'The result may be a wildly exaggerated fear, unjustified by reality,
and this fear may be deliberately promoted by the wicked cynical
manipulators.'
Don’t worry – this will be a data driven post, as usual. But first I must set the context without which this post would be unnecessary.
There are cynical people who will exploit heinous crimes, perhaps the murder of an individual or perhaps the homicide of a class of people, to progress a political or personal agenda. This is highly reprehensible.
There is another type of person who is driven by emotional reasoning and who will easily be manipulated by this kind of exploitation. What may result is a widespread reaction which lacks any balance, judgment or fairness and is actually motivated by pre-existing prejudice. The latest event is just the spark that sets off a conflagration whose true cause is the endemic prejudice that has been long in the building. Consequently, this second type of person is also reprehensible, but with the mitigation that they may have been misled.
The misleading of this second class of person is greatly assisted by a
characteristic from which many who are inclined to emotional reasoning
suffer: innumeracy. Innumeracy is usually understood to mean merely a
poor grasp of elementary arithmetic. But actually the condition is more
serious, and may be a cognitive impairment for which the sufferer is
blameless. The condition causes such people to have no real feel for the
significance of statistics which differ by being large, small or
extremely small.
The result may be a wildly exaggerated fear, unjustified by reality, and this fear may be deliberately promoted by the wicked cynical manipulators.
It is more than six years since I last attempted to put death statistics into proper perspective as a counter to the issue discussed above. I do so again here, via the Table below. The Table relates to England and Wales and shows the number of people dying from selected causes in a recent year. For each of these figures I have found the size of the relevant demographic, i.e., the total population of the same sex and age range, which allows the death data to be expressed as a percentage of the corresponding demographic population. The purpose is to give a feel for probability (or prevalence, if you prefer).
For in-utero deaths, neonatal deaths, infant deaths and abortions, the corresponding population defining the denominator is the sum of the number of live births and the number of deaths/destructions in-utero.
For the deaths of rough sleepers the corresponding population defining the denominator is the number of rough sleepers. For non-resident parents the denominator is the size of the sample to which the death statistic relates.
Notes describing in more detail how the data were derived, and the sources used, follow the Table.
All death data are “per year” and hence the percentage figures, interpreted as probabilities of dying of that cause, are also probabilities per year.
Hence, the percentages give the probability of dying per year of that cause for a randomly chosen person or foetus from the sub-population in question.
As always with probabilities, one has to be careful about interpretation. For example, for deaths due to alcohol or drugs I have used the total population of England and Wales as the denominator. In reality, the risk from these causes is (obviously) hugely concentrated upon those who abuse these substances (and whose individual probability of dying as a result will be far larger than the average). Nevertheless, for a person chosen at random from the whole population, the probabilities are correct.
Another important interpretational issue is the age at death. Without wishing to be callous or to “kill granny”, the death of an 80-year-old is not the same as the death of a much younger person who might have expected many more decades of healthy life. In this respect, deaths from (actually, with) Covid-19 are not comparable with most of the other causes listed, as the average age at death with Covid is actually rather greater than the overall average age at death. The same applies to the bulk of deaths due to diseases (natural causes). Of greater relevance for comparison purposes, therefore, are early deaths from diseases. Here I refer you to The Empathy Gap, Table 3.3, which breaks down early death, defined as death before age 45, into its various causes. In the Table below I give only the totality of early deaths.
The Table is in order of increasing probability.
One of the points being made, of course, is that the probability of homicide, of all kinds, is small compared with other causes of death. The probability of homicide is greatest for adult men. The probability of homicide for a boy is comparable with, perhaps somewhat larger than, the probability of homicide for a woman (even when domestic and non-domestic homicides are summed).
Domestic homicides are the only category where deaths of females exceed that of males. In all other categories (where the numbers of deaths are far larger) deaths of males outnumber those of females.
Across all causes, early deaths of men (before age 45) exceed early deaths of women by 78%.
Premature deaths of men (before age 75) exceed premature deaths of women by 43%.
