{"id":124866,"date":"2021-04-27T15:27:20","date_gmt":"2021-04-27T19:27:20","guid":{"rendered":"https:\/\/www.bibula.com\/?p=124866"},"modified":"2021-05-03T11:21:25","modified_gmt":"2021-05-03T15:21:25","slug":"fikcja-covid-matematyczno-logiczna-metoda-ustalenia-prawdziwej-liczby-smiertelnych-ofiar-damian-rafal","status":"publish","type":"post","link":"https:\/\/www.bibula.com\/?p=124866","title":{"rendered":"Fikcja Covid. Matematyczno-logiczna metoda ustalenia prawdziwej liczby \u015bmiertelnych ofiar – Damian Rafal<\/em>"},"content":{"rendered":"

Przedstawiamy poni\u017cej opracowanie, kt\u00f3re w spos\u00f3b naukowy, z zastosowaniem oryginalnego aparatu matematyczno-logicznego ukazuje zak\u0142amanie dotychczasowej narracji o tak wielkiej liczbie „ofiar Covid-19”. Opracowanie (ca\u0142o\u015b\u0107 w j\u0119zyku angielskim) zosta\u0142o ju\u017c przes\u0142ane do redakcji kilku naukowych periodyk\u00f3w.<\/em><\/strong><\/p>\r\n

Dzi\u0119kujemy Autorowi za podzielenie si\u0119 opracowaniem oraz za polskie wprowadzenie.<\/em><\/strong><\/p>\r\n

Redakcja BIBU\u0141Y<\/em><\/strong><\/p>\r\n

 <\/p>\r\n

Fikcja Covid. Matematyczno-logiczna metoda ustalenia prawdziwej liczby \u015bmiertelnych ofiar<\/h1>\r\n

T\u0141O<\/strong>: Co oznaczaj\u0105 dane zaprezentowane w tabelach CDC \u201eDeaths involving coronavirus\u201d. Jedyna obiektywna informacja to: \u201exxx tysi\u0119cy ludzi zmar\u0142o, przeci\u0119tnie w wieku 76,5 lat, b\u0119d\u0105c prawdopodobnie zainfekowanymi koronawirusem Covid-19\u201d. Ale ilu z tych ludzi na pewno by nadal \u017cy\u0142o, gdyby nie infekcja Covid-19? Celem tego artyku\u0142u jest pokazanie, w jaki spos\u00f3b metoda matematyczno-logiczna ustala prawdziw\u0105 liczb\u0119 \u015bmiertelnych ofiar Covid-19.<\/p>\r\n

REZULTATY<\/strong>: Tylko oko\u0142o 30% z oficjalnie uznanych za ofiary Covid-19 w USA w roku 2020 naprawd\u0119 zmar\u0142o wskutek udzia\u0142u Covid-19, a cala reszta i tak by zmar\u0142a w tym samym czasie, nawet bez Covid-19, poniewa\u017c ich zgony wynika\u0142y wy\u0142\u0105cznie z normalnej struktury zgon\u00f3w w USA, tworz\u0105cej przeci\u0119tn\u0105 d\u0142ugo\u015b\u0107 \u017cycia. Tylko niedu\u017ca mniejszo\u015b\u0107 z oficjalnych \u201czgon\u00f3w z powodu Covid-19\u201d tworzy\u0142a nadmiarowe zgony rok do roku; przyczyny nadmiarowych zgon\u00f3w by\u0142y zupe\u0142nie inne.<\/p>\r\n

INTERPRETACJA<\/strong>: Oficjalnie podawane liczby ofiar s\u0105 w zdecydowanej wi\u0119kszo\u015bci podw\u00f3jnym liczeniem tych, kt\u00f3rzy i tak by zmarli w tym samym czasie i bez Covid-19. Analiza \u2018ex post\u2019 jest konieczna, aby pozna\u0107 rzeczywista liczb\u0119 przypadk\u00f3w synergii skutkuj\u0105cej wcze\u015bniejszym zgonem.<\/p>\r\n

