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The risk/benefit of Covid vaccines is arguably most accurately measured by comparing the all-cause mortality rate of vaccinated against unvaccinated, since it not only avoids most confounders relating to case definition but also fulfils the WHO/CDC definition of "vaccine effectiveness" for mortality. We examine two of the most recent UK ONS vaccine...
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Context 1
... example, the two-dosed vaccinated non-Covid mortality rate is consistently far lower than the baseline, while the greater than 21 days 1-dose vaccinated non-Covid mortality rate is consistently far higher than the baseline. This is illustrated in the 70-79 age group in Figure 11, but the other age groups show very similar patterns. ...
Context 2
... possible miscategorisation processes are summarised in Figure 15. If we accept the possibility of miscategorisation, then how might the ONS data be adjusted to take account of it? ...
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... We plot the new adjusted mortalities for the vaccinated and unvaccinated and compare to the vaccine roll out periods for each of the age groups. Figures 16 to 18 show the adjusted mortalities for each of the three age groups for vaccinated and unvaccinated, along with the percentage of that age group being vaccinated for first and second doses. The similarity between them all is notable. ...
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... 22 shows a significant increase in people in very poor health in the greater than 21 days after first dose cohort around the time of dose two rollout, with the percentage of people in very poor health in June being around two times higher than April. However, the non-Covid mortality rate in this cohort shows something in the order of a ten-fold rise (see Figure 13). Very poor health cannot, therefore, account for the apparent increase in mortality observed. ...
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Objective:
To establish the impact of the first 6 months of the COVID-19 outbreak response on gastrointestinal (GI) infection trends in England.
Design:
Retrospective ecological study using routinely collected national and regional surveillance data from seven UK Health Security Agency coordinated laboratory, outbreak and syndromic surveillance...
Citations
... 7,8 The effect of this misattribution error on causing grossly overestimated COVID-19 vaccine efficacy has been shown using real-world data from Israel and the UK. 9,10 The data used in the Bagshaw et al study is no longer publicly available from Alberta Health Services, making correction for these errors impossible. ...
We aim to use a recently published research study as an example in order to demonstrate how data can be misinterpreted and result in deriving misleading policy implications. Bagshaw et al wrote that unvaccinated patients with COVID-19 in Alberta, Canada “had substantially greater rates of ICU admissions, ICU bed days, and ICU related costs than vaccinated patients did. This increased resource use would have been potentially avoidable had these unvaccinated patients been vaccinated.” The authors in Bagshaw et al then concluded that their findings “have important implications for discourse on the relative balance of increasingly stringent public health protection (restrictions), including mandatory vaccination policies, and the sustainability and function of health system infrastructure and capacity during the ongoing COVID-19 pandemic.” Here we show the following. First, the effect of vaccination on intensive care admissions were grossly over-estimated. Second, an effect of vaccination on access to acute care and on all-cause excess deaths was grossly over-stated. Third, policy implications were overstated and at best unclear. Overall, the data cannot support what Bagshaw et al called “increasingly stringent public health protection (restrictions), including mandatory vaccination policies”.
... The UK Government has been significantly better than most countries in providing detailed data on Covid cases and deaths indexed by vaccine status. However, despite these efforts we revealed a range of fundamental inconsistencies and flaws in ONS mortality data [1]. Specifically, two of the most recent UK ONS (Office for National Statistics) vaccine mortality surveillance reports [2,3] reveal a range of fundamental inconsistencies and anomalies in the data. ...
... Specifically, two of the most recent UK ONS (Office for National Statistics) vaccine mortality surveillance reports [2,3] reveal a range of fundamental inconsistencies and anomalies in the data. Analysis of these identified the most likely explanations for the observed anomalies are a combination of four possibilities: (1) systemic miscategorisation of deaths between the different categories of unvaccinated and vaccinated; (2) delayed or non-reporting of vaccinations; (3) systemic underestimation of the proportion of unvaccinated; and/or (4) incorrect population selection for Covid deaths. ...
... Likewise, we compared the death counts registered for England in [7] with the ONS dataset and found that 13,593 deaths were missing from the ONS dataset (taking account for the fact that the ONS use only a subset of the population). The mortality rate in the vaccinated and unvaccinated population omitted from the dataset is disproportionately high when compared to historical norms, whilst that reported for the vaccinated are disproportionately low, as previously reported in [1]. ...
The accuracy of any data purporting to show covid 19 vaccine effectiveness or safety is critically dependent on the accuracy of four measurements: (1) people classified as having the disease; (2) vaccination status; (3) reported deaths; and (4) the population of vaccinated and unvaccinated (the so called 'denominators'). Errors in any of these could undermine claims of vaccine effectiveness or safety. We have previously identified anomalies in the UK Government's ONS deaths by vaccination status data (ONS dataset)-specifically that some deaths occurring shortly after vaccination are being wrongly classified as unvaccinated deaths. In this paper we identify a further problem that appears to explain anomalies in the ONS data: the total deaths reported by ONS are significantly lower than we would expect compared to other government datasets, even allowing for the fact that the ONS use only a subset of the population. For both non-covid and covid deaths respectively the number of deaths reported for the within 21 days of first dose vaccination category tally almost perfectly with the number of deaths that would be expected should they have occurred in the third week alone. Thus, for both covid and non-covid deaths, the two weeks of post first vaccination deaths appear to have been omitted from the ONS dataset. This pattern is repeated in all age groups over 60. A variety of factors could have led to deaths in the first 14 days being omitted in the ONS dataset, including miscategorisation, reporting lags and data handling or transcription errors. The dataset is therefore corrupted, making any inferences about vaccine efficacy or safety that are reliant on the data, moot. Accordingly, the ONS should publicly withdraw their dataset and call for the retraction of any claims made by others that are based upon it.
