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Conspiracy Archive
The Coronavirus Conspiracies
Claim: This virus isn't harmful
This is the claim that the virus is an overblown hoax. A 99.9% recovery rate shows this virus isn't harmful.
I’m going to look into 5 countries and various points of interest such as population density and infections over time. While I demonstrate that some countries have done better than others, this isn’t a full analysis. The variables at play in are enormous in number.
For example: What is the density of the cities vs the entire population? Does each country have socialised health care? What is the capacity of their hospitals? How many hospitals do they have and what is the quality of care? What is the social attitude towards government advice? Do the public have an attitude of science acceptance or science denial? How competent was the government response?
Another further point: the standard of care increases over time. Doctors become more efficient at treating covid patents over time. How long and aggressive were the lockdowns per country? Do they have local lockdowns? How does the impact on mental health affect deaths overall? How much are they testing? What is the quality of those tests and how are they handled? Who is being tested?
You can go on and on for a long time. So don’t misunderstand - this isn’t a definitive grading per country.
To examine whether the virus is dangerous or not, we need to do more than just repeat things we’ve heard from others or the media. We need to look at the statistics for multiple countries and examine that against population density, risk factors, and demographics of infected populations to see if they line up.
To investigate I will look at 5 countries:
USA - Because they’re doing the worst
Italy - A country hit hard in the early months
UK - Because I live here and we haven’t done that well
South Korea - A country known for doing very well in the early stages of the pandemic
New Zealand - Another country known the world over for taking down the virus early and well
So how do we investigate whether their deaths are real or fake?
First Let’s look at:
-Deaths vs confirmed cases
-Positive test rate
This will be:
-Death toll over time
-Confirmed cases over time
-Number of excess tests
Excess tests show you whether tests are showing the infection is more widespread than is being reported. If more tests show on average less cases, that’s good. If you test more and find more cases it means the problem is potentially worse than you currently know.
Then for 3 months: February, May, and August, let’s compare this year’s excess death count vs the last 4 years. Excess testing means the number of tests carried out per confirmed case. If you tested 20 tests and 1 came back positive, that would mean you have 20 tests per confirmed case.
Consider that all of these months are spaced out evenly, and include months where people are mostly locked down and unable to go out.
It’s worth noting that South Korea became infected in January, and the other 4 countries started around March.
Sources:
The three big health institutions reports of covid cases
Cululative confirmed cases by country
Excess tests - tests per confirmed case
The statistics
UK - 67,886,011 people - Density 279
Deaths on Feb 1st - 0 | Cases: 2 | Excess Tests: 0
Deaths on May 1st - 26 788 | Cases: 172 592 | Excess Tests: 6
Deaths on Aug 1st - 46 119 | Cases: 303 181 | Excess Tests: 32
USA - 331,002,651 people - Density 35
Deaths on Feb 1st - 0 | Cases: 7 | Excess Tests: 0
Deaths on May 1st - 63 006 | Cases: 1 070 000 | Excess Tests: 6
Deaths on Aug 1st - 153 314 | Cases: 4 560 000 | Excess Tests: 12
Italy - 60,461,826 people - Density 201
Deaths on Feb 1st - 0 | Cases: 3 | Excess Tests: 0
Deaths on May 1st - 27 967 | Cases: 205 463 | Excess Tests: 7
Deaths on Aug 1st - 35 141 | Cases: 247 537 | Excess Tests: 17
South Korea - 51,269,185 people - Density 512
Deaths on Feb 1st - 0 | Cases: 12 | Excess Tests: 31
Deaths on May 1st - 248 | Cases: 10 774 | Excess Tests: 57
Deaths on Aug 1st - 301 | Cases: 14 336 | Excess Tests: 108
New Zealand - 4,822,233 people - Density 18
Deaths on Feb 1st - 0 | Cases: 0 | Excess Tests: 0
Deaths on May 1st - 19 | Cases: 1 132 | Excess Tests: 129
Deaths on Aug 1st - 22 | Cases: 1 212 | Excess Tests: 388
So to put this into a proportion we can visually see, this is the case-fatality rate which isn't a reliable way to calculate these things which I will explain below.
UK
Feb 1st - 2 cases, no deaths, no excess testing
May 1st - 0.0025% population infected, 15.552% death rate - 6 excess tests
Aug 1st - 0.0045% population infected, 15.212% death rate - 32 excess tests
USA
Feb 1st - 7 cases, no deaths, no excess testing
May 1st - 0.0032% population infected, 5.888% death rate - 6 excess tests
Aug 1st - 0.0147% population infected, 3.362% death rate - 12 excess tests
Italy
Feb 1st - 3 cases, no deaths, 0 excess testing
May 1st - 0.0034% population infected, 13.612% death rate - 7 excess tests
Aug 1st - 0.0041% population infected, 14.196% death rate - 17 excess tests
South Korea
Feb 1st - 0 deaths, 12 cases, 31 excess testing
May 1st - 0.0002% population infected, 2.301% death rate - 57 excess tests
Aug 1st - 0.0003% population infected, 2.1% death rate - 108 excess tests
New Zealand
Feb 1st - 0 deaths, 0 cases
May 1st - 0.0002% population infected, 1.678% death rate, 129 excess tests
Aug 1st - 0.0003% population infected, 1.815% death rate, 388 excess tests
America is doing quite badly as you can see. Some states are doing much worse than others, and if we broke it down by state we would see a clearer picture. On a country-wide level they have a low population density, yet have the highest proportion of their population infected out of the 5, yet a low death rate.
So how could a country like America who is doing really badly in this pandemic have such a low proportion of death? A confounding variable here is the role age and pre-existing conditions play in fatality from this virus.
Source:
As you can see from this mortality risk chart, the risk of death doesn’t reach 2% until you get over 60. So how might this come into play with the numbers we’re seeing? Where does this 99.9% figure come from?
This is the infection fatalatiy rate (IFR). As the CDC puts it:
"The number of individuals who die of the disease among all infected individuals (symptomatic and asymptomatic). This parameter is not necessarily equivalent to the number of reported deaths per reported case because many cases and deaths are never confirmed to be COVID-19, and there is a lag in time between when people are infected and when they die. This parameter also reflects the existing standard of care, which may vary by location and may be affected by the introduction of new therapeutics."
Some have shared the CDC Planning Scenarios page as a way to show the IFR is 0.0001% or lower. What does it say?
"0-19 years: 0.00003
20-49 years: 0.0002
50-69 years: 0.005
70+ years: 0.054"
Isn't that 99.95% recovery for 70+? An IFR of 0.054%? I've had multiple people claim this. Notice the lack of a percentage. It's a decimal. 1 = 100%. 0.054 = 5.4%. The people sharing this are misunderstanding that this is a decimal and not a percentage are off by 100x.
If you notice next to "Infection Fatality Ratio" on the chart, there is a † symbol.
"† These estimates are based on age-specific estimates of infection fatality ratios from Hauser, A., Counotte, M.J., Margossian, C.C., Konstantinoudis, G., Low, N., Althaus, C.L. and Riou, J., 2020. Estimation of SARS-CoV-2 mortality during the early stages of an epidemic: a modeling study in Hubei, China, and six regions in Europe. PLoS medicine, 17(7), p.e1003189"
This study is in PloS Medicine titled Estimation of SARS-CoV-2 mortality during the early stages of an epidemic.
"Estimates of IFR ranged from 0.5% (95% CrI 0.4%–0.6%) in Switzerland to 1.4% (1.1%–1.6%) in Lombardy, Italy. In all locations, mortality increased with age. Among individuals 80 years or older, estimates of the IFR suggest that the proportion of all those infected with SARS-CoV-2 who will die ranges from 20% (95% CrI 16%–26%) in Switzerland to 34% (95% CrI 28%–40%) in Spain"
Update: The source for this data is now this study from European Journal of Epidemiology titled Assessing the age specificity of infection fatality rates for COVID-19: systematic review, meta-analysis, and public policy implications.
"we construct age-specific IFRs using the seroprevalence data in conjunction with cumulative fatalities 4 weeks after the midpoint date of each study; see Supplementary Appendix F. We have also conducted sensitivity analysis using cumulative fatalities 5 weeks after the midpoint date, and we flag studies as having an elevated risk of bias if the change in cumulative fatalities between weeks 4 and 5 exceeds 10%."
"after an initial screening of 1146 studies, we reviewed the full texts of 113 studies, of which 54 studies were excluded due to lack of age-specific data on COVID-19 prevalence or fatalities"
"Studies of non-representative samples were excluded as follows: 13 studies of blood donors; 5 studies of patients of hospitals, outpatient clinics, and dialysis centers; 4 studies with active recruitment of participants, and 6 narrow sample groups such as elementary schools"
"Consequently, our meta-analysis encompasses 27 studies of 34 geographical locations, of which 28 are included in our metaregression and 6 are used for out-of-sample analysis"
"Evidently, the SARS-CoV-2 virus poses a substantial mortality risk for middle-aged adults and even higher risks for elderly people: The IFR is very low for children and young adults (e.g., 0.002% at age 10 and 0.01% at age 25) but rises to 0.4% at age 55, 1.4% at age 65, 4.6% at age 75, 15% at age 85, and exceeds 25% for ages 90 and above"
"population IFR (computed across all ages) ranges from about 0.5% in Salt Lake City and Geneva to 1.5% in Australia and England and 2.7% in Italy. The metaregression results indicate that about 90% of the variation in population IFR across geographical locations reflects differences in the age composition of the population and the extent to which relatively vulnerable age groups were exposed to the virus."
Here are some of the graphs within this paper.
