Thursday, 17 June 2021

NFL Covid-19 protocols: a glimpse into the future restrictions for all unvaccinated - and a problem for studies into vaccine effectiveness


These are the new Covid-19 protocols for NFL players. Eventually these kinds of differences will apply everywhere. 

Apart from the civil liberties issues, the fact that only unvaccinated will be routinely tested also means that data on vaccine effectiveness will be massively biased because the vast bulk of people being tested will be asymptomatic unvaccinated people. When real infection rates are low (as they are now) this bias will exaggerate vaccine effectiveness because almost all new 'cases' will be asymptomatics (i.e. unvaccinated) - most of which will be false positives.  Yet, if infection rates are high the bias could underestimate vaccine effectiveness as the proportion of symptomatics among those tested will be higher for the vaccinated. 

We raised this issue of how different testing strategies for unvaccinated compared to vaccinated compromised the big Pfizer study in Israel .......... and are still waiting for a response from The Lancet 

Links

Monday, 17 May 2021

Is the Pfizer vaccine as effective as claimed?

There was massive media fanfare over the study (published in The Lancet) in Israel on the effectiveness of the Pfizer vaccine.


Notwithstanding the fact that 8 of the 15 authors "hold stock and share options in Pfizer"* the results look genuinely impressive and provide support for the hypothesis that the vaccine is effective in preventing infection. In particular, the raw data (Table 2 of the paper**) states the following

  • Between 24 Jan 2021 and 3 April 2021 there were 109,876 'cases' of SARS-Cov-2 found among those unvaccinated*** compared to just 6,266 'cases' found among those vaccinated.
  • The table also provides the 'incident rate per 100,000 person days' which is: 91.5 for unvaccinated compared to 3.1 for vaccinated 
  • Based on these data the (adjusted) 'vaccine effectiveness' measure**** is calculated as 95.3% (hence the headline figure picked up by all main stream media).

There are, however, issues with the study and its analysis which mean the 95% effectiveness measure is exaggerated. In this article Will Jones argues that the researchers have not adjusted for the declining infection rate during the study period and that when you do so, the effectiveness drops to 74% (in the over 65's). 

A different problem with the study (that we focus on here) aises from the statement found on page 8 of the paper:

What this is saying is that, whereas unvaccinated people continued to be regularly and routinely subject to PCR tests, vaccinated people no longer had to be. The number of 'cases' stated in Table 2 is, of course, simply the number of positive PCR test outcomes (which includes false positives). If you stop testing vaccinated people then you are not going to find any 'cases' among them. The paper says that 19% of the tests were, however, on 'exempted', i.e. vaccinated people. But, this still means unvaccinated people were much more likely to be tested than vaccinated people, so we have to take account of the absolute number of tests performed on both vaccinated and unvaccinated.

We know that there were 4.4 million PCR tests and that 19% of these were on vaccinated people.  Hence, we can conlude that there were:

  • 836,000 tests on vaccinated people (of whom there were 4,714,932, making up 72.1% of the population; so on average approximately one in six vaccinated people received a PCR test);
  • 3,564,000 tests on unvaccinated people (of whom there were 1,823,979;  so, on average, each unvaccinated person received two PCR tests)

So, the number of 'cases' per 1000 tests were:

  • 30.8 for unvaccinated people  (109,876 divided by 3,564,000 times 1000)
  • 7.5 for vaccinated people  (6,266 divided by 836,000 times 1000)

Using the simple 'cases per 1000 tests' (rather than the biased 'incident rate per 100,000 person days'),   results in an approximate 'vaccine effectiveness' measure of 75.7%. While this is much less than the 95% headline figure, it is still impressive, so it is strange why the study failed to account for the difference in proportions tested. 

It appears that the failure to adjust the vaccine effectiveness calculation for different testing protocols for vaccinated and unvaccinated people is not restricted to this Pfizer study in Israel. The data in the FDA briefing document on the Pfizer vaccine (dated 10 Dec 2020) suggests there was a similar problem with the phase 3 trial of the vaccine. This was a randomized, double-blinded and placebo-controlled trial of the vaccine in 44,000 uninfected participants. It similarly reports a 95% effectiveness measure based on the fact that (post injection) there were 162 confirmed Covid-19 cases among the placebo participants compared to just 8 among the vaccinated participants.  However, the study also reports that there were a much larger number of 'suspected but unconfirmed' cases and that these were more evenly spread between placebo participants (1,816 such cases) and vaccinated participants (1,594 such cases). This seems to suggest that a disproportionately small number of vaccinated participants with symptoms received PCR tests compared to placebo participants with symptoms.

