Covid-19: politicisation, “corruption,” and suppression of science

  • timer7 min
  • today2021-01-21 12:36

Politicians and governments are suppressing science. They do so in the public interest, they say, to accelerate availability of diagnostics and treatments. They do so to support innovation, to bring products to market at unprecedented speed. Both of these reasons are partly plausible; the greatest deceptions are founded in a grain of truth. But the underlying behaviour is troubling.

Science is being suppressed for political and financial gain. Covid-19 has unleashed state corruption on a grand scale, and it is harmful to public health.1 Politicians and industry are responsible for this opportunistic embezzlement. So too are scientists and health experts. The pandemic has revealed how the medical-political complex can be manipulated in an emergency—a time when it is even more important to safeguard science.

The UK’s pandemic response provides at least four examples of suppression of science or scientists. First, the membership, research, and deliberations of the Scientific Advisory Group for Emergencies (SAGE) were initially secret until a press leak forced transparency.2 The leak revealed inappropriate involvement of government advisers in SAGE, while exposing under-representation from public health, clinical care, women, and ethnic minorities. Indeed, the government was also recently ordered to release a 2016 report on deficiencies in pandemic preparedness, Operation Cygnus, following a verdict from the Information Commissioner’s Office.34

Next, a Public Health England report on covid-19 and inequalities. The report’s publication was delayed by England’s Department of Health; a section on ethnic minorities was initially withheld and then, following a public outcry, was published as part of a follow-up report.56 Authors from Public Health England were instructed not to talk to the media. Third, on 15 October, the editor of the Lancet complained that an author of a research paper, a UK government scientist, was blocked by the government from speaking to media because of a “difficult political landscape.”7

Now, a new example concerns the controversy over point-of-care antibody testing for covid-19.8 The prime minister’s Operation Moonshot depends on immediate and wide availability of accurate rapid diagnostic tests.9 It also depends on the questionable logic of mass screening—currently being trialled in Liverpool with a suboptimal PCR test.1011

The incident relates to research published this week by The BMJ, which finds that the government procured an antibody test that in real world tests falls well short of performance claims made by its manufacturers.1213 Researchers from Public Health England and collaborating institutions sensibly pushed to publish their study findings before the government committed to buying a million of these tests but were blocked by the health department and the prime minister’s office.14 Why was it important to procure this product without due scrutiny? Prior publication of research on a preprint server or a government website is compatible with The BMJ’s publication policy. As if to prove a point, Public Health England then unsuccessfully attempted to block The BMJ’s press release about the research paper.

Politicians often claim to follow the science, but that is a misleading oversimplification. Science is rarely absolute. It rarely applies to every setting or every population. It doesn’t make sense to slavishly follow science or evidence. A better approach is for politicians, the publicly appointed decision makers, to be informed and guided by science when they decide policy for their public. But even that approach retains public and professional trust only if science is available for scrutiny and free of political interference, and if the system is transparent and not compromised by conflicts of interest.

Suppression of science and scientists is not new or a peculiarly British phenomenon. In the US, President Trump’s government manipulated the Food and Drug Administration to hastily approve unproved drugs such as hydroxychloroquine and remdesivir.15 Globally, people, policies, and procurement are being corrupted by political and commercial agendas.16

The UK’s pandemic response relies too heavily on scientists and other government appointees with worrying competing interests, including shareholdings in companies that manufacture covid-19 diagnostic tests, treatments, and vaccines.17 Government appointees are able to ignore or cherry pick science—another form of misuse—and indulge in anti-competitive practices that favour their own products and those of friends and associates.18

How might science be safeguarded in these exceptional times? The first step is full disclosure of competing interests from government, politicians, scientific advisers, and appointees, such as the heads of test and trace, diagnostic test procurement, and vaccine delivery. The next step is full transparency about decision making systems, processes, and knowing who is accountable for what.

Once transparency and accountability are established as norms, individuals employed by government should ideally only work in areas unrelated to their competing interests. Expertise is possible without competing interests. If such a strict rule becomes impractical, minimum good practice is that people with competing interests must not be involved in decisions on products and policies in which they have a financial interest.

Governments and industry must also stop announcing critical science policy by press release. Such ill judged moves leave science, the media, and stock markets vulnerable to manipulation. Clear, open, and advance publication of the scientific basis for policy, procurements, and wonder drugs is a fundamental requirement.19

The stakes are high for politicians, scientific advisers, and government appointees. Their careers and bank balances may hinge on the decisions that they make. But they have a higher responsibility and duty to the public. Science is a public good. It doesn’t need to be followed blindly, but it does need to be fairly considered. Importantly, suppressing science, whether by delaying publication, cherry picking favourable research, or gagging scientists, is a danger to public health, causing deaths by exposing people to unsafe or ineffective interventions and preventing them from benefiting from better ones. When entangled with commercial decisions it is also maladministration of taxpayers’ money.

Politicisation of science was enthusiastically deployed by some of history’s worst autocrats and dictators, and it is now regrettably commonplace in democracies.20 The medical-political complex tends towards suppression of science to aggrandise and enrich those in power. And, as the powerful become more successful, richer, and further intoxicated with power, the inconvenient truths of science are suppressed. When good science is suppressed, people die.


  • Competing interests: I have read and understood BMJ policy on declaration of interests and have no relevant interests to declare.
  • Provenance and peer review: Commissioned; not externally peer reviewed.

This article is made freely available for use in accordance with BMJ’s website terms and conditions for the duration of the covid-19 pandemic or until otherwise determined by BMJ. You may use, download and print the article for any lawful, non-commercial purpose (including text and data mining) provided that all copyright notices and trade marks are retained.


  1. Geoghegan P. Cronyism and clientelism. London Review of Books 2020 Nov 5.
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  4. Department of Health and Social Care. Policy paper. Annex B: Exercise Cygnus report. 5 Nov 2020.
  5. Public Health England. Disparities in the risk and outcomes of COVID-19. 2020.
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  7. Horton R. Tweet, 15 Oct 2020.
  8. Boseley S. Antibody tests bought by UK government “less accurate than maker claims.” Guardian 2020 Nov 12.
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  15. Cohen K, Kupferschmidt K. The “very, very bad look” of remdesivir, the first FDA-approved COVID-19 drug. Science 2020 Oct 28.
  16. Transparency International. Corruption and covid-19—the story so far. 8 Jul 2020.
  17. Ennals E. Government test tsar has £770,000 shares in drugs firm that sold us £13million of “pointless” antibody screening kits—after it emerged that Sir Patrick Vallance has a financial interest in company racing to find vaccine. Daily Mail 2020 Sep 26.
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Source: Ilgiornale.It – Roberta Damiata – 13 janvier 2021 / Traduction et présentation :

Une véritable bombe médiatique est lancée sur les vaccins Pfizer et Moderna par le professeur Peter Doshi, qui a analysé les données de la demande d’approbation des deux sociétés pharmaceutiques, constatant que leur efficacité est bien inférieure aux données publiées.

Au fur et à mesure que les jours passent depuis la sortie du vaccin Pfizer et maintenant également du vaccin Moderna, apparaissent de plus en plus d’informations sur leur efficacité et sur les éventuels effets secondaires qu’ils pourraient avoir.

Une véritable bombe a été lancée dans le British Medical Journal par Peter Doshi, un associé de l’Université du Maryland chargé de recherche sur les services de santé pharmaceutiques; auteur qui, dans un article daté du 26 novembre, avait déjà posé quelques réserves sur l’efficacité présumée du vaccin.

À l’époque, avec les données en sa possession sur des deux vaccins concernés, Doshi avait pu déceler des différences évidentes avec ce qui était alors affirmé par l’ensemble de la communauté scientifique.

Dans le British Medical Journal il avait fortement critiqué les vaccins Covid : “Il y a un manque de transparence sur les données. Il n’est pas clair s’ils fonctionnent ou pas, et il n’y a pas eu suffisamment de personnes âgées, de personnes immunodéprimées et d’enfants scolarisés testés pour analyser leurs effets sur une période moyenne à longue. J’ai soulevé des questions sur les résultats des essais du vaccin Covid-19 par Pfizer et Moderna, car tout ce que l’on en connaissait était les protocoles d’étude réalisés par les firmes elles-mêmes et quelques communiqués de presse”, avait déclaré le professeur.

Cinq semaines après son premier article, Doshi a eu l’occasion d’étudier plus de 400 pages de données soumises à la Food and Drug Administration (Fda) avant que celle-ci ne délivre l’autorisation de diffusion dans le cadre de l’urgence sanitaire; et après en avoir fait l’analyse, il a publié quelques considérations importantes toujours dans la section opinion du British Medical Journal : “aurait été compromise l’efficacité des vaccins parce que ceux-ci ont été faits en partie sur des patients “suspects de covid” et sur des covid asymptomatiques non confirmés”.

Son étude aurait conduit Doshi à suggérer une efficacité beaucoup plus faible que celle affirmée jusqu’à présent : “bien en dessous du seuil d’efficacité de 50 % fixé par les autorités réglementaires pour l’approbation”.

Ce chiffre, selon ce qui est écrit dans le British Medical Journal, qui fait autorité, ne serait donc pas de 95% mais bien en dessous, entre 19% et 29%. Ces calculs, indique la note, ont été obtenus avec le calcul suivant : 19% = 1 – (8 + 1594) / (162 + 1816) ; 29% = 1 – (8 + 1594 – 409) / (162 + 1816 – 287). “J’ai ignoré les dénominateurs car ils sont similaires entre les groupes” (écrit le professeur pour clarifier la façon dont les pourcentages ont été calculés).

Si ces données avaient été présentées et analysées, il n’y aurait pas été possible d’obtenir d’autorisation de diffusion de ces vaccins par les autorités compétentes.

Mais ce n’est pas tout : “Même après avoir éliminé les cas survenus dans les 7 jours suivant la vaccination (409 sur le vaccin Pfizer contre 287 sur le placebo), ce qui devrait inclure la plupart des symptômes dus à la réactogénicité du vaccin (l’efficacité. ndlr) à court terme, celle-ci reste faible et atteint 29%. Les seules données fiables – dit Doshi – pour comprendre la capacité réelle de ces vaccins, sont les cas d’hospitalisation, les patients en soins intensifs et les décès.

Il est évident qu’à partir de ces considérations, il serait nécessaire de mener des enquêtes plus approfondies. Le rapport de 92 pages de Pfizer, par exemple, ne fait aucune mention des 3410 cas de : “suspicion de Covid-19″, ni de leur publication dans le New England Journal of Medicine, ni des rapports de Moderna sur les vaccins.

La seule source qui semble les avoir signalé est l’étude de la Food and Drug Administration sur le vaccin de Pfizer”. “Il est nécessaire de comprendre la véritable efficacité des données brutes”, déclare M. Doshi, “mais aucune entreprise ne semble les avoir partagées. Pfizer dit qu’il met les données à disposition sur demande mais que celles-ci sont encore soumises à examen, et Moderna dit que ses données pourraient être disponibles, toujours sur demande, une fois l’étude terminée”.

Ce qui nous ramène à la fin de l’année 2022 puisque le contrôle nécessite deux ans. Il en va de même pour le vaccin Oxford/AstraZeneca, qui publiera ses données à la fin de sa propre étude de viabilité.

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Peter Doshi: Pfizer and Moderna’s “95% effective” vaccines—let’s be cautious and first see the full data – The BMJ

  • imer6 min
  • today2021-01-22 12:39
  • create2020-11-26 14:24
  • person

Only full transparency and rigorous scrutiny of the data will allow for informed decision making, argues Peter Doshi

In the United States, all eyes are on Pfizer and Moderna. The topline efficacy results from their experimental covid-19 vaccine trials are astounding at first glance. Pfizer says it recorded 170 covid-19 cases (in 44,000 volunteers), with a remarkable split: 162 in the placebo group versus 8 in the vaccine group. Meanwhile Moderna says 95 of 30,000 volunteers in its ongoing trial got covid-19: 90 on placebo versus 5 receiving the vaccine, leading both companies to claim around 95% efficacy.

Let’s put this in perspective. First, a relative risk reduction is being reported, not absolute risk reduction, which appears to be less than 1%. Second, these results refer to the trials’ primary endpoint of covid-19 of essentially any severity, and importantly not the vaccine’s ability to save lives, nor the ability to prevent infection, nor the efficacy in important subgroups (e.g. frail elderly). Those still remain unknown. Third, these results reflect a time point relatively soon after vaccination, and we know nothing about vaccine performance at 3, 6, or 12 months, so cannot compare these efficacy numbers against other vaccines like influenza vaccines (which are judged over a season). Fourth, children, adolescents, and immunocompromised individuals were largely excluded from the trials, so we still lack any data on these important populations.

I previously argued that the trials are studying the wrong endpoint, and for an urgent need to correct course and study more important endpoints like prevention of severe disease and transmission in high risk people. Yet, despite the existence of regulatory mechanisms for ensuring vaccine access while keeping the authorization bar high (which would allow placebo-controlled trials to continue long enough to answer the important question), it’s hard to avoid the impression that sponsors are claiming victory and wrapping up their trials (Pfizer has already sent trial participants a letter discussing “crossing over” from placebo to vaccine), and the FDA will now be under enormous pressure to rapidly authorize the vaccines.

But as conversation shifts to vaccine distribution, let’s not lose sight of the evidence. Independent scrutiny of the underlying trial data will increase trust and credibility of the results. There also might be important limitations to the trial findings we need to be aware of.

Most crucially, we need data-driven assurances that the studies were not inadvertently unblinded, by which I mean investigators or volunteers could make reasonable guesses as to which group they were in. Blinding is most important when measuring subjective endpoints like symptomatic covid-19, and differences in post-injection side-effects between vaccine and placebo might have allowed for educated guessing. Past placebo-controlled trials of influenza vaccine were not able to fully maintain blinding of vaccine status, and the recent “half dose” mishap in the Oxford covid-19 vaccine trial was apparently only noticed because of milder-than-expected side-effects. (And that is just one of many concerns with the Oxford trial.)

In contrast to a normal saline placebo, early phase trials suggested that systemic and local adverse events are common in those receiving vaccine. In one Pfizer trial, for example, more than half of the vaccinated participants experienced headache, muscle pain and chills—but the early phase trials were small, with large margins of error around the data. Few details from the large phase 3 studies have been released thus far. Moderna’s press release states that 9% experienced grade 3 myalgia and 10% grade 3 fatigue; Pfizer’s statement reported 3.8% experienced grade 3 fatigue and 2% grade 3 headache. Grade 3 adverse events are considered severe, defined as preventing daily activity. Mild and moderate severity reactions are bound to be far more common.

One way the trial’s raw data could facilitate an informed judgment as to whether any potential unblinding might have affected the results is by analyzing how often people with symptoms of covid-19 were referred for confirmatory SARS-CoV-2 testing. Without a referral for testing, a suspected covid-19 case could not become a confirmed covid-19 case, and thus is a crucial step in order to be counted as a primary event: lab-confirmed, symptomatic covid-19. Because some of the adverse reactions to the vaccine are themselves also symptoms of covid-19 (e.g. fever, muscle pain), one might expect a far larger proportion of people receiving vaccine to have been swabbed and tested for SARS-CoV-2 than those receiving placebo.

This assumes all people with symptoms would be tested, as one might expect would be the case. However the trial protocols for Moderna and Pfizer’s studies contain explicit language instructing investigators to use their clinical judgment to decide whether to refer people for testing. Moderna puts it this way:

It is important to note that some of the symptoms of COVID-19 overlap with solicited systemic ARs that are expected after vaccination with mRNA-1273 (eg, myalgia, headache, fever, and chills). During the first 7 days after vaccination, when these solicited ARs are common, Investigators should use their clinical judgement to decide if an NP swab should be collected.

This amounts to asking investigators to make guesses as to which intervention group patients were in. But when the disease and the vaccine side-effects overlap, how is a clinician to judge the cause without a test? And why were they asked, anyway?

Importantly, the instructions only refer to the first seven days following vaccination, leaving unclear what role clinician judgment could play in the key days afterward, when cases of covid-19 could begin counting towards the primary endpoint. (For Pfizer, 7 days after the 2nd dose. For Moderna, 14 days.)

In a proper trial, all cases of covid-19 should have been recorded, no matter which arm of the trial the case occurred in. (In epidemiology terms, there should be no ascertainment bias, or differential measurement error). It’s even become common sense in the Covid era: “test, test, test.” But if referrals for testing were not provided to all individuals with symptoms of covid-19—for example because an assumption was made that the symptoms were due to side-effects of the vaccine—cases could go uncounted.

Data on pain and fever reducing medicines also deserve scrutiny. Symptoms resulting from a SARS-CoV-2 infection (e.g. fever or body aches) can be suppressed by pain and fever reducing medicines. If people in the vaccine arm took such medicines prophylactically, more often, or for a longer duration of time than those in the placebo arm, this could have led to greater suppression of covid-19 symptoms following SARS-CoV-2 infection in the vaccine arm, translating into a reduced likelihood of being suspected for covid-19, reduced likelihood of testing, and therefore reduced likelihood of meeting the primary endpoint. But in such a scenario, the effect was driven by the medicines, not the vaccine.

Neither Moderna nor Pfizer have released any samples of written materials provided to patients, so it is unclear what, if any, instructions patients were given regarding the use of medicines to treat side effects following vaccination, but the informed consent form for Johnson and Johnson’s vaccine trial provides such a recommendation:

“Following administration of Ad26.COV2.S, fever, muscle aches and headache appear to be more common in younger adults and can be severe. For this reason, we recommend you take a fever reducer or pain reliever if symptoms appear after receiving the vaccination, or upon your study doctor’s recommendation.”

There may be much more complexity to the “95% effective” announcement than meets the eye—or perhaps not. Only full transparency and rigorous scrutiny of the data will allow for informed decision making. The data must be made public.

Spanish translation of this article

German translation of this article

Peter Doshi, associate editor, The BMJ.

Competing interests: I have been pursuing the public release of vaccine trial protocols, and have co-signed open letters calling for independence and transparency in covid-19 vaccine related decision making.