Science Matters

The hands of frontline medical practitioners have been tied treating Covid19 cases because the drugs that work on the virus work against Big Pharma profits. Computers were employed to produce attack papers  overwhelming the growing confidence that HCQ  helps patients.  A recent paper later on describes how medical science is corrupted to follow the money rather than fight disease.

Context:  The Attack Against Medical Practitioners Using HCQ

What happened in the publishing and press worlds is summarized by Elizabeth Woodworth, Canadian Health Sciences librarian specialized in medical statistics. Leaked: “Deadly” Hydroxychloroquine (HCQ) to treat Covid 19: How the World’s Top Medical Journals, The Lancet and NEJM, Were Cynically Exploited by Big Pharma  Excerpts in italics with my bolds.

On May 22, 2020, the time-honoured Lancet[vi]– one of the world’s two top medical journals – published the stunning claim that 671 hospitals on six continents were reporting life-threatening heart rhythms in patients taking hydroxychloroquine (HCQ) for Covid-19.

The headlines that followed were breath-taking.

Although wider access to the drug had recently been urged in a petition signed by nearly 500,000 French doctors and citizens,[vii] WHO and other agencies responded to the article by immediately suspending the clinical trials that may have cleared it for use.

North American headlines did not mention that HCQ has been on the WHO list of essential drugs since the list began in 1977. Nor did they mention an investigative report on the bad press that hydroxychloroquine had been getting prior to May 22, and how financial interests had been intersecting with medicine to favour Gilead’s new, more expensive drug, Remdesivir.[viii]

The statistics behind the headlines

As a Canadian health sciences librarian who delivered statistics to a large public health agency for 25 years, I sensed almost immediately that the article had to be flawed.

Why? Because health statistics are developed for different purposes and in different contexts, causing them to exist in isolated data “stovepipes.”[ix] Many health databases, even within a single region or country, are not standardized and are thus virtually useless for comparative research.

On May 28, an open letter from 200 scientists to the authors and The Lancet requested details of the data and an independent audit. The letter was “signed by clinicians, medical researchers, statisticians, and ethicists from across the world.”[xii]

The authors declined to supply the data, or even the hospital names. Meanwhile, investigative analysis was showing the statistics to be deeply flawed.[xiii][xiv]

If this were not enough, the lead author was found to be in a conflict of interest with HCQ’s rival drug, Remdesivir:  In view of the foregoing, the article was retracted by three of its authors on June 5.

How did this fraud get past The Lancet reviewers in the first place?

The answer emerges from what has remained an obscure French interview, although it has been quoted in the alternative media.[xvi]

On May 24, a closed-door Chatham House expert meeting about Covid included the editors-in-chief of The Lancet and the NEJM. Comments regarding the article were leaked to the French press by a well-known health figure, Dr. Philippe Douste-Blazy,[xvii] who felt compelled to blow the whistle.

His resulting BFM TV interview was posted to YouTube with English subtitles on May 31,[xviii] but it was not picked up by the English-speaking media.

These were The Lancet editor Dr. Richard Horton’s words, as reported by Dr. Douste-Blazy:

“If this continues, we are not going to be able to publish any more clinical research data because pharmaceutical companies are so financially powerful today, and are able to use such methodologies as to have us accept papers which are apparently methodologically perfect, but which, in reality, manage to conclude what they want to conclude.” [xix]

Doust-Blazy made his own comments on Horton’s words:

“I never thought the boss of The Lancet could say that. And the boss of the New England Journal of Medicine too. He even said it was ‘criminal’. The word was used by them.”[xx]

The final words in Doust-Blazy’s interview were:

“When there is an outbreak like Covid, in reality, there are people like us – doctors – who see mortality and suffering. And there are people who see dollars. That’s it.”[xxi]

The scientific process of building a trustworthy knowledge base is one of the foundations of our civilization. Violating this process is a crime against both truth and humanity.

Evidently the North American media does not consider this extraordinary crime to be worth reporting.

How the Science was Rigged Against HCQ

This paper describes how scientific data can be manipulated to serve an agenda. Clinical Efficacy of Chloroquine derivatives in COVID-19 Infection: Comparative meta-analysis between the Big data and the real world  Excerpts in italics with my bolds.

In the context of a pandemic with an unknown virus, development of new drugs is a major opportunity for “big pharma” industry, and this is potentially associated with a very high risk of conflicts of interest. This led us to consider these conflicts of interest as a moderator variable in the present work. As major financial issues are at stake, and may impact the interpretation of scientific data, we felt it was important to mention that none of us have conflict of interest with any pharmaceutical company.

The following outcomes were considered: hospitalization rate, duration of cough, duration of fever, clinical cure, lymphocyte count, C-reactive protein level, Interleukin-6 level, thoracic CT-scan imaging, aggravation to severe, death, transfer to intensive care unit (ICU), ventilation, length of hospital stay and persistent viral shedding as assessed by PCR.

Only studies comparing a group of COVID19 patients treated with a chloroquine derivative to a control group without chloroquine derivatives were included. Non-comparative (single arm) studies and studies comparing two groups treated with chloroquine derivatives at different dosages or with different delay of treatment were excluded. Studies were classified as “big data” studies when conducted on electronic medical records extracted by public health specialists and epidemiologists who did not care COVID106 19 patients themselves. Conversely, studies were classified as “clinical studies” when mentioning details of treatments (dosages, duration, contraindications, monitoring…) and conducted by authors physicians (infectious diseases and internal medicine specialists, and pulmonologists) who cared for COVID-19 patients themselves.

Twenty studies were identified involving 105,040 patients (19,270 treated patients) from nine countries (Brazil, China, France, Iran, Saudi Arabia, South Korea, Spain, and USA). Big data observational studies were associated with conflict of interest, lack of treatment dosage and duration, and absence of favorable outcome. Clinical studies were associated with favorable outcomes and details on therapy. Among clinical studies, three of four randomized controlled trials reported a significant favorable effect.

Among clinical studies, a significant favorable summary effect was observed for duration of cough (Odds ratio (OR), 0.19, p = .00003), duration of fever (0.11, p = .039), clinical cure (0.21, p = .0495), death (0.32, p = 4.1×10-6) and viral shedding (0.43, p = .031). A trend for a favorable effect was noted for the outcome “death and/or ICU transfer” (0.29, p = .069) with a point estimate remarkably similar to that observed for death (∼0.3). In conclusion, a meta-analysis of publicly available clinical reports demonstrates that chloroquine derivatives are effective to improve clinical and virological outcomes but, more importantly, it reduces mortality by a factor 3 in patients infected with COVID-19. 

 All “big data” studies reported a lack of beneficial effect of the treatment and were significantly more likely associated with “Cons” variable (5/5 vs 3/15, p = .004). This was also observed by examination of the meta-analysis forest plot (Figure 2, Table S3 to S8). In addition, both “conflicts of interest” (p = .01) and “not described treatment protocol” variables (p = .004) were associated with “Cons” variable. Conversely, clinical studies were more likely to report a favorable effect of chloroquine derivatives in COVID-19 patients (p <.05). Consistently, clinical studies with detailed treatment protocol were more likely associated with the observation of a favorable effect of the treatment (p < .05).

In the big data analyses, 4 comparisons reported a significant effect, and all were deleterious (4/4). In the clinical studies, 17 comparisons reported a significant effect, and all were beneficial.

The clinical studies used a standardized treatment protocol with methods that included assessment of contraindications, daily dosage, adjuvant measures and duration of treatment with at least 48 hours of treatment before the objective could be assessed. For example, assessment of kalemia and electrocardiogram is critical prior to treatment, especially when the chloroquine derivative is combined with AZ (37). At the same time, we observed that virtual big data studies did not mention these elements and considered the presence of chloroquine derivative prescription in electronic records in a binary fashion. Obviously, the number of patients included in the database analyses was much higher than the number of patients included in the clinical studies, because these databases are made up of thousands of electronic medical records (EMR). As mentioned in the past (4), this type of studies have tremendous statistical power but are limited by clinical inaccuracy that makes their conclusions difficult to believe.

The major elements of this study are that, overall there is an extremely significant difference between the analyses of data not collected directly by the doctors who cared for patients and the studies carried out by the physicians who set up these studies and cared for patients, including the randomized studies. The second thing is that in these studies conducted electronically, the treatment is never really specified, with the dosage and duration of treatment making it impossible to assess efficacy (dose too low) or toxicity (dose too high). In addition to this major bias, we also noted a significant bias when the authors had conflicts of interest due to their relationship with industrialists trying to market molecules in the same therapeutic framework competing with HCQ.

Overall, and as previously published, the relevance of the analysis of important medical data depends on clinical accuracy (4). Indeed, the discrepancy between clinicians and epidemiologists reflects a major trend, that of the analysis of large medical data, with database warehouse more or less well filled by individuals who are not directly included in the work reported. This analysis is unrelated to the observations made by physicians who are in direct contact with patients and which lead to divergent interpretations and opposite conclusions, which are of real interest and show that the world predicted by Baudrillard (39); that of a parallel world of numerical analysis completely disconnected from reality; is being born.

Under these conditions, a meta-analysis allowing for the combination of different studies makes it possible to identify a general trend. This makes it possible to reconcile the chloroquine derivative efficacy that many doctors have perceived with the results of the first published studies. This meta-analysis is based on several studies, including four RCTs, and identifies a favorable trend toward the benefit of chloroquine derivatives in the treatment of COVID-19 patients, enabling us to make a grade I recommendation for its use against the disease.

Footnote:  The “parallel world of numerical analysis disconnected from reality” sounds a lot like climate science.  We are in a Brave New World where narratives are primary, while facts and data are servants to the agenda. See Climate Science Was Broken

More Bad News for Big Pharma:

Doctors in Italy, Israel and U.S. say the coronavirus may be losing its potency and becoming less deadly even as it spreads. Some wonder if there will be enough cases around when a vaccine becomes available. See Covid Decline in Canada and World June 8

 

In reporting on Covid19 pandemic, governments have provided information intended to frighten the public into compliance with orders constraining freedom of movement and activity. Statistics of interest to the public have been hidden; for example only cumulative numbers are presented to show ever increasing cases and deaths. For example, the above map of the Canadian experience is all cumulative, and the curve will continue upward as long as cases can be found and deaths attributed.  As shown below, we can work around this myopia by calculating the daily differentials, and then averaging newly reported cases and deaths by seven days to smooth out lumps in the data processing by institutions.

A second major deficiency is lack of reporting of recoveries, including people infected and not requiring hospitalization or, in many cases, without professional diagnosis or treatment. The only recoveries presently to be found are limited statistics on patients released from hospital. The only way to get at the scale of recoveries is to subtract deaths from cases, considering survivors to be in recovery or cured. Comparing such numbers involves the delay between infection, symptoms and death. Herein lies another issue of terminology: a positive test for the SARS CV2 virus is reported as a case of the disease COVID19. In fact, an unknown number of people have been infected without symptoms, and many with very mild discomfort.

For this discussion let’s assume that anyone reported as dying from COVD19 tested positive for the virus at some point prior. A recent article by Nic Lewis at Climate Etc. referred to evidence that the average time from infection to symptoms is 5.1 days, and from symptoms to death 18.8 days. A separate issue, of course, is that 95+% of those dying had one or more co-morbidities contributing to the patient’s demise. Setting aside the issue of dying with/from Covid19, it is reasonable to assume that 24 days after testing positive for the virus, survivors can be considered recoveries.

Recoveries are calculated as cases minus deaths with a lag of 24 days. Daily cases and deaths are averages of the seven days ending on the stated date. Recoveries are # of cases from 24 days earlier minus # of daily deaths on the stated date. Since both testing and reports of Covid deaths were sketchy in the beginning, this graph begins with daily deaths as of April 24, 2020 compared to cases reported on May 31, 2020. Another view of the data is shown below.

The scale of testing has increased and is now well above 35,000 a day, while positive tests (cases) are dwindling.  The shape of the recovery curve resembles the case curve lagged by 24 days, since death rates are a small portion of cases.  The recovery rate has grown from 83% to 96% steady over the last 11 days.  This approximation surely understates the number of those infected with SAR CV2 who are healthy afterwards, since antibody studies show infection rates multiples higher than confirmed positive tests. In absolute terms, cases are now down to 364 a day and deaths 41 a day, while estimates of recoveries are 986 a day.

Background from previous post May 31, 2020
It took a lot of work, but I was able to produce something akin to the Dutch advice to their citizens.

The media and governmental reports focus on total accumulated numbers which are big enough to scare people to do as they are told.  In the absence of contextual comparisons, citizens have difficulty answering the main (perhaps only) question on their minds:  What are my chances of catching Covid19 and dying from it?  The map shows a lot of cases, and the chart looks like an hockey stick, going upward on a straight line. So why do I say canadians are safer than it looks like from such images?

First let’s look at daily numbers to see where we are in this process.  All the statistics come from Canada Public Health Coronavirus disease (COVID-19): Outbreak update.

By showing daily tests, new cases and reported deaths, we can see how the outbreak has built up, peaked and declined over the last 2.5 months.  The green line shows how testing grew to a sustained daily rate of 29,000, with a recent drop and recovery (all numbers are smoothed with 7 day averages ending with the stated date.) Note that the curve is now descending after peaking at 1800 on April 22, now down to 893 new cases per day.  This lower rate of infections is despite the highest rate of testing since the outbreak began. Deaths have also peaked at 177 on May 6, down to 104 May 30. (Reported deaths bumped upward yesterday due to a data transmission catchup in Quebec, explained below).  The percentage of people testing positive is down to 3%, and deaths are 0.42% of the tests administered.

But it matters greatly where in Canada you live.  In the map at the top, Quebec is the dark blue province leading the nation in both cases and deaths.  Quebec has always celebrated being a distinct society, but not in this way. Below is the same chart for the Quebec epidemic from the same dataset. The province has about 23% of the national population and does about 26% of the tests.  But Quebec contributes 56% of the cases and 64% of the deaths, as of yesterday.  Here how the outbreak has gone in La Belle Province.

The Quebec graph is more lumpy showing cases peaking May 1-9, including several days inflated by data catchups. Cases have dropped off recently, from 1100 May 7 down to 521 yesterday.  Deaths are also slowing, declining from 110 on May 7 to 71 May 30. Yesterday the reported deaths in Quebec jumped to 202 due to 165 previously unrecorded data, while the actual new deaths were 37 . The animation below shows the epidemic in Canada with and without Quebec statistics.

But clearly everywhere else in Canada, people are much safer than those living in Quebec.  So what is going on?

The graph shows that people in Quebec are dying in group homes, the majority in CHSLD (long term medical care facilities) and also in PSR (private seniors’ residences).  The huge majority of Quebecers in other, more typical living arrangements have very little chance of dying from this disease. Not even prisoners are much at risk.

Of course the other dimension is years of age, since this disease has punished mostly people suffering from end-of-life frailties.  A previous post reported that the Netherlands parliament was provided with the type of guidance everyone wants to see.

For canadians, the most similar analysis is this one from the Daily Epidemiology Update: :

The table presents only those cases with a full clinical documentation, which included some 2194 deaths compared to the 5842 total reported.  The numbers show that under 60 years old, few adults and almost no children have anything to fear.

Update May 20, 2020

It is really quite difficult to find cases and deaths broken down by age groups.  For Canadian national statistics, I resorted to a report from Ontario to get the age distributions, since that province provides 69% of the cases outside of Quebec and 87% of the deaths.  Applying those proportions across Canada results in this table. For Canada as a whole nation:

Age	Risk of Test +	Risk of Death	Population per 1 CV death
<20	0.05%	None	NA
20-39	0.20%	0.000%	431817
40-59	0.25%	0.002%	42273
60-79	0.20%	0.020%	4984
80+	0.76%	0.251%	398

In the worst case, if you are a Canadian aged more than 80 years, you have a 1 in 400 chance of dying from Covid19.  If you are 60 to 80 years old, your odds are 1 in 5000.  Younger than that, it’s only slightly higher than winning (or in this case, losing the lottery).

As noted above Quebec provides the bulk of cases and deaths in Canada, and also reports age distribution more precisely,  The numbers in the table below show risks for Quebecers.

Age	Risk of Test +	Risk of Death	Population per 1 CV death
0-9 yrs	0.13%	0	NA
10-19 yrs	0.21%	0	NA
20-29 yrs	0.50%	0.000%	289,647
30-39	0.51%	0.001%	152,009
40-49 years	0.63%	0.001%	73,342
50-59 years	0.53%	0.005%	21,087
60-69 years	0.37%	0.021%	4,778
70-79 years	0.52%	0.094%	1,069
80-89	1.78%	0.469%	213
90  +	5.19%	1.608%	62

While some of the risk factors are higher in the viral hotspot of Quebec, it is still the case that under 80 years of age, your chances of dying from Covid 19 are better than 1 in 1000, and much better the younger you are.

Mr. Trudeau, Take Down This Wall !

Alex Berenson explains in a twitter thread (here). Text in italics with my bolds

1/ This panic is likely to prove even more embarrassing than previous panics. Here’s why: the media is both confused and conflating several different data points in an effort to stir hysteria. (Stop me if you’ve heard this before.)

What do I mean?
2/ So: you’ve heard positive tests are up in several states. True. The media refers to these as “cases,” as if positive tests have clinical significance by themselves. They do not. The vast majority of people with positive tests do not become ill enough to need hospitalization…

3/ Much less intensive care or ventilator support. For people under 50, this is true in the extreme. But the daily age distribution of positive tests is rarely if ever supported…

4/ Second: you’ve heard overall hospitalizations are up in some states. This is also true. THIS IS A FEATURE, NOT A BUG. Overall hospitalizations are rising because people are returning to hospitals for elective (and in some cases very necessary) surgeries that were postponed…

5/ Now, in some of these states COVID-related hospitalizations have also risen (though they make up a tiny fraction of overall hospitalizations). Scary, right?

No. When people go to the hospital for elective surgeries they are now routinely tested for COVID...

6/ Whether or not they are symptomatic. Hospitals have financial and legal as well as medical incentives to do this. IF THEY ARE POSITIVE, hospitals will report them as COVID patients (since, technically, they are), EVEN IF THEY HAVE NO COVID SYMPTOMS.

7/ This fact accounts for the bizarre disconnect between the fact the number of people going to emergency rooms with influenza-like or COVID-like symptoms is NOT rising (and remains in the low single digits) even in states reporting more hospitalizations…

8/ As well as the fact that fewer hospitalized cases are now progressing to ventilators (I can’t swear to this in every state, but it appears to be a trend)…

9/ And the fact that deaths no longer seem to have any relationship to case counts in many states (true even accounting for the fact that deaths lag).

The question you should be asking yourself: why aren’t Europe and Asia seeing post-lockdown spikes if this trend is real?

10/ And don’t say masks. Masks are not routine in Europe. So either COVID is somehow different post-lockdown in Europe (and different in different states, too)… or this is just one last gasp of panic porn.

And if you want confirmation from epidemiologists, here Facts about Covid-19 from Swiss Policy Research.

Fully referenced facts about Covid-19, provided by experts in the field, to help our readers make a realistic risk assessment. (Regular updates below)

“The only means to fight the plague is honesty.” (Albert Camus, 1947)

Overview

1.According to the latest immunological and serological studies, the overall lethality of Covid-19 (IFR) is about 0.1% and thus in the range of a strong seasonal influenza (flu).

2. Even in global “hotspots”, the risk of death for the general population of school and working age is typically in the range of a daily car ride to work. The risk was initially overestimated because many people with only mild or no symptoms were not taken into account.

3. Up to 80% of all test-positive persons remain symptom-free. Even among 70-79 year olds, about 60% remain symptom-free. Over 95% of all persons develop at most moderate symptoms.

4. Up to 60% of all persons may already have a certain cellular background immunity to Covid19 due to contact with previous coronaviruses (i.e. common cold viruses).

5. The median or average age of the deceased in most countries (including Italy) is over 80 years and only about 4% of the deceased had no serious preconditions. The age and risk profile of deaths thus essentially corresponds to normal mortality.

6. In many countries, up to two thirds of all extra deaths occurred in nursing homes, which do not benefit from a general lockdown. Moreover, in many cases it is not clear whether these people really died from Covid19 or from weeks of extreme stress and isolation.

7. Up to 30% of all additional deaths may have been caused not by Covid19, but by the effects of the lockdown, panic and fear. For example, the treatment of heart attacks and strokes decreased by up to 60% because many patients no longer dared to go to hospital.

8. Even in so-called “Covid19 deaths” it is often not clear whether they died from or with coronavirus (i.e. from underlying diseases) or if they were counted as “presumed cases” and not tested at all. However, official figures usually do not reflect this distinction.

9. Many media reports of young and healthy people dying from Covid19 turned out to be false: many of these young people either did not die from Covid19, they had already been seriously ill (e.g. from undiagnosed leukaemia), or they were in fact 109 instead of 9 years old. The claimed increase in Kawasaki disease in children also turned out to be false.