... The reliability of the data source should be checked, particularly with regard to the categorisation of vaccination status. Independent analysis of a similar database in the United Kingdom, for example, found a major categorisation bias 25 . † It should be noted that the product was exempted from any pre-clinical genotoxicity study. ...
The objective of this note is to analyse the safety assessment of Comirnaty vaccination of pregnant women in the manufacturer's risk management plan (RMP) and in the European Medicines Agency (EMA) fact sheet, and to measure the impact on the recommendations that led to the mandatory vaccination of pregnant women caregivers and health-related professionals in France.
The evaluation of this safety was carried out in two phases. In the first phase, which runs from late 2020 to early 2022, the safety profile of the vaccine is not known in pregnant women. In the second phase, which runs from early 2022, the RMP and the EMA report data that are considered reassuring for short-term safety, but are limited. Long-term safety is still unknown. The RMP remains cautious and expects that intentional vaccination of pregnant women will remain limited.
The detailed analysis of risk management by the manufacturer, the European Agency and the French authorities reveals, to varying degrees, a lack of rigour. The EMA has disregarded certain elements of prudence maintained by the manufacturer, while the latter has allowed the only real clinical trial capable of determining an individual benefit-risk balance to lapse, which was, moreover, restricted to the third trimester of pregnancy. The French authorities recommended mandatory vaccination of pregnant women caregivers and health-related professionals at a time when the manufacturer and the EMA provided no guarantees.
La présente note a pour objectif d’analyser l’évaluation de la sécurité de la vaccination par le Comirnaty des femmes enceintes dans le plan de gestion des risques (PGR) du fabricant et dans la fiche de l’Agence Européenne du Médicament (AEM), et d’en mesurer l’incidence sur les recommandations qui ont conduit à l’obligation vaccinale des femmes enceintes soignantes et assimilées en France.
L’évaluation de cette sécurité a connu deux phases. Dans la première, qui s’étend de fin 2020 à début 2022, le profil de sécurité du vaccin n’est pas connu chez les femmes enceintes. Dans la seconde, qui court depuis début 2022, le PGR et l’AEM font état de données qui sont jugées rassurantes quant à la sécurité à court terme, mais elles sont limitées. La sécurité à long terme est, elle, toujours inconnue. Le PGR, encore à l’heure actuelle, demeure prudent et s’attend à ce que la vaccination intentionnelle des femmes enceintes reste limitée.
L’analyse détaillée de la gestion du risque par le fabricant, l’agence européenne et les tutelles françaises révèle, à des degrés divers, un manque de rigueur. L’AEM s’est affranchie de certains éléments de prudence maintenus par le fabricant, tandis que ce dernier a laissé péricliter le seul véritable essai clinique à même de déterminer une balance bénéfices-risques individuelle, par ailleurs restreinte au seul troisième trimestre de grossesse. Les tutelles françaises ont recommandé l’obligation vaccinale des femmes enceintes soignantes et assimilées à une époque où le fabricant et l’AEM ne fournissaient aucune garantie.
De nombreux biais statistiques entraînent une surestimation de l'efficacité vaccinale contre le SARS-CoV-2. Cette note, après avoir rappelé le biais qui dissimule une efficacité vaccinale négative dans les deux premières semaines suivant la première dose, en présente trois autres. Le premier est lié à la redéfinition récente de la population de référence à partir de laquelle l'efficacité vaccinale est mesurée : cette population n'est plus celle des individus non-vaccinés mais devient celle des individus vaccinés, à six mois de leur deuxième dose. Ce changement de référentiel non seulement ne permet plus de mesurer l'efficacité vaccinale réelle mais dissimule en outre une possible efficacité vaccinale négative à distance de la deuxième dose-d'autant moins facile à mettre en évidence que la plupart des statistiques privilégient des calculs d'efficacité vaccinale contre le risque d'infection uniquement symptomatique. Le deuxième est l'erreur de catégorisation du statut vaccinal dans certaines statistiques nationales, qui attribuent des décès de vaccinés aux non vaccinés. Ainsi au Royaume-Uni, dont l'analyse corrigée des données montre que l'estimation du bénéfice global de la vaccination en termes de mortalité devient finalement très incertaine, même chez les personnes âgées. Le troisième, qui relève sans doute plus du paralogisme que du biais proprement dit, concerne la succession d'extrapolations erronées à partir desquelles on peut conclure à, et imposer, une efficacité pratique de la vaccination là où il n'y en a peut-être absolument aucune. L'avis de la Haute Autorité de Santé du 21 juillet 2022 en donne une illustration.