Clearly the failure to properly adjust for both a decreasing infection rate and different testing protocols for vaccinated and unvaccinated people casts doubt on the validity of the studies. 

It is also worth noting that, even if we ignore all of the above issues and accept as undisputed the number for 'COVID-19 related deaths' in the Israel study (715 among the unvaccinated and 138 among the vaccinated), then the absolute percentage increase in risk of death for an unvaccinated person is just 0.036%. That means that, even if we accept the 95% effectiveness measure, for every 10,000 unvaccinated people, about 3 or 4 would die as a result of not being vaccinated. And this brings us to the final (and critical) problem with the study. It does not provide any information about the number of adverse reactions - in particular the number of deaths - due to the vaccine. Hence, it does not provides the necessary information to make an informed decision about the overall risk/benefit of the vaccine.

We submitted a 250-word response to The Lancet over a week ago summarising the above concerns about the article, but the response is still "With the Editor".

*screenshot of declared interests in the paper:



**Table 2 screenshot from the paper:


***Although Table 2 states that there were a total of 109,876 'cases' among the unvaccinated, there seems to be an error in the table in that the total number of asymptomatic cases (49,138) and symptomatic cases (39,065) do not sum to 109,876

****The 'vaccine effectiveness' measure is defined as: 100 times (1 -  the incident rate ratio). The incident rate ratio is (approximately) the incident rate of vaccinated divided by the incident rate of unvaccinated.

 

Postscript: The study provides interesting insights into the separate issue of 'asymptomatic' infection that we have covered extensively on this blog. For example, Table 2 shows that, among the unvaccinated, there were 49,138 asymptomatic 'cases' compared to 39,065 symptomatic 'cases', i.e. 56% of all those testing positive (and classified as a 'case') were asymptomatic. It is likely that most of the positives among the asymptomatics were false positives. This is because, especially at times when the infection rate is low, a false positive PCR test rate of, say just 0.4%, would still mean that the majority of positive tests among asymptomatics are false. See  here and here.

Friday, 30 April 2021

COVID-19: Discrepancy between 'cases' and 'illness'

It's been a while since we last highlighted the difference between Covid-19 'case' numbers (and by extension this means also hospitalisation numbers and death numbers) and actual Covid-19 illness.

The NHS pathways coronavirus triages website (see digital.nhs.uk/dashboards/nhsactual illness due to Covid-19 as it combines all 999, 111, online and ambulance calls relating to Covid-19 triages. Previous articles (see links) make clear what the caveats are.


The triage data confirms the real pandemic of spring 2020. I've still yet to see any better evidence that the (vast) majority of 'cases' (i.e. positive PCR test results) since the summer of 2020 have been false positives. 

Links 


Friday, 16 April 2021

Revisiting the issue of disparity in Covid-19 death rates by ethnicity

 

An article we first wrote in June 2020 has been published today in Significance magazine. It was triggered by an ONS report on Covid-19 deaths by ethnicity published in May 2020. For reasons possibly explained in our article, that ONS report is no longer available in its original form and the one now on the ONS website covering that period contains caveats and information that were not in the original report when we did our analysis. There was extensive national media coverage of the results of the original report, with every major newspaper and TV news channel in the UK focusing on the following highlighted conclusion:

"When taking into account age in the analysis, Black males are 4.2 times more likely to die from a Covid-19-related death and Black females are 4.3 times more likely than White ethnicity males and females". 
This information caused understandable fear and anguish among the Black community. But, when we analysed the report we found that there was insufficient data provided to support the conclusions and we also suspected that the ONS was basing its analysis on the 2011 census data which would skew the results (we wrote to the ONS asking them if this was the case, but did not receive a reply). Using publicly available data we showed that, while there was indeed a disparity, the death rate was likely to be about 2.1 times greater for Blacks, not 4.2 to 4.3 as claimed, and that when using the WHO defined measure to adjust for age there was little difference between Blacks and non-Blacks. We first published our analysis on ResearchGate in July 2020 and there was a widely read blog posting about it on 5 August 2020, which eventually led to the article's submission and publication in Significance today.

