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Serious Comparison of flu deaths with COVID-19 deaths

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Source: https://www.news-medical.net/news/20200514/Comparison-of-flu-deaths-with-COVID-19-deaths.aspx


Comparison of flu deaths with COVID-19 deaths

By Dr. Liji Thomas, MDMay 14 2020

A new study published in the journal JAMA Internal Medicine in May 2020 reports that false comparisons between the count of COVID-19 deaths as reported by various hospitals in the US, and the estimated flu-related deaths, causes significant confusion and may impair the ability to design public health policies.

By the beginning of May 2020, about 65,000 deaths had been reported in the USA as a result of the ongoing COVID-19 pandemic – a number roughly equivalent to the estimate issued by the Centers for Disease Control and Prevention (CDC) for influenza deaths this flu season.

With frontline health workers pushed to the limit, and sometimes beyond, with a lack of ventilators and PPE in large cities like New York, and the unsparing demand on hospitals for weeks on end, the pandemic has placed a heavy burden on these resources unlike that experienced in the heaviest of flu seasons. Though the number of deaths in both conditions is apparently comparable, the panic and the suffering caused by COVID-19 does not compare with that of the flu, says the study.

Why, then, do health officials continue to compare the death rates between the two conditions directly? The answer may relate to a lack of understanding of how the two epidemics are reported publicly. In the case of influenza, the reported case estimates are not actual counts of patients or death, but estimates made on the basis of the specific disease codes submitted to the healthcare authorities.

Flu Death Estimates Vs. COVID-19 Death Counts

The yearly estimates of flu deaths ranged between 23,000 and 61,000 from 2013-14 to the 2018-19 seasons. However, the actual reported counts were between 3,500 and 15,600 deaths, which is almost six times less.

On the other hand, the COVID-19 mortality data is a raw count. The right comparison would be, therefore, between weekly deaths from COVID-19 and those from the flu.

For the week ending April 15, 2020, the reported number of COVID-19 deaths was 15,500, and the previous week it was 14,500. On the other hand, the CDC stats show death counts during the peak flu week in all the flu seasons from 2013-2014 to 2019-2020 stayed in the range 350 to 1,600, for an average of 752 deaths each year during the peak week of influenza.

This shows the count of COVID-19 deaths during the week ending April 21 to be anywhere from almost ten-fold to 44 times higher than the highest weekly flu death count, depending on which season is in question. It is more than 20 times higher than the average flu death count.

Even the CDC’S provisional COVID-19 death count for this week shows it to be 15 times higher than the flu deaths for this season’s peak week (ending February 29) – though the reported number is less than that obtained from other sources of data.

The Reason for The Difference

These figures help understand why frontline conditions are so appalling with the current pandemic compared to current flu death estimates. Either the latter consistently over-represents the actual number of flu deaths – or else, the currently reported COVID-19 death count is much less than the actual figure.

Which of these scenarios is more likely? The researchers contend that either is probable. COVID-19 testing restrictions and false negatives, especially in advanced illness, may lead to a falsely low number of COVID-19 deaths being reported.

On the other hand, flu death reports are notoriously unreliable because hospitals do not mandatorily have to report flu deaths in adults. The flu death figures, therefore, depend on surveillance, which are adjusted for potential under-reporting.

Are All COVID-19 Death Numbers Reliable?

Some COVID-19 death counts are becoming less reliable, as in New York City, where probable deaths from this condition are being included with the confirmed deaths, pushing the line between actual reporting and estimation of such deaths.

Some deaths included in this class may not have been due to COVID-19 at all, as when a person who has tested positive but is not seriously sick suffers a cardiac arrest. Thus, the future reappraisal of this epidemic will have to include a recount of deaths, both direct and indirect, to arrive at the number of deaths that occurred in excess of the expected. This will still be more reliable if it includes the deaths that occurred because patients with other medical conditions could not get the care they needed or if the care was delayed, because of the COVID-19-induced strain on the hospital system in severely affected areas.

Another confusing parameter is the case fatality rate. While some areas estimate the case fatality to be less than 1%, others peg it at 15%, mainly due to the limitations in the calculation. For instance, the lack of adequate test facilities means the ratio of infected persons who die to the total number of infections is falsely high since only severely symptomatic people are tested. Another source of error is the inability to include critically ill patients who were still alive at the time of counting, which falsely reduces the numerator. Serologic testing will help provide a more accurate infection number.

