Will Jones

Guardian Article Claims Covid in Hospitals Has “Largely Become a Disease of the Unvaccinated” – Yet Data Shows 71% of Adults Hospitalised with Covid Are Vaccinated

An article appeared in the Guardian this week written by an anonymous NHS respiratory consultant claiming that “in hospital, COVID-19 has largely become a disease of the unvaccinated”.

Of course, there are people who have their vaccinations but still get sick. These people may be elderly or frail, or have underlying health problems. Those with illnesses affecting the immune system, particularly patients who have had chemotherapy for blood cancers, are especially vulnerable. Some unlucky healthy people will also end up on our general wards with Covid after being vaccinated, usually needing a modest amount of oxygen for a few days.

But the story is different on our intensive care unit. Here, the patient population consists of a few vulnerable people with severe underlying health problems and a majority of fit, healthy, younger people unvaccinated by choice. … If everyone got vaccinated, hospitals would be under much less pressure; this is beyond debate. Your wait for your clinic appointment/operation/diagnostic test/A&E department would be shorter. Your ambulance would arrive sooner. Reports of the pressure on the NHS are not exaggerated, I promise you. … Most of the resources that we are devoting to Covid in hospital are now being spent on the unvaccinated.

This reads to me like a blatant attempt to stigmatise the unvaccinated as selfish, a burden on society and a threat to the vaccinated. (The clue is in the headline: “ICU is full of the unvaccinated – my patience with them is wearing thin.”) Given the polling (which may not be very reliable of course) showing that 45% of U.K. adults would support an indefinite lockdown of the unvaccinated, this is all starting to look and sound rather ugly.

The most frustrating thing about this anonymously written article is it doesn’t cite any data even though its arguments are based on claims which only data can validate. It consists instead only of a single medic’s subjective impressions, with no sources provided to see if his claims holds water.

Are the hospitalised mostly unvaccinated? Not according to Government data from the UKHSA. Here is the breakdown of hospitalisations by vaccination status in England for the four weeks up to November 14th from the latest Vaccine Surveillance report.

Vaccine Passports Make No Sense as the Vaccinated Are More Likely to Be Infected, Scientists Tell MPs

It makes “little sense” to impose any kind of vaccine certification scheme, an expert panel of scientists has told a cross-party group of MPs, since Government data indicates that “vaccinated people over 30 years are now more likely to be infected than the unvaccinated”.

The comments were made at a hearing for an enquiry into Covid passes set up by the Pandemic Response and Recovery All Party Parliamentary Group (APPG). The group, established in the autumn to scrutinise the Government’s response to the pandemic, will gather information from doctors, public health officials, business owners and parliamentary colleagues before setting out its conclusion to Government ministers.

Co-chaired by Conservative MP Esther McVey and Labour MP Graham Stringer, the APPG says it will examine the pros and cons of such a scheme and the rationale behind it, as well as global evidence of whether they work. 

Esther McVey said:

Will the U.K. Face a Winter Covid Surge?

I’m slightly surprised to be writing this post as to my mind the answer is obvious – of course the U.K. will face a winter Covid surge. It’s winter. That’s what happens in winter; the dominant respiratory virus surges and, most years, taxes the capacity of the health service. The only question is how big it will be – unusually large like 2020-21, or unusually small like 2019-2020 before Covid hit? It’s worth remembering that more people died in England and Wales per head of population in 2008 (once adjusted for age etc.) and every year prior to it than died in 2020 or 2021, many of them succumbing during the winter flu season, as the chart below from the Institute and Faculty of Actuaries shows. In other words, there’s a winter surge in deaths-by-virus every year, and I see no reason why 2021-22 will be any different.

Standardised mortality rates (SMRs) in England and Wales

As I see it, the only realistic way there would not be a Covid surge on some scale is if another influenza-like virus takes over, which seems unlikely right now as flu is almost nowhere to be seen.

Nonetheless, I am writing this post, and that’s because some people seem to think that this year it’s not going to happen. Dr Sebastian Rushworth argues that places hit hard already, such as Sweden, New York and Lombardy, have developed enough immunity to avoid “another big wave” altogether. Andrew Lilico in the Telegraph maintains that owing to “infection saturation” and vaccine third doses, “for us, the Covid crisis is over”. Even the usual doom-mongers at SAGE are predicting a decline in hospitalisations and deaths in December, according to new modelling released on Friday. A decline in flu-like hospitalisations and deaths in December? Whoever heard of such a thing?

I freely admit that the winter surge may, because of acquired immunity, be relatively small in places like the U.K. which have already faced widespread exposure. Perhaps that’s all that Sebastian Rushworth and Andrew Lilico mean, and in which case our positions are not so far apart. But will it really be a non-event, as SAGE at least appears to be implying, so that Covid deaths decline during the winter and don’t put any further pressure on the health service?

New Analysis of ONS Data Finds that Vaccine Effectiveness Against Death Has Been Overestimated and Uncovers an Alarming Spike in Covid Deaths Post-Vaccination

There follows a guest post by ‘Amanuensis’, an ex-academic and senior Government researcher/scientist with experience in the field, who has undertaken a re-analysis of ONS data on deaths by vaccination status and concluded that vaccine effectiveness against death has been significantly overestimated owing to a failure to take into account the delay between infection and death. His analysis also uncovers an alarming spike in Covid deaths following vaccination during a Covid surge which, he says, needs urgent investigation. This post is also available on his Substack page.

Recently a blog post was brought to my attention. This was a very interesting piece of work that is directly related to a previous post of mine analysing the deaths by vaccination status figures published by the Office for National Statistics.

In Norman Fenton’s excellent analysis he considers the impact of a delay in the reporting of a death on the shape of the deaths curve; he finds that such a delay during a vaccination campaign will naturally result in the creation of a spike in unvaccinated deaths and an under-estimation of deaths in the vaccinated – indeed, he notes that you would see that spike in deaths even if the vaccines do nothing. If you want to read more about his analysis his site can be found here – and I recommend at least a quick skim of his work because I’ll be building on the fundamentals of data analysis that he considers.

However, there is a small but significant flaw in his argument; that there was a delay in reporting deaths which has then resulted in the spike in cases that we see in the data. Unfortunately, a check of the data source reveals that the deaths data were given by the date at which the death occurred, not the date at which it was reported. Thus there is little scope to introduce a delay in the data using this mechanism.

This then seems like a conundrum – we have a mechanism that might explain the spike in deaths in the unvaccinated apparent in the deaths data for last spring, but we can’t explain how the necessary delay might have occurred. But there is a potential explanation.

Vaccine Effectiveness Remains Negative in 30-79 Year-Olds, Government Data Shows, Despite Boosters Starting to Kick In

The latest UKHSA Vaccine Surveillance report came out yesterday, allowing us to update our estimates of unadjusted vaccine effectiveness. Last week I noted that vaccine effectiveness was stabilising, and this week we can see it rising in the older age groups. Despite this, it is still negative for those aged 30-79, highly so for 40-69 year-olds, and barely positive in the over-80s and 18-29 year-olds.

Part of the reason for the recent rises in the older age groups may be the boosters that have been rolled out since September 20th – you can see a staggered stabilising and then rise across the age groups in the graphs above and below. This means that we are no longer seeing clean data for double-vaccinated versus unvaccinated, as some are triple-vaccinated. The UKHSA report doesn’t include figures for ‘dose three’ and appears to include the triple-jabbed in its ‘received two doses’ category, though oddly does not clarify either way.

The report still claims of course that its data is too biased to be used to estimate vaccine effectiveness, and lists the usual reasons. We await any actual data on the differences between vaccinated and unvaccinated populations, such as testing rates, seroprevalence and prior Covid positives, that would help to account for these biases.

While the raw data shows infection rates often much higher in the double-vaccinated than the unvaccinated, and a number of studies have shown negligible vaccine effectiveness after six months, the official line is that the vaccines remain positively efficacious. Test-negative case-control studies are often used to demonstrate this, which we criticise here. A recent UKHSA study on boosters put the pre-booster effectiveness at 44.1% for AstraZeneca and 62.5% for Pfizer, five months after dose two. Such estimates must be considered upper bounds, given the biases in the case-control design that seem consistently to inflate vaccine effectiveness estimates.

Comprehensive Review of Face Mask Studies Finds No Evidence of Benefit

The Cato Institute has published its latest working paper, a critical review of the evidence for face masks to prevent the spread of Covid. Entitled “Evidence for Community Cloth Face Masking to Limit the Spread of SARS‐​CoV‑2: A Critical Review” and written by Ian Liu, Vinay Prasad and Jonathan Darrow, the paper is an admirably thorough and balanced overview of the published evidence on the efficacy of face masks. While even-handedly acknowledging and summarising the studies that show benefit, the authors’ overall conclusion is that: “More than a century after the 1918 influenza pandemic, examination of the efficacy of masks has produced a large volume of mostly low- to moderate-quality evidence that has largely failed to demonstrate their value in most settings.”

At 61 pages in length, however, not everyone will make it through to the end, so here’s a TL;DR, with some key quotes to serve as a handy overview. The paper is, of course, worth reading in full, though.

Here’s the authors’ own summary from the abstract:

The use of cloth facemasks in community settings has become an accepted public policy response to decrease disease transmission during the COVID-19 pandemic. Yet evidence of facemask efficacy is based primarily on observational studies that are subject to confounding and on mechanistic studies that rely on surrogate endpoints (such as droplet dispersion) as proxies for disease transmission. The available clinical evidence of facemask efficacy is of low quality and the best available clinical evidence has mostly failed to show efficacy, with fourteen of sixteen identified randomised controlled trials comparing face masks to no mask controls failing to find statistically significant benefit in the intent-to-treat populations. Of sixteen quantitative meta-analyses, eight were equivocal or critical as to whether evidence supports a public recommendation of masks, and the remaining eight supported a public mask intervention on limited evidence primarily on the basis of the precautionary principle. Although weak evidence should not preclude precautionary actions in the face of unprecedented events such as the COVID-19 pandemic, ethical principles require that the strength of the evidence and best estimates of amount of benefit be truthfully communicated to the public.

The authors open by recalling the initial advice on masks from the WHO and others and the pre-Covid evidence it was based on.

Until April 2020, World Health Organization COVID-19 guidelines stated that “[c]loth (e.g. cotton or gauze) masks are not recommended under any circumstance”, which were updated in June 2020 to state that “the widespread use of masks by healthy people in the community setting is not yet supported by high quality or direct scientific evidence”. In the surgical theatre context, a Cochrane review found “no statistically significant difference in infection rates between the masked and unmasked group in any of the trials”. Another Cochrane review, of influenza-like-illness, found “low certainty evidence from nine trials (3,507 participants) that wearing a mask may make little or no difference to the outcome of influenza-like illness (ILI) compared to not wearing a mask (risk ratio 0.99, CI 0.82 to 1.18).”

Can Anything About the Pfizer Vaccine Trial be Trusted?

In a recent article in the BMJ, a whistle-blower exposed serious problems she had observed first-hand in the Pfizer vaccine trial in Texas.

A regional director who was employed at the research organisation Ventavia Research Group has told the BMJ that the company falsified data, unblinded patients, employed inadequately trained vaccinators, and was slow to follow up on adverse events reported in Pfizer’s pivotal phase III trial. Staff who conducted quality control checks were overwhelmed by the volume of problems they were finding. After repeatedly notifying Ventavia of these problems, the regional director, Brook Jackson, emailed a complaint to the US Food and Drug Administration (FDA). Ventavia fired her later the same day. Jackson has provided the BMJ with dozens of internal company documents, photos, audio recordings, and emails.

Another Ventavia employee said of the data the company generated for the Pfizer trial: “I don’t think it was good clean data. It’s a crazy mess.”

The six-month trial results for the Pfizer vaccine have now been published in the New England Journal of Medicine. These findings, the researchers note, “contributed to the full approval of BNT162b2 [the Pfizer vaccine] in the United States”. A close inspection of the study, however, reveals a number of problems that raise serious questions about the reliability of its findings, as well as about the safety of the vaccine.

Here is the graph of cumulative incidence for the two trial arms, vaccine and placebo, over the six months of the study period, showing how the symptomatic Covid PCR-positives added up following receipt of the first dose.

Vaccine Safety Update

This is the 18th of the round-ups of Covid vaccine safety reports and news compiled by a group of medical doctors who are monitoring developments but prefer to remain anonymous in the current climate (find the 17th one here). By no means is this part of an effort to generate alarm about the vaccines or dissuade anyone from getting inoculated. It should be read in conjunction with the Daily Sceptic‘s other posts on vaccines, which include both encouraging and not so encouraging developments. At the Daily Sceptic we report all the news about the vaccines whether positive or negative and give no one advice about whether they should or should not take them. Unlike with lockdowns, we are neither pro-vaccine nor anti-vaccine; we see our job as reporting the facts, not advocating for or against a particular policy. The vaccine technology is novel and the vaccines have not yet fully completed their trials, which is why they’re in use under temporary and not full market authorisation. This has been done on account of the emergency situation and the trial data was largely encouraging on both efficacy and safety. For a summary of that data, see this preamble to the Government’s page on the Yellow Card reporting system. (Dr Tess Lawrie in June wrote an open letter to Dr June Raine, head of the MHRA, arguing that: “The MHRA now has more than enough evidence on the Yellow Card system to declare the COVID-19 vaccines unsafe for use in humans,” a claim that has been ‘fact checked’ here.) Boris Johnson said in October that being double vaccinated “doesn’t protect you against catching the disease, and it doesn’t protect you against passing it on”. We publish information and opinion to inform public debate and help readers reach their own conclusions about what is best for them, based on the available data.

Summary of Adverse Events in the U.K.

According to an updated report published on November 11th, the MHRA Yellow Card reporting system has recorded a total of 1,261,714 events based on 383,644 reports. The total number of fatalities reported is 1,766: 1,118 with AstraZeneca, 597 with Pfizer, 19 with Moderna, 32 unspecified. (Note: updated dose counts were not available in this week’s MHRA report so figures based on dose counts have been omitted in this update. All other data has been updated.)

Norway Study Finds ZERO Vaccine Effectiveness Against Death for Covid Hospital Patients

A new pre-print study from Norway looking at differences in outcomes between vaccinated and unvaccinated hospital patients has found that being vaccinated makes zero difference to the risk of dying once hospitalised for COVID-19.

“There was no difference in the adjusted odds of in-hospital death between vaccinated and unvaccinated patients in any age group,” the researchers write. They also observed no difference between vaccinated and unvaccinated in the length of hospital stay for patients not admitted to ICU. These findings are adjusted for age and other risk factors so are not simply due to the vaccinated being older or at higher risk (though, as always, the validity of the adjustments may be questioned). The findings also only include patients admitted primarily due to Covid, so aren’t confounded by patients admitted for other reasons who also tested positive at some point.

The researchers did however find that vaccinated patients aged 18-79 had “43% lower odds of ICU admission” and an estimated 26% shorter hospital stay than unvaccinated patients.

It is curious that vaccinated patients were 43% less likely to need ICU but no less likely to die. Did the antibodies from the vaccines just mean that those who were going to fight it off did so a bit more quickly and easily, but the vaccine antibodies weren’t actually able to save anyone who wasn’t going to survive anyway? That appears to be the researchers’ conclusion:

Our results suggest that once hospitalised the risk of death among vaccinated and unvaccinated patients in Norway is similar. However, for survivors the disease trajectory is milder in vaccinated patients, with reduced need for hospital care and organ support.

The Flaw at the Heart of the UKHSA’s Vaccine Effectiveness Study

There follows a guest post by Daily Sceptic reader ‘Amanuensis’, as he is known in the comments section below the line. He is an ex-academic and senior Government researcher/scientist with experience in the field, who says he is “a bit cross about how science has been killed by Covid”. It was originally posted on his Substack page, but I thought it was such an excellent analysis of the UKHSA’s favoured test-negative case-control approach and its problems – especially why it seems consistently to exaggerate vaccine effectiveness – that Daily Sceptic readers should be treated to it too.

There has been much consideration in recent months about the effectiveness of the Covid vaccines, and this leads to thoughts about how vaccine effectiveness is calculated in the first place. The trouble with any attempt to calculate vaccine effectiveness is bias – that is, are the vaccinated and unvaccinated similar enough to make the calculation, or, rather, can we remove any bias to get an unbiased estimate.

As an example of bias, in the early days of the Covid vaccinations the majority of the vaccinated were old, and the unvaccinated were young – so if there was an effect of age then we’d get a biased result simply by comparing overall case rates (per 100,000) in the vaccinated versus the unvaccinated groups. In this case the bias might be resolved by splitting the analysis into different age groups, but what about other factors? Most of all, what is the bias associated with willingness to become vaccinated (maybe the vaccinated are in general more likely to be the healthy ones, say)?

Some time ago, statisticians came up with a really great way to remove rather a lot of the ‘difficult’ bias – it is called the Test Negative Case Control approach (TNCC).  With this approach you don’t simply count infections, but compare the rates of infections amongst those who get tested – more specifically, you compare the ratio of positive to negative results in the vaccinated against the positive-to-negative ratio in the unvaccinated groups.

The great thing about this method is that it automatically compensates for many behavioural effects in the vaccinated compared with unvaccinated groups – so, say the unvaccinated are half as likely to go and get tested compared with the vaccinated, the TNCC should remove most of this effect. Of course, many demographic things are of interest (particularly the impact of age and gender), so you’ll usually separate out these variables, but the advantages of the TNCC method remain.

Anyway, pretty much every study on Covid vaccine effectiveness makes use of TNCC – it gives such a powerful and unbiased estimate.  You can read more about it in this review article.

Oh, but what’s this I see in that paper?