10. The normal overall mortality per day is about 8000 people in the US, about 2600 in Germany and about 1800 in Italy. Influenza mortality per season is up to 80,000 in the US and up to 25,000 in Germany and Italy. In several countries Covid19 deaths remained below strong flu seasons.

11. Regional increases in mortality can occur if there is a collapse in the care of the elderly and sick as a result of infection or panic, or if there are additional risk factors such as severe air pollution. Special regulations for dealing with the deceased sometimes led to additional bottlenecks in funeral or cremation services.

12. In countries such as Italy and Spain, and to some extent the UK and the US, hospital overloads due to strong flu waves are not unusual. In addition, up to 15% of doctors and health workers are now being put into quarantine, even if they develop no symptoms.

13. The often shown exponential curves of “corona cases” are misleading, as the number of tests also increased exponentially. In most countries, the ratio of positive tests to tests overall (i.e. the positive rate) remained constant at 5% to 25% or increased only slightly. In many countries, the peak of the spread was already reached well before the lockdown.

14. Countries without curfews and contact bans, such as Japan, South Korea or Sweden, have not experienced a more negative course of events than other countries. Sweden was even praised by the WHO and now benefits from higher immunity compared to lockdown countries.

15. The fear of a shortage of ventilators was unjustified. According to lung specialists, the invasive ventilation (intubation) of Covid19 patients, which is partly done out of fear of spreading the virus, is in fact often counterproductive and damaging to the lungs.

16. Contrary to original assumptions, various studies have shown that there is no evidence of the virus spreading through aerosols (i.e. tiny particles floating in the air) or through smear infections (e.g. on door handles or smartphones). The main modes of transmission are direct contact and droplets produced when coughing or sneezing.

17. There is also no scientific evidence for the effectiveness of face masks in healthy or asymptomatic individuals. On the contrary, experts warn that such masks interfere with normal breathing and may become “germ carriers”. Leading doctors called them a “media hype” and “ridiculous”.

18. Many clinics in Europe and the US remained strongly underutilized or almost empty during the Covid19 peak and in some cases had to send staff home. Numerous operations and therapies were cancelled, including many cancer screenings and organ transplants.

19. Several media were caught trying to dramatize the situation in hospitals, sometimes even with manipulative images and videos. In general, the unprofessional reporting of many media maximized fear and panic in the population.

20. The virus test kits used internationally are prone to errors and can produce false positive and false negative results. Moreover, the official virus test was not clinically validated due to time pressure and may sometimes react positive to other coronaviruses.

21. Numerous internationally renowned experts in the fields of virology, immunology and epidemiology consider the measures taken to be counterproductive and recommend rapid natural immunisation of the general population and protection of risk groups.

22. At no time was there a medical reason for the closure of schools, as children hardly ever transmit the virus or fall ill with it themselves. There is also no medical reason for small classes, masks or ‘social distancing’ rules in schools.

23. The claim that only severe Covid-19 but not influenza may cause venous thrombosis and pulmonary (lung) embolism is not true, as it has been known for 50 years that severe influenza greatly increases the risk of thrombosis and embolism, too.

24. Several medical experts described vaccines against coronaviruses as unnecessary or even dangerous. Indeed, the vaccine against the so-called swine flu of 2009, for example, led to sometimes severe neurological damage and lawsuits in the millions.

25. The number of people suffering from unemployment, depressions and domestic violence as a result of the measures has reached historic record values. Several experts predict that the measures will claim more lives than the virus itself. According to the UN millions of people around the world may fall into absolute poverty and famine.

26. NSA whistleblower Edward Snowden warned that the “corona crisis” will be used for the massive and permanent expansion of global surveillance. The renowned virologist Pablo Goldschmidt spoke of a “global media terror” and “totalitarian measures”. Leading British virologist professor John Oxford spoke of a “media epidemic”.

27. More than 500 scientists have warned of an “unprecedented surveillance of society” through problematic apps for “contact tracing”. In some countries, such “contact tracing” is already carried out directly by the secret service. In several parts of the world, the population is already being monitored by drones and facing serious police overreach.

28. A 2019 WHO study on public health measures against pandemic influenza found that from a medical perspective, “contact tracing” is “not recommended in any circumstances.

See also: Pandemonia Funnies Madebyjimbob