What is especially curious about this story is that at some point after we first published our analysis the ONS 'updated' their original report; in fact, even though the report is still dated 7 May 2020 they seem to have been continually updating the report, and the link which says Previous releases points to the National Archives where a search for previous releases draws a blank. In October we noticed that the report revised the difference in death rate between black and white males down from 4.2 to 2.9, and for females from 4.3 to 2.3 and this version specifically says it uses the 2011 census data. Looking at the report today it has been changed again. The original headline figures of 4.2 and 4.3 have been reinstated but critically the report now says:
After taking account of age and other socio-demographic characteristics and measures of self-reported health and disability at the 2011 Census, the risk of a COVID-19-related death for males and females of Black ethnicity reduced to 1.9 times more likely than those of White ethnicity.
There is also a later report dated 19 June with death statistics up to 15 May 2020 (rather than up to 10 April). This report says:
  • This analysis showed that for all ages the rate of deaths involving COVID-19 for Black males was 3.3 times greater than that for White males of the same age, while the rate for Black females was 2.4 times greater than for White females.
  • After adjusting for region, population density, socio-demographic and household characteristics, the raised risk of death involving COVID-19 for people of Black ethnic background of all ages together was 2.0 times greater for males and 1.4 times greater for females compared with those of White ethnic background.
What is clear is that the original figures, which were so widely seized on by the media, were exaggerated - as we originally said. And even the current figures are also likely to be exaggerated by failure to account for demographic changes since the 2011 census. Yet it is the figures in the original report that remain in the popular narrative and which have created an unjustified level of fear and anxiety among the Black community.



Monday, 12 April 2021

The barriers to academic publication for work that challenges the ‘official narrative’ on Covid-19

 

Our paper about the “1 in 3 people with Covid-19 have no symptoms”  claim has had (at time of writing this) 4093 reads since we posted it on researchgate on Friday,  and 336,755 impressions to the tweet about it. The video summary has been watched by 7,422 people in 2 days.

But, this was the response we got less than 24 hours after we submitted it to the BMJ:

“Thank you for sending us your paper. We read it with interest but I am sorry to say that we do not think it is right for the BMJ. In comparison with the many other papers we have to consider, this one is a lower priority for us. We do not send out for external peer review manuscripts whose subject matter, design or topic do not meet our current priorities and are unlikely to make it through our process.”

Even more bizarrely, neither the medRxiv or  arXiv sites (where we routinely post pre-prints of our research) would accept the paper. MedRxiv said:

“Thank you for submitting your manuscript to medRxiv. We regret to inform you that your manuscript is inappropriate for posting. medRxiv is intended for research papers, and our screening process determined that this manuscript fell short of that description.”

while arXiv (which initially said: “Your article is currently scheduled to be announced at Fri, 9 Apr 2021 00:00:00 GMT”) quietly changed the status of the article to “on hold” as the submission “was identified by arXiv administrators or moderators as needing further attention.” UPDATE: we now have the following response:

"Our moderators have determined that your submission is not of sufficient interest for inclusion within arXiv. The moderators have rejected your submission after examination, having determined that your article does not contain sufficient original or substantive scholarly research.

As a result, we have removed your submission.Please note that our moderators are not referees and provide no reviews with such decisions. For in-depth reviews of your work, please seek feedback from another forum.

Please do not resubmit this paper without contacting arXiv moderation and obtaining a positive response. Resubmission of removed papers may result in the loss of your submission privileges"

Compare this with what happened in April 2020 when we first investigated the Covid-19 data. Whereas our latest work shows that Covid-19 'case' numbers have been exaggerated and that mass testing of asymptomatic people is counter-productive, at that time we were actually concerned that 

a) the numbers infected were being UNDERESTIMATED and 
b) the data was being skewed by the fact that ONLY people with extreme covid symptoms were being tested (and hence we argued for the need for more random testing). 

These views were not considered threatening to the "official narrative" and of course random testing WAS widely implemented after August. We had no problem getting those articles published in academic journals (see e.g. here  and here are some of our other articles on Covid-19)

But things are completely different when you challenge the "official narrative". Given that even researchgate has been censoring such articles it is possible that our latest paper may be removed. If so a copy can be found here.

 

See: