Ok, so Ioannidis has updated his preprint This is good - hats off to him for acknowledging issues and working to improve Let's see what's changed 1/n
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3/n If you missed it, my original thread on this preprint is here:https://twitter.com/GidMK/status/1262956011872280577?s=20 …
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4/n So, reading the paper, most of the issues are still apparent: - no clear search methodology - strange inclusion/exclusion criteria - odd 'adjustments' that only ever decrease IFR - including strange studies - excluding the most robust estimates
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5/n What has changed is that some of these things are now justified This is good, but not really adequate to improve the rigor of the paper
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6/n For example, we now have this explanation of using the estimates derived from the included papers even if they didn't account for right-censoring The thing is, the issue still remains, we now just have a few words addressing itpic.twitter.com/xUetr3smfa
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7/n There are also still obvious errors in the paper. Ioannidis claims that the Spanish seroprevalence estimate cannot be included because it has only been published as a press release This is wrongpic.twitter.com/HlwMLV5mGa
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8/n In fact, the Spanish seroprevalence estimate is a lengthy government report that is FAR more detailed than many preprints. Excluding it from the main estimate makes no sense scientificallypic.twitter.com/8ammTyzDOk
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9/n Similarly, the Czech Republic seroprevalence study is (while published in Czech) very comprehensive. The same is true of the Danish and English estimates (although not of the Swedish and Slovenian ones)pic.twitter.com/T4jz0po4tW
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10/n In fact, it appears that Ioannidis has continued to exclude any government reports, which is still an issue (remember, governments are doing most of the testing!)
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11/n So, the results Broadly speaking, we have a bunch of new studies included but the exact same issues as beforepic.twitter.com/KKzZFDUUH5
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12/n Studies with inappropriate samples to infer population IFR (such as the Kobe study) are still in there, while random, population-wide estimates (i.e. Spain) are excluded
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13/n It's also worth noting that Ioannidis has violated his own inclusion criteria, with at least one study under the arbitrary 500-person sample size that has been includedpic.twitter.com/ard0rwK6T4
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14/n While it is hard to know why this is still the case, again the decisions made in the paper exclusively work to suggest a lower IFR than that actually implied by most research, which is worrying
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15/n If we again only look at studies using a population-wide estimate of IFR, we see that the lowest estimate is still Ioannidis' Santa Clara study, with the estimates ranging from 0.18%-0.78%pic.twitter.com/7dHABZRXHI
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16/n This is still a bit low - for some reason, this paper uses an incorrect IFR for the Brazilian estimate (0.3% instead 1% given by the authors) - but much more in line with the estimate from our updated meta-analysis of 0.64%https://www.medrxiv.org/content/10.1101/2020.05.03.20089854v3 …
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17/n One thing worth noting - the paper still makes the clear error in comparing the IFR of COVID-19 to influenza This is a common mistake, so I thought I'd highlight itpic.twitter.com/Oscf9UZn4q
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18/n Here, Ioannidis is comparing the IFR of influenza used by the CDC - which is ~0.1% - to the IFR of COVID-19 inferred from seroprevalence studies These two figures, however, are not comparable
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19/n The IFR estimate for influenza generated by the CDC is the result of a complex modelling process that inflates the numerator (deaths) according to hospitalization data for pneumonia and other ICD codespic.twitter.com/YMS8AkDyh7
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20/n Why is this a problem? Well, we are not comparing apples with apples here. Numerous efforts have demonstrated that the death count of COVID-19 in many places is a significant underestimate (by 50%+)pic.twitter.com/vR5VxuCUFg
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21/n If we instead compare the IFR of influenza calculated from seroprevalence studies and official death counts to the same for COVID-19, we see a VERY different picture
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22/n The HIGHEST IFR estimate for influenza using this methodology, based on a 2014 systematic review, is 0.01% That's 18x lower than the lowest reasonable estimate of COVID-19 IFRhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3809029/ …
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23/n More broadly, if we look at the total range, the IFR of COVID-19 calculated from seroprevalence data appears to be around 50-100x higher than the same number for influenza
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24/n This is actually a serious flaw with the paper - the author has chosen only to pursue corrections of the data that push the IFR lower. If we were to account for excess mortality attributable to COVID-19 - based on published research - the IFRs would all jump substantially
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25/n Now, there are some excellent improvements to the paper For example, much of the language in the discussion/conclusion has been correctedpic.twitter.com/KkVboq3eDO
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26/n There are still odd, emotive phrases ("blind lockdown"), but the paper no longer describes COVID-19 as common and mild, which was clearly incorrect
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27/n However, overall this paper still suffers from many of the issues I previously raised, and seems to still substantially underestimate the IFR of COVID-19
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28/n I should be clear that I am not speculating in any way about the reasoning behind these decisions. The fact that the paper underestimates IFR is a problem, but we can't really know why these decisions were made
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End of conversation
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