"our results indicate that most of the variation in population IFR across locations reflects differences in the extent to which vulnerable age groups were exposed to the virus."
"One key implication of our findings is that the incidence of fatalities from a COVID-19 outbreak depends crucially on the age groups that are infected, which in turn reflects the age structure of that population and the extent to which public health measures limit the incidence of infections among vulnerable age groups [140]. Indeed, even if an outbreak is mainly concentrated among younger people, it may be very difficult to prevent the virus from spreading among older adults [141]."
"A further implication of our results is that the risks of infection to the middle aged cannot be neglected. This is important for pandemic management strategies that aim to avoid large influxes of patients to healthcare. Indeed, it is likely that an unmitigated outbreak among middle-aged and older adults could have severe consequences on the healthcare system."
"it is absolutely clear that the COVID-19 pandemic has had devastating consequences for lower-income and developing countries. For example, as of late October 2020, the reported COVID-19 death counts were nearly 160 thousand in Brazil, 120 thousand in India, and 90 thousand in Mexico. And in many countries, measures of excess mortality are much higher than official tabulations of COVID-19 fatalities."
"In summary, our analysis demonstrates that COVID-19 is not only dangerous for the elderly and infirm but also for healthy middle-aged adults. The metaregression explains nearly 90% of the geographical variation in population IFR, indicating that the population IFR is intrinsically linked to the age-specific pattern of infections. Consequently, public health measures to protect vulnerable age groups could substantially reduce the incidence of mortality."
Honestly, this is the most sobering paper I have read regarding the IFR of covid-19, and ironically this is the source for the table people are using to pretend that the IFR is 99.98%.
On the topic of the IFR, this is what the WHO has to say.
"some have thus far suggested substantial under-ascertainment of cases, with estimates of IFR converging at approximately 0.5 - 1%"
Now this analysis is too simple for the topic at hand. The IFR changes by age and demographic, location, and time. The more data we have access to the more accurate the assessments will be. Additionally, over time health care workers have got better at treating covid-19 infections, which will lower the IFR. Early estimates are important, and newer estimates are also important. It shows where we've been and where we are. We've also been incrementally testing more giving us more data.
The IFR varies by location, by age, and by sex. There isn't a single IFR. There is no "single death rate". It might very well be we're undercounting infections (it's quite likely), and it's also possible we're undercounting deaths (also likely), so the best we can do is look to statistical analysis like this.
In a Nature study titled Age-specific mortality and immunity patterns of SARS-CoV-2, thery write:
"more than 20% of all reported COVID-19-associated deaths occurring in nursing homes in countries such as Canada, Sweden and the UK3. In other countries, such as South Korea and Singapore, few COVID-19-associated deaths have been reported in nursing homes3. In this context, simply comparing the total number of deaths across countries may provide a misleading representation of the underlying level of transmission"
"We use our model to produce ensemble IFR estimates by age and sex in a single harmonized framework as well as estimates of the proportion of the population that has been infected in each country"
"To translate the relative risks of death by age to the underlying IFR, we combine age-specific death data with 22 seroprevalence surveys, representing 16 of the 45 countries"
"We find that age-specific IFRs estimated by the ensemble model range from 0.001% (95% credible interval, 0–0.001) in those aged 5–9 years old (range, 0–0.002% across individual national-level seroprevalence surveys) to 8.29% (95% credible intervals, 7.11–9.59%) in those aged 80+ (range, 2.49–15.55% across individual national-level seroprevalence surveys)"
"We estimate a mean increase in IFR of 0.59% with each five-year increase in age (95% credible interval, 0.51–0.68%) for ages of 10 years and older. We estimate that the risk of death if infected with SARS-CoV-2 is significantly higher for men than for women (Fig. 2a) particularly in older individuals with ensemble IFR estimates of 10.83% for men aged 80+ (95% credible interval, 9.28–12.52%"
Here is the graph data showing the IFR by variable.
Getting onto some more information:
"seroprevalence studies from New York City (2.28; 95% credible interval, 2.15–2.42%), Scotland (1.49%; 95% credible interval, 1.25–1.82%) and England (1.41%; 95% credible intervals, 1.38–1.44%) suggest a significantly higher IFR whereas studies in Kenya (0.24%; 95% credible interval, 0.23–0.25%), Slovenia (0.25%; 95% credible interval, 0.24–0.30%) and Denmark (0.26%; 95% credible interval, 0.24–0.32%) support a lower IFR than that of the ensemble model."
So England (where I'm from) has an estimates IFR of 1.4%. That's not a "99.9% death rate", that's a 98.6% infection fatality rate. Understanding that the idea of "the death rate" isn't as simple as that is extremely important. To demonstrate this here is another extract:
"in France, including deaths that occurred in nursing homes increases the IFR from 0.74% for the general population (95% credible interval, 0.64–0.86%) to 1.10% overall (95% credible interval, 0.95–1.28%). This highlights the complexity of comparing headline IFR estimates across populations in which very different levels of transmission may have occurred in these hyper-vulnerable communities."
This is why combatting misinformation is so difficult. In the 2 seconds it took someone to type "it's a 99.9% recovery rate", it took hours or reading to debunk it.
In the UK, the majority of the infections are below 60 which is where the risk of fatality increases substantially. It’s the same for New Zealand, USA, and South Korea. Italy is about 50/50 which has a higher death rate compared to the other 4.
The breakdown of the population percentages 60 or over infected are as follows:
UK - 35% - 15.212% case fatality rate of the country
USA - 32% - 3.362% case fatality of the country
Italy - 52.4% - 14.196% case fatality of the country
South Korea - 23.93% - 2.1% case fatality rate of the country
New Zealand - 19% - 1.815% case fatality rate of the country
UK Statistics
Italy Statistics
USA Statistics
South Korea Statistics
New Zealand Statistics
So already we can see that age is a huge confounding variable. Older people are at massively greater risk than those under 60, and most people infected are under 60. That's not to say that this virus can't kill those under 60 because it can and it has.
Also note from the source above, 0.9% of deaths are by those without pre-existing conditions. Were those people also over 60? I don't know, it isn't clear. But age and medical condition are huge factors to take into account.
So already this idea that 99% people recover (which isn't true anyway) therefore this virus isn't a problem is problematic. If we look at the pandemic success story South Korea with a massive polulation density, who tested loads of people and got the virus under control in the early stages - if you only look at above 60, your death rate would go from 0.02%, to 1.8%, to 6.2%, to 13%.
Looking at South Korea's age demographics, 42% of their polulation is 60 or over - that's 17 million people. We now have a case-fatality rate of 2.1%. Let's say that half of those people became infected. That's 357,000 deaths. That figure contains the higher ages which have a drastically increased risk of death, too. That means this 357,000 figure is much lower than it would actually be.
Cardiovacsular Disease
From the Health and Social Care Information website, we can see that 20-30% of CVD cases are 65-74, and 30-40% are over 75.
(left graph)
Diabetes
Let's look at the UK. On Diabetes.Org, the figure for Diabetes shows that over half of the population - 50.2% of the people with diabetes are 65 or over. (Upper right table)
Chronic Respiratory Disease
According to the NDC Alliance:
"The term chronic respiratory diseases (CRDs) describes a range of diseases of the airways and the other structures of the lungs. They include asthma and respiratory allergies, chronic obstructive pulmonary disease (COPD), occupational lung diseases, sleep apnea syndrome and pulmonary hypertension. Allergic rhinitis or “hay fever”, sleep apnea and pulmonary hypertension are other chronic respiratory conditions that affect the lives of millions worldwide."
Or to put it another way:
Chronic - Meaning long term or reoccuring
Respiratory - Releating to the respiratory system
Asthma for example has an incidence rate of 13% in over 75's according to HSCIC. In terms of diagnosis by age, the statistics looks relatively even. Asthma UK reports that 5,400,000 people in the UK has asthma, and of those cases 200,000 are classed as severe.
That's just asthma. I won't spend any more time on this section as we're going down a rabbit hole now.
Hypertension
Again from the HSCI website, roughly 25-30% of hypertension cases are people over 65. Additionally, over 10% of all cases are untreated. You can see a full breakdown by age and sex here. There are more middle-aged people with hypertension than there are old people. Furthermore, the most worrying this is that around a third of the population has hypertension.
Cancer
1 in 2 people in the UK will contract cancer according to Cancer Research UK. A lot of people recover from cancer in the modern day, so let's look deeper. Looking at cancer incidence from Cancer Research UK:
-More than a third of cancer cases are diagnosed over 75
-People 85 to 89 have the highest rick of developing cancer
According to Macmillan - There are currently (as of 2015) 2,500,000 people living with cancer and that number is rising.
Putting this all together
So the confounding variables which need to be considered when looking at blanket statistics like the overall death rate is:
-What are the risk factors
-How many people who get infected have those risk factors
Age is arguably the biggest risk factor here. Age also correlates with all by one (asthma) of the pre-existing conditions which increase your risk of death from the virus.
It looks clear to me that the statistics of who dies is skewed by the younger infected population who are less likely to have those conditions, and consequently recover at a much higher rate than those over 60.
If we assume that this virus' only negative effect is death then we'd be incorrectly assuming that recovered means there was no problem.
We have to account for:
-Hospitalisation
-ICU Admission
Before we move on - a confounder here is that very old people have a great risk of death if put on a ventilator because it's just too invasive of a procedure, so you either don't get it, or you risk death from the treatment.
In this risk assessment from the European CDC:
"Hospitalisation occurred in 32% (48755 of 152375) of cases reported from 26 countries (median country-specific estimate, interquartile range (IQR): 28%, 14–63%)"
Now 14 - 63 is a large proportion. My first impression would be countries that don't test much are skewing the results. Their statistical analysis is 32%, so it's very likely they've adjusted for these variables.
"Severe illness (requiring ICU and/or respiratory support) accounted for 2859 of 120788 (2.4%) cases reported from 16 countries (median, IQR: 1.4%, 0–33%)."
So from this we get our worldwide pooled estimate for the percentage of hospitalisations and ICU admissions.
Or if we rejig the numbers and see what proportion 2859 is of 48755, we see that from this, 17% of those hospitalised will need to go to the ICU.
Their graph on page 6 also shows a how much age is a factor in hospitalisation.
"R0 is proportional to the contact rate and will vary according to the local situation. A recent review of 12modelling studies reports the mean basic reproductive number (R0) for COVID-19 at 3.28, with a median of 2.79."
CDC Report
From the CDC on hospital admissions, ICU admission, and death in a one month span:
"Overall, 31% of cases, 45% of hospitalizations, 53% of ICU admissions, and 80% of deaths associated with COVID-19 were among adults aged ≥65 years with the highest percentage of severe outcomes among persons aged ≥85 years. In contrast, no ICU admissions or deaths were reported among persons aged ≤19 years. Similar to reports from other countries, this finding suggests that the risk for serious disease and death from COVID-19 is higher in older age groups."
"The percentage of persons hospitalized increased with age, from 2%–3% among persons aged ≤19 years, to ≥31% among adults aged ≥85 years."
"Among 121 patients known to have been admitted to an ICU, 7% of cases were reported among adults ≥85 years, 46% among adults aged 65–84 years, 36% among adults aged 45–64 years, and 12% among adults aged 20–44 years"
"These preliminary data also demonstrate that severe illness leading to hospitalization, including ICU admission and death, can occur in adults of any age with COVID-19."
"Data on age and outcomes, including hospitalization, ICU admission, and death, were missing for 9%–53% of cases, which likely resulted in an underestimation of these outcomes."
While this report has limitations which are stated within the report itself, it shows that covid-19 isn't a simple life or death outcome. You might end up in the hospital, or end up in the ICU which can happen at any age. Death isn't the only negative outcome of contracting this virus.
If we look at the UK. August 1st we had 303 181 cases. According to the Coronavirus government data page, 132,673 people have been admitted to hospital. Thats a 40% hospitalisation rate. That sits in the same area as the ECDC report above.
British Medical Journal Study
From a study titled Features of 20 133 UK patients in hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: prospective observational cohort study
"The ISARIC WHO CCP-UK (National Institute for Health Research Clinical Research Network Central Portfolio Management System ID: 14152) study is an ongoing prospective cohort study in 208 acute care hospitals in England, Scotland, and Wales."
"We also included patients who had been admitted for a separate condition but had tested positive for covid-19 during their hospital stay."
"The nature of the study means that a large amount of data were missing, particularly during the later parts of the growth curve of the UK outbreak. Because this paper is mainly descriptive, we have not performed any imputation for missing data, and describe the data as they stand. To reduce the impact of missing data on outcome analyses, we restricted these analyses to patients who had been admitted for at least two weeks before data extraction."
The results:
"On behalf of ISARIC WHO CCP-UK, 2468 research nurses, administrators, and medical students enrolled 20 133 patients who were admitted with covid-19 to 208 hospitals in England, Scotland, and Wales between 6 February and 14:00 on 19 April 2020"
"The median age of patients was 73 years (interquartile range 58-82, range 0-104; fig 1); 310 patients (1.5%) were less than 18 years old and 194 (1.0%) were less than 5 years old. More men (59.9%, n=12 068) than women (40.1%, n=8065) were admitted to hospital with covid-19. One hundred women (10%) of reproductive age (n=1033) were recorded as being pregnant."
Note down the pregnant part, as it correlates with data higher on the page.
"The most common major comorbidities were chronic cardiac disease (30.9%, 5469/17 702), diabetes without complications (20.7%, 3650/17 599), chronic pulmonary disease excluding asthma (17.7%, 3128/17 634), chronic kidney disease (16.2%, 2830/17 506), and asthma (14.5%, 2540/17 535). Of 18 525 patients, 22.5% (4161) had no documented major comorbidity. There was little overlap between the three most common comorbidities"
It's important to note that 22% had no comorbidity, but that doesn't mean it wasn't there. That's a confounder to be mindful of as the proportions of the comorbidities might actually be higher.
Here's where the problem comes in:
"A high proportion of patients required admission to high dependency or intensive care units (17%, 3001/18 183; fig 3), and 55% (9244/16 849) received high flow oxygen at some point during their admission. Sixteen per cent of patients (2670/16 805) were treated with non-invasive ventilation, while 10% (1658/16 866) received invasive ventilation."
Of those admitted to hospital, over half needed high flow oxygen. 17% had to go to the intensive care unit.