The most accurate and complete data set at present comes from the Diamond Princess cruise ship, which had a case fatality rate of 1.8%, reflecting 13 deaths out of 712 cases. If adjusted for the age composition, this would have been 0.5%, which is five times that of the typical case fatality rate for seasonal influenza in adults.

Implications for Public Health

In essence, direct comparisons between mortality statistics for two different conditions when the numbers are counted in two different ways lead to wrong information being relayed to the public. Secondly, when these false comparisons inform the attitude of government officials and others in power, it hinders the shaping of proper public health policies. For instance, such comparisons may increase the tendency to accelerate the reopening of the economy while relaxing movement and social restrictions.

The researchers summarize their conclusion, peremptorily, “Although officials may say that SARS-CoV-2 is “just another flu,” this is not true. Our analysis suggests that comparisons between SARS-CoV-2 mortality and seasonal influenza mortality must be made using an apples-to-apples comparison, not an apples-to-oranges comparison.” This will allow the right public health interventions to be made to mitigate the health and economic impact of COVID-19.

Journal reference:

Faust, J. S., and del Rio, C. (2020). Assessment of Deaths From COVID-19 and From Seasonal Influenza. JAMA Internal Medicine. Published online: May 14, 2020. doi:10.1001/jamainternmed.2020.2306
 

Leongsam

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The best way to tell is to look at excess mortality as a result of the arrival of Covid-19.

 

Leongsam

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Leongsam

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Counting COVID-19 ‘Cases’ is Misleading Everybody

8 October 2020
by Professor Norman Fenton, Dr. Scott McLachlan, Professor Martin Neil and Dr. Magda Osman

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The focus of almost all current reporting on COVID-19 is on the sharp increase in number of ‘new cases’ – as shown in the above graph – and the accompanying narrative that we are in the midst of an exponential ‘second wave’ increase. For example, there were 12,000 ‘new cases’ on October 2nd compared to just 3,000 on September 2nd – a four-fold increase in four weeks, and more than double the new cases per day now than we had at the peak of the pandemic in March.

This looks frightening. But the massive increase in ‘new cases’ is almost completely explained by factors that have nothing to do with an increasing population health risk. New cases are simply the count of those who get a positive test result. But almost all of those – as can be seen from the university student ‘cases’ – are either asymptomatic or false positives., i.e. they do not – and will not – show any symptoms of a ‘COVID-19 illness’. Nor will they ‘spread the virus’ to others.

Also, contrary to widely believed assumptions, there is no ‘gold standard’ test for COVID-19. A diagnostic process, namely PCR, has been used, but since the outbreak there has been no attempt to determine its accuracy. It might be shocking to find that research on lab grown ‘live’ cultures of the virus, taken from patients, had not been published until early August – eight months after the virus outbreak. These have been used to assess the accuracy of PCR and the results are not good. It has been shown it is possible to return a positive PCR test where a sample taken from the same patient never grows a viral culture – meaning the patient does not have an active COVID-19 infection despite the positive PCR test. The implications of this for the false positive rate of PCR tests are obvious and significant.

The other obvious explanation for the increase in number of ‘cases’ is that far more people are being tested – 280,000 per day now compared to 10,000 at the peak in March. So, while there are twice the number of ‘new cases’ per day now compared to the March peak, the number of ‘new cases’ per 1000 people tested now is actually only ONE-TENTH of that in the March peak (45 compared to 450). See graph below.

Much of this is borne out by simply looking at the more important trend of “COVID-19” hospital admissions and deaths (see graph below). For both we see increases – but at much slower rates than ‘cases’, and not beyond what we would expect at the beginning of any flu season. But, critically, these counts of ‘COVID-19’ admissions and deaths are inevitably ALSO inflated for the same reason that ‘cases’ are: anybody admitted to hospital – or dying – with a positive test is classified as ‘COVID-19’ irrespective of the actual reason for admission or cause of death AND regardless of whether they showed any symptoms of a ‘COVID-19 illness’. The analogous situation would be assigning the cause of hospitalisation or death to ’a cold’ if a patient harboured a cold virus without showing any symptoms of it.

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Professor Norman Fenton is the Professor of Risk and Information Management, School of Electronic and Electrical Engineering, Queen Mary University of London
Dr. Scott McLachlan is a Postdoctoral Research Assistant, School of Electronic and Electrical Engineering, Queen Mary University of London
Professor Martin Neil is the Professor of Computer Science and Statistics, School of Electronic and Electrical Engineering, Queen Mary University of London
Dr. Magda Osman is a Reader in Experimental Cognitive Psychology, School of Biological and Chemical Sciences, Queen Mary University of London
 
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