WNIOSKI:<\/strong><\/p>\r\n

a) Ka\u017cde \u017cycie ko\u0144czy sie \u015bmierci\u0105, a jej przyczyny s\u0105 wzgl\u0119dem siebie swego rodzaju konkurencj\u0105. Covid-19 to raczej slaby czynnik, przy kt\u00f3rym wymagana jest zwykle znacz\u0105ca dominacja og\u00f3lnej s\u0142abo\u015bci organizmu w przyczynowo\u015bci. Stan zdrowia ludzi oficjalnie uznanych za zmar\u0142ych z powodu Covid-19 w USA w roku 2020 by\u0142 przeci\u0119tnie tylko lekko-umiarkowanie gorszy od standardu przy danym wieku. Analiza \u2018ex post\u2019 jest konieczna, aby pozna\u0107 rzeczywista liczb\u0119 przypadk\u00f3w synergii skutkuj\u0105cej wcze\u015bniejszym zgonem.<\/p>\r\n

b) Tylko zdecydowana mniejszo\u015b\u0107 z oficjalnych \u201czgon\u00f3w z powodu Covid-19\u201d tworzy\u0142a nadmiarowe zgony rok do roku Jeszcze wyra\u017aniej wida\u0107 to przy analizie dla kraj\u00f3w europejskich. Najwa\u017cniejszymi przyczynami nadmiarowych zgon\u00f3w by\u0142y:<\/p>\r\n

– utrudniony dost\u0119p do leczenia chor\u00f3b innych ni\u017c Covid-19<\/p>\r\n

– obawy przed p\u00f3j\u015bciem do szpitala (panika), aby nie zarazi\u0107 sie tam.<\/p>\r\n

– \u201cdeaths of despair\u201d.<\/p>\r\n

c) Oficjalnie podawane liczby ofiar powstaj\u0105 w zdecydowanej wi\u0119kszo\u015bci poprzez podw\u00f3jne liczenie tych, kt\u00f3rzy i tak by zmarli w tym samym czasie i bez Covid-19. Zatem, w USA w roku 2020 nie by\u0142o \u017cadnych 363 tysi\u0119cy zgon\u00f3w z powodu Covid-19, a jedynie oko\u0142o 109,000 tysi\u0119cy.<\/p>\r\n

d) Mo\u017cna przypuszcza\u0107, ze jednym z powod\u00f3w znacznego zawy\u017cenia \u015bmiertelno\u015bci Covid-19 jest wliczanie tych, kt\u00f3rzy mieli jedynie pozytywny wynik testu PCR.<\/p>\r\n

e) Je\u015bli oficjalna \u015bmiertelno\u015b\u0107 jest na poziomie 0.27% (badania na przeciwcia\u0142a), to rzeczywista \u015bmiertelno\u015b\u0107 z powodu Covid-19 jest bardzo niska i wynosi (USA) oko\u0142o: 0.27% x 0.2991 = 0.081%<\/p>\r\n

f) Por\u00f3wnawcza, \u0142\u0105czna analiza przypadk\u00f3w Covid-19 + grypa + zapalenie p\u0142uc bez Covid-19 jest konieczna.<\/p>\r\n

 <\/h4>\r\n
\r\n

 <\/h4>\r\n

ASSUMED COVID-19 MORTALITY IS STRONGLY OVERESTIMATED<\/strong><\/h1>\r\n

I. <\/strong>The math-logic method to measure the real number of Covid-19 victims in the U.S. in 2020, <\/strong>the revision due to the sudden CDC announcement of the new and higher number of comorbidities.<\/strong><\/p>\r\n

by Damian Rafal<\/em><\/strong><\/h4>\r\n

 <\/p>\r\n

 <\/p>\r\n

ABSTRACT<\/strong><\/p>\r\n

BACKGROUND<\/strong>: What do the data presented in the CDC tables \u201eDeaths involving coronavirus\u201d mean? The one objective information is: \u201exxx thousands of people have died at the age of 76.5 on average and being infected probably with Covid-19\u201d. But how many of these people would for sure still live if not Covid-19 infection? The aim of this paper is to show how to use the math-logic method to reveal the real Covid-19 number of lethal victims in the US. METHODS: The ideas for solutions are fully original, mathematical – logical, including the real number of Covid-19 lethal victims discovering. The calculated data are usually slightly rounded, because the method presentation is the main aim of the article. FINDINGS: Only about 30% of those reported as Covid-19 lethal victims in the US in 2020 died from Covid-19 complicity, and all the rest would have died in the same time anyway, also without Covid-19, because their deaths resulted only from the normal age structure of deaths in the United States, creating the average length of life. Only a minority of the official \u201cCovid-19 related deaths\u201d numbers mean excessive deaths year-over-year. The reasons of the excessive deaths appearing are quite different. INTERPRETATION: The official numbers of Covid-19 lethal victims are in a majority \u201cthe double counting\u201d of those who would die whatsoever in the same time even without Covid-19. The \u2018ex post\u2019 analysis is necessary to discover the real number of cases with synergy causing earlier deaths. FUNDING: None<\/p>\r\n