The above graphcs show ICU admission, high flow oxygen, non invasive entilation, and invasive ventilation by age.
What this shows is that you don't need to be in the ICU to need high flow oxygen.
"Overall, 41% (8199/20 133) of patients were discharged alive, 26% (5165/20 133) died, and 34% (6769/20 133) continued to receive care at the date of reporting (fig 4). The median age of patients who died in hospital from covid-19 in the study was 80 years"
So looking at this, the virus on the surface has a 99% survival rate but if you have an infection that requires you to go to the hospital, then your mortality rate jumps to an estimated 26% minumum. 34% of the study patients hadn't left hospital or died. In the case of the 18 thousand people monitored, the death rate for hospitalisation was a bare minimum 26%.
"The proportion of patients admitted to critical care in our study was similar to that reported in Italy (17%),18 19 and New York (14.2%),11 12"
"However, an inherent reporting bias exists because the sickest of patients, particularly those admitted to intensive care, have the longest hospital stays; mortality rates in hospital could therefore increase. These mortality rates were considerably higher than the 24% mortality rate in hospital seen in patients in intensive care units in Italy19 and the US.1112 The lower rate in the US could in part be explained by differences in healthcare systems and the proportion of intensive care unit beds to hospital beds between the two countries. In Italy, a lower proportion of patients received mechanical ventilation, and most of their patients (72%) remained in hospital at the time of the analysis.19"
"although age adjusted mortality rates are high in elderly patients, most of these patients were admitted to hospital with symptoms of covid-19 and would not have been in hospital otherwise."
"We do not currently have data on the inpatients that were not enrolled, or people managed in community settings, such as usual domestic residences and older people’s care homes."
So from this to summarise it up, if we look at hospitalisation cases, most of them need high flow oxygen, and 17% of them get sent to the intensive care unit. 26% will die, and while the numbers are low, young people can still end up in the ICU and need invasive ventilation.
The CDC says the 32% of cases on average require hospitalisation.
By looking at the number of cases and government statistics, 40% of covid cases in the UK require hospitlisation.
The BMJ study shows that among hospitalisations, 55% require high flow oxygen, and 17% die.
If these numbers pan out over time, from the UK's 303 181 cases on august 1st, that would imply:
-121,272 hospitalisations
-66,699 people needing high flow oxygen
-20,616 deaths
Now wait a moment. We have 46,000 deaths. Does this mean the hospital death rate is twice that of the study? I don't know and I don't want to start doing DIY statistics. I'm not a biostatician.
I don't know if the numbers have changed in a way since this study, or if there are confounders at play I haven't analysed yet.
Excess deaths are the deaths in a population you wouldn't expect to see based off of previous trends.
Let's take a look at the excess deaths in Britain, USA, and Italy.
The Economist has a tracker for global excess deaths.
As you can see, there is an abundance across the board of excess death.
They also have a section that allows you to look at various regions. Let's look at the UK.
This section I've highlighted is mid-March until the end of May.
Across the country we saw a spike in excess mortality.
This is important because the UK went into lockdown after the Prime Minister announced it on March 23rd.
Considering the virus has a 2 week incubation pariod, and deaths in hospital can be between 1-2 weeks, that means that during lockdown we had a spike in excess death.
If that doesn't immediately seem relevant, allow me to explain.
When the UK went into lockdown, what do you think happened when everyone stayed home?
Let's look at the ONS breakdown of deaths from 2016 through to July 2020.
Below I have screenshot from the start of March until mid-June. They go in order from 2016 - 2020. This isn't Covid-19 suspected deaths ,or covid confirmed deaths. These are all deaths reported.
Death is up by 100% in various places. This correlates with The Economist graph above.
It's not that clear cut, however.
The flip side of all of this is: How many of these people who will be infected with covid-19 who have pre existing conditions are keeping up with their medication and doctor visits during lockdown?
How many people have died from not getting cancer treatement during the pandemic? I don't know. I can't find statistics on this.
In an NHS publication they said:
"Cancer patients will want to discuss with their clinicians whether the risks of beginning or continuing their cancer treatment could outweigh the benefits, given that many patients receiving systemic therapies in particular are more at risk of becoming seriously unwell if they contract the coronavirus infection. In the event of disruption to cancer services, clinicians may also need to prioritise treatment for those most in need. It is important that all guidance for trusts on the management of non-coronavirus patients requiring acute treatment: cancer decisions taken are done so with multidisciplinary team(MDT)input and clearly communicated with patients."
You also have to consider whether cancer patients who aren't visiting hospitals are at a lower risk as they aren't around a covid-19 environment with all of the infection cases appearing daily. Are they higher risk because they are avoiding going to the hospital? The evidence so far suggests the scales tip in favour of an increased risk, yet the data isn't enough to make an accurate prediction.
Additionally, the less people get infected at once, the less this scenario of clinicians needing to choose between patients based on urgency.
