods 2020-2022 and 2020-2023-H1 include different numbers of points (countries),
as do different half years (as above), depending on availability of mortality data for each
half year. **Appendix H** lists all the corresponding extracted Pearson correlation
coefficients.

<figure><img src="image_200.png" /></figure>

<figure><img src="image_201.png" /></figure>

<figure><img src="image_202.png" /></figure>

<figure><img src="image_203.png" /></figure>
<figure><img src="image_204.png" /></figure>

<figure><img src="image_205.png" /></figure>

<figure><img src="image_206.png" /></figure>

<figure><img src="image_207.png" /></figure>
<figure><img src="image_208.png" /></figure>

<figure><img src="image_209.png" /></figure>

<figure><img src="image_210.png" /></figure>

<figure><img src="image_211.png" /></figure>
<figure><img src="image_212.png" /></figure>

<figure><img src="image_213.png" /></figure>

<figure><img src="image_214.png" /></figure>

<figure><img src="image_215.png" /></figure>
<figure><img src="image_216.png" /><figcaption>Figure 34. Excess all-cause mortality for the Covid period (2020-2022 and 2020-2023-H1) and for each half year 2020-H1 to 2023-H1, as labelled, top to bottom twin panels, expressed as a fraction of 2019 population, versus GDP per capita, with 1σ error bars (left panels) and country codes (right panels). The colours correspond to the three clusters from the cluster analysis. The corresponding Pearson correlation coefficient is given in each twin panel.</figcaption></figure>
<figure><img src="image_217.png" /></figure>

<figure><img src="image_218.png" /></figure>

<figure><img src="image_219.png" /></figure>

<figure><img src="image_220.png" /></figure>
<figure><img src="image_221.png" /></figure>

<figure><img src="image_222.png" /></figure>

<figure><img src="image_223.png" /></figure>

<figure><img src="image_224.png" /></figure>
<figure><img src="image_225.png" /></figure>

<figure><img src="image_226.png" /></figure>

<figure><img src="image_227.png" /></figure>

<figure><img src="image_228.png" /></figure>
<figure><img src="image_229.png" /></figure>

<figure><img src="image_230.png" /></figure>

<figure><img src="image_231.png" /></figure>

<figure><img src="image_232.png" /></figure>
<figure><img src="image_233.png" /><figcaption>Figure 35. Excess all-cause mortality for the Covid period (2020-2022 and 2020-2023-H1) and for each half year 2020-H1 to 2023-H1, as labelled, top to bottom twin panels, expressed as a fraction of 2019 population, versus Gini inequality index, with 1σ error bars (left panels) and country codes (right panels). The colours correspond to the three clusters from the cluster analysis. The corresponding Pearson correlation coefficient is given in each twin panel.</figcaption></figure>
<figure><img src="image_234.png" /></figure>

<figure><img src="image_235.png" /></figure>

<figure><img src="image_236.png" /></figure>

<figure><img src="image_237.png" /></figure>
<figure><img src="image_238.png" /></figure>

<figure><img src="image_239.png" /></figure>

<figure><img src="image_240.png" /></figure>

<figure><img src="image_241.png" /></figure>
<figure><img src="image_242.png" /></figure>

<figure><img src="image_243.png" /></figure>

<figure><img src="image_244.png" /></figure>

<figure><img src="image_245.png" /></figure>
<figure><img src="image_246.png" /></figure>

<figure><img src="image_247.png" /></figure>

<figure><img src="image_248.png" /></figure>

<figure><img src="image_249.png" /></figure>
<figure><img src="image_250.png" /><figcaption>Figure 36. Excess all-cause mortality for the Covid period (2020-2022 and 2020-2023-H1) and for each half year 2020-H1 to 2023-H1, as labelled, top to bottom twin panels, expressed as a fraction of 2019 population, versus share of population living with less than $5.50 per day, with 1σ error bars (left panels) and country codes (right panels). The colours correspond to the three clusters from the cluster analysis. The corresponding Pearson correlation coefficient is given in each twin panel.</figcaption></figure>
<figure><img src="image_251.png" /></figure>

<figure><img src="image_252.png" /></figure>

<figure><img src="image_253.png" /></figure>

<figure><img src="image_254.png" /></figure>
<figure><img src="image_255.png" /></figure>

<figure><img src="image_256.png" /></figure>

<figure><img src="image_257.png" /></figure>

<figure><img src="image_258.png" /></figure>
<figure><img src="image_259.png" /></figure>

<figure><img src="image_260.png" /></figure>

<figure><img src="image_261.png" /></figure>

<figure><img src="image_262.png" /></figure>
<figure><img src="image_263.png" /></figure>

<figure><img src="image_264.png" /></figure>

<figure><img src="image_265.png" /></figure>

<figure><img src="image_266.png" /></figure>
<figure><img src="image_267.png" /><figcaption>**Figure 37.** Excess all-cause mortality for the Covid period (2020-2022 and 2020-2023-H1) and for each half year 2020-H1 to 2023-H1, as labelled, top to bottom twin panels, expressed as a fraction of 2019 population, versus share of population living with less than $30 per day, with 1σ error bars (left panels) and country codes (right panels). The colours correspond to the three clusters from the cluster analysis. The corresponding Pearson correlation coefficient is given in each twin panel.</figcaption></figure>
<figure><img src="image_268.png" /></figure>

<figure><img src="image_269.png" /></figure>

<figure><img src="image_270.png" /></figure>

<figure><img src="image_271.png" /></figure>
<figure><img src="image_272.png" /></figure>

<figure><img src="image_273.png" /></figure>

<figure><img src="image_274.png" /></figure>

<figure><img src="image_275.png" /></figure>
<figure><img src="image_276.png" /></figure>

<figure><img src="image_277.png" /></figure>

<figure><img src="image_278.png" /></figure>

<figure><img src="image_279.png" /></figure>
<figure><img src="image_280.png" /></figure>

<figure><img src="image_281.png" /></figure>

<figure><img src="image_282.png" /></figure>

<figure><img src="image_283.png" /></figure>
<figure><img src="image_284.png" /><figcaption>Figure 38. Excess all-cause mortality for the Covid period (2020-2022 and 2020-2023-H1) and for each half year 2020-H1 to 2023-H1, as labelled, top to bottom twin panels, expressed as a fraction of 2019 population, versus median income per day, with 1σ error bars (left panels) and country codes (right panels). The colours correspond to the three clusters from the cluster analysis. The corresponding Pearson correlation coefficient is given in each twin panel.</figcaption></figure>
<figure><img src="image_285.png" /></figure>

<figure><img src="image_286.png" /></figure>

<figure><img src="image_287.png" /></figure>

<figure><img src="image_288.png" /></figure>
<figure><img src="image_289.png" /></figure>

<figure><img src="image_290.png" /></figure>

<figure><img src="image_291.png" /></figure>

<figure><img src="image_292.png" /></figure>
<figure><img src="image_293.png" /></figure>

<figure><img src="image_294.png" /></figure>

<figure><img src="image_295.png" /></figure>

<figure><img src="image_296.png" /></figure>
<figure><img src="image_297.png" /></figure>

<figure><img src="image_298.png" /></figure>

<figure><img src="image_299.png" /></figure>

<figure><img src="image_300.png" /></figure>
<figure><img src="image_301.png" /><figcaption>Figure 39. Excess all-cause mortality for the Covid period (2020-2022 and 2020-2023-H1) and for each half year 2020-H1 to 2023-H1, as labelled, top to bottom twin panels, expressed as a fraction of 2019 population, versus life expectancy at age 65, with 1σ error bars (left panels) and country codes (right panels). The colours correspond to the three clusters from the cluster analysis. The corresponding Pearson correlation coefficient is given in each twin panel.</figcaption></figure>
<figure><img src="image_302.png" /></figure>

<figure><img src="image_303.png" /></figure>

<figure><img src="image_304.png" /></figure>

<figure><img src="image_305.png" /></figure>
<figure><img src="image_306.png" /></figure>

<figure><img src="image_307.png" /></figure>

<figure><img src="image_308.png" /></figure>

<figure><img src="image_309.png" /></figure>
<figure><img src="image_310.png" /></figure>

<figure><img src="image_311.png" /></figure>

<figure><img src="image_312.png" /></figure>

<figure><img src="image_313.png" /></figure>
<figure><img src="image_314.png" /></figure>

<figure><img src="image_315.png" /></figure>

<figure><img src="image_316.png" /></figure>

<figure><img src="image_317.png" /></figure>
<figure><img src="image_318.png" /><figcaption>**Figure 40.** Excess all-cause mortality for the Covid period (2020-2022 and 2020-2023-H1) and for each half year 2020-H1 to 2023-H1, as labelled, top to bottom twin panels, expressed as a fraction of 2019 population, versus share of population aged 60+, with 1σ error bars (left panels) and country codes (right panels). The colours correspond to the three clusters from the cluster analysis. The corresponding Pearson correlation coefficient is given in each twin panel.</figcaption></figure>
<figure><img src="image_319.png" /></figure>

<figure><img src="image_320.png" /></figure>

<figure><img src="image_321.png" /></figure>

<figure><img src="image_322.png" /></figure>
<figure><img src="image_323.png" /></figure>

<figure><img src="image_324.png" /></figure>

<figure><img src="image_325.png" /></figure>

<figure><img src="image_326.png" /></figure>
<figure><img src="image_327.png" /></figure>

<figure><img src="image_328.png" /></figure>

<figure><img src="image_329.png" /></figure>

<figure><img src="image_330.png" /></figure>
<figure><img src="image_331.png" /></figure>

<figure><img src="image_332.png" /></figure>

<figure><img src="image_333.png" /></figure>

<figure><img src="image_334.png" /></figure>
<figure><img src="image_335.png" /><figcaption>Figure 41. Excess all-cause mortality for the Covid period (2020-2022 and 2020-2023-H1) and for each half year 2020-H1 to 2023-H1, as labelled, top to bottom twin panels, expressed as a fraction of 2019 population, versus share of population aged 90+, with 1σ error bars (left panels) and country codes (right panels). The colours correspond to the three clusters from the cluster analysis. The corresponding Pearson correlation coefficient is given in each twin panel.</figcaption></figure>

## 4.17 Excess all-cause mortality correlations with vaccination by population

The number of vaccine injections by population for a given vaccination period, like socio-economic variables, is itself a variable. We consider the vaccination periods 2021-2022, 2021, and 2022.