| Cause | Number of deaths | Demographic Population | Percentage |
| Domestic homicide (men) | 28 | 23,800,000 | 0.00012% |
| Non-domestic homicide (women) | 85 | 24,500,000 | 0.00035% |
| Domestic homicide (women) | 91 | 24,500,000 | 0.00037% |
| Homicide of girls | 28 | 6,450,000 | 0.00043% |
| Homicide of boys | 53 | 6,450,000 | 0.00082% |
| Non-domestic homicide (men) | 445 | 23,800,000 | 0.00187% |
| Accidents (female) | 986 | 30,300,000 | 0.00325% |
| Drugs (female) | 1,425 | 30,300,000 | 0.00470% |
| Suicide (female) | 1,388 | 26,600,000 | 0.00522% |
| Accidents (male) | 2,531 | 29,500,000 | 0.00858% |
| Alcohol (female) | 2,622 | 30,300,000 | 0.00865% |
| Drugs (male) | 2,968 | 29,500,000 | 0.01006% |
| Suicide (male) | 4,303 | 26,000,000 | 0.01655% |
| Alcohol (male) | 4,998 | 29,500,000 | 0.01694% |
| All early death (<45 years), female | 6,098 | 17,000,000 | 0.03587% |
| All early death (<45 years), male | 10,829 | 16,600,000 | 0.06523% |
| Women 25 to 55, all causes | – | – | 0.11600% |
| Men 25 to 55, all causes | – | – | 0.19100% |
| Covid-19 (female) | 60,940 | 30,300,000 | 0.20112% |
| Neonatal death (<28 days) | 1,771 | 867,062 | 0.20425% |
| Covid-19 (male) | 72,322 | 29,500,000 | 0.24516% |
| All infant deaths (<1 year) | 2,387 | 867,062 | 0.27530% |
| Still births / death in-utero | 2,689 | 867,062 | 0.31013% |
| Non-resident parents in CMS | 1,189 | 362,245 | 0.32800% |
| All diseases (both sexes) | 579,653 | 59,800,000 | 0.96932% |
| Rough sleepers (women) | 91 | 4,950 | 1.83838% |
| Rough sleepers (men) | 687 | 28,050 | 2.44920% |
| Abortion | 207,384 | 867,062 | 23.91801% |
Population: I have used death data from a mix of recent years, from 2016/17 to 2019/20, but for simplicity I have used time-invariant populations of England & Wales as follows,
- All: 29.5M men, 30.3M women.
- 16 and above: 23.8M men, 24.5M women
- Under 18s: 6.45M boys, 6.45M girls
- 10 and above: 26.0M males, 26.6M females.
- Under 45: 16.6M men, 17.0M women.
Homicides: Domestic homicide data are taken from [1], using the average over the three years April 2016 to March 2019. However this source appears misleading for non-domestic homicides, for which I have used [2]. Here I have averaged over the four years 2016/17 to 2019/20. This source breaks down data into age ranges. Note that the terms “men” and “women” in this context relate to homicides of those aged 16 and above. In the Table, “boys” and “girls” refers to those under 18 (so there is some double accounting with the adult range). As [2] gives data in the age range 16-24, I have used 2/9 of these data to represent the age range 16-17, which is crude but adequate for the present purposes.
Suicide: Data from [3] relates to those aged 10 and above (2019).
Covid-19: I have used the official statistics based on “death within 28 days of a positive test”. This is, at best, death with Covid, not necessarily of Covid. Hence these data are upper bounds. Ref.[4] gives data to end-February 2021, which is a reasonable cut-off as it represents just 12 months of Covid data, the epidemic within the UK taking off in March 2020.
Alcohol: I have used data from 2016, [5], after which the definition changed. There is potential ambiguity here as many cancers are epidemiologically related to alcohol consumption but, being non-deterministic, will not be included here. The data here relates only to cases which can be unambiguously related to alcohol.
Drug Poisoning: Data from [6] for 2019.
Accidents: These data include all accidents: road accidents, accidents at work or elsewhere, accidental deaths due to falls, fire, drowning, noxious substances, etc. I have been a bit lazy here and used data from The Empathy Gap, Table 3.1, which is for 2016 and actually relates only to deaths before age 75. So these data are likely to be under-estimates.
Rough Sleepers: The number of deaths of rough sleepers in 2019 has been taken from [7]. The size of the population of rough sleepers is subject to serious ambiguity, as explained at length in The Empathy Gap chapter 16. Sources like [8] and [9] only attempt an estimate of how many people are sleeping rough on a specific night. Even this is misleading because it fails to account for people in night shelters on that night, who might be back on the street the next night. More importantly, though, the rough sleeping population is not static. There is a huge difference between the number of people sleeping rough on a given night and the number of people who have slept rough at some time during a given year. Data from CHAIN in London suggest the latter is 7 or 8 times larger than the former. On this basis, in The Empathy Gap I estimated that around 36,000 people had slept rough in England at least once in 2017/18. Some rough corroboration of a figure of this magnitude comes from the “Everyone In” project in 2020, to get rough sleepers off the streets during the Covid pandemic, which supported 33,000 people to do just that, Ref. [9]. In short, I have used 33,000 as the relevant population. Based on [8], 85% of rough sleepers are men. Note that [7] indicates 88.3% of deaths of rough sleepers were men in the same year, 2019. Consequently it would seem that male rough sleepers are somewhat more likely to die than female rough sleepers. (37% of rough sleeper deaths are attributable to drugs, and 14.4% to suicide).
Abortion data taken from [10] for 2019 (all time high), 207,384.
Births & Infant Death: Taken from [11]: live births 656,989; still births (including deaths in-utero) from week 24 onwards 2,689; neonatal deaths (i.e., <28 days) 1,771; all infant deaths (<1 year) 2,387. Denominator for this category, and abortion, 656,989 + 2,689 + 207,384 = 867,062.
Early Death: I define this as deaths from all causes before age 45, see The Empathy Gap, Table 3.3 (2016).
Non-Resident Parents paying into CMS: Data here is taken from my earlier post Deaths Whilst Paying Child Maintenance, such non-resident parents (overwhelmingly fathers) have an elevated death rate compared with men or women of comparable age, i.e., mostly 25 to 55. The latter are also included in the Table for comparison. Note that I have converted the data in Table 1 of the earlier post to a yearly figure by dividing by 2.75.
Sources
- Domestic abuse prevalence and victim characteristics – Appendix tables All data related to Domestic abuse victim characteristics, England and Wales: year ending March 2020 – Office for National Statistics (ons.gov.uk)
- Homicide in England and Wales: year ending March 2020 – Appendix Tables Appendix tables: homicide in England and Wales – Office for National Statistics (ons.gov.uk)
- Suicides Suicides in England and Wales – Office for National Statistics (ons.gov.uk)
- Covid deaths Deaths | Coronavirus in the UK (data.gov.uk)
- Alcohol-related deaths by sex, age group and individual cause of death Alcohol-related deaths by sex, age group and individual cause of death – Office for National Statistics (ons.gov.uk)
- Deaths related to drug poisoning, England and Wales Deaths related to drug poisoning, England and Wales – Office for National Statistics (ons.gov.uk)
- Rough deaths Deaths of homeless people in England and Wales – Office for National Statistics (ons.gov.uk)
- Rough sleeping snapshot in England: autumn 2019 – GOV.UK (www.gov.uk)
- Rough sleeping snapshot in England: autumn 2020 – GOV.UK (www.gov.uk)
- Abortion statistics for England and Wales: 2019 Abortion statistics for England and Wales: 2019 – GOV.UK (www.gov.uk)
- Infant mortality (birth cohort) tables in England and Wales Infant mortality (birth cohort) tables in England and Wales – Office for National Statistics (ons.gov.uk)
- Deaths registrations 2020 to Mar’21 Deaths registered weekly in England and Wales, provisional – Office for National Statistics (ons.gov.uk)
I don't think you are making enough of an issue of the fact that the tests for covid themselves are stated by many experts to be a running joke. That we have apparently had a year free from influenza, that an African PM duped the WHO by sending pheasant, papaya and goat samples all of which were stated by the WHO to be positive for covid, etc.
ReplyDeleteUtterly dissatisfied with the validity of testing I have chosen to rely on the general daily, weekly, monthly, yearly govt. death figures to decide for myself the validity of the Orwellian reality in which we find ourselves today in the UK, Greece, Cyprus, occupied Palestine [a true Guinea Pig Vaxi-Nation in the last two months] and much of the rest of the world. I was looking for enough of an increas in Englands' death rates to justify the current Orwellian status quo.
I note that after September 2020 it was clear that the ONS was clearly nobbled by political forces. That less people died as a whole in 2020 than in 2018 in England and Wales. That in August and September over 1000 less died per month [and a similar proportion in Wales] v the five year average, ...before the ONS was nobbled.
My 12-year-old daughter Sophia made much shorter work of the whole affair last week when we were discussing the issue and she asked me to simply look up the world population figures for 2018, 2019 and 2020. From the mouth of my babe I found a rather neat progression of approximately 100,000,000 additional souls per year without interruption by any so-called extraordinary deadly pandemic.
I also note that our percentages were no different than those of Sweden where they didn't shut down and North and South Dakota in the USA where one shut businesses and made masks are requirement and one kept businesses open and did not require masks the graphs are more or less identical.
In short, as a very gifted mathematician and having an excellent understanding of medical science v the general public and better than some doctors in my personal experience, it is my informed opinion that the lockdown that has put many more nails in the coffin of the middle classes around the world, has had much more to do with 'Agenda 21' and 'Agenda 2030' and Bill Gates' recent acquisition of many vaccine producers, and very little to do with the wellbeing of the citizenry.
The figures coming out of “Guinea Pig Vaxi-Nation” the Jewish anti-Gentile apartheid regime 'Israel' over the last weeks reinforce the above assessment.
Delete...Not to mention MIND UK's reports on the effects on mental health and Cancer Research UK's reports on general health and many others, about the negative effects of the unwarranted lame-stream media hysteria excusing Orwellian lockdowns.
Furthermore I note that whereas we were more or less done with the last seasons colds [influenza, covid, etc.] the recent vaccinations drive has caused the rates of colds and associated deaths to rocket rather than decline.
In general I am rather appalled with the sheeple of Great Britain and of much of the rest of the world for so easily laying down and giving up all our liberties to the new Orwellian techno-bank-ocracy.
As the man said in French Kiss, they all make my arse twitch. ...The same morons allowed destruction of fatherhood, the second class citizenship of males and by proxy the destruction of the family that has destroyed so many children's' lives.
As I understand how the human immune system works and am confident that flues offer very little risk to healthy people, I refuse to wear masks unless absolutely forced to do so and I refuse to be super cautious and use the alcohol hand treatments hoisted on us at every port. I refuse to actively flush my strong immune system down the toilet by those aforementioned methods that the general public has been encouraged to use.
I refuse vaccines for myself and my children, especially when I know that they are often quite different to what we have understood historically to be a vaccine, or a benign form of a particular virus introduced into our body to induce the body to create antibodies to guard against a particular virus. Some of these so-called vaccines today are in fact a chemical inducement of the body to attack itself in order to produce general antibodies, ...a highly suspect method in my opinion.