An article from CambridgshireLive said
"With nearly 2,000 people a week going undiagnosed and untreated due to Covid-19 concerns in hospitals and GP surgeries, Macmillan Cancer Support has warned the UK faces a "ticking time bomb" with the disease.
It follows evidence of patients having appointments cancelled or postponed, while others awaiting possible diagnosis say they are put off attending hospitals due to fears about contracting the virus.
The study from the Institute of Cancer Research, London, suggested putting off cancer surgeries for three months could lead to almost 5,000 excess deaths in England alone."
From MacMillan:
"Meanwhile, COVID-19 has left many cancer patients more vulnerable than ever as they now face delays in diagnosis and treatment. Modelling carried out by experts at the ICR has demonstrated that just a three-month delay in cancer surgeries could lead to 5,000 extra deaths, highlighting the ever-growing urgency to understand cancer better and design smarter, kinder and more effective treatments."
This references a study from The Lancet titled Effect of delays in the 2-week-wait cancer referral pathway during the COVID-19 pandemic on cancer survival in the UK: a modelling study
"We assessed the scenario of a 3-month period of lockdown during which a proportion of symptomatic patients delayed their presentation until after lockdown (ie, backlog patients), set at 25%, 50%, and 75% of normal monthly volumes"
"We considered different scenarios of extra capacity for catching up on this backlog applied across months 1–8 after lockdown"
"By age group and tumour type, we compared the benefit of prompt investigatory referral versus different periods of delay or no referral (absolute survival benefit). We estimated benefit in proportional survival and life-years gained from gain in cancer survival versus the combined fatality risk (COVID-19 and technical)."
"For several cancers, including those of the colorectum, oesophagus, lung, liver, bladder, pancreas, stomach, larynx, and oropharynx, a 3-month delay to diagnosis is predicted to result in a reduction in long-term (10-year) survival of more than 10% in most age groups"
"The aggregated impact of universal delays in the 2-week-wait pathway on lives lost and life-years lost varies widely by tumour type (figure 2). These predicted outcomes are driven by aspects of the model including age-specific incidence, the proportion of cancers diagnosed via the 2-week-wait pathway, the proportion of cancers diagnosed as stage I–III via the 2-week-wait pathway, and the aggressiveness of the tumour"
"Across the 20 cancer types, on average an estimated 243 098 cancers are diagnosed annually. Of these, an estimated 96 289 are diagnosed via the 2-week-wait pathway, of which 75 369 (78·3%) are diagnosed at stage I–III (table). 20 293 (26·9%) of 75 369 patients would be predicted to die due to cancer within 10 years of diagnosis, representing a loss of 304 129 life-years. A uniform per-patient delay of 1 month would be predicted to result in attributable additional lives lost of 1412 and life-years lost of 25 812 and a per-patient delay of 6 months would be attributed to an additional 9280 lives and 173 540 life-years lost over the subsequent 10 years for an annual cohort of cancer cases diagnosed via 2-week wait at stage I–III"
"We estimated the national toll of presentational delay accrued over a 3-month lockdown period to be 181 attributable additional lives and 3316 life-years lost for a backlog rate of 25%, 361 additional lives and 6632 life-years lost for a backlog rate of 50%, and 542 additional lives and 9948 life-years lost assuming a backlog rate of 75%, with an average presentational delay of 2 months per patient"
"However, it is unlikely that all extra diagnostic capacity required can be provided in a single month; therefore, we estimated the additional lives and life-years that might be lost due to subsequent diagnostic delays. Rapid provision of additional capacity over months 1–3 would result in 90 additional lives and 1662 live-years lost due to diagnostic delays for the 25% backlog scenario, 183 additional lives and 3362 life-years lost under the 50% backlog scenario, and 276 additional lives and 5075 life-years lost under the 75% backlog scenario"
"If the risk of infection is high (≥2·5% per referral), for patients older than 70 years, the risk associated with investigatory referral might exceed the absolute survival benefit for tumour-referral groups with poorer outcomes, such as upper gastrointestinal (pancreas, oesophagus, liver, and stomach) and brain tumours"
"COVID-19-related delays in presentation, diagnosis, and subsequent treatment, will result in loss of life and life-years that varies widely according to patient age and tumour type. Data regarding the true duration and extent of service disruption and per-patient cancer pathway delay across the UK as a result of the COVID-19 lockdown are currently immature. Direct predictions regarding attributable cancer deaths will be possible once more accurate patient-level data become available"
"the proportion diagnosed with stage I–III tumours, the age profile of patients diagnosed with those cancers, and the diagnostic-conversion rate, which inevitably means that the overall impact of delays in referral via the 2-week-wait pathway is far from uniform between cancers."
"Diagnostic delays will affect patient groups differently. For younger patients (<70 years), all delays should be avoided. Our data show that survival decrement for even small delays (ie, 2 months) is substantial for most tumours."
"Conversely, for older groups (≥70 years), per-referral risk of death from nosocomial infection is much higher and might exceed the average decrement of a moderate delay, in particular for more indolent cancer types (eg, prostate cancer) or cancers with a poor overall prognosis (eg, upper gastrointestinal tract cancers)."
"The impact of COVID-19-related disruption on cancer care is likely to be an ongoing issue until a vaccine or effective treatment is identified. Our modelling suggests a clinically significant impact in lives and life-years lost if delays to the 2-week-wait pathway are extensive and prolonged. Unlike acute pathologies, such as stroke and myocardial infarction, the true excess mortality due to COVID-19-related disruption to cancer pathways will not be fully evident for 10 years or longer."
As you can see this is a very complicated and complex issue. There is no simple way to look at this pandemic. If you say it has a 99+% survival rate therefore it isn't a problem, then you're grossly simplifying the situation.
If you look at the case fatality rate it's significanty higher than the death rate when you eliminate pre existing conditions. Pre existing conditions as well as age are siginificant confounding variables to account for.
Even so, a proportion of those infected will be hospitalised, and of those half will need high flow oxygen. Roughly 17% will need to visit the ICU.
It's also not good enough to just claim the numbers are wrong as people are saying they slap Covid on any old diagnosis. We know this is misleading because we see a huge spike in excess mortality despite the world being confied to their homes which means less people going out.
As far as the number of people who die being old or having pre existing conditions - you could argue that these people had health problems and as such will have a short lifespan. I don't like this. My reasoning is because that would disregard those who will have covid-related death who:
-Have cancer but who would otherwise recover
-Are among the 5.4 million people with asmthma of any age
-Are among the 200 000 people with severe asthma of any age
On the flip side the delay in cancer diagnosis and treatement directly from lockdown will result in thousands of deaths depending on the length of delay, the age of the individuals, their cancer, and their age.
So while direct death from the virus is 98-99%, that doesn't mean that all people infected will recover in the conventional sense.
Case fatality is not the same as the direct death rate or IFR. If you ignore one and focus on the other you're only looking at half of the picture. Case fatality rate isn't a robust measurement to base your opinions on by any means, but it shouldn't be ignored.
Also think of the implication. Are we saying that those who die with pre existing conditions don't matter because they are going to die anyway? Or because they're old they don't have long left?
Don't get me wrong, I'm not saying that we should treat all deaths as a reflection of the virus' deadliness. But I also refuse to accept that we shouldn't be concerned for those who will die, whether or not it is covid-related or directly because of covid.
On the other hand we should provide the best medical care for as many other conditions as we can during this time, which means keeping those people at high risk safe from covid-19. The depressingly sad irony here is that from my own experience, many of those flaunting a 99% recovery rate are also anti-mask and claim Hydroxychloraquine is a miracle drug. These anti-science uncritical alternative narratives are going to cost more lives.
The longer people refuse to wear masks, the longer resitrictions will apply, the more cancer diagnosis will be delayed, and more people will lose their lives. The people who are anti-masks and sharing conspiracies are doing the exact harm they're claiming the government is doing to them, and it's a real problem.
I rate this very misleading as while young people and those without pre existing conditions are at a very low risk, those who have pre existing conditions and are older are. The IFR is not 0.01% as some are claiming, but ranges by age, sex, demographics, and country and can range from 0.5% to 1% or over. The CDC table I've seen used to prove the super low percentages is actually a decimal so people using this are off by 100x. The source of the table's information is a study which shows the IFR for the elderly is 5%, not 0.05%.
Originally I rated this misleading, but I have downgraded it on light of new information.
Even if 0.1% of people die directly from the virus, if 0.1% of Americas 331,002,651 people die, that's 331,002 people. If their case fatality rate was 1% (which it isn't) then that's another 2,979,024 people who are going to die. And that is exactly why this virus is a threat to be avoided.
Many cofounding variables have to be considered such as:
-Death rate with age and pre-existing conditions
-Death rate purely from the virus
-Demographics of people infected
-How many unaccounted for infections there are
-Excess deaths
-Delays in other life-threatening diagnosis and treatement such as cancer
-What testing methods countries are using
-What reporting methods countries are using
Without mitigation: lockown, hand washing, social distancing, face masks - the virus would be free to sweep its way through the population and infect many more people than it has. Whether people die directly of covid, or people die due to covid related issues, people still die.
When a country gets locked down and other illnesses get left unchecked, more people will die.
Isn't death worth avoiding? That's the purpose of the mitigation effort.
The longer these conspiracy theories float around like Plandemic, anti-mask rhetoric, Controllavirus, the virus being a hoax, the virus being a bio-weapon, the virus being the flu, the virus being the common cold, the virus being 5G, the virus being released from a lab, and so on - the less people are going to comply with mitigation. This means longer lockdown and more people die.
We should not strive for lockdown to contain this virus. It is going to have to be a community effort to get back to normal.