We showed above that vaccination by population is correlated to poverty or wealth variables, and to other socio-economic variables (Figure 25).

The main results of the correlations between excess all-cause mortality during the Covid period and vaccine injections (WHO data) by population for given vaccination periods are shown in the figures below, for vaccination periods 2021-2022, 2021, and 2022. In
each case, the excess all-cause mortality is for the Covid period itself (2020-2022) and for each half year in the Covid period, 2020-H1 to 2023-H1.

Figure 42, Figure 43, and Figure 44 are for excess all-cause mortality expressed as a percentage of the predicted (extrapolated historic trend, see Methods) mortality for the same mortality period (“P-score”), whereas Figure 45, Figure 46, and Figure 47 are for excess all-cause mortality expressed as a fraction of 2019 population.

A table of the corresponding Pearson correlation coefficients is in **Appendix I**.

<figure><img src="image_336.png" /></figure>

<figure><img src="image_337.png" /></figure>

<figure><img src="image_338.png" /></figure>

<figure><img src="image_339.png" /></figure>
<figure><img src="image_340.png" /></figure>

<figure><img src="image_341.png" /></figure>

<figure><img src="image_342.png" /></figure>

<figure><img src="image_343.png" /></figure>
<figure><img src="image_344.png" /></figure>

<figure><img src="image_345.png" /></figure>

<figure><img src="image_346.png" /></figure>

<figure><img src="image_347.png" /></figure>
<figure><img src="image_348.png" /><figcaption>**Figure 42.** Excess all-cause mortality for the Covid period (2020-2022) and for each half year 2020-H1 to 2023-H1, as labelled, top to bottom twin panels, expressed as a percentage of the predicted (extrapolated historic trend, see Methods) mortality for the same period ("P-score"), versus number of vaccine injections by 2019 population for the vaccination period 2021-2022 (WHO data), with 1σ error bars (left panels) and country codes (right panels). The colours correspond to the three clusters from the cluster analysis. The corresponding Pearson correlation coefficient is given in each twin panel.</figcaption></figure>
<figure><img src="image_349.png" /></figure>

<figure><img src="image_350.png" /></figure>

<figure><img src="image_351.png" /></figure>

<figure><img src="image_352.png" /></figure>
<figure><img src="image_353.png" /></figure>

<figure><img src="image_354.png" /></figure>

<figure><img src="image_355.png" /></figure>

<figure><img src="image_356.png" /></figure>
<figure><img src="image_357.png" /></figure>

<figure><img src="image_358.png" /></figure>

<figure><img src="image_359.png" /></figure>

<figure><img src="image_360.png" /></figure>
<figure><img src="image_361.png" /><figcaption>**Figure 43.** Excess all-cause mortality for the Covid period (2020-2022) and for each half year 2020-H1 to 2023-H1, as labelled, top to bottom twin panels, expressed as a percentage of the predicted (extrapolated historic trend, see Methods) mortality for the same period ("P-score"), versus number of vaccine injections by 2019 population for the vaccination period 2021 (WHO data), with 1σ error bars (left panels) and country codes (right panels). The colours correspond to the three clusters from the cluster analysis. The corresponding Pearson correlation coefficient is given in each twin panel.</figcaption></figure>
<figure><img src="image_362.png" /></figure>

<figure><img src="image_363.png" /></figure>

<figure><img src="image_364.png" /></figure>

<figure><img src="image_365.png" /></figure>

<figure><img src="image_366.png" /></figure>

<figure><img src="image_367.png" /></figure>
<figure><img src="image_368.png" /></figure>

<figure><img src="image_369.png" /></figure>

<figure><img src="image_370.png" /></figure>

<figure><img src="image_371.png" /></figure>

<figure><img src="image_372.png" /></figure>

<figure><img src="image_373.png" /></figure>
<figure><img src="image_374.png" /><figcaption>**Figure 44.** Excess all-cause mortality for the Covid period (2020-2022) and for each half year 2020-H1 to 2023-H1, as labelled, top to bottom twin panels, expressed as a percentage of the predicted (extrapolated historic trend, see Methods) mortality for the same period ("P-score"), versus number of vaccine injections by 2019 population for the vaccination period 2022 (WHO data), with 1σ error bars (left panels) and country codes (right panels). The colours correspond to the three clusters from the cluster analysis. The corresponding Pearson correlation coefficient is given in each twin panel.</figcaption></figure>
<figure><img src="image_375.png" /></figure>

<figure><img src="image_376.png" /></figure>

<figure><img src="image_377.png" /></figure>

<figure><img src="image_378.png" /></figure>
<figure><img src="image_379.png" /></figure>

<figure><img src="image_380.png" /></figure>

<figure><img src="image_381.png" /></figure>

<figure><img src="image_382.png" /></figure>
<figure><img src="image_383.png" /></figure>

<figure><img src="image_384.png" /></figure>

<figure><img src="image_385.png" /></figure>

<figure><img src="image_386.png" /></figure>
<figure><img src="image_387.png" /></figure>

<figure><img src="image_388.png" /></figure>

<figure><img src="image_389.png" /></figure>

<figure><img src="image_390.png" /></figure>
<figure><img src="image_391.png" /><figcaption>Figure 45. Excess all-cause mortality for the Covid period (2020-2022 and 2020-2023-H1) and for each half year 2020-H1 to 2023-H1, as labelled, top to bottom twin panels, expressed as a fraction of 2019 population, versus number of vaccine injections by 2019 population for the vaccination period 2021-2022 (WHO data), with 1σ error bars (left panels) and country codes (right panels). The colours correspond to the three clusters from the cluster analysis. The corresponding Pearson correlation coefficient is given in each twin panel.</figcaption></figure>
<figure><img src="image_392.png" /></figure>

<figure><img src="image_393.png" /></figure>

<figure><img src="image_394.png" /></figure>

<figure><img src="image_395.png" /></figure>
<figure><img src="image_396.png" /></figure>

<figure><img src="image_397.png" /></figure>

<figure><img src="image_398.png" /></figure>

<figure><img src="image_399.png" /></figure>
<figure><img src="image_400.png" /></figure>

<figure><img src="image_401.png" /></figure>

<figure><img src="image_402.png" /></figure>

<figure><img src="image_403.png" /></figure>
<figure><img src="image_404.png" /></figure>

<figure><img src="image_405.png" /></figure>

<figure><img src="image_406.png" /></figure>

<figure><img src="image_407.png" /></figure>
<figure><img src="image_408.png" /><figcaption>Figure 46. Excess all-cause mortality for the Covid period (2020-2022 and 2020-2023-H1) and for each half year 2020-H1 to 2023-H1, as labelled, top to bottom twin panels, expressed as a fraction of 2019 population, versus number of vaccine injections by 2019 population for the vaccination period 2021 (WHO data), with 1σ error bars (left panels) and country codes (right panels). The colours correspond to the three clusters from the cluster analysis. The corresponding Pearson correlation coefficient is given in each twin panel.</figcaption></figure>
<figure><img src="image_409.png" /></figure>

<figure><img src="image_410.png" /></figure>

<figure><img src="image_411.png" /></figure>

<figure><img src="image_412.png" /></figure>

<figure><img src="image_413.png" /></figure>

<figure><img src="image_414.png" /></figure>
<figure><img src="image_415.png" /></figure>

<figure><img src="image_416.png" /></figure>

<figure><img src="image_417.png" /></figure>

<figure><img src="image_418.png" /></figure>

<figure><img src="image_419.png" /></figure>

<figure><img src="image_420.png" /></figure>
<figure><img src="image_421.png" /></figure>

<figure><img src="image_422.png" /></figure>

<figure><img src="image_423.png" /></figure>

<figure><img src="image_424.png" /></figure>

<figure><img src="image_425.png" /></figure>

<figure><img src="image_426.png" /></figure>
Figure 47. Excess all-cause mortality for the Covid period (2020-2022 and 2020-2023-H1) and for each half year 2020-H1 to 2023-H1, as labelled, top to bottom twin panels, expressed as a fraction of 2019 population, versus number of vaccine injections by 2019 population for the vaccination period 2022 (WHO data), with 1σ error bars (left panels) and country codes (right panels). The colours correspond to the three clusters from the cluster analysis. The corresponding Pearson correlation coefficient is given in each twin panel.

## 4.18 Excess all-cause mortality correlations with products of socio-economic variables

We examined correlations of excess all-cause mortality with products of two socio-economic variables. We considered many products between one economic variable (share of population living with less than $X/day, mean income per day) and one population health variable (share of population aged X+, life expectancy at age X).

The main high correlation coefficient occurred for the product of share of population living with less than $30/day and share of population aged 60+, for excess all-cause mortality expressed as a fraction of 2019 population. The Pearson correlation coefficient between this excess all-cause mortality during the Covid period (2020-2023-H1) and this product is +0.78 ("strong", following Evans, 1996), whereas the correlation coefficients to the individual variables are +0.49 ("moderate", share of population living with ≤$30/day, Figure 37) and +0.37 ("weak", share of population aged 60+, Figure 40). The correlations to the product is shown in Figure 48.
<figure><img src="image_427.png" /></figure>

<figure><img src="image_428.png" /></figure>

<figure><img src="image_429.png" /></figure>

<figure><img src="image_430.png" /></figure>

<figure><img src="image_431.png" /></figure>

<figure><img src="image_432.png" /></figure>
<figure><img src="image_433.png" /></figure>

<figure><img src="image_434.png" /></figure>

<figure><img src="image_435.png" /></figure>

<figure><img src="image_436.png" /></figure>

<figure><img src="image_437.png" /></figure>

<figure><img src="image_438.png" /></figure>
<figure><img src="image_439.png" /></figure>

<figure><img src="image_440.png" /></figure>

<figure><img src="image_441.png" /></figure>

<figure><img src="image_442.png" /></figure>

<figure><img src="image_443.png" /></figure>

<figure><img src="image_444.png" /></figure>
**Figure 48.** Excess all-cause mortality for the Covid period (2020-2022 and 2020-2023-H1) and for each half year 2020-H1 to 2023-H1, as labelled, top to bottom twin panels, expressed as a fraction of 2019 population, versus the product of share of population living with less than $30/day and share of population aged 60+, with 1σ error bars (left panels) and country codes (right panels). The colours correspond to the three clusters from the cluster analysis. The corresponding Pearson correlation coefficient is given in each twin panel.

Table 2 compares the Pearson correlation coefficients for the two individual variables and for the product variable, for the different time period during the Covid period (from Figure 37, Figure 40, and Figure 48, respectively).

<table><caption>**Table 2.** Pearson correlation coefficients of excess all-cause mortality by population with two variables, their product, and for different time periods in the Covid period *</caption><thead><tr><th>Time period</th><th>Living ≤$30/day</th><th>Share pop aged 60+</th><th>Product variable</th></tr></thead><tbody><tr><td>2020-2022</td><td>+0.39</td><td>+0.38</td><td>+0.73</td></tr><tr><td>2020-2023-H1</td><td>+0.49</td><td>+0.37</td><td>+0.78</td></tr><tr><td>2020-H1</td><td>-0.07</td><td>+0.07</td><td>-0.08</td></tr><tr><td>2020-H2</td><td>+0.29</td><td>+0.25</td><td>+0.63</td></tr><tr><td>2021-H1</td><td>+0.49</td><td>+0.12</td><td>+0.64</td></tr><tr><td>2021-H2</td><td>+0.38</td><td>+0.24</td><td>+0.72</td></tr><tr><td>2022-H1</td><td>+0.16</td><td>+0.44</td><td>+0.64</td></tr><tr><td>2022-H2</td><td>-0.56</td><td>+0.70</td><td>-0.06</td></tr><tr><td>2023-H1</td><td>-0.45</td><td>+0.24</td><td>-0.39</td></tr></tbody></table>

## 4.19 Age dependence of excess all-cause mortality in European countries during the Covid period

Several European countries have available all-cause mortality data discriminated by age group and by sex. Twenty five countries have such data of sufficient quality to allow the analyses of the present section: Austria, Belgium, Bulgaria, Croatia, Czechia,

<footnote>* Here, the orange and blue colours are used to draw attention to differences between values for individual variables and the product variable, for the full Covid period and some half years, respectively.</footnote>
Denmark, Estonia, Finland, France, Greece, Hungary, Italy, Latvia, Lithuania, Netherlands, Norway, Poland, Portugal, Romania, Serbia, Slovakia, Slovenia, Spain, Sweden, and Switzerland.

First, we examine the age dependence of yearly all-cause mortality itself, by sex, for the years 2019, 2020, 2021 and 2022, normalized by population of sex and age group per given year. For this, and below, we define the thirteen age groups: 0-19, 20-29, 30-39, 40-44, 45-49, 50-54, ..., 80-84, and 85+.

Figure 49 shows yearly population-normalized all-cause mortality, by sex, versus age group for the 25 European countries (as labelled) and for the years 2019 through 2022 (as labelled), for both linear (top panels) and logarithmic (bottom panels) mortality scales. Exponential fits of the non-sex-discriminated data, over the age-group range 30-39, 40-44 through 80-84 are also shown. The values of the doubling age ($\tau$, in years, with $1\sigma$ fitting error) corresponding to each of the exponential fits are given in Table 3. In the exponential fits, the x-coordinate of a point is set as the starting age of the age group. Using the age-group range 40-44 through 80-84 makes a negligible difference in the exponential fit outcomes.
<figure><img src="image_445.png" /></figure>

<figure><img src="image_446.png" /></figure>

<figure><img src="image_447.png" /></figure>

<figure><img src="image_448.png" /></figure>

<figure><img src="image_449.png" /></figure>

<figure><img src="image_450.png" /></figure>

<figure><img src="image_451.png" /></figure>

<figure><img src="image_452.png" /></figure>

<figure><img src="image_453.png" /></figure>

<figure><img src="image_454.png" /></figure>

<figure><img src="image_455.png" /></figure>

<figure><img src="image_456.png" /></figure>

<figure><img src="image_457.png" /></figure>

<figure><img src="image_458.png" /></figure>

<figure><img src="image_459.png" /></figure>

<figure><img src="image_460.png" /></figure>
<figure><img src="image_461.png" /></figure>

<figure><img src="image_462.png" /></figure>

<figure><img src="image_463.png" /></figure>

<figure><img src="image_464.png" /></figure>

<figure><img src="image_465.png" /></figure>

<figure><img src="image_466.png" /></figure>

<figure><img src="image_467.png" /></figure>

<figure><img src="image_468.png" /></figure>

<figure><img src="image_469.png" /></figure>

<figure><img src="image_470.png" /></figure>

<figure><img src="image_471.png" /></figure>

<figure><img src="image_472.png" /></figure>

<figure><img src="image_473.png" /></figure>

<figure><img src="image_474.png" /></figure>

<figure><img src="image_475.png" /></figure>

<figure><img src="image_476.png" /></figure>
<figure><img src="image_477.png" /></figure>
<figure><img src="image_478.png" /></figure>

<figure><img src="image_479.png" /></figure>

<figure><img src="image_480.png" /></figure>

<figure><img src="image_481.png" /></figure>

<figure><img src="image_482.png" /></figure>

<figure><img src="image_483.png" /></figure>

<figure><img src="image_484.png" /></figure>

<figure><img src="image_485.png" /></figure>

<figure><img src="image_486.png" /></figure>

<figure><img src="image_487.png" /></figure>

<figure><img src="image_488.png" /></figure>

<figure><img src="image_489.png" /></figure>

<figure><img src="image_490.png" /></figure>

<figure><img src="image_491.png" /></figure>

<figure><img src="image_492.png" /></figure>

<figure><img src="image_493.png" /></figure>
<figure><img src="image_494.png" /></figure>
<figure><img src="image_495.png" /></figure>

<figure><img src="image_496.png" /></figure>

<figure><img src="image_497.png" /></figure>

<figure><img src="image_498.png" /></figure>

<figure><img src="image_499.png" /></figure>

<figure><img src="image_500.png" /></figure>

<figure><img src="image_501.png" /></figure>

<figure><img src="image_502.png" /></figure>

<figure><img src="image_503.png" /></figure>

<figure><img src="image_504.png" /></figure>

<figure><img src="image_505.png" /></figure>

<figure><img src="image_506.png" /></figure>

<figure><img src="image_507.png" /></figure>

<figure><img src="image_508.png" /></figure>

<figure><img src="image_509.png" /></figure>

<figure><img src="image_510.png" /></figure>
<figure><img src="image_511.png" /></figure>

<figure><img src="image_512.png" /></figure>

<figure><img src="image_513.png" /></figure>

<figure><img src="image_514.png" /></figure>

<figure><img src="image_515.png" /></figure>

<figure><img src="image_516.png" /></figure>

<figure><img src="image_517.png" /></figure>

<figure><img src="image_518.png" /></figure>

<figure><img src="image_519.png" /></figure>

<figure><img src="image_520.png" /></figure>

<figure><img src="image_521.png" /></figure>

<figure><img src="image_522.png" /></figure>

<figure><img src="image_523.png" /></figure>

<figure><img src="image_524.png" /></figure>

<figure><img src="image_525.png" /></figure>

<figure><img src="image_526.png" /></figure>
<figure><img src="image_527.png" /></figure>

<figure><img src="image_528.png" /></figure>

<figure><img src="image_529.png" /></figure>

<figure><img src="image_530.png" /></figure>
<figure><img src="image_531.png" /></figure>
<figure><img src="image_532.png" /></figure>

<figure><img src="image_533.png" /></figure>

<figure><img src="image_534.png" /></figure>

<figure><img src="image_535.png" /></figure>

<figure><img src="image_536.png" /></figure>

<figure><img src="image_537.png" /></figure>

<figure><img src="image_538.png" /></figure>

<figure><img src="image_539.png" /></figure>

<figure><img src="image_540.png" /></figure>

<figure><img src="image_541.png" /></figure>

<figure><img src="image_542.png" /></figure>

<figure><img src="image_543.png" /></figure>

<figure><img src="image_544.png" /></figure>

<figure><img src="image_545.png" /></figure>

<figure><img src="image_546.png" /></figure>

<figure><img src="image_547.png" /></figure>
<figure><img src="image_548.png" /></figure>

<figure><img src="image_549.png" /></figure>

<figure><img src="image_550.png" /></figure>

<figure><img src="image_551.png" /></figure>

<figure><img src="image_552.png" /></figure>

<figure><img src="image_553.png" /></figure>

<figure><img src="image_554.png" /></figure>

<figure><img src="image_555.png" /></figure>

<figure><img src="image_556.png" /></figure>

<figure><img src="image_557.png" /></figure>

<figure><img src="image_558.png" /></figure>

<figure><img src="image_559.png" /></figure>

<figure><img src="image_560.png" /></figure>

<figure><img src="image_561.png" /></figure>

<figure><img src="image_562.png" /></figure>

<figure><img src="image_563.png" /></figure>
<figure><img src="image_564.png" /></figure>

<figure><img src="image_565.png" /></figure>

<figure><img src="image_566.png" /></figure>

<figure><img src="image_567.png" /></figure>

<figure><img src="image_568.png" /></figure>

<figure><img src="image_569.png" /></figure>

<figure><img src="image_570.png" /></figure>

<figure><img src="image_571.png" /></figure>

<figure><img src="image_572.png" /></figure>

<figure><img src="image_573.png" /></figure>

<figure><img src="image_574.png" /></figure>

<figure><img src="image_575.png" /></figure>

<figure><img src="image_576.png" /></figure>

<figure><img src="image_577.png" /></figure>

<figure><img src="image_578.png" /></figure>

<figure><img src="image_579.png" /></figure>
<figure><img src="image_580.png" /><figcaption>Figure 49. Yearly all-cause mortality, by sex, versus age group for 25 European countries (as labelled) and for the years 2019 through 2022 (as labelled), for both linear (top panels) and logarithmic (bottom panels) mortality scales. Normalization is by population of the age group and sex in each specific calendar year (January 1 of the year). Exponential fits of the non-sex-discriminated data, over the ten-age-group range 30-39, 40-44 to 80-84 are also shown (black lines), as are the corresponding values of the doubling age $\tau$ (in years, with $1\sigma$ fitting error).</figcaption></figure>