 <\/p>\r\n

INTRODUCTION<\/strong><\/p>\r\n

In my opinion there is no correct essay analyzing the real Covid-19 net mortality to find. What do the data presented in the CDC tables \u201eDeaths involving coronavirus\u201d mean? The one objective information is: \u201exxx thousands of people have died at the age of 76.5 on average and being infected probably with Covid-19\u201d. But how many of these people would for sure still live if not Covid-19 infection? The main reason of deaths is \u2018aging\u2019 = advancing age and any diseases the frequency and deadly effects of which are very strongly correlated with it (what means, with the overall weakness of the organism) and main sources of which are in the body itself – these are \u2018internal causes\u2019. Apart from that there are also deaths caused by external causes like different injuries, and by external causes like infections which mostly have only burdening actions. The aim of this paper is to show how to calculate the real Covid-19 number of lethal victims.<\/p>\r\n

 <\/p>\r\n

METHODS<\/strong><\/p>\r\n

The ideas for solutions are fully original, including the real number of Covid-19 lethal victims calculating. At first I calculated the average expected death age of a close to identical group (like the one assumed to be killed by Covid-19) if nobody was infected. Then I calculated the average further life expectancy for the people from the whole \u201cdeaths involving Covid-19\u201d group if they were alive. I used constructed by me estimators. To understand the procedures and what is the consequence a reader must follow the resolving and explanations given below. The data from the calculations are further slightly rounded, but when more precision is needed then even of 0.05-year accuracy. In general, the data are rounded to chase calculations because the method presentation is the main goal of this article.<\/p>\r\n

 <\/p>\r\n

DETAILED PROCEDURE & RESULTS<\/strong><\/p>\r\n

Basing on the CDC.gov tables \u201cProvisional Deaths Counts for Covid-19\u201d (NCHS data) and on \u2018actuarial life table\u2019 I calculated\/estimated, in January 2021, the average age of those who died from Covid-19 to be 76.5 years.<\/p>\r\n

 <\/p>\r\n

How many of the US \u201cdied from Covid-19\u201d had in real their date of death accelerated.<\/strong><\/p>\r\n

a)<\/strong> At the beginning we must calculate what the average death age should be in a close to identical group (like the one assumed to be killed by Covid-19) if nobody was infected. As the average length of life in the US I take 78.5 years (the last World Bank data, for 2018). But this value needs to be revised upwards due to some factors. People from the \u201cdeaths involving Covid-19\u201d group just before the death were 76.5 y. old on average so they have already bypassed some risks of death\u2019s causes not directly dependant on aging, plaguing mainly people much younger. Deadly injuries shorten average life expectancy and their impact is unique because they are not derivatives of already \u2018not far from deadly\u2019 health status! Any death due to, for example, mechanical accidents excludes the possibility of assuming the Covid-19 causative participation, so the at-birth life expectancy of our group must exclude the negative impact of injuries in their broad meaning. We can find the CDC.gov data named „Leading Causes of Deaths” and see there are some groups of causes not directly dependent on \u2018aging\u2019 of the organism.<\/p>\r\n

-Accidents (unintentional injuries): 167127 cases in 2018<\/p>\r\n

-Intentional self-harm (suicides): 48344<\/p>\r\n

-Assaults: 18830<\/p>\r\n

Going deeper into it (data for 2018, imported in January 2021 from the website: https:\/\/injuryfacts.nsc.org<\/a>), we can see there are some sub-categories concerning \u2018Accidents\u2019, with given age structures of their victims.<\/p>\r\n

-„Poisoning’ 19.9 per 100,000 (deaths per 100,000 population)<\/p>\r\n

-\u2018Motor-vehicle crashes\u2019 12.4 per 100,000<\/p>\r\n

-\u2018Falls\u2019 11.2 per 100,000 (before the site revised it to 12.0 in February 2021)<\/p>\r\n

-\u2018Choking\u2019 1.6 per 100,000<\/p>\r\n

-\u2018Drowning\u2019 1.1 per 100,000<\/p>\r\n

-\u2018Fires\/smoke\u2019 0.9 per 100,000<\/p>\r\n

-\u2018Mechanical suffocation\u2019 0.4 per 100,000<\/p>\r\n

I calculate the negative contribution of \u2018Poisoning\u2019- (P) to the average at-birth life expectancy in the following way. The share of all \u2018accidental\u2019 deaths in the structure of US deaths is 0.0589 and the share of the \u2018Poisoning\u2019 category in \u2018accidental\u2019 deaths is 0.37 (0.0589 x 0.37 = 0.0218). I calculate using the following constructed by me estimator [when the average length of life is 78.5 and the average lethal poisoning age is 41.5 years (estimate); LE \u2013 life expectancy at age 41.5, taken from \u2018actuarial Life Table\u2019] (it would be more exact if used the average actuarial life expectancy of a victim, in all estimators, instead of life expectancy at the given average age, otherwise we can receive a slight underestimation):<\/p>\r\n

 <\/p>\r\n

(1 – 0.0218) x [78.5 + P x (78.5 +P – 41.5) \/LE)] + 0.0218 x 41.5 = 78.5<\/p>\r\n

0.9782 x 78.5 + 0.9782 x P x [(37 + P) \/39.13] + 0.9047 = 78.5<\/p>\r\n

P x (37 + P) = 0.8246 x 39.13 = 32.2656<\/p>\r\n

P^2 +37 x P – 32.2656 = 0<\/p>\r\n

P = 0.8524<\/p>\r\n

 <\/p>\r\n

^ -exponentiation<\/p>\r\n

 <\/p>\r\n

The \u2018Poisoning\u2019 category by about 0.85 y. has its negative impact on the average at-birth life expectancy of a US citizen. The estimates of the influence of the less important factors in the US: \u2018Suicides\u2019, \u2018Moto-vehicle crashes\u2019 and \u2018Assaults\u2019 give for our group: 0.5, 0.45 and 0.3 year respectively. \u2018Drowning\u2019, Choking\u2019, \u2018Fires\u2019\/\u2018Smoke\u2019 and \u2018Mechanical suffocation\u2019 are all trifles and add up together to the additional 0.2 year. There is one category = \u2018Falls\u2019 but the average age of a victim is bigger than the average length of life this time. I estimated (basing on the Injuryfacts.nsc.org table and chart) the average \u2018Falls\u2019 victim age as 79.5 years. The share of all \u2018accidental\u2019 deaths in the structure of the US deaths is 0.0589 and the share of the \u2018Falls\u2019 category in all \u2018accidental\u2019 deaths is 0.22. So again: 0.0589 x 0.22 = 0.013. But due to the average age of a victim higher than the average length of life I must construct another (more universal) estimator. If to think a little more, then it has no meaning what the age of someone dying X years earlier, than he otherwise would, is; 10 years subtracted from any age is always 10 years subtracted from the total value\/sum, that divided into the total number of deaths gives the average length of life. So the estimator can also be as simple as [LE1 \u2013 life expectancy at age 79.5]:<\/p>\r\n

 <\/p>\r\n

1.0 x (78.5 + F) – 0.013 x LE1 = 78.5<\/p>\r\n

78.5 + F – 0.013 x LE1 = 78.5<\/p>\r\n

F = 0.013 x LE1 = 0.013 x 9.2675 = 0.1205<\/p>\r\n

 <\/p>\r\n

Let\u2019s control what we receive if to calculate P (the \u2018Poisoning\u2019 negative impact) this way:<\/p>\r\n

 <\/p>\r\n

1.0 x (78.5 + P) – 0.0218 x LE = 78.5<\/p>\r\n

78.5 + P – 0.0218 x 39.13 = 78.5<\/p>\r\n

P = 0.0218 x 39.13 = 0.8530 (= the very similar result)<\/p>\r\n

 <\/p>\r\n

There are some more causes of \u201cpreventable injuries\u201d (Accidents) and their share is 9% in total (Injuryfacts). But their age structures are unknown to me, so I take its influence as 0.09 \/0.91 of the summed rest of the \u201cpreventable injuries\u201d categories what gives 0.148<\/p>\r\n

Looking through the list of all the death causes, there are still factors that will noticeably revise upwards the expected average length of life in our group, but these factors are associated with the very lowest age ranges; mainly infant mortality. We can look at the \u2018actuarial Life Table\u2019 (CDC.gov or SSA.gov) [1] to see that the lowest age ranges factors are almost totally \u201cconsumed\u201d in the age range 0-1. The negative impact of infant mortality (congenital malformations, low birth weight and the rest of the causes) is 0.5 year (but 0.05 must be subtracted not to repeat \u2018mechanical suffocation\u2019 and \u2018motor-vehicle incidents\u2019 cases – Injuryfacts). As it could be expected, the weight of this age sub-group in the \u201cdeaths involving Covid-19\u201d group (CDC.gov) is close to none (about 80 times less than the 0-1 group normal weight in all deaths in the society, estimated with the help of the \u2018life table\u2019). Looking at the age of 19 in the actuarial life table, and comparing with the age of 1, we can see already only under 0.3-year impact on life expectancy. But according to statistics about 80% of deaths at that age range is due to external causes (accidents \/poisonings, suicides) [2], the total influence of what is already calculated above. Due to this, rounding off the result, I take 0.05 year by which our group\u2019s average expected length of life must be additionally increased (taking into account adolescents huge deficit amongst Covid-19 victims).<\/p>\r\n

At the same time we can see that, when excluding \u2018injuries\u2019 and infant mortality, there can be left only factors adding up to less than 0.05 year of the diminishing effect on life expectancy (the 1-19 life period), because \u2018aging\u2019 shows very close to none deadly effects, in persons at age under 20 !!<\/p>\r\n

Thus, the total value of upwards adjusting is:<\/p>\r\n

0.85 + 0.5 + 0.45 + 0.3 + 0.2 + 0.1 + 0.15 + 0.45 + 0.05 = 3.05<\/p>\r\n

So: 78.5 + 3.05 = 81.55 years.<\/p>\r\n

 <\/p>\r\n

However, there is also one factor that in turn forces our group\u2019s average expected length of life to be adjusted downwards. This is the group state of health factor. According to the CDC.gov data, 94% of those who died from Covid-19 had chronic conditions, on average, 4.0 per person). At the same time, the CDC.gov publishes the tables (\u201cPercent of U.S. Adults 55 and Over with Chronic Conditions\u201d) with the information on how many older adults have chronic conditions:<\/p>\r\n

the group 65+ =85.6%<\/p>\r\n

the group 55-64 =60.50%.<\/p>\r\n

for the group <55 =on the basis of a number of American and Canadian data, not always very similar, (the sources mentioned in the text below) I take a guideline of 45% taking into account the dominance of the 45-54 age subgroup among those <55 y. old from \u201cdeaths involving Covid-19\u201d. I calculate taking into account the weights of the groups:<\/p>\r\n

the group 65+ : 0.79956<\/p>\r\n

the group 55-64 0.122<\/p>\r\n

the group <55 : 0.078<\/p>\r\n

 <\/p>\r\n

0.79956 \u00d7 85.6 + 0.122 x 30.5 +0.078 x 45, so: 68.442 + 7.381 + 3.51 = 79.33 (%)<\/p>\r\n

Thus, with the same age composition of the comparative group of the US citizens, only about 79.3% of the comparative group has any comorbidity.<\/p>\r\n

According to NCOA.org even 20% of people over 65 do not suffer from any \u2018chronic condition\u2019. The share of people without a chronic condition drops to 20% at the age of 75, but at the age of 85 this value is still 20% (not falling more) according to the Canadian data (CIHI.ca 2011). There are studies [3,4] according to which people who do not abuse alcohol +do not smoke +are physically active +eat healthy live on average 9-10 years longer than the US average is, being free, in a majority, of chronic conditions. A similar effect was encountered in other developed countries [5,6]. The approach from the assessment of single added chronic conditions influence [7] in our group would require to subtract 0.2 year from the average, but if there is some considerable lack of the strongest ones in our group, then it could require to subtract a year from the average. A separate article with the analysis is needed here, but there is no guarantee that some assumptions would not be obstacles in finishing the analysis. However the deviation by +\/- 0.3 year could change the final analysis result by less than 2% only, so to chase further calculations I subtract 0.6. Any underestimation or overestimation, however, is partially reduced by subtracting the corresponding value from the result of the next calculation, in the B part.<\/p>\r\n

\u2026But my previous analysis (January-February) must be by way of supplement revised because the CDC suddenly increased (in April) the average number of conditions from 2.9 to 4.0 for those 94% with conditions. So the health status of that 94%-subgroup was not rather normal (as previously estimated) but slightly-moderately worse than the standard one for a comparative group of the same age structure (please look into the Discussion part !). An additional analysis is not in the scope of this article, so it will be done here in a very simplified way. Basing on Life Table and processed by me data from the work of DuGoff et al. [7] the increased number of conditions would require to deduct, from the expected average length of life, about 2.75 year, but for a statistical 59-year-old individual if he next lived with those conditions for 22 years, and about 1.75 year for a statistical 67-year-old individual if he next lived with those conditions for 18 years. We do not have such an information. But we can use the British data guideline [8] to see that the crude %-increase in multimorbid patients is stable after age 55 and till about 80 years when the %-increase considerably slows down. At the same time, in the US, the prevalence of 2+, 3+ and 4+ chronic conditions in the group of the average age equal 55 is already about: 77%, 62% and 47%, respectively, of that in the oldest group of age 65++, according to another guideline [9]. Let\u2019s concentrate on 4+ conditioned, because the lower values are always much ahead, on the average age of a victim, and on the fact that the actuarial life expectancy at age 65 is 19 years [1].<\/p>\r\n

0.47 + [4 \/(84 \u2013 55)] x 0.53 = 0.543 \u2026so:<\/p>\r\n

[0.543 x 17.5 \/22 x 2.75 + 0.53 x (12 \/29) x (9.5 \/18) x 1.75] x 0.94 = [1.1878 + 0.2026] x 0.94 = 1.307<\/p>\r\n

\/This additional \u2018analysis\u2019 is very simplified, so the estimate above is rough.\/<\/p>\r\n

 <\/p>\r\n

The previous 0.6 I diminish by 1\/3, because it should be partially already incorporated in the second factor. So I deduct, in total, 1.70 year to obtain 79.85 years<\/u> as the final result.<\/p>\r\n

 <\/p>\r\n

 <\/p>\r\n

b)<\/strong> Since people from the \u201cdeaths involving Covid-19\u201d group were allegedly killed by Covid-19 (accelerated deaths), it means that without its \u2018intervention\u2019 these people should still live. Thus, I calculate the average further life expectancy for the people from the whole \u201cdeaths involving Covid-19\u201d group if were not killed by Covid-19. I plot their death-age structure plus the share of women and men on the \u2018actuarial Life Table\u2019 [1]. I calculate the average value for each age subgroup, and then, taking into account those age subgroups weights, I finally calculate the average \u2018further life expectancy\u2019 for the whole group. Careful calculations made by me in January 2021 gave the result of 12.3 year. But these data also have to be revised upwards because our group consists of those who could not die (if to be included into the group) because of external causes. For each mentioned category we must calculate the still existing, after the age at which the deceased formed the \u201cdeaths involving Covid-19\u201d group, potential length of life diminishing effect (X). For example, there are still quite many people in that group at the age range 45-75 which could otherwise be important in number victims of lethal \u2018Poisoning\u2019. The calculation is the sum of the partial ones (Xn) for different age ranges (including 75+ too).<\/p>\r\n

 <\/p>\r\n

1.0 x [78.5 + Xn x (Sn \/SN) \/(Cn \/CN)] – 0.0218 x (Pn \/PN) x LE = 78.5<\/p>\r\n

Xn x (Sn \/SN) \/(Cn \/CN) = 0.0218 x (Pn \/PN) x LE<\/p>\r\n

Xn = LE x 0.0218 x (Pn \/PN) x (Cn \/CN) \/ (Sn \/SN)<\/p>\r\n

 <\/p>\r\n

Xn -the potential length of life diminishing effect for an \u2018n\u2019 age range in the \u201cdeaths involving Covid-19\u201d (DIC) group<\/p>\r\n

Pn -the number of Poisoning victims in an \u2018n\u2019 age range; PN -the number of all Poisoning victims<\/p>\r\n

Cn -the number of persons in an \u2018n\u2019 age range of the DIC group; CN \u2013 the whole DIC group size.<\/p>\r\n

Sn, SN -the same as above (C) but in the whole society<\/p>\r\n

LE \u2013the average actuarial life expectancy of a victim from an \u2018n\u2019 age range, or at least life expectancy at the average age<\/p>\r\n

 <\/p>\r\n

We must repeat the same kind of calculations with all of the mentioned earlier categories. After that, the calculations results concerning different categories must be summed up all together. All needed data, concerning age ranges of victims of different types of injury, are in the tables and charts of https:\/\/injuryfacts.nsc.org<\/a> . The calculations gave me the following final values (the same order like in the A part) to sum up:<\/p>\r\n

0.25 + 0.2 + 0.15 + 0.05 + 0.1 + 0.05 + 0.05 = 0.85<\/p>\r\n

 <\/p>\r\n

Next I add the calculated 0.85, but at the same time I could subtract 1.70 (the worse state of health of our group; the same value like subtracted in the A part) to obtain 11.45 years, but to keep the proportions:<\/p>\r\n

79.85 \/81.55 = 0.9792<\/p>\r\n

(79.85 + 12.30 + 0.85) x 0.9792 – 79.85 = 11.2156<\/p>\r\n

 <\/p>\r\n

\u2026I finally take the more diminished value of 11.20 years<\/u> for the further analysis. But why, for example, for the age of 76 an alive person should live, on average, for over 11 more years (\u2018life table\u2019)? Because some persons have already died being much younger, and any person aged 76 is the one who is lucky to still live. Those who died earlier lower the average age of death and the still living will increase it. The average \u2018length of life\u2019 and the average \u2018life expectancy at a given age\u2019 are equal only at birth.<\/p>\r\n

 <\/p>\r\n

 <\/p>\r\n

c)<\/strong> What are the conclusions so far and what next?<\/p>\r\n

-If 100% of persons would die due to the normal age structure of deaths, excluding \u2018injuries\u2019 and infant mortality (so almost exclusively due to \u2018aging\u2019) in the US, that is, if Covid-19 would not kill any of them, the average expected death age should be about 79.85 years. The Covid-19 burden (superimposing) cannot increase but only lower this value, because Covid-19 is a life-shortening factor. The average number of chronic conditions in the \u201cdeaths involving Covid-19\u201d group is not meaningfully lowered, but it is moderately increased ! (what is already taken into account). The worst possible state of health (pre-deadly\/deadly) is nothing like at age 85; the worst one is, on average, at age 79.85 and only within the whole group some persons have their worst health status even at age 90 or more while at the same time some persons have their worst possible health status at age 70 or less.<\/p>\r\n

-At the same time, if Covid-19 killed all persons from the \u201cdeaths involving Covid-19\u201d group then it means that without the virus \u2018intervention\u2019 all of them should be still alive, for the next 11.20 years on average! It also means that each individual genuine \u2018Covid-19 related death\u2019 shortened its victim life, on average, by 11.20 years (Life Table)..<\/p>\r\n

-Persons from the \u201cdeaths involving Covid-19\u201d group died at the average age of 76.5, not of 79.85, so there is the 3.35-year loophole caused probably by lethal effects of Covid-19. The average contribution of each individual genuine \u2018Covid-19 related death\u2019 to the size of this gap is as follows:<\/p>\r\n

 <\/p>\r\n

11.20 x 1\/N. (N is the size of the entire group).<\/p>\r\n

The total Covid-19 contribution to the size of the gap cannot be more than the gap itself is. Let\u2019s count exactly:<\/p>\r\n

 <\/p>\r\n

C x 11.20 \/ N = 3.35<\/p>\r\n

(\u2018C\u2019 is the potential number of real\/genuine Covid-19 related deaths *)<\/p>\r\n

C\/N = 3.35 \/ 11.20 = 0.2991 (= 29.91%)<\/p>\r\n

(C\/N \u2013the potential share of real Covid-19 related deaths in the \u201cdeaths involving Covid-19\u201d group in the US *)<\/p>\r\n

 <\/p>\r\n

\/* potential, because the \u201cintrinsic loop\u201d (described later in the text) will further diminish the share<\/p>\r\n

 <\/p>\r\n

So only 30% of those from the official \u201cdeaths involving Covid-19\u201d group died from Covid-19 complicity and all the rest would have died in the same time anyway, also without Covid-19, because their deaths resulted only from the normal age structure of deaths (due to causes already existing before Covid-19) in the United States, creating the average length of life.<\/p>\r\n

The US genuine Covid-19 deaths share is among larger ones. There are countries in Europe with the official average \u201cCovid-19 death age\u201d as high (or only its contribution to the calculus as big), so the basic share of real Covid-19 deaths will be considerably lower than the US one; down even to hardly 20%, like in England & Wales with their 82.4 years of the official average \u201cCovid-19 death age\u201d and with the average number of chronic conditions 2.3 in that group (the number still not revised in the end of January 2021). The average number of chronic conditions and the prevalence of multimorbidity within different age groups are meaningfully smaller in England than in the US [8].<\/p>\r\n

 <\/p>\r\n

The \u201cintrinsic loop\u201d<\/strong><\/u><\/p>\r\n

Some of patients with other diseases are not provided with immediate help because access to treatment for the diseases that most contribute to deaths (cardiology, oncology and lung diseases) has worsened with the pandemic in a number of countries. Some of hospital clinics have been closed due to revealed Covid-19 outbreaks. There are also people who are afraid of going to the hospital because of their apprehension of becoming Covid-19 infected there (panic). Covering the face with a mask enables the creation of a dangerous concentration of microorganisms and a statistical mask user probably do not change it often enough to limit that problem. Staying at home means limited physical activity what is negative for overall health. When a number of people die because of these reasons earlier that they otherwise would, they additionally reduce the assumed average length of life and the share of genuine Covid-19 deaths. These factors role can be only considerably bigger over time.<\/p>\r\n

 <\/p>\r\n

Influenza and Pneumonia<\/strong><\/p>\r\n

I calculated the average flu victim age as 72 years in the US in the last 10 years. It seems to me that the role of influenza can be underestimated compared to Covid-19. After all, the lower the average age of death, the lower the share of the overall weakness (aging and diseases the frequency of which is strongly and directly correlated with it) is required for a virus to be effective in killing. The flu reported numbers of cases, even up to 90%, diminished in the world in the year 2020. It was already visible in the very beginning of the Covid-19 appearance [10]. Maybe some of the flu cases are also treated as Covid-19 this year due to the tests limited reliability, or maybe there is another explanation.<\/p>\r\n

Comparative joint counting of Covid-19, influenza and pneumonia-without-Covid-19 cases is necessary because when looking at the CDC table: \u201cDeaths involving coronavirus disease\u201d we can see that virtually all cases of \u201cDeaths involving Covid-19 and Pneumonia\u201d are further claimed to be Covid-19 lethal victims. Also, in the UK when influenza, pneumonia and Covid-19 were on a Medical Certificate Cause of Death (MCCD) together, without a postmortem, then almost 96% of these deaths were counted as Covid-19 deaths; assuming Covid-19 deaths was practiced even without testing for Covid-19 [11].<\/p>\r\n

 <\/p>\r\n

DISCUSSION<\/strong><\/p>\r\n

The very specific variant: \u201cCovid-19 kills mainly the very weakest among the elderly\u201d should be rejected. <\/u>In the US, according to NCOA.org, 77% of persons aged 65+ have two or more chronic conditions each; over 60% of persons aged 67+ have three or more [7], and additionally, according to the rand.org study: \u201cMultiple Chronic Conditions in the United States\u201d [12] \u2013about 12% of the US adult population have five or more chronic conditions each. But the prevalence of 2+, 3+ and 4+ chronic conditions is about: 2.4 times, five times and up to ten times, respectively, greater in the age group 65+ than in the age group 20-44 years; at the same time, when comparing to the age group 45-64, this prevalence is 1.3, 1.6 and 2.1 times, respectively, greater in the age group 65+ [9]. So, with the average of (4.0 x 0.94) = 3.8 conditions, the health status of the \u201cdeaths involving Covid-19\u201d group was slightly worse than the normal one for a comparative group of the same age structure, which should have, on average, 3.2 conditions according to my detailed estimates. Life expectancy and comorbidities numbers are strongly correlated; the average number of chronic conditions would have to be \u2265 10.0 to diminish life expectancy to 80 years for a still alive 75-year-old US woman, what means shortening the remaining life to five years; at the same time a 75-year-old woman with \u201conly\u201d 5.0 chronic conditions will live, on average, to the age of 87, what is by one year shorter than the average for a 75-year-old woman in the US! [7]. The marginal decline in life expectancy increases with an additional chronic condition when numbers are low but this decline starts with low values -first ones conditions sum up to the much less effect than the next conditions do [7]. At the same time, selected conditions give differences in life expectancy at age 67, but the differences meaningfully diminish with increasing age [7]. The clear relationship between the number of comorbidities and life expectancy has been discovered also by other authors [13].<\/p>\r\n

So, with only 3.8 (4.0) chronic conditions on average, the very specific variant for the \u201cdeaths involving Covid-19\u201d group (= killing by shortening the remaining life, on average, by 3.35 years; = the remaining life to be, on average, by almost eight years shorter than the norm in the US society !) is a fallacy. The state of health of this group was only slightly to moderately worse than the standard one. Further increasing the average number of chronic conditions could only diminish the share of real Covid-19 deaths in the officially announced \u201cCovid-19 related deaths\u201d because the relation of the final values (in the C-part) would decrease.<\/p>\r\n

 <\/p>\r\n

CONCLUSIONS<\/strong><\/p>\r\n

a) Every life will end with death so the causes of death are a kind of competition with each other. Covid-19 is rather a weak factor where, on average, the considerable dominance of the factor of the general weakness of the organism is required in the causality of death. The \u2018ex post\u2019 analysis is necessary to discover the real number of cases with synergy causing earlier deaths.<\/p>\r\n

b) The official \u2018Covid-19 deaths\u2019 numbers do not mean excessive\/net deaths year-over-year but only a limited minority of it. It would be more conspicuous when looked at the analysis results for European countries. The main causes of excessive deaths most likely are:<\/p>\r\n

– the worsened access to treatment for diseases other than Covid-19<\/p>\r\n

– some of patients\u2019 fear of going to the hospital (panic); when they finally go there it is too late<\/p>\r\n

– \u201cdeaths of despair\u201d.<\/p>\r\n

c) The \u2018Covid-19 deaths\u2019 official numbers are in a vast majority the double counting of those who would die whatsoever in the same time even without Covid-19. So in the US in the year 2020 there were not about 363,000 \u201cdeaths involving Covid-19\u201d but only about 109,000.<\/p>\r\n

d) It can be supposed that another reason of the official numbers of \u2018Covid-19 deaths being strongly overestimated is including those who have had only a positive PCR test result (even 2 months prior to the death, like in the US or in the UK).<\/p>\r\n

e) If the official Covid-19 mortality in the US is at the most commonly accepted level (= 0.27%, based on antibody tests) then the genuine mortality is about: 0.27% x 0.2991 = 0.081%<\/p>\r\n

f) Comparative joint counting of Covid-19 + influenza + pneumonia-without-Covid-19 cases is necessary.<\/p>\r\n

 <\/p>\r\n

CONFLICT OF INTEREST<\/strong><\/p>\r\n

There is no conflict of interest.<\/p>\r\n

 <\/p>\r\n

REFERENCES<\/strong><\/p>\r\n