<table><caption>Table 3. Exponential-fit doubling age $\tau$ (in years, with $1\sigma$ fitting error) in yearly all-cause mortality (both sexes) normalized by population (per age group), using the ten age groups 30-39, 40-44 to 80-84 fitting range, by country, and by year 2019-2022.</caption><thead><tr><th>Country</th><th>2019</th><th>2020</th><th>2021</th><th>2022</th></tr></thead><tbody><tr><td>Austria</td><td>6.5 (0.3)</td><td>6.4 (0.2)</td><td>6.9 (0.1)</td><td>6.8 (0.1)</td></tr><tr><td>Belgium</td><td>7.0 (0.2)</td><td>6.6 (0.2)</td><td>7.1 (0.1)</td><td>6.9 (0.1)</td></tr><tr><td>Bulgaria</td><td>7.6 (0.2)</td><td>7.8 (0.2)</td><td>8.1 (0.1)</td><td>7.6 (0.2)</td></tr><tr><td>Croatia</td><td>6.7 (0.2)</td><td>6.9 (0.2)</td><td>7.0 (0.1)</td><td>6.4 (0.2)</td></tr><tr><td>Czechia</td><td>7.1 (0.1)</td><td>6.7 (0.1)</td><td>7.4 (0.1)</td><td>6.9 (0.1)</td></tr><tr><td>Denmark</td><td>6.6 (0.2)</td><td>6.6 (0.1)</td><td>6.6 (0.1)</td><td>6.5 (0.1)</td></tr><tr><td>Estonia</td><td>8.1 (0.2)</td><td>8.2 (0.3)</td><td>8.1 (0.2)</td><td>7.9 (0.3)</td></tr><tr><td>Finland</td><td>6.7 (0.1)</td><td>6.6 (0.2)</td><td>6.4 (0.2)</td><td>6.6 (0.1)</td></tr><tr><td>France</td><td>7.6 (0.3)</td><td>7.5 (0.2)</td><td>7.7 (0.2)</td><td>7.6 (0.2)</td></tr><tr><td>Greece</td><td>6.4 (0.3)</td><td>6.6 (0.3)</td><td>7.0 (0.3)</td><td>6.6 (0.2)</td></tr></tbody></table>
<table><tr><td>Hungary</td><td>8.0 (0.3)</td><td>7.9 (0.3)</td><td>8.5 (0.3)</td><td>8.0 (0.3)</td></tr><tr><td>Italy</td><td>6.1 (0.2)</td><td>6.1 (0.1)</td><td>6.4 (0.1)</td><td>6.1 (0.1)</td></tr><tr><td>Latvia</td><td>8.4 (0.3)</td><td>8.5 (0.2)</td><td>8.7 (0.3)</td><td>8.7 (0.3)</td></tr><tr><td>Lithuania</td><td>8.7 (0.3)</td><td>8.9 (0.1)</td><td>8.7 (0.1)</td><td>8.2 (0.2)</td></tr><tr><td>Netherlands</td><td>6.1 (0.1)</td><td>6.0 (0.1)</td><td>6.3 (0.1)</td><td>6.2 (0.1)</td></tr><tr><td>Norway</td><td>6.1 (0.1)</td><td>6.0 (0.1)</td><td>6.1 (0.1)</td><td>6.0 (0.1)</td></tr><tr><td>Poland</td><td>8.4 (0.2)</td><td>8.2 (0.2)</td><td>8.4 (0.1)</td><td>8.1 (0.1)</td></tr><tr><td>Portugal</td><td>6.4 (0.3)</td><td>6.4 (0.3)</td><td>6.5 (0.2)</td><td>6.4 (0.3)</td></tr><tr><td>Romania</td><td>7.8 (0.3)</td><td>7.9 (0.2)</td><td>8.1 (0.1)</td><td>7.6 (0.2)</td></tr><tr><td>Serbia</td><td>6.8 (0.2)</td><td>7.1 (0.2)</td><td>7.1 (0.1)</td><td>6.8 (0.1)</td></tr><tr><td>Slovakia</td><td>7.4 (0.2)</td><td>7.2 (0.1)</td><td>7.6 (0.1)</td><td>7.1 (0.1)</td></tr><tr><td>Slovenia</td><td>7.1 (0.2)</td><td>6.3 (0.2)</td><td>7.0 (0.1)</td><td>6.7 (0.2)</td></tr><tr><td>Spain</td><td>6.6 (0.2)</td><td>6.1 (0.3)</td><td>6.7 (0.2)</td><td>6.7 (0.2)</td></tr><tr><td>Sweden</td><td>5.9 (0.1)</td><td>5.7 (0.1)</td><td>6.0 (0.1)</td><td>5.7 (0.1)</td></tr><tr><td>Switzerland</td><td>6.2 (0.1)</td><td>6.0 (0.1)</td><td>6.3 (0.1)</td><td>5.9 (0.2)</td></tr></table>

Next, we examine the age dependence of yearly excess all-cause mortality (see Methods), by sex, for the period 2020-2022 (nominally the Covid period), and for the individual years 2020, 2021 and 2022, normalized by population of sex and age group per given year, and using the population of 2021 for the 2020-2022 period. We use the same thirteen age groups as introduced above: 0-19, 20-29, 30-39, 40-44, 45-49, 50-54, ..., 80-84, and 85+.

Figure 50 shows yearly population-normalized excess all-cause mortality, by sex, versus age group for the 25 European countries (as labelled) for the period 2020-2022 and for the years 2020 through 2022 (as labelled), for both linear (top panels) and logarithmic (bottom panels) mortality scales. Exponential fits of the non-sex-discriminated data, over the age-group range 30-39, 40-44 through 80-84 are also shown. The values of the doubling age ($\tau$, in years, with $1\sigma$ fitting error) corresponding to each of the exponential fits are given in Table 4.
<figure><img src="image_581.png" /></figure>

<figure><img src="image_582.png" /></figure>

<figure><img src="image_583.png" /></figure>

<figure><img src="image_584.png" /></figure>
<figure><img src="image_585.png" /></figure>
<figure><img src="image_586.png" /></figure>
<figure><img src="image_587.png" /></figure>

<figure><img src="image_588.png" /></figure>

<figure><img src="image_589.png" /></figure>

<figure><img src="image_590.png" /></figure>

<figure><img src="image_591.png" /></figure>

<figure><img src="image_592.png" /></figure>

<figure><img src="image_593.png" /></figure>

<figure><img src="image_594.png" /></figure>

<figure><img src="image_595.png" /></figure>

<figure><img src="image_596.png" /></figure>

<figure><img src="image_597.png" /></figure>

<figure><img src="image_598.png" /></figure>

<figure><img src="image_599.png" /></figure>

<figure><img src="image_600.png" /></figure>

<figure><img src="image_601.png" /></figure>

<figure><img src="image_602.png" /></figure>
<figure><img src="image_603.png" /></figure>
<figure><img src="image_604.png" /></figure>
<figure><img src="image_605.png" /></figure>
<figure><img src="image_606.png" /></figure>

<figure><img src="image_607.png" /></figure>

<figure><img src="image_608.png" /></figure>

<figure><img src="image_609.png" /></figure>

<figure><img src="image_610.png" /></figure>

<figure><img src="image_611.png" /></figure>

<figure><img src="image_612.png" /></figure>

<figure><img src="image_613.png" /></figure>

<figure><img src="image_614.png" /></figure>

<figure><img src="image_615.png" /></figure>

<figure><img src="image_616.png" /></figure>

<figure><img src="image_617.png" /></figure>

<figure><img src="image_618.png" /></figure>

<figure><img src="image_619.png" /></figure>

<figure><img src="image_620.png" /></figure>

<figure><img src="image_621.png" /></figure>
<figure><img src="image_622.png" /></figure>
<figure><img src="image_623.png" /></figure>
<figure><img src="image_624.png" /></figure>
<figure><img src="image_625.png" /></figure>
<figure><img src="image_626.png" /><figcaption>Figure 50. Yearly excess all-cause mortality, by sex, versus age group for 25 European countries (as labelled) for the period 2020-2022 and for the years 2020 through 2022 (as labelled), for both linear (top panels) and logarithmic (bottom panels) excess mortality by population scales. Normalization is by population of the age group and sex in each specific calendar year (January 1 of the year). Exponential fits of the non-sex-discriminated data, over the ten-age-group range 30-39, 40-44 to 80-84 are also shown (black lines), as are the corresponding values of the doubling age τ (in years, with 1σ fitting error).</figcaption></figure>

<table><caption>Table 4. Exponential-fit doubling age τ (in years, with 1σ fitting error) in yearly excess all-cause mortality (both sexes) normalized by population (per age group), using the ten age groups 30-39, 40-44 to 80-84 fitting range, by country, for the period 2020-2022 and for the years 2020 through 2022.</caption><thead><tr><th>Country</th><th>2020-2022</th><th>2020</th><th>2021</th><th>2022</th></tr></thead><tbody><tr><td>Austria</td><td>4.4 (0.1)</td><td>nan (nan)</td><td>5.0 (0.1)</td><td>4.5 (0.2)</td></tr><tr><td>Belgium</td><td>6.1 (1.6)</td><td>4.6 (0.3)</td><td>6.9 (2.2)</td><td>8.7 (3.0)</td></tr><tr><td>Bulgaria</td><td>8.1 (0.7)</td><td>9.0 (0.9)</td><td>8.6 (0.6)</td><td>6.1 (0.7)</td></tr><tr><td>Croatia</td><td>8.3 (1.9)</td><td>8.2 (1.8)</td><td>8.3 (1.4)</td><td>7.3 (1.5)</td></tr><tr><td>Czechia</td><td>3.9 (0.2)</td><td>3.9 (0.2)</td><td>5.9 (0.2)</td><td>2.5 (0.2)</td></tr><tr><td>Denmark</td><td>7.9 (2.0)</td><td>14.6 (16.2)</td><td>8.3 (2.4)</td><td>7.2 (1.0)</td></tr><tr><td>Estonia</td><td>6.8 (2.7)</td><td>9.5 (1.9)</td><td>8.0 (1.8)</td><td>5.2 (2.0)</td></tr><tr><td>Finland</td><td>6.6 (0.7)</td><td>1.7 (0.5)</td><td>6.9 (0.6)</td><td>7.0 (0.9)</td></tr></tbody></table>
<table><tr><td>France</td><td>7.1 (3.1)</td><td>5.9 (0.8)</td><td>7.6 (3.4)</td><td>8.7 (4.1)</td></tr><tr><td>Greece</td><td>8.8 (3.4)</td><td>7.7 (2.2)</td><td>9.4 (2.3)</td><td>10.4 (5.3)</td></tr><tr><td>Hungary</td><td>6.6 (1.0)</td><td>6.7 (0.4)</td><td>8.4 (1.0)</td><td>4.7 (1.4)</td></tr><tr><td>Italy</td><td>5.3 (0.4)</td><td>5.1 (0.4)</td><td>6.2 (0.5)</td><td>5.0 (0.4)</td></tr><tr><td>Latvia</td><td>7.9 (3.2)</td><td>8.2 (5.2)</td><td>8.3 (2.0)</td><td>10.6 (3.1)</td></tr><tr><td>Lithuania</td><td>10.6 (2.0)</td><td>11.2 (2.1)</td><td>9.7 (1.1)</td><td>10.6 (2.7)</td></tr><tr><td>Netherlands</td><td>6.7 (0.7)</td><td>4.1 (0.4)</td><td>5.7 (1.3)</td><td>7.6 (1.3)</td></tr><tr><td>Norway</td><td>5.8 (0.1)</td><td>nan (nan)</td><td>5.7 (0.2)</td><td>5.9 (0.5)</td></tr><tr><td>Poland</td><td>8.0 (1.4)</td><td>7.2 (1.1)</td><td>8.3 (1.0)</td><td>11.0 (5.2)</td></tr><tr><td>Portugal</td><td>6.4 (1.1)</td><td>5.3 (0.4)</td><td>6.8 (1.1)</td><td>6.6 (1.6)</td></tr><tr><td>Romania</td><td>9.0 (2.0)</td><td>8.0 (1.0)</td><td>9.0 (1.2)</td><td>19.0 (24.5)</td></tr><tr><td>Serbia</td><td>8.4 (1.4)</td><td>9.3 (1.2)</td><td>7.9 (0.7)</td><td>10.3 (2.8)</td></tr><tr><td>Slovakia</td><td>5.5 (0.4)</td><td>4.9 (0.2)</td><td>6.8 (0.3)</td><td>3.6 (0.4)</td></tr><tr><td>Slovenia</td><td>3.7 (1.5)</td><td>3.2 (0.6)</td><td>5.1 (1.6)</td><td>7.7 (0.7)</td></tr><tr><td>Spain</td><td>6.6 (1.4)</td><td>5.8 (0.6)</td><td>7.4 (1.9)</td><td>6.9 (2.1)</td></tr><tr><td>Sweden</td><td>5.6 (0.2)</td><td>3.8 (0.5)</td><td>7.5 (0.5)</td><td>4.9 (0.3)</td></tr><tr><td>Switzerland</td><td>4.1 (0.7)</td><td>4.3 (0.3)</td><td>6.9 (1.9)</td><td>4.1 (0.6)</td></tr></table>

Finally, we examine the age dependence of yearly excess all-cause mortality (see Methods), by sex, for the period 2020-2022 (nominally the Covid period), and for the individual years 2020, 2021 and 2022, normalized by expected all-cause mortality (from the extrapolated pre-Covid historic trend, see Methods) for the same time period or year and for each age group and sex (age and sex-discerned P-score, expressed as a percentage). We use the same thirteen age groups as introduced above: 0-19, 20-29, 30-39, 40-44, 45-49, 50-54, ..., 80-84, and 85+.

Figure 51 shows yearly excess all-cause mortality, by sex, versus age group for the 25 European countries for the period 2020-2022 and for the years 2020 through 2022, normalized by expected all-cause mortality (from the extrapolated pre-Covid historic trend, see Methods) for the same time period or year and for each age group and sex (age and sex-discerned P-score, expressed as a percentage).
<figure><img src="image_627.png" /></figure>

<figure><img src="image_628.png" /></figure>

<figure><img src="image_629.png" /></figure>

<figure><img src="image_630.png" /></figure>

<figure><img src="image_631.png" /></figure>

<figure><img src="image_632.png" /></figure>

<figure><img src="image_633.png" /></figure>

<figure><img src="image_634.png" /></figure>

<figure><img src="image_635.png" /></figure>

<figure><img src="image_636.png" /></figure>

<figure><img src="image_637.png" /></figure>

<figure><img src="image_638.png" /></figure>

<figure><img src="image_639.png" /></figure>

<figure><img src="image_640.png" /></figure>

<figure><img src="image_641.png" /></figure>

<figure><img src="image_642.png" /></figure>
<figure><img src="image_643.png" /></figure>
<figure><img src="image_644.png" /></figure>

<figure><img src="image_645.png" /></figure>

<figure><img src="image_646.png" /></figure>

<figure><img src="image_647.png" /></figure>

<figure><img src="image_648.png" /></figure>

<figure><img src="image_649.png" /></figure>

<figure><img src="image_650.png" /></figure>

<figure><img src="image_651.png" /></figure>

<figure><img src="image_652.png" /></figure>

<figure><img src="image_653.png" /></figure>

<figure><img src="image_654.png" /></figure>

<figure><img src="image_655.png" /></figure>

<figure><img src="image_656.png" /></figure>

<figure><img src="image_657.png" /></figure>

<figure><img src="image_658.png" /></figure>

<figure><img src="image_659.png" /></figure>

<figure><img src="image_660.png" /></figure>

<figure><img src="image_661.png" /></figure>

<figure><img src="image_662.png" /></figure>

<figure><img src="image_663.png" /></figure>
<figure><img src="image_664.png" /></figure>

<figure><img src="image_665.png" /></figure>

<figure><img src="image_666.png" /></figure>

<figure><img src="image_667.png" /></figure>

<figure><img src="image_668.png" /></figure>
<figure><img src="image_669.png" /></figure>

<figure><img src="image_670.png" /></figure>

<figure><img src="image_671.png" /></figure>

<figure><img src="image_672.png" /></figure>
<figure><img src="image_673.png" /><figcaption>Figure 51. Yearly excess all-cause mortality, by sex, versus age group for 25 European countries (as labelled) for the period 2020-2022 and for the years 2020 through 2022 (as labelled), normalized by expected all-cause mortality (P-score, from the extrapolated pre-Covid historic trend, see Methods) for the same time period or year and for each age group and sex.</figcaption></figure>

Note that, if the doubling ages ($\tau$) on perfectly exponential increases with age were the same for excess and baseline mortalities, then there would be constant values with age of the baseline-normalized excess all-cause mortalities shown in Figure 51, with constant value equal to the ratio of excess to baseline mortalities. The by-country median all-ages value of the said ratio for the 2020-2022 period is 14.1 % (Table 1). Constant values for different age groups are not observed, and the observed variations with age are statistically significant (Figure 51).
# 5 Discussion

## 5.1 Limitations in attributing cause of death to vaccine toxicity

The main limitation regarding acquiring a truly global perspective is that weekly or monthly data is not available for China, India, equatorial (most of) Africa, and some large countries such as Indonesia.

Furthermore, whereas all-cause mortality data typically may include information about the age, sex, cultural or racial group, and place of residency or of death of the deceased individuals, it generally excludes all information about prior health status of the deceased individuals, or medical history, or past medical interventions, or COVID-19 vaccination status, or inferred cause of death.

Our hypotheses about the causes of excess all-cause mortality during the Covid period are based on patterns and correlations using available all-cause mortality data (and common available socio-economic variables), and are meant as interpretive guides for further research. Much needed follow-up research would include: extensive and multi-disciplinary field work (including institutional medical protocols and practices, living and social environments, etc.), and in-depth case studies (including autopsies).

Recently, the present authors have contributed to several studies that both: (1) find strong temporal correlations between rapid COVID-19 vaccine rollouts and accompanying sharp peaks in excess all-cause mortality, and (2) quantify corresponding values of risk of death per injection, assuming the deaths are induced by the injections, or would not have otherwise occurred within the time of the associated peak in excess mortality. These temporally correlated peaks in vaccine rollout and mortality are particularly evident in age-stratified mortality and vaccination data, and for the later (booster) doses of the vaccines, or in countries in which there was no excess all-cause mortality prior to the first COVID-19 vaccine rollouts (Rancourt, 2022; Rancourt et al., 2022a, 2023a, 2023b). Many similar temporal correlations between
mortality and vaccine-rollout peaks are seen in the present study, such as in Section 4.6, and with the December-2021-January-2022 peak, or 22-0 peak (Section 4.5, and **Appendix B**).

While it is useful to calculate such a population-wide risk of death per injection, and its age dependence, to appreciate its possible magnitude, it is possible that the observed strong correlations occur due to one or several hidden factors, rather than from a direct causal relationship due to challenge via toxicity of the injected substance.

For example, we might postulate that the teams of attendants who walk into the various institutions housing frail people to administer the latest booster during the period of a rapid rollout, are accompanied by or serve the dual function of a team of attendants who test for positive cases of presumed COVID-19. Each positive test or diagnostic determination, in turn, whether real or false, could have significant negative health consequences for the individual, such as isolation, removal to a different location, confinement, and aggressive chemical and mechanical medical treatment.

It is also possible that the vaccine injections, especially with multiple or booster doses, suddenly induce significant immunosuppression, thus making many vulnerable, frail, and stressed individuals susceptible to infections, including fatal respiratory tract infections, whether predominantly viral or bacterial. Possible mechanisms for COVID-19 vaccine-induced immunosuppression have been suggested (Palmer et al., 2023, ss. 3.3—3.5; Seneff et al., 2022).

To constrain whether or not vaccine toxicity directly causes measurable mortality, versus (for example) the fatal impact of other and concomitant large-scale public health interventions, or versus (for example) vaccine rollouts causing sudden and significant immunosuppression making many vulnerable to fatal respiratory disease, the researcher should have access to vaccine-status-discriminated all-cause mortality data. Such data will constrain more definitively whether the COVID-19 vaccination rollouts have life-saving benefits, or cause additional mortality, and the degree of these
relations. This data is needed for the same countries in which strong temporal
associations are present between rapid vaccine rollouts and sharp peaks in excess all-
cause mortality.

For any of the three scenarios considered above (vaccine toxicity; concomitant
aggressive medical and/or health interventions; vaccine-induced immunosuppression),
the systematic medical intervention (injection and/or testing and treatment) would have
caused the deaths which otherwise would not have occurred in the relevant short time
interval of the sharp mortality peak that follows. In this regard, it is important to keep in
mind that the practice of medicine itself has long been authoritatively recognized to be a
leading and underestimated cause of death (Makary and Daniel, 2016).

In the Covid context, for example, a study by Rancourt et al. (2021a) showed that there
was a large epidemic of fatal bacterial pneumonia in the USA, which apparently was not
treated, and the authors proposed that COVID-19-death-assignment was an incorrect
cause-assignment for what was in fact bacterial pneumonia. This suggests a Covid-
period institutional environment of widespread fatal medical malpractice.

## 5.2 Magnitude of the excess mortality rate during the Covid period, compared to past mortality events

Before delving into the spatiotemporal and socio-economic variations of excess all-cause mortality during the Covid period, it is useful to calculate and comparatively interpret the average magnitude of the net Covid-period excess all-cause mortality, inferred from the many countries studied.

The total excess all-cause mortality in the period 2020-2022 for the 93 countries having sufficient data is 9.086 million, for a total historic baseline value on the same period of 56.737 million, which gives an excess-to-baseline ratio, for all 93 countries together, of 16.01 %, whereas the by-country median value is 14.1 % (Section 4.9).
As also described in Section 4.9, the excess death rate during the Covid period, treating the 93 countries with mortality data throughout 2020-2022 as one jurisdiction, is 0.3844 ± 0.0021 % of 2019 population. Normalizing to mid-Covid-period 2021 population is achieved by assuming a pro-rata increase based on estimated world populations for 2019 and 2021: x (7.888B/7.742B) or x 1.019. This gives an excess death rate for three years (2020-2022) of the Covid period:

$$ 
\begin{aligned}
& 0.3844 \pm 0.0021 \% \times 1.019 \\
& = \\
& 0.3917 \pm 0.0021 \% \text{ of 2021 population} \\
& = \\
& 1 \text{ death per 770 persons per year during 2020-2022} \\
& = \\
& (0.3917 \pm 0.0021 \%) \times 7.888 \text{ billion} = 30.90 \pm 0.17 \text{ million} \\
& \text{excess deaths globally during 2020-2022}
\end{aligned}
$$

This is for all excess all-cause mortality during the Covid period (2020-2022), or approximately 0.13 % of population per year. The number of COVID-19-assigned deaths is significantly less (Section 4.13).

Here we obtain 30.9 ± 0.2 million projected excess deaths globally for the 3-year period 2020-2022, which is almost twice as many deaths as the 17 million deaths that have previously been projected globally to be associated with the COVID-19 vaccine rollouts (Rancourt et al., 2023a).

For comparison, India (which is not included in the present study) had an April-July 2021 peak in excess all-cause mortality of 3.7 million deaths for its 2021 population of approximately 1.41 billion, which corresponds to an excess death rate of 0.26 % for 2021 alone (and measurably 0 % for 2020, undetected) (Rancourt, 2022; and references therein).
Msemburi et al. (2023), using different methods to account for missing data for large countries, obtained similar numbers for global excess all-cause mortality per population as: 0.06 % in 2020 and 0.13 % in 2021, or 0.19 % for 2020-2021, which can be compared to our 0.39 % for 2020-2022.

Our yearly excess all-cause mortality during the Covid period (0.13 % of population) is less than one tenth of the global average death rate per year of 1.4 % of population, which corresponds to the mean global life expectancy at birth ($e_0$) of 71 years.

The yearly excess all-cause mortality during the Covid period (0.13 % of population) corresponds to reducing $e_0$ from the global average of 71 years to approximately 65 years during the three years of the declared pandemic. For the relationship between life expectancy and death rate, see: Liang et al. (2023).

These numbers can be compared to the excess mortality rate during the 1918 pandemic. Arguably the best estimate of the global death toll of the 1918 pandemic is from Spreeuwenberg et al. (2018), and see Roser (2020). The said best estimate is 17.4 million, at a time when the global population was 1.8 billion.

The latter numbers for the 1918 pandemic correspond to a 1918 pandemic death rate, during the course of the pandemic of:

$$ \begin{array}{l} 17.4 \text{ million} / 1.8 \text{ billion} \\ = \\ 0.97 \text{ % of population} \end{array} $$

which is more than 2 times larger than the excess all-cause mortality death toll per population, for the Covid period (0.39 %, 2020-2022), in the countries studied herein.

Both pandemics (1918 and 2020-2022) were entirely or largely epidemics of bacterial pneumonia, untreated or undertreated with antibiotics, respectively (Rancourt et al.,
2021a; and references therein). Both pandemics (1918 and 2020-2022) had death rates that were highly correlated to socioeconomic status (Mamelund, 2006; Grantz et al., 2016; Mamelund, 2021; Rancourt et al., 2021a, 2022b). Rancourt et al. (2022b) showed proportionality, with a Person correlation coefficient of +0.86, between Covid-period excess all-cause mortality by population and share of the population living in poverty, by state in the USA. However, a major difference between the two pandemics (1918 and 2020-2022) is that Covid-period excess all-cause mortality was generally exponential with age (e.g., Section 4.19) whereas excess mortality during the 1918 pandemic in the USA affected primarily young adults, and did not produce excess all-cause mortality for those aged 50+ (Rancourt et al., 2021a). Bailey et al. (2024) argue that the high mortality rate of young (mostly male) adults is due to surviving the horrendous war conditions of the First World War (waged in Europe, Africa, Middle East, Asia, Pacific Ocean, Pacific Islands), which ended on 11 November 1918.

The two pandemics (1918 and 2020-2022) have comparable death rates by population (0.97 % and 0.39 %, respectively) but both have death rates that do not approach the large death rate of the Black Death (bubonic plague) pandemic of 1347-1352, which had high selectivity on health status or frailty (DeWitte and Wood, 2008), and which is documented to having been associated with the loss of some 30-50 % of the European population, in a historic period and environment when average human life expectancy at birth was barely more than 30 years, and in which the leading cause of death generally was infections (Finch, 2009).

## 5.3 Large-scale jurisdictional, spatial and temporal variations of Covid-period excess all-cause mortality

The Results show much complexity apparent in the spatial and temporal variations of excess all-cause mortality in the world (125 countries) during the Covid period (2020-2023).
For the countries with available data in the present study, we have the following large-scale behaviour of excess all-cause mortality by country:

* Large country to country variability in excess all-cause mortality during the Covid period (Appendix A, Appendix B, Figure 16, Appendix D, Table 1, and also Figure 1, Figure 2, etc.), including both intra-continent and inter-continent variability

* Large temporal variations in excess all-cause mortality during the Covid period, in each individual country (Appendix A, Appendix B, Figure 16, Appendix D, Table 1, and also Figure 1, Figure 2, etc.)

* Three large clusters of countries (cluster analysis, with countries having populations greater than 1 million) having similar yearly-resolved temporal patterns of all-cause mortality (2019-2022), which largely group geographically (Figure 12, Figure 13)

* The mostly geographically grouped clusters of countries (cluster analysis, with countries having populations greater than 1 million) have distinct temporal variations of their distributions of values of excess all-cause mortality, as characterized by the statistical parameters of mean, median, standard deviation, and coefficient of variation (cv = standard deviation / mean) (Figure 17)

* Correspondingly, continental-scale groupings of countries have similar excess all-cause mortality magnitudes and temporal patterns (e.g., Eastern Europe with Russia and Northern Asia) (Figure 17, Figure 18, Figure 19, Figure 21)

* Large spatial gradients of excess all-cause mortality, across countries and between countries sharing large land borders, such as: east-west in Europe and north-south in North America (Figure 3, Figure 4, Figure 18, Figure 19, Figure 21)

* Spatially heterogeneous (country to country) large temporal peaks in excess all-cause mortality nominally occurring March-April 2020 (20-3 feature), October-November 2020 (20-10 feature), December-2021-January-2022 (22-0 feature), December-2022-January-2023 (23-0 feature) (Sections 4.2, 4.3, 4.4, 4.5, and Figure 5, Figure 6)

* Absence of excess all-cause mortality, up to a late and sharp massive peak in excess all-cause mortality, often synchronous with a vaccine rollout, in mostly
island and small coastal countries (also seen with India, Rancourt, 2022)
(Section 4.6, Figure 7, Figure 8, Figure 9, Figure 10, Figure 11)

* Thirty two (32) of the 76 countries allowing reliable quantification have a high statistical certainty of having persistent and significant excess all-cause mortality in the available data for 2023, up to June 2023 and excluding excess mortality from peak residuals extending out from any 23-0 feature (by selecting an integration start date a few weeks or more later than 1 January 2023), notably after the 5 May 2023 WHO declaration of an end of a pandemic (Section 4.8, Figure 14, Figure 15)

Notably, the large geographical and temporal variations in excess all-cause mortality (normalized either by population or by historic baseline all-cause mortality) are systematically different for the three clusters identified in Section 4.7. This is shown in Figure 17, which has the mean, median, standard deviation, and coefficient of variation (cv = standard deviation / mean) values of the excess all-cause mortality (normalized either by population or by historic baseline all-cause mortality) for all countries (blue) in the present study and for countries in each of the identified clusters of countries (Section 4.7: cluster 1, orange; cluster 2, green; cluster 3, red; no cluster, black, population ≤ 1 million, or data ends prior to end of 2022), and for each of several different time periods of integration (2020-2022, 2020-2023-H1, and each half year 2020-H1 through 2023-H1).

## 5.4 East-West divide in Europe

Our analysis shows a large distinction regarding excess all-cause mortality between Western European countries and Eastern European countries including Russia, with Eastern European nations having significantly larger Covid-period mortality, and among the largest in the world.
The said large East-West distinction in excess all-cause mortality is strikingly captured in the cluster analysis (Section 4.7, Figure 13; Figure 17) and in the maps of spatial distribution of excess all-cause mortality (Section 4.11, Figure 18, Figure 19).

The structure of the excess all-cause mortality by time (week or month) is also distinctly different for Eastern and Western European countries (Appendix A). Western European countries can have large March-April 2020 peaks (20-3 feature, Section 4.2) whereas Eastern European countries do not. In addition, Eastern European countries typically have remarkably large October-November 2020 peaks (20-10 feature, Section 4.3) whereas Western European countries do not.

We would not accept the hypothesis that more virulent strains or variants of the presumed SARS-CoV-2 virus were acting in Eastern Europe than in Western Europe, and in different time periods. The gymnastics needed to advance such a hypothesis would be too strenuous.

Rather, we would propose the following observations to help explain the large difference in magnitude of excess all-cause mortality (rate or P-score) between Eastern European and Western European countries.

We hypothesize that the relatively high excess mortality in Eastern European countries is due to a large share of the population aged 60+ corresponding to former workers who lost their economic and employment security after the dissolution of the Soviet Union in the early 1990s, and who presently live in poverty with low social status. This is supported by the strong correlation that exists between excess mortality rate and the product of share of population aged 60+ and share of population living with < $30/day (Section 4.18, Figure 48, Table 2). It is also evident in world maps of such socio-economic factors as life expectancy at 65 and share of population living with < $30/day (Section 4.11, Figure 20). See Section 5.12.
Our hypothesis is further supported by world maps of post-early-1990s prevalence of tuberculosis by country (Dye et al., 1999, their Figure 1; Glaziou et al., 2015, their Figure 1). See Section 5.12.

Similarly, Bonnet et al. (2024) reported the same excess all-cause mortality East-West disparity in Europe, and tentatively interpreted it this way:

> "This outcome is in line with the results of a recent study33, who concluded that the East-West differences in excess mortality are related to structural and psychosocial traits that have their roots in the communist era. On the one hand, this includes differences in the connectivity of populations, driving the later onset of the pandemic in the East (from October 2020 onwards), while the West was hit more in the first wave (March to May 2020). On the other hand, this likely includes profound disparities in levels of vulnerability to the disadvantage of the East, e.g. in terms of pre-existing diseases, intensified by lagging economic development and selective migration due to their impact on risk-relevant behaviour. Lower levels of compliance with policy interventions (e.g. social distancing and vaccination) and a generally lower level of trust in authorities might also stem from the communist past of CEE countries."

We disagree with the notion advanced by Bonnet et al. (2024) and the authors they cite that excess mortality in the Eastern European countries is delayed 1st wave mortality compared to the Western countries (20-3 versus 20-10 features) because of differences in interpersonal contacts between the two types of societies. We argue that all-cause mortality disproves the notion of spread (Section 5.6). If anything, we would argue that "lower levels of compliance with policy interventions" and "a generally lower level of trust in authorities" may have saved Eastern Europeans from the "1st wave" March-April 2020 peak in excess mortality (20-3 feature) that we take to be due to aggressive institutional response (fatal treatment in hospitals and care facilities) (see below: Sections 5.12.2 and 5.12.3.2).
## 5.5 Exceedingly large country-to-country heterogeneity of excess all-cause mortality

The time-period-specific excess all-cause mortality described above gives an inescapable result:

* The country-to-country differences are remarkably large (Figure 16, **Appendix D**, Table 1, Figure 17, Figure 18, Figure 19, Figure 21, **Appendix E**).

For example, this is reflected in values of the coefficient of variation (cv = standard deviation / mean) for different time periods, presented in Table 1 and shown in Figure 17, which are the order of 1 and can be as large as 3 or more.

This is true not only with population-normalized excess mortality, but also with excess mortality normalized by the expected (historic trend) mortality (Table 1, Figure 17), which takes into account country-to-country age structure and health status differences.

A good example is the integrated excess all-cause mortality for the first half-year of 2020 (2020-H1 period), during which a pandemic was declared by the WHO on 11 March 2020. The prominent 20-3 feature (March-April 2020 peak in excess all-cause mortality) occurs in this period, but only in relatively few countries. The 2020-H1 panel in Figure 16 shows that the great majority of countries had zero or near-zero excess all-cause mortality in this half-year, in which the median value is 0.7 % of expected mortality. Relatively few outlier countries had large (and essentially synchronous) 20-3-type excess mortality peaks (**Appendix A**), and the resulting coefficient of variation is 3.0, which is extraordinarily large, and it is even much larger in cluster 2 (green cluster, cv > 70, due to a small value of the mean) and cluster 3 (red cluster, cv > 8) (Figure 17).

In June 2020, Rancourt (2020) argued — based largely on early data from USA states — that the synchronous and heterogeneously distributed 20-3 feature (March-April 2020 peak) was incompatible with a viral respiratory pandemic and instead was due to
iatrogenic deaths in vulnerable groups. The main elements of Rancourt's argument are confirmed in the present study based on 125 countries.

The same kind of large inter-country variability in excess all-cause mortality occurs with a period of integration in time over essentially the entire Covid period, 2020-2022. Here (Table 1), the coefficient of variation is 0.55 (cv = standard deviation / mean = 8.1 % / 14.7 % = 0.55) — with excess mortality normalized by the expected (historic trend) mortality, which takes into account country-to-country age structure and health status differences. And see Figure 17.

In other words:

* The country-to-country (N = 93, Table 1) 2σ range in excess all-cause mortality for essentially the entire Covid period (2020-2022) spans values between approximately 0 % and 31 % of the extrapolated historic trend all-cause mortality.

* That is, the excess all-cause mortality expressed as a P-score for the 2020-2022 Covid period spans values between approximately 0 % and 31 %.

This (0-31 %) is an outstandingly large country-to-country variability; for example, compared to the historic record of large but robustly constant seasonal variations in all-cause mortality (**Appendix A, Appendix B**).

We stress that the above described large heterogeneity in country-to-country excess all-cause mortality normalized by the expected historic trend mortality (P-score, which takes into account country-to-country age structure and health status differences), using several different integration time periods (N ≤ 125, Table 1) is incompatible with a pandemic model in which a new virulent and contagious pathogen causes the increased mortality:

* The pandemic model would require the pathogen to carry a passport and to unnaturally change its virulence in attacking vulnerable populations in different national jurisdictions.
* Since the observed variability is by national jurisdiction, it seems more plausible to us that the largely varying mortality rates are due to different institutional treatments and societal assaults against vulnerable groups in the different countries, and to country-specific vulnerabilities that are not accounted for by normalization using historic trend mortality.

* The correlations with socio-economic variables reported above and discussed below support this hypothesis.

## 5.6 Absence of spread across national boundaries

In addition to the above-described country-to-country heterogeneity in normalized excess all-cause mortality, another striking feature is that large normalized excess all-cause mortality often does not cross national boundaries into directly neighboring countries, for extended periods of time.

In other words, continental and subcontinental-scale geographical patterns and gradients of large national differences in normalized excess all-cause mortality do not attenuate or homogenize for many months and years, contrary to the hypothesis of human-contact-mediated spreading of the cause of excess mortality.

For example, the large north-to-south Canada-USA-Mexico gradient of increasing normalized excess all-cause mortality, seen for time-period integration over the entire Covid period (2020-2022) (Figure 19), is maintained in every half year from 2020-H2 through 2021-H2 (Figure 21). Only as the magnitude of excess mortality winds down globally does this gradient attenuate into 2022 (Figure 21).

Another example is the case of Germany, wherein Germany has low normalized excess all-cause mortality, while being adjacent to and surrounded by countries with relatively high relative mortalities, during the period 2020-H1 through 2021-H1 (Figure 21).
A further illustration is provided by the east-to-west brochette of border-sharing countries Portugal-Spain-France-Germany-Poland, which has dramatically different all-cause mortality versus time curves during the Covid period, not in any sequence consistent with human-contact-mediated spreading of the cause of excess mortality (Figure 4). In particular the large 20-3 March-April 2020 feature in Spain is accompanied by a synchronous weaker such peak in France and does not give rise to delayed similar features in neighbouring countries, which would be expected in a spread hypothesis (Figure 3, Figure 4).

An absence of all-cause mortality spread is also apparent in several similar USA states that share land borders, in which differences are associated with lockdown policies (Johnson and Rancourt, 2022).

On a larger geographical scale than simply neighbouring countries, it is remarkable that the three large groups of populous (> 1 million) countries identified in the cluster analysis performed using not-normalized yearly raw all-cause mortality for the years 2019-2022 (Section 4.7) have distinct normalized excess all-cause mortality temporal behaviours (Figure 13, Figure 17, Figure 19, Figure 21). This is clear in Figure 17 and, if the risk of death were primarily determined by viral properties, would require that three different pathogens (with different contagiousness and virulence) would have separately infected three groups of countries in the present study, dividing the world into three, not to mention the puzzling and distinctive excess all-cause mortality patterns that occur in island nations (Section 4.6).

Our conclusion that the country-to-country heterogeneity of excess all-cause mortality (Section 5.5) and absence of spread of high excess all-cause mortality across national boundaries (this section) are incompatible with a pandemic model in which a new virulent and contagious pathogen causes the increased mortality is supported by higher spatial resolution data for the USA and Europe (not shown), and by further considerations described below.
## 5.7 Comparison of excess all-cause mortality and infection fatality rate (IFR) determinations

Infection fatality rate (IFR) determinations are problematic because they are often accepted at face value rather than being fundamentally questioned. The IFR is an operational parameter defined to be the number of deaths from the presumed specific pathogen divided by the number of infections with the pathogen, occurring in a given population, over a sufficiently long time period.

The number of infections is ascertained using seroprevalence determinations, which use blood tests to detect post-infection residual antibodies presumed to be specific for the presumed pathogen. Even reviews that describe the many challenges and uncertainties in performing seroprevalence determinations (McConnell et al., 2021) do not question whether the molecular antibody test itself is specific or has been reliably validated, which is often not the case with new commercial tests (e.g., Rancourt, 2021).

This means that there is a large uncertainty in the denominator of the IFR.

The numerator of the IFR, in this context, is the number of counted COVID-19-assigned deaths. However, there are also large uncertainties with PCR testing for SARS-CoV-2 (Borger et al., 2020), and cause-of-death assignation is itself prone to bias, with and without laboratory testing.

Nonetheless, if we take current IFR determinations for SARS-CoV-2 during the Covid period essentially at face value, as do the authors of the following studies, then:

* Ioannidis (2021) in a review estimated a global whole-population (all ages) IFR to be 0.15%, typical values of IFR for Europe and the Americas to be 0.3-0.4%, a global number of infections to be 1.5-2.0 billion by February 2021, and noted "substantial differences in IFR and in infection spread across continents, countries and locations".
* COVID-19 Forecasting Team (2022) in a systematic analysis found all-country whole-population (all ages) median values of IFR to be 0.466 % (0.223–0.840, interquartile range) on 15 April 2020, 0.314 % (0.143–0.551) on 1 January 2021, and noted “substantial heterogeneity in the IFR by age, location, and time ... IFRs varied by a factor of more than 30 among 190 countries and territories in this analysis”.

* Pezullo et al. (2023) in a systematic review for 29 countries found median IFR values of 0.034 % (0.013–0.056) for ages < 60, 0.095 % (0.036–0.119) for ages < 70, 0.506 % for ages 60-69, their Figure 1 showing large country-to-country differences, and an exponential increase with age with quadrupling of IFR for every 10 years in age.

Whereas these reported values of IFR have magnitudes and heterogeneity consistent with our values of excess all-cause mortality by population (assuming that most excess-mortality deaths were COVID-19 deaths and that most of the populations were infected with SARS-CoV-2), this does not constitute support for the hypothesis that the excess mortality was due to a presumed new and virulent virus.

The reason is twofold:

* First, the reported heterogeneities in IFR may be largely due to the above mentioned inescapable uncertainties in IFR determinations, which cannot easily be deconvoluted from any actual heterogeneity.

* Second, the said “consistency” (of having heterogeneity) would arise with any cause of excess mortality, if the deaths are assigned to COVID-19 and if seroprevalence estimates suggest that most of the population was infected, irrespective of whether most excess mortality is associated with respiratory conditions.

More importantly, and this is not directly addressed by the above named authors of reviews of seroprevalence studies and IFR estimations, the large spatiotemporal heterogeneities in inferred IFR values, if real — like the large spatiotemporal
heterogeneities in excess all-cause mortality normalized by population or by historic baseline mortality, which are real (Figure 16, **Appendix D**, Table 1, Figure 18, Figure 19, Figure 21, **Appendix E**) — are incompatible with an interpretation in which the excess deaths are caused by a distinct virulent pathogen.

The whole idea of an IFR is that it is characteristic of a specific presumed virulent pathogen. The pathogen hypothesis becomes nonsensical if the resulting death rate is essentially determined by presumed or unknown local population and environmental characteristics, in order to explain a large range of seemingly random values of IFR.

In other words, if degree (population share) of inferred exposure to the presumed pathogen (seroprevalence) is not a predictor of excess mortality (if IFR is virtually random in this regard), then what is the pathogen? If the main predictors of (age and frailty adjusted, historic baseline normalized, P-score) excess mortality are moderate poverty, type of government and historical circumstances (Sections 4.11, 4.12, 4.15, 4.18, 5.3, 5.5, 5.6), then how can the presumed specific new and globally prevalent pathogen be considered “virulent” whatsoever?

## 5.8 Correlations of excess all-cause mortality and socio-economic variables by country

The large country-to-country heterogeneity in normalized excess all-cause mortality (Section 4.9, Section 5.5) compels one to look for correlations with national variables or factors that are expected to be valid socio-economic indicators, and suggests that correlations should be detectable.

Recently, Ioannidis et al. (2023) have done this — for 34 countries, with fewer tested variables and using relatively inaccurate quantification of excess all-cause mortality — and found statistically significant differences between countries grouped as less and more vulnerable, with the latter defined as having “per capita nominal GDP < $30,000, Gini > 0.35 for income inequality and/or at least ≥2.5% of their population living in
poverty". For comparison and completeness, in **Appendix J** we show a recalculation for the 34 countries studied by Ioannidis et al. (2023) using our excess mortality analysis method, add the countries from the present study not included by Ioannidis et al., and apply their same "less and more vulnerable" country categorization to all the available countries. Their conclusions are valid despite the systematic error and the smaller selection of countries.

In the present study, continental and subcontinental spatial patterns and large spatial gradients of normalized excess all-cause mortality in themselves (Section 4.11, Section 5.3) directly show significant correlations with poverty or socio-economic and politico-cultural factors, such as the north-south Canada-USA-Mexico gradient in North America and the east-west Western European to Eastern European gradient in Europe and into Asia, which are persistent in time (Figure 18, Figure 19, Figure 21; and compare to Figure 20).

In addition, as discussed above, there is a global-scale clustering of countries, with cluster-analysis country groups having significantly different mortality behaviours, which is apparently tied to underlying socio-economic, historic and political differences (Section 4.7, Section 4.10, Section 4.11), again suggesting the potential for correlations with factors other than pathogen characteristics.

Therefore, we explored and quantified correlations between excess all-cause mortality (on different given time periods, normalized either by population or by expected all-cause mortality from the extrapolated pre-Covid historic trend for the same time period) with several socio-economic variables, and their products (Sections 4.11, 4.15, 4.16, and 4.18, and appendixes referenced therein).

We remind the reader that when excess mortality on a given time period is expressed as a percentage of (or normalized by) expected all-cause mortality from the extrapolated pre-Covid historic trend for the same time period (i.e., as a "P-score") it is measured in comparison to the intrinsic baseline mortality rate (by time period) in the
society or jurisdiction. The latter baseline mortality is a weighted average of all the country-specific factors affecting baseline (pre-Covid) mortality, such as age structure and health frailty or vulnerability. Thus-normalized excess all-cause mortality therefore is an age structure- and health status-adjusted specific national mortality.

If we prefer to measure the excess mortality directly on a per population basis, irrespective of the health status and age structure of the population, then we express the excess all-cause mortality as a fraction of a recent census population. We use census year 2019. The thus normalized excess all-cause mortality is highly dependent on and reflects age structure and societal health frailty.

The results (scatter plots and their analyses) were organized as follows (Section 4.15, Section 4.16):

* The excess all-cause mortality was normalized either by expected (historic baseline) all-cause mortality (Section 4.15) or by population (Section 4.16). (x2 outcomes)

* Each normalized excess all-cause mortality was calculated on eight or more different time periods (2020-2022, and each half year from 2020-H1 through 2023-H1; and sometimes additionally 2020-2023-H1 and calendar years 2020, 2021 and 2022). (x8 or more outcomes)

* A subset of eight socio-economic variables was selected for illustration of scatter plots with single (non-product) variables (x8 single variables):

i. GDP per capita (Figure 26, Figure 34)

ii. Gini inequality coefficient (Figure 27, Figure 35)

iii. share of population living on ≤ $5/day-person (Figure 28, Figure 36)

iv. share of population living on ≤ $30/day-person (Figure 29, Figure 37)

v. income per day (Figure 30, Figure 38)

vi. life expectancy at age 65 (Figure 31, Figure 39)

vii. share of population aged 60+ (Figure 32, Figure 40)

viii. share of population aged 90+ (Figure 33, Figure 41)
* Here, each of the resulting 2x8x8=128 or more scatter plots illustrated use points (with error bars, and labelled by country code) in four different colours, for the three clusters identified by cluster analysis and those countries not assigned to clusters (population < 1 million, or data ends before the end of 2022) (see Section 4.7).

Associated scatter plots and corresponding tables of Pearson correlation coefficients are:

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

* **Appendix G:** Table of Pearson correlation coefficients between socio-economic variables

* **Appendix H:** Table of Pearson correlation coefficients between all-cause mortality, excess all-cause mortality (both normalizations) and socio-economic variables

Of the eight selected socio-economic variables listed above, two consistently have the largest Pearson correlation coefficient magnitudes ($|r|$), for the periods and half-years of the Covid period. These are: (iv) “share of population living on ≤ $30/day-person”, and (v) “income per day” (median income or expenditure per day).

Regarding excess all-cause mortality as percentage of the expected (extrapolated historic trend) all-cause mortality (P-score), these correlation coefficient values are typically $r(≤ $30/d) ~ +0.5$ to $+0.6$ and $r(income) ~ -0.5$ to $-0.6$, which is “moderate” to “strong” (Evans, 1996). Strangely, these correlations change sign in the half-years 2022-H2 and 2023-H1, relative to the earlier periods. The situation is similar with excess all-cause mortality by population. (**Appendix H**)

Thus, as Rancourt et al. (2022b) found for the states of the USA (their Figure 20, $r = +0.86$, and see Rancourt et al., 2021a), poverty is the dominant single factor that correlates to excess mortality during the Covid period. This is generally true across the
125 countries providing data, specific short-time-period anomalies notwithstanding
(Appendix H).

In virtually every country studied, people living in poverty died predominantly in excess
compared to the pre-Covid-period historic record. This result holds in taking into
account age structure and baseline frailty (P-score), and is even stronger than with
purely population normalization (Appendix H).

Indeed, even though bulk yearly all-cause mortality rate itself (total yearly all-cause
mortality divided by population) correlates strongly with age structure (i.e.: "share of
population aged 60+", r ~ +0.75 to +0.80, years 2015-2022, see Appendix H), as
expected from the Gompertz Law (e.g., Neilsen et al., 2024; and Section 5.10, below),
this is not the case for excess all-cause mortality rate (r = +0.38, 2020-2022), and even
less so for P-score mortality (r = -0.15, "negligible") (Appendix H).

Therefore, excess all-cause mortality during the Covid period is not dominantly and
directly associated to (correlated to) age structure of the population. P-score mortality,
in particular, which accounts for age and frailty, is negligibly to very weakly correlated to
age structure of the population. Factors other than age play dominant roles, and are
linked to prevalence of poverty.

Interestingly, the correlation to “poverty” is reversed in the late Covid period (2022-H2
and 2023-H1). This relates to “persistent excess all-cause mortality into 2023”
(Section 4.8) since persistent excess all-cause mortality late in and beyond the Covid
period is largely associated with richer countries (Section 4.8).
## 5.9 Late and post-Covid-period persistent excess all-cause mortality

In Section 4.8 we described and quantified the widespread phenomenon of “persistent excess all-cause mortality in 2023”, and its systematics. In Section 5.8 we suggested that sign reversals of correlation coefficients to poverty, in the half-year periods 2022-H2 and 2023-H1, are possibly due to this same phenomenon, since wealthy (Green cluster) countries are most affected.

We submit that the said persistent excess all-cause mortality is a real phenomenon: Most countries have all-cause mortalities that return to pre-Covid levels in 2023, while the countries in issue have significant excess all-cause mortality into 2023. There are few cases of significant negative excess all-cause mortality into 2023. The analytic methods, including error propagation, appear to be robust and reliable (Appendix A, Appendix B). We have extensively tested the methodology on all the states of the USA (not shown).

The persistent excess all-cause mortality into 2023 (half-year 2023-H1), as a P-score (which accounts for age and frailty of the population), correlates to the following socio-economic variables considered (variables i—viii, Section 5.8; see Appendix H for all correlation coefficients, by time period):

iv. share of population living on ≤ $30/day-person

r = -0.42 (2023-H1, Figure 29)

v. income per day

r = +0.35 (2023-H1, Figure 30)

vi. life expectancy at age 65

r = +0.44 (2023-H1